Posted on Authorea 21 Jan 2020 — CC BY 4.0 — https://doi.org/10.22541/au.157963812.23974358 — This a preprint and has not been peer reviewed. Data may be preliminary. en Nord´enJenni scales spatial different at trends projecting population for models colonisation-extinction versus Occupancy ag-cl ouainted fe eutfo eaouaindnmc,truhtelclpoessof processes local landscapes, the fragmented through In dynamics, 2007). metapopulation pervasive al. the from et mitigate result (Pressey to change often actions climate trends planning and when population fragmentation population necessary large-scale in loss, also aim habitat key is a of knowledge is them effects Such behind drivers 2003). the (Turchin and trends ecology population temporal and spatial Understanding Introduction should applied quality models habitat the units, Further, for resource suitable projections conducted. of conducting is availability and the fitting as species, model such the the dynamics, connectivity. by which spatial metapopulation utilizable at and the resolution is driving on variables resource that strongly key and resolution depended models, include scale resource-unit occupancy occupancy over in spatial finest models increase same the colonisation-extinction future than the using at of ratio encourage process We magnitude projected colonization:extinction The We the resolution. and resource statistics. modelling rate and patches. these colonization scale colonisation-extinction 174 of occupancy, modelling the estimates at lower spatial to higher the with fungi compared with species and models species the occupancy deadwood the for the hierarchicalfor of difference using The survey greater trends repeated with dynamics. positive extensive more colonization-extinction models, an and and probabilities to occurrence We occurrence fitted species data. higher were on deadwood resolution variables applied coarse explanatory or models fine key the Bayesian using colonization- over of and against affected effects scales, models are spatial tested occupancy alternative occupancies on also at based landscape-scale substrates. modelling projections differing the distinct compared conducting with utilize We models, species they extinction assumptions. of and modelling rarity scales occupancies different different an spatial scale by have century different large are species coming at these projected fungi as structured the Deadwood-dependent species are how sessile populations assumptions. tested of We the modelling models dynamics, However, predictive of population different climate. range of spatial and a performance landscapes and changing to the by testing sensitive term able for are long be system the trends to excellent in need affected population further are We future populations ecology. of of population sizes predictions in and aim dynamics key the a how is predict drivers to their and trends population spatiotemporal Understanding Abstract 2020 5, May 7 6 5 4 3 2 1 Kindvall vrgslantbruksuniversitet Sveriges AB Sciences Calluna Agricultural of University Swedish Finland Institute Resources Natural University Newcastle Oslo University Research Uppsala Nature for Institute Norwegian 6 n odSnall Tord and , 1 hlpHarrison Philip , 7 2 oieMair Louise , 1 3 uaSiitonen Juha , 4 nesLundstr¨om Anders , 5 Oskar , Posted on Authorea 21 Jan 2020 — CC BY 4.0 — https://doi.org/10.22541/au.157963812.23974358 — This a preprint and has not been peer reviewed. Data may be preliminary. ao su npeitv clg stesaea hc clgclpoesssol ecniee (Chave considered be should processes ecological which at scale the is of attention. ecology little estimates predictive received while date in 2015), to issue Hernandez fo- have and major have sizes Avalos A development population Rosario or population (del occupancies base future dependence patterns summed their to of distribution future and the better Projections the extinction in be 2015). and changes should al. colonization on local it et cused (Yackulic of and rates conditions 2002), both environmental Hanski incorporate on and that to slow (Ovaskainen models expected with place patterns on be species take predictions occupancy for can that Especially future relationships events sources. occupancy–environment biased dy- dispersal colonisation on produce the colonisation-extinction based re- because of for SDMs locations we influential, current dynamics, models less what the colonization-extinction Under reflect becomes using surveys models 2003). two structure instance dynamics the al. landscape for between occupancy et past 2014), models, (MacKenzie and the occupancy al. change models) dynamic namics, et temporal (aka occupancy (Gimenez the models multiseason acknowledge patterns colonisation-extinction or to as the preferable to generated is here it which fer points processes time the the multiple of over model available connectivity and are area data the When where situations time. structure in over landscape projections decreased current