ÉPIDÉMIOLOGIEDU CHAMPIGNON PURPUREUM, .GENT DE -SE BIOLOGIQUE DE LA REPRODUCT~ON~ÉGETATIVE DES FEUILLUS DE LUMIÈRE EN MILIEU FORESTIER

EPIDEMIOLOGY OF THE CHONDROSTEREUM PURPUREUM, A BIOLOGICAL CONTROL AGENT OF THE ASEXUAL REPRODUCTION OF DECIDUOUS SPECES IN FOREST ENVIRONMENT

Mémoire présenté à la Faculté des études supérieures de l'université Laval pour l'obtention du grade de maître ès sciences (M.Sc.)

Département des sciences du bois et de la forêt FACULTÉDE FORESTERIE ET DE GEOMATIQUE UNIVERSITÉLAVAL

JANVIER 1998

O André Goulet, 1998 Nationai Library Bibliothèque nationale 1*1 of Canada du Canada Acquisitions and Acquisitions et Bibliographie Services services bibliographiques 395 Wellington Street 395. rue Wellington Ottawa ON K1A ON4 OtiawaON KlAON4 Canada Canada

The author has granted a non- L'auteur a accordé une licence non exclusive licence allowing the exclusive permettant à la National Libraq of Canada to Bibliothèque nationale du Canada de reproduce, loan, distribute or sell reproduire, prêter, distribuer ou copies of this thesis in microforni, vendre des copies de cette thèse sous paper or electronic formats. la forme de microfiche/fh, de reproduction sur papier ou sur format électronique.

The author retains ownership of the L'auteur conserve la propriété du copyright in this thesis. Neither the droit d'auteur qui protège cette thèse. thesis nor substantial extracts fiom it Ni la thèse ni des extraits substantiels may be printed or otherwise de celle-ci ne doivent être imprimés reproduced without the author's ou autrement reproduits sans son permission. autorisation.

AVANT-PROPOS

Ce mémoire de maîtrise est le fruit de trois années de travail aché. Mes études graduées. sans vouloir déprécier leur contenu et leur apport intellectuels appréciables, m'ont avant toui permis de décowrir le monde et, par le fait même, de me découvrir moi-même: stage à l'université Agricole de Wageningen (Pays-Bas), congrès a Mol (Belgique), présentatior; scientifique si Rotonia (Nouvelle-ZdIande) et congrés de I'IüFRO a Tampere (Finlande). Pour m'avoir permis de vivre ces beaux moments, pour m'avoir fait découvrir de manière réaliste et passionnée les multiples défis qu'exige la recherche appliquée, pour m'avoix inculqué des valeurs telles la rigueur et la transparence en ce domaine, mais surtout pour m'avoir encadré de manière exemplaire tout au long de cette aventure inoubliable, je tiens sincèrement à remercier mon co-directeur de recherche, Dr. Robert Jobidon. Je tiens également a souligner l'aide remarquable qui m'a dté apportée par mon directeur, Dr. Louis Bernier. Je remercie Dr. Richard Leduc pour avoir aimablement révisé ce document, et spécialement le chapitre 3 de la thèse. J'aimerais rendre un sincère hommage au Dr. Meindert de Jong qui, malgré une maladie, m'a brillamment guidé lors de mon stage aux Pays-Bas. Je dois remercier Dr. Wopke van der Werf ainsi que Dr. Jan Goudriaan, tous deux du département de 'Theoretical Production Ecology' de 17Université Agricole de Wageningen, pour leurs judicieux conseils en ce qui concerne le monde fantastique (mais complexe!) de la modélisation mathématique. Je remercie aussi du fond du coeur toutes les personnes qui ont participé aux travaux terrain et de serres ainsi qu'aux analyses en laboratoire, particulièrement Mme Lyne Gosselin, M. Simon DéAliers, M. Claude Fortin, M. Jacques Carignan et M. Réjean Poliquin. Enfin, il m'est important de signaler que ce projet de maitrise a pu être réalisé grâce a la précieuse aide financière du ministère des Ressources naîurelles du Québec. TABLE DES MATIEHES...... 4

Chapitre I .Introduction generuïe...... 6 l .I Contexte fores rier actuel...... 7 1.2 i.u muifrise biologique de lu vkgétation ...... 8 1.4 Chondrmtereum purpureum en tant qu'agent de maîtrise biologique .historique ...... IO 1.5 Clzondrostcreum purpureum - Revue de littérature...... 12 1.6 Objectifi de l .etude...... 14

Chapitre 2 .Sporulation of ïhe silverleuffngus Chondrostereum purpureum as aflected by environrnental vuriubles ...... 16 2.1 /ntroduction ...... 17 2.2 Aiiaterials and methods...... 18 2.3 Resulfs and discussion ...... 19

Chapitre 3 .Development of OPEA$ a model simulatirtg release and dispersul of Chondrostereumpurpureum ...... 29 3.1 Introduction ...... 30 3.2 hiarerials and methods...... 32 3.2.1 Sfu& sife...... 32 3.2.2 Meteorological data collection...... 32 3.2.3.2.1 Meteorology...... 37 3.2.3.2.2 Dispersal within the foresr clearcut...... 40 3.3.3 Transmission ...... 4 3.4 Discussion ...... 5 3.5 Conclusion ...... 5.

Chapitre 4 .Virulence ofselected isolures of Chondrostereum purpureum ro several Canadian deciduous nee species ...... Si 4. l Introduction ...... 5! 1.2 Materials and rnerhods ...... 61 4.3. Results ...... 6- 4.4 Discussion ...... 7;

Chapitre 5 .Conclusion generole...... 7:

Rij&ences ...... 81

Annexe A .. Code dc programmation du iogiciel de simulation OPEM

Annexe 3 .Guide de I 'uiilisateur du logiciel de simulation OPEM

Annexe C .Code informarique d'acquisirion de clonn2e.s météorologiques Chapitre 1

Introduction générale La phase d'établissement d'une régénération forestière est cruciale pour la remise er production de temtoires forestiers. Actuellement au Québec, environ 85 % des aire! récoltées se régénèrent naturellement, Ie 15 % restant doit être reboisé pour assurer cent régénération. Alors que les terrains qui se régénèrent naturellement subissent peu les effet! de la compétition interspécifique, ceux reboisés la subissent fortement On identifie, aL Québec, trois grands groupes de végétation de compétition: 1) le groupe hboisier-épiIobel 2) le groupe feuillus de lumière; et 3) le groupe graminées-herbacées. La maitrise de la végétation du premier groupe se réaiise traditionnellement à l'aide d'un herbicide, Ic glyphosate (visionm) étant le plus utilise. La maitrise du second groupe se réalise par la coupe mécanisée a l'aide de débroussailleuses, soit le dégagement mécanique.

Les deux approches, mécanique ou chimique, se caractérisent par divers avantages et inconvénients qui leur sont propres. Alors que les phytocides offrent des résultats de maîtrise de la végétation satisfaisants tout en réduisant les coûts d'opération au minimum, le dégagement mécanique s'avère une pratique encouragée par la population en raison de la création d'emplois et d'une perception d'effets moindres sur l'environnement. Au cours des années 1980, l'innocuité des traitements mécaniques a été soulignée lors de nombreuses consultations organisées par le Bureau d'audiences publiques sur l'environnement (BAPE). Par ailleurs, le ministère des Ressources naturelles du Québec a rédigé la Stratégie de protection des forêts qui est entrée en vigueur en 1994. Ce document prévoit, comme son nom l'indique, l'optimisation de l'utilisation des différentes ressources forestières tout en préconisant des pratiques sylvicoles prdventives. Rationaliser davantage l'usage des phytocides en forèt tout en assurant le rendement soutenu de matière ligneuse est l'une de ces pratiques mises de l'avant par le MRN. Ainsi, iI a été prévu que, au plus tard en 2001, l'utilisation des phytocides chimiques en forèt soit complètement éliminée.

Présentement, le dégagement mécanique semble être la seule alternative opérationnelIe possible à l'application de phytocides chimiques. Toutefois, cette pratique n'offre souvent que de piètres performances lorsque des essences de lumière caractérisées par un mode de reproduction végétative très agressif sont visées par le traitement. C'est pourquoi la Stratégie de protection des forêts prévoit égaiement la mise en place de programmes de recherche visant à développer des solutions de rechange aux pesticides chimiques. Parmi les alternatives possibles, la mise au point de méthodes de maîtrise biologique de la végétation semble une voie prometteuse et privilégiée par la population et donc par le Gouvernement.

Selon Jobidon (1991), trois voies de recherche en maîtrise biologique semblent prometteuses; ce sont l'allélopathie, les phytotoxines produites par des micro-organismes et la maîtrise biologique par des organismes phytopathogènes. C'est précisément ce dernier point qui fait et qui a fait l'objet de maintes études (surtout en agriculture), le principe étant d'utiliser des champignons, des bactéries ou des vims en tant que phytocides biologiques1 (Strobel, 1991). Sur le sujet, Scheepens et van Zon (1982) affirment que certains virus ne sont pas spécifiques envers leur espéce cible et qu'ils ont souvent besoin d'un vecteur afin de se déplacer d'un milieu à un autre. Vincent et Coderre (1992), de leur côté, mentionnent que l'utilisation des bactéries est limitée par leur mode de pénétration, qui nécessite la présence d'orifices naturels ou de blessures sur les plantes. Cependant, Templeton et TeBeest (1979) et Templeton (1982) soutiennent que certains champignons, malgré leur temps d'action généralement plus long que les herbicides chimiques, peuvent effectivement être employés afin de supprimer certains végétaux indésirables. Dans quelques cas, des champignons seraient même plus efficaces que les phytocides chimiques couramment utilisés. Enfin, Strobel (1991) ajoute que les champignons sont souvent plus sélectifs que leurs homologues chimiques. C'est notamment pour ces raisons qu'on utilise surtout les champignons dans la fabrication des phytocides biologiques (Strobel, 199 1; Vincent et Coderre, 1992).

' Phytocide biologique: Ltinoculum d'un pathogène, /ormulé et appliqué de h même foçon qu'un herbicide chimique et utilisé pour réprimer la végétation indésirable (Vincent et Coderre, 1992). 1.3 LRs dew tacriques de mirrise biologique

Traditionnellement, on distingue deux tactiques de lutte en maîtrise biologique, la tactiqur classique (aussi dite tactique par augmentation) et la tactique par inondation (aussi dit€ tactique par phytocide biologique ou par dispersion massive) (Templeton et TeBeest, 1979; Scheepens et van Zon, 1982; Schroeder, 1983; TeBeest et Templeton, 1985). La première, el la plus répandue, consiste a introduire un agent de maîtrise biologique qui attaque une espèce cible. Cette tactique est souvent pratiquée dans les régions où l'espèce cible a été introduite et où les ennemis naturels sont pratiquement absents (Vincent et Coderre, 1992). Dans ce cas, le succès de la maîtrise de liagent nuisible est dépendant de l'implantation de l'agent de maîtrise biologique dans Ie nouveau milieu et de son efficacité de propagation. Le but ultime de cette tactique est de faire en sorte que t'agent introduit puisse vivre a perpétuité et de façon synchrone avec Iliôte nuisible dans leur nouveau milieu. Le pourcentage de maîtrise de la végétation par cette tactique varie généralement de 30 a 35% (Schroeder, 1983). L'objectif premier de cette opération n'étant cependant pas de supprimer toute l'espèce cible mais bien de la maintenir sous des niveaux épidémiques, ces chiffres sont donc acceptables dans plusieurs cas (Schroeder, 1983; Vincent et Coderre, 1992). Malheureusement, puisqu'il est à toutes fins pratiques impossible de conthler la propagation d'un agent biologique après son implantation, l'approche classique ne peut être utilisée sur un même site si la maîtrise d'une espéce est nécessaire a un endroit et non a un autre (Schroeder, 1983). Quelques exemples de cette tactique meublent la littérature: la maîtrise de Centaura difia par Puccinia jaceae (Watson et Alkhouri, 1981; cité par Scheepens et van Zon, 1982) ou celle de ChondriIfa juncea par Puecinia chondriIfina(CulIen, 1978; cité par Scheepens et van Zon, 1982) ne sont que deux exemples parmi tant d'autres.

La deuxième tactique, moins dépendante de la propagation et de la survie de I'agent de maîtrise biologique, est dite 'par inondation'. Cette dernière consiste i imposer, à un moment précis, une quantité anormdement élevée de l'agent en question (phytocide biologique) dans un milieu OU une espèce cible est présente. Cette méthode est très pratique lorsqu'un traitement rapide est désiré (contrairement a !a tactique classique oii I'agent de maîtrise biologque doit d'abord s'implanter adéquatement dans son nouveau miIieu). Des niveaux de maîtrise frôlant les 100% sont couramment atteints avec cette tactique. De plus, la tactique par inondation peut être appliquée aussi souvent que nécessaire.

Quelques études sur la formulation de phytocides bioIogiques menées au cours des dernière: années ont porté fiuit. Par exemple, citons les travaux de Daniel et al. (1973), de Templeton et 02. (1979-1992), de Weidemann et al. (1988) et de TeBeest et al. (1985-1992) qui on1 conduit à I'hornologatioa en 1982 de COU EGO^^, le premier phytocide biologique a être utilisé a des fins agro-industrielles mondialement. Plus récemment, mentionnons les travaw de Mortensen (1988) et de Makowski et Mortensen (1992) qui ont conduit a l'homologation du premier phytocide biologique à vocation agro-alimentaire au Canada: BIOMAL~~.La principale différence existant entre les deux méthodes de maîtrise biologique tient du fait que l'approche ciassique est plutôt écologique alors que l'approche par augmentation est plutôt technologique (Vincent et Coderre, 1992). Par le fait même, la deuxième tactique (par augmentation) nécessite beaucoup plus d'efforts dans Ia préparation de formulations viables et applicables de façons opérationnelles (Vincent et Coderre, 1992).

Enfin, dans fe même ordre d'idée, Schroeder (1983) mentionne l'existence d'une troisième tactique dite de "conservation" visant essentiellement 8 manipuler l'environnement de l'espèce nuisible pour la réprimer. Toutefois, ce type d'approche a reçu jusqu'à présent très peu d'attention dans le domaine scientifique. Vincent et Coderre (2992) ajoutent également a cette liste la méthode d'utilisation des herbivores, limitée cependant a certain habitats terrestres ou aquatiques.

1.4 Chondrosîereum purpureum en tant qu'agent de matrisebiologique - historique

C'est en poursuivant ce même courant de souci environnemental évoqué par la population que des efforts de recherche ont été déployés afin de développer des moyens de maitrise biologique en foresterie. Parmi ces moyens, si i'on s'en tient aux conclusions de plusieurs études, Chondrostereum pwpraeum (Pers. ex F.) Pouzar semble être, selon une tactique pa inondation, un champignon ayant un bon potentiel en tant qu'agent de maîtrise de 1 reproduction végétative (z.e. rejets de souche et drageonnement) de certaines espèce forestières feuillues.

C'est à Scheepens (1980) que revient I'initiative d'avoir utilisé C. purpureum comme agent d maîtrise biologique afin de supprimer la reproduction végétative de serotina Ehrh une espèce indésirable dans les forêts de conifëres aux Pays-Bas. Depuis, la plupart de projets d'études traitant de C. pu'pureum n'est plus réaiisée à titre de recherche fondamentales proprement dites mais plutôt a titre d'approfondissement des connaissance entourant l'utilisation du champignon en tant que phytoçide biologique (Scheepens, 1989 Wall, 1990; Wall, 1994; Ekramoddouiiah et al., 1993; Gosselin et Jobidon, 1995; Wall et al 1996).

Wall (1990) a introduit l'idée au Canada en énidiant la vinilence de quelques isolats sur de tiges au champ. Il a égaiement vérifié l'effet pathologique du champignon sur le bouleai jaune (Berula alleghaniensis Britton). Des recherches ont aussi été entreprises au Québel pour étudier l'efficacité d'isolats du pathogène à maîtriser la reproduction végétative di diverses espèces feuillues rencontrées en milieu forestier (Jobidon, 1995) et dans de corridors de transport d7é1ecmcité (Gosselin et Jobidon, 1995). Plus tard, Gosselin et al

(1996) et Ramsfield et al. (1996) ont déterminé et caractérisé la diversité génétique di quelques isolats du pathogène provenant du Québec et du Canada.

De Jong et al. (1990a, 1990b et 199 1) ainsi que De Jong (1988 et 1992) se sont grandemen intéressés au risque environnementai relié à l'utilisation de C. purpureum comme phytocidi biologique. Ces travaux qui, selon Schroeder (1983), représentent habituellement l'étape 1; plus cruciale d'un projet de maitrise biologique, ont porté fit puisque le Service dr Protection des Plantes des Pays-Bas a désormais permis l'utilisation officielle du charnpignor à condition de respecter toutefois certaines règles environnementales (une zone tampon dr 500 mètres doit être établie entre les zones traitées et les vergers). Au Canada, De Jong et al: (1996) ont évalué qu'une population ajoutée de C. purpurem par la pratique de la maitrise biologique ne représenterait pas une augmentation des risques d'infection d'arbres non- cibles. Enfin, Goulet et al. (1995) ont vérifié la susceptibilité de quelques espèces résineuses commerciales à quelques isolats du Québec en mettant au point une méthode d'évaluation appropriée. Cette dernière étude confirme que le risque d'infection des espèces résineuses telles les épinettes et le sapin baumier par C. purpureum est virtuellement inexistant,

1.5 Chondrostereumpurpureum - Revue de littérature

C. purpureum (aussi nommé stéréon pourpre) est un champignon non spécifique appartenant a la famille des Stéréacées et a l'ordre des Aphyllophorales. Il est un envahisseur primaire des arbres blessés dans lesquels il croit très rapidement (Grosclaude, 1973b; Rayner, 1977 et 1979). La pénétration du champignon dans son hôte se produit exclusivement par une blessure fraiche (moins d'un mois) exposant le bois à l'air ambiant (Brooks et Moore, 1926). La croissance longitudinale de C. purpureum dans l'hôte peut atteindre, sous des conditions environnementales optimales, un centimètre par jour (Setliff et Wade, 1973; Rayner, 1979; Rayner et Boddy, 1988). Sa présence sur les souches et les tiges récemment coupées ou blessées témoigne de sa nature d'envahisseur primaire (Lindsey, 1978; Stalpers, 1978; Bishop, 1979; Kendrick, 1992). Les auteurs soulignent en outre que C. purpurem est très peu compétitif et qu'il est très rapidement remplacé par d'autres pathogènes lors des premières années suivant l'infection (Rayner, 1978; Rayner et Boddy, 1988). C. purpureurn se nourrit essentiellement des éléments nutritifs du xylème des arbres dans lesquels il s'est introduit (Beever, 1970; Spiers and Hopcrofi, 1988b). Finalement, ce pathogène se propage uniquement par voie aérienne (basidiospores) et sa spodation atteint un sommet au cours de la nuit a des températures variant de 180C a 20% (Grosclaude, 1969; Spiers, 1985).

C. purpureum est reconnu depuis environ un siècle comme étant l'agent responsable de la maladie du plomb, maladie s'attaquant principalement aux espèces ligneuses de la famille des rosacées (surtout des genres Prunus spp. et spp.) (Rayner et Boddy, 1988; Scheepens et Hoogerbrugge, 1989; Ivlaruyarna et Hiratsuka, 1991). On peut cependant 1s rencontrer sur tous les arbres a feuilles caduques des régions tempérees circurnpolaire! (Setliff et Wade, 1973; Bennett, 1962). Bien qu'étant un symptôme facultatif, la couleui

argentée des feuilles des arbres infectés (d'où le nom de la maiadie) provient, selon Rayner e' Boddy (1988), d'une séparation des cellules de l'épiderme du mésophylle des feuilles. Cettt division, ou cette couleur de plomb, serait causée à l'origine par une toxine produite par le champignon (non identifiée à ce jour). Toutefois, cette toxine, qui ne serait pas enzymatique selon Bishop (1979)' ne serait pas reliée à la mort de i'arbre. Cette dernière serait plutôi attribuable à I'obstmction des vaisseaux du xylème et du phloeme par la masse mycélienne du champignon qui, par le fait même, engendrerait des zones de coloration brune chez le bouleau bIanc (Berula papyrifera Marsh.) (Scheepens et Hoogerbnigge, 1989; McLaughlin el Setliff, 1990). McIaughlin et Setliff (1990) et McLaughh (199 1) ont même proposé une certaine association entre l'infection de C. purpureum et le déclin des bétulaies blanches eri Ontario. Bien que déjà aperçu dans des cas isolés chez quelques conifêres dont 1'Abie~ balsarnea (L.) Mill. (Ethridge et Morin, 1963) et sur Picea murianu (Mill.) BSP. (Gosselin- communication personnelle), on n'attribue a C. purpureum aucune maladie chez les résineux (Wall, 1990; Goulet et al., 1995).

Depuis le début du sikcle, plusieurs efforts de recherche ont été déployés afin de lutter contre la maIadie du piomb. Certains essais de protection chimiques (Dye et Wheeler, 1968; Trandafirescu et Topor, 1995) et biologiques (Grosclaude, 1970 et 1 973a; Rishbeth, 1988), de mème que plusieurs études ayant pour objet de mettre en lumière la dynamique de Ia maladie (Ethridge et Morin, 1963; Grosclaude, 1973b; Beever, 1970; Dye, 1974; Bishop, 1979; BieIinin et Malewski, 1982; Spiers, 1985; Spiers et Hopcroft, 1988b; Trandafirescu et Topor, 1985; McLaughlin, 1991), ont ainsi été réaiisés. L'ignorance de 1a dynamique de la maladie et conséquemment des techniques phytosanitaires adéquates requises afin d'éviter la mort des arbres engendrée par celle-ci, ont probablement contribué a faire en sorte qu'elle est apparue être plus nuisible à l'époque qu'aujourd'hui (Maruyama et Hiratsuka, 1991). Effectivement, l'application de traitements adéquats, tel un bon élagage effectué au moment opportun ou l'application préventive ou curative de fongicides (Trandafirescu et Topor, 1995), a fait en sorte que la mdadie du plomb passe presque inaperçue de nos jours. 1 semble également que plusieurs facteurs contraignent le champignon à demeurer en deçà de! seuils épidémiques, par exemple des facteurs tels les conditions environnementales (unc température égale ou supérieure à 370C peut être létale) (Spiers, 1985), certaine! particularités propres à l'organisme (les se dessèchent rapidement, la faile épaisseu de ses parois cellulaires, etc.), la susceptibilité de hôte et Ia concentration du champignon er nature (Setliff et Wade, 1973; Wall, 1986 et 1990; BoIand, 1990). Un bon exemple de l'impact de la concentration du champignon rencontrée dans la nature est celui cité par Setlifl et Wade (1973): en 1971 au Wisconsin, un verger a été infecté par C. purpureum provenanl en grande partie des débris de bois adjacents au verger causés par une tornade.

1.6 Objectifs de 1 'dtude

Lors de son cycle vital, C. purpureum produit habituellement des fnictifications (carpophores) sur les branches, les troncs ou les souches infectes, La formation de hctifications et la libération de spores représentent le seul moyen par lequel Ie pathogène parvient à se disperser. L'apparition des carpophores sembIe toutefois être fonction de quelques paramètres environnementaux tels l'humidité et Ia température de l'air me, 1974). Les travaux d'inoculations réalisés au champ (Jobidon, 1995; Gosselin et Jobidon, 1995) ont démontré que le cycle du pathogène est similaire lorsqu'introduit artificiellement en nature. Les carpophores du champignon demeurent présents sur Ies souches pour un maximum de deux ans suivant un traitement de maîtrise biologique (observations personnelles). Le fait que ces hctifications rejettent des quantités très élevées de spores dans I'environnement lorsque les conditions climatiques sont favorables était déjà connu en Nouvelle-Zélande me, 1974; Spiers, 1985). Étant donnée la vaste gamme d'hôtes du champignon, ces spores sont susceptibles d'infecter des arbres de valeur non visés par le traitement de maîtrise biologique et qui sont situés en périphérie des aires traitées. Un arbre de valeur susceptible a la maladie du plomb peut se définir comme une tige ligneuse d'espèce feuillue ayant une valeur commerciale, ornementale, patrimoniale ou sentimentale et portant une blessure fraîche exposant le cambium depuis moins d'un mois (Brooks et Moore, 1926; Grosclaude, 1969 1974; Wall, 1991). 11 importe également de considérer que si un arbre non-cible, dei empilements de bois ou des débris ligneux sont infectts en conséquence d'un traitement, il: pourront à leur tour en infecter d'autres par les carpophores qu'il produira.

La présente hde tente d'évaluer quelques aspects de l'épidémiologie du champignoi pathogène C. purpurem suite a la pratique d'une maîtrise biologique de la reproductioi végétative de feuiILus de Iurniere. Dans un premier temps, nous vérifions I'hypothèse qu'u échantilIon d'isolats de la population canadienne de C. purpurem produise des symptômer équivalents chez une espèce hôte donnée et nous vérifions aussi l'hypothèse qu'un group d'espèces hôtes ont une susceptibilité équivalente aux isolats. Dans un deuxième temps, unr évaluation du devenir environnemental du pathogène est proposée en (1) quantifiant avet exactitude la sporulation du pathogène en laboratoire et en l'évaluant au champ et (2) er simulant la dispersion de ces spores au pourtour des aires traitées. Chapitre 2

Sponrlation of the silverteaf fungus Chondrostereumpurpureum as affected by environmental variables

An indoor srudy was initiated to quanti@ the spomlation of the silverleaf funp Chondrostereum purpurem. Overalt, 288 basidiocarps of one single isolate of the pathoger were subjected, using a factorial design including 6 replicates, to 3 regimes of ail temperature (10, 20 and 30°C) and to 4 regimes of relative humidity (60, 80, 90 and 100%). For each treatment, a spore flux (spores per cm2 of hymenium and per second) wz quantified at 4 different times (12 h, 24 h, 48 h and 96 h) following the beginning of the experiment using a hemacytometer and a digital LI-COR 3 IO0 area rneter. Results indicated that a maximal mean spore flux of 123 sporeicm2/sec was reached at an air temperature of 20°C, a relative humidity of 100% and at 48 hom following the beginning of the experiment. Ln contrast, spomlation was severely reduced when air temperature reached 30°C while stiI1 being significant at IOoC.It was show that spore discharge cm reach values as hi& as 260 spores/cm2/sec. The method used in this experiment was found to be reliable and easy to use, which makes it interesting for such quantitative study. Results are discussed in relation to modelling spore dispersai. Chondrostereum prrrpureum (Pers. ex Fr,) Pouzar is a fungus of the order of the aphylIophorales (Talbot, 1973) that is indigenous and widespread throughout Canada (Wall, 1986). This fungus is the causa1 agent of the siIverIeaf disease, a disease mostly reported on both stone fruit and pip fniit tree species (Bennett, 1962; Dye and Wheeler, 1968; Setliff and Wade, 1973; Borecki et al., 1978; Pittevils et al., 1979; Bielenin and Malewski, 1982) but also observed on several Canadian forest tree species (Wall, 1986; McLaughlin and Setliff, 1990). It has been shown that most Canadian coniferous tree species are not likely to be infected by the pathogen (Goulet et al., 1995) although an exceptional case has been reported (Ethridge and Morin, 1963). Men infection occurs, necessarily via airbome basidiospores and through fresh wounds (Brooks and Moore, 1926; Grosclaude, 1973b), cankers can develop on the bark while staining and hyphae spread within vascuiar tissues (Wall, 1990; Goulet et al., 1995). Fniiting bodies of the fungus appear as small brackets on infected stubs or branches when environmental conditions are suitable and the process can be repeated up to 2 years following infection. It is known that basidiocarps may release large quantities of spores when clhatic conditions are favourable (Dye, 1974; Spiers, 1985). These spores are suspected to be dissiminated over some distances and to infect fieshly wounded (Grosclaude, 1973b; de Jong, 1988). Spores of C. purpurem are typically small, measuring 3-4 p by 5-6p.

Previou studies have demonstrated that sponilation of C. purputeum depends to a great extent upon the water content of the basidiocarps which is, in tum, function of environmental factors such as air temperature and atmospheric humidity (Grosclaude, 1969; Dye, 1974; Spiers, 1985). Grosclaude (1 969) first studied the sporuiation of C. pwpureum in France, but the experiment was conducted using maIl samples of basidiocarps and was informative rather than quantitative. Spiers (1985) presented resdts about spore liberation fiom basidiocarps and cultivated mycelium of the fungus in both laboratoxy and field experiments in New Zealand. Results fiom both studies reveded that spore discharge was maximal close to an air temperature of 20°C. However, no attempt was made to quanti@ the sporulation as a spore flux (spores per cm2 of hymenium and per second) and as a continuous function ol both air temperature and relative humidity in either of the above studies. It is also of concerr whether the spodation of a North American strain differs fiom those on which Grosclaude (1969) and Spiers (1985) investigated.

The present study aims to examine the sporuiation dynamics of an isolate of C. purpurem and to quantifi both mean and maximal sporuiations of the isolate according to relevani environmental variables.

2.2 Materials and methods

Young and even-aged basidiocarps of a single isolate of C. purpureunt were collected fiom ai exprirnental site located in eastem Québec in which stubs of paper (Betufapapyrifen Marsh.) were inoculated with myceiium of the sarne isolate in the previous year (Jobidon unpub1ished)- The isolate was genetically and pathologically characterized in earlier studie and was coded as Q2 (Goulet et al., 1995; Chapter 4) and as iB (Gosselin et al., 1996) Considering their xerophytic nature and in order to maintain their spodation potential basidiocarps were stored in a dry cabinet under a mean relative hurnidity of less than 259 until their use, as suggested by Grosdaude (1969). Using a factoriai design, fniiting bodie: were subjected to three levels of air temperature (Y) (10, 20, 30 OC) and four levels O relative hurnidity (RH.) (60%, 80%, 90% and 200%). Sporulation was rneasured at 4 differen times following the beginning of the expriment (t) (12 h, 24 h, 48 h and 96 h) and eacl treatrnent was repeated sixfoId, hence requiring 288 fniiting bodies for the entire study (3 T" >

4 R.H.x 4 t x 6 replicates).

Prior to each treatment, basidiocarps were steeped in fiee water as required to initiatc sponilation @ye, 1974; Spiers, 1985). A preliminary study conducted on 10 basidiocarp! having an initial water content of 25% showed that wetting the fructifications for 15 minute: was enough to let them reach a maximal water content (Fig. 2.1)- which is in agreement witl Spiers (1985). However, fnictifications were soaked one hour pnor to the experiment tc optimize basidia and basidiospore formation and to ensure an adequate and uniforn moisturizing. Spiers (1985) observed that spore release from basidiocarps was ni1 whei plunged into fke water. Following wetting, basidiocarps were attached with lanolin on thc inside of lids of 15 ml screw cap tubes (sarstedta) that were modified to reduce their volurnc to 3 ml. Tubes, lids and basidiocarps were disposed on a hand-made pewter rack fixed to thc iid of impermeable 250 ml glas jars in which a specific saturated saline solution wa

previously poured (Fig. 2.2). These saturated solutions permitted to obtain accurate levels O relative humidity at a precise air temperature as described by Winston and Bates (1960 (Table 2.1). The lids of the small3 ml tubes were pierced to dlow the humidity to be uniforn throughout the jars as well as within the tubes. A portable RH-200 digital hygrometer (Omegi inc.) was used to evaluate the accuracy of the relative humidity within each glas jar. Thc latter, including tubes and basidiocarps, were randomly placed in a growth cabinet under : given air temperature and for a given period of time. The experiment was conducted undei constant darkness to prevent underestimation of spore release (Grosclaude, 1969; Spiers 1985). For al1 treatment, spore discharge was estimated by diluting the spores with a know volume of distilled water mixed with Tween 20" (to prevent clustenng of spores) and bj counting them using a hemacytometer. The surface area of hyrnenium of each basidiocaq was measured using a LI-3100 Area Meter (LI-COR"). Spore discharge, surface area oi basidiocarps and time of sporulation were related ta obtain a spore flux (spores per cm2oi hymenium and per second).

2.3 Results and discussion

For a11 treatments, first spore deposits were observed within the first 6 hours following wetting, as already pointed out in earlier studies (Exell, 1924; Dye, 1974; Spiers, 1985). Within each treatment, a great variation in sporulation was observed among basidiocarps. However, maximal spore fluxes occured at 48 hours following immersion into water under an air temperature of 20°C and a relative humidity of 100% (Fig. 2.3b). A mean spore flux of 123 spores/cm2/secwas found under these environmental conditions although the maximum valut obtained was 260 spores/cm2/sec for the same conditions (Fig. 2.4). At 30°C, spore fluxe: were severely reduced, showing values of Iess than 1 spore/cm2/sec in al1 cases (Fig 2.3~) while the process was still significant at 10°C (Fig. 2.3a). In the latter, a maximum spore flur of 45 spores/cm21sec was observed at 48 hours following soaking and at a relative humidity oj 100%.

4m-

350 --

300 -- - - g .$= 2B.- z0 200- z=* 3 y lm--

100 .-

60 --

O. -1 O 16 M 45 Ti me following immersion (minutes)

Fig. 2.1 Water content (% of dry weight) of basidiocarps immersed into fiee water in function of time (n=10). Vertical bars indicate standard deviation. Fig. 2.2 Schematic representation of the experimental design used to assess the sporulation. Table 2.1 Saturated saline solutions used to obtain accurate levels of relative humidity (R.H.) at a precise air temperame (T) (Winston and Bates, 1960). The number in parentheses is the exact value of relative humidity obtained.

90 Kcl (88%) RH.

Fig. 2.3 PunctuaI mean spore fluxes in function of tirne foIIowing tetting at a) 10°C, b) 20°C and c) 30°C.Dashed arrows represent the approximate the following the begiming of the experiment beyond which sporulation is reguIated by the ambient relative humidity. Relative humidity (%)

0 5 10 15 20 25 30 Air temperature (Co)

Fig. 2.4 Maximal spore fluxes measured (regardless of time of rneasurement) in function of air temperature and relative hurnidity. R.H.

O 12 24 36 48 60 72 84 96 Time (hours)

Time (hours)

O 12 24 36 48 60 72 84 96 Time (hours)

Fig. 2.5 Cumulative mean spore release in function of tirne following wetting at a) 1O0C, b) 20°C and c) 30°C. For each regime of air temperature, relative humidity did not seem to influence tht sporulation for the first hours following the beginning of the experiments. The chaotic pattern of the curves within these time periods promotes this idea (al1 time periods an identified with a dashed mow on the x-axis in Fig. 2.3% 2.3b and 2.3~).This phenomenor was also outlined by Spiers and Hopcroft (1988a) and might be explained by the fact tha basidiocarps were fully hydrated, hence not affected by the ambient humidity. Thi! hypothesis is mercoherent in that the time pend in which humidity does not affeci spomlation seems to be shorter for higher air temperature levels than for lower ones (Fig, 2.3~).This variation in time is indeed due to the hygroscopic nature of the air that varies ir: fiinction of its temperature. However, relative bumidity was found to strongly regulate the sporulation beyond this period of time.

At 10°C, spore emission ceased afler 48 hours for al1 levels of relative humidity (Fig. 2.5a). At 3O0C,only lowest levels of relative humidity (60% and 80%) yielded ni1 values oi sporulation following a similar period of tirne (Fig. 2.5b). Although greatly reduced, development and release of basidiospores continued under highest levels of humidity (90% and 100?4) up to 96 hours following wetting at this temperature. Under favourable conditions, the longest continuous spore release period observed in the field by Spiers (1 985) was 106 hours, which is in agreement with OUT results. At 30°C, spore discharge continued at hi$er Ievels of humidity but was extrerneIy reduced (Fig. 2.5~).Since it has been verified that basidium and basidiospore development depends greatly upon available moishm (Spiers and Hopcroft, I988a), dehydration cm be regarded as the principal cause of termination of sporulation. In the present study, ail basidiocarps subjected to the lowest levels of relative humidity (i-e.60% and 80%) were dry and crusty afier 48 hours following the beginning of the experiment, which sustains this hypothesis. However, we are also aware that ceasing of spore discharge could have been due to the fact that basidiocarps were not attached to any nutrient source, which could have possibly played a role in the formation of new basidia or basidiospores. Considering the hi& generative potential of the hymenium of C. purpureum that has been outlined by Exell (1924) and Spiers (2985), we are tempted to believe uiat this influence bas been small. It has already been pointed out that strains of C. purpureum fiom Europe and fiom Nen ZeaIand were capable of releasing spores under a wide range of environmental conditions (Grosclaude, 1969; Spiers, 1985). Results shown in the present study demonstrate that it is also the case for a Canadian isolate of the fungus. As already reported, rainfdl is essential to initiate spore liberation in the field and a mean relative hurnidity above 90% is required ta aUow a continuation of spore discharge Oye, 1974; Spiers, 1985). Our results fit with these observations, but oniy at air temperature closer to 20°C (Fig. 2.5b). The highest spore releases were obsened 48 hours following soaking at an air temperature of 20°C and under a reIative humidity of 100%. According to this, Grosclaude (1 969) could have underestirnated spore discharge since in his study, mictifications were subjected to sporulation for only 24 hours. At the experimentd site where basidiocarps were collected, 40 basidiocarps were averaged on every inoculated stubs of B. puprifera. Under optimal climatic conditions such as the ones defined in ths study, and considering the high sporulation potential of the fungus, tremendous arnounts of spores might be expected to be reIeased in the vicinity of heavily infested areas. Grosclaude (1973b) argued on the infectiveness of C. ptapureum basidiospores and concluded that as Meas 2 spores could induce pathological syrnptoms on certain susceptible tree species. It follows that one could expect the disease to spread beyond epidemic tresholds, which is not the case. Therefore, this leads us to believe that dissemination of the disease seems to be greatly restricted by other extemal sources such as the environment itself. Indeed, micro and rnacro-climatic conditions are not only regulating spore reIease, but also spore flight, spore survival, spore germination (Grosclaude, 1969), appearance of basidiocarps and to a greater extent, host susceptibility.

In the present study, a single indigenou isolate of C. purpurezm was investigated Yet, there is no information on whether different isolates have different sporulation potentials. We are tempted to hypothesize that, due to the high variation in sporulation within an isolate obtained in this study, there may not be any significant difference in sponilation arnong many isolates. Saturated saline solutions were found to give very accurate values of relative humidity without king, as a side-effect, affected by the change in water content of the basidiocarps. This couId partly explain why saturated saline solutions yield more satisfactor results than do different concentrabons of H2S04or KOH (Winston and Bates, 1960). In fact the latter do not change in vapor pressure as much as saline solutions do with varying ai temperature, hence ofkn yieIding unprecise relative humidity leveis. Moreover, the frequen evaluations of concentrations needed when using those HzSOJ or KOH solutions are tirne consuming and laborious. Our method was also found to be highly versatile in that it migh be fully operational for similar studies using other microorganisms. Chapitre 3

Development of OPEM, a mode1 simulating spore release and dispersal of Chondrostereum purpureum

In Canada, the indigenous pathogen Cltondrosiereum purpureum (Pers. ex Fr.) Poux is s prornising candidate for biological contrai of stump sprouting of several deciduous tree species. When climatic conditions are welt suited, the fungus is producing basidiocarps or treated stubs from which tremendous quantities of spores are released. These spores are Iikely to infect nontarget deciduous vegetation in the vicinity of the treated areas. As an environmental fate assessment, emission and dispersal of spores of C. purpureum from a biocontrol area was simulated through the development of OPEM, a versatile Operational EpidemioIogical Model. The mode1 has ken smctured according to indoor and outdoor studies, to intensive meteorological data collected in situ, to exhaustive field observations and to updated theory of atmospheric diffusion. OPEM partly bases its dispersai computations upon an adapted version of the Gaussian Plume Model (GPM). Depletion fiom both source and plumes of spores due to low windspeed, to scrubbing by rain or to interception by vegetation has been implernented in the algorithm of the program. To avoid an underestirnation of risk related to biocontrol, special attention was paid not to underestimate any biologicaI or aerobiological processes in each step of the simulation process. Predicted results clearly indicate that spore dispersai above a sunounding forest canopy is non-negligible and that wounded trees might be at risk even up to 2 kilometers downwind a treated site. Because of the different parameters and approaches that have beer used, simulated spore concentrations are one order of magnitude lower than the one: reported earIier by de Jong in the Netherlands. Operational and ethical impIications of suck biological treatrnent, as well as limitations of OPEM as a decision support system an discussed.

3.1 Introduction

Recently in the province of Québec, Canada, environmental concerns have led to a political decision airning at banishing the use of chemical phytocides on forest areas by year 2001 (Stratégie, aménager pour mieux protéger les forêts, 1994). This policy is part of a global stratejg based upon the concepts of integrated vegetation management, sustainable forestry and biodiversity maintenance. Nowadays, several silvicultural treatments, such as conife~ release operations, site preparation or vegetation management in rights-of-way, mut be done using chernicals to obtain satisfactory results. Mechanical treatments, which are regarded as the most realistic and operational alternative to chemical herbicides, are in some cases costly and possibly as noxious to humans and to wildlife as the currently registered herbicides are. Moreover, the efficacy of mechanical treatments is iraen altered by the vigourous vegetzttive reproduction potential of many competing or undesirable deciduous tree species. Therefore, there is a strong need to develop alternatives or enhance the efficacy of actual silviculturai treatments. Biological control appears to be a promising research avenue and warrants further attention (French and Schroeder, 1969; Jobidon, 199 1).

In 1992, several experimental sites were established in Québec to evaluate the potential of the indigenous fungus Chonclrostereum purpureum (Pers. ex Fr.) Pouzar to inhibit sturnp sprouting of several deciduous tree species (Gosselin and Jobidon, 1995; Jobidon, 2995). C. purpureum is known as the causal agent of silverleaf disease, a disease occuring on most broadleaf tree species throughout temperate regions of the world (Wall, 1986). In the early eighties, Scheepens (1980) first mentiomed the potential of this fungus to control sprouting of Pmmsertlrim in the Netherlands. The idea was retrieved in Canada by Wall (1990) who tested the fungus against some deciduous tree species under nanual conditions. In al1 of th< above cases, results shuwed that the microorganism could control the vegetative reproductior of many cornpetitive or undesirable tree species, which rnakes it a good candidate as 5 biocontrol agent. However, considering its life cycle, C. purpurem may produce abundm fruiting bodies on treated stubs when environmental conditions are well suited (1-e. mosi ofien during sprïng and autumn)- Basidiocarps are able to hit up to two years following 2 biocontrol treatment and are releasing, wben the weather is favourable, millions of spore! into the environment. Due to the wide host range of the fungus, its airborne basidiospore: may infect susceptible valuable nontarget tree species nearby treated areas (de Jong, 1988 Wall, 1990). A susceptible valuable tree rnight be defined as any ligeous deciduou' vegetation having an economical, ornemental or heritage value and bearing less than z month-old wound(s) (Brooks and Moore, 1926; Grosclaude, 1969, 1973b; Wall, 199 1). It ha: been show1 that most coniferous rree species are not likely to be infected by the pathogeri (Goulet el a/., 1995). In contrast to de Jong (1988) who perfomed a risk analysis foi nontarget vegetation consisting of fruit trees in orcharch, the situation exposed here involvec most Canadian valuable deciduous tree species since these are subjected to infection by C. purpureum (Wall, 1990; Chapter 4).