have optimistic the overly habitat than in rather resulting past e.g. the modelling, landscape reflect changing will With 2002). patterns (Sn Hanski distribution slow not and (Ovaskainen may with species landscape patterns species the the distribution sessile of structure, the for structure plants, spatial contrast, and present In fungi species, the to many trends. such reflect due probably For population as structure. e.g. future such new of changes, dynamics, the the projections colonization-extinction structure on to reliable adjust landscape depend produce promptly much the may will to If SDMs distribution assumed 2002). species be the Hanski indeed many operations, and can e.g. varymanagement (Ovaskainen dynamics, pattern to colonization-extinction structure distribution high expected species landscape with the current are species assumption insects, For relationship this and 2015). occupancy-environment of al. birds Violations et disequilibrium, mammals, (Yackulic environment. habitat at space occurrence its with and current as with time landscape the results over equilibrium that the metapopulation biased assume in however, at produce populations data, is the can and pattern species of snapshot (Elith the associate subset to landscape of SDMs fitted a pattern herein. the models across Such projection across occurrence data. for species’ data climate evaluate a and presence/absence we of of which pattern models snapshot spatial Distribution occupancy (static) the Species include single use These to and a is 2009). changes to populations, Leathwick environmental future fit local usually to (SDMs) are responses and data species Models Such predict patches to 2002). habitat method Cain sought. common and be have all A (Higgins must ideally spatially species of solutions should the of other distribution one of thus models range forecasts, and and and of unexpected parameterise lacking rate size basis To or dispersal a the 2013). the adverse as about on al. to used information species et repeatedly be rise on (Guisan to data give actions 1999) overturn may collected conservation (Hanski to decisions and dynamics difficult management metapopulation today’s be realistic of because may effects accurate, that long-term regional and allow consequences the realistic to of extinc- resource time are forecasts local through new populations that frequency the of important and balance colonisations space is to confined in It local Sn are need density 2002, with enough that species Gourbiere high disappearance) species These and have of (Gourbiere unit to trees. persistence persistence need resource dead units the from or These for resulting living units. critical 2009). or as (Kuuluvainen is (stochastic such actions units conservation units tions resource and resource new management ephemeral of by to supply land- replaced production local in largely continuous but are disturbance, A 2011). and processes patterns succession al. connectivity, these through et spatial disappear scapes (Johst and habitat landscapes), these appear suitable forest naturally between of patches in interactions Habitat longevity and structures and creation, tree size and availability, appropriate destruction the or patch as stands of such old processes strongly (e.g. and viability patches metapopulation features that landscape suggest studies on Theoretical depends 1999). l ta.20,Plt ta.20) hsfrssieseis D a eiaporaefrpredictive for inappropriate be may SDM a species, sessile for Thus 2006). al. et Paltto 2004, al. et ¨ all l ta.2003). al. et ¨ all 2 Posted on Authorea 21 Jan 2020 — CC BY 4.0 — https://doi.org/10.22541/au.157963812.23974358 — This a preprint and has not been peer reviewed. Data may be preliminary. fdffrn g rte pce Fg ) h uvypo a ftesz 0mx10madsbiie into subdivided and m 100 x m 20 size the of forests was or plot types survey land The other by 1). surrounded (Fig. is species that tree area and forest or models. homogeneous survey age occupancy and second different the contiguous colonization-extinction revealed for of the a the basis is surveys and in patch the two occupancy first forest formed in A These survey the change first 2014. between of the Finland rate place in central from for) taken them and Data (parameters had southern estimate models. resurveying to across that then data patches events the and forest extinction constituted 174 2013) and in al. dead- fungi colonisation (Nord´en spruce et polypore the surveying 2003-2005 focal by the in dynamics of once colonization-extinction bodies on fruit data and extensive wood large-scale, the obtained We COLLECTION DATA AND PATCHES STUDY methods and occupancy Materials the our explain resolutions. of variables resource-unit part environmental and was regional scales and models spatial the local different which Building at test dynamics resolutions. The colonization-extinction colonization-extinction Sweden. to resource-unit of and and (4) and zone occupancy species specifically scales boreal the model aim, spatial whole two the fourth using different across of simulations at production occupancy stochastic fitted from of through aside models predictions three projections set influence obtained different land on (2) resolution were in (two and based at and projections resolutions scale made land data how resource-unit were production out two the Inferences forest find in (3) trends. to modelling and population included) between patch), be future occupancies to colonization- of or species. deadwood future common slower plot for than have (cell, projected diameters delay minimum scales to greater in a modelling expected differences with spatial be for landscapes different forest can test in species further changes in track We rare difference species therefore a the because and for that rates greater occupancy hypothesise extinction is We models landscape-scale occupancy). realistic colonisation-extinction (of lower more and changes occupancy be with of between to rate occupancy expected future on colonisation-extinction are projected focus by and models the they revealed and trends Colonisation-extinction as ones available the scarce. changes the are how currently predicting from out models are for find differ occupancy data to reveal colonisation-extinction which for important models the for suitable is occupancy while models it data that areas, of time As geographical change time. in occupancies many of in and point future magnitudes points species one the many two colonisation-extinction from in for from versus used data differences data frequently occupancy on for on based based test using are are to when models was models occupancy aim occupancy used landscape-scale first The seldom Bader different The models. are and study. with (Zotz they our species dynamics if with (1) population survey aims its the four influence were from significantly There units not 2015). heterogeneity resource do al. within-habitat therefore small and et the they the the species Loos if in cover focal exclude 2011, and the them would to for species including that justifiable general the abundant, design is is by are that covered survey It units relationship area a type. occupancy-environment resource survey attain the habitat the of about to and kinds information difficult diameter), particular give and deadwood it or the minimum make trees small or (e.g. living may size If as included minimum survey 2011). such the be patch, Bader i.e. to habitat resolution, and the unit resource (Zotz in resource between unit trade-off the accurately resource a of particular involve relatively al. types a often et be surveys to (Guisan field restricted can species are deadwood, of that or that type species systems the of on for studies has strongly In especially performance depends quantity effect model size, and this SDM grain quality of 2015). strength chosen resource al. and 2007). in the et direction (Mouquet heterogeneities the on undetected but local modelling depend modelled, go spatial scale, When to will different modelling at question shown 1999). data spatial in (Le´on-Cort´esbeen al. to species a conclusions et fit the large different models for too from relevant drastically made at to Predictions performed 2015). lead is al. can et scales Mouquet 2013, modelling al. et Evans 2013, 3 Posted on Authorea 21 Jan 2020 — CC BY 4.0 — https://doi.org/10.22541/au.157963812.23974358 — This a preprint and has not been peer reviewed. Data may be preliminary. vns seilyi h la-u ace/lt/el Tbe1.Tecerctfrssls lotalo their of place. all took almost colonisations lost few forests very clear-cut and The surveys, extinction two 1). several (Table the and patches/plots/cells between patches/plots/cells, clear-cut occurrences mature the the in in especially especially events, events, colonization several observed We EVENTS EXTINCTION AND COLONISATION an given were 26-63 ages in with Results species the those for or 2017). suitable spruce deadwood al. dead spruce et of no (Mair param- absence range with the typical age of plots the this distribution of NFI of posterior because forests All joint zero, the of models. from probability values fitted occupancy 1000 the drawing model on from occupancy based eters fitted made final were NFI the projections the step. For models, All time on values. occupancy each parameter dynamics on for 2010. estimated based extinction used here its projections and with instead step, the making was model time colonisation species, of colonisation-extinction first subsequent effect polypore fitted the the final the each in investigating the For simulate we states using Next to occupancy 2110 period. steps S1). the until time plots initialise Appendix time same to 2015; 10-year the utilised al. Forest used for was et We National species model Scenario Eriksson Forestry the occupancy on 2015, nationwide of fitted conditions al. the final dynamics et 2110, forest occupancy (Claesson and of the Agency 2010 Forest projected projections between Swedish Sweden available the boreal by utilized adjacent Analysis we in plots questions, (NFI) study Inventory our answer To CENTURY COMING National THE and Swedish OCCUPANCIES data the using POLYPORE The in fit PROJECTING and 2005). archived were are deviance al. models analyses et in The (Sturtz statistical reduction R2OpenBUGS and (2007). library 0, simulations, Hill the models, Service, with through and Data for intervals R Gelman used in credible by code selection. 