The present study aims to evaluate spore release and spore dispersa1 of C. purpurem from treated stubs of a forest plantation above a surrounding forest canopy using simulation and meteorological data recorded in siru. This can be done through the development of a pdwalent OPerationaI Epidemiological Mode1 (OPEM) that is based upon updated theoretical and ernpirical approaches of air pollution sciences and aerobiology. 3.2 Marerials and me~hods

3.2. I Studj?site

The experirnental site consisted of a forest clearcut (48'06' of northern latitude and 6717' of western longitude) of 0.4 hectare located near the village of Sainte-Florence, Québec, Canada (Fig. 3.1). The previous forest stand was invaded by young paper birch (Betda pupyrfera ivlarsh.) and pin cherry @unus pensylvanica LX), two studied target tree species, and by mountain ash (Surbus americana Marsh). The total stub density was 15200 per hectare and both previous and surrounding forests had a mean height (H) of 7.5 metres and a mean leaf area index of 1.92. When felled in summer 1994, young hardwood trees were inoculated with myceiium of a selected isolate of C. purpureum. This isolate coded 42 was collected on a papa birch in the province of Québec (Canada) and was used in previous studies (Jobidon, 1995; Chapter 2 and 4). Basidiocarps of the fungus first appeared on the treated stubs in autumn 1994 and in both spring and autumn 1995.

3.2.2 ~MeteoroIogicaldata collection

During spring 1995, a 15 metre mast \vas erected in the center of the clearcut, the closest forest edge being at 20 metres (3H) from it (Fig. 3.2). Wind speed (u), wind direction (0) and the standard deviation of wind direction (ce)were recorded at two levels (7 and 15 rnetres). Air temperature (0was recorded on the entire profile of the mast (O. 1, 2, 7 and 15 metres) while other atmospheric scalars such as relative humidity (H), rainfall (R), net radiation (Rn) and giobal radiation were recorded at one height. Al1 data were recorded on a minute bais and averaged and stored in the memory module every 30 minutes. Steady state conditions were assumed to prevail in these half-hour perds so that a time-series analysis could be performed. Sarnpling was made frorn June 1" to November 10' 1995, which accounted for 162 days of data col tection. No meteorological data was missing during this perïod. 3.2.3 Conception of OPEM

OPEM was stmctured to simuiate aerobiological processes in two general, but distinct, steps i.e. by quantifLing (1) the emission of spores (or sporulation) and (2) the transmission (01 passive dispersion) of spores above the contiguous forest. Rough or hilly temain was no1 taken into consideration when developping OPEM, which therefore assumes a flat anc homogeneous topography in its computational domain. No special treatment was made tc handle intermittent winds (gusts) either since this parameter was not recorded. The prograrr was coded in Microsoft, FORTRAN development system version 5.1,

Fig. 3.1 Geographical location of the study site (the arrow indicates the experimentai site). Spodation depends firstly upon the development of basidiocarps. Field observations during summer 1995 Ied us to assume that 7 days with mean ground-level air temperature above 0°C and below 18°C was enough to anticipate the appearance of fiuiting bodies of C. purpureum. Maximal spore emission was quantified according to exhaustive indoor measurements of spore flux (spores per cm2 of hymenium and per second) as a function of two environmental variabIes: relative humidity and air temperature (Fig. 3.3) (Chapter 2). Determination of a rnean surface area of hymenium per basidiocarp was done by measuring the surface of 164 basidiocarps sampled on IO stubs in the field with a digital LI-COR 3 100 area meter. A mean number of basidiocarps per stub was evaluated by counting fiuiting bodies on every stub inoculated. Here, let SAH be the surface area of hymeniurn (cm2) contributing to spomlation so that:

where MSH is the mean surface area of hymeniurn per basidiocarp (cm2), lMBS is the mean number of basidiocarps per stub, S.is the stub density (stubsfm') and SA is the surface area of the experimental site (rn'). Spore discharge was compiled every half-hours and assumed to be constant for those periods.

Sporulation of C. purpureum was previously shown to be govemed mainly by water content of basidiocarps which in turn is regulated mostIy by three environmental factors, namely rainfdl, relative air humidity and air temperature (Grosclaude, 1969; Dye, 1974; Spiers, 2985). In OPEM, spore emission was computed only when a sigiificant rainfall (objectively taken as 2 0,2mm/hour) was initiating the process (Dye, 1974; Spiers, 1985). When ground- ievel air temperature was above 15OC and for the first 24 hours following the last rainfall, spomlation was simulated until relative humidity was dropping below 50% (Chapter 2). In this case, low relative humidity was not impairing on sponilation since the moisture needed to allow the biotogical process was supplied by the water stored in the fiuiting bodies. Beyond 24 hours, sporulation was modelled to cease when relative hurnidity fell below 85% which agrees with observations reprted by Dye (1974) and Spiers (1985). When ground- level air temperature was below 1S0C, a similar approach was used but with a period of 48 hours (Chapter 2). Logically, this phenornenon can be explained by the hygromeû-ic capacitj of the air in function of its temperature. No sporulation was wmputed when heavy showers (2 6mmlhour) were recorded (adapted from Dye, 1974). The drymg effect of the wind un the water content of basidiocarps was not îaken into consideration at any time. Fig. 3.2 Disposition of meteorological instruments.

300 250 200 Spo~ktion (spoms/crn2~s) 100 50 O O 6 10 15 20 26 30 Air tenqmmture (C)

Fig. 3.3 Sporulation in f'unction of relative humidity and air temperature (Chapter 2). 3.2.3.2 Transmission (dispersion)

Planetary boundary layer turbulent parameters, such as friction velocity lu*),Monin-Obulihov length (L)and sensible heat flux (C) are needed as input in OPEM. These are processed iteratively according to the resistance method proposed by Berkowicz and Prahrn (1982). The foIlowing expressions are used:

Monin-Obukhov length (Monin and Obukhov, 1954):

Logarithmic wind speed profile according to flux profile relationships of Businger el al. (1971):

Sensible heat flux (Berkowicz et al., 1985):

where Sis the air density, c, is the specific heat of the air at constant pressure, k is the von Kiirmin constant taken as 0.35, g is the gravity acceleration, r, is the aerodynamic resistance to evaporation, r, is the surface resistance to evaporation, Dq is the humidity deficit of the air, y is the psychometer constant, A is the gradient of saturated specific humidity in function of air temperature and a, is a proportionality parameter depending on the actual conductivity of the soil. The roughness length (zO)of the treated area is evaluated by OPEM using the following aerodynamic equation under neutral lapse rate, high wind speed conditions and during penod with low to moderate solar radiations (Monteith and Unsworth, 1990: Berkowicq personnal communication):

where u, is tbe windspeed at height ,x. Displacement length (6) for ta11 vegetation i! calculated according to the following expression (de Bruin and Moore, 1985):

where h is the mean height of residual (post-logging) vegetation within the treated area. Dg i! defined as:

where q,(T) is the saturated vapor pressure which is function of air temperature T, and wherf H,the recorded relative humidity, has units between O and 100. In OPEM, a, is set to O.: seeing the bareness of the ground at the experimentai site (Berkowicz, Olesen and Torp 1985). y, is an ernpiricai stability correction fùnction for momentum (Berkowicz and Prahrn 1982; van Ulden and Holtslag, 1985): in cases where computation of Eqs. (2), (3) and (4) is overwhelming a maximum iteratim step value (i.e. 50), L is computed using the gradient Richardson nurnber (Ri,) as describer: by Golder (1972):

L = =/Ri, for Ri, < O (9a)

L = z (1-7RiJ/Rig for Ri, > O (9b)

where Ri, is given by (Goudriaan, 1977):

The aerodynamic resistance to evaporation r, is computed iteratively dong hith Eqs. (2), (3) and (4) and using the following formula (Berkowicz and Prahrn, 1982):

where R equals 0.74 and zt is taken as 15 metres. y/h is an ernpirical stability correctior function for sensible heat (Berkowicz and Prahm, 1982):

The surface resistance to evaporation r, is cornputed by OPEM using the folIowing relationship (Berkowk and Prahrn, 1982): where F is an energy flux related to the surface water vapor flux which is function of both rneasured net radiation (Rn)and hourIy-accumulated net radiation (ZR,,).

The Eulerian approach used in OPEM requires that the aforementionned 2-D profile schemes are assurned to be homogeneous throughout the computational domain. This assumption is not reflecting reality, especially during summer time when stability above a forest is largely unreiated to that over an opened area (McNaughton, 1989). However, since spore emission and transmission occurs mostly in spring or in auturnn under eastern Canadian climatic conditions (when tree foliage is absent), the influence of the forest on the turbulent scalars is greatly damped and the assumption may hold.

3.2.3.2.2 Dispersal within the forest dearcut

Forest clearcuts are generally characterized by chaotic fluxes of heat and momentum (Bergen, 1975). Following an emission, spores can either impact on the forest clearcut floor, on elements composing the nearby forest or escape above forest height (Fig. 3.4). An escape fraction UBS quantified according to dynarnic stability of the atmosphere using both stability parameters L and Ri,. When the turbuient flow was found to be near neutral (ILI > IOOO), the escape fraction was first estimated using simulated steady state horizontal and vertical windspeeds and strearnlines provided by several nins of the program UCFWF conceptualized at the University of Connecticut (Miller er al., 1991). As needed by the program, a representative vertical leaf area distribution of the surrounding forest was rneasured at the experimental site using a portable LI-COR LAI-2000 (LI-CORInc., Lincoln, Nebraska) and implemented as an input file (Fig. 3.5). Fraction of spores escaping fiom the computational domain of the forest clearcut was also compared with graphical estimations fiom Raynor (1971) and from Raynor et al. (1974). Under neutral conditions, a the-average escape fraction of less than 30% of the amount of spores emitted for the half-hour was estimated This percentage was estimateci to increase up to 40% when convective iapse rate (-1000 IL < O), leading to stronger vertical air motions, was fond As described in de Jong (1988), no spores were assumed to escape when a strong thermal inversion (Ri, 2 0.1) or low windspeed conditions (c 0.5 ds) occurred. Below this vebcity, sedimentarion process was assumed to dorninate over other dispersion processes such as mechanical or convective dispersai. Fig. 3.4 Schematic representation of spore dispersal within the computational domain of the forest clearcut.

Fig. 3.5 Mean vertid distribution of foliage at experimental site in Ste-Florence. 3.2.3.2.3 Dispersal above the surromdingforesr: The Gaussfan Plume Model

No special attempt was made to handle spore dispersal within or beneath the canopy of th1 nearby forest. Findings of Raynor et al. (1974) which showed that less than 10% of particle with a mean diameter of 15 p were remaining airborne at 100 metres inside the forest, art suggesting that trançportation within and beneath a canopy is of smailer magnitude thar transportation above it. It appears that impaction of spores on the forest floor, trunks branches and leaves in conjunction with reduced wind speed encountered beneath a canopy account for this limited transportation (Raynor, 1971; Raynor et al., 1974; Legg am Bainbridge, 1978; Fritschen, 1983; Stull, 1988; Aylor, 1990; Jacobs and van Boxel, 1991) Besides, Tauber (1967) observed a strong filtration of pollen by forest vegetation. Partich transportation seems to be further limited in that updrafis and backdrafi from the forest tc: the open area appear to be cornmon (Van Arsdel, 1967).

For dispersai in the contiguous field and considering the low sedimentation velocity of tb 5pn spores of C. purpureum, an adapted version of the well known Gaussian Plume Mode: (GPM) was applied. Much use is made of the GPM and the model serves nowadays as a regdatory tool for atmospheric pollutants dispersal in many counîries. Models such as Oh4L in Den..uk, HPDM in the USA and ADMS in the UK have been built upon a Gaussian dispersion scheme (Berkowicz et al., 1985; Hanna and Paine, 1989; Carmthers et al., 1992). Gregory (1945) suggested the use of this rnodel for aerobiological purposes. Mason (29791 and McCartney and Fitt ( 1985) promoted the same idea in their respective reviews. Elkinton et al. (1984) applied the GPM for simulating pheromone dispersal in a deciduous forest while Carney and Dodd (1989) used the model for dispersal of maiodours. De Jong (1988) modelled spore dispersal of C. purpureuni within and outside a forest in îhe Netherlands using this model. In Canada, Di-Giovanni et al. (1989) investigated the use of the GPM to simulate tree pollen dispersal within a forest canopy.

In the Gaussian Plume Model, the time-average spore concentration C (spores per cubic metre) at a distance x downwind is given by (McCartney and Fitt, 1985): where Q is the source strength (spores emitted per second) seen as a point source, x, y and = are the Cartesian coordinates of the location where C is calculated, q.and a, are the standard deviation of the Gaussian distribution and h is the source height. In the present study, r is rneasured with respect to crop height and h = O. Furthemore, we want to obtain the canopy

level concentration aIong the plume centerline (z = O) where the maximum concentration of spores is expected. Equation (9) then simplifies to:

The vertical standard deviation (02 is modeIled according to formdas cited in Berkowicz et al (1985). Briefly, this scheme is expressed as a surn of a convective and mechanical contributions that are assumed to be uncorrelated:

Mechanical contribution is a direct fùnction of PBL stabiIity parameters such as the ones computed wlth Eqç. (2), (3) and (4) while convective part is fünction of the mixing depth (z,) and of the convective velocity scale (W.). Sensitivity analysis showed that Eq. (16) has sirnilar performance during convective daytime conditions as (Hanna, 1982):

where r is the travel time defined as du. Mixing depth of the planetary boundary layer (z,)is computed under neutral stratifications according to the method described in Hanna and Paine

(1989) while OPEM uses the following expression for W. (Berkowicz et al., 1985) : No reflection on the top of the boundary-layer is assumed. Lateral dispersion (5)is computed from direct measurement of half-hourly averaged lateral wind direction fluctuations at forest height (m)by the mean of the following expression (Draxler, 1976):

where F, is a universal function of both r and horizontal Lagrangean time scale TV and is defined as:

Here, we use Draxler's (1976) method and T, is taken as 300 s for a ground level source and

as 0.001t2 when 1 is greater than 550 S. In the Gaussian equation, no loss of particies is assumed from the plume. However, OPEM inteptes analytical formulas of Belot et al. (1976) who modelled depletion of 5 p particles from a plume above a -pical forest canopy in function of friction veloci~.Scmbbing of particies by rainfall, leading to a fraction of spores that are still being windborne (E,4), was also modelled in OPEM by implementing a simple algorithm adapted from Chamberlain (1953) cited in Pasquill and Smith (1983):

Where E is the number of spores escaping above the forest height and R is the rainfall expressed in mm.

Logically, spore transmission would be expected to be higher in the prevailing wind direction. However, since spore emission may not coincide with winds blowing in the prevailing wind direction, transmission was computed in dl directions around the biocontra area. Dispersion output of OPEM is calculated from time series of spore concentrations ani is mainly: (2) maximal half-hourly-averaged spore concentrations (Cm) at any Iocatioi around the area and (2) any percentile of daily mean spore concentrations at the saml locations. De Iong (1988) suggested b rely upon tbe 90' percentile of daily mean spori concentrations (Cw)as an indicator of spore concentration in the air. This value represent the highest daily mean spore concentration at one location to be expected for 90% of thi days. In the present study, we have chosen a more severe criterion, the 9gh percentile of hatf hourly averaged daily mean spore concentrations (&) as an indicator. in the present case the use of such a sensitive criterion may be usefùl under dry and Sam summer condition: that would lead to very few periods of sporuiation. We believe that using the 90~percentile which is less sensitive, wouId greatly underestimate the sporuiation under such climatic conditions. Therefore, a pessimistic approach was adopted throughout the study so that thr environmental risk associated to inundative release of C. purpurem os a biocontrul agen was not biaised toward an underestimation.

OPEM was fed with 7780 haIf-hours of meteoro~ogical data. Summer 1995 wac characterized by an unusual warrn weather while darnper air coupled with lowei temperatures were observed in autumn 1995. Overall, 439 mm of min were recorded withir: only 6% of the period under investigation. June was exceptionnaly dry shouing a few puncruai, but strong, rainfalls totallzing 41 mm. Heavy rainfall (2 6mmlhour) occured for only 0.2% of the time. More than 70% of total rainfall occured &er first appearance of basidiocarps which was simulated to be in Iate Aupt (Julian &y 240) (Fig. 3.6). OPEM attnbuted to the experirnentd site a mean roughness length (q,)and a displacernent length (6) of 1 metre and 0.07 metre, respectiveIy. Using treshoids suggested by Erbrink (1994), stable atrnosphere (O 5 L ,< 1000) occured for more han 50% of the time while unstable lapse rate (-1000 5 L < O) accounted for 36% of the tirne. Neutra1 stratification (KI > 1000) occured fo only 13% of the half-bours analyzed SimuIated rnixing depths were mostly occuring in th1 range 0-100 metres but ranged up to 2400 metres. Prevailing wind direction was in the 70" 80" sector while fiequency of wind direction between 180" and 280' fiom North was Iov (Fig. 3.7).

Mean surface of hymeniurn per basidiocarps \vas estimated to 1.98 cm2 and an average of 4( basidiocarps per stub were counted for B. pWnferu. Only 3 and no basidiocarps per stul were observed on S. americana and P. pen.sy/vanica,respectively although the latter specie! is sometimes known to bear many fniiting bodies (L.Gosselin, persona1 communication) This accounted for a total hymenial surface of 43.7 m2/ha Spore discharge was predicted fo: only 16% of the entire period andyzed Limiting factors were mainly the absence O: basidiocarps (54% of the tirne), the absence of rainfall coupled with dry weather (55% of thc time) and unfavourable air temperature (13% of the time). The mode1 computed a mear spore discharge of 1 568 770 spores per second for the entire period analyzed. As describec above, a significant rainfalt \vas essential to initiate sporulation (Fig. 3.8). When spores wert emitted without any rainfall (e-g. Julian day 246), this meant that a sufficient basidiocaq water content or the occurrence of a mean relative hurnidity that was moistunzing the basidiocarps was allowing discharge of spores. The longest predicted unintempted spore release period was 59 hous and occured between Julian days 244 and 246 which w characterized by a mean reIative humidity above 89%.

3.3.3 Transmission

Slightly more than 29% of total spore discharge was predicted to escape fiom the clearcut area and above the mean forest height for the entire period under investigation. Washout by rain, low windspeeds (< 0.5 m/s) and the stability of the attnosphere accounted for îhi! percentage. Low windspeeds oçcured for only 8% of the timc analyzed while no thermal inversion was predicted. Integration of equations of particies interception sketched fiom Belot et al. (1976) had a negligible quantitative effect on spore depletion from the plumes since only few rainfalls occurred. The main reason explaining this observation is the proportionnality that exists between interception rate and particle size.

Highest Cg9values were not forecasted to be in the mean wind direction sector, confirming that spore release and wind bIowing in mean unid directions are not necessarily coinciding. Highest spore concentrations were rather predicted in the 290" - 300" sector (Fig. 3.9). In this direction, OPEM predicted a ni1 Cg9value at 2.5 km downwind fiom the treated area. At 500 metres downwind, the mode1 predicted a maximal C99value of 16 spores/m' and at 1000 metres, this value dropped to 4 spores/m3(Fig. 3. IO). Fig. 3.6 Daily mean temperature and simulated spore ernission.

Fig. 3.7 Frequency distribution of wind direction as measured in Ste-FIorence (Canada) in summer 1995. Fig. 3.8 Variation of simulated daily sporulation according to observed precipitations.

Fig. 3.9 Predicted values of Cw in the vïcinity of the biocontrol site. The area covered by the map is equal to 25 km2 and the biocontrol site is in the center of the map. 3.3.4 Epidemiological impacts

Defining a maximal treshold of spore concentration in the air (CMAX,)can be done througl the detemination of two different concepts, namely the deposition velocity (va and thi infectiveness (minimal number of basidiospores required to induce infection in a tree) of thc spores of C. purpureum (MNUprind)in such a way that:

InitialiUng v, requires knowledges of particle size and of on-site informations such a: vegetation type and u. (E3elot er al., 1976; Little, 1977; Bache, 1979). T is the time O: exposure (e-g. 24 hours in our case) and A is the surface area of a typical wound on fores trees objectively taken as 20 cm2. The value MINWoundcm be estimated using data fiom th€ literature. Grosclaude (1973b) argued on the Iow effectiveness of C. purpureum to infec, wounded trees. Accordmg to his observations, it seems unlikely that one spore coulc establish infection in a wound. Instead, it appears that infection of a susceptible tree (Pruma sp.) requires at least 22 spores to induce significant silverleaf symptoms while inverseIy, nc infection occurs if too many spores are germinating on a wound. However, infection of 2 fresh wound has been exceptionally reported in the same study using only 2 spores of the pathogen. Spiers and Hopcrofi (1988a) dso reported that only a few basidiospores are required to initiate infection while de Jong et al. (1990a) showed that 10 spores on a wound were enough to cause infection at 20% probability.

Thus, adopting a pessimistic approach, if MNWodis set to 2 spores and that v,, under reasonable friction velocity, is taken as 0.01 mls (Belot et al., 1976; Bache, 1979), the maximal steady state spore concentration allowed in the air to induce infection would be as low as 1 spore!m3. Beyond 2.5 kiiomeees downwind fiom the treated ara, this value is not reached in 99% of the time. This is not the case for distances less than 2.5 kilometres fiom the treated area. Fig. 3.10 Predicted maxima1 Cg9values in function of distance from the experimental site (predicted at 290" from North). Although uing a more severe criterion (C9& spore concentrations predicted by OPEM an one order of magnitude lower than those presented by de Jong (1988). Many factors explaii this difference and one of them is the fact that the expetimental site used in the present stud! was more than 16 times srnaller than the mode1 forest conceptualized in the Netherlands

Another aspect explaining this disparacy is the presmce of basidiocarps. En OPEM, ai ernpiricd procedure has ken implemented to simulate their appearance on treated stubi while it was assumed that basidiocarps were present at al1 times in the Netherlands. A thirc factor is both a;. and a, schemes computed by OPEM which were higher than thosc computed by de Jong, when compared under similar dimatic conditions. In the mode presented here, these schemes are continuous functions of stability parameters which an believed to give a more sophisticated representation of atmospheric turbulence in terms o. convective velocity scale, friction velocity and Mo&-Obukhov Iength. Furthermore, thesr scalars are processed according to real on-site meteorological data measurements. Pasquill'! dispersion parameters used by de Jong are based upon ernpirical stability classes that arc strongly biaised toward neutral stability when convective conditions prevail (Weil, 1985) anc are only valid for opened areas and for sampling times of 3 to 10 minutes, hence limiting their use for the present study (Pasquill and Smith, 1983; SchuIze, 1994). Moreover, rainfall was not considered as the initiator process of sporulation in de Jong's model, aithough previous studies have show the importance of this aspect (Dye, 1974; Spiers, 1985; Chaptei 2). Instead, de Jong supposed that sporulation was solely fùnction of air temperature and was predicted to be initiated each time that the relative humidity was above 90%. This increased substantially the predicted frequencies of spore emissions in his case. Ln the present study, spore discharge kvas simulateci in function of both relative humidity and temperature while the process was predicted to be initiated by a significant rainfall. Finally, the summer 1995 was charaçterized by both dry and warm weather, which could explain Iower spore concentrations in the air predicted by OPEM. However, spore emission rates integrated in OPEM were consistently higher under certain conditions than those used by de Jong (Chapter 2). For the sake of cornparison, the highesi spore release modelled by de Jong (1988) was 176 spores per cm2 of hymenium and pei second at 15°C- This vaiue was extrapoiated fiom results outlined by Grosciaude (1969) wha studied sporulation of C. purpureum for periods of 24 hours. It has been shown that a peak level of spore £luof 260 spores/cm2/s cm be attained but at 48 hours following the lm hydration (or rainfalI) (Chapter 2). This maximum value is typical for temperature closer to 20°C, which fi& observations reported by Grosclaude (1969) and Spiers (1985). It is probable that this dissimilarity could have been introduced by intrinsic sporulating potentid of the different isolates studied in each of the study.

Whether one should rely on fiequency-based parameters such as percentiles of spore concentrations or on the most pessirnistic approach, 1.e. the maximal half-hourly-averaged spore concentration, is questionable. In OPEN the choice of relying on either parameter is up to user's discretion. However, one must recall that a tree is susceptible to spores of C purpureum only when it has been fieshly wounded. Many other factors influence or limit both spread and development of the silverleaf disease into the environment: climatic conditions regulating rel ease, dispersion, deposition, suMvaI and germination of spores and both the stress level and intrinsic susceptibility of nontarget hosts (Grosclaude, 1973; Wall, 1991). Similarly, these factors would greatly restrict the development of the disease introduced artificialiy into the environment. Moreover, target tree species treated also limit the occurrence of the siIverleaf disease by influencing the presence and the number of basidiocarps. Following a biologica1 control treatrnent, added spore concentrations in the air would then be expected to drop rapidly below natural tresholds. in Canada as well as in other Nordic countries such as Finland and Sweden, C. purpureum is cornmonly found in the forest and Our experience as well as informations available in the literature demonstate that stumps in logged areas, woodpiles or pruned trees are the most important vectors for silverleaf disease (Setliff and Wade, 1973; Goulet, personal observations). It is not unlikely to observe a significant punctual increase of the natural population of C. purpureum dose to these sites, which definitly have similar environmental impacts as a biocontrol area would Considering dl these biological aspects of the situation and despite that predicted maximal half-howly- averaged spore concentrations (Cd were significantly higher than Cg9values, we are tempted to state that a choice based on muencies of spore concentrations such as daily perceutiles seems to be logical, safe and redistic.

in this study, no attempt was made to mesure the natural occurrence of C. purpurem spores nor to define the relative risk defined as the ratio of added spores load to the background spore load (de Jong et al., 1990a). We have assurned that a relative risk of 1 as modelled by de Jong et al. (1990a) wouid be uncertain in our circumstances since (1) this wouid double the spore load in the atmopshere, (2) most Canadian deciduous tree species are susceptible to be infected by the microorganism (Chapter 4) and (3) some of these species are potentially more susceptible than those tested in the NetherIands (Chapter 4). Instead, we have merely supposed that risk was non-significant where ni1 values of Cg9were predicted.

Spore concentrations were computed in OPEM using probabilities and a statisticd approach, and many aspects iduencing spore dispersion, such as the wind field around the biocontrol are- the topography or the heterogeneity of the vegetation affecting microclimatic conditions, have been deliberatly neglected or cmdely defined when deveioping the model. Therefore, as de Jong et al. (1990a) outlined, the order of magnitude of predicted spore concentration downwind fiom a treated area is more of relevance than any punctual concentrations (in time) simulated by the model.

The environmental fate assessment presented herein was based upon on-site observations, concepts of aerobiology and updated theory of air pollution. Results were predicted fiom a Canadian forest clearing using a meteorological data set recorded in summer 1995. It has ken simuiated that spores of C. purpureurn can indeed be passively disseminated over relatively long distances. Topics on epidemics of the disease such as the viability of the disseminated spores, the period of susceptiàility of specific nontarget wounded hosts and the infectiveness of spores to Canadian me species have yet to be defined to proceed with a complete environmenta1 risk assessment related to the use of C. pwpureum as a phytocide. This would ascertain decisions about defiriing the extent of a possible baer zone between treated areas and sites with sirsceptib1e tree species.

A main feature of OPEM is its use of on-site haIf-hourly-averaged meteorological data in order to simulate time series of spore concentrations. The rnodel is also implemented with exhaustive data of sporulation that has been quantified using an isolate that has been chosen for biocontrol operations in the field. In the development of the model, emphasize was put on creating a polyvalent tool, z.e. the mode1 had to be operational and easily adaptable to other forestry realities. Four years of observations made on eight experimental biocontrol sites served to provide OPEM with this high versatiiity (Gosselin and Jobidon, 1995; Jobidon, 1995). This feature permits the use of the mode1 for performing Merrisk analyses related to biocontrol in forestry, and thereby, allows the fiitwe users to enter through a validation phase which would be highly desirable. This codd be done by plotting observed values against either predicted values of daiiy mean, total or maximal half-hourly-averaged (Cm) spore concentrations, three outputs provided by OPEM. However, because of the small size and the cornmon shape of the spores of C. purpurem, direct validation would require laborious and fastidious work. Indirect validation could be seen as a more feasible and realistic solution. This might be achieved by wounding susceptible trees al1 around a biocontrol site while basidiocarps are sporulating and by relating occurrence of infection by the introduced organisrn (confinned by Koch's postulate and genetic fingerprints) to spore concentrations predrcted by OPEM. Development of a model handiing particle dissemination within a forest clearcut that would be vaiid under ai1 atmospheric turbulence classes (stable, neutral and unstable) wodd also be desirable to add accuracy to OPEM. Because the model has been structured in a rnoduktr fomt ailowing further modifications to the source code, this could be made easily. Recently, an indirect validation phase has been initiated to calibrate predictions computed by the mode1 to observed levels of disease encountered in the field. Biological control with a fungai pathogen having such a broad range of hosts cm hardly be made without nsing ethical questions about the assoçiated environmental risk Suppression of sporuiation of C. purpurem can undoubtly be regardecl as the best option fiom an environmental risk point of view if the pathogen is appiied as a biocontrol agent ExampIes such as suppression of sporuiation of Bonytis spp., a pathogen of agriculfural crops, confirm that this idea is getting accepted as a good biocontrol strategy (Kohl er al., 1995). Selection of a strain of C. purpureum that would combine virulence and an asporogenic behavior would definitiy void the uncertainties and risk related to a standard biocontrol treatment. Genetic transformation couId also be regarded as a soIution to obtain asporogenic isolates. However, as required by the actual Canadian IegisIation (Agriculture and Agri-Food Canada, 1993), introducing these strains in an ecosystem would cal1 for exhaustive studies to assess their environmental fate. Chapitre 4

Virulence of selected isola tes of Chondrosiereum purpureum to several Canadian deciduous tree species

Abstract

The virulence of welve selected Canadian isolates of Chondrosfereumpurpureum wai verified in a tunnel experïment using 1690 seedlings of nine common tree species amon{ which one species \vas represented by five geographical provenances. The experirnent wa! conducted over two consecutive growing seasons to ver@ the long term effects of sud treatment. Results showed that al1 species under study were susceptible to infection by thr mycelium of the pathogen. Following one growing season, only 9.2 % of the inoculatec seedlings in which the fungus was recovered died despite the fact that they were previouslj topped at 10 cm above ground level. At the end of the study, mortality reached a level of 28.C %. When confounding al1 tree species, a slight but significant difference in vinilence wa( found arnong the isolates @ < 0.05). A significant variation @ c 0.05) in susceptibiIity to the pathogen as also found arnong tree species. Al1 seedlings of Paper birch (Betula papyrifer~ Marsh.) showed a certain sensitivity to isolate 492, an isolate causing very litde mortali~ among other species. This outlines that mortality induced by C. purpureum is governed bj the host-pathogen interaction and, to a greater extent in this case, by the susceptilbility of the host. B. papyriferu was the most susceptible species to be killed by dl strains of the pathogen. In terms of virulence, this study promotes the view that geographic races are absent within the Canadian population of the pathogen. Operational implications such a selection of sh-ains for field applications and nontarget impacts of such biocontrol treatments are discussed

4.1 Introduction

In Canada, mechanical clearing treatments are largely used to release young coniferous species fiom hardwood competition and also to manage undesirable vegetation in rights-of- ways. Although highly versatile, these treatments are ofien of low efficacy in cases where tree species having high potential for vegetative reproduction (e.g. stump sprouting) are involved. Enhancing the efflcacy of this type of treatment appears essential to provide a viable alternative to chernical phytocides by controlling such vegetative reproduction. Otherwise, the long term econornical rentability of these clearing treatments might not be justifie4 several treatrnents in a site presenting tree species reproducing themselves asexually being needed. Among the most promising alternatives in forest vegetation management, biological control is of great interest (Jobidon, 1991). Recently, studies have demonstrated the effkacy of the fungus Chondrosfereumpurpureuln (Pers. ex Fr.) Pouzar to inhibit sturnp sprouting of several inoculated hardwood tree species (Wall, 1990; Wall, 1994; Gosselin and Jobidon, 1995; Jobidon, 1995). C. purpureum is an indigenous pathogen in Canada (Wall, 1986; Maruyama and Hiratsuka, 199 1) causing silverleaf disease, a disease encountered on most deciduous tree species but mostly reported on ornemental and hit trees (Brooks and Moore, 1926; Bennett, 1962; Bishop, 1979; Spiers, 1985). This microorganism could be regardeci as a potential biophytocide for release treatments in forest plantations and for vegetation management in rights-of-ways.

In Canada, little information is available to underlay the selection of an isolate as a biocontrol agent in operational treatments. Little genetic variation has been observed in several isolates of the fungus (Shamoun et al., 199 la; 199 1b) while the absence of biotypes or geographic races within C. purpureum has been suggested using biochemical techniques (Ekramoddouilah et al., 1993) and analysis of DNA polymorphisms (Gosselin et al., 1996) Afongside, the panrnictic nature of the pathogen led Gosselin et al. (1996) to hypothesize tha a single genotype could be used as inoculurn accross Canada without significantly modifyiq the genetic structure of the population.

In regard to vinilence of a selected strain, ElÛ.amoddoulIah et al. (1993) found that mos isolates of C. purpureum tested on balsam poplar cuttings ( balsamfera L.) variec slightly in virulence and fell within an intermediate group of virulence. In a different study Wall et ai. (1996) assessed the virulence of a selection of rnonokaryotic and dikaryotic isolates of C: purpureum on plant tissue cultures and on rooted baisam poplar cuttings. Feci significant differences in virulence arnong isolates were observed in this study whilc virulence did not seem to be associated with family origin.

The present study aims to compare the viruience of a selection of Canadian isolates of C. purpureum towards a broad spectnun of target and nontarget cornmon deciduous tree species inchding different ecotypes of one tree species, and to compare the response (i.e. susceptibility) of these tree species.

4.2 Malerials and methods

In a first expriment, the virulence of twelve dikaryotic isolates of C. purpureum towards one year-old seedlings fiom eight tree species was evaluated. A factorid design was used to allow evaluation of both the vindence of each fungal isolate and susceptibility of each species. These tree species were: paper birch (Betula papyriferu Marsh.),red (Quercus nrbra L.), black cherry (Prunus serorina Ehrh.), silver (Acer saccharinurn L.), trembling (Populus trernuloides Michx,), eastern cottonwood (P. deltoides Bartr.) and balsarn poplar (P. balsamrfea), sis well as McIntosh trees (Malus spp.) known to be one of the most susceptible fittree species (Borecki et al., 1978). Both P. balsamfera and P. deltoides were grown fiom 30 cm cuttings while Malus spp. were grafts (those will be nmed seedlings for the sake of simplicity). AI1 seedlings were obtained ffom provincial oi private tree nurseries. The isolates used were randomly selected within their respective Canadian geographical originç (Table 4.1) and were taken fiom a bank conserved in Iiquic nitrogen at our laboratov. in a second study, a similsu factorial design was used to evaluate the virulence of the same twelve isolates to two year-old yellow birch (Betula alleghaniensi~ Britton) seedlings fiom five geographical origins within the province of Québec (Table 4.2).

Mean height of a11 tree species ranged ffom 64cm to 96cm. Seedlings of the first study were potted in 1.5 L recipients one month and a half pnor to inoculation while the five provenances of B. alleghaniensis seedlings were potted one year pnor to inoculation. Seedlings were fertilized weekly with sdutions of N-P-K,fiom potting (May 11" 1995) to seedling dormancy (late September 1995), and fiom early spring of the following year (May 1996) to the end of the study (July 1996).

Al1 seedlings were healthy and photosynthetic when inoculated (June 19' 1995). Balsarn poplar cuttings were also rooted when inoculated. in both experiments, treatment consisted in topping every seedling at 10 cm above soi1 surface (at least two buds remained on each stem) and in applying 2.5 % malt-extract agar discs infected with actively growing myceliurn of C. pzcrpureunt on the cut surface. Mycelium was previously found to induce more symptoms than spore suspensions on different host trees (Grosclaude, 1964; Trandafirescu et al., 1985). Control treatments consisted in applying stenle 1.5 % malt-extract agar discs on the cut surface. Each treatment was repeated tenfold for both experiments, hence requiring 1690 seedlings. Agar discs were covered with a plastic wrap (ParafilmfM)to prevent dessication of the inoculum, hence promoting infection. This technique of inoculation, although inducing severe stresses to the host, was previously found to be the most capable of differenciating groups of virulence among several isolates (Goulet et al., 1995). Table 4.1 Characteristics of the isolates used

holate' Host Origin (Canadian e~ozone)~

B. papyrfera 2 (Montane cordille ra) B. papyrflera 2 (Montane mrdillera) Alnus mbra 1 (Pacific maritimes) B. papyferu 4 (Mixedwood plains) Malus spp. 1 (Pacific maritimes) Prunus spp. 4 (Mixedwood plains) Malus spp. 4 (Mixedwaad plains) B. papyrferra 5 (Boreal shietd) P. tremufuides 5 moreal shield) B. papyrifera 5 (Boreal shield) P. frcmuloides 5 (Boreal shield) B. papyrfem 4 (Mixedwood plains)

I Prefix in the code refers to the origin of the isolate; (BC)British columbia, (E) Canada outside the provinces ofQuébec and British Cohbia, (Q)prownce of Québec; Ecozone: large and very generalized ecologically distinctive areas based on interplay of landforni. water, mil, clirnate. flora, tàuna and human factors (Agicdture and Agri-food Canada, 1993).

Table 4.2 Gtographical ongin of yellow birch seediings.

Code Ecological region' Location

A Ab zefet um balsamcae - Bel ulet um papyr fera (5d) Harvey Township

B Berula alIeghanzensis - Aceratum sacchari (3b) Blake Township

Betulu alleghniensis - Aceratum sacchi C and Madawaska Seigieury Abietetunz halsnmeae - Berulerum ppyrr;fera (4a)

D Betula alIeghaniensis - Aceratum sachari (30 Batiscan Seigneury

E Betulu alleghaniensis - Acerarum sacchari (3j) Campeau Township

I Codes in parentheses were taken hmecological region mapping from Thibault (1 987). Following the treatrnent, each pot was placed in a covered tunnel according to a completel randomized design. The roof of the tunnel was semi-transparent, hence allowing naîural lia to penetrate in the tunnel. Symptoms such as leaf silvering, leaf wiltering and bark necrosi! as well as mortality were noted bi-weekly. Signs such as the presence of basidiocarps o mycelium were also monitored SarnpIing of woody tissues was made to correlate rnortalit to the presence of the pathogen. This was done by plating samples taken at different height along the stem (1, 5 and 9 cm above root collar) on 1.5 % malt-extract agar in which 5 ppn of Benomyl was added to restnct the growth of other microorganisrns without affecting th development of C. pzrpureum (Pittevils er al., 1979). Seedlings were considered infecte4 when the fungus was re-isolated hm at least one sample. The experiment wa conducted in both summer and autumn 1995 and in spring 1996 while seeding overwintered in the tunnel between these periods.

Analysis of both fiingal virdence and tree susceptibility was done using two distinc approaches. in a first analysis, a relative vinilence aiming at comparing mortalities caused b! every isolate was determined. This method allowed to identie groups of virulence thar coulc lead to an eventual selection of an isolate as a biocontrol agent. A similar analysis wai performed to measure a relative susceptibility arnong the different tree species under study Ï.e. identifying groups of susceptibitity. Because of the binomial distribution of the dependen variable (mortality frequencies), the iikelihwd G-test (Sokal and Rohlf, 1981) was used tc evaluate both relative fungal virulence and relative tree susceptibility.

In a second analysis, the absolute virulence of every isolate under investigation was assessed This approach sought to detemine the hgal virulence in regard to a total control(l00 % 01 mortality). The absolute virulence (1.3 was ciassified according to a method outlined bj Trmdafirescu et al. (1985) but using a different nomenclature such as the one suggested bj Andrivon (1 993): V = i~ocuous(1) when the isoIate \vas causing no mortality (O %); V = Slightly virulent (SV) when causing mortaliiy rates between 1 % and 20 %; V = Moderatelj virulent (MV) when causing mortality rates between 2 1 % and 40 %; V = virulent (V) when causing mortality rates between 41 % and 60 %; V = very vident (VV) when causing rnortality rates behveen 6 1 % and 80 %; V = Extremely vident (EV)when causing mortality rates higher than 81 %. Sirnilarly, an absolute tree susceptibility (S) for every tree species was quantified as foIlow: S = Resistant (R) if mortality rates were ni1 (O %); S = Tolerant (T) if mortality rates fell within a range between 1 % and 20 %; S = Moderately toIemt (MT) if mortality rates were between 21 % and 40 %; S = SIightly tolerant (ST) if mortaiity rates were between 42 % and 60 %; S = Sensitive (S) if mortality rates were between 6 1 % and 80

%; S = Very sensitive (VS) if mortality rates were higher than 81 %. For both analyses (relative and absolute), mortality induced by the fungus (when the pathogen was recovered from woody tissues) was the sole &ta considered and anaiyzed.

After one growing season (up to week 12, see Fig. 4.1), 217 inoculated seedlings out of the 1560 treated were dead (13.9 %). Among these dead seedlings, C purpureum was recovered fiom only 144 seedlings, which is equivalent to a recovery rate of 66.4 %. Recovery of the fungus proved that al1 tree species were susceptible, to different degrees, to infection by the rnicroorganism. In spring 1996, mortality of 3 15 additional seedlings was observed despite a constant decrease in previous auturnnal levels of rnortality (Fig. 4.1). This phenornenon suggests that the pathogen remained active foliowing seediing dormancy or preceding bud- break.

C. purpureuni was recovered fiom 293 of these 3 15 seedlings, yielding a higher recovery rate of 93.0 %. The low autumnal recovery level of the fungus, mainly observed within the first four weeks of the study (Fig. 4. l), suggests that early cases of mortaIity were rather the result of a severe physical stress induced by manual cutting instead of by the sole lethal effects of the pathogen. Precocious mortality amonç controls sustains this hypothesis. Low recovery levels were especially observed among P. balsamrfera and P. rremuloides seedlings, with autumnal recovery levels of only 5.3 % (111 9) and 26.3 % (1 0/38), respectively. This low susceptibility contrasts with the interpretation made by Ekramoddouilah et al. (1993) who correlated death of P. bahamifëra cumngs to pathological effects of the fiuigus. However, in their study, no attempt was made to recover the pathogen fiom dead 6-10 cm cuttings in order to correlate mortality with the development of the fungus within host tissues. The low recovery level of the fungus in balsarn poplars obtained in the present study also contrasts with the levels obtained by Wall et al. (1996). In their experiment, the pathogen was recovered from 77 % of dead rooted cuttings.

re-isolation

O Dead

2 4 6 8 10 12 SQ Total

Fig. 4.1 Bi-weekly progression of seedling mortality.