2009) 95% suggested computer model al. as of stepwise et forwards overlap species, (Lunn with the OpenBUGS on determined on based were knowledge models was biological fitted selection final model the estimated. in were This retain parameters) to rate probability in covariates the colonisation provided The the (i.e. is and intercepts probability level covariates only extinction of cell patches, the effects For clear-cut the the including patches. of at allowed mature recorded of models extinctions probability and colonisation-extinction colonisation colonisations on and of the number occupancy The from the S1. data Appendix of to description models detailed occupancy A fitted For also S1). we (Appendix hypothesised we species models, that survey. focal colonisation-extinction scales the first the modelling of [?]5 with dynamics spatial (diameter comparison different colonization-extinction resolutions for and the resource collected occupancies deadwood covariates two the included and explain We patch) would cm). and species plot the [?]15 of (cell, or data scales cm presence-absence the modelling to spatial models three state-space Bayesian at hierarchical fitted we species, each For species. cells). COLONISATION-EXTINCTION focal 5 the AND x ( and OCCUPANCY 65 collection species MODELLING data plots, fungal the two 65 woodland of the patches, cells), description surveyed (65 detailed 5 we forests a x 2014), managed for 16 and S1 and plot, (2003-2005 Appendix cells); a surveys 5 had both which x in of ferrugineofuscus 56 patch, 16 plots, forest patches, each 56 ( (53 In spruce patches, trees Norway (56 by retention dominated habitats with were key patches clear-cuts All types: m. 20 forest x of m 20 of cells survey https://snd.gu.se/en and .viticola P. n edodbt nec eladi h eann ac ra See area. patch remaining the in and cell each in both deadwood and ) . 4 ie abies Picea n oee range a covered and ) Phellinus Posted on Authorea 21 Jan 2020 — CC BY 4.0 — https://doi.org/10.22541/au.157963812.23974358 — This a preprint and has not been peer reviewed. Data may be preliminary. h antd fftr nraei cuac eeddsrnl ntesailmdligsae(el plot, (cell, scale modelling spatial the on strongly depended the occupancy of S1. in combination Appendix increase fitted see a future the links, from of of these covariates magnitude resulted of the The species description of forecasts a polypore For the two and S2-1). 1) the (Table (Table of models rates colonization-extinction occupancies or the occupancies of general forecasts differing for probabilities The occurrence higher predicted) predicted We (where S3-1-4). declines the Figs. on than for 2, depended and Table compensate patches 3, for could scales forest and viticola set-asides connectivity 2 the modelling P. the all and (Figs. in spatial across land age trends production occurrence all positive stand the in in across the change deadwood, which relative set-asides of to The the degree volume S4-2). the and in and colonisation-extinction density S4-1 increase the increasing (Figs. an using set-asides to when projected owing than we resolutions, C-D) species resource 3 model C-D, both 2 A-B). For trajecto- 3 (Figs. projection A-B, an the models 2 of scale among occupancy (Figs. modelling probability variation the models spatial larger the and using was resolution species, when there resource chosen Specifically, model ries from the the ranged both models. to using but For sensitive colonisation-extinction models when more than occupancy were land S3-1-4). the models on production Occupancy based Figs. in models. projections for colonization-extinction 2, the decrease 0.96 for Table the smaller to unity species was 3, than predicted 0.59 patches model and models the forest our all 2 was occupancy over oc- of this increase the (Figs. the probability for using model on reason general higher occupancy based main The was The projections land the forest models. models. from all colonisation-extinction changes across the relative increasing than and probabilities models occurrence cupancy higher predicted We dispersal PROJECTIONS selected mean was FUTURE logs a of to density the corresponds whether for that on models influence distance an the a mean had in resolution within a not resource forests to or Deadwood old corresponds km. in that 10 spruce of distance distance of a For for presence/absence within connectivity km. forests or of spruce 1 spruce measure and years) scales, fitting of plot best ([?]120 distance and The old cell dispersal the scale. of at patch presence/absence connectivity the and the at logs) was logs small For spruce many of reflecting required. density (here hence were logs the and plot-scale selection S2-1) spruce (Table the of covariates model S2- two density whereas (Table of or the connectivity logs), one rounds by or of large two age effects of or the stand estimating one presence by allowed available responses only the data patch-scale reflecting of the amount (here and The scale age, 1). cell stand by the explained at were logs responses spruce of volume the For MODELS surveys. FITTED both host in OF from recorded both SUMMARIES were with logs resulted suitable Extinctions decreased modelling where rates. rate stochastically spatial extinction and largest extinction modelling lowest decomposition the and spatial the to resolution, and due largest increased resource rates disappearing the rate coarse colonisation logs at highest the colonization the at resolution the had Similarly, resource resolution, scale scale. coarse resource modelling the fine spatial and increasing for the scale At rates modelling extinction spatial scale. highest smallest the the observed at We 1). species, (Table ferrugineofuscus, resolutions frequent resource less both viticola the landscape, and than the scales colonization/extinction in modelling of occupancy highest ratios the higher with species The .ferrugineofuscus P. .ferrugineofuscus P. ttefiersuc eouin(?5c)adtesals pta oeln cl 2 0m.For m). 20 x (20 scale modelling spatial smallest the and cm) ([?]5 resolution resource fine the at hnfor than .ferrugineofuscus P. h ooiainadetnto ae eecmaal ewe h n eoreresolution resource fine the between comparable were rates extinction and colonisation the .ferrugineofuscus P. h epne rbblte focrec n ooiain–wr xlie by explained were – colonisation and occurrence of probabilities – responses the , .viticola. P. costetotpso oes pta oeln clsadrsuc resolutions. resource and scales modelling spatial models, of types two the across .viticola P. n rm08 o1for 1 to 0.88 from and n nices nftr cuac a oelkl for likely more was occupancy future in increase an and , w esrso onciiywr motn:tevlm of volume the important: were connectivity of measures two , 5 .viticola P. .viticola P. .viticola, P. ae ntecolonisation-extinction the on based a ihrclnzto ae and rates colonization higher had , h aersosswr explained were responses same the .ferrugineofuscus P. .ferrugineofuscus P. talspatial all at .viticola P. P. P. Posted on Authorea 21 Jan 2020 — CC BY 4.0 — https://doi.org/10.22541/au.157963812.23974358 — This a preprint and has not been peer reviewed. Data may be preliminary. oteclnsto-xicinmdl,teocpnymdl rdce ihrocrec rbblte and probabilities occurrence higher knowledge predicted Compared on models decades. based occupancy recent the is the during conclusion species models, focal Our colonisation-extinction the of the development. development predicted to population 2010), population and Miller system positive and study (Franklin unrealistically the SDMs about is applied believe frequently we the to what corresponding models, occupancy The REALISTIC MORE PREDICTIONS PRODUCE conducted. MODELS is same fitting COLONISATION-EXTINCTION the model for the projections which colonisation-extinction conducting and at of species, resolution use the resource by the and process utilizable encourage the is scale We modelling that spatial SDMs), resolution considerable. Models, resource-unit Distribution finest also Species the generally, was effect at more fitting the (or vs. models while model (occupancy occupancy change, of over model predicted models species, scale of frequently the Type model that spatial of data. species our the magnitude resolution For the the of coarse for affected modelling data. the especially substantially from resource-unit of predictions excluded model) the were type the colonisation-extinction colonization-extinction that of on the deadwood resolution their impact smaller to the strong affects the – a and uses which had fitting addressed species the resolution model questions resource-unit on model of show four the scale Based the we all spatial of data, to the 2013). use occupancy projection sensitive rates, landscape-scale to forest were al. the realistic predicted models et with trends performed, which (Evans colonisation- combined future performed outset extensive fungi, the an be the to deadwood-dependent that fitted should from on models modelling clear Bayesian dataset hierarchical the seldom extinction from resolution distribution is parameters and it posterior scale joint systems what ecological at for and predictions making When Discussion S3-4). occupancy Fig. The the 2, in resolution. Table than resource 3; models fine colonisation-extinction the the (Fig. coarse at in zero the modelling pronounced was less when at models. was decline occupancy for modelled resolution of future we resource models in probability of when occupancy increase effect the