Overall, C. purpurmm was re-isolated fiom 437 out of 532 dead seedlings (82.1 %) at the end of the experiment, which represents a total mortality level related to the fungus of 28.0 % (Fig. 4.1). When not considering both aforementiomed poplar species, this percentage of recovery in dead seedlings increased to 90.6 %, which is closer to previous results and to detection treshold of the sampling method (Goulet er al., 1995). However, leaf silvenng or bark necrosis on dead secdlings in which C. purpureum was not recovered were such symptoms suggesting greater frequencies of infection. When gathenng a11 Mates into one single population, a significant difference (p < 0.05) i relative susceptibility among tree species was found (Table 4.3). B. papyrfera u.os by far th most susceptible species showing 76.7 % (92/120)of mortality at the end of the study. Th pathogen was re-isolated fiom 94.8 % of dead seedlings for this species. in contrast, A saccltarinum and al1 poplar species were the least susceptible species under study showing ii al1 cases less than 10 % of mortality. The poplar species, namely P. deltoides, P. balsamfer4 and P. iremuloides, showed sirniIar @ > 0.05) treatment responses to the pathogen (Tabll 4.3). Despite showing severe leaf silvering throughout the study and contrary to expectations Mcintosh apple tree seedlings were unlikely to be kiIled by C. purpurem with oniy less thai 3 % (3i120) of mortality. Noticeably, isolates E20 and 44 that were both previously collectec on an appie tree did not cause any mortality on A4ulus spp. (TabIe 4.1). Leaf silvering was alsc observed on both Q. rubra and P. serottna, Iasting up to natural leaf senescence in most cases

The five provenances of B. olleghaniensis showed similar responses to the pathogen (Table 4.31, suggesting that al1 origins might be regarded as one single ecotype, with regard to resistance to C. purpureum. Mtogether, the five provenances of yellow birch yielded an average of only 4 % of mortality twelve weeks following inoculation and, surprisingly, 40.5 % at the end of the study. Again, it seems that the pathogen remained active while B. alleghuniensis seedlings were dormant. This resuIt contrasts with mortality levels obtained in a previous study in which 6 1 % of mortality was reached for the same tree species over the same period of time (Goulet er al., 1995). It appears that a longer time period between potting and inocdation in the present experiment caused this difference. In other words, the seedlings had more time to acclimatize to their new environment in the present study and low mortality Ievels encountered could therefore be explained by their healthier conditions. This suggests strongly that seedling mortality is more related to host susceptibility than to intnnsic pathological characteristics of the isolates thernselves.

In terms of absolute susceptibility, most tree species were tolerant (T) to al1 isolates used whereas the five provenances of B. alleghaniemis, considerd as one single ecotype, fell into an intermediate range of tolerance (MT) to C. pwpureurn (Table 4.4). On average (al1 isolates confounded), B. pap1.rfera was the sole species found to be sensitive (S) to the fungus. In some cases, this tree species was classified as very sensitive (VS) to C. purpureum, i.e. presenting levels of mortality of more than 81% (> 8/10), especially to isolates 428, Q104, BC32 and ES1 (Table 4.4). Although most tree species showed resistance (R) or tolerance (T) to isolate E2, paper birch was found to be very sensitive (VS) to this particular isolate. With the exception of isolate E5 1, al1 of these five virulent strains (Q28, Q104, BC32, E2) were previously collected on B. papyrifeu (Table 4.1).

When gathering al1 tree species into one single group and regarding mortality, a significant difference (p < 0.05) in relative virulence among isolates was fond (Table 4.5). Qudbec isolates 428, QI04 and Q4 caused the highest percentages of mortality, ranging between 42.5 % (52i120) and 57.5 % (57/120) of mortaliîy, and were al1 classified as virulent (V) as for their absolute virulence. Nevertheless, no very virulent (VV)nor extremely vident (EV) isolate was identified among these strains. Isolates sampled in British Columbia (BC) were found to be moderately virulent (MV) and were not significantly different @ > 0.05) among themselves (Table 4.3). Both the most (428) and least (Q92) virulent isolates under study were collected in the province of Québec. Isolate Q92 induced very littie mortality, killing less than 7 % of the treated seedlings overall, Nevertheless, seedlings of B. papyrrjéru showed some sensitivity to isolate Q92, 50 % (910) of the seedlings being killed for this treatment (Table 4.4). This outlines the fact that mortality induced by C. purpureurn is govemed by the host-pathogen interaction and, to a greater extent in the present case, by the susceptibility of the host. in addition, it is hardly possible to identifL any physiological races arnong C. purpureum within the limits of the present study, a study that was designed with too few replicates to explore this avenue.

Since no isolate induced more îhan 50 % of mortality in al1 cases, no very virulent (W)nor extremely vident (EV) strain was identified when al1 tree species were confounded. However, mortality patterns for given isolates were not reproducible among tree species, i-e. an isolate showing a higher level of absolute virulence on a given tree species \vas not necessarily found to yield sirnilar results on another species. From this, it appears that verifying the vinilence of several isolates on one single species would yield biaised results if one is interested in defining overall pathological effects of C. purpzcreum on many tree species. Table 4.3 Seedling mortaliiy per tree species at the end of the experirnent.

II.ialus spp. 2.5 %"

B. alleghaniensis (A) 43.3 %='

B. alleghon iensis (El 45.0 %

Calcdated by establishg proportion of 120 seedlings (all isolates confounded) that were dead. Frequencies followed by the same letter are not significantly different atp = 0.05 according to G statistics. Code letters are refering to Table 4.2. Table 4.4 Mortaiity of tree speciesiat the end of the experiment.

lmlatr PED Mika PEB ER4 PET CET BO.1' CHR BOP Tom?

O. (RIJ 3m 0 (RI 8 (SI O (Ri 5 (Sn O (RI 6 (ST) 0 iR) 7 (Si O(R) 5fm 1 (Tl 4 (MT) 1 Tr, 3 (MD 0 (RI lm O (Ri 3 (rn 1 in I m 0 (RI O (RI

3 46

2.5 %' 38 3 ?ad

T MT

75 'a 93.1 4 ' PEB, P. balsamijiera: PET, P. cremulordes; PED. P. deltoide; CET, P. seronna CHR.Q. rubra: ERA. A. sacchannuni; BOJ, B. dlcgbaniuisis; BOP, B. paptnfirci. ~eanof the five provenances. Total made wtof 90 sadlinçs. ' Mortality fiequenc). 0bsar.d on 10 seedlings. ' Code in parcnhcscs refers to inc sirrccptibility: R rcsisrant; T, toIcrant; MT. modcmtely tolerant; ST. slightly tolcrant; S. sensitive: VS. vccy sensitive. On atotal of t 080 seedlings. 7 Pacaitage (calculaml out of 120 ding)followal by tbe same lctter are nor sigüfican& diflmt at p = 0.05 acmrding to G statistics. 1 Pcrctntage or dead scçdlings hmwfüch C.prrrprtmm recovacd ai the end of the nu&. Table 4.5 Seedling mortality per isolate. Isolate Mortality Mortality Mo-lity Relative Absolute (autumn (spring (Total) vindence virulence (Total) 1995) 1996) (~otal')

492 8 O 8 6.7 %* SV

Total 144 293 437

' Percentages made on 120 wedlings. al1 tree species confounded. Frquencies foiiowed by the same Inter are not significantly dmerent at p = 0.05 according to G statistics. 4.4 Discussion

Al1 tree species studied were susceptible to infection by C. purpurem and none of them wa found resistant (R) to the pathogen. However, not al1 of them were likely to be killed in spite of the hi& level of virulence rewrded for some of the isoIates under investigation. Most tree species studied showed a certain tolerance to the microorganism, yielding in almost al1 cases less than 50 % rnortality (Table 4.4). In fact, only three tree species revealed a mortality level

of more than 20 %, narnely rd O& yellow birch and papa birch. This variation in tree susceptibility to the fungus outfines the relative specificity of (7. pwpureurn regarding its hosts, which further implies that the pathogen would behave as a selective biophytocide. Mortaiity levels observed on certain tree species in the present study were abnormally low when compared to results obtained in previous field studies (Gosselin and Jobidon, 1995; Jobidon, 1995) and indoor studies (Goulet et al., 1995). Considering these levels, enhancement of the eficacy of the fungus to maintain high control of sturnp sprouting of tolerant target tree species would have to be regarded for any operational treatrnents. This could be done through genetic transformation of the microorganism or by finding a naturally more agressive strain or mutant in the field. However, these approaches would indred require an exhaustive environmental risk assessment. Creating an operational formulation that wouid promote the infection (host penetration), the survival of the fungus within the host and that would reduce fungal cornpetition without adding substantial risk to the environment, would also be of interest while being more realistic.

Selection of an isolate as a potential candidate for biological control of target species would ideally be made upon its absolute fiingal viruience. A perfect candidate wodd be a biotype of the fungus that would be more specific to these target species. However, a specificity behavior has never ken observed in any canadian population of C purpweum so far (Wall, 1990; Gosselin and Jobidon, 1995; Goulet et al., 1995). Selection could also be made using criteria other than virulence. Criteria based upon environmental risk concepts such as the fruiting, sponilation or cornpetitive potential of the candidate, or upon practical and operational concepts such as its shelf life, its mycelial development within the host or it adaptability to an eventual application formulation could be regarded

The present study showed that the Canadian population of C. purpeum varies in vindencl in that both high and low levels of fungal virulence may be encountered in the province where the isolates were sampled (Table 4.5). It has been demonstrated that two provinces a distant as Québec and British Columbia are colonized by both vident and Iess vinilen isolates that are respectively statistically sirnilar in their relative virulence. in addition, nc evidence of any link between the vidence of the isolates and their geographic origii (Canadian ecozone) was noted in this study.

In terms of environmental risk associated to biological control, it is unlikely that mortdi- levels attained in the present study wouId be observed on nontarget tree species in the field in the study of Wall (1 99 1 ), no rnortality was observed following several field inoculations O yellow birch (DBH of 6 cm). In the present expriment, special attention was paid tc optimize the infection throughout this study by: (1) inducing severe physical stresses to hos seedlings that increased significantly their sensitivity (Goulet et al., 1995), (2) wounding th seedlings, hence allowing infection to occur, (3) inoculating immediately &er wounding a! it is known that spores geminate better on fiesh wounds (Grosclaude, 1973b), (4) usini mycelium as inoculurn as it is known that natural infecting bodies (basidiospores) are mort fragile and lead to disease intensities that are at lest two times lower (Trandafirescu et al.. 1985; Spiers and Hopcroft, 1988a), (5) introducing high arnounts of inocu1um on the wound' (i.e. infection points) and (6) protecting the inoculurn with parafllmm. In the field wherc such conditions do not happen nor coincide, the probability to obtain higher infection rates, disease severity or rnortality levels on similar nontarget species following a biocontrol treatment would indeed be low. Moreover, natural control, such as other more cornpetitive bgi, are present in the forest ecosystem that ensure C. purpurem wilI not persist ai epidemic levels (Wall et al., 1992). Using one of the twelve strains tested, an eventuai risk to nontarget tree species would be damped since, according to the results presented herein, none of the isolates tested caused more than 50 % mortality under our experimental conditions. In the present study, paper birch was at least 30 times more susceptMe to infection by C. purpureum and to subsequent mortality than was Mcintosh apple tree (genus Maius), and 8 times more than was black cherry (genus Pnmus), two genera previously known as very susceptible to the fungus. In some cases, paper birch was classified as very sensitive (VS) to some isolates whereas the species was overall classified as sensitive (S) to the fungus. Regarding environmental risk related to biocontrol, the repercussions of these results are of concern since B. pupyr$eru is a major commercial species throughout Canada. Nohÿithstanding this species might be considered as a target species in many sites such as forest plantations and in rigths-of-ways, it also represents a nontarget valuable species in other cases. Furthemore, the high level of susceptibiiity of this species could underlay the possible association between the pathogen and the deciine of B. papyr~rain Ontario (McLaughlin and Setliff, 1990). Although it is widely accepted that susceptible trees must be wounded to be infected by the pathogen (Ethridge and Morin, 1963; Spiers and Hopcroft, 1988; Wall, 1991), it is quite obvious that better understanding of the epidemiology of the silverleaf disease (eg. spore dispersal, period of receptivîty of nontarget tree species wounds to these spores, number of spores required to induce infection to nontarget hosts (spore effectiveness)) is desirable prior to entering through the registration process of C7. purpureum as a biophytocide. Chapitre 5

Conclusion générale Le champignon pathogène Chondrosterem purpwewn est reconnu comme étant un envahisseur primaire des blessures fraîches causées chez des essences ligneuses feuillues (Ethridge et Morin, 1963). Ses caractéristiques pathologiques en font un candidat idéal pour la pratique de la maîtrise biologique de la reproduction végétative des feuillus de lumière en milieu forestier. Sa virulence en égard aux essences se reproduisant de manière vkgetative, son avinilence envers les essences résineuses commerciales, sa vitesse de croissance rapide et son cycle de vie plutôt court et peu complexe sont en effet des atouts intéressants pour en faire un phytocide biologique. Toutefois, le cycle d'infection de l'agent pathogène comportant la formation de carpophores, la libération de spores et, ultérieurement, l'infection d'arbres non ciblés par le traitement de maîtrise biologique, sont des éléments importants à considérer préalablement à son utilisation de manière opérationnelle. La présente étude, divisée en trois sous-études, avait pour objectif d'évaluer quelques aspects de i7épidérniologiede C. purpureum.

Les résultats de la premiere sous-étude ont démontré le pouvoir de régénération élevé des carpophores d'un isolat canadien de C. purpurem, les hctifications du champignon ne nécessitant qu'une courte période de temps (moins de 6 heures) afin d'initier la sporulation suivant un mouillage. Conformément aux résultats d'autres travaux (Dye, 1974; Spiers, l985), le mouillage en laboratoire, équivalant aux précipitations sur le terrain, est un élément déclencheur essentiel à la libération de spores. Dans notre étude, il a été démontré qu'il fallait une humidité relative minimale de 90% afin que les carpophores poursuivent temporairement la formation et la libération continuelle de spores. A des niveaux d'humidité moins élevés (60% et 80%), nous avons observé la déshydratation des fnictifications et, éventuellement, la cessation de la sponilation.

Les résultats confirment également la très grande aptitude de C. purpurem a sporuler sous des conditions environnementales propices. A une température de l'air de 20°C et à une humidité relative maximale (100 %), un taux de sponilation maximum de 260 spores par centimètre carré d'hyménium et par seconde a été observé sous des conditions contrôlées. Par ailleun, nous avons mesuré dans certaines stations expérimentales une surface hyméniaie totale arîeignant 43,7 mha. Alors que les conditions environnementales favorables a la spodation coïncident tres fréquemment sur le terrain et puisque le champignon a la capacité de produire une multitude de fiuctifications et donc une slrrface hyméniale totale très élevée sur le tenain, on pourrait s'attendre a observer des &missionsde spores considérables des fiuctifications présentes dans un site traité de manière opérationnelle.

Cette quantité ajoutée importante de spores 8 la population fongique naturelle déjà en place ne nous renseigne que tres peu sur l'augmentation des risques d'infection d'espèces non- cibles. En effet, pratiquement aucune information sur le devenir et l'impact environnemental de C purpureum, notamment sur la phase de dispersion ainsi que sur la phase infectieuse de la maladie, n'est présentement disponib1e dans le contexte canadien. Toutefois, nous soupçonnons fortement que le devenir et l'impact environnemental de C. purpureum sont d'abord, voire presqu'entierement régis par des conditions qui sont extrinsèques au champignon, i-e. par les conditions environnementales, la nature de l'hôte ainsi que les conditions physioIogiques de l'hôte. C'est d'ailleurs ce qui expliquerait la faible occurrence de la maladie du plomb en forêt ainsi que l'absence de niveaux de population atteignant un seuil épidimique. II y a fort à parier qu'à cause de ces facteurs, une population ajoutée aurait un impact plutôt minime sur Ia ressource ligneuse forestière noncible. Cependant, nous devrions être en mesure de pouvoir évaluer le devenir environnementai de l'agent pathogène de même qu'un éventuel impact sur cette ressource précédant son application opérationnelle au champ. Récemment, des études ont été entreprises en ce sens.

L'objectif de la deuxième sous-étude consistait à évaiuer, par le biais de la simulation, le devenir environnemental (la sporuIation et la dispersion) des spores de C. purpureum suivant un traitement de maîtrise biologique. Le logiciel de simulation ainsi créé a été construit en utilisant des données théoriques et empiriques, dont Ia période d'apparition des carpophores sur le terrain, des données de sponilation effectuées en laboratoire, des données météorologiques semi-horaires enregistrées in situ et le modèle de dispersion à distribution normale (Gaussian Plume Model). Les résultats de dispersion indiquent que 99 % du temps, on retrouve une concentration de spores dans l'air moindre qu'une spore par mètre cube d'air a 2'5 kilomètres d'une aire traitée de manière opérationnelle. Les prédictions formulées par le logiciel nous révèlent donc qu'étant donnée la source importante de spores émise d'une aire traitée, ces dernières peuvent se retrouver que légèrement diluées dans l'air et ce, à des distances non négligeables de cette aire. Encore une fois, les résultats de dispersion ne renseignent en rien sur l'impact réel d'une telle dissémination sur la ressource forestière non- cible. ER ce sens, des informations concernant les autres phases de l'épidémiologie du micro- organisme, informations telles le nombre de spores minimum nécessaire à l'induction de l'infection chez différents hôtes, la période de réceptivité d'une blessure à l'infection chez plusieurs hôtes ainsi que la viabilité des spores dans I'air, sont nécessaires afin d'évaluer l'impact environnemental de la maîtrise biologique.

Dans la troisième et dernière étude, une méthode d'évaluation de la virulence en milieu serni-contrôlé mise au point préalablement (Godet er al., 2995) a été appliquée afin de vérifier la variation de la virulence d'une séIection canadienne d'isolats de C. purpureum provenant de plusieurs écozones. L'étude poursuivait comme second objectif la détermination de la susceptibilité de plusieurs espéces ligneuses feuillues a ces isolats. Les résultats ont démontré que la virulence des douze isolats a l'étude est très varïabIe, le Québec possédant des isolats de grande et de faible virulence alors qu'il en est de même pour les isolats provenant de l'extérieur du Québec. Aucun lien entre la provenance des isolats et leur ongine géographique n'a donc étk observé dans la présente expérience. Une forte mortalité printanière chez les plants traités avec C. purpureum suggère que celui-ci demeure actif suivant l'aoûtement des plants et précédant leur débourrement. Ce facteur pourrait en partie expliquer le succès du champignon en tant qu'agent de maîtrise biologique.

Les résultats nous indiquent également que toutes les espèces 5i l'étude sont susceptibles à l'infection par l'agent pathogène. En conséquence, aucune espèce ne s'est avérée résistante a la maladie. Toutefois, la plupart des espèces se sont montrées tolérantes au champignon, présentant un niveau de mortalité infërieur a 20 %. Ceci est le cas pour les peupliers (Populus spp.), le censier tardif (P. serotina), l'érable argenté (A. succharinum) et le pommier Macintosh (Malus spp.). A l'opposé, il a été démontré que le bouleau à papier (B. pupifera) est l'essence testée la plus susceptiile à C. purpweum, 76-7 % des plants subissant la mortalité pour cette espéce, suivie par le chêne rouge (Q.rubra) (44,2 %) et par le bouleau jaune (B. alleghaniensis) (38'3 %). Au Canada et dans le cadre de la présente étude, le genre Betula s'est avéré environ 30 fois plus susceptible que le genre Malus et environ 8 fois plus que le genre Pnrnuq, ce qui est coniraire aux croyances jusqu'ici véhiculées dans le domaine scientifique, croyances voulant que ces derniers genres soient particulièrement susceptibles à la maladie du plomb. Ce phénomène pourrait d'ailleurs expliquer l'association faite entre le déclin du bouleau a papier et C. purpureum en Ontario (McLaughlin et Setliff, 1990). Ces résultats pourraient avoir de sérieuses répercussions en ce qui concerne le risque environnementai associé à la pratique de la maîtrise biologique dans le contexte forestier canadien actuel, contexte où le bouleau à papier est une espèce commerciale majeure. En dépit du fait que cette espèce représente une espèce cible fréquente en foresterie, il n'en demeure pas moins qu'elle est également considérée comme étant une espèce non-cible dans bien des cas. Ceci vient souligner, une fois de plus, le besoin pour la réalisation d'études supplémentaires portant sur l'impact environnemental associé à la pratique de la maitrise biologique avec C. purpureum. Des études similaires menées sur le terrain seraient également grandement souhaitées.

Présentement, les biophytocides suscitent beaucoup d'intérêt comme méthode alternative aux phytocides chimiques et tout porte a croire qu'ils en susciteront davantage dans les années à venir. Ceci est d'autant plus évident si on remarque les bouleversements majeurs que subit actueIlement la foresterie au Québec: l'adoption de la Stratégie de protection des forêts par le ministère des Ressources naturelles du Québec, la tendance qu'ont les intervenants forestiers vers des pratiques forestières plus respectueuses de l'environnement, la certification environnementale de ces pratiques forestières, les efforts importants déployés en recherche et développement sur ces produits, etc. Aux yeux de plusieurs, les biophytocides semblent très prometteurs en tant qu'outils syIvicoIes curatifs en milieu forestier. Il n'en demeure pas moins que l'utilisation des biophytocides comporte, tout comme leurs prédécesseurs chimiques, un risque pour l'environnement. Il faut bien comprendre qu'un risque, se définissant comme la probabilité qu'un événement non-désiré se produise, est présent dans toutes interventions humaines, si minimes soient-elles. Et ce risque doit idéalement &tri connu, défini et réduit au minimum comme dans le cas de n'importe quelle intervention el forêt. Tel que stipulé dans la Stratégie de protection des forêts, on se doit d'abord di favoriser des pratiques sylvicoies préventives (le reboisement hâtif, les plants a forte: dimensions, les paillis, les coupes de régénération telles la coupe avec protection de 1i régénération et des sols ou la coupe progressive d'ensemencement, etc.) pour en arriver t réduire au minimum le recours aux phytocides, qu'ils soient chimiques ou biologiques. Références

AgricuIture and Agi-Food Canada, 1993. Regulatory proposai Pro9345 - Research permit guidelines for microbial pest control agents. Food production and inspection branch 1 Plant industry directorate. Canada. 22 p.

Anclrivon, D., 1993. Nomenclature for pathogeneicity and vinilence: The need for precision. Phytopathology, 83 (9): 889-890.

Aylor, D.E., 1990. The role of intermittent wind in the dispersal of fimgal pathogens. Annu. Rev. Phytopathology, 28: 73-92.

Bache, D.H., 1979. Particulate transport within plant canopies - ii. Prediction of deposition veiocities. Atm. Env., 13: 168 1-1687.

Beever, D.J., 1970. The relationship behveen nutrients in exrracted xylem Sap and the susceptibiIity of fniit trees to silver-leaf caused by purpureum (Pers.) Fr. Am. appl. Biol., 65: 85-92.

Belot, Y., Baille, A., and Delmas, J.L., 1976. Modèle numérique de dispersion des polluants atmosphériques en présence de couverts végétaux - Applications aux couverts forestiers. Atm. Env., 10: 89-98.

Bennet, M., 1962. Susceptibility of victoria trees to different isolates of Stereum purpureum. J. Hort. Sci., 37: 235-238.

Bergen, J.D., 1975. Air movement in a forest clearing as indicated by smoke drift. Agricultural Meteorology, 15: 165-179. Berkowicz R, and Prahrn, L.P., 1982. Sensible heat flux estimated fiom routin meteorological data by the resistance method. J. Appl. Met., 2 1: 1845-1 864.

Berkowicz, R, Olesen, H.R, and Torp, U., 1985. The Danish air pollution mode1 (OML: Description, test and sensitivity analysis in view of regdatory applications. In: C. D Wispelaere, F.A. Schienneier and N.V. Gillani (Editors), Proceedings of the 1st NATOICCMS international Technical Meeting on air pollution modeling and it application. St-Louis, USA, pp.453-48 1.

Bielenin, A., and Malewski, W., 1982. Changes in the content of sugars in sour cheme xylem sap and their effect on the growth of S~ereumpurpureum (Pers.) Fr. fungus. Fnii Science Reports, 9: 83-89.

Bishop, G.C., 1979. Infection of cherry trees and production of a toxin that causes folia silvering by different isolates of Chondtosrereunl purpureum. Aus. J. Ag. Res., 30: 659 665.

BoIand, G.J., 1990. Biologicai control of plant diseases with fimgal antagonists: Challenge! and opportunities. Canadian Journal of Plant Pathology, 12:295-299.

Borecki, Z., Czynczyk, A., and Millikan, F., 1978. Susceptibility of several cultivars of appIt to bark canker fungi. Plant Dis. Reptr., 62: 8 17-819.

Brooks, F.T., and Moore, W.C.1926. SiIver-leaf disease V. J. Pornol., 5: 11-97.

Businger, J.A., Wyngaard, J.C.,hmi, Y., and Bradley, E.F., 1971. Flux profile relationship! in the atmospheric surface layer. J. Atmos. Sci., 28: 181-1 89.

Carney, P.G.,and Dodd, V.A, 1989. A cornparison between predicted and measured values for the dispersion of malodours fiom sluny. J. Agric. Eng. Res., 44: 67-76. Carruthers, D.J., Hoiroyd, RJ., Hunt, J.C.R, Weng, W.S., Robins, AG., Apsley, D.D., Thompson D.J., and Smith, F.B., 1992. UK-ADMS - A new approach to modelling dispersion in the earth's atmosphenc boundary layer. In: H.R. Olesen and T. Mïkkelsen (Editors), Proceedings of Objectives for next generation of practical short-range atmospheric dispersion rnodels, Riso National Laboratory, Denmark, pp. 143-146.

Daniel, J.T., Templeton, G.E., Smith Jr, RJ., and Fox, W.T., 1973. Biological control of Northem Joinvetch in rice with an endemic fungal disease. Weed science, 2 1(4):303- 307.

De Bruin, H.A.R., and Moore, C.J., 1985. Zero-plane displacement and roughness length for taIl vegetation, derived from a simple mass conservation hypothesis. Bound.-Layer Met., 3 1 : 39-49.

De Jong MD., 1988. Risico voor fmitbomen en inheemse bomen na bestrijding van Amerikaanse vogelkers (Pnmus serorina) met Ioodglansschirnrnel (Chonclrrostereum purpureunz). PhD Thesis, Wageningen Agricultural University (WAU), the Netherlands, 138 pp.

De Jong, M.D.,.Scheepens, P.C.,and Zadoks, J-C., 1990a. Risk analysis applied to biological control of a forest weed, using the gaussian plume model. Grana, 29:139-145.

De Jong, M.D.,Scheepens, P.C., and Zadoks, J-C., 1990b. Risk analysis for biological control: A Dutch case study in biocontrol of Prunus serotina by the fungus Chondrosrereum purpureum. Plant Disease, 74 (3): 189- 194.

De Jong, MD., Wagenrnakers, P.S., and Goudriaan, J., 1991. Modelling the escape of Chondrosrerewnpurpureum spores from a larch forest with biological control of Prunus serotina. Neth.J.PI.Path., 97554 1. De Jong, M.D.,1992. Risk assessrnent for the application of biological control of a forest weed by a cornmon plant pathogenic fungus. Risk analysis, Letter to the editor, 12(4):465-466.

De Jong, M.D., Sela, E., Shamoun, S.F., and Wall, R.E., 1996. Natural occurrence of Chondrostereum purpureum in relation to its use as a biological control agent in Canadian forests. Biological control, 6: 347-352.

Di-Giovanni, F., Beckett, PM., and FlenIey, J.R, 1989. Modelling of dispersion and deposition of tree pollen within a forest canopy. Grana, 28: 129- 139.

Mer,R.R., 1976. Determination of atmospheric dimision parameters. Atm. Env., 10: 99- 105.

Dye, M.H.,1974. Basidiocarp development and spore release by Srereunl purpureum in the field. N. Z. J. Agr. Res., 17: 93- 100.

Dye, M.H.,and Wheeler, P.J., 1968. Wound dressing for the prevention of silver-leaf in hit trees caused by Sferetmpurprrreum (Pers.) Fr. N.Z.J. Agr. Res., 1 1: 874-882.

Ekramoddoullah, A.K.M., Shamoun, S.F., and Wall, R.E., 1993. Cornparison of Canadian isolates of Chondrostereum purpureum with respect to temperature response, virulence, and protein profiles. Can. J. Plant Pathol., 15: 7-13.

Etkinton, AS., Cardé, R.T., and Mason, C.J., 1984. Evaluation of time-average dispersion models for estimating pheromone concentration in a deciduous forest. J. Chem. Ecol., 10 (7): 1081-1 108.

Erbrink, J.J., 1994. Use of boundary-layer meteorological parameters in the Gaussian mode1 'STACKS'. Bound.-Layer Met., 74: 2 1 1-235. Ethridge, D.E., and Morin, L.A, 1963. Colonization by decay hgiof living and dead stems of balsam fi followlng artificial injury. Canadian Journal of Botany, 4 1: 1532- 1534.

ExeIl, A.W., 1924. An investigation of the hymenium of three species of Stereum. Tram Br. Mycol. Soc., 10: 207-215.

French, D.W., and Schroeder, D.B., 1969. Oak wilt fungus, Ceratocystisfagaceanrm, as a selective silvicide. For. Sci., 15 (2): 198-203

Fritschen, L. J., 1983. Characterization of boundary conditions affecting forest environmental phenornena. In: B.A. Hutctiison and B.B. Hicks (Editors), The forest-atrnosphere interaction; Proceedings of the forest environmenta1 rneasurements conference, Oak Ridge, Tennessee. pp. 3-23.

Golder, D., 1972. Relations mong stability parameters in the surface layer. Bound-Layer Met., 3: 47-58.

Gosse1in, L., and Jobidon, R., 1995. Biologicai control of deciduous tree species in rights-of- way by the fungus Chondrostereurnpzupureum (Pers. ex Fr.) Pouz. in: RE. Gaskin and J.A Zabkiewicz (Editors), Proceedings of the second International Conference on Forest Vegetation Management, Rotorua, New Zealand, pp. 243-245.

Gosselin, L., Jobidon, R, and Bernier, L., 1996. Assessrnent of genetic variation within Chondrostereum purpurem fiom Québec by random amplified polyrnorphic DNA analysis. Mycol. Res., 100: 15 1-158.

Goulet, A., Gosselin, L., and Jobidon, R., 1995. Susceptibility of selected Canadian tree species to different isolates of Chondrostereum purpureum. In: R.E. Gaskin and J.A. Zabkiewicz @&tors), Proceedings of the second International Conference on Forest Vegetation Management, Rotorua, New Zealand. pp. 246-248. FRI Bulletin No 192. Goudriaan, J., 1977. Crop rnicrometeorology: A simulation study. Wageningen Centre fa Agricultd Publishing and Documentation (PUDOC), Wageningen, the Netherlandz 249 pp.

Gregory, PM., 1945. The dispersion of air-borne spores. Trans. Br. Mycol. Soc., 28: 26-72.

Groclaude, C., 1964. Le plomb des arbres hitiers IV. L'infection du pêcher et du prunier pa le Slereum purpureum. Résultats d'inoculation artificielles et d'essais in vitro. Annua Epiphyties, 1559.

Grosclaude, C., 1969. Le plomb des arbres hitiers. VU. Observations sur les carpophores e les spores du Sfereumpurpureum. Pers. Ann. Phytopathol., 1: 75-85.

Grosciaude, C., 1970. Premiers essais de protection biologique des blessures de taille vis-à vis du Stereum purpureum pers. Annales de phytopathologie, 2(3):507-516.

Grosclaude, C., hcard, 3. and Dabos, B. 1973a. Inoculation of Trichodernza viride spores vi: pruning shears for biological control of Chondrosrereum purpureum on plum tret wounds. Plant Disease Reporter, 57(1):25-28.

Grosclaude, C., 1973b. Le plomb des arbres fruitiers. L'infection des blessures de taille pal les spores du Stereum purpureum Pers. Étude expérimentale. Rev. 2001. Agric. Pathol Vég., 72: 13-26.

Hanna, SR.. 1982. Applications in air pollution modeling. In: F.T.M. Nieuwdstat and H. van Dop (Editors), Atmospheric turbulence and air pollution modelling, Reidel, Dordrecht. the Neîherlands. pp. 275-3 10.

Hanna, S.R., and Paine, R.J., 1989. Hybrid Plume Dispersion Mode1 (HPDM) deveiopmeni and evaluation. J. Appl. Meteor., 28: 206-224. Jacobs, AF.G., and van Boxel, J.H., 1991. Horizontal and verticai disnibution of wind speel in a vegetation canopy. Neth. J. Agric. Sc., 39: 165-178.

Jobidon, R, f 991. Some firture directions for biologically based vegetation control il forestry research. For. Chronicle, 67 (5): 514-5 19.

Jobidon, R., 1995. Search for alternatives to chernical herbicides in Québec forestry: Use O large transplants and biological control. In: R.E. Gaskin and J.A. Zabkiewicz (Editors] Proceedings of the second international Conference on Forest Vegetation Managemeni Rotorua, New Zealand, pp. 255-257. FR1 Bulletin No 192.

Kendrick, B., 1992. The fifth kingdom. 2nd Ed. Focus texts, Focus Information Group Inc Newburyport, MA., 406pp.

KohI, J., Molhoek, W.M.L., van der Plash, CH.,and Fokkema, N.J., 1995. Suppression o sporulation of Borq~isspp as a valid biocontrol strategy. Eur. J. Plant Pathol., 1 0 1 (3) 25 1-259.

Legg, B.J., and Bainbridge, A., 1978. Air movement within a crop: spore dispersal anc deposition. In: Scott, P.R. and Bainbridge A (Editors), Plant disease epidemiolorg BlacLwell, Oxford, pp. 103-1 10.

Little, P., 2977. Deposition of 2.75,5.O and 8.5 mm particles to plant and soi1 surfaces. Envir Pollut. 12: 293-305.

Lindsey, J.P. and Gilbertson, R.L., 1978. Basidiomycetes that decay aspen in North America EdJ.Crarner. Germany, 406pp. Makowski, RM.D. and Mortenson, K., 1992. The fht mycoherbicide in canada Coiietobictrum gloeosporioides f.sp.malvae for round-leaved mallow control Proceedings of the fis? International Weed Control Congress. Melbourne, Australia 2:298-300.

Marayuma, P.J., and Hiratsuka, Y., 199 1. Stéréon pourpre. For. Cm.,Rég. Nord-Ouest, Cent for. Nord, Edmonton (Alberta). Dépliant for. 1.

Mason, C.J., 1979. Air pollution dispersion models. Aerobiology: The ecological system! approach, U.S.ilnt. Biol. Progr. Synth. Ser. no. 10. Dowden, Hutchinson & Ross Pennsylvania, U.S.A., pp. 284-297.

McCartney, HA., and Fitt, B.D.L.,1985. Construction of dispersal models. Adv. Plan' PathoI., 3: 107-143.

McLaughlin, J.A., 1991. A study of Chondrcwtereum purpurmm in Thunder Bay. MSc Thesis, Lakehead University, 1 19pp.

McLaugIiIin, J.A., and Setliff, E.C., 1990. Chondrostereum purpureum associated with decline of Betulcrpapyr@~ain Thunder Bay, Ontario. Plant Disease, 74: 33 1.

McNaughton, KG.,1989. Micrometeorology of shelter belts and forest edges. Phil. Trans. R. Soc. Lond., B 324: 35 1-368.

Miller, DR., Lin, J.D., and Lu, ZN.,199 1. Air flow accross an alpine clearing: A mode1 and field rneasurements. Agr. For. Met., 56: 209-225.

Monin, AS., and Obukhov, A.M., 2954. Basic laws of turbulent mixing in the atrnosphere near the gound. Tr. Akad. Nauk., SSSR Geophiz Inst., 24 (1 5 1 ): 1963-1987. Monteith, J.L., and Unsworth, M.H., 1990. Principles of environmental physics (2nt Edition), Chapman and Hall, New York, U.S.A., 29 1 pp.

Mortensen, K., 1988. The potential of an endemic fungus C. gloeosporiozdes, for biologica control of round-teaved mallow (Malva pusilla) and velvetleaf (Abutilon theophrmfi) Weed science, 36:473-478.

Pasquill, F., axid Smith, F.B., 1983- Atmosphenc diffusion (2nd edition), Wiley, New York U.S.A., 429 pp.

Pittevils, J., Vandergeten J., and Herinckx, D., 1979. Prévention et lutte contre la maladie di plomb, Stereum purpureum (Pers. ex. Fr.) Fr sur arbres fruitiers. Rev. Hort. Suisse, 52 5-14.

Ramsfield, T.D.,Becker, E.M., Rathlef, S.M.,Tang, Y.J.,Vrain, TC., Sharnoun, S.F. anc Hintz, W.E., 1996. Geographic variation of Chondrostereum purpureum detected bj polymorphisms in the ribosomial DNA. Cm. J. Bot., 74 (12): 19 19-1929.

Rayner, A.D.M., 1977. Fungal cotonization of hardwood sturnps from naturd sources. il. Basidiomycetes. Transactions of the British mycological Society, 69: 303-3 11.

Rayner, A.D.M., 1978. Interactions between fûngi colonising hardwood stumps and their possible role in determiring patterns of colonisation and succession. Annd Applied Biology, 89: 13 1-134.

Rayner, A.D.M., 1979. Intemal spread of fungi inoculated into hardwood stumps. New phytol., 82: 505-517.

Rayner A.D.M., and Boddy, L., 1988. Fungal decomposition of wood: Its biology and ecology. John Wiley & sons, England, 587 pp. Raynor, G.S., 1971. Wind and temperature structure in a coniferous forest and a contiguou field. For. Sci., 17 (3): 351-363.

Raynor, G.S., Hayes, J.V., and Ogden, E.C., 1974. Particdate dispersion into and within i forest. Bound.-Layer Met., 7: 429-456.

iùshbeth, J., 1988. Biological control of air-borne pathogens. Phil. Trans. R. Soc. Lond. B. 3 18265-28 1.

Scheepens, P.C., 1980. Bestrijding van de Arnerikaanse vogelkers met pathogene schimmels een perspectief Cent. Agrobiol. Onderz., Wageningen, Verslag Nr. 29.

Scheepens, P.C.,and van Zon, H.C.J.,1982. Microbial herbicides. In: Microbial and vira pesticides. Ed. : E-Kurstak.,623-64 1.

Scheepens, P.C., and Hoogerbrugge, A., 1989. Controi of Pnrnuv serorino in forests with thr endemic fungus Chondrostereum purpureum. Proceedings of the VII Intemationa Symposium on Biological Control of Weeds. Rome, Italy., 545-55 1.

Schroeder, D., 1983. Biological control of weeds, p.41-78. In: Recent advances in Weec Research. Ed. W.W.Fletcher. London: Commonwealth Agicultural Bureaux., 266 pp.

Schulze, R.H., 1994. Balancing simplicity with accuracy in the use of dispersion modeling ir the United States. In: J. Kretzschrnar, G. Maes and G. Cosemans (Editors), Proceeding: of the third workshop on harmonisation within atrnopheric dispersion modelIing fo~ regdatory purposes, Mol, Belgium, pp. 159-168.

Setiiff, E.C., and Wade, E.K.,1973. Stereum purpureum associated with sudden decline and death of apple trees in Wisconsin. Plant Dis. Rep., 57 (5): 473474. Shamoun, S.F., E~oddoullah,A.K.M., and WaIl, RE., 1991a Isoenzyme analysis ol Chondrostereum pvpureum used as a mycoherbicide. Cm. J. Plant Pathol., 13: 285. (Abstr.)

S-oun, S.F., Vrain, TC., and Wall, RE., 199Ib. Genetic variability among isolates of Clzondrosrereumpurpweum. Can- J. Plant Pathol., 13: 285. (Abstr.)

Sokal, R.R., and F.J. Rohlf, 198 1. Biometry. 2nd Edition. W.H. Freeman and Company (Ed). New York. 859 p.

Spiers, A.G., 1985. Factors Secting basidiospore release by Chondrostereum purpureum in New Zealand. Eur. J. For. Path., 15: 1 1 1 - 126.

Spiers, A.G., and D.H. Hopcroft, 1988a. Factors af5ecting Chondrosrereum purpurem infection of Salk. Eur. J. For. Path., 18: 257-278.

Spiers, A.G., and Hopcroft, D.H., 1988b. Ultrastructural studies of basidid and basidiospore release in ('hondroslereumpurpureum. Eur. J. For. Path., f 8: 367-38 1.

Stalpers, J.A., 1978. Identification of wood-inhabiting aphytlophorales in pure culture. Study in mycology, Instihrte of the Royal Netherlands, No. 16,

Stratégie (Une), aménager pour mieux protéger les forêts, 1994. Direction des programmes forestiers, ministère des Ressources naturelles du Québec. ISBN 2-550-29288-X, 197 P.

StrobeI, G.A., 199 1. Biologicat control of weeds. Scientific Amerîcan, July 199 1,72-78.

Stull, R.B., 1988. An introduction of boundary layer meteorology. Kluwer Academic Publishers, London. 660 pp. Talbot, P.H.B.,1973. Aphyllophorales 1: General characteristics; Thelephoroid and Cupuloic fadies. Pp - . In: The bgi, an advance treatise. Eds., Ainsworth, G.C., F.K.Sparrov and A.S. Sussman. Academic Press, New York, 504 p.

Tauber, H., 1967. Investigation of the mode of pollen transfert in forested areas. Rev Palaeobotan. Palynol., 3: 277-286.

TeBeest, DO., and Templeton, G.E., 1985. Mycoherbicides: Progress in the biologicai control of weeds. Plant disease, 69(1):6-10.

TeBeest, D.O., and Yang, XB., 1992. Spread of Colletotrichzcm gloeosporioides bj grasshoppers and green tree fiogs. Arkansas Farm Research, 41(3):5-6.

Templeton, GE.. and TeBeest, D.O.,1979. Biological weed control with mycoherbicides. Annual review of phytopathology, 17:30 1-3 10.

Templeton, G.E., 1982. Biological herbicides: discovery, deveiopment, deployment. Weed sci., 30:430-433.

Templeton, G.E.,1986. Mycoherbicide research at the University of Arkansas-Past, present and future. Weed science, 34(1):35-37.

Templeton, G.E., 1992. Use of Collerotrichum strains as mycoherbicides. In: Colletoirichum. Éditions C.A.B. international- Wallingford, UK, 358-379.

Thibault, M, 1987. Les régions écologiques du Québec méridional (2* approx). Carte. Ministère de l'énergie et des ressources, Bibliothèque nationaie du Québec. Trandafirescy M., and Topor, E., 1985. Sensitivity of some apricot varieties to the attack of Stereum purpurm. Acta Hort., 192: 239-345.

Trandafirescu, M, and Topor, E., 1995. Investigations on Stereum purpureum (Pers. ex Fr.) Fr. control on apricot. Acta Hoa, 384: 595-599.

Van Arsdel, E.P., 1967. The noctural diffusion and transport of spores. Phytopathology, 57: 1221-1229.

Van Ulden, A.P., and Holtslag, A.A.M, 1985. Estimation of atmospheric boundary layer parameters for diffusion applications. J. Clim. Appl. Met., 24: 1 196-1207.

Vincent, C., and Coderre, D., 1992. La lutte biologique. Éditions Gaétan Morin. Bouchenille, Québec, 671 pp.

Wall, RE, 1986. Pathogenicity of Chorulrostereum purpureum to yellow birch. Plant Disease, 70 (2): 158-160.

Wall, R.E., 1990. The fungus Chondrostereum purpureum as a silvicide to control stump sprouting in hardwoods. North. J. of Appl. For., 7: 17-19.

Wall, R.E., 199 1. Pathological effects of Chondrostereum purpureum in inoculated yellow birch and . Can. J. Plant Pathol., 13: 8 1-87.

Wall, R.E., Prasad, R., and Shamoun, S.F., 1992. The development and potential role of mycoherbicides for forestry. For. Chron. 68: 736-74 1.

Wall, R.E., 1994. Biological control of red using stem treatrnents with the fungus Chondrostereurnpurpureum. Can. J. For. Res., 24: 1527-1 530. Wall, RE., Macey, D.E., and Sela, E., 1996. Virulence and interfertility of Chondn,srereuj purpurem isoIates. Biological control, 7: 205-2 11.

Weidemann, G.J., TeBeest, D.O., and Cm&t, R.D., 1988. Host specificity ( Colletotrichum gloeosporioides fsp aeschynomene and Chuncarum in th leguminosae. Phytopath, 78(7):986-990.

Weil, J.C., 1985. Updating appIied diffusion models. J. Clim. Appl. Met., 24 (1 1): I 11 l 1130.

Winston, P.H.,and Bates, D.H., 1960. Saturateci solutions for the control of humidity i biological research. Ecology, 41: 232-237. Annexe A

Code de programmation du logiciel de simulation OPEM ********************************************************************** ************************************************************** * **************************************************** * * O000 PPPPP EEEEEE M M * O O P P E MM MM * O O PPPPP EEEE MM MM * O O P E M MM M * ****************************************************O000 P EEEEEE MMM * ************************************************************** * ********************************************************************** This Operational Epidemiological Model reads meteorological data from a biocontrol site and simulates spore dispersa1 above a forest using a mode1 of sporulation and the well known Gaussian Plume Model. The program is fed with real data recorded in a forest clearcut in sumrner 1995. Authors: Andre Goulet and Robert Jobidon Date: May 15th 1996 DATA DICTIONARY: Description of the arrays used in the model:

CARPSP( ) : Mean number of basidiocarps per stub for each tree species COGAGE ( ) : Added spore concentration at a location in a julian day (sp/m3 COMEAN ( ) : Daily mean of spore concentration (sp/m3) at a location CONCEN ( ) : Spore concentration at a precise location (sp/rn3) CONTOT ( ) : Added spore concentration at a precise point (sp/m3) FORHEI ( ) : Forest height of the each of the forest edges (ml FRQC ( ) : Frequency of a spore concentration at a location GREATX ( ) : Greatest X coordinate value of the treated area (ml ICONTO : Integer spore concentration at a precise point (sp/m3) ID( Integer dummy value IMAXO : Integer maximum spore concentration at a precise point (sp/m3 Intercept in a Cartesian grid of each of the forest edge equat ion LARGE ( ) : Temporary largest width of the forest clearcut (ml LOWERX ( ) : Lowest X coordinate value of the treated area (ml MAXCON ( ) : Maximum spore concentration at a precise point (sp/m3) NSPDAY ( ) : Julian day at which the basidiocarps are disappearing from stubs PERC ( ) : XXth percentile of daily mean spore concentration at a precise location (sp/m3) RD( ) Real dummy value REALX ( ) : Treated site X-coordinate REALY ( ) : Treated site Y-coordinate SPDAY ( ) : Julian day at which the sporulation starts SLICEX ( ) : X-value of a forest edge SLICEY ( ) : Y-value of a forest edge SLSCANO : Slope in a Cartesian grid between SCNMXY and each treated area coordinate SLPSIDO : Slope in a Cartesian grid of each of the forest edges SPDAY() : Julian day at which basidiocarps are appearing on stubs TEMD ( ) : Mean daily temperature (Celcius) TEMN ( ) : Mean nightly temperature (Celcius) TREE ( ) : Tree density (#stubs/m2)

FRQ Added frequency of spore concentration FY Universal function for calculating Sigma Y GAGE Variable used to proceed with the sporulation or not GAMA Psychometer constant (~/m2*~) GRAD Global radiation (m/m2) GRAV Earth gravity (m/s2) HDRY Hours with dry weather (< 50% R.H.) HDRYP Percentage of hours with dry weather (< 50% R.H.) HEATS Dummy value of sensible heat flux (~/m2) HE12 2 metres (height) HEIGHT Height of a forest edge (ml HFREE Hours with freeze HFREEP Percentage of hours with freeze HHOT Hours with hot temperature (>32.5 C) HHOTP Percentage of hours with hot temperature (>32.5 C) HHRAIN Hours with heavy rainfall (> 3 mm) HHRAIP Percentage of hours with heavy rainfall (> 3 mm) HINV Hours with thermal inversion HINVP Percentage of hours with thermal inversion HNOSP Percentage of hours without sporulation HNOSPO Hours without sporulation HNOSPP Percentage of hours without rainfall and R.H. > 50% HNOTRA Hours without spore transportation HNOTRP Percentage of hours without spore transportation HNOWIN Hours without significant wind HNOWIP Percentage of hours without significant wind HNSPDP Percentage of hours of delay in sporulation HNSPOD Delay between beginning of rain and sporulation (hours) HNSP02 Hours without rainfall and H.R. > 50% HOUR Number of hours analyzed HRAIN Hours with rainfall (>0.1 mm) HRAINP Percentage of hours with rainfall (>0.1mm) HS Variable for the calculation of Sigma Z (dimensionless) HUMDEF Humidity deficit of air (h~/m2) HUM1 Relative humidity at a 2 metres height (%) HYMEN Total surface of hymenium of the treated site (m2) 1 Dummy variable 11 Distance along the plume centerline (m) INDEX Value for drawing graphic elements INDICE Value for drawing graphic elements J1 Crosswind distance from the plume centerline (m) JULDAY Julian day (from ... to ... ) KARMAN Von Karman constant LAGRA Lagrangean time scale (SI LARG Largest width of the forest clearcut (m) LATITU Northern latitude (degrees) LIBSPO Number of spores emitted per cm2 of basidiocarp per second LNGEST Longest crosswind vector within the treated area (m) LOWSLP Lowest slope value of the array SLSCANO LSTRAD Last rainy day LSTRAT Last rainy time MAXX Greatest absolute X-value of the treated area X-coordinates (ml Greatest absolute Y-value of the treated area Y-coordinates (ml MIXDEP Mixing depth of the PBL (m) MIXY Dummy mixing depth value (ml MONIN Monin-Obukhov length (ml MNWS7 Minimum wind speed at 7 metres above ground level for the current 30 minutes MNWS15 Minimum wind speed at 15 metres above ground level for the current 30 minutes MXWS7 Maximum wind speed at 7 metres above ground level for the current 30 minutes MXWS15 Maximum wind speed at 15 metres above ground level for the current 30 minutes MXXPIX Maximum number of X-pixels of the video screen MXYPIX Maximum number of Y-pixels of the video screen NBS IDE Number of forest sides of the treated area NETRAD Net radiation (~~/m2) NEU Frequency of occurence of neutral atmosphere NEUP Percentage of hours with neutral stratification OBUK Dummy Monin-Obukhov length (m) OLDDAY Previous Julian day PARA Variable needed for the calculation of the universal function PSYH PARAM Variable needed for the calculation of the universal function PSYM PERCE Percentile entered by the user (e.g. Cg0 or C99) PERCET Percentage of calculation done of the chosen percentile PERIOD Period of biocontrol treatrnent (before or after August) PERPWI Perpendicular wind direction (degrees) PI Pie (3,14159) PLACE Variable used for drawing graphic elements POSITI Fartest point of the GRID MAP (km and m) PSYH Universal function for calculating RS PSYM Universal function for calculating USTAR RAIN Amount of rain (mm) REAxx Dummy real value for meteorological variables mco Year at which meteorological data have been recorded REFHE 1 Reference height (15 m) RICH Maximum gradient Richardson number RICHS Gradient Richardson number between O and 2 metres above ground level RICH7 Gradient Richardson number between 2 and 7 metres above ground level RICH1 5 Gradient Richardson number between 7 and 15 metres above ground level RRR Constant (0.741 RS Surface resistance to evaporation (1 /s) RSUBA Aerodynamic resistance (l/s) SATVAP Saturated vapor pressure (h~/m2) SCNMXX X-value on the X-axis from where the scan of the treated area will start SCNMXY Y-value on the Y-axis from where the scan of the treated area will start SDWD7 Standard deviation of wind direction at 7 metres above ground level (degree) SDWD15 Standard deviation of wind direction at 15 metres above ground level (degree ) SDWS7 Standard deviation of wind speed at 7 metres above ground level (m/s) SDWS15 Standard deviation of wind speed at 15 metres above ground level (m/s) SENSH Sensible heat flux (~/m2) SIGY Sigma Y (variable in the Gaussian formula) (m) S IGZ Sigma Z (variable in the Gaussina formula) (m) SIGZC Part of sigma Z brought by convective turbulence (ml SIGZM Part of sigma Z brought by mechanical turbulence (ml SLOP : Value for fransforming scÏ-een coordinates into field coordinates Cm) SLPERW : Slope of the perpendicular wind vector in a cartesian grid SPEED : Wind speed at a given height (m/s) SPOABO : Total number of spores escaping above forest height SPORUL : Total number of spores emitted from basidiocarps for the current 30 minutes SPOSEC : Mean number of spores shed per second SPOTOT : Total number of spores shed by basidiocarps STA : Frequency of occurence of stable atmosphere STAP : Percentage of hours with stable stratification STATUS : Video mode of the screen (text/graphics) SUMSPC : Total density of stubs within the treated area (#stubs/m2) SUMZO : Sum of roughness lengths used to calculate an averaged ZO (ml SUPERF Surface area covered by the GRID MAP (km2) TO Averaged air temperature at ground level (Celcius) T2 Air temperature at 2 metres above ground level (Celcius) T7 Air temperature at 7 metres above ground level (Celcius) Tl 5 Air temperature at 15 metres above ground level (Celcius) TCHEK Time of the day at which the sporulation begins TDM Mean daily temperature (Celcius) TER1 Variable used in the calculation of the sensible heat flux TIM Dummy time value TIME Time at which meteorological data have been recorded TIMEP Numerical counter of half-hours in a julian day TIMER Dumrny variable for time TIMEY Dummy variable for time TK Current temperature (K) TM0 Mean temperature at ground level (Celcius) TM1 5 Mean temperature at 15 metres above ground level (Celcius) TNM Mean nightfy temperature (Celcius) TORAIN Total rainfall (mm) TOTARE Surface area of the treated site (m2) TOTRAD Accumulated net radiation (W*hr/mS) TRANSF Value used to transform radians into degrees or vice-versa TRAVEL Mean travel time for a spore to get at a precise position !s) TTT Variable used for the calculation of Sigma 2 (dimensionless) VARZM Variance of sigma Z : (SIGZ)**2 UNS Frequency of occurence of unstable atmosphere UNSP Percentage of hours with unstable stratification USTAR Friction velocity (m/s) VANG Angular speed of rotation of the earth (rad/s) VHEI Mean vegetation height within the treated area (m) W7 Wind speed at 7 metres above ground level (m/s) W15 Wind speed at 15 metres above ground level (m/s) WSTAR Convective velocity scale (m/s) XCOOR X coordinate of the GRID MAP (m) XX Dimensionless value of relative temperature XXXX Computer screen coordinate (X) YCOOR Y coordinate of the GRID MAP (m) YYYY Computer screen coordinate (Y) 20 Roughness length (m) INCLUDE Declaring variables: Basic variables: REAL TO,T2,T7,T15,HUMI,GRAD,KARMAN, NETRAD, RAINIW7, W15,HOUR, ~SDWS7~SDWS15tDIR7,DIR15ISDWD~CSDWD15ITOTARErMXWS15~LARGIMXWS7t !MNWS15,MNWS7,SUMSPCIVHEIIZOIGRAV,REFHEIIVANG,HAFDrHAFN, !DISPLArLATITUlTDAY,TNIGHT,TDM,TNMIHALFDIHALFNITORAINfHNRAINf !HRAIN,HNSP02,HNSPODIHNOSPOINEUrSTA,UNS INTEGERfl ANSW9,GAGE2 INTEGER*2 NBSIDE,ANSW7,ANSW8,RECOIPER1OD INTEGER JüLDAY,TIMZ,DIVERG CHARACTER* 1 ANSW 5

SAVE GAGE2

Calling a graphical subroutine: CALL GRAPH() Calling the introduction message subroutine: : IF (GAGE2.EQ.O) THEN CALL CLEARSCREEN($GCLEARSCREEN) GOTO 1300 END IF

Re-initializing data: TREE(1 )=O. TREE(2)=O. TREE(3)=O. TREE(B)=O. TREE(S)=O. SUMSPC=O . VHEI=O. TOTARE=O. HOUR=O . HNSP02=0. TORAIN=O . HHRAIN=O . HRAIN=O . HNOSPO=O . HNSPOD=O . SPDAY(1 )=O. SPDAY(2)=0. SPDAY(3)=0. DIVERG=O. STA=O UNS=O NEU=O HALFD=O . HALFN=O . 2alling the subroutine MENU: Calling a subroutine INPUT which allaws the user to enter his own parameters if wanted : 00 CALL INPUT(NBSIDE,REALX,REALYfFORHEIITREEISUMSPCfTRANSFtVH~~l !ANSW7,LATITU,RECOtPER1OD) Calling the subroutine CARAC which determines a few caracteristics of the-treated area: CALL CARAC(NBSIDE,REALX,REALYlSLPSIDtINTRSI,LO~~fGREATXtTOT~l !LARG ) Calling the subroutine DRAWTA which draws the forest clearcut on

Calculating the coriolis parameter (Hanna and Chang, 1991): CORIOL=~.*VANG*SIN(LATITU/TRANSF) Opening data and output files: CALL FILE() Reading data file DATA.DAT: CALL CLEARSCREEN($GCLEARSCREEN) PRINT*,tFIRST PHASE: OPEM is now reading meteorological data; Plea Ise wait.. . PRINT*, ' PRINT*, O READ (12,525,END=99) ID(~),ID(~),RD(~),RD(~),RD(~),RD(~), !RD(S),RD(~),RD(~),RD(~) ,RD(g) IRD(lO)rRD(ll )tm(12)tRD(13)tRD(14), !RD(15),RD(16),RD(l7),~D(18)tRD(19)fRD(20)l~(21 ),m(22) 5 FORMAT (13,5X,I4,11X,7F8.2,F8.3,F8.2,7F8.3,F8.1fF8.2tF8.3tF8~11 !F8.2,F8.1 ) IF (ID(l).EQ.O) GOTO 99 JULDAY = ID ( 1 ) TIME=ID (2) TO=(RD(~)+RD(~)+RD(~))/~.O Tl 5=RD(4) T7=RD(5) T2=RD(6) HUMI=RD(7) IF (HUMI.GT.100.0) HUMI=100.0 GRAD=1000.O*RD(8) NETRAD=RD(9) MxWSlS=RD(l O) MXWS7=RD(f 1 ) MNWS15=RD(12) MNWS7=RD(f 3) SDWSlS=RD(14) SDWS7=RD(lS) W1 S=RD(l6) Transforming DIRIS from upwind (where the wind cornes from) to downwind (where the wind goes) direction: DIR15=RD(i7)+180. IF (DIR15.GT. 360. ) DIR15=DIRl5-360. Transforming DIR7 from upwind to downwind direction: DIR7=RD(20)+180. IF (DIR7.GT.360,) DIR7=DIR7-360.

Computing the number of hours analyzed: HOUR=HOUR+0.5 Displaying on the screen a counter (for the user): WRITE (*,604) JULDAY,TIME 4 FORMAT(f+DAY: fr13r~TIME: ',14) Writing the file METEO.OUT: WRITE (22,535) JULDAY,TIMErTOrT2rT71T15,H~IIGRAD,NETRAD,w15f !MXWS15 ;DIRI5, SDWD15 ,W7r DIR7 r SDWD7 ,RAIN 5 FORMAT (1X,I3,5X,I4,4Xl15F8.3) Computing the number of half-hours in the current julian day (night and day separately) and adding the temperatures: IF (GRAD.GT.0.) THEN HALFD=HALFD+l . TDAY=TO+TDAY ELSE HALFN=HALFN+l . TNIGHT=TO+TNIGHT END IF

Checking if the julian day is finished and if sol calculating the mean daily and mean night temperature at ground level: IF (TIME.EQ.2400) THEN TDM=TDAY/HALFD TNM=TNIGHT/HALFN TDAY = O. TNIGHT=O . HAFD=HALFD HAFN=HALFN HALFD=O . HALFN=O . END IF Writing the file MEANT.OUT: IF (TIME.EQ.2400) THEN WRITE (102,600) JULDAYtTDMtHAFDtTNMtHmN 0 FORMAT (1X,14,2X,F8.3,F5.0,F8.31F5.0) END IF Now finding the mean forest side on which the wind is blowing by calling the çubroutine BLOW: CALL BLOW (TIMErJULDAY,NBSIDE,INTRSIISLPSIDIREALX,REALYf !DXR~,TRANSF,LOWERX,GREATX,FORHEI)

CLOSE (UNIT=12) CLOSE (UNIT=22) CLOSE (UNIT=32) CLOSE ( UNIT=102 )

k********************************************************************* SECOND PART OF OPEM - COMPUTING THE SPORULATION ...... k*********************************************************************

Opening output file SPORE.OUT: OPEN (~~,STATUS=~OLD',FILE='C:\FORTRAN\OUT\SPO~.OUT~,E~=~~~) CALL CLEARSCREEN($GCLEARSCREEN) PRINT*ffThe file SPORE.OUT already exists; OK to overwrite (y/n)?, READ (*,SIS) ANSWS 2 FORMAT (At ) CLOSE (42) IF (ANSW5.EQ.fYf.0R.ANSW5.EQ.tyf)THEN 1 OPEN (42, FILE=~C:\FORTRAN\OUT\SPORE.OUT'~MODE='WRITEf) ELSE STOP 'Program aborted! Goodbye!' END IF Calling the subroutine SPO which computes the sporulation: CALL SPO(RECO,PERIOD,TOTARE,TREE,TREE,ANSW7fTORAINIHHRAINfHRAINt !HNSPOS,HNSPOD,SPDAY,HNOSPO)

CLOSE (UNIT=42) Calculating the displacement height and the roughness length of the vegetation within the treated area: CALL AERO(VHEI,DISPLA,ZO,GRAVIREFHEI) ********************************************************************** THIRD PART OF OPEM - PROCESSING METEOROLOGICAL DATA ***************** ********************************************************************** CALL PRO(PI,KARMAN,REFHEIIDISPLA,ZO,GRAVICORIOLfDIVERGtSTAtUNSfNEU ! 1 ***********************************************~********************** FOURTH PART OF OPEM - CALCULATING THE AMOUNT OF SPORES ESCAPING ***** ABOVE THE FOREST HEIGHT *********************** ********************************************************************** CALL ESCA(GRAV,DISPLA,RECO,PERIOD) ********************************************************************** FIFTH PART OF OPEM - APPLYING THE GAUSSIAN PLUME MODEL ************** ...... CALL GAUSS(HOURfDISPLA,PI,TRANSFIANSW9INE:UISTAIUNSIDIVERG,TORAINt !HHRAIN,HRAIN,HNSPO2,HNSPODtSPDAYfHNOSPO,ZOtSUMSPCfTOTARE)

GOTO 444 01 CALL CLEARSCREEN($GCLEARSCREEN) STOP END Caiier: PRESE INCLUDE 'FGRAPH.FDt Declaring local variables: INTEGER*2 STATUS INTEGER MXXPIX,MXYPIX,COLO REAL CENX,CENY,PLACE RECORD /XYCOORD/ XY RECORD /VIDEOCONFIG/ MYSCREEN STATUS=SETVIDEOMODE($VRES16COLOR) CALL GETVIDEOCONFIG (MYSCREEN) MXXPIX=MYSCREEN.NUMXPIXELS-1 MXYPIX=MYSCREEN.NUMYPIXELS-1 IF (SETVIDEOMODE($VRES16COLOR).EQ.O) THEN STOP 'Error: Cannot set graphic mode on this cornputer!' END IF IF (REGISTERFONTS(~C:\FORTRAN\FONT\TMSRB.FON~).LT.O) THEN STOP 'Errer: Cannot open C:\FORTRAN\FONT\TMSRB.FON!~ END IF IF ( SETFONT( "TI TMS RMN' Hl 4~1OB" ) .LT. O ) THEN STOP 'Errer: Cannot load typeface!' END IF

CALL SETVIEWORG(CENX,CENY,XY) Setting the line style as a full line: CALL SETLINESTYLE(#FFFF)

Drawing the letter IOt: CALL MOVETO (((-(~~./~~.)*CENX)-~.*CENX/~~.),(-~.*CEW/~.)~XY) PLACEzLINETO (((-(~~./~~.)*CENX)-~.*CENX/~~.),(-CENY/~.)) CALL MOVETO (((-(~~./~~.)*cENx)-~.*CENX/~~.),(-CENY/~.)~XY) PLACE=LINETO (((-(S./~.)*CENX)-~.*cENx/~~.),(-~.*CENY/~.)) CALL MOVETO (((-(S./~.)*CENX)-~.*CENX/~~.),(-S.*CENY/~.)~XY) PLACE=LINETO ((((-~./~.)*cENx)-~.*cENx/~~.),(-~.*cENY/~.)) CALL MOVETO ((((-~./~.)*cENx)-3-*CENX/48.),(-~.*cENY/~.)~XY) PLACE=LINETO ((((-I~./~~.)*cENx)-~.*CENX/~~.),(-~.*CEW/~.)) CALL MOVETO ((((-~~./~~.)*CENX)-~.*CENX/~~.),(-~.*CENY/~.)~XY) PLACE=LINETO ((((-~./~.)*cENx)-~.*cENx/~~.),(-~.*CENY/~.)) CALL MOVETO ((((-I./~.)*cENx)-~,"CENX/~~.)~(-~.*CENY/~.),XY) PLACE=LINETO ((((-~~./~~,)*cENx)-~.*CENX/~~.),(-CENY/~*)) CALL MOVETO ((((-~~./~~.)*cENx)-~.*cENx/~~.)~(-cENY/) PLACE=LINETO ((((-S./~.)*CENX)-~.*CENX/Q~.)~(-CENY/~-)) CAU MOVETO ((((-~./~.)*cENx)-~.*cENx/~~,),(-cENY/~.),~Y) PLACE=LINETO (((-(~~./S~.)*CENX)-~.*CENX/~~.),(-CENY/~.)) CALL MOVETO (((-(19./24.)*C~~~)-CENX/S~.),(-~.*cENY/)IXY) PLACE=LINETO ((((-~~./~~.)*cENx)-~.*cENx/~~.),(-~.*CENY/~~.)) CALL MOVETO ((((-~~./~~.)*CENX)-~.*CENX/~~.)~(-~.*CENY/~~.)~XY) PLACE=LINETO ((((-~S./~~.)*CENX)-~.*cENx/~~.),(-~.*cENY/I~.)) CALL MOVETO ((((-I~./S~.)*CENX)-~.*CENX/~~.),(-~.*CENY/~~.)~XY) PLACE=LfNETO ((((-S./3.)*CENX)-~.*cENx/~~.),(-~.*CENY/~.)] CALL MOVETO ((((-~./~.)*CENX)-~.*CENX/~~.)~(-~.*CENY/~.)~XY] PLACE=LINETO (((-(I~./~~.)*cENx)-CENX/~~.),(-~.*cENY/~.)) Drawing the letter 'Pt: CALL MOVETO ((((-~./S.)*CENX)-~.*cENx/~~.),(-CENY/~.)~XY) PLACE=LINETO ((-~.*cENx/~~.-CENX/~~.),(-~.*cENY/~.)) CALL MOVETO ((-S.*CENX/IS.-CENX/~~.)~(-~.*CENY/~.),XY). PLACE=LINETO ((-CENX/~.-CENX/~~.),(-~.*cENY/~.)) CALL MOVETO ((-CENX/~.-CENX/~~.),(-~.*cENY/~.),xY) PLACE=LINETO ((-CENX/IS.-CENX/~~.),(-~.*cENY/~.)) CALL MOVETO ((-CENX/IS.-CENX/~~.),C-~.*cENY/~.),xY) PLACE=LINETO ((-CENX/IS.-CENX/~~.),(-~.*CENY/I~.)) CALL MOVETO ((-CENX/~S.-CENX/S~.)~(-~.*CENY/~~.),XY) PLACE=LINETO ((-cENx/~.-cENx/~~.),(-~.*cENY/I~.)) CALL MOVETO ((-CENX/~.-CENX/~~.)~(-~.*CENY/~~.)~XY) PLACE=LImTO ( (-1 5. *CENX/~~.-CENX/ 24. ), (-5. *cENY/1 6. ) ) CALL MOVETO ((-~s.*cENx/~~.-CENX/~~.),(-~.*CENY/~~.)~XY) PLACE=LINETO ((-~I.*cENx/~~.-CENX/~~.)~(-CENY/~.)) CALL MOVETO ((-~I.*cENx/~~.-CENX/~Q.),(-CENY/B.),XY) PLACE=LINETO ((((-~./~.)*cENx)-~.*cENx/~~.),(-CENY/~.)) CALL MOVETO ((-~~.*cENx/~~.-cENX/~~.),(-~.*CENY/~~.)~XY) PLACE=LINETO ((-~.*cENx/~~.-CENX/~~.),(-~.*cENY/~~.)) CALL MOVETO ((-~.*CENX/~~.-CENX/~~.),(-~.*cENY/~~.),xY) PLACEzLINETO ((-CENX/~.-CENX/~~.),(-~.*CENY/~~.)) CALL MOVETO ((-CENX/~.-CENX/S~.),(-~.*CENY/I~.),XY) PLACE=LINETO ((-S.*CENX/~~.-CENX/~~.),(-CENY/~.)) CALL MOVETO ((-~.*CENX/~~.-CENX/~~.),(-CENY/~.),XY) PLACE=LINETO ((-CENX/~.-CENX/~~.),(-~.*cENY/~~.)) CALL MOVETO ((-CENX/~.-CENX/~~.),(-~.*CENY/~~.),XY) PLACE=LINETO ((-29.*~~~~/96.-CENX/~~.)~(-~.*CENY/~~~))

Drawing the letter 'El : CALL MOVETO (O.,(-CENY/~.),XY) PMCE=LINETO (O.,(-~.*cENY/~.)) CALL MOVETO (O.,(-~.*cENY/~.),xY) PLACE=LINETO ((~.*cENx/~~.-CENX/~~.),(-~.*cENY/~.)) CALL MOVETO ((~.*cENx/~~.-CENX/~~.),(-~.*CENY/~.),XY) PLACE=LINETO ((29.*~~~~/96.-CENX/~~.),(-~.*cENY/~.)) CALL MOVETO ((29.*~~~~/96.-CENX/~~.),(-~.*cENY/~.),xY) PLACE=LINETO ((~.*cENx/~~.-CENX/~~.)~(-~.*cENY/~.)) CALL MOVETO ((~.*cENx/~~.-CENX/~~.),(-~.*cENY/~.),xY) PLACE=LINETO ((13.*~~~~/96.-CENX/~~.),(-~.*cENY/~~.)) CALL MOVETO ((13.*~~~~/96.-CENX/~~.),(-~.*cENY/I~.),XY) PLACE=LINETO ((~.*cENx/~~.-CENX/S~.)~(-~.*CENY/~~.)) CALL MOVETO ((s.*cENx/~~.-CENX/~~.)~(-~.*CENY/~~.),XY) PLACE=LINETO ((I~.*cENx/~~.-CENX/~~.),(-~.*CENY/~~.)) CALL MOVETO ((~I.*CENX/~~.-CENX/~~.),(-~.*CENY/~~.)~XY) PLACEzLINETO ((I~.*cENx/~~.-CENX/~~.),(-~.*CENY/I~.)) CALL MOVETO ((~s.*cENx/~~--cENx/~~.),(-~.*cENY/~~.)~XY) PI;rACE=LINETO ((cENx/~.-CENX/~~.),(-~.*CENY/~.)) CAU MOVETO ((cENx/~.-CENX/~~.),(-~.*CENY/~.)~XY) PLACE=LINETO ((cENx/~.-CENX/~~.),(-~.*cENY/~.)) CALL MOVETO ((cENx/~.-CENX/~~.),(-~.*cENY/~.),xY) PLACE=LINETO ((~.*cENx/s~.-CENX/~~.),(-CENY/~.)) CALL MOmTO ((~.*cENx/~~.-CENX/S~.),(-CENY/~.),XY) PLACE=LINETO (O.,(-CENY/~.)) Drawing the letter 'Mt: CALL MOVETO ((CENX/S.-CENX/~~.),(-CENY/~.),XY) PLACE=LINETO ((~.*cENx/Is.-CENX/S~.),(-~.*cENY/~.)) CUL MOVETO ((s.*cENx/IS.-CENX/~~.),(-~.*cENY/~.),XY) PLACE=LINETO ((I~.*cENx/s~.-CENX/~~.),(-~.*cENY/~.)) CALL MOVETO ((~~.*CENX/S~.-CENX/~~.),(-~.*CENY/~.),XY) PLACE=LINETO ((~I.*CENX/~~.-CENX/~~~)~(-~.*CENY/~.)) CALL MOVETO ((~I.*cENx/~~.-CENX/~~.)~(-S.*CENY/~.),XY) PLACE=LINETO ((I~.*CENX/~~.),(-~.*CENY/~.)) CALL MOVETO ((~~.*CENX/S~.),(-~.*cENY/~.),xY) PLACEzLINETO ((~.*cENx/~.-CENX/S~.),(-~.*cENY/~.)) CALL MOVETO ((~.*CENX/~.-CENX/~~,),(-~.*CENY/~.),XY) PLACE=LINETO ((CENX-CENx/48.),(-CENY/8.)) CALL MOVETO ((CENX-CENX/~~.),(-CENY/~.)~XY) PLACE=LINETO ((S.*CENX/~.-CENX/~~.),(-CENY/~.)) CALL MOVETO ((~.*CENX/~.-CENX/~~.)~(-CENY/~.),XY) PLACEzLINETO ((~.*cENx/~.-CENX/~~.),(-CENY/~.)) CALL MOVETO ((~.*cENx/~.-CENX/~~.)~(-CENY/~.),XY) PLACE=LINETO ((I~.*CENX/~~.-CENX/~~.),(-~.*CENY/~.)) CALL MOVETO ((~~.*CENX/~Q.-CENX/~~.),(-~.WENY/~.),XY) PLACE=LINETO ((~.*cENx/~.-CENX/~~.),(-~.*cENY/~.)) CALL MOVETO ((~.*CENX/~.-CENX/S~.)~(-~.*CENY/~.),XY) PLACE=LINETO ((~.*cENx/~~.-CENX/~~.),(-CENY/~.)) CALL MOVETO ((~.*cENx/~~.-CENX/~~.),(-CENY/~.),XY) PLACEzLINETO ((S.*CENX/~.-CENX/~~.),(-CENY/~.)) CALL MOVETO ((S.*CENX/~.-CENX/~~.),(-CENY/~.),XY) PLACE=LINETO ((CENX/~.-CENX/~~.),(-CENY/~.))

Drawing text on the screen: COLO=SETCOLOR(2) CALL MOVETO (~.~~*(((-~./S.)*CENX)-~.*CENX/~~.),O.~~*CENY~XY) CALL OUTGTEXT(' AN OPERATIONAL EPIDEMIOLOGICAL MODEL '1 CALL MOVETO (~.~~*(((-~./~.)*CENX)-~.*CENX/~~.),~.~~~*CENY~XY) CALL OUTGTEXT('F0R BIOCONTROL APPLICATIONS IN FORESTRYt)

Drawing the boxes: COLO=SETCOLOR(9) CALL MOVETO ((((-~./~.)*CENX)-~.*CENX/~~.)*~.~~O.~*CENY,XY) PLACE=LINETO ((((-I./~.)*CENX)-~.*CENX/~~.)*~.~,O.~~~S*CEW) CALL MOVETO ((((-1./2.)*CENX)-3.*~~~~/48.)*1.3,0.3875*CE~,XY) PLACEzLINETO (-(((-1 ./~.)*cENx)-3.*~~~~/48.)*1.3,0.3875*CENY) CALL MOVETO (-(((-~./~.)*CENX)-~.*CENX/~~.)*~.~,O.~~~~*CENY,XY) PLACE=LINETO (-(((-~./S.)*CENX)-~.*CENX/~~.)*~.~~O.~*CENY) CALL MOVETO (-(((-~./~.)*cENx)-~.*cENx/~~.)*~.~,~.~*CENY,XY) PLACEzLINETO ((((-~./~.)*CENX)-~.*cENx/~~.)*~.~,~.~*cENY) PLACE=LINETO CALL MOVETO PLACE=LINETO CALL MOmTO PLACE=LINETO Waiting for ENTER to be pressed: READ (*,*)

RETURN END

This subroutine displays on the screen a main menu: Caller: MAIN Declaring local variable: CHARACTER*i ANSWS Displaying a message on the screen: PRINT*, ' PRINT*,IDo you want to use OPEM again (y/n)? READ (*,886) ANSW2 6 FORMAT (Al) IF (ANSW2.EQ.rY1.0R.ANSW2.EQ.1yr) THEN RETURN ELSE STOP tGoodbye!t END IF RETURN END

********************************************************************** SUBROUTINE INPUT (NBSIDE,REALX,REALY~FORHEI~TREE,SUMSPC~TRANSF~ **********************************************************************!VHEI,ANSW~,LATITU,RECO,PERIOD) This subroutines allows the user to enter his own parameters in the mode1 : Caller: MAIN Declaring global variables: INTEGER*2 NBSIDE,RECO,PERIOD REAL AZIMUT,SUMSPC,TRANSF,VHEI,LATITU Declaring local variables: REAL FHFJGH INTEGER*S ANSW7,ANSW3,HOMOGE,DUMIRESPON CKARACTER* 1 ANSW4

I CALL CLEARSC~EN($GCLEARSCREEN) PRINT*, ' PRINT*,'WELCOME INTO OPEM!' PRINT*, PRINT*,lDo you want to use default parameters (treated area coordi !nates, forest PRINT*,'edges height, etc.) or enter new ones (1 = Default, 2 = Ne !w)?' READ*, ANSW7 DO WHILE (ANSW7.NE.l.AND.ANSW7.NE.S) PRINT*, ' ' PRINT*,tPleaset enter 1 (default) or 2 (new)' READ*, ANSW7 END DO IF (ANSW7. EQ. 1 ) THEN

The following data are real data obtained £rom a forest clearcut (used as default in the program as wished by the user) in Ste-Florence, Quebec, Canada. NBSIDE is the number of forest edges: NBSIDE=6

The following are the forest clearcut coordinates of the experimental site (Sainte-Florence, Quebec, canada): REALX(1 ]=O REALY (1 )=O REALX(S)=-96 -1 REALY(2)=31.2 REALX(3)=-97.5 REALY(3)=-9.8 REALX(4)z-57.1 REALY(4)=-21.4 REALX(5)=-16.7 REALY(S)=-33.0 REALX(6)=7 -8 REALY(6)=-28.2 The following are the forest height of every forest edge of the experimental site (Sainte-Florence, Quebec, canada): FORHEI(1)=6.2 FORHEI(2)=6.9 FORHEI(3)=8,5 FORHEI(4)=9.8 FORHEI(S)=7.1 FORHEI(6)=7.1

The following are the densities of stubs per m2 per tree species of the experimental site (Sainte-Florence, Quebec, canada): TREE(1)=0.46 TREE(S)=O. TREE(3)=O. TREE(4)=0.51 TREE(5)=0.55 SUMSPC=l. 52 Defining the mean vegetation height within the treated area of the experimental site (Sainte-Florence, Quebec, canada): VHEI=O. 1 Defining the Northern latitude of the experimental site (Sainte-Florence, Quebec, canada): LATITU=48.08 Defining the meteorological data recording period (RECOI and the treatment period (PERIOD): RECO=2 PERIOD=2

Now entering into an INPUT phase: CALL CLEARSCREEN($GCLEARSCREEN) PRINT*ftEntering the forest clearcut coordinate~:~ PRINT*, ' PRINT*,rHow many sides (edges) does the forest clearcut have (MAX= !15)?' READ*, NBSIDE IF (NBSIDE.GT.15) GOTO 51 IF (NBSIDE.LT.3) THEN PRINT*,'This is impossible ...Enter new value..,' GOTO 51 ELSE PRINT*, ' CALL CLEARSCREEN($GCLEARSCREEN) PRINT 611rYoumay enter thet, NBSIDEttcoordinatesof the forest cl !earcut in metresf PRINT*, land in a ANTI-CLOCKWISE way (POINT 1 being (0,O)) PRINT*, ' ' DO DUM=2, NBSIDE PRINT 65,f(POINTt,DUM,t)X:f READ*, X4 (DUM) PRINT 65,r(POINTt,DUM,t)Y:' READ*, Y4 (DUM) CALL CLEARSCREEN($GCLEARSCREEN) FORMAT (1X,A17,1X,12t1X,A45) FORMAT (1X,A6, 1Xr12,A4) END DO END IF IF (RESPON.EQ.1) GOTO 939

PRINT*,fWhat is the angle from North of point 1 (O,O)f PRINT 86,Ito point 2 (',X4(2),',rtY4(2),')?' PRINT*, ,N.B. An angle that is North-West is between 270 and 360 de !grees READ*,AZIMUT DO WHILE (AZIMUT.GE. 360) AZIMUT=AZIMUT-360. O END DO FORMAT (1X,~12,F8.2,A1,F8.2,A3) 2 CALL CLEARSCREEN($GCLEARSCREEN) PRINT*, PRINT*rrIn terms of height, is the surrounding forest homogeneous !(l=yes, 2=n0)?~ 7 PRINT*, ' ' READ*, HOMOGE IF (HOMOGE.NE.I.AND.HOMOGE.NE.~) THEN PRINT*,tPleasel enter 1 (yes) or 2 (no)...' GûTO 907 ELSE IF (HOMOGE.EQ.1) GOTO 859 END IF CALL CLEARSCREEN($GCLEARSCREEN) PRINT*, ' ' PRINT*,fPlease, enter the height of each forest side.' PRINT*,IJust to help you, here are the sides and their coordinat !es:' PRINT*,' DO DUM=1 ,NBSfDE IF (DUM.EQ.NBSIDE) THEN X4(DUM+l )=O Y4(DUM+1 )=O END IF PRINT 91ltfSIDE#f,DUM,r: POINT (r,X4(DUM),f,t,Y4(DUM)fr)to PO1 !NT (',X~(DUM+~),~,~,Y~(DUM+~),~)~ 1 FOF?MAT (1X,A6,12,1XlA9,F7.2,A1,F7.2tA12rF7-2rA1,F7.2,Al) END DO DO DUM= 1 ,NBSIDE PRINT*, l PRINT 91StrFORESTSIDE #t,DUM,lHEIGHT = ' FORMAT (lX,Al3,1XrI2,4XtA10) READ*, FORHEI (DUM) END DO CALL CLEARSCREEN($GCLEARSCREEN) IF (HOMOGE.EQ.S) GOTO 860 CALL CLEARSCREEN($GCLEARSCREEN) 3 PRINT*, ' ' PRINT*,'What is the mean height of the surrounding forest (m)?:' PRINT*, ' READ*, FHEIGH DO DUM=1,NBSIDE FORHEI(DUM)=FHEIGH END DO IF (RESPON.EQ.S) GOTO 939

3 CALL CLEARSCREEN($GCLEARSCREEN) PRINT*,IWhat is the density of the following tree species in the f !orestl PRINT*, clearing (#stubs/ha)? PRINT*, ' PRINT*,'Prunus pennsylvanica (pin cherry):' READ*, TREE ( 1 ) PRINT*, PRINT*,fPopulus tremuloides (trembling aspen):' READ*, TREE ( 2 ) PRINT*, ' PRINT*,fAcer saccharum (sugar maple):, READ*, TREE ( 3 ) PRINT*, PRINT*,fBetula papyrifera (paper birch):, READ*, TREE ( 4 ) PRINT*, * PRINT*,'Other deciduous trees (ERA,ERR,BOG,BOJ,~~C.):~ Transforming the density of stubs per hectare into density of stubs per square metres, and totalizing them: DO DUM=1,5 TE~EE(DUM)=TREE(DUM)/IOOOO. SUMSPC=TREE(DUM)+SUMSPC END DO Calculation of the distance of each coordinate from point (0,O): DO DUM=2,NBSIDE HYPOT(DUM)=SQRT(X4(DUM)**2.+Y4(DUM)**2.) END DO Calculation of angles of each point entered by the user in the cartesian plan of the user (angle from the Y axis) (TETA(2) being angle £rom Y axis of coordinate 2) DO DUM=L,NBSIDE IF (X4(DUM).GE.O.AND.Y4(DUM).GE.O) TETA(DUM)=(ASIN(ABS(X~(DUM)/HYP !OT(DUM)))*TRANSF) IF (X4(DUM).GE.O.AND.Y4(DUM).LT.O) TETA(DUM)=(ASIN(ABS(Y~(DUM)/HYP !OT(DUM)))*TRANSF)+90 IF (X4(DUM).LT.O.AND.Y4(DUM).LT.O) TETA(DUM)=(ASFN(ABS(X~(DUM)/HYP !OT(DUM)))*TRANSF)+IôO IF (X4(DUM).LT.O.AND.Y4(DUM).GE.O) TETA(DUM)=(ASIN(ABS(Y~(DUM)/HYP !OT(DUM)))*TRANSF)+270 END DO Calculating the angles of each coordinate of forest clearcut (angle ptl to pt2 being O degree): DO DUM=3, NBS IDE BETA(DUM)=TETA(DUM)-TETA(S) IF (BETA(DUM).LT.O) BETA(DuM)=BETA(DUM)+~~O. END DO BETA(2)=0 Adapting user's cartesian plan to real geographical plan (ANGLE0 being the real angle of a coordinate £rom North and REALXO and Y() being the real coordinates in the real plan: DO 301 DUM=2,NBSIDE ANGLE(DUM)=AZIMUT+BETA(DUM) s IF (ANGLE(DUM).LE.~O.AND.ANGLE(DUM).GE.O) THEN REALX(DUM)=HYPOT(DUM)*SIN(ANGLE(DUM)/TRANSF) REALY(DUM)=HYPOT(DUM)*COS(ANGLE(DUM)/TRANSF) ELSE IF (ANGLE(DUM).GT.90.AND.ANGLE(DUM).LEE180) THEN REALX(DUM)=HYPOT(DUM)*COS((ANGLE(DUM)-~O)/TRANSF) REALY(DUM)=-(HYPOT(DUM)*SIN((ANGLE(DUM)-~O)/TRANSF)) ELSE IF (ANGLE(DUM).GT.180.AND.ANGLE(DUM).LE.270 THEN REALX(DUM)=-(HYPOT(DUM)*S~N((ANGLE(DUM)-~~O)/T~SF)) REALY(DUM)=-(HYPOT(DUM)*COS((ANGLE(DUM)-~~O)/TRANSF)) ELSE IF (ANGLE(DUM).GT.270.AND.ANGLE(DUM).LEE360) THEN RERLX(DUM)=-(HYPOT(DUM)*COS((ANGLE(DUM)-~~O)/TRANSF)) REALY(DUM)=HYPOT(DUM)*SIN((ANGLE(DUM)-~~O)/TRANSF) ELSE IF (ANGLE(DUM).GT.~~O)THEN DO WHILE (ANGLE(DUM).GE.~~O) ANGLE(DUM)=ANGLE(DUM)-360 END 00 WTO 275 ELSE IF (ANGLE(DUM).LT.O) THEN DU WHILE (ANGLE(DUM).LT,O) ANGLE(DUM)=ANGLE[DUM)+~~O END DO GOTO 275 ELSE AZIMUT=AZIMUT END IF END IF END IF END IF END IF END IF 1 CONTINUE