only the greatest land resolution the on production fine predicted based the We the stable in Projections at more resolution. decline were resolution; trends fine [?]0.99) S3-2). resource the (probability the resolution certain Fig. fine used the For almost 2, that for an Table resolution; models units. resource 2; coarse occupancy deadwood the on on (Fig. resolution based based for models small the 0.38) models the for was occupancy and only for the prediction (0.47 on account the probable based not less of Projections did but precision we types) the when model and land For predictions. cm), production S3-3-4). ferrugineofuscus future ([?]15 the P. Figs. the in resolution 2, (Table on decline coarse resolution impact a the coarse great the with a for than higher had cm) included) ([?]5 deadwood versus resolution large (0.08 which only models increase, plot-scale or of For probability the (all 1-0.08=0.92 than resolution. resolution a cell- resource Resource means the fine also the using decline by lower of detected much probability thus was 0.08 is S3-1-4). land the projections Figs. here production for the 2, However, the (Table model when 0.66). in model colonisation-extinction species cell-scale cm) decline model the ([?]5 a both using resolution of than the for resource models that higher fine patch-scale were was the or rates land Applying plot- extinction forest the and rates spatial using all the extinction colonization all conducted overall across at the at were for lowest these increase common true were of the future events was consequence were of extinction opposite The rates and probability the rare colonisation 1). while were (Table forests, scales, events scales patch colonisation mature modelling to forests, in clear-cut plot species In via model up 1). (Table going both increased For and 3). scale cell and 2 (Figs. patch) .viticola P. epeitdceryamr oiieftr ouaindvlpetwt h n resource fine the with development population future positive more a clearly predicted we , uuedciei rdcinln emdcranbsdo h orerslto (both resolution coarse the on based certain seemed land production in decline future , .ferrugineofuscus P. 6 .viticola P. hwdadciei h rdcinland production the in decline a showed epeitd rbbyerroneously, probably predicted, we .viticola P. .viticola P. iial showed similarly h probability the , Posted on Authorea 21 Jan 2020 — CC BY 4.0 — https://doi.org/10.22541/au.157963812.23974358 — This a preprint and has not been peer reviewed. Data may be preliminary. orlto ewe h itlpeitr n h eore hyrpae oevr fteei isi this in bias is strong there a if assumes projections. Moreover, it biased as into replace. analyses, transferred of they the is use these resources into bias The – the uncertainty this selected 2014). of and then are al. level correlation, predictors age) et higher assumed distal other (Merow stand a the replace the bring (e.g. they between On may more predictors predictors correlation predictors proximal distal be acceptable. the distal more may is than more scale, indirectly patch) resolution the spatial more conducting or spatial Thus, larger species aggregated plot the a region. more affect (here or at landscape a scale modelling a (here at when larger concerns simulations patch hand, study a the each projection at of and in question simulation general modelling slow logs projection the with of if and chosen species especially proportion fitting appropriate, the rare model small for across a species, However, dynamics such on population patches. occurrences between and few including dispersal deadwood especially and including place, complete projections dynamics ideally take of making required, colonization-extinction simulation dynamics for is of Moreover, region, population recommendation scale or this required. local spatial However, power landscape the computational patches. a which the among for and ignores at patch dynamics level modelling each detailed allows within the simulating This variables We at scale. proximal projections. spatial for dynamics the the small accounting in of a the applied were at scale models simulation projecting three spatial and the and fitting of chosen which model on the conducting depending recommend on 42%, generally to frequent strongly 0% from less depend ranged the 2110 development For year population fitting. future model the statistical of predictions The FIT- MODEL OF TING SCALE SPATIAL environment. APPROPRIATE the OF with The CONSIDERATIONS assume equilibrium turnover. models a metapopulation deadwood occupancy at with and while is environment stand change, pattern its forest occurrence temporal of in current the rate changes the for highest the that the explicitly for tracking with account so thus large forest models lower especially is production colonisation-extinction very project is species the to deadwood- This in The models of especially confined colonization-extinction delay, models. colonization/extinction. species not Despite the occupancy of many