4 CALL CLEARSCREEN[$GCLEARSCflEEN) PRINT*,'What is the mean height of the residual vegetation in the !treated area?' PRINT*, ' READ*, VHEI IF (RESPON.EQ.4) GOTO 939 5 CALL CLEARSCREEN($GCLEARSCREEN) PRINT*,'What is the latitude of the treated site?' PRINT*, ' READ*, LATITU IF (WSPON.EQ.5) GûTO 939 6 CALL CLEARSCREEN($GCLEARSCREEN) PRINT*, 'When have the rneteorological data been recorded?' PRINT*,' ' PRINT*,t(l) In the year of the treatment with the biosilvicide;' PRINT*,t[2) One year following the treatment with the biosilvicide 1.1 . t PRINT*,#(3) Two years following the treatment with the biosilvicid !e;' PRINT*,'(4) Three years or more following the treatrnent with the b !iosil~icide;~ PRINT*, ' PRINT*,fEnter one of the above number: PRINT*, READ*, RECO DO WHILE (RECO.NE.l.AND.RECO.NE.2.AND.RECO.NE,3.A.4) PRINT*,' ' PRINT*,8Please, enter a nurnber from 1 to 4: READ*, RECO END DO IF (RESPON.EQ.6) GOTO 939

7 CALL CLEARSCREEN($GCLEARSCREEN) PRfNT*,'At which time of the year has the treatment with the biosi !lvicide PRINT*,'been applied?' PRINT*, ' ' PRINT*,f(l) Before August;, PRINT*,'(2) In August or later;' PRINT*, ' READ*,PERIOD DO WHILE (PERIOD.NE.l.AND.PERIOD.NE.2) PRINT*, ' PRINT*, Please, enter 1 or 2 : ' READ*,PERIOD END DO IF (RESPON.EQ.7) GOTO 939 Asking the user if al1 inputs are ok: 19 CALL CLEARSCREEN($GCLEARSCREEN) PRINT*,'Are you sure that the above inputs are correct (l=yes, 2=n !O)?' PRINT*, READ*, ANSW3 10 IF (ANSW3.NE.I.AND.ANSW3.NE.2) THEN PRINT*,'The answer is not valid, please enterf PRINT*,t(l=yes, 2=no):, PRINT*, ' READ*, ANSW3 GOTO 360 ELSE IF (ANSW3.EQ.2) THEN CALL CLEARSCREEN($GCLEARSCREEN) PRINT*,'What must be corrected (enter the number)?' PRINT*, ' PRINT*,rl-) The forest clearing coordinates;' PRINT*,f2-; The forest edges height(s)it PRINT*,,3-) The stub density;' PRINT*,'4-) The mean height of the vegetation within the !d area; PRINT*,t5-) The northern latitude of the site;' PRINT*,t6-) The time of recording the meteorological data PRINT*,t7-) The time of treatment;' PRINT*, ' READ*, RESPON DO WHILE (RESPON.GT.7) PRINT*,tPleasefenter a number from 1 to 7...' PRINT*, ' READ*, RESPON END DO IF (RESPON.EQ.I) GOTO 851 IF (RESPON.EQ.2) GOTO 852 IF (RESPON.EQ.3) GOTO 853 IF (RESPON.EQ.4) GOTO 854 IF (RESPON.EQ.5) GOTO 855 IF (RESPON.EQ.6) GOTO 856 IF (RESPON.EQ.7) GOTO 857 ELSE GOTO 858 END IF CALL CLEARSCREEN($GCLEARSCREEN) END IF END IF RESPON=O Writing some parameters into a file named FHEI-OUT: 8 OPEN (521~~~~~~='~~~tt~~~~='~:\~~~~~~~\~~~\~~=616) CAU CLEARSCREEN($GCLEARSCREEN) PRINT*, ' ' PRINT*,#The file FHEI.OUT already exists; OK to overwrite (y/n)?, READ (*,617) ANSW4 FORMAT (Al ) CLOSE (52) IF (ANSW4.EQ.' Y' .OR.ANSW4.EQ. 'y' ) THEN OPEN (52, FILE=~C:\FORTRAN\OUT\FHEI.OUT', MODE='WRITE1) ELSE STOP 'Program aborted! Goodbye!' END IF WRITE (52,618) NBSIDE DO DUM= 1 ,NBS IDE WRITE (52,619) FORHEI(DUM) END DO FORMAT (12) FORMAT (15F5.2) CLOSE (52) RETURN END

k********************************************************************* SUBROUTINE CARAC (NBSIDE,REALX, REALY ,SLPSID, INTRSI ,LOWERX , !GREATX,TOTARE,LARG) k************X******************************************************** This subroutine determines a few caracteristics of the treated asea (the surface area, the largest width of the area and both slope and intercept of each forest clearcut edge in the cartesian plan): Caller: MAIN Declaring local variables: INTEGER*2 DUM,DUM4 REAL AREA ,LARGE ( 1 5 ) Declaring global variables: REAL REALX(~S),REALY(~~),SLPSID(~S),INTRSI(~~RG~ !GREATX( 15) INTEGER*2 NBSIDE Calculating the surface area of the forest clearcut (square rnetres): REALX(NBSIDE+I )=O. REALY (NBSIDE+1 )=O. TOTARE=O . AREA=O . DO DUM=1,NBSIDE AREA= (REALX(DUM+I1-REALX(DUM) ) * (REALY (DUM+I) +REALY(DUMI ) TOTARE=AREA+TOTARE END DO T~TARE=ABS(TOTARE/~.) Finding the largest width of the forest clearcut: LARG=O DO DUM=1,NBSIDE DO DUM4=1,NBSIDE LARGE(DUM~)=SQRT((REALY(DUM)-UEALY(DUM~))**~.+(REALX(DUM)- !REALX(DUM4))**2.) IF (LARGE(DuM~).GT.LAE~G) LARG=LARGE(DUM~) END DO END DO Calculating the slope of each side of the clearcut on the cartesian grid (SLPSIDII) being the slope between ptl and pt2 and so on): DO DUM=1,NBSIDE IF (DUM.EQ.NBSIDE) THEN REALY (DUM+l)=REALY ( 1 ) REALX(DUM+I )=REALX(I ) ELSE REALY(DUM)=REALY(DUM) REALX(DUM)=REALX(DUM) END IF IF ((REALX(DUM+l)-REALX(DUM)).NE.O) THEN SLP~ID(DUM)=~REALY(DUM+~)-REALY(DUM))/(RX(DUM+~)- !REALX(DUM) ) ELSE SLPSID(DUM)=9.9E+19 END IF END DO Calculating the intercept (point on Y axis) of each side of the forest clearcut. INTRSI(1) is the intercept of side 1: DO DUM=1 ,NBS IDE INTRSI(DUM)=REALY(DUM)-(SLPSID(DUM)*REALX(DUM)) END DO INTRSI(NBSIDE)=O LOWERX and GREATX are the boundary points beyond which the comrnon point (see in subroutine BLOW) is considered an outlier of the forest clearcut side analyzed: DO DUM=I,NBSIDE IF (REALX(DUM).LT.REALX(DUM+l)) THEN LOWERX(DUM)=REALX(DUM) GREATX(DUM)=REALX(DUM+l) ELSE LOWERX (DUM)=REALX ( DUM+1 ) GREATX(DUM)=REALX[DUM) END IF END DO RETURN END

********************************************************************** **********************************************************************SUBROUTINE DRAWTA(REALX,REALY,NBSIDE,LARG,TOTARE,ANSW8,ANSW7) This subroutine returns a drawing of the forest clearcut on the screen using Microsoft FORTRAN graphics libraries: Caller: MAIN INCLUDE 'FGRAPH.FD' Declaring global variables: Declaring local variables: INTEGER MXXPIX,MXYPIX INTEGER*2 PLACE DUM, STATUS INTEGERR4 COL0 INTEGER X1 (15),Y1(15) REAL INDEXfCENXfCENY,INDICE RECORD /XYCOORD/ XY RECORD /VIDEOCONFIG/ MYSCREEN DO 1210 DUM=l,NBSIDE Xl(DUM)=NIrn(REALX(DUM)) YI(DVM)=-NINT(REALY(DUM)) 10 CONTINUE CALL CLEARSCREEP ($GCLEARSCREEN) STATUS=SETVIDEOMODE($VRES~~COLOR) CUL GETVIDEOCONFIG (MYSCREEN) MXXPIX=MYSCREEN.NUMXPIXELS-1 MXYPIX=MYSCREEN.NUMYPIXELS-1

CALL SETVIEWORG(CENX,CENY~XY) Setting the line style as a full line: CALL SETLINESTYLE(#FFFF)

CALL MOVETO {O,(CENY/~.~),XY)

DO DUM= 2, NBS IDE PLACE=LINETO (X~(DUM)*INDEX,(Y~(DUM)*INDEX)+CENY/~.O) CALL MOVETO (X~(DUM)*INDEX,(Y~(DUM)*INDEX+CENY/~.O)~XY 1) END DO PLACE=LINETO (O,(CENY/~.~))

IF (REGISTERFONTS(~C:\FORTRAN\FONT\TMSRB.FON O THEN STOP 'Errer: Cannot open C:\FORTRAN\FONT\TMSRB.FON!~ END IF IF (SETFONT("TrTMS RMNfHl 4W1 OB") .LT. O) THEN STOP 'Errer: Cannot load typeface!# END IF

CALL MOVETO (-(CENX*O.~),(CENY*O.~)~XY) Drawing the scale on the screen: COLO=SETCOLOB( f 4 ) CALL OUTGTEXT(rscalet) CALL MOVETO (-(CENX*0.7)t((CENY*0.9)+8.)tXY) IF (LARG.LT.200.AND.LARG.GE.O) THEN INDICE=LARG/1 o. LONGE=LONGE/INDICE PLACE=LINETO((-(CENX*O.~)+LONGE)~(CENY*O.~+~.)) CALL MOVETO (-(CENX*O.~)~((CENY*O.~)+~.)~XY) PLACE=LINETO(-(CENX*O.~)~((CEW*O.~)+IO.)) CALL MOVETO ((-(CENX*O.~)+LONGE),((CENY*O.~)+~~)~XY) PLACE=LINET~((-(CENX*O.~)+LONGE),((CENY*O.~)+IO.)) CALL MOVETO ((-(CENX*O.~)+LONGE+~.),(CENY*O.~)~XY) CALL OUTGTEXT(t= 10 Metrest) END IF IF (LARG.LT.400.AND.LARG.GE.200) THEN INDICE=LARG/SO. LONGE=LONGE/INDICE PLACE=LINETO((-(CENX*O.~)+LONGE),(CENY*O.~+~.)) CALL MOVETO (-(cENx*O.~)~((CENY*O.~)+~.),XY) PLA~E=LINETO(-(cENx*O.~),((CENY*O.~)+IO.)) CALL MQVETO ((-(cENx*O.~)+LONGE)~((CENY*O.~)+~.),XY) PLACE=LINET~((-(CENX*~.~)+LONGE)~((CENY*~.~)+~~.)) CALL MOVETO ((-(CENX*O.~)+LONGE+~.)~(CENY*O.~)~XY) CALL OUTGTEXT(I= 20 Metrest) END IF IF (LARG.LT.600.AND.LARG.GE.400) THEN INDICE=LARG/SO. LONGE=LONGE/INDICE PLACE=LINETO((-(CENX*0.7)+LONGE)f(CENY*0.9+8.)) CALL MOVETO (-(CENX*O.~)~((CENY*O.~)+~.)~XY) PLAcE=LINETo(-(cENx*O.~)~((CENY*O.~)+IO.)) CALL MOVETO ((-(cENx*O.~)+LONGE)~((CENY*O~~)+~.),XY) PLACE=LINETO((-(CENX*~.~)+LONGE),((CENY*~.~)+~~.)) CALL MOVETO ((-(CENX*0.7)+LONGE+4.)t(~~~~*0.9),~~) CALL OUTGTEXT('= 50 Metrest ) END IF IF (LARG.GE.600) THEN INDICE=LARG/I00. LONGE=LONGE/INDICE PLACE=LINETO((-(CENX*0.7)+LONGE),(CENY*O.9+8.) CALL MOVETO (-(cENx*O.~),((CENY*O.~)+~.),XY) PLACE=LINETO(-(CENX*O.~),((CENY*O.~)+W.)I CALL MOVETO ((-(CENX*~.~)+LONGE),((CENY*~.~)+~.),XY) PLACE=LINET~((-(CENX*~.~)+LONGE)~((CEN*~.~)+~~.)) CALL MOVETO ((-(CENX*O.~)+LONGE+~.)~(CENY*O.~),XY) CALL OUTGTEXT(t= 100 Metrest) END IF Displaying the North direction on the screen: COLO=SETCOLOR(lQ) CALL MOVETO ((CENX*O.~),(CENY*O.~~),XY) PLACE=LINETO((CENX*O.~)~(CENY*O.~)) PLACE=LINETO((CENX*0.94)t (CENY*0.94)) PLACE=LINETO((CENX*O.~~),(CENY*~.~)) CALL MOVETO ((CENX*O.~),(CENY*O.~~),XY) PLAcE=LINETO((CENX*O.~~),(CENY*~.~)) PLACE=LINETO((CENX*O.~~),(CENY*~.~~)) Displaying the total area treated from which sporulation vil1 occur: PRINT 150tfTotalarea treated:t,TOTAREtlm~t 3 FORMAT (1X,A19,fXtF11.2,1X,A3) PRINT*tfThisis an aerial view of the forest clearcut treated with ! the myc~herbicide;~ PAUSE tPress ENTER to continue. ..'

IF (ANSW7.EQ. 1 ) THEN ANSW8=1 WTO 866 ELSE PRINT*ttIs the aerial view of the forest clearcut correct accordin !g to the coordinatest PRINT*, ' you entered (1 =YES, 2=NO ) ? ' READ*, ANSW8 DO WHILE (ANSW8.NE.l.AND.ANSW8.NE.2) CALL CLEARSCREEN ($GCLEARSCREEN) PRINT*tlPlease,enter 1 or 2' READ*, ANSW8 END DO END IF

RETURN END

...... SUBROUTINE FILE ( ) This subroutine opens several files (meteorological data and output files) : Caller: MAIN Declaring local variables: CHARACTER*l ANSW16 Opening data file DATA.DAT: OPEN (12t~~~~~~=r~~~~t~~~~=~:\~~~~~~\~~~~~\~~~~.~~~t~~~~=~~~~ GOTO 504 3 STOP 'The file DATA.DAT is missing! Program aborted!'

Opening output file METEO.OUT : 4 OPEN (22,STATUS=tOLDt,~~~~=t~:\~~~~~~\~~~\~~~~~.~~~tt~~~=5~6~ CALL CLEARSCREEN($GCLEARSCREEN) PRINT*,,The file METEO.OUT already exists; OK to overwrite (y/n)ll READ (*,507) ANSW16 7 FORMAT (Al) CLOSE (22) IF (ANSW16.EQ. 'Y' .OR.ANSW16.EQ. 'y' ) THEN 6 OPEN (22, FILE=~C:\FORTRAN\OUT\METEO.OUT', MODE='WRITEt) ELSE STOP 'Program aborted! Goodbye!' END IF Opening output file FSIDE. OUT: OPEN (~~,STATUS=~OLD~,FILE='C:\FORT~N\OUT\FSIDE.~UT~,E~=~~~) CALL CLEARSCREEN($GCLEARSCREEN) PRINT*, ' ' PRINT*,fThe file FSIDE.OUT already exists; OK to overwrite (y/n)?' READ (*,509) ANSW16 P FORMAT (Al ) CLOSE (32) IF (ANSW16.EQ.fYt.0R.ANSW16.EQ.ryp) THEN 3 OPEN (32, FILE=~C:\FORTRAN\OUT\FSIDE.OUT',MODE='WRITEf) ELSE STOP ,Program aborted! Goodbye!' END IF Opening output file MEANT.OUT: OPEN (~~~,sTATuS=~OLD~,FILE='C:\FORTRAN\OUT\~=~~~) CALL CLEARSCREEN($GCLEARSCREEN) PRINT*, ' ' PRINT*,'The file MEANT.OUT already exists; OK to overwrite (y/n)? Il READ (*,515) ANSW16 5 FORMAT (A1 ) CLOSE (102) IF (ANSW16.EQIfYf.OR.ANSW16.EQ.'y') THEN 1 OPEN (102, FILE='C:\FORTRAN\OUT\MEANT.OUT', MODE='WRITEf) ELSE STOP 'Program aborted! Goodbye! END IF

RETURN END

k********************************************************************* SUBROWINE BLOW(TIME,JULDAY,NBSIDE,INTRSI,SLPSID,REALXt~~Y,DIR7, ~*********************************************************************!TRANSF,LOWERX,GREATX,FORHEI) This subroutine tells the program where to find the mean height of the forest edge on which the wind is blowing, and this, every 30 minutes : Caller: MAIN Declaring local variables: REAL SCNMXX,SCNMXY,MAXX,MAXYfSLOPWIILNGEST,PE,INTVAXf !INTVAY,LOWSLP INTEGER*2 DUM9,DUM14,DUM15,DUM18IDUM23,DUM24IDUM25fDUM26tDUM27I !DUM28,NBSIDEIIDüMMY Declaring local arrays: REAL X~(~~)~Y~(~~)~SLSCAN(~~),INTERY(~),INTE~(~)IPTSLIX(~~)~ !PTSLIY(~~),X~(~~),Y~(~~)~INTSLI(~~~SLICEX(~~~~L~CEY(~) INTEGER SIDSLI(2),SIDE(6) Dedaring global variables: INTEGER TIME,JULDAY REAL DIR7,TRANSF Initializing variables: DUM28=O

Calculating- the slope of the wind (SLOPWI) for the present time period: IF (DIR7.EQ.O.OB.DIR7.EQ.90.OR.DIR7.EQ.180.OR.DIR7.EQ.27~~ !DIR7=DIR7+0.5 SLOPWI=COS(DIR~/TRANSF)/SIN(DIR~/TRANSF) Calculating the slope of the crosswind direction (SLPERW) which is 90 degrees from wind direction for the present time period: LNGEST=O .O1

Finding the reference intercept (SCNMXX is the point on the X axis calculated from MAXX and SCNMXY is the point on the Y axis calculated from MAXY) from which the scanning will start in order to find the three slices (2-D locations) in the treated area: DO DUM9=1 ,NBSIDE IF (DUM9.EQ.NBSIDE) REALY(DUM9+1)=REALY(l) IF (ABS(REALY(DUM9+1)).GT.ABS(REALY(DUM9))) THEN MAXY=REALY (DUM9+1) ELSE MAXY=MAXY END IF END DO DO DUM14=7 ,NBSIDE IF (DUM14.EQ.NBSIDE) REALX(DUM14+1)=REALX(l) IF (ABS (REALX(DUM14+1)) .GT. ABS (REALX (DUM14)) ) THEN MAXX=REALX (DUMI 4+1 ) ELSE MAXX=MAXX END IF END DO

Determining whether the scanning will be done on the X axis or Y axis: LOWSLP=1 OEf 5 DO DUM15=1 ,NBSIDE Avoiding a numerical error in the next equation: IF (REALX(DUM15) .EQ.O) REALX(DUM15)=0.01 The variable SLSCAN is the slope between SCNMXY (defined above) and each of the forest clearcut coordinates: SLSCAN (DUMI 5 ) = (REALY(DUM15 ) -SCNMXY ) /REALX(DW~5 ) IF (ABS(SLSCAN(DUM~~)).LT.LOWSLP)LOWSLP=ABS(SLSCAN(DUM~S)) END DO In the following case, the scanning is done on the X axis: IF (ABS(SLPERW).GE.LOWSLP) GOTO 585 Scanning on the Y axis: CALL SCANY(X5,Y5,LONTX,LONTY,SLPSIDtSCNMXY,SLPERW,NBSZDEIMAXYt !INTRSI,LOWERX,GREATXILNGEST)

GOTO 755

Scanning on the X axis: i CALL SCANX(X5,Y5,LONTX,LONTYISLPSID,SCNMXX,SLPERWt !NBSIDE,MAXX,INTRSItLOWERXIGREATXILNGEST) INTVAX is the value of the variable LNGEST transposed on the X axis (always positive) and INTVAY is the one but transposed on the Y axis (the sign of INTVAY depends on the sign of the slope): ; INTVAX=SQRT(LNGEST**~./(~+(SLPERW**~.))) INTVAY=INTVAX*SLPERW Identifying the 3 points (6 coordinates) on the LNGEST segment (INTERX AND INTERY): DO DUM23=1,3 INTERX(DUM~~)=LONTX(~)+(DUM~~*(INTVAX/~.O)) INTERY(DUM~~)=LONTY(~)+(DUM~~*(INTVAY/~.O)) Finding the intercept (point Y axis) of each wind slope passing by each of the 3 points on the LNGEST segment INTSLI(DUM23)=INTERY(DUM23)-(SLOPWI*INTERX(DUM23)) END DO Calculating the 2 common points of the wind slope and side slope for each of the 3 points on the LNGEST segment (12 coordinates) For the first point on the LNGEST segment (INTERX and INTERY) DO 910 DUM25=1,3 DUM26=0 DO 920 DUM24=1 ,NBSIDE X6 and Y6 are the common coordinates between forest clearcut side (DUM24) and the crosswind equation with intercept (INTERC}: In case of a zero denorninator: IF ((SLOPWI-SLPSID(DUM~~)).EQ.O)SLPSID(DUM24)=SLPSID ! (DUM24)+O. 001

Finding if the cornmon points (X6 and Y6) are points of the forest clearcut side or of an extension of it (PTSLIX and PTSLIY are the common points between the slice and the forest clearcut: IF (X~(DUM~~).LT.LOWERX(DUM~~).OR.X~(DOMS~).GT.GREATX(DUM~~)) !THEN GûTO 920 ELSE DUM26=DüM26+1 PTSLIX(DUM26)=X6(DUM24) PTSLIY(DUM26)=Y6(DUM24) SIDSLI is the number of the forest side on which PTSLIX and PTSLIY are: SZDSLI(DUMSG)=DUM~~ END IF O CONTINOE If there are 3 common points for one given slice: IF (DUM26 .EQ. 3) THEN IF (PTSLIX(l).EQ.PTSLIX(2).AND.PTSLIY(l).EQ.PTSLIY(2)) !THEN PTSLIX(L)=PTSLIX(3) PTStIY(2)=PTSLIY(3) PTSLIX(3)=0 PTSLIY(3)=0 ELSE DUMMY=DUMMY END IF IF (PTSLIX(l),EQ.PTSLIX(3).AND.PTSLIY(l).EQ.PTSLIY(3)) !THEN PTSLIX(3)=0 PTSLfY(3)=0 ELSE DUMMY =DUMMY END IF IF (PTSLIX(~).EQ.PTSLIX(~).AND.PTSLIY(~).EQ.PTSLIY(~)) !THEN PTSLIX(3)=0 PTSLIY(3)=O ELSE DUMMY=DUMMY END IF DUM26=2 ELSE DUMMY=DUMMY END IF DUM28 is the number of common points between the forest clearcut sides and the current slice: DUM28=DUM28+2 DUMMY=1 DO 930 DUM27=(DUM28-1),DUM28 SLICEX(DUM27)=PTSLIX(DUMMY) SLICEY(DUM27)=PTSLIY(DUMMY) SIDE(DUM27 ) =SIDSLI (DIJMMY) DUMMY= 2 O CONTINUE O CONTINUE

Calculating the length of each slice: DO DUMl8=1,3 IF (DIR7.GT.180) THEN IF (SLICEx((DUM18*2)-I).GT.SLICEX(DUM18*2)) THEN DUMMY =Dm ELSE IDUMMY=SIDE( (DUM18*2)-1) SIDE( (DUMI 8*2)-1 ) =SIDE(DUM18*2) SIDE(DUM18*2)=IDUMMY DUMMY=SLICEX((DUM18*2)-1) SLICEX((DUM18*2)-l)=SLICEX(DUM18*2) SLICEX(DUPII18*2)=DUMMY DüMMY=SLICEY( (DUMI 8*2 1-1 1 SLICEY((DUMI~*~)-~)=SLICEY(DUMI~*~) SLICEY (DUMI 8*S)=DUMMY END IF ELSE IF(SLICEX((DUM~~*~)-~).GT.SLICEX(DUM~~*~))THEN IDUMMY=SIDE( (DM18*2)-1) SIDE((DUM~~*~)-~)=SIDE(DUM~~*~) SIDE (DUMI 8*2 ) =IDüMMY DUMMY=SLICEX( (DuMt 8*2)-1) SLICEX ( (DUMI 8*2} -1 ) =SLICEX (DUMI 8*2 ) SLICEX(DUM18*2)=DUMMY DUMMY=SLICEY( (DUMI 8*2) -1 ) SLICEY ( (DUMl8*2)-1 )=SLICEY (DUMI 8*2 ) SLICEY (DUMI 8*2 ) =DUMMY ELSE DUMMY=DUMMY END IF END IF Downloading the length, the related coordinates and the related sides into a file named FSIDE.OUT: WRITE (32,750) JULDAY,TIME,DIR7,SIDE((2*DUMI8)-I)t~~~~(2*~~~I8~~ !FORHEI(SIDE((~*DUM~~)-~)),FORHEI(SIDE(~*DUMI~)) 1 FORMAT (1Xt13j2XfI4I2XIF8.2I2XII2I2XtI2~2XtFS~2,2X~F5~2) 3 END DO RETURN END

t********************************************************************* SUBROUTINE SCANY(X5,Y5,LNGESX,LNGESYtSLPSIDISCNMXY, !SLPERW,NBSIDE,MAXY,INTRSItLOWERX,GREATXtLNGEST) t********************************************************************* This subroutine scans the treated area on the Y axis (north to south) in order to find the location of the forest edge on which the wind is blowing and from this, the equivalent forest height: Caller: Subroutine BLOW

Declaring local variables: INTEGER*2 DUMMY,DUMI 0,DUM11, DUM12 ,DUM13 REAL LONGER,INTERC,MIDGRX~MIDGRY~ABMAXY~LONX(~),LONY(~) ! ,POINTX(15) ,POINTY(15)tGRTXIGRTY,LOWESX~LOWESYtMDLOWXt !MDLOWYfLEN1,LEN2 Defining the scanning interval (DUM11) on the Y axis: ABMAXY=ABS(MAXY) IF (ABMAXY.GE.O.AND.ABMAXY.LT.500) DUM11=1 IF (ABMAXY.GE.500) DUMfl=10

Scanning : DO 475 DUMlO=-SCNMXY,SCNMXYtDUM1l Calculating al1 comrnon point of crosswind slope and side slope from which the longest crosswind vertor can be found: DO 600 DUM12=1 ,NBSIDE X5 and Y5 are the common coordinates between forest clearcut side (DUM11) and the crosswind equation with intercept (INTERC): IF ((SLPSID(DUM12)-SLPERW).EQ.O) SLPSID(DUMlL)=SLPSID

Finding if the common point (X5 and YS) is a point of the forest clearcut side or an extension of it: POINTX and POINTY are the common point inside the forest clearcut: IF (X~(DUM~~).LT.LOWERX(DUM~~).OR.XS(DUM~~).GT-G~ATX(DUM~~)) !THEN LONGER=O GoTO 600 ELSE DUM13=DUM13+1 POINTX(DUM13)=X5(DUM12) POINTY(DUM~~)=YS(DUM~~) END IF O CONTINUE

Finding the longest segment if there are 2 common points on the forest clearcut side:

!**2.) LONX ( 1 ) =POINTX ( 1 ) LONY ( 1 ) =POINTY ( 1 ) LONX(2)=POINTX(2) LONY(S)=POINTY(2) ELSE DUMMY=DUMMY END IF Finding the longest segment if there are 3 common points on the forest clearcut side (should be very seldom!): IF (DUM13.EQ.3) THEN IF (POINTX(1).EQ.POINTX(2).AND.POINTY(1).EQ.POINTY(2)) THEN LONGER=SQRT((POINTX(1)-POINTX(3))**2.+(FOINTY(1)-POINTY(~)) !**2.) LONX ( 1 ) =POINTX ( 1 ) LONY ( 1 ) =POINTY ( 1 ) LONX(2)=POINTX(3) LONY(2)=POINTY(3) ELSE DUMMY=DUMMY END IF LONX ( 1 ) =POINTX ( 1 ) LONY(1 )=POINTY(1) LONX(~)=POINTX(Z) LONY(2)=POINTY(2) ELSE DUMMY=DUMMY END IF IF (POINTX(~).EQ.POINTX(~).AND.POINTY(~).EQ.POI(~))THEN LONGER=SQRT((POINTX(1)-POINTX(2))**2.+(POI~Y(1)-POINTY(2)) !**2. ) LONX ( 1 ) =POINTX ( 1 ) LONY ( 1 ) =POINTY ( 1 ) LONX(2)=POINTX(2) LONY ( 2 ) =POINTY ( 2 ) ELSE DUMMY=DUMMY END IF ELSE DUMMY=DUMMY END IF

Finding the longest segment if there are 4 comrnon points on the forest clearcut side: IF (DUM13.EQ.4) THEN

1 POINTX ( 2 .LE I POINTX ( 4) THEN LOWESX=POINTX(S) LOWESY=POINTY(S) ELSE IF (POINTX(3).LE.POINTX(1).AND.POINTX(3).LE.POI~X(2 I POINTX(3).LE.POINTX(4)) THEN LOWESX=POINTX(3) LOWESY=POINTY(3) ELSE IF (POINTX(4).LE.POINTX(l).AND.POfNTX(4).LE.P01NTX I POINTX(4).LE.POINTX(3)) THEN LOWESX=POINTX(~) LOWESY=POINTY(4) ELSE POINTX(1 )=POINTX( 1 ) END IF END IF END IF END IF POINTX(4).GE.POINTX(3)) THEN GRTX=POINTX(4) GRTY=POINTY(4) ELSE POINTX ( 1 ) =POINTX ( 1 ) END IF END IF EM) IF END IF

In the case of 4 common points, finding the middle points: IF (LOWESX.EQ.POINTX(~).AND.GRTX.EQ.POINTX(~))THEN IF (POINTX(3).GT.POINTX(4)) THEN MDLOWX=POINTX(~) MDLOWY=POINTY(4) MIDGRX=POINTX(3) MIDGRY=POINTY(3) ELSE MDLOWX=POINTX(3) MDLOWY=POINTY(3) MIDGRX=POINTX(4) MIDGRY=POINTY(4) END IF ELSE IF (LOWESX.EQ.POINTX(1).AND.GRTX.EQ.POINTX(3) THEN IF (POINTX(S).GT.POINTX(4)) THEN MDLOWX=POINTX(4) MDLOWY=POINTY(4) MIDGRX=POINTX(L) MIDGRY=POINTY(2) ELSE MDLOWX=POINTX(2) MDLOWY=POINTY(S) MIDGRX=POINTX(4) MIDGRY=POINTY (4) END IF ELSE IF (LOWESX.EQ.POINTX(~).AND.GRTX.EQ.POINTX()) THEN IF (POINTX(3).GT.POINTX(2)) THEN MDLOWX=POINTX(2) MDLOWY=POINTY(2) MIDGRX=POINTX(3) MIDGRY=POINTY(3) ELSE MDLOWX=POINTX(3) MDLOWY=POINTY(3) MSDGRX=POINTX(2) MIDGRY=POINTY(2) END IF ELSE IF (LOWESX.EQ.POINTX(2).ANDNDGRTX.EQ.POINTX(1) THEN IF (POINTX(3).GT.POINTX(4)) THEN MDLOWX=POINTX(4) MDLOWY=POINTY(4) MIDGRX=POINTX(3) MIDGRY=POINTY(3) ELSE MDLOWX=POINTX(3) MDLOWY=POINTY(3) MIDGRX=POINTX(4) MIDGRY=POINTY(4) END IF ELSE IF (LOWESX.EQ.POINTX(2).ANDANDGRTX.EQ.PO1NTX(3THEN IF (POINTX(l).GT.POINTX(4)) THEN MDLOWX=POINTX(4) MDLOWY=POINTY(4) MIDGRX=POINTX( 1 ) MIDGRY=POINTY( 1 ) ELSE MDLOWX=POINTX( 1 ) MDLOWY=POINTY( 1 ) MIDGRX=POINTX(4) MIDGRY=POINTY(4) END IF ELSE IF (LOWESX.EQ.POINTX(~).AND.GRTX.EQ.POINTX(~)) THEN IF (POINTX(3).GT.POINTX(l)) THEN MDLOWX=POINTX( 1 ) MDLOWY=POINTY( i ) MIDGRX=POINTX(3) MIDGRY=POINTY(3) ELSE MDLOWX=POINTX(3) MDLOWY=POINTY(3) MIDGRX=POINTX( 1 ) MIDGRY=POINTY( 1 ) END IF ELSE IF (LOWESX.EQ.POINTX(~).AND.GRTX.EQ.POINTX(~)) THEN IF (POINTX(2).GT.POINTX(4)) THEN MDLOWX=POINTX(4) MDLOWY=POINTY(4) MIDGRX=POINTX(2) MIDGRY=POINTY(2) ELSE MDLOWX=POINTX(S) MDLOWY=POINTY(2) MIDGRX=POINTX(4) MIDGRY=POINTY(4) END IF ELSE IF (LOWESX.EQ.POINTX(3).AND.GRTX.EQ.POINTX()) THEN IF (POINTX(l).GT.POINTX(4)) THEN MDLOWX=POINTX(4) MDLOWY=POINTY(Q) MIDGRX=POINTX( 1 ) MIDGRY=POINTY ( 1 ) ELSE MDLOWX=POINTX ( 1 ) MDLOWY=POINTY ( 1 ) MIDGRX=POINTX(4) MIDGRY=POINTY(4) END IF ELSE IF (LoWESX.EQ.POINTX(~).AND~GRTX.EQ.POINTX(~))THEN IF (POINTX(l).GT.POINTX(2)) THEN MDLOWX=POINTX(2) MDLOWY=POINTY(2) MIDGRX=POINTX( 1 ) MIDGRY=POINTY ( 1 ) ELSE MDLOWX=POINTX( 1 ) MDLOWY=POINTY( 1 ) MIDGRX=POINTX(2) MIDGRY=POINTY(Z) END IF ELSE

MDLOWX=POINTX(2] MDLOWY=POINTY(S) MIDGRX=POINTX(3) MIDGRY=POINTY(3) ELSE MDLOWX=POINTX(3) MDLOWY=POINTY(3) MIDGRX=POINTX(L) MIDGRY=POINTY(2) END IF ELSE IF (LOWESX.EQ.POINTX(~).AND.GRTX.EQ.POINTX()) THEN IF (POINTX(3).GT.POINTX(l)) THEN MDLOWX=POINTX ( 1 ) MDLOWY=POINTY( 1 ) MIDGRX=POINTX(3) MIDGRY=POINTY(3) ELSE MDLOWX=POINTX(3) MDLOWY=POINTY(3) MIDGRX=POINTX(l) MIDGRY=POINTY (1 ) END IF ELSE IF (LOWESX.EQ.POINTX(~).AND.GRTX.EQ.POINTX(~)) THEN IF (POINTX(l).GT.POINTX{2)) THEN MDLOWX=POINTX(2) MDLOWY=POINTY(2) MIDGRX=POINTX ( 1 ) MIDGRY-POINTY ( 1 ) ELSE MDLOWX=POINTX ( 1 ) MDLOWY=POINTY ( 1 ) MIDGRX=POINTX(S) MIDGRY=POINTY(2) END IF ELSE POINTX ( 1 ) =POINTX ( 1 ) END IF END IF END IF END IF END IF END IF END IF END IF END IF END IF END IF END IF Finding the longest of the two segments on the same crosswind vector LENI=SQRT((LOWFSX-MDLOWX)**~.+(LOWESY-MDLOWY)**~.) LEN2=SQRT((GRTX-MIDGRX)**22+(GRTY-MIDGRY)**2.) IF (LENl.GT.LEN2) THEN LONGER=LENl LONX ( 1 ) =LOWESX LONY ( 1 ) =LOWESY LONX(S)=MDLOWX LONY ( 2 ) =MDLOWY ELSE LONGER=LEN2 LONX ( 1 ) =MIDGRX LONY ( 1 ) =MIDGRY LONX(2)=GRTX LONY(L)=GRTY END IF

In the case of 4 common points of which 2 are the same: IF (MDLOWX.EQ.MIDGRX.AND.MDLOWY.EQ.MIDGRY) THEN LONGER=LENl+LEN2 LONX ( 1 ) =LOWESX LONY ( 1 ) =LOWESY LONX(S)=GRTX LONY(2)=GRTY ELSE DUMMY=DUMMY END IF END IF Comparing actual length and previous length and choosing the longest: IF (LONGER.GT.LNGEST) THEN LNGEST=LONGER LNGESX ( 1 ) =LONX ( 1 ) LNGESX(S)=LONX(2) LNGESY ( 1 ) =LONY ( 1 ) LNGESY(S)=LONY(S) ELSE LNGEST=LNGEST LNGESX ( 1 ) =LNGESX ( 1 ) LNGESX(~)=LNGESX(S) LNGESY(l)=LNGESY(l) LNGESY(S)=LNGESY(S) END IF 5 CONTINUE Interverting point 1 and point 2 in order to give to point 1 the lowest X values: IF (LNGESX(I).GT.LNGESX(î!)) THEN DUMMY=LNGESX(L) LNGESX(S)=LNGESX(I ) LNGESX(l)=DUMMY DUMMY=LNGESY(L) LNGESY (2 ) =LNGESY ( 1 ) LNGESY ( 1 ) =DU= ELSE LNGESX ( 1 ) =LNGESX ( 1 ) LNGESX(S)=LNGESX(S) LNGESY ( 1 ) =LNGESY ( 1 ) LNGESY(L)=LNGESY(2) END IF RETURN END

h********************************************************************* SUBROUTINE SCANX(X5,Y5,LNGESX,LNGESYlSLPSID,SCNMXX, ! SLPERW,NBSIDE, MAXXJNTRSI LOWERXIGREATXr LNGEST) k**********************f*X*********************************************** This subroutine scans the treated area on the X axis (West to East) in order to find the location of the forest edge on which the wind is blowing and from this, the equivalent forest height: Caller: Subroutine BLOW Declaring global variables: REAL X~(~~)~Y~(~~),LNGESX[~)~LNGESY(S)~SLPSID(~~),SCNMXX, !SLPERW, MAXX ,INTRSI ( 1 5 ) ,LOWERX ( 1 5 ) ,GREATx( 1 5 ) ,LNGEST INTEGER*S NBSIDE Declaring local variables: INTEGER*2 DUM14,DUM16, DUM17, DUMMY REAL INTERC,LONGER,MIDGRX,MIDGRYIABMAXXILONX(2)tLONY(2) !,P01NTX[15),P0INTY[15),GRTX,GRTY,L0WESX,L0~SY,MDL0WX, !MDLOWY,LEN1 ,LENZ Defining the scanning interval (DUM16) on the X axis: ABMAXX=ABS(MAXX) IF (ABMAXX.GE.O.AND.ABMAXX.LT.500) DUM16=1 IF (ABMAXX-GE.500) DUMl6=lO The variable INTERC is the value on the Y coordinate (intercept) of the slope of perpendicular wind passing by the current scanning value on the X axis. The following loop (485) is the one that scans along the X axis: DO 485 DUM17=-SCNMXX,SCNMXXtDUM16 DUM13=O INTERCZ-(SLpERWkDUM17) Finding the common points (intersection) of the current crosswind slope and side dope from which the longest crosswind vertor can be f ound : DO 61 O DUM14=1,NBSIDE

Arrays X5 and Y5 are the common coordinates between forest clearcut side (or its extension) and the crosswind equation with intercept ( INTERC ) Finding if the common point (X5 and YS) is a point of the forest clearcut side or an extension of it: POINTX and POINTY are the common point inside the forest clearcut: IF (X~(DUM~~).LT.LOWERX(DUM~~) .OR,X~(DUM~~) .GT.GREATX(DUMI~) !THEN LONGER=O GOTO 61 0 ELSE DUM13=DUM13+1 POINTX (DUM13 ) =X5 (DUM14) POINTY (DUM13)=Y5 (DUM14) END IF 1 CONTINUE Finding the longest segment if there are 2 common points on the forest clearcut side:

!**2.) LONX ( 1 ) =POINTX ( 1 LONY ( 1 ) =POINTY ( 1 ) LONX(S)=POINTX(2) LONY(2)=POINTY(2) ELSE DUMMY=DUMMY END IF

Finding the longest segment if there are 3 common points on the forest clearcut side (should be very seldom!): IF (DUM13.EQ.3) THEN IF (POINTX(1).EQ.POINTX(2).ANDDPOINTY(I ).EQPOINTY(2)) THEN LONGER=SQRT((POINTX(1)-POINTX(3))**2.+(POINTY(1)-POINTY(3)) !**2.) LONX(1 )=POINTX(l ) LONY ( 1 ) =POINTY ( 1 ) LONX(~)=POINTX(~) LONY(S)=POINTY(3) ELSE DUMMY=DUMMY END IF IF (POfNTX(1).EQ.POINTX(3).AND.POINTY('1).EQ.POINTY(3)) THEN LONGER=SQRT((POINTX(I)-POINTX(2))**2.+(POINTY(1)-POI~Y(~)) !**2. ) LONX ( 1 ) =POINTX ( 1 ) LONY ( 1 ) =POINTY ( 1 ) LONX(~)=POINTX(~) LONY(2)=POINTY(2) ELSE DUMMY=DUMMY END IF LONGER=SQRT((POINTX(~)-POINTX(~))**~~+(POINTY(~)-POINTY(~)) !**2.) LONX( 1 )=POINTX ( 1 ) LONY ( 1 ) =POINTY ( 1 ) LONX(S)=POINTX(2) LONY(~)=POINTY(~) ELSE DüMMY=DUMMY END IF ELSE DUMMY=DUMMY END IF

Finding the longest segment if there are 4 common points on the forest clearcut side: IF ( DUM13. EQ. 4 ) THEN

! POINTX(2).LE.POINTX(4)) THEN LOWESX=POINTX(2) LOWESY=POINTY(S) ELSE IF (POINTX(3).LE.POINTX(1).AND.POINTX(3).LE.POINTX(2).~D. I POINTX(3).LE.POINTX(4)) THEN LOWESX=POINTX(3) LOWESY=POINTY(~) ELSE IF (POINTX(4).LE.POINTX(1).AND.POINTX(4).LE.POINTX(2).AND. I POINTX(4).LE.POINTX(3)) THEN LOWESX=POINTX(4) LOWESY=POINTY(4) ELSE POINTX ( 1 ) =POINTX ( 1 ) END IF END IF END IF END IF POINTX(4).GE.POINTX(3)) THEN GRTX=POINTX(4) GRTY=POINTY(4) ELSE POINTX ( 1 ) =POINTX ( 1 ) END IF END IF END IF END IF

In the case of 4 common points, finding the middle points: IF (LOWESX.EQ.POINTX(~).AND.GRTX.EQ.POINTX(~)) THEN IF (POINTX(3).GT.POINTX(4)) THEN MDLOWX=POINTX(4) MDLOWY=POINTY(4) MIDGRX=POINTX(~) MIDGRY=POINTY(3) ELSE MDLOWX=POINTX(3) MDLOWY=POINTY(3) MIDGRX=POINTX(4) MIDGRY=POINTY(4) END IF ELSE IF (LOWESX.EQ.POINTX(l).AND-GRTX.EQ.POINTX( THEN IF (POINTX(S).GT.POINTX(~)) THEN MDLOWX=POINTX(4) MDLOWY=POINTY(4) MIDGRX=POINTX(2) MIDGRY=POINTY(S) ELSE MDLOWX=POINTX(2) MDLOWY=POINTY(2) MIDGRX=POINTX(4) MIDGRY=POINTY(4) END IF ELSE IF (LOWESX.EQ.POINTX(l).AND.GRTX.EQ.POINTX(4)) THEN IF (POINTX(3).GT.POINTX(2)) THEN MDLOWX=POINTX(L) MDLOWY=POINTY(2) MIDGRX=POINTX(3) MIDGRY=POINTY(3) ELSE MDLOWX=POINTX(3) MDLOWY=POINTY(3) MIDGBX=POINTX(2) MIDGRY=POINTY(L) END IF ELSE MIDGRY=POINTY(4) END IF ELSE IF (LOWESX.EQ.POINTX(S).AND.GRTX~EQ~POINTX(~)THEN IF (POINTX(~).GT.POINTX(~))THEN MDLOWX=POINTX(~) MDLOWY=POINTY(4) MIDGRX=POINTX ( 1 ) MIDGRY=POINTY(1 ) ELSE MDLOWX=POINTX ( 1 ) MDLOWY=POINTY (1 ) MIDGRX=POINTX(4) MIDGRY=POINTY(4) END IF ELSE IF (LOWESX.EQ.POINTX(2).AND.GRTX.EQQ~OINT~(4))THEN IF (POINTX(3).GT.POINTX(l}) THEN MDLOWX=POINTX ( 1 ) MDLOWY =POINT'Y ( 1 ) MIDGRX=POINTX(3) MIDGRY=POINTY(3) ELSE MDLOWX-POINTX(3) MDLOWY=POINTY(3) MIDGRX=POINTX ( 1 1 MIDGRY=POINTY ( 1 ) END IF ELSE IF (LOWESX.EQ.POINTX(~).AND.GRTX~EQ.POINTX(~))THEN IF (POIJ!?TX(2).GT.POINTX(4)) THEN MDLOWX=POINTX(Q) MDLOWY=POINTY(4) MIDGRX=POINTX(2) MIDGRY=POINTY(S) ELSE MDLOWX=POINTX(S) MDLOWY=POINTY(S) MIDGRX=POINTX(4) MIDGRY=POINTY(4) END IF ELSE IF (LOWESX.EQ.POINTX(~).AND.GRTX.EQ.POINTX(~)) THEN IF (POINTX(1 ).GT.POINTX(4)) THEN MDLOWX=POINTX(4) MDLOWY=POINTY(4) MIDGRX=POINTX ( 1 ) MIDGRY=POINTY ( 1 ) ELSE MDLOWX=POINTX ( 1 ) MDLOWY=POINTY(l) MIDGRX=POINTX(4) MIDGRY=POINTY(Q) END IF ELSE IF (LOWESX.EQ.POINTX(~).AND.GRTX.EQ.POINTX(~)) THEN IF (POINTX(1 }.GT.POINTX(S)) THEN MDLOWX=POINTX(S) MDLOWY=POINTY(2) MIDGRX=POINTX ( 1 ) MIDGRY=POINTY(I) ELSE MDLOWX=POINTX(l) MDLOWY=POINTY ( 1 ) MIDGRX=POINTX(2) MIDGRY=POINTY(L) END IF ELSE IF (LOWESX.EQ.POINTX(4).AND.GRTX.EQ.POINTX(l)) THEN IF (POINTX(3}.GT.POINTX(2)) THEN MDLOWX=POINTX ( 2 ) MDLOWY=POINTY(2) MIDGRX=POINTX(3) MIDGRY=POINTY(3) ELSE MDLOWX=POINTX(3) MDLOWY=POINTY(3) MIDGRX=POINTX(S) MIDGRY=POINTY(S) END IF ELSE IF (LOWESX.EQ.POINTX(4).ANDtGRTX.EQ.P0INTX(2)) THEN IF (POINTX(3).GT.POINTX(l)) THEN MDLOWX=POINTX ( 1 ) MDLOWY=POINTY ( 1 ) MIDGRX=POINTX(3) MIDGRY=POINTY(3) ELSE MDLOWX=POINTX(3) MDLOWY=POINTY(3) MIDGRX=POINTX(I) MIDGRY=POINTY(I) END IF ELSE IF (LOWESX.EQ.POINTX(~).AND.GRTX.EQ.POINTX(~))THEN IF (POINTX(l).GT.POINTX(S)) THEN MDLOWX=POINTX(S) MDLOWY=POINTY(S) MIDGRX=POINTX ( 1 ) MIDGRY=POINTY ( 1 ) ELSE MDLOWX=POINTX ( 1 ) MDLOWY=POINTY ( 1 ) MIDGRX=POINTX(S) MIDGRY=POINTY(2) END IF ELSE POINTX ( 1 ) =POINTX ( 1 ) END IF END IF END IF END IF END IF END IF END IF END IF END IF END IF END IF END IF Finding the longest of the two segments on the same crosswind vector LEN1=SQRT((LOWESX-MDLOWX)**2.+(LOWESY-MDLOWY)**2.) LEN2=sQRT((GRTX-MIDGRX)**2.+(GRTY-MIDGRY)**2.) IF (LENI .GT .LENZ ) THEN LONGER=LENl LONX ( 1 ) =LOWESX LONY ( 1 ) =LOWESY LONX(2)=MDLOWX LONY(2)=MDLOWY ELSE LONGER=LEN2 LONX ( 1 ) =MIDGRX LONY ( 1 ) =MIDGRY LONX(Z)=GRTX LONY(2)=GRTY END IF In the case of 4 common points of which 2 are the same: IF (MDLOWX.EQ.MIDGRX.AND.MDLOWY.EQ.MIDGRY) THEN LONGER=LENl +LENS LONX ( 1 ) =LOWESX LONY ( 1 ) =LOWESY LONX(S)=GRTX LONY(L)=GRTY ELSE DUMMY=DUMMY END IF

END IF Comparing actual length and previous length and choosing the longest: IF (LONGER.GT.LNGEST) THEN LNGEST=LONGER LNGESX(1 )=LONX(l) LNGESX(2)=LONX(2) LNGESY ( 1 ) =LONY ( 1 ) LNGESY(S)=LONY(S)

ELSE LNGEST=LNGEST LNGESX ( 1 ) =LNGESX( 1 ) LNGESX(L)=LNGESX(L) LNGESY ( 1 ) =LNGESY ( 1 ) LNGESY(S)=LNGESY(2) END IF 35 CONTINUE

Interverting point 1 and point 2 in order to give to point 1 the lowest X and Y values: IF (LNGESX(I).GT.LNGESX(2)) THEN DUMMY=LNGESX(2) LNGESX ( 2 ) =LNGESX ( 1 ) LNGESX ( 1 ) =DUMMY DUMMY=LNGESY(L) LNGESY(2)=LNGESY(1 ) LNGESY ( 1 ) =DUMMY ELSE LNGESX ( 1 ) =LNGESX ( 1 ) LNGESX(2)=LNGESX(S) LNGESY ( 1 ) =LNGESY ( 1 ) LNGESY ( 2 ) =LNGESY ( 2 ) END IF RETURN END

This subroutine calculates the number of basidiocarps appearing on the stubs and from there, the amount of spores emitted in the treated area: Caller: MAIN Declaring global variables: REAL TOTARE,TORAIN,HHRAIN,HRAIN,HNSP~D~HNOSP~ INTEGER*2 ANSW7,RECO,PERIOD Declaring local variables: REAL HYMEN,CARPSU, TO, HUMI,RAIN INTEGER JULDAY,TIME, DUM INTEGER*1 1 Declaring arrays: REAL CARPSP(S),TREE(~),SPDAY(~),NSPDAY(~) Initializing data (This mean surface of hyrnenium per basidiocarp has been calculated from measurements made on 164 basidiocarps using a LI-COR 2000 surface area rneter: DATA CARPSU/~.98/ OPEN (~~,STATUS='OLD~,FILE='C:\FORTRAN\OUT\METEO.OUT~) Displaying a message on the screen: CALL CLEARSCREEN($GCLEARSCREEN) PRINTk,*SECONDPHASE: OPEM is now computing the number of basidioc !arps expected andf PRINT*, r the amount of spores emitted from those; Ple !ase, wait . . . PRINT*, ' PRINT*, '

First defining if basidiocarps are present. 7 consecutive days with BOTH daily and nightly mean temperature below 18 degrees are needed. This simple assumption is based on observations made in the field during summer 1995 at Ste-Florence, Quebec, Canada: CALL CHECK(SPDAY,NSPDAY,I,PERIOD,RECO) IF (REC0.EQ.4) SPDAY(l)=O Calling the subroutine CARPS which computes the number of basidiocarps per stub per tree species (CARPSP) according to the season of treatment, the time following the treatment and the daily mean temperature: CALL CARPS(RECO,PERIOD,CARPSP,ANSW7) Reading data file METEO.OUT: READ (22,578,END=10) ifZJLDAY,TIME,TO,HUMI,RAIN 1 FORMAT (1X,I3,5XtI4,4X,F8.3,24X,F8.3,72X,F8.3)

WRITE (*,22) JULDAY,TIME FORMAT('+DAY: 8i13,8 TIME: ',I4) Comparing the current julian day to SPDAY, the julian day at which the fruiting bodies should appear: IF (I.EQ.l) THEN Then there is only 1 period of appearance of basidiocarps without any disappearance: IF (JULDAY.LT.SPDAY(1)) THEN HYMEN=O . GOTO 13 END IF END IF

Then there might be 2 periods of appearance of basidiocarps: IF (JULDAY.LT.SPDAY(1)) THEN HYMEN=O . GOTO 13 END IF IF (JULDAY.GE.SPDAY(I).AND.JULDAY.LT.NSpDAY()) MT0 498 IF (SPDAY(~).NE.O) THEN IF (JULDAY.GE.NSPDAY(~).AND.JULDAY.LT~SPDAY(~))THEN HYMEN=O . GûTO 13 END IF IF (JULDAY.GE.SPDAY(2)) GOTO 498 END IF END IF

Then there might be 3 periods of appearance of basidiocarps: IF (JULDAY.LT.SPDAY(1)) THEN HYMEN=O . GOTO 13 END IF IF (JULDAY.GE.SPDAY(l).ANDDJULDAY.LT.NSPDAY(l)) GOTO 498 IF (JULDAY.GE.NSPDAY(1).AND.JULDAY.LTTSPDAY2 THEN HYMEN=O . GOTO 13 END IF IF (JULDAY.GE.SPDAY(~).AND.JULDAY.LT.NSPDAY)) GOTO 498 IF (SPDAY(~).NE.O) THEN IF (JULDAY.GE.NSPDAY(S).AND.JULDAY.LT.SPDAY(~ THEN HYMEN=O . GOTO 13 END IF IF (JULDAY.GE.SPDAY(3)) GûTO 498 END IF END IF Calculating the total area of hymenium (variable HYMEN) (crn2) that sporulates: DO DUM=1,5 ~YMEN=~TOTARE*TREE(DUM)*CARPSP(DUM)*CARPSU)+HYMEN END DO Calling the subroutine SPORU which calculates for each period of 30 minutes the total sporulation from the basidiocarps in the treated area : CGL SPORU (T0,SP0RUL,HUM1,HYMEN,RAIN,RA1NIJULDAY,T1~,T0RA1N, !HHRA1N,HRA1N,HNSP02,HNSP0D,HN0SP0)

WTO 11

CLOSE (UNIT=22)

RETURN END

k********************************************************************* SUBROUTINE CHECK(SPDAY,NSPDAY,I,PERIOD,RECO) k********************************************************************* This subroutine verifies at which julian day the appearance and disappearance of fruiting bodies of Chondrostereum purpureum would occur : Caller: Subroutine SPO Declaring global variables: INTEGER*l I INTEGER*2 PERIOD,RECO REAL SPDAY(3),NSPDAY(3) Declaring local variables: INTEGER DUM,JULDAY ,DUMY REAL TEMD(7),TEMN(7) REAL TDM,TNM

First, finding the julian days with presence of basidiocarps: OPEN (~~~,STATUS='OLD',F?LE='C:\FORTRAN\OUT\MEA~.OUT~) In this case, no basidiocarps are appearing on the stubs; Preparing to read next temperatures and keeping the last 12 temperatures (6 during daytime and 6 during nighttime) in memory: DO DUMY=l,6 TEMD (DUMY)=TEMD (DVMY+1) TEMN (DLJMY ) =TEMN (DUMY+1 ) END DO READ (102,601tEND=24)JULDAY,TDM,TNM TEMD ( 7 ) =TDM TEMN ( 7) =TNM GOTO 7 ELSE Basidiocarps have appeared and the day is noted (SPDAY). No fruiting body of the fungus appears before that day: IF ( RECO .EQ .1 .AND. PERIOD .EQ -1) THEN IF (JULDAY.LE.213) THEN DO DUMY=1,6 TEMD(DUMY)=TEMD(DUMY+l) TEMN(DUMY)=TEMN(DUMY+I) END DO READ (102,6OlrEND=24)JULDAY,TDM,TNM TEMD(7)=TDM TEMN(7)=TNM GOTO 7 ELSE SPDAY(I)=JULDAY GOTO 7 9 END IF END IF SPDAY(I)=JULDAY GOTO 19 END IF ELSE GCITO 6 END IF Now finding the day when the basidiocarps are going to vanish due to high or low temperature (NSPDAY): DUM= 0

In this case, basidiocarps are NOT disappearing from the stubs; Preparing to read next temperatures and keeping the last 12 temperatures (6 during daytime and 6 during nighttime) in memory: DO DUMY=1,6 TEMD ( DUMY ) =TEMD ( DüMY + 1 ) TEMN(DUMY)=TEMN(DUMY+l) END DO READ (10Sf601,END=24) JüLDAY,TDM,TNM TEMD(7)=TDM TEMN ( 7 ) =TNM WTO 25 ELSE Basidiocarps have now disappeared and the day is noted (NSPDAY): NSPDAY(I)=JULDAY I=I+1 GOTO 6 END IF ELSE GOTO 26 END IF

FORMAT (lX,I4,2X,F8.3,5X,F8.3)

CLOSE (UNIT=102) RETURN END

t********************************************************************* SUBROUTINE CARPS(RECOfPERIOD,CARPSPfANSW7) t********************************************************************* This subroutine computes the number of basidiocarps per stub per tree species according to the season of treatment and the time following the treatment. The calculations are based on observation in the field done over a period of three years and in 8 different experirnental sites (Gosselin, Jobidon and j ou let): Caller: Subroutine SPO Declaring global variables: INTEGERfS RECO, PERIOD,ANSW7 REAL CARPSP ( 5 ) If the meteorological are recorded during the same year of treatment or during the year following the treatment: IF (RECO.EQ.l.OR.RECO.EQ.2) THEN If treatment has been applied before August: IF (PERIOD.EQ. 1 ) THEN CARPSP(1)=10.4 CARPSP(S)=Q.O CARPSP(3)=1 .5 CARPSP(4)=28.0 CARPSP(S)=3.0 END IF If treatment has been applied after August: IF (PERIOD.EQ.S) THEN CARPSP(1)=3.3 CARPSP(2)=7.1 CARPSP(3)=0.2 CARPSP(4)=0.4 CARPSP(5)=3.0 END IF END IF If the meteorological are recorded 2 years following the treatment: IF (REC0.EQ.3) THEN If treatment has been applied before August: IF (PERIOD .EQ .1 ) THEN CARPSP(1)=1 .4 CARPSP(2)=0.5 CARPSP(3)=1 .5 CARPSP(4)=1 .O CARPSP(5)=1 .O END IF If treatment has been applied after August: IF (PERIOD.EQ.2) THEN CARPSP(1)=1.3 CARPSP(2)=1 .O C24RPSP(3)=2.0 CARPSP(4)=0.2 CARPSP(5)=1 .O END IF END IF If the meteorological data have been recorded 3 years following the treatment : IF (RECO.EQ. 4) THEN CARPSP(1 )=O. CARPSP(L)=O. CARPSP(3)=0. CARPSP(4)=0. CARPSP(5)=0. END IF If the meteorological data have been recorded during the year of the treatment and if the treatrnent has been applied after August: IF (RECO.EQ.l.AND.PERIOD.EQ.2) THEN CARPSP(1 )=O. CARPSP(2)=O. CARPSP(3)=0. CARPSP(4)=0. CARPSP(S)=O. END IF Values of CARPSP for default operations. The variable CARPSP has been assigned values according to observations in the field (Frorn R.Jobidon and A-Goulet): IF (ANSW7.EQ. 1 ) THEN CARPSP ( 1 )=O. !Prp CARPSP(2)=7.1 !Pet CARPSP(3)=0.2 !Ers CARPSP(4)=40.0 !Bop CARPSP(S)=3.0 !Others END IF

RETURN END

********************************************************************** SUBROUTINE SPORU(TO,SPORUL,HUMI~HYMEN,RAINfJüLDAY,TINI ...... !HHRAIN,HRAIN,HNSP02rHNSPODtHNOSPO) This subroutines calculates the total amount of spores that are released from the basidiocarps in the treated area (forest clearcut) for every period of 30 minutes (Goulet and Jobidon): Caller: Subroutine SPO Declaring global variables: REAL HYMEN,TOfSPORUL,RAIN,HUMI,TORAIN,HHRAIN,HRAIN,HNSP02,HNSPOD, !HNOSPO INTEGER JULDAY,TIME Declaring local variables: REAL LIBSPO INTEGER GAGE,BEGINDfBEGINTITCHEKIDCHEK,LSTRADILSTRAT SAVE GAGE,DCHEK,TCHEK,LSTRAD,LSTRAT Spore liberation begins necessarily with a rainfall of at least 0.2 mm and last until relative humidity falls below 50% (valid for the first 24 hours)(Goulet and Jobidon). According to Dye (19741, dew do not induce sporulation and hence, this is not taken into account: IF (RAIN.GT.O.1 ) THEN LSTRAD=JULDAY LSTRAT=TIME HRAIN=HRAIN+0.5 TORAIN=TORAIN+RAIN BEGINT is the time ak which the rainfall greater than 0-lmm occurs and BEGIND is the corresponding julian day: IF (GAGE.EQ.0) THEN BEGINT=TIME BEGIND=JULDAY TCHEK and DCHEK are the time and julian day at which the sporulation starts (i.e. 3 hours following rain): TCHEK=BEGINT+300 DCHEK=BEGIND IF (TCHEK.GT.2400) THEN TCHEKzTCHEK-2400 DCHEK=BEGIND+l END IF END IF GAGE=1 ELSE IF (GAGE.EQ.l.AND.HUMI.GE.50.) THEN GAGE=1 ELSE GAGE=O LIBSPO=O . HNSP02=HNSP02+0.5 GOTO 71 1 END IF END IF Atmospheric conditions are presently suitable for sporulation (i.e. There has been a recent rainfall (occured in the last 24 hours) coupled with a relative humidity above 50%): Accounting for time delay in sporulation (3 hours) (Dye, 1974; Spiers, 1985 and Goulet, 1996: IF (JULDAY.EQ.DCHEK) THEN Sporulation starts in the same day than rainfall: IF (TIME.LT.TCHEK) THEN LIBSPO=O . HNSPOD=HNSPOD+0.5 GOTO 7 1 1 END IF END IF Sporulation starts the day next to the current one: IF (JULDAY.LT.DCHEK) THEN Sporulation starts in the same day than rainfall: IF (TIME.LT.BEGINT+300) THEN LIBSPO=O . HNSPOD=HNSPOD+O.5 GOTO 7 1 1 END IF END IF Now computing the sporulation: IF (HUMI.GE.50.0.AND.HUMI.LE.75.0) THEN IF (TO.LE.0.) LIBSPO=O.O IF (TO.GT.O.O.AND.TO.LE.7.5) LIBSPO=25. IF (TO.GT.7.5.AND.TO.LE.12.5) LIBSPO=49. IF (TO.GT.12.5.AND.TO.LE.17.5) LIBSPO=54. IF (TO.GT.17.5.AND.TO.LE.22.5) LIBSPO=183. IF (TO.GT.22.5.AND.TO.LE.27.5) LIBSPO=2. IF (TO.GT.27.5.AND.TO.LE.32.5) LIBSPO=O.6 IF (TO.GT.32.5) LIBSPO=O.O END IF IF (HUMI.GT.75.0.AND.HUMI.LE.8S.O) THEN IF (TO.LE.0.) LIBSPO=O.O IF (TO.GT.O.O.AND.TO.LE.7.5) LIBSPO=33. IF (TO.GT.7.5.AND.TO.LE.12.5) LIBSPO=66. IF (TO.GT.12.5.AND.TO.LE.17.5) LIBSPO=76. IF (TO.GT.17.5.AND.TO.LE.22.5) LIBSPO=243. IF (TO.GT.22.5.AND.TO.LE.27.5) LIBSPO=10. IF (TO.GT.27.5.AND.TO.LE.32.5) LIBSPO=O.6 IF (TO.GT.32.5) LIBSPO=O.O END IF IF (HUMI.GT.85.0.AND.HUMI.LE.95.0) THEN IF (TO.LE.0.) LIBSPO=O.O IF (TO.GT.O.O.AND.TO.LE.7.5) LIBSPO=33. IF (TO.GT.7.5.AND.TO.LE.12.5) LIBSPO=66. IF (TO.GT.12.5.AND.TO.LE.17.5) LIBSPO=86. IF (TO.GT.17.5.AND.TO.LE.SZ.5) LIBSPO=243. IF (TO.GT.22.5.AND.TO.LE.27.5) LIBSPO=13. IF (TO.GT.27.5.AND.TO.LE.32.5) LIBSPO=0.7 IF (TO.GT.32.5) LIBSPO=O.O END IF IF (HUMI.GT.95.) THEN IF (TO.LE.0.) LIBSPO=O.O IF (TO.GT.O.O.AND.TO.LE.7.5) LIBSPO=33. IF (TO.GT.7.5.AND.TO.LE.12.5) LIBSPO=66. IF (TO.GT.12.5.AND.TO.LE.17.5) LIBSPO=99. IF (TO.GT.17.5.AND.TO.LE.22.5) LIBSPO=260. IF (TO.GT.22.5.AND.TO.LE.27.5) LIBSPO=77. IF (TO.GT.27.5.AND.TO.LE.32.5) LIBSPO=1.6 IF (TO.GT.32.5) LIBSPO=O.O END IF During heavy rainfall, no sporulation occurs. This is adapted frorn Dye (1974) who found that during a rainfall of 155mm of rain in 33 hours (155/33 = Sm/h = 2.5mrn/half-hour), no sporulation was occuring : IF (RAIN.GT.3.) THEN LIBSPO=O . HHRAIN=HHRAIN+O.S END IF With an air temperature above 15C, if the last significant rainfall has been more than 24 hours ago and if the relative humidity is below 85%, there is no sporulation (Goulet and Jobidon): IF (TO.GE.15) THEN IF ((LSTRAD-JULDAY).EQ.O) THEN GOTO 7 1 1 ELSE IF ((LSTRAD-JULDAY).EQ.l) THEN IF (LSTRAT.LE.TIME) THEN IF (HUMI.LT.85.) LIBSPO=O. END IF ELSE IF (HUMI.LT. 85. ) LIBSPO=O . END IF END IF

With an air temperature below 15C, if the last significant rainfall has been more than 48 hours ago and if the relative humidity is below 85%, there is no sporulation (Goulet and Jobidon): IF ((LSTRAD-JULDAY),LT.2) THEN GûTO 711 ELSE IF ((LSTRAD-JULDAY).EQ.2) THEN IF (LSTRAT.GE.TIME) THEN GûTO 711 ELSE IF (HUMI.LT.85.) LIBSPO=O. END IF END IF IF ((LSTRAD-JULDAYI-GT.~)THEN IF (HUMI.LT.85.) LIBSPO=O. END IF END IF Calculating the total number of spores emitted from the basidiocarps per second for the current half-hour: 1 SPORUL=HYMEN*LIBSPO

Writing the current half-hourly sporulation rate (spores/sec) in the output file SPORE.OUT: WRITE (42,707) JULDAY,TIME, SPORUL 7 FORMAT (lX,f3,2X,I4,2X,F20.0)

RETURN END

********************************************************************** ...... SUBROUTINE AERO(VHEI,DISPLA,ZO,GRAVtREFHEI) This subroutine calculates aerodynamic properties of the forest clearcut: Caller: MAIN Declaring global variables: REAL DISPLA,ZO,REFHEI,GRAV, VHEI Declaring local variables: INTEGERk2 DUM71 INTEGER JULDAY ,TIME REAL TOrT2,T7,T15,W1S,W7~SUMZ~,GRAD Initializing variables: SUMZO-O . DUM71 =O Calculating the displacement height (DISPLA) within the treated area using a simple formula (De Btuin and Moore, 1985): DISPLA=0.69*VHEI Calculating the roughness length of the treated site under certain atmospheric conditions; i.e. wind speed at 15 metres above ground level greater than 4 m/s, neutral atmosphere, daytime with no strong solar radiation (fxom Monteith and Unsworth, 1990). Current atmospheric stability is verified using the gradient Richardson number (Goudriaan, 1977): OPEN (221STATUS=tOLD~,~~~~=~:\~~~~~~~\~~~\~~~~0.0~~t) READ (22,541tEND=6)JULDAY,TIME,T0,T2,T7,T15,GRADtW15tW7 1 FORMAT (1X,I3,5X,I4,4X,4F8.3,8X,F8.3,8X,F8.3,24XIF8.3) GOTO 7 END IF GOTO 7 CLOSE (UNIT=22) IF (DUM71.NE.O.AND.SUMZO.NE.O) THEN ZO=SUMZO/DUM~I ELSE ZO=O .1 *WEI END IF RETURN END

********************************************************************** SUBROUTINE PRO(PI,KARMAN,REFHEIlDISPLAIZOIGRAVtCORIOLlDIVERG, **********************************************************************!STA,UNS,NEU) This subroutine processes the boundary layer parameters (U*, LI W*, Zi, H, etc.) using meteorological data: Caller: MAIN Declaring global variables: REAL Pf~KARMANfREFHEIfDISPLA~GRAVlCORIOLIZOtSTAtUNSINEU INTEGER DIVERG,MIX ( 3 1 ) Declaring local variables: CHARACTER"1 ANSW6 INTEGER*2 DUM INTEGER TIM,JULDAY,TIME REAL REA1 ~REA2~REA3lREA4tREA5rREA6IREA7l~A8rTOrT2~T7tTIS~HUMIt !NETRAD,W15lW7,RAINtTOTRADIFO1IFO2IFO3IFO4tFO5lFO6lFOHEI Opening the output file METEO.OUT: OPEN (~~,STATUS='OLD~,FILE=~C:\FORTRAN\OUT\METEO.~UT~ Opening the output file FSIDE.OUT: OPEN (~~,STATUS=~OLD~,FILE='C:\FORTRAN\OUT\FSIDE.OUT~~ Opening the output file PBLPAR.OUT: OPEN (~~,STATUS=~OLD~~FILE='C:\FORTRAN\OUT\PBLPAR.OUT~~ERR=~~~) CALL CLEARSCREEN($GCLEARSCREEN) PRINT*,'The file PBLPAR-OUT already exists; OK to overwrite (y/n) l?' READ (*,716) ANSW6 6 FORMAT (Al) CLOSE (82) IF (ANSW6.EQ.1Yr.0R.ANSW6.EQ.'y1) THEN 7 OPEN (82, FILE=~C:\FORTRAN\OUT\PBLPAR.OUT', MODE='WRITE') CALL CLEARSCREEN($GCLEARSCREEN) PRINT*,lTHIRD PHASE: OPEM is now processing the boundary-layer p !arameters;' PRINT*, ' Please, wait ...# PRINT*, ' ' PRINT*, ' ELSE STOP 'Program aborted! Goodbye!, END IF Reading data file METEO.OUT on a hourly basis: READ (22,54SlEND=8) JULDAY,TIMEtTO,T2,T7,T15,HUMI,NETRAD,W15,W7, !RAIN 2 FORMAT (1Xt13t5XtI4t4Xr5F8-3I8XI2F8-3t24XtF8.3,16XtF8-3) Averaging the half-hour data to obtain hourly data: DUM=DUM+ 1 TIM=REAL(TIME) IF (DUM.EQ.~.AND.((TIM/IOO.)-(INT(TIM/~~~.)WTO 91 IF ((~1~/100.)-(INT(TIM/~OO.)).NE.O) THEN REAl =TO REA2=T2 REA3=T7 REA4=T15 REA5=HUMI REA6=NF,TRAD REA7=W15 REA8=RAIN GOTO 91 ELSE TO=(TO+REA~)/~. T~=(T~+REA~)/~. T~=(T~+REA~)/~. ~15=(~15+~~~4)/2. HUMI=(HUMI+REA~)/~. NETMD=(NETRAD+REA~)/~. ~15=(~1S+REA~)/~. RAIN=RAIN+REA8 GOTO 923 END IF Finding the hourly accumulated net radiation from the last rain to get a crude idea about the dryness of the soi1 (from Berkowicz and Prahm, 1982): 3 TOTRAD=NETRAD+TOTRAD IF (RAIN.NE.0) TOTRAD=O. Reading the output file FSIDE-OUT: READ (32,660,END=661 ) FOI READ (32,660tEND=661) F02 READ (32,660,END=661 ) F03 READ (32,66OtEND=661)F04 READ (32,660,END=661 ) F05 READ (32,66OtEND=66l) F06 FOHEI= (FOI+F02+F03+F04+F05+F06 ) 16. 10 FORMAT (37XtF5.2) Calling the subroutine MON1 which calculates the current stability of the atmosphere : 11 CALLMONI(REFHEI,DISPLA,ZOIW15tKARMANIPItHUMItTOT~,NETRADt !GRAVtCORIOL,T15,TIME~JULDAY,T7,FOHEI,W7U,TOtT2t !MIX) GOTO 91 CLOSE (UNIT=22) CLOSE (UNIT=32) CLOSE (UNIT=82) Writing the half-hourly mixing depth into a file named MIXING-OUT: OPEN(~~~,STATUS='OLD\FILE='C:\FORTRAN\OUT\MIX~NG~OUT~,ERR=~~~) GOTO 889 8 OPEN(~~~,FILE=~C:\FORTRAN\OUT\MIXING.~UT~I~~~~=f~~~~~l) 9 DO 887 DUM=1,31 WRITE (172,886) MIX(DUM) 6 FORMAT (lX,I8) 7 CONTINUE CLOSE (UNIT=l72)

RETURN END

********************************************************************** SUBROUTINE MONI(REFHEI,DISPLA,ZOIW75IKARMANIPIfHU~~ITOT~f~T~DI !GRAV,C0R10L,T15,TIMEfJULDAYIT7,F0HE1,WU,T0,T2, **********************************************************************!MIX) This subroutine calculates the Monin-Obukhov length, the friction velocity and the sensible heat flux iteratively using the resistance method described in Berkowicz and Prahm (1982). Mixing depth and convective velocity scale are also computed: Caller: Subroutine PRO Declaring global variables: REAL REFHEI,DISPLA,ZO,W15,KARMAN,PIfHUMI,TOTRAD,NETRAD,T7,W7, !GRAV,CORIOL,T15,FOHEIITOIT21STAIUNSfNEU INTEGER TIME,JULDAY,DIVERG,MIX(31) Declaring local variables: INTEGER DUM84 REAL MONIN,OBUKfEPSI1SIEPSILOfUSTARIRRRISENSHISATVAPIDELTAfHUMDEF~ !RS,RSUBAfALFAIMIXDEP~WSTARIDIF~ADDITER1tTKfA1~A2IA3fXXIAIRHEAI !GAMA Initializing some variables: ALFA=O. 3 A1 =25. O78 A2=52M A3=6846.4 RRR=O. 74 TK=273.l6+TI 5 ~~=273.76/TK AIRHEA=1305.*XX GAMA=l6l5.6/(3lS6.-2.4*TK) SATVAP=~.~~*XX**A~*EXP(A~*(~.-XX)) HUMDEF=SATVAP*(I.-HUMI/IOO.) DELTA=A~*~ATVAP/(TK**~.) First, a neutral stratification (L=infinite) is assumed; DUM84=0 MONIN=9999. RS=RSUBS(NETRAD,TOTRAD~AIRHEA,GAMA,HUMDEF~ Beginning the iteration The parameter Dm84 serves as a counter for the iteration: 6 DUM84=DUM84+1

Calculating the variable EPSI15 and EPSILO (stability parameters z/L): EP~IIS=(REFHEI-DISPLA)/OBUK EPSILO=ZO/OBUK Calculating the friction velocity (USTAR): USTAR=B~/(B~-PSYM(EPSI~~,PI)+PSYM(EPSILO,PI)) IF (USTAR.LT.0.001) USTAR=0.001 Calculating the aerodynamic resistance (r sub a) according to Berkowicz and Prahm (1982): RSUBA=(C~/USTAR)*(B~-PSYH(EPSI~~,RRR)+PSYH(EPSIL~~R~)) Calculating the sensible heat flux (from Berkowicz and Prahm, 1982): TER1 =NETWD* (RSUBA+RS) SENSH= (TER1-Dl ) / (D~*RSUBA+D~) IF (SENSH.EQ.0) SENSH=0.001 Calculating a new value of the Monin-Obukhov length with the latest value of the sensible heat flux: MONIN=E1 *USTAR**3. /SENSH Giving a value of 100*Z0 to L when very stable conditions occur: IF (MONIN.GE.O.O.AND.MONIN.LT.E2) MONIN=E2 Calculating relative sum and difference: DIF=ABS((MONIN-OBUK)/OBUK) ADD=ABS((MONIN+OBUK)/OBUK) If the number of iterations is greater than 50, the iteration is ending : IF (DUM84.GE.50) GOTO 549 Ending the loop if the wanted accuracy is obtained: IF (DIF.LT.O.01) GOTO 543 In case of divergence (near-neutral conditions): IF (ADD.LT.O.01) GOTO 543

Ending the iteration

In case of divergence in the iteration 050 iterations), the Monin-Obukhov length is re-calculated according to the method proposed by Golder (1972), and USTAR and SENSH are re-calculated using the new value of M-O length: The atmosphere is unstable: MONIN=FOHEI/RICH(W~,W~~,GRAV,TO,T~,T~~) EPSI~~=(REFHEI-DISPLA)/MONIN EPSIL~=ZO/MONIN USTAR=B? / (~2-PSYM(EPSI~S,PI ) +PSYM(EPSILO,PI) ) SENSH=E~*USTAR**~./MONIN IF (SENSH-GT.350. ) SENSH=100. IF (SENSH.LT.-100.) SENSH=-100. ELSE The atmosphere is stable: MONIN=FOHEI*(l.-(7,*~ICH(W7,W15IGRAVIToIT2IT7IT~5IDIs~~~~ RICH(W7,W15fGW4VrT0,TS,T7fT15,DISPLA) EPSI~~=(REFHEI-DISPLA)/MONIN EPSILO=ZO/MONIN USTAR=B~/(B~-PSYM(EPSI~~,PI)+PSYM(EPSIL~,P~)) SENSH=E~*USTAR**~./MONIN IF (SENSH.GT.350.) SENSH=350. IF (SENSH.LT.-100. ) SENSH=-100. END IF Giving the M-O length and the sensible heat flux a maximum value (for practical purposes): 3 IF (MONIN.GT.9999.) MONIN=9999. IF (MONIN.LT.-9999.) MONIN=-9999. IF (SENSH.GT.350.) SENSH=lOO. IF (SENSH.LT.-100.) SENSH=-100.

WRITE ( *, 6 1 0 ) JULDAY,TIME 3 FORMAT('+DAY: f,13,1 TIME: ',I4) Calculating the mixing depth (MIXDEP) of the current hour (according- to Hanna and Chang, 1 993 ) and checking the f requencies of stability of the atmosphere using the stability parameter (LI (According to Erbrink, 1994): During stable lapse rate: IF (MONIN.GE.O.AND.MONIN.LE.1000.) THEN MIXDEP=(MONIN/~.~)*(-~+(SQRT(ABS(~+(~.~~*USTAR/(COR~OL*M~NIN~~ !I))) STA=STA+1. END IF During unstable lapse rate: IF (MONIN.LT.O.AND.MON1N.GE.-1000.) THEN MIXDEP=~.~*USTAR/CORIOL UNS=UNS+1. END IF During neutrai hpse rate: IF (ABS(MONIN).GT.1000.) THEN MIXDEP=~.~*USTAR/CORIOL NEU=NEU+1 . END IF Calculating the convective velocity scale (W*) according to Berkowicz et al. (1992): WSTAR=(ABS(GRAV*SENSH*MIXDEP/(T~*AIRHEA)))**O.~~~~ Writing the results of turbulent parameters in the output file PBLPAR.OUT: WRITE (62,544) JULDAYtTIME,DUM84,MONIN,SENSH,USTAR,MIXDEP,WSTAR 4 FORMAT (1X,13,2Xt14,2X,12,2Xt 3F10.4,F7.1 ,F8.4) Cornputing the frequency of mixing depth in 100 metre sectors: IF (MIXDEP.GE.O.AND.MIXDEP.LT.100) MIX(l)=MIX(l )+1

RETURN END

********************************************************************** **********************************************************************REAL FUNCTION RSUBS(NETRAD,TOTRAD,AIRHEA,GAMA,HUMDEF) This subroutine computes the surface resistance to evaporation as a function of humidity deficit (HUMDEF), net radiation (NETRAD) and accumulated net radiation (TOTRAD): Caller: Subroutine MON1 Declaring global variables: REAL TOTRAD,NETRAD,AIRHEA,GAMA,HUMDEF Declaring local variables: REAL AM, FFF ,DDD IF (HUMDEF.GT.0.) GOTO 2 RSUBS=O . RETURN Calculating the flux F (variable FFF) used in the calculation of the surface resisitance (r sub s) according to Berkowicz and Prahm (1982) : AAA = 0.6 - O.~~*TOTRAD/~OOOO. IF (AAA.LT. 0.12) AAA=0.12 DDD = 221. * EXP( -TOTRAD/~O~~~.) IF (DDD.GT.67.) DDD=67. FFF=AAA*ABS(NETRAD)+DDD Calculating the surface resistance (r sub s) according to Berkowicz and Prahm (1982): RSUBS=AIRHEA*HUMDEF/(GAMA*FFF) RETURN END

********************************************************************** **********************************************************************REAL FUNCTION PSYH (EPSI,RRR This function returns the value of PSYH (universal function) according to Berkowicz and Prahm, 1982: Caller: Subroutine MON1 Declaring global variables: REAL EPS 1,RRR Declaring local variables: REAL PARA

IF ((EPSI).GE.O.) THEN PSYH=-(~.~/RRR)*EPSI ELSE IF ((1.-(9.*EPSI)).LT.O.) THEN PARA=O . GOTO 499 END IF PARA=SQRT(~.-(g.*EPSI)) 9 PSYH=~.*ALOG((~.+PARA)/~.) END IF

RETURN END

This function returns the value of PSYM (universal function); from Erbrink ( 199 1 ) : Caller: Subroutine MON1 Decïaring variables: REAL EPSrPARAM, PI

PARAM=O. CoTO 399 END IF PARAM=(1 .-(15.*EPS))**O025 3 PSYM=ALOG(((I.+PARAM)/~.)**~.*((~.+PARAM**~ !(~.*ATAN(PARAM))+PI/~. END IF RETURN END

r************X**X******Jr.kkR*.k***k*k****.******************************* SUBROUTINE ESCA(GRAV,DISPLA,RECO,PERIOD) t*****************t*************************************************** This subroutine calculates the fraction of spores emitted from basidiocarps that are escaping above the forest height. The gradient Richardson number is used here. There are three (3) scenarios that may occur: (1) a thermal inversion (stable atmosphere) captures the spores within the treated area - spores are assumed to impact onto the ground of the treated area or ont0 the nearby forest floor or vegetation; (2) a neutral statification is found and the escape fraction is calculated accordinq to observations from Raynor et al. (1974) and to several program rÜns made with the mode1 UCFWF of University of Connecticut (Miller et al., 1990); and, (3) the atmosphere is found to be unstable and it is assumed that 40% (biaised toward overestimation) of the total number of spores emitted £rom basidiocarps are transmitted above the forest height due to strong convection: Caller: MAIN Declaring global variables: REAL GRAV,DISPLA INTEGER*2 REC0,PERIOD Declaring local variables: INTEGER JULDAY,TIME REAL TO1T2,T7,Tl 5, W1 SI W7,SPORUL, ESCAP,RAIN, HE1 CHARACTERfl ANSWl Opening the output file SPORE.OUT: OPEN (~~,STATUS=~OLD',FILE=~C:\FORTRAN\OUT\SPO~.OUT~) Opening the output file ESCAPE-OUT: OPEN (~~,STATUS=~OLD',FILE=~C:\F~RTRAN\OUT\ESCAPE~OUT~,E~=~~I) CALL CLEARSCREEN($GCLEARSCREEN) PRINT*,'The file ESCAPE.OUT already exists; OK to overwrite (y/n)? ! f READ (*,622) ANSWl CALL CmSCREEN($GCLEARSCREEN) PRINT*,'FOURTH PHASE: OPEM is now computing the amount of spores e !scaping above' PRINT* ,' the forest height; Please, wait.. .' PRINT*,' PRINT*, ' !2 FORMAT (Al) CLOSE (92) IF (ANSWf.EQ.fYt.OR.ANSW1.EQ.fyr) THEN !1 OPEN (92, FILE=~C:\FORTRAN\OUT\ESCAPE.OUT', MODE=fWRITE') ELSE STOP 'Program aborted! Goodbye!, END IF Opening the output file METEO.OUT: OPEN (~~,STATUS='OLD~,FILE='C:\FORTRAN\OUT\METEO.OUT~) Reading the output file METEO.OUT: 11 READ (22,6fO,END=88) JULDAY,T1MEtTO,T2,T7,T15,W15,W7,RAIN O FORMAT (1X,I3,5X,I4,4X,4F8.3,24X,F8.3I40XIF8.3,16XrF8.3) Reading the output file SPORE.OUT: READ ( 42,6 1 1 ) SPORUL 1 FORMAT (12X,F20.0) WRITE (*,667) JULDAY,TIME i 7 FORMAT(I+DAY: f,13,f TIME: ',14) Checking the stability of the atmosphere: IF ((RICH(W7,W1S,GRAV,TQIT2IT7rT15tDISPLA).GT.-O.Ol).AND !.(RICH(W7,W1S,GRAV,TO,T2rT7IT15,DISPLA).LT.O.1)) THEN The atmosphere is near neutral and mixing allows a certain quantity of spores to escape above forest height: ESCAP=SPORUL*0.3 ELSE IF (RICH(W7,W15,GRAV,T0,T21 T7 ,Tl 5,DISPLA).GE 1 ) THEN A thermal inversion occurs and there is no spore escaping above forest height (from de Jong, 1988 ) : ESCAP=O . ELSE The atmosphere is unstable and convection is responsible for transportfng spores above the forest height: ESCAP=SPORUL*0.4 END IF END IF Washout of spores by rainfall (from Fig. 5.8 in Pasquill and Smith, 1983): ESCAP=ESCAP-(0.016*RAIN*ESCAP)

In case of low windspeed, spores are sedimenting ont0 the forest floor (De Jong, 1988): HEI=2. IF (SPEED(HEIfDISPLA,W15,W7).LT.0.5) ESCAP=O. In the following cases, there is no basidiocarp, then no sporulation, then no escape fraction: Writing the number of spores escaping above the forest height per second for each half-hour into the output file ESCAPE.OUT: WRITE (92,612) JULDAY,TIME,ESCAP ! FORMAT (1X,13t2X,14t2X,F20.0)

CLOSE (UNIT=22) CLOSE (UNIT=42) CLOSE (UNIT=92) RETURN END

t********************************************************************* SUBROUTINE GAUSS(HOUR,DISPLA,PIITRANSFIANSW9tNEUISTAfUNSlDIVERG, !TORAIN,HHRAIN,HRAINIHNSPO2,HNSPOD,SPDAYfHNOSPO,ZOlSUMSPC,TOTA~~ t********************************************************************* This subroutine alfows the user to enter some inputs and perform a Gaussian plume. The user rnay visualize the spore concentrations according to these inputs: Caller: MAIN Declaring global variables: REAL POSITI,HOUR,PI,TRANSF~DISPLA~TORAIN~HH~IN~HNSPO~, !HNSPOD,HNOSPO,NEUtSTArUNSfZOrSUMSPCfTOTARE INTEGER*l ANSW9 INTEGER DIVERG,DUMZ,PERCE Declaring local variables: INTEGER*2,DUM, DUMY Declaring the arrays: REAL CONTOT(~~,~~),MAxCON(~~,~I),SPDAY(~) INTEGER COMEAN(11,11,200)

6 CALL CLEARSCREEN($GCLEARSCREEN) PRINT*ttAtthis point, you may want:' PRINT*, PRINT*, ' PRINT*,t(l) The rnap of mean spore concentrations for the entire pe !riod analyzed;' PRINT*, ' PRINT*tf(2) The map of total spore concentrations for the entire p !eriod analyzed;' PRINT*, ' ' PRINT.*,I(3) The map of maximum half-hourly spore concentrations;' PRINT*, ' ' PRINT*,I(4) The map of a percentile of daily mean spore concentrat !ions for ther

PRINT*, f entire period analyzed;, PRINT*, ' ' PRINT*tt(S) The meteorological and spore release statistics of the ! entiret PRINT*, period analyzed;' PRINT*, ' PRINT*,'(6) Leave this menu; PRINT*,' READ*, ANSW9 IF (ANSW9.EQ.1.OR.ANSW9.EQ.2.OR.ANSW9.EQ.3.OR.~SW9.EQ.4) TWEN CALL CLEARSCREEN ($GCLEARSCREEN) PRINT*,'Up to which distance downwind do you want to visualize the ! concentrationt PRINT*,'of spores at forest height (O to 20 km)?' READ*, POSITI DO WILE (POSITI.GT.20) PRINT*,tPlease, enter a distance between O and 20 km:' PRINT*, ' ' READ*, POSITI CALL CLEARSCREEN ($GCLEARSCREEN) END DO CALL CLEARSCREEN($GCLEARSCREEN) Asking the user which percentile is required: IF (ANSW9.EQ.4) THEN PRINT*,tWhich percentile of the daily mean of half-hourly spor !e concentrations dot PRINT*,Iyou wish to use (e.g. 90)?t PRINT*, ' READ*, PERCE DO WHILE (PERCE.GE.100) PRINT*, PRINT*,'Please, enter a percentile lower than 100 ...' PRINT*, ' ' READ*, PERCE END DO END IF Changing the distance from kilometres to metres: POSITI=POSITI*1000. Calling the subroutine GPM which cornputes the dispersion of the spores emitted from the treated area: CALL GPM(POSITI,DISPLA,PItTRANSF,CONTOT,MAXCONtCOMEAN,DUMZ) END IF Calling the subroutine MEANC which displays the mean half-hourly spore concentration on the screen: IF (ANSW9.EQ. 1 ) THEN CALL MEANC(POSITI,CONTOT,HOUR) PAUSE Press ENTER to go back to the menu..., END IF Calling the subroutine CONC which displays the cumulative half-hourly spore concentrations on the screen: IF (ANSW9.EQ.2) THEN CALL CONC(POSIT1,CONTOT) PAUSE Press ENTER to go back to the menu...' END IF Calling the subroutine MAX which calculates for every point of the GRID MAP the maximum half-hourly concentrations of spores encountered for the time period analyzed: IF (ANSW9.EQ.3) THEN CALL MAX(POSITI,MAXCON) PAUSE Press ENTER to go back to the menu...' END IF Calling the subroutine PER which calculates for every points of the GRID MAP a percentile of the daily mean of half-hourly spore concentration: IF (ANSW9.EQ.4) THEN CALL PER(POSITI,COMEAN,DUMZIPERCE) PAUSE Press ENTER to go back to the menu...' END IF

Calling the subroutine STAT which displays some statistics about meteorological data, boundary layer scalars and dispersion: IF (ANSW9.EQ.5) CALL STAT(HOUR,NEU,STA,UNSIDIVERGIDISPLA, !TORAIN,HHRAIN,HRAIN,HNSPO~~HNSPOD~SPDAY~HNOSPO~ZO,SUMSPC~TOTARE)

Re-initializing the arrays to zero: 1 DODUM=1,11 DO DUMY-1,11 MAXCON(DUM,DUMY)=O. CONTOT(DUM,DUMY)=O. END DO END DO GOTO 976 RETURN END

~********************************************************************* ~*********************************************************************SUBROUTINE GPM(POSITI,DISPLA,PIITRANSFICONTOTIMAXCONICOMEAN,DuMZ) This subroutine computes Gaussian plumes using the output file ESCAPE.OUT and the meteorological file METEO.OUT as inputs to calculate dispersion parameters: Caller: Subroutine GAUSS Declaring global variables: REAL DISPLA,PI,POSITI,TRANSF INTEGER DUMZ Declaring local variables: REAL T7,T15,W15,W7,MONIN,SENSHlUSTAR,MIXDEP,WSTAR,FOHEIT,TIMEY, !SIGZ,SIGY,XCOOR,YCOORIBETAXYISDWD7,SDWD15,BETAIJtI1JI tSLOPt !TRA~L,DIRFOH,DIR7~DIR15IFOHEI1IFOHEI2~FOHEI3rESCAPtTIMEP INTEGER COORX,COORYIJULDAY,TIME~DUMX~DUMYIOLDDAY Declaring the arrays: REAL CONCEN(~~,I~),MAXCON(~~,~~),CONTOT(~I,II C CO GAGE(^^,^^) INTEGER COMEAN(11,11,200) OLDDAY=O DUMZ= 1 TIMEP=O. CALL CLEARSCREEN ($GCLEARSCREEN) PRINT*,'FIFTH PHASE: OPEM is now applying the Gaussian Plume Mode1 ! in thet PRINT*, t computational domain; Please, wait,..' PRINT*, ' PRINT*, ' Opening output files: OPEN (~~,STATUS=~OLD~,FILE=~C:\FORTRAN\OUT\METEO.UUT~) OPEN (~~,STATUS=~OLD~,FILE='C:\FORTRAN\OUT\FSIDE.OUT~) OPEN (~~,STATUS='OLD~,FILE=~C:\FORTRAN\OUT\PBLPAR.OUT~) OPEN (~~,STATUS=~OLD~,FILE='C:\FORTRAN\OUT\ESCAPE.OUT~) Reading the files on a half-hourly basis: iO READ (22,621tEND=88)JULDAYITIME,T7,T1S,W15,DIR15,SDWD15,W7, !DIR7,SDWD7 IF (OLDDAY.EQ.0) OLDDAY=JULDAY Since the PBL parameters have been computed on a hourly basis, and seeing that meteorological data have been recorded each half-hour, the same PBL parameters are read twice during an hour: TIMEY=REAL(TIME) IF ((TIMEY/~~~.)-(INT(T~MEY/~OO.)).NE.O) THEN BACKSPACE (UNIT=82) READ (82,770) MONIN,SENSH,USTAR,MIXDEP,WSTAR ELSE READ (82,770) MONINfSENSHfUSTARfMIXDEPrWSTAR O FORMAT (76X,3F10.4,F7.1,F8.4) END IF READ (92,624) ESCAP READ (32,625) FOHEII READ (32,625) FOHEI2 READ (32,625) FOHEI3 FOHEIT=(FOHEI~+FOHEI~+FOHEI~)/~.

1 FORMAT (1X,I3,5XtI4,2OX,2F8.3~,F8.3,8X~8.3) 3 FORMAT (16X,3F10.4,F7.1 ,F8.4) 4 FORMAT (12XtF20.0) 5 FORMAT (37XrFS.2) WRITE (*,668) JULDAYfTIME 8 FORMAT('+DAY: f,13,f TIME: ',14)

Counting the nurnber of half-hours in the Julian day: TIMEP=TIMEP+l.

The julian day is over, then computing the daily mean spore concentration: DO 52 DUMX=1,11 DO 53 DUMY=1,11 COMEAN(DUMX,DUMY,DUMZ)=NINT(COGAGE(DUMX,DUMY)/TIMEP) COGAGE(DUMX,DUMY)=O. 1 CONTINUE , CONTINUE TIMEP=O. DUMZ is the number of days: DUMZ=DUMZ+l END IF Generating a map of spore concentrations at forest height; COOM and COORY are dumrny coordinates along X and Y axis respectively: DO 50 COORX=1 ,Il DO 51 COORY=1 ,Il PT (6,6) is the origin where the biocontrol site is (concentration of spores is equal to O): IF (COORX.EQ.6.AND.COORY.EQ.6) GOTO 51 Transforming COORX and COORY in real field coordinates (XCOOR and YCOOR) (1 = -POSITI km and 11 = +POSITI km): SLOP=~.*POSITI/I O. XC~~R=(~L~P*REAL~COORX))-(POSITI+SLOP) YCOOR=(SL~P*REAL(COORY))-(POSITI+SLOP) Transforming XYZ coordinates into IJK coordinates (BETAXY is the angle between the current coordinate and the North): IF (XCOOR.NE.0.) THEN IF (XCOOR.GT.0) THEN IF (YCOOR.LT.O.) BETAXY=(~O.+ABS(TRANSF*ATAN(YCOOR/XCOOR))) IF (YCOOR.GE.O.) BETAXY=(~O.-(TRANSF*ATAN(YCOOR/XCOOR))) IF (BETAXY.GE.360.) BETAXYzBETAXY-360. ELSE IF (YCOOR.LT.0.) BETAXY=~~O.-(TRANSF*ATAN(YCOOR/XCOOR)) IF (YCOOR.GE.0.) BETAXY=~~~.+ABS(TRANSF*ATAN(YCOOR/XCOOR)) IF (BETAXY.GE.360.) BETAXYzBETAXY-360. END IF ELSE IF (YCOOR.LT.0) BETAXY=180. IF (YCOOR.GE.0) BETAXY=O. END IF BETAIJ=BETAXY-DIRFOH(DIR~~DIR~~~FOHEIT)

11 and JI are the X and Y coordinates according to the wind direction (JI is perpendicular to wind direction, thus to the plume as well, and 11 is on the X axis, thus along the plume and wind direction): I~=(SQRT((XCOOR**~.)+(YCOOR**~.)))*COS(BETA~J/T~NSF) J~=(SQRT((XC~~R**~.)+(YCOOR**~.)))*S~N(BETAIJ/T~NSF) Computing CONCEN (spore concentration) if Y is outside the plume (P=0.99) or if the plume is not in the same direction than the coordinates analyzed: IF (BETAIJ.GT.90.O.OR.BETAIJ.LT.-9O.O.OR.I1.LE.O) THEN CONCEN(COORX,COORY)=O. ELSE TRAVEL is the time required to the plume to travel from the source to 11 : TRAVEL=I~/SPEED(FOHEIT~DISPLA~W~S~W~)

Calculating dispersion parameters (Sigma y and Sigma 2): CALL SIGMAZ(SIGZIFOHEITIMONINIUSTAR,MIXDEP,WSL) CALL SIGMAY(SIGY,FOHEIT,SDWD7ISDWD15ITRAVELtW7IW1SITRANSF~ !DISPLA) Now applying the Gaussian dispersion scheme for the curent half-hour: CALL PLUME(CONCEN,ESCAPtSIGY,SIGZ,P1,J1,COORX,COORY, !USTAR,I1,FOHEITrDISPLArW15,W7) Calculating the maximum concentration of spores for each point: IF (CONCEN(COORX,COORY) .GT.MAXCON(COORXICOORY)) THEN MAXCON(COORX,COORY)=CONCEN(COORXtCOORY) END IF END IF IF (SPEED(FOHEIT,DISPLA,W15,W7).LT.0.5) CONCEN(COORX,COORY)=O. Adding the concentrations at al1 points: CONTOT(COORX,COORY)=CONCEN(COORX,COORY)+~~NT~T(~~~R~,~~~RY) COGAGE(COORX,COORY)=CONCEN(COORX,COORY)+~~GAGE(~~~~,~~~RY)

CONTINUE I CONTINUE GOTO 620 CLOSE (UNIT=22) CLOSE (UNIT=32) CLOSE (UNIT=82) CLOSE (UNIT=92) RETURN END

~********************************************************************** ...... SUBROUTINE SIGMAZ(SIGZ,FOHEIT,MONIN,USTAR~MIXDEP~WSTAR,TRAVEL~ This subroutine calculates Sigma Z, the vertical standard deviation of the plume. As it is calculated in OML, Sigma Z is calculated in 2 parts (mechanical and convective) (Berkowicz et al., 1992): Caller: Subroutine GPM Declaring global variables: REAL SIGZ,FOHEIT,MIXDEP,WSTAR,MONINIUSTARITRAVEL Declaring local variables: REAL TTT,HS,AA,BB,TE~1ITERME2ISIGZC,VARZM,SIGZMtVARZMN

Calculating the convective part of Sigma z: IF (HS.GE.BB) THEN SIGZC=M*(BB**0,3333)*TTT IF (TTT.LT.TERMEI) !S1GZC=AA*(HS**0,3333)*TTT IF (TTT.GE.TERMEl.AND.TTT.LT.TERME2) !~1~~~=((0.6666*A~*~TT)+(0.3333*HS**O.6666))**1.5 IF (TTT.GE.TERME2) SIGZC=((AA*(BB**O.~~~~)*TTT)+ !(0.5*(BB**0.3333)*(HS**O06666))-(0.SfBB)) END IF

Calculating the mechanical part of Sigma z: Under unstable conditions: IF (MONIN.LT.O) THEN IF ((TRAVEL*USTAR/F~HEIT).LT.~.)THEN VARZM=~.~*(USTAR**~.)*(TRAVEL**~.)* !EXP(-O.~*TRAVEL*USTAR/FOHEIT) ELSE VARZM=1.2*(USTAR**2.)*(TRAVEL**2.)*EXP(-o.6) END IF Under stable conditions: ELSE IF ((TRAVEL*USTAR/FOHEIT).LT.~.) THEN VARZMN=1.2*(USTAR**2.)*(TRAVEL**2.)* !EXP(-O.~*TRAVEL*USTAR/FOHEIT) ELSE VARZMN='I.S*(OSTAR**2.)*(TRAVEL**2.)*EXP(-O.6) END IF IF ((~.~~*TRAVEL*USTAR/MONIN).NE.-1.)THEN VARZM=VARZMN/( 1 . + ( 1 .11 *TRAVEL*USTAR/MONIN)) ELSE VARZM=VARZMN/O.~O~ END IF SIGZM=SQRT(ABS(VARZM)) END IF SIGZ=SQRT(SIGZM**2.+SIGZC**2.) IF (SIGZ.EQ.O) SIGZ=O.OI RETURN END

...... SUBROUTINE SIGMAY(SIGY,FOHEIT,SDWD7,SDWD15ITRAVELIW7IW15ITRANSF, ...... !DISPLA) This subroutine calculates Sigma Y, the lateral standard deviation of the plume from standard deviation of wind direction at 7 metres above ground level and a universal function (FY) (From Hanna, 1982; Gryning et al., 1987): Caller: Subroutine GPM Declaring global variables: REAL SDWD7rSDWD15,TRAVELtSIGYtW7tW15tTRANSFtDISPLA First choosing among SDWD7 and SDWD15: IF (ABS(FOHE1T-7.).LE,ABS(IS.-FOHEIT)) THEN SIGY=(SDWD~/T~CANSF)*SPEED(FOHEIT~DISP~~W~~,W~)*TRA~L* !FY(TRAVEL) ELSE SIGY=( SDWDl5 /TRANSF) *SPEED (FOHEIT,DISPLAL* !FY(TRAVEL) IF (SIGY.EQ.O.) SIGY=O.OOOI ENI3 IF RETURN END

...... **********************************************************************REAL FUNCTION FY (TRAVEL) This Eunction returns the value of Fy (universal function); from Draxler (1976): Caller: Subroutine SIGMAY Declaring global variables: REAL TRAVEL Declaring local variables: REAL LAGRA LAGRA is the Lagrangian time scale for the lateral dispersion: IF (TRAVEL.LT.550.) THEN LAGRA=300. ELSE LAGRA=O.OOl*(TMVEL**2.) END IF

RETURN END

********************************************************************** SUBROUTINE PLUME (CONCEN,ESCAP,SIGY,SIGZtPIIJIICOORXICOORYl **********************************************************************!USTARII1,FOHEIT,DISPLA, W1 5, W7) This subroutine calculates the ground level spore concentration at precise locations in the plume: Caller: Subroutine GPM Declaring global variables: REAL CONCEN(11,ll) ,S1GYtSIGZIPII JI IESCAPfUSTAR, II FOHEITtDISPLAt !W15,W7 INTEGER COORX,COORY Applying the Gaussian Plume Mode1 (according to McCartney and Fitt, 1985) at forest height. Plume height is taken as O and CONCEN is computed as a ground level concentration (Z=O). In the following formula, no loss of spores from the plume is assumed: Depletion of spores from the plume in function of the friction velocity and distance downwind (from Belot et al., 1976 cited in Chamberlain and little, 1981). Equations are adapted from Fig.9.5 on page 162: IF (USTAR.LE.0.3) THEN IF (II.LE.500) CONCEN(COORX,COORY)=((-0000008*11)+1.)* !CONCEN(COORX,COORY) IF (I1.GT.500.AND.11.LE.1000) CONCEN(COORX,COORY)= ! ( (-0.00006*11) +1, )*CONCEN(COORXICOORY) IF (11.GT.1000.AND.I1.LE.2000) CONCEN(COORX,COORY)= ! ((-O.OOOO2*11)+1. )*CONCEN(COORXtCOORY) IF (Il.GT.2000) CONCEN(COORX,COORY)=0.9*CONCEN(COORX~COORY) END IF IF (USTAR.GT.O.3.AND.USTAR.LE.O.S) THEN IF (11.LE.500) CONCEN(COORX,COORY)=((-0.0002*11)+1.)* !CONCEN(COORX,COORY) IF (II.GT.500.AND.I1.LE.1000) CONCEN(COORX,COORY)= ! ( (-O.OOOOS*II)+I. )*C~NCEN~COORX~C~~RY) IF (11.GT.1000.AND.I1.LE.2000) CONCEN(COORX,COORY)= ! ((-0.00004*11)+1. )*CONCEN(COORX,COORY) IF (Il.GT.2000) CONCEN(COORX,COORY)=O08*CONCEN(COORX,C00RY) END IF IF (USTAR.GT.0.5) THEN IF (Il.LE.250) CONCEN(COORX,COORY)=((-0.0004*11)+1.)* !CONCEN(COORX,COORY) IF (11.GT.2SO.AND.II.LE.500) CONCEN(COORX,COORY)= ! ((-O.OOO28*Il)+l. )*CONCEN(COORX,COORY) IF (I1.GT.500.AND.II.LE.1000) CONCEN(COORX,COORY)= ! ((-O.OOOI2*11)+1. )*CONCEN(COORXICOORY) IF (II.GT.IOOO.AND.I~.LE.~OOO)CONCEN(COORX,COORY)= ! ((-0.00006*11 )+1. )*CONCEN(COORXICOORY) IF (Il.GT.2000) CONCEN(COORX,COORY)=O.7*CONCEN(COORX,C00RY) END IF RETURN END

********************************************************************** **********************************************************************SUBROUTINE MEANC(POSITI,CONTOT,HOUR) This subroutine display the map of half-hourly mean spore concentrations to the screen: Caller: Subroutine GAUSS INTEGER XXXX,YYYY,ICONTO(11,11) REAL POSITI,CONTOT(11,11),HOUR,SUPERF Transforming concentrations in mean concentration at each point in the GRID MAP: CALL CLEARSCREEN($GCLEARSCREEN) DO 622 XXXX=1,11 DO 622 YYYY=1, 11 IF (XXXX.EQ.6.AND.YYYY.EQ.6) GOTO 622 ICONTO(XXXX,YYYY)=NINT(CONTOT(XXXX~YYYY)/HOUR) 2 CONTINUE

Display the GRID MAP: CALL CLEARSCREEN($GCLEARSCREEN)

PRINT* ,f GRID MAP OF MEAN SPORE CONCENTRATIONS (Spores/m3 !Ir PRINT*, ' ------1-1 PRINT*, ' PRINT*, ' DO 630 YYYY=11,1,-1 PRINT 800,(ICONTO(XXXX,YYYY), XXXX=1,11) CONTINUE PRINT*, ' ' PRINT*,' North is up the screen. Biocontrol site is in the middle !of the map. PRINT*,' ' PRINT 887,'Distance between each row and each column: r,(PO~~~~/5. !),'metresf PRINT*, ' PRINT 775,'Area covered by this map: r,SUPERF,r km2f PRINT*, ' Format of al1 output: 7 FORMAT (lx, A44, F6.0, A7) 5 FORMAT (lx, A27, F7.2, A4) 1 FORMAT (lx, 19(16)) RETURN END

...... SUEROUTINE CONC[POSITI,CONTOT) t********************************************************************* This subroutine displays the added spore concentrations around the treated area: Caller: Subroutine GAUSS Declaring global variables: REAL POSITI,CONTOT(11,11) Declaring local variables: INTEGERk2 XXXX,YYYY INTEGER ICONT ( 7 1 ,11) REAL SUPERF' Convesting from real to integer: CALL CLEARSCREEN($GCLEARSCREEN) DO 480 XXXX=1,11 DO 480 YYYY=1,11 IF (XXXX.EQ.6.AND.YYYY.EQ.6) GOTO 480 ICONT(XXXX,YYYY)=NINT(CONTOT(XXXX,YYYY)) O CONTINUE Displaying the GRID MAP: CALL CLEARSCREEN($GCLEARSCREEN) SUPERF=((POSITI*~.)/~OOO.)**~. PRINT*, t GRID MAP OF TOTAL SPORE CONCENTRATIONS (spores/ !rn3It PRINT*, ' ------PRINT*, ' ' PRINT*, ' DO 482 YYYY=11,1,-1 PRINT 483, (ICONT(XXXX,YYYY) , XXXX=1,11) 2 CONTINUE PRINT*, ' PRINT*,' North is up the screen. Biocontrol site is in the middle !of the map. PRINT*, ' ' PRINT 88gtrDistancebetween each row and each column: r,(~~~~~~/5. !),trnetresf PRINT*, ' PRINT 481tfAreacovered by this map: t,SUPERF,fkm2' PRINT*, ' Formating ail output: 9 FORMAT (lx, A44, F6.0, A7) 1 FORMAT (lx, A27, F7.2, ~4) 3 FORMAT (lx, 19(16)) RETURN END

********************************************************************** SUBROUTINE MAX(POSIT1,MAXCON) *****Xk***X**XX*XX**X***kkX**X*XXRk*k*f***************************%*** This subroutine displays the half-hourly maximum spore concentration encountered around the treated area for the whole time period analyzed : Caller: Subroutine GAUSS Declaring global variables: REAL POSITI,MAXCON(11,11) Declaring local variables: INTEGER*2 XXXX,YYYY INTEGER IMAX ( 1 1 ,Il ) REAL SUPERF Converting from real to integer: CALL CLEARSCREEN($GCLEARSCREEN) DO 494 XXXX=1,11 DO 494 YYYY=1,11 IF (xxxX.EQ.6.AND.YYYY.EQ.6) MT0 494 IMAX(XXXX,YYYY)=NINT(MAXCON(XXXXtYYYY)) 4 CONTINUE Displaying the GRID MAP: CALL CLEARSCREEN($GCLEARSCREEN) SUPERF=((POSITI*~.)/~~~~.)**~. PRINT* ,t GRID MAP OF MAXIMAL SPORE CONCENTRATIONS (Spores ! /m3) PRINT*, ' ------I -r PRINT*,' ' PRINT*, ' DO 496 YYYY=11,1,-1 PRINT 497,(IMAX(XXXX,YYYY), XXXX=1,11) 5 CONTINUE PRINT*, PRINT*,r North is up the screen. Biocontrol site is in the middle !of the map. PRINT*, ' ' PRINT 888,rDistance between each row and each column: r,(~~~~~~/5. ! ),fmetrest PRINT*, ' PRINT 49StrAreacovered by this map: t,SUPERF,fkm2' PRINT*, '

Formaking al1 output: 3 FORMAT (lx, A44, F6.0, A7) 5 FORMAT (lx, A27, F7.2, A4) 7 FORMAT (lx, 19(16)) RETURN END

t********************************************************************* SUBROUTINE PER(POSITI,COMEAN,DUMZ,PERCE) t********************************************************************* This subroutine display the map of (PERCE)th percentile of daily mean spore concentrations of half-hourly values to the screen: Caller: Subroutine GAUSS Declaring global variables: INTEGER COMEAN(11,11,200),DUMZIPERCE REAL POSITI Declaring local variables: INTEGER FRQC(~OOOO),XXXX,YYYY~ZZZZ~CO,PERC(~~~~) REAL SUPERF,FRQ,FR,COUNT,PERCET Calculating the XXth percentile at each point: CALL CLEARSCREEN($GCLEARSCREEN) PRINT 911rr0PEMis now computing thet,PERCE,rthpercentile of dail !y mean sporet PRINT*,fconcentration; Please, wait ...' PRINT*, ' PRINT*, ' I FORMAT (A26,1Xt13,A33) DO 622 XXXX=1 ,Il DO 622 YYYY=1,11 IF (XXXX.EQ.6.AND.YYYY.EQ.6) GOTO 622 For point (XXXX,YYYY), computing for al1 days: DO 623 ZZZZ=1 ,DUMZ CO=1 -7 IF (COMEAN(XXXX,YYYY,ZZZZ).EQ.(CO-1)) THEN FRQC(CO)=FRQC[CO)+I GOTO 623 ELSE CO=CO+1 GOTO 747 END IF 3 CONTINUE Displaying a counter on the screen: COUNT=COUNT+l . PERCET=(COUNT/~~~.)*IOO. WRITE ( * ,333 ) PERCET 3 FOE?MAT(f+Percentage done: f,F5.0,f %O Now computing the (PERCE)th percentile: FRQ=O . CO=1 DO WHILE (FRQ.LE.((REAL(PERcE))/~OO.)) FR=REAL(FRQC(CO))/REAL(DUMZ) FRQ=FRQ+FR CO=CO+1 END DO PERC(XXXX,YYYY)=CO-2 Reinitializing to zero: DO KKK=1,20000 FRQC (KKK)=O END DO CONTINUE

Displaying the GRID MAP: CALL CLEARSCREEN($GCLEARSCREEN)

I ------t 9 FORMAT (4XlA12,1XfI3,A49) PRINT*, ' ' PRINT*, ' DO 630 YYYY=11,1,-1 PRINT 800,(PERC(XXXX,YYYY), XXXX=1,11) O CONTINUE PRINT*, PRINT*,' North is up the screen. Biocontrol site is in the middle !of the map.' PRINT*, ' ' PRINT 887,'Distance between each row and each column: t,(~~~~~~/5. !),'metrest PRINT*, ' PRINT 77SftAreacovered by this map: r,SUPERF,tkmSf Formating al1 output: 7 FORMAT {lx, A44, F6.0, A7) 5 FORMAT (lx, A27, F7.2, A4) O FORMAT (lx, 19(16)) RETüRN END

********************************************************************** SUBROUTINE STAT(HOUR,NEUrSTA,UNS,DIVERGrDISPLA,TORAINIHHRAINf ...... !HRAIN,HNSP02,HNSPOD,SPDAYfHNOSPOIZOrSUMSPCITOTARE~ This subroutine computes some statistics about meteorological data, boundary layer scalars and spore dispersal: Caller: Subroutine GAUSS Declaring global variables: REAL HOUR,TORAIN,HNSPO2,HNSPODtHNOSPO,HHRAIN,HRAIN,NEU,STA,UNS, !ZO,SUMSPC,TOTARE,DISPLA INTEGER DIVERG Declaring local variables: REAL TO,T2,T7,T15,HUMIrGRAD,NETRAD,W15,MXWS15,DIR15,SDWD15,W7, !DIR7rSDWD7fRAINrESCAPIMONINtSENSHIUSTARIMIXDEPrWSTARISPORULr !SPOAB0,SPOTOT,SPOSECIAVEWIS,TMOfTM15,HHLI !HDRYfHNOWIN,HINV,HNOTRA~HHOTPtHFREEP~HDRYPIHNOWIP,HNOTRPfHNSPDPf ! HNOSPP, HINVP, TIMER, HNOSP INTEGER JULDAY,TIME Declaring arrays: REAL SPDAY(3) INTEGER WDIR(36) CALL CLEARSCREEN($GCLEARSCREEN) PRINT*, Please, wait a few seconds. . . PRINT*, ' PRINT*,' SUMSPC=SUMSPC*10000. First, opening the output files: OPEN (~~,STATUS=~OLD',FILE='C:\FORTRAN\OUT\METEO.OUT~) OPEN (~~,STATUS=~~LD~,FILE='C:\FORTRAN\OUT\SPORE.OUT~) OPEN (~~,STATUS=~OLD~,FILE=~C:\FORTRAN\OUT\ESCAPE.~UT~) OPEN (~~,STATUS='OLD',FILE='C:\FORTRAN\OUT\PBLPAR.OUT~) 2 READ {22,7511~~~=767)JULDAYrTIMEtT0,T21T71T151HUMIIGRADINETRADr !W15tMXWS15rDIR15,SDWD151W71DIR7,SDWD7,RAIN 1 FORMAT (1X,I3,5XtI4,4X,15F8.3)

E@,AD (42,753) SPORUL 3 FORMAT (12XtF20.0) READ (92,746) ESCAP 6 FORMAT (12X,F20.0) Since the PBL parameters have been computed on a hourly basis, and seeing that meteorological data have been recorded each half-hour, the same PBL parameters are read twice during an hour: TIMER=REAL(TIME) IF ((TIMER/IO~.)-(INT(TIMER/~OO.)).NE.O) THEN BACKSPACE (UNIT=82) READ (82,748) MONIN,SENSH,USTAR,MIXDEP,WSTAR ELSE READ (82,748) MONIN,SENSH,USTAR,MIXDEP,WSTAR L 8 FORMAT (16X,3F10.4,F7.1 ,F8.4) END IF WRITE ("1750) JULDAYITIME iO FORMAT('+DAY: ,1 TIME: (14) Some calculations for statistics: Calculating the total amount of spores escaping above the forest height (SPOABO) and being transported downwind: SPOABO=SPOABO+(ESCAP*~~OO.) Calculating the total amount of spores released from basidiocarps SPOTOT=SPOTOT+(SPORUL*1800.) Number of spores escaping above the forest height per second: SPOSEC=SPOTOT/(HOUR*~~~~.) SPOSEC=NINT(SPOSEC) Computing the frequency of wind directions with 10 degrees sectors: IF (DIR15 .EQ. 360) DIR15=DIRl5-360. IF (DIR15.GE.O.AND.DIR15.LT.10) WDIR(l)=WDIR(l)+l IF (DIR15.GE.lO.AND.DIR15.LT.20) WDIR(S)=WDIR(L)+l IF (DIR15.GE.20.AND.DIR15.LT.30) WDIR(3)=WDIR(3)+7 IF (DIRlS.GE.30.AND.DIR15.LT.40) WDIR(4)=WDIR(4)+1 IF (DIR15.GE.40.AND.DIR15.LT.50) WDIR(S)=WDIR(5)+1 IF (DIR15.GE.5O.AND.DIR15.LT.60) WDIR(6)=WDIR(6)+1 IF (DIR15.GE.60.AND.DIR15.LTT70) WDIR(7)=WDIR(7)+1 IF (DIR15.GE.70.AND.DIR15.LT.80) WDIR(8)=WDIR(8)+1 IF (DIRlS.GE.80.AND.DIR15.LT.90) WDIR(S)=WDIR(9)+1 IF (DIRlS.GE.90.AND.DIRl5.LT.100) WDIR(10)=WDIR(10)+1 IF (DIR1S.GE.lOO.AND.DIR15.LT.110) WDIR(ll)=WDIR(11)+1 IF (DIR15.GE.11O.AND.DIR15.LT.120) WDIR(12)=WDIR(12]+1 IF (DIR15.GE.120.AND.DIR15.LT.130) WDIR(13)=WDIR(13)+1 IF (DIR15.GE. 130,AND.DIR15.LT.l40) WD1R(14)=WD1~(14)+1 IF (DIR15.GE.140.AND.DIR15.LT.150) WDIR(~~)=WDIR(~~)+~ IF (DIR15.GE.150.AND.DIR15.LT.160) WDIR(16)=WDIR(16)+1 IF (DIR1S.GE.160.AND.DIRi5.LT.170) WDIR(17)=WDIR(17)+1 IF (DIR15.GE.170.AND.DIR15.LT.180) WDIR(18)=WDIR(18)+1 IF (DIR15.GE.180.AND.DIRf5.LT.190) WD1R(19)=WD1~(19)+1 IF (DIR15.GE.190.AND.DIRlS.LT.200) WDIR(2O)=WDIR(SO)+l IF (DIR15.GE.200.AND.DIRf5.LT.210) WDIR(21)=WDIR(21)+1 IF (DIR15.GE.210.AND.DIR15.LT.220) WDIR(22)=WDIR(22)+1 IF (DIR15.GE.220.AND.DIR1S.LT.230) WDIR(23)=WDIR(23)+1 Averaging wind speed: AVEWIS=AVEWIS+W15 Averaging temperature at a 2 and 15 metres height: TMO=TMO+TO TM1 S=TMl5+Tl5 Averaging relative humidity: AVEREL=AVEREL+HUMI Calculating hours without full transmission due to: Weak wind velocity: IF (W7.LT.0.5) HNOWIN=HNOWIN+0.5 Inversion layer (Garratt, 1992, McIntosh and Thom, 1978): IF (RICH(W7,W15,GRAV,T0,T2,T71T151DISPLA).GT.0.1) HINV=HINV+O.S Calculating hours without transmission: IF ((RICH(W7,W1S,GRAV,TO,T2,T7IT15I~ISPL~).G~.O.1).OR. !(W7.LT.0.5)) HNOTRA=HNOTRA+0.5

GOTO 752 Closing the loop: Calculating mean values for statistical purposes:

Writing the half-hourly wind directions into the output file PWIND.OUT: OPEN(~~~,STATUS=~OLD',FILE='C:\FORTRAN\OUT\PWIND.~UT~~ERR=~~~) GOTO 225 4 OPEN(~~~,FILE='C:\FORTRAN\OUT\PWIND.OUT~~MODE=~WR~TE') 5 DO 223 DUM=1,36 WRITE (132,222) WDIR(DUM) 2 FORMAT (lX,I8) 3 CONTINUE CLOSE (UNIT=132) Calculating percentages: HNSPDP=(HNSPOD/HOUR)*~OO. HR.AINP=(HRAIN/HOUR)*~~O. HHRAIP=(HHRAIN/HOUR)*~OO HHOTP=(HHOT/HOUR)*IOO. HFREEP=(HFREE/HOUR)*~OO. HDRYP=(HDRY/HOUR)*~~~. Displaying general resuits: CALL CLEARSCREEN($GCLEARSCREEN) PRINT*, ' -- 1 PRINT*, ' GENERAL RESULTS ' PRINT*, ' ------I PRINT*, ' ' PRINT* , ' - ATMOSPHERIC CONDITIONS -' PRINT*, ' ' PRINT*, ' PRINT 179,tTotalhours analyzed:', HOUR PRINT 180ffMeanwind speed (m/~):~,AVEWIS PRINT 1801fMeantemperature at ground îevel (C):', TM0 PRINT 18Ot1Meantemperature at 15 a metres height (C):',TM15 PRINT 18OItMeanrelative humidity:', AVEREL PRINT 1801fTotalrainfall (mrn):l,TORAIN PRINT 181,'Frequency of rainfall 00.1 mm) (hours and %):', !HRAIN,HRAINP PRINT 18lIfFrequencyof heavy rainfall (>3,0 mm) (H + %):#, !HHRAIN, HHRAIP PRINT 181,'Frequency of stable atmosphere (hours and %):',STA, !PSTA PRINT 181,1Frequency of unstable atmosphere (hours and %):fIUNS, !PUNS PRINT 1 81 , 'Frequency of neutral atmosphere (hours and % ) : NEU, !PNEU PRINT*,, Number of iterations exceeding 50 (M-O length): If !DIVERG PRINT*, '

9 FORMAT (lx, A48, F7.0) O FORMAT (lx, A48, F7.2) 1 FORMAT (lx, A48,F6.0f2X,F5.1) PAUSE Press ENTER for results about sporulation ...' CALL CLEARSCREEN($GCLEARSCREEN) PRINT*, ' -,-,--,,,,--- r PRINT*, GENERAL RESULTS' PRINT*, ,------1 PRINT*, ' PRINT*, ' - SPORULATION -' PRINT*, ' PRINT 300frHourswithout sporulation (hours and %):f,HNOSPO, !HNOSP PRINT*, I ,,,~~~~~,~~~~~~~~~~~~~,~~~~~~~~~~~l PRINT 3001t Low Temperature (

FORMAT ( 1 X, A47, SX, F6. O, SX, F6.2) FORMAT (SX, A43, SX, F6.0, 2X, F6.2) FORMAT (lx, A47, F10.0) FORMAT (A27, 16, Al , 16, Al ) FORMAT (All, lx, F8.0, Al6) FORMAT (lx, 19(F3.0t1X)) PAUSE Press ENTER for more results on sporuiation ...' CALL CLEARSCREEN($GCLEARSCREEN) PRINT*, ' ,----,-f PRINT*, ' GENERAL RESULTS ' PRINT*, ' ------f PRINT*, ' PRINT*, ' - SPORULATION -' PRINT*, ' PRINT*, ' PRINT*, t Total spore liberation for the analyzed heurs:', !SPOTOT PRINT*, t Mean spore liberation (sp/sec):', !SPOSEC PRINT*,, Total amount of spores escaping above forest height:', !SPOABO PRINT 501,fFirst day of appearance of basidiocarps: ',SPDAY(l) PRINT*, ' PRINT*, ' '

PAUSE Press ENTER to see more resufts. ..'

CAL L CLEARSCREEN($GCLEARSCREEN) PR1 NT*, ' --,,,,,,,,,,,-,,,t PR1 !TT*, GENERAL RESULTS ' PR1 YT*, ------,--, 1 PR1 YT* , ' PR1 YT*, ' - FOREST CLEARCUT -' PR1 !TT*, ' PR1 W*,' PR1 YT 567,fForest clearcut surface area (m2): ,TOTARE PR1 YT 567,'Forest clearcut roughness length (m): t,ZO PR1 YT 567,fForest clearcut displacement height (m): t,DISPLA PR1 YT 567,,Forest clearcut stub density (# stubs/ha): f,SUMSPC PR1 w*,, ' PR1 YT* , '

PAUSE 'Press ENTER to exit resuits ...# Re-initializing data to zero: SPOABO=O . SPOSEC=O . SPOTOT=O . AVEWID=O . AVEWIS=O. AVEXEL=O. TMO=O. TM1 5=0. HNOWIN=O . HNOTRA=O . HINV=O . HFREE=O . HHOT=O . HDRY=O. DO 111 DUM=1,36 WDIR(DUM)=O 1 CONTINUE SUMSPC=SUMSPC/~0000.

CLOSE (UNIT=22) CLOSE (UNIT=42) CLOSE (UNIT=82) CLOSE (UNIT=92) RETURN END

t********************************************************************* REAL FUNCTION RICH(W7,W15,GRAV,T0,T2,T7tT15fDISPLA) r**t****************************************************************** This function calculates the maximum value (the most stable) of the gradient Richardson number for the current 30 minutes (according to Goudriaan, 1977 and De Jong, 1988): Caller: Subroutines AERO, PREPRO, ESCA and STAT Declaring global variables: REAL W7,W15,GRAV,TOtT2,T7,T15,DISPLA Declaring local variables: REAL HEIL,D72,DSO

Avoiding a numerical error: IF ( (TL-TO ) .EQ. O. ) T2=T2-0.01 IF ((T7-T2l.EQ.O.) T7=T7-0.01 IF ((T15-T7I.EQ.O.) T15=T15-0.01 IF ((TL+TO).EQ.O.) T2=T2-0.01 IF ((T7+T2).EQ.O.) T7=T7-0.01 IF ( (Tl 5+T7),EQ. O. ) Tl 5=T15-O. 01 IF ( (W15-W7). EQ. O) W1 S=Wl5+O.01 D72=W7-SPEED (HEI2,DISPLA, W15 ,W7 ) IF (D72.EQ.O) D72=D72+0.01 D20=SPEED(HE12,DISPLAtW15tW7)-0. IF (D2O.EQ.O) DSO=D20+0.01 Computing the Richardson number at several heights: RICH~~=((GRAV*(T~~-T~))/~.)/((((T~~+T~)/~.)*(WI~-W~)/~.)**~.) RICH~=((GRAV*(T~-T~))/~.)/((((T~+T~)/~.)*D~~/~.)**~.) RICH~=((GRAV*(T~-TO))/~.~~)/((((T~+TO)/~.)*D~U/~~~~)**~.) Choosing the highest Richardson number: RICH=RICH15 IF (RICH7.GT.RICH) RICH=RICH7 IF (RICH2.GT.RICH) RICH=RICH2

RETURN END

~********************************************************************** ~**********************************************************************REAL FONCTION DIRFOH(DIR7,DIR1S,FOHEIT) This Function computes the wind direction at forest height (plume height) : Caller: Subroutine GPM Declaring global variables: REAL FOHEIT,DIR7, DIRIS Declaring local variables: REAL DIFDIR Computing the wind direction at forest height assuming a linear relationship between wind directions at 7 metres and 15 metres: IF (DIR15.GT.DIR7) THEN IF ( (DIR15-DIR7 ) .LE. 180. ) THEN DIFDIR=DIR15-DIR7 DIFDIR=DIFDIR/~. DIRFOH=DIR7+((FOHEIT-7.)*DIFDIR) ELSE DIFDIR=360.-(DIR15-DIR7) DIFDIR=DIFDIR/~. DIRFQH=DIR7-((FOHEIT-7.)*DIR) IF (DIRFOH.LT.0.) DIRFOH=DIRFOH+360. END IF ELSE IF ( (DIR7-DIRI5 ) .LE. 1 80. ) THEN DIFDIR=DIR7-DIRIS DIFDIR=DIFDIR/~. DIRFOH=DIR7-((FOHEIT-7.)*DIFDIR) ELSE DIFDIR=36O.-(DIR7-DIR15) DIFDIR=DIFDIR/~. DIRFOH=DIR7+((FOHEIT-7.)*DIFDIR) IF (DIRFOH.GT.~~~.)DIRFOH=DIRFOH-360. END IF END IF IF (FOREIT.LT.7.) DIRFOH=DIR7 IF (FOHEIT.GT.15.) DIRFOH=DIR15

RETURN END ********************************************************************** **********************************************************************REAL FUNCTION SPEED(HEIGHTtDISPLA,W1S,W7) This function cornputes the wind speed at forest height (plume height ) : Caller: Subroutines GPM and PLUME, and function RICH Declaring local variables: REAL HEIGHT, DISPLA, W15, W7 Computing the wind speed at height 'HEIGHTr from the logarithmic wind speed profile (from Stull, 1988; page 382): SPEED=W~+((WI~-W~)*(ALOG((HEIGHT-DISPLA)/(~.-DISPLA))/ I ALOG((~~.-DISPLA)/(~.-DISPU)))) IF (SPEED.LE.0.) SPEED=O.O1

RETURN END Annexe B

Guide de l'utilisateur du logiciel de simuhtion OPEM OPEM

an Operational Epidemiological Model for biocontrol applications in forestry (version 1.0)

User's Guide

André Goulet, hg. f. / Louis Bernier, Ph.D. Centre de Recherche en Biologie Forestière (CRBF) Université Laval Québec, Canada

Robert Jobidon, ing. f., Ph.D. Direction de la Recherche Forestière Ministère des Ressources naturelles du Québec Québec, Canada

March 1st 1997 Foreword

This user's guide is the main report on the application of OPEM, an Operationi Epidemiological Model for biocontrol applications in forestry. The document focuses on th description of the model while emphazing the specifications and limitations for an efficier and adequate use of OPEM. It is strongly recommended to take cognizance of the preser document prior to using the model in order to avoid misinterpretations of any output yielde by OPEM The following rnatters are discussed throughout the guide:

1. Introduction 2. Specifications and limitations of OPEM 3. Hardware / Software requirements 4. Instalation procedures 5. Program execution 6. Outputs of the mode1 7. Description of the output files 8. Meteorological instruments and data storage 9. Description of the program, subroutines and functions

1. Introduction

Recently in the province of Québec, Canada, environmental concems have led to a politica decision aiming at banishing the use of chemical phytocides in forest areas by year 2001 This poiicy is part of a global strategy based upon the concepts of integrated vegetatior management, sustainable forestry and biodiversity maintenance. Nowadays, sevek silvicdtural treatments, such as conifer release operations, site preparation or vegetatior management in rights-of-way, must be done using chernicals to obtain satisfac tory resul ts. Mechanicd treaments, which are regarded as the most realistic and operational altemative to chemical herbicides, are ofien of poor eficacy when applied to tree species having vigourous vegetative reproduction. Therefore, there is a strong need to develop alternatives or enhance the efficacy of actual silvicultural treatments. BioIogical control appears to be a promising research avenue and warrants merattention.

In 1992, several experimental sites were established in Québec to evaluate the potentia1 of the indigenous fungus Chondrosterem pwpureum (Pers. ex Fr.) Pouzar to inhibit stump sprouting of several deciduous tree species. C. purpweum is the causa1 agent of silverleaf disease, a disease occuring on most broadleaf tree species, but especialIy on fniit tree species such as apple, , , peach and nectarines in orchards, throughout temperate regions of the world. As an alternative silvicultural method, the use of the pathogen wrnbined to mechanical treatments codd yield interesting prospects in conifer release treatments in forest pIantations or for other purposes. Results, coupled to those fiom other studies, have show that the pathogen couid control sprouting of selected deciduous tree species very efficiently. The wide host range of C. purpurm is indeed an interesting aspect when regarded as i biocontral point of view. However, this characteristic soon becomes a disadvantage when th environmentai risk associated to biocontrol is outlined Indeed, depending on the life cycle O the fungus, on the tree species îreated and on environmental conditions, C pürpu'eum mal produce numerous fiuiting bodies in form of brackets on îreated stubs following a biocontro operation. Basidiocarps are fruiting up to two years following a biocontrol treatment and art releasing, when the weather is fkvomble, millions of spores into the environment Basidiospores are dispersed in contiguou fields and may infect freshIy wounded deciduou tree species. A susceptible valuable tree might be defined any Iigneous deciduou: vegetation having an economicd, ornemental or sentimental value and karing less than e month-old wormd(s). It has been shown that most coniferous tree species are not likely to be infected by the pathogen. Although several environmental conditions must coincide to lead tc infection, (ie. basidiocarps must be present, sporulation and dispersion are strongly reguIated by ciimatic conditions, basidiospores are fragile and subjected to dessication, germination and infection necessitate moistened conditions, trees must be fieshly wounded), it remaim that risk is not secludeci Environrnenatl risk assesment related to biocontrol operations rnainly MpIies two types of assessment, namely a fate assessment and an impact assessrneni of the trearment. While the first term tries to illustrate the survival, growth and spread of the introduced disease and can be seen as a a priori analysis, the secong term deals with the undesirable environmental effects of the treatment (thus a a posteriori analysis). Fate assessment of C. pwpureum, or more precisely the ernission and transmission of its spores, has been done by modelling the dispersal part of the epidemiologicai process chain through the development of OPEM. Elements and concepts of severai disciplines such as forestry, aerobiology, meteorology and forest pathology have served as input to simulate the dissemination of the disease, as similarly done by De Jong (1988) in the Netherlands.

2. Specifications and limitations of OPEM

OPEM was developed at Lavai University in collaboration with the Quebec Ministry of NaturaI Resources and the Theoretical Production Ecology department (TPE) of the Wageningen Agricultural University (WAU) in the Netherlands. The mode1 was built in 1994-1996 as part of a USc. degree and was designed to serve exclusively for biocontrol applications in the context of eastern Canadian forestry. The model simulates spore dispersal of C. purpurem following a biocontrol treatment, and this above a forest canopy surrounding the treated area. OPEM is operational since it was stnictured to be versatile and easily adaptable to several forest conditions.

T'he mode1 was built using observations made in siru (meteorological data, appearance and densities of basidiocarps, leaf area index of surrounding fore$ etc.), in the Iaboratory (spore emission according to environmental conditions, surface area of basidiocarps) and using data in the Merature. OPEM was conceived using a Gaussian Plume Mode1 scheme to simulate spore dispersal over short range distances downwind of a biocontrol site., i.e. up to 20 kiiornetres. The model simulates plumes of spores for time periods of 30 minutes. Steady state conditions are assumed to prevail in these penods. A flat topography is also assumed by the mode1 whiIe no reflection of spores is simuiated on the top of the boundary laye: Therefore, the output given by OPEM, such as spore concentrations, are averages hence nc exact in time nor in space.

3. Hardware / Software reuwirements

Before installing OPEM on a PC, the user must be aware that the computer must be equippei

with a 386 or larter processor, a mathematicai CO-processor(xxx87), 4 MEG RAM and a VGA screen. Moreover, the PC mut be equipped with 640 Kb of memory of which 440 K1 must be fiee for the program executioa

4. Installation ~rocedures

Attached to this document, the user will fïnd a diskette containing the source code of OPEh that has been &en in Microsok FORTRAN@ developing systern 5.1. The name of tha file is OPEM-FOR and the corresponding executable file is named OPEMEXE. A simila: compiler-linker and defadt libraries provided by Microsob must be used to recompile tht code if wanted. On the same diskette, the user will find the file TMSRB.FON which is a fon Iibrary that allows OPEM to display graphical outputs on the screen. In addition, a sewnc diskette containing a sample of the meteorological data set (DATADAT)recorded on 2 biocontrol site (described as defadt site in OPEM) in 1995 is enclosed. These data are haIf: hourly averaged and cover a fraction of the entire period analyzed, i.e. fiom June 1* 1995 tc October 27' 1995. Steady Rate conditions are assurned during these 30 minute penods. The user must create the following directories on the PC prior to run OPEM:

(1) C:\FORTRAN\ MODEL (2) CA FORTRAN\ METEO (3) C:\FORTRAN\ FONT (4) C:\FORTRAM OUT

The aforementioned source (.FOR)and executable (.EXE)files must be copied to directory (1). The meteorological data file @ATA.DAT) must be downloaded into the second directory (2 ) while the file TMSRB.FON must be copied to path (3). The duectory C:\ FORTRAN\ OUT serves as an output directory in which OPEM downloads 9 different files (Fig. 1).

S. Program exeeution

To nui the program, the user must type OPEM under the path C:\ FORTRAN under a DOS environment. A graphicaI introduction message will appear on the screen. When ready, the user presses the ENTER key and the following text appears: WELCOME mm OPEM!

Do you want to use default parameters (treated area coordinates, forest edges height, etc.) or enter new ones (1 - default, 2 = new) ?

Default parameters are the ones recorded during summer 1995 at the study site of Ste Florence, Quebec, Canada. When choosing this option, OPEM simulates spore dispersa fiom this experimental forest clearcut whkh was treated with C. pu'pivm in 1994 Simulation is done using empirical inputs such as the number of different forest edges anr their corresponding forest height, the area treated, the density of the stubs treated per tm species, the latitude of the site, and so on When choosing to enter his mer) own parameters i.e. when the answer typed is 2, the user is asked to enter the clearcut coordinates in i cartesian plane:

Entering the forest clearcut coordinates:

How many sides (edges) does the forest dearcut have (max==l5)?

As written, a maximum of 15 forest edges is dlowed. Logically, a minimum of three (3: forest edges must be entered. Here, a forest edge is defined as an edge having a differes orientation OR height. When entered, another message is sent to the screen:

You may enter the 6 coordinates of me forest clearcut in motres and in an ANTI-CUXKWISE =y (POINT 1 being (0,O))

(POINT 2) XI

OPEM then asks the user to enter every coordinate (except point 1 being (0,O)) in metres and in an anti-clockwise way. These aspects must be respected to perform a correct simuIation. There is no need to enter the coordinates according to real geographic situation of the treated site under investigation, Le. North does not have to be taken into consideration. For instance, for the following forest plantation treated with C. purpureum that has 6 forest edges, entering the coordinates in the following order wouId be erroneous: POINT 3 1 POINT 2 dl

POINT 1

POINT 5

in this particular case, the correct procedure to follow would be:

POINT 3

POINT 4 cPOINT POINT 1 When this step is done, the user is asked (pretending that coordinates of POINT 2 are (76.: 102)):

What is the angle from North of point 1 (Oro) to point 2 (76.3, 102)? N.B. An angle that is North-West is between 270 and 360 degrees

This input is needed to translate screen coordinates into real field coordinates. The nen procedure is used to enter the mean height of every forest edge:

In terms of height, is the surrounding forest hamgeneous (l-yes, 2-01?

Men entering 2 (no), the next message appears (values are dumrny):

Please, enter the height of each forest side. Just to help you, here are the sides and their coordinates:

SIDE # 1: POINT ( -00, .O0 ) ta POINT (102.30, 4.20 ) SIDE # 2: PORJT ( 102 -30, 4.20 ) to WINT ( 65.20, 6.80) SIDE # 3: POINT ( 65.20, 6.80 ) to POINT ( 67.00, 87.00 ) SIDE # 4: POINT ( 67.00, 87.00 to POINT ( 9.00, 132.00 ) SIDE # 5: POINT ( 9-00, 132.00 ) to WfNT ( -00, .O0 )

FOREST SIDE # 1 HEIGflT =

Heights in metres are entered and enable OPEM to compute (or extrapolate) windspeed a forest height for each 30 minute periods. Then, the mode1 passes to another question: mat is the density of the following tree species in the forest dearing (Ystub/ha)?

Prunus pensylvaniea (pin cherry) :

OPEM asks the user to enter the density (stubs per hectare) of every target tree species thai has been investigated The user enters stub density of pin cherry (Pnmus pensyivanica), trembling aspen (Poplus tremzdoides), sugar maple (Acer saccharum) and paper birch (Betula papyrfera). If the target species is (are) not part of that group, the user mut enter its (their) cumulative density(ies) in the last group:

Other deciduous trees (ERAI ERR, BOG, BOJ, etc.):

ERA refers to silver maple (A. saccharinwn), ERR to red maple (A. nrbnmt), BOG to grey birch (B. populfilia) and BOJ to yeIlow birch (B. alleghaniemis). Mountain ash (Surbus urnericana), (Sulix spp.) and (Alnus crispa and Alnus rugosu) must also be considered in that group. From these inputs, OPEM computes the nurnber of basidiocarps, hence the total surface of hyrnenium that is predicted to sporuiate. Soon, a message appears: What is the mean height of the residuai vegetation in the treated area?

This height serves to cornpute the displacement lengtù (4 for the calculation of the half- hourly Iogarithmic windspeed profile, If there is no significantly dense cover on the ground, this height should be set to zero. The ne- step wnsists of entering the latitude of the treated site. In Quebec, forested lands are inciuded between 45" and 52" North. The latitude permit5 the computation of the coriolis force which in tum serves to calculate the height of the mixed Iayer (zJ. When this step is done, the nex? message is displayed:

men have the meteorological data been recordai?

(1) In the year of the treatment with the biosilvicide; (2) One year following the treatfnent with the biosilvicide; (3) Two years following the treawt with the biosilvicide; (4) Three years or more following the treatment with the biosilvicide;

Enter one of the above number:

This parameter is needed to compute the number of basidiocarps expected according to the time following the treatment. The period at which the biocontrol treatment was applied is also needed:

At which time of the year has the treatment with the biosilvicide ben applied?

(1) Before August; (2) In August or later;

In Eastern Canada, treatment can be applied at anytime except during winter. Time of treatment is indeed another parameter influencing the appearance of basidiocarps in the field. Following this step, OPEM asks the user if al1 inputs are correct If one information has been entered erroneously, it is possible to re-enter a new value with the next menu:

What must be ccrrrected (enter the number) ?

1-) The forest dearcut coordinates; 2-) The forest edges height (s ; 3-) The stub density; 4-) The mean height of the vegetation within the treated area; 5-) The Northern latitude of the site; 6-) The tima of recording the meteordopical data; 7-1 The time of treatment; In both cases (default or user's parameters), OPEM displays an aerial view of the area treatec with the biosilvicide so that the user may have a graphitai support to add further correction ij needed. When the user confis that al1 inputs are right, OPEM creates three output files, i.e. FHEI-OUT, FSIDE.OUT and MEANT-OUT (see section 7 and Fig. 1). If the user bas used OPEM for more than once, a message appears on the screen:

The file FHEI.0UT already exists; OK to ovemite (y/n)?

By answering 'y7 to this question, the old file FHEI.OUT is automatically deleted while repIaced by a new one. if 'n7 is entered, OPEM displays a note on the screen:

As the user runs OPEM, similar questions are asked for most files having the extension '.OUT'. This feature has been implemented in order to protect output files that would be of any relevance to the user. The mode1 next passes to the first of the five main phases of cornputation. At this moment, the user sees the next message:

FIRST PHASE : OPEM is now reading meteorological data; Please uai t . . .

As displayed, a counter has been conceived at each of the five phases to allow the user to follow the computation progression. if an error message appears on the screen, OPEM usually returns the nature of the error, e.g. the meteorological &ta file DATA.DAT is missing or has not been downloaded in the path C:\ FORTRAN METEO. Al1 relevant meteoroIogica1 data inchded in DATADAT are read and saved into the output file METEO.OUT (Fig. 1). At this phase, OPEM also operates the complex subroutine BLOW that Ends in each half-hou. the forest edge on which the wind is bIowing. This is done by scanning the clearcut using the subroutines SCANX and SCANY (see section 7 and Fig. 1). According to the processor in your PC and to the size of the rneteoroIogical data set, this step, as for the next ones, can last several seconds to a few minutes, OPEM then gets through the next phase btis identified as:

SECQNrl PME: OPEM is now camputing the number of basidiocarps expected and the amount of spores emitted from those; Please mit...

This phase cornputes the half-hourly emission (or spore discharge) hmthe basidiocarps (done by subroutine SPO) and stores the results into the output file SPOREOUT. The third phase enables OPEM to process the mixed layer turbulent parameters such as the Monin- Obukhov length (L), the fiction velocity (u.), the sensible heat flu.. (H), the convective velocity scale (W.) and the mixing depth (q). This phase is done by calling the subroutin PRO and the attached subroutines and functions, and creates the output file PBLPAROU? The turbulent parameters are cssential to compute the vertical standard deviation of th Gaussian plume (a;).The values of L, u. and H are caiculated iteratively with an accuracy a 1%. A maximum of 50 iterations is performed by OPEM and when this number is reachec which is exceptional, the model computes these parameters using other formulas and notes il The user sees the next message on the screen while OPEM proceeds through this çtep:

THIRD PHASE: OPEM is now processing the boundary-layer parameters; Please wait.. .

DAY: 153 TIME: 2200

Since spore dispersal is sirnulated above a forest canopy, the fourth phase has beel irnplemented to compute the haif-hourly ktions of the total arnount of spores discharge( that are escaping above the forest edge on which the wind is blowing (subroutine ESCA) Results are saved in the output file ESCAPE.OUT while this message is visible on thr screen:

FOURTH PHASE: OPEM is now computing the amount of spores escaping abow th€ forest height; Please wait. ..

DAY: 153 TIME: 2200

6. Outuuts of the model

Following the fourth phase, OPEM displays a menu on the screen:

At Mis point, you may mt to see:

(1) The map of man spore concentrations for the entire peciod analyzed;

(2) The map of total spore concentrations for the entire period analyzed;

(3) The map of maximum half-hourly spore concentrations;

(4) The map of a percentile of daily mean spore concentrations for the entire period analyzed;

(5) The meteorological and spore release statistics of the entire period analyzed ;

(6) Leave this menu;

The first option displays a map of mean spore concentrations for several locations surrounding the treated area. With this option, the mean spore concentration at one precise location is merely calculated by dividing the added half-hourly mean spore concentrations of the whole period analyzed by the related number of half-hours. When choosing this option, and as for the three next ones (2,3 and 4), the foilowing message appears on the screen:

Up tm which distance downwind do you want to visualize the concentration of spores at forest canopy height (1 to 20 km)?

This sep ailows the user io visualize spore concentrations in the air up to 20 kilornetres downwind the treated site, i.e. up to a critical distance beyong which the Gaussian P!ume Model does not descnbe satisfmorily the dispersion of particles. The second option displays the added half-hourly mean spore concentrations at the same locations downwind. Aithough not very useful to perform a risk anaiysis since this is a fiequency-independent option, the values calculated give an idea of the total exposure of trees to spores of C. purpureum for the whole period andyzed. The third option gives the simulated maximum haif-hourly mean spore ckcentratio& in the air en~&tcredat precises locations for the entire period under investigation. This option is indeed the most severe parameter to perform a risk analysis. The fourth option yields any percentile of the daily mean spore concentrations at given locations. This is the parmeter retained in many studies to perfonn a risk assessment. When choosing this option, the next message appears: mich percentile of the daily mean of half-hourly spore concentrations do you wish to use (9-g. go)?

The due entered (e.g 98, which should always be less than 100) serves to calculate the spore concentration that is computed fiom a cumulative frequency (occurrence) of daily mean spore concentrations and for which a lower daily mean spore concentration was computed, in this example, in 98% of the days. Seeing the particular biological aspect of the risk analysis, it is believed that a percentile equal or above 90 is adequate as a cntical value for perforrning a risk assessrnent When choosing one of these four options, the user sees the next message:

FIFTEI PHASE : OPEM is nov applying the Gaussian Plume Mode1 in the computational domain; Please, wait.. .

DAY: 153 TIME: 2200

This phase is the last one before displaying a grid map of spore concentrations on the screen. Depending on the distance entered, the size of meteorological &ta set and the type of processor instdled in the computer, this phase may take one to several minutes to be completed. A grid map then appean (here, a durnmy distance of 10 km and the 90' percentile are used): GRID MAP OF 90TH PERCENTILE OF SPORES CONCENTRATIONS (Spres/m3)

North is up the screen. Biacontrol site is in the middïe O£ the map.

Distance between each row and column: 2000. metres

Area covered by this map: 400.0 Lm2

Press ENTER to go back to the menu...

The treated area is represented by the value O in the middIe of the map. In this example, it is located on the left handside of number 23. The value of one spore per cubic metre in the right col- is located at 11.3' hmNorth and, therefore, is located at d(l0h)' + (2k1n)~= 10.2 km 6.om the treated area The concentration of 23 spores/m3 is perdicted at 2 km fiom the site. When pressing the ENTER key, OPEM goes back to the 1st menu where the user rnay choose another option.

The fifi option yieids generai results and sratistics about amospheric conditions and sporuiations that prevailed during the whole period analyzed, and about the area treated with the biosilvicide. When choosing the sixth option, the user may reset al1 variables and perform another environmentai fate (dispersd) analysis by answering positively to the question:

Da you want to use OPEM again (y/n)?

7,Description of the out~utfiles

In addition to the results displayed on the screen when perfoning a simulation, OPEM mates and loads nine output files under the directory C:\ FORTRAM OUT.These are:

METEO.OUT : Re-formatted meteorological data file. The data are in the following order (1) Julian &y, (2) time, (3) averaged ground level air temperature CC), (4) air temperature at 2 metres above ground level (OC), (5) air temperature at 7 rnetres above ground level (OC), (6) air temperature at 15 metres above ground leveI CC), relative humidity (%), (8) global radiation (W/m2), (9) net radiation (w/mZ),(10) wind speed at 15 metres above ground level (mls), (1 1) maximum wind speed at 15 rnetres above ground level, (12) wind direction at 15 metres above ground level (degrees), (13) standard deviation of wind direction at 15 metres above ground level (degrees), (14) wind speed at 7 metres above ground leveI (mis), (15) wind direction at 7 metres above ground level (degrees), (16) standard deviation of wind direction at 7 metres above ground level (degrees) and (17) rainfaU (mm).

FHEI.OUT :Number of forest edges and their respective height (metres).

FSIDE.OUT : (1) Juiian &y, (2) time, (3) windspeed at forest height (mis), (4) forest sides on which the wind is blowing and their respective height (metres).

PWTND.OUT : Frequency of wind direction (in wind sectors of ten degrees according to North) for the whole period analyzed The first value is the fiequency of wind direction in the 0-10 degrees sector while the last is the one but in the 350-360 degrees sector.

PBLPAROUT : Processed planetary boundary-Iayer parameters: (1) Juiian day, (2) time, (3) number of iterations required to reach an accuracy of 1%, (4) Monin-Obukhov length (metres), (5) sensible heat flux (IV/m2), (6) fiction veIocity (ds), (7) mixing depth (metres) and convective velocity scale (ds).

SPORE.OUT : Total number of spores emitied fiom basidiocarps for each haIf-hour.

ESCAPE.OUT : Total number of spores escaping above forest height for each half-hour.

MEANT.OUT: Daily and nightly mean air temperature at gound Ievel (OC).

MIXING-OUT : Frequency of mixing depth. The first value in this database is the fiequency of zi lower than 100 metres for the entire period analyzed, the second is the one for 1 that are incIuded between 100 metres and 200 metres, and so on, until3000 metres.

8. MeteoroloPical instruments and data storage

The meteoroIogica1 instruments used to record data were al1 purchased fiom Campbell Scientific Inc. and were disposed on a 15 metre mast erected in the forest clearing. These were:

Five air temperature probes (type 107) - three at ground Ievel, one at 7 metres above ground level and one at 15 metres above ground level; One HMP35C (relative humidity and air temperature) sensor - futed at two rnetres above ground level; Two wind monitors (R.M. Young Inc.) - one at 7 metres above ground level and one at 15 metres above ground level; One 46 net radiometer - fmed at 1.3 metres above ground level; One LUOOS psychometer &&COR) - fmed at 15 metres above ground level; One tiping bucket pluviorneter - hxed at 1.3 metres above ground level;

A CR10 datalogger/controler was used to operate the system and to store the data in ; memory module. A solar panel was also used to recharge a 12 volts baîtery which wa feeding the datalogger. Data were dowdoaded in the file DATADAT on a 1.4 Mb diskett~ using a CRIO-KD keyboard in conjuction with the software PC208. This software was aIsc used to format the data in colirmns, ready to be read by OPEM. A copy of the recording codl may be available fiom the authors upon request.

9. Descri~tionof the aropram. subroutines and fnnctions

OPEM is fed by the rneteorological &ta fiIe DATADAT as well as by inputs provided b! the user. The rnmeof the program @WIN, see below) calls 12 suroutines as shown ii Figure 1. These subroutines cd22 other functioas and subroutines. Each subroutine/functioi has a defined purpose, in most cases apparent in its name, that is detailed in the source codi (OPEM-FOR)just before the subroutine/fÜnction calls and in the subroutine/function body a well. What follows is a descriptron of each subroutinelfunction in OPEM:

MAIN Reads the rneteorological data file DATA-DAT, creates the output file METEO.OUT and MEANT.OUT and calls the foIlowing subroutines.

Subroutine GMH Displays a graphical introduction message on the screen.

Subroutine MENU DispIays a menu on the screen.

Subroutine INPUT Allows the user to choose among default or new parameters (size of treated area, forest height, etc.) to perform a spore dispersal simulation.

Subroutine CARAC Determines a few physical characteristics of the treated area

Subroutine DRAWTA Retums a drawing (aerial view) of the treated area to the screen.

Subroutine FILE Opens several input and output files.

Subroutine BLOW Identifies the forest edge(s) on which the wind is blowing for every haIf-hour in order to compute windspeed at forest height. This is done by calling two subroutines: SCANX and SCANY. Creates the output file FSlDE.OUT. Subroutine SCANX Scans the treated area on the X axis (East to West) to hdthe forest edge(s) on which the wind is blowing.

Subroutine SCANY Scans the treated area on the Y axis (North to South) to hdthi forest edge(s) on which the wind is blowing.

Subroutine SPO Computes the sporuiation be calling three subroutines: CHECK CARPS and SPORU.

Subroutine CHECK Verifies at which Jdian day the appearance and disappearance of basidiocarps occurs.

Subroutine CARPS Cornputes the number of basidiocarps per stub per tree species.

Subroutine SPORU Computes the number of spores emitted from basidiocarps eacl half-hour. Creates the output file SPORE.OUT

Subroutine AERO Computes some aerodynamc properties of the treated area

Subroutine PRO Processes the rneteorological data to cornpute boundary-layer turbulent parameters by calling the subroutine MONI and the function RICH. Downioads the parameters into the output fiIe PBLPAItOuT.

Subroutine MONI Cornputes iteratively hourly values of the Monin-Obukhov length, the friction velocity, the sensible heat flux, the convective velocity scale and the mixing depth.

Subroutine RSUBS Computes the surface resistance to evaporation.

Subroutine PSYH Cornputes the value of Yh(universal bction).

Subroutine PSYM Cornputes the value of Y, (universal fùnction).

Subroutine ESCA Cornputes the fraction of spores emitted escaping above forest height every half-hour. Creates the output file ESCAPE-OUT.

Subroutine GAUSS Asks the user to enter the type of outputs wanted by caIling several subroutines (GPM, MEANC, CONC, MAX, PER and STAT).

Subroutine GPM Cornputes Gaussian plumes every half-hour by calhg several subroutines (SIGMAZ, SIGMAY, PLUME)and fimctions (RICH and DIRFOH). Subroutine SiGW Computes the vertical dispersion parameter a;,

Subroutine SIGMAY Computes the lateral dispersion parameter q. using onsite obsewaiions of Iateral wind direction fluctuations and by calling the function FY.

Function FY Returns the value of Fy (universal function).

Subroutine PLUME Cornputes the haif-houriy mean spore concentrahon in a plume using the Gaussian Plume Mode1 scheme.

Subroutine WC Displays a map of mean half-hourly spore concentrations at severaI locations around the treated ara

Subroutine CONC DispIays a map of cumulative half-hourIy spore concentrations at severai locations around the treated area.

Subroutine MAX Displays a map of the maximal half-hourly spore concentrations at several locations around the treated area.

Subroutine PER Displays a map of any percentile of daily mean spore concentrations at several locations around the treated area.

Subroutine STAT Computes some statistics about rneteorological data, PBL scalars, spore dispersal and the treated area Creates the output file PWND-OUT.

Function RlCH Computes the highest gradient Richardson number.

Function DDRFOH Extrapolates wind direction at forest height using wind directions at 7 and 15 metres above ground level.

Function SPEED Extrapolates wind speed at forest height using wind speeds recorded at 7 and 15 metres above mound level. INPUT PROGRAM OUTPUT

CHECK cms SPORU

RICH t "'* SIGMAY -FY T GPMTSIGMM PLUME -SPEED DiRFOH iSPEED MEANC CONC MAX

PER STAT - RICH PWIND-OUT

Fig. 1 -Scbrmatic organization of the main program, subroutines and functions in OPEM. Annexe C

Code informatique d'acquisition de données météorologiques PROGRAMME D'ACQUISITION DE DONNEES AVEC CR10 PROJET DE A. GOULET - C. WRPURmTM, UNIV. LAVAL

COMPREND LE FONCTIONNEMENT DE LA TRAPPE A SPORE SUR UN PROGRAMME DE 6 OU DE 7 JOURS. CETTE ROUTINE EST ACTIVEE OU ARRETEE PAR LE CR10-KD SEULEMENT. POUR UN FONCTIONNEMENT DE 6 JOURS, AU CLAVIER: *6 A D 1 POUR UN FONCTIONNEMENT DE 7 JOURS, AU CLAVIER: *6 A D 2 POUR ARRETER LE FONCTIONNEMBNT DE LA TRAPPE, AU CLAVIER *6 A D 3. NE JAMAIS DEMANDER A LA FOIS UN 6 ET 7 JOURS! POUR VERIFIER LE STATUT DE FONCTIONNEMENT DE LA TRAPPE, AU CLAVIER: *6 A D ; LE KEYBORD AFFICEfE DES ZEROS; SI 1 A GAUCHE = 6 JOURS; SI 2 A GAUCHE = 7 JOURS. DELAIS MAXIMUM DE 1 I4INI;JTE POUR LE DEBUT DE FONCTION DE LA TRAPPE. STATION DE STe. FLORENCE, R. JOBIDON. 95-05-24.

it 1 Table 1 ~&rams 01: 60 Sec. Execution Interval LECTURE A CHAQüE MINUTE DU VENT (vitesse et direction)

01: P91 If Flag/Port INSTRUCTIONS 1 A 8 POUR CR10-KD POUR TRAPPE A SPORES. 01: 13 Do if flag 3 is high 02: 30 Then Do

02: P30 Z=F 01: O F 02: O Exponent of 10 03: 18 Z Loc :

03: P30 Z=F 01: O F 02: O Exponent of 10 03: 19 Z Loc : 04: P86 Do 01: 21 Set low Flag 1

05: P86 Do 01: 22 Set low Flag 2

06: P86 Do 01: 51 Set low Port 1

07: P86 Do 01: 23 Set low Flag 3 08: P95 End

09: P91 If Flag/Port INSTRUCTIONS 9 A 17 POUR TRAPPE 6 JOURS DE FONCTION. SI CETTE INSTRUCTION EST ACTIVEE PAR LE CR10-KD, ALORS LE RELAIS EST MIS EN MARCHE ET LE COURANT VA A TRAPPE. 01: 11 Do if Elag 1 is high 02: 30 Then Do Page 2 Table I

10: P32 Z=Z+1 COMPTEUR DE MINUTES. 01: 18 Z LOC : 11: P86 Do 01: 41 Set high Port 1

12: P95 End 13: P89 1f Xc=>F 01: 18 X Loc 02: 3 >= 03: 8640 F 8640 MINUTES = 6 JOURS. 04: 30 Then Do

14: P86 Do ARRET DE FONTIONNEMENT DE LA TRAPPE. 01: 21 Set low Flag 1

15: P30 Z=F REMISE A ZERO DU COMPTEUR. 01: O F 02: O Exponent of 10 03: 18 Z Loc : 16: P86 Do 01: 51 Set low Port 1

17: P95 End

18: P91 If Flag/Port INSTRUCTIONS 18 A 26 POUR TRAPPE 7 JOURS DE FONCTION. 01: 12 Do if flag 2 is high 02: 30 Then Do

19: P32 Z=Z+l COMPTEüR DE MINUTES. 01: 19 Z LOC :

20: P86 Do 01: 41 Set high Port 1

21: P95 End

22: P89 If X<=>F 01: 19 X Loc 02: 3 >= 03: 10080 F 10080 MINUTES = 7 JOURS. 04: 30 Then Do

23: P86 Do 01: 22 Set low Flag 2 Page 3 Table 1

24: P30 Z=F 01: O F 02: O Exponent of 10 03: 19 Z Loc :

25: P86 Do 01: 51 Set low Port 1

26: P95 End

27: P3 Pulse 2 SONDES DE VITESSE ET DIRECTION DU VENT. BUCK OF RED & BUCK--G BLACK OF GREEN & BLACK--AG GREEN (ler SONDE) --6H. GREEN (2er SONDE) --6L. BUCK--E3 RED (ler SONDE) --PI. RED (2er SONDE) --P2. 01: 2 Reps 02: 1 Pulse Input Chan 03: 21 Low level AC; Output Hz. 04: 13 Zloc : 05: 0.098 Mult 06: O Off set

28: P4 Excite, Delay,Volt (SE) DIRECTION DU VENT. BRANC~S= INSTRUCTION 27. 01: 2 Reps 02: 5 2500 mV slow Range 03: 11 IN Chan 04: 3 Excite al1 reps w/EXchan 3 05: 2 Delay (units .Olsec) 06: 2500 mV Excitation 07: 15 Loc : 08: 0.142 Mult 09: O Offset

29: P92 If time is POUR FAIRE EXECUTER LES INSTRUCTIONS SUIVANTES AUX 15 MINUTES. 01: O minutes into a 02: 15 minute interval 03: 30 Then Do

30: Pl0 Battery Voltage 01: 1 Loc : I 31: Pl7 Module Temperature 01: 2 Loc : Page 4 Table 1

32: Pl1 Temp 107 Probe ler SONDE ROUGE--IH, NOIR--EI, MAUVE--AG, ACIER--G. i 2er SONDE ROUGE--IL, NOIR--EI, ---AG, ACIER--G. !i 3er SONDE ROUGE--2H, NOIR--El, MAUVE--AG, ACIER--G: i 4er SONDE ROUGE--2L. NOIR--El, MAUVE--AG, ACIER--G J \ 5er SONDE ROUGE--3H, NOIR--El, MAUVE--AG, ACIER--G. I 6er SONDE: BRANCHEMENT DE SONDE HMP35C. 01: 6 Reps 02: 1 IN Chan 03: 1 Excite al1 reps w/EXchan 1 04: 3 Loc : 05: 1 Mult 06: O Off set

33: P4 Excite, Delay, Volt (SE) SONDE HMP35C. ROUGE--12V DU CR10. JAUNE--E2. VIOLET--AG. CLEAR--G . VERT--4H (HUKIDITE RELATIVE)- NOIR--El. ORANGE--3L. (TEMPERATURE DE L'AIR) BLANC--AG . 01: 1 ReP 02: 5 2500 mV slow Range 03: 7 IN Chan 04: 2 Excite al1 reps w/EXchan 2 05: 15 Delay (units .Olsec) 06: 2500 mV Excitation 07: 9 LOC : 08: 0.1 Mult 09: O Offset

34: Pl Volt (SE) 1 SONDE LI-COR LI-200S, RADIATION GLOBALE. ROUGE--4L. NOIR--AG. CLEAR--G . 01: 1 ReP 02: 3 25 mV slow Range 03: 8 IN Chan 04: 10 Loc : 05: 0.1300 Mult 06: O Offset Page 5 Table 1

35: PZ Volt (DIFF) 1 SONDE 46, RADIATION NETTE. ROUGE--5H. NOIR--5L. ACIER--G. 01: 1 ReP 02: 4 250 mV slow Range 03: 5 IN Chan INPUT CHANNEL No 5 CAR DIFFERENTIEL. 04: 11 Loc : 05: 13.2 Mult 06: O Offset 36: P95 End TERMINE LA LOUPE DES 15 MINUTES.

37: P92 If the is MOYENNES ET TOTAUX AUX 30 MINUTES. 01: O minutes into a 02: 30 minute interval 03: 10 Set high Flag O (output)

38: P77 Real The 01: 220 Day,Hour-Minute 39: P74 Minimize VOLTAGE MINIMUM. 01: 1 R@=P 02: O Value only 03: 1 Loc

40: P73 Maximize TEMP. MAX. DU CR10. 01: 1 ReP 02: O Value only 03: 2 Loc 41: P71 Average MOYENNES TEMP. AIR, HüMIDITE ET RADIATION(L1-200s + Q6) 01: 9 Reps 02: 3 Loc

42: P73 Maximize VALEURS MAX DE VITESSE DU VENT (2 SONDES) . 01: 2 Reps 02: O Value only 03: 13 Loc 43: P74 Minimize VALrmRS MIN DE VITESSE DU VENT (2 SONDES). 01: 2 Reps 02: O Value only 03: 13 Loc Page 6 Table 1

44: P82 Standard ~eviation ECART-TYPE DE VITESSE DU VENT DU 30 MINUTES (2 SONDES) . 01: 2 Reps 02: 13 Sample Loc 45: P69 Wind Vector ECART-TYPE DE DIRECTION DU VENT DU 30 MINUTES (2 SONOES 01: 2 Reps 02: 30 Samples per sub-interval 03: 00 Polar Sensor/ (S, Dl, SD1) CETTE OPTION DONNE: MEAN HORIZONTAL WIND SPEED UNIT VECTOR MEAN WIND DIRECTION STANDARD DEVIATION OF WIND DIRECTION 04: 13 Wind Speed/East Loc 05: 15 Wind Direction/Nortfi Loc

46: P91 If Flag/Port INSTRUCTIONS 46 A 53 ET TABLE 3 POUR PLWIOMETRE CAR IL MANQUE UN PULSE. (VOIR PAGE 8.6 DU MANUEL) . . BRANCHEMENT DU TE-525: NOIR--C8. BMC ET ACIER--G PLACER RESISTANCE 100 X ENTRE C8 ET 5 VOLTS. SOUDER ENSEMBLE RESISTANCE C8 ET NOIR DE TE-525 POUR MEILLEUR CONTACT (important). 01: 10 Do if flag O (output) is high 02: 30 Then Do 47: P70 Sarnple 01: 1 Reps 02: 12 Loc

48: P30 Z=F 01: O F 02: O Exponent of 10 03: 12 Z Loc :

49: P86 Do 01: 20 Set low Flag O (output) 50: P33 Z=X+Y 01: 17 X Loc 02: 12 Y LOC 03: 12 Z LOC : 51: P30 Z=F 01: O F 02: O Exponent of 10 03: 17 Z Loc : 52: P95 End

53: P96 Serial Output ENREGISTREMENT AU MODULE DE MEMOIRE 01: 71 SM192/SM7 16 Page 7 Table 1

54: P End Table 1

.~t 2 Table 2 Programs 01: 0.0000 Sec. Execution Intenral

End Table 2

Table 3 Subroutines Beginning of Subroutine Subrout ine Number If Flag/Port Do if flag O (output) is high -Then -- Do Z=X+F X LOC F Z Loc :

Z=X+F X Loc F z Loc : End Excitation with Delay EX Chan Delay w/EX (units=. Olsec) Delay after EX (units=.Olsec) mV Excitation End

End Table 3

Mode 10 Memory Allocation Input Locations

Intermediate- Locations SI ERREUR 4, AJOUTER AVEC LE CR10-KD DE LA MEMOIRE INTERMEDIAIRE (PORTER A 100 OU A 150) 03: 0.0000 Final Storage Area 2

* C Mode 12 Security 01: 0000 WCK 1 02: 0000 mcx 2 03: 0000 LOCK 3 Page '8 Input Location Assignments (with comments) :

Key : =able Number E=Entry Number >Location Number T: E: L: 1: 30: 1: Loc : 1: 31: 2: Loc : 1: 32: 3: Loc : 1: 33: 9: Loc: 1: 34: 10: Loc : 1: 35: 1 Loc : 1: 48: 12: Z Loc : 1: 50: 12: Z Loc : 3: 5: 12: z Loc : 1: 27: 13: Loc: 1: 28: 15: Loc : 1 51: 7 Z Loc : 3: 3: 17: Z Loc : 1: 2: 18: Z Loc : 1 O: 18: Z LOC : 1: 5 8 ZLoc: 1: 3: 19: Z Lac : 1:9 ZLoc: 1: 24: 19: Z Loc : TEST TARGET (QA-3)

APPLIED IMAGE. lnc 1653 Easl Main Street ,-*- Rochester. NY 14609 USA ,---- Phone: 7161482-0300 --- Far 71ôi2ûE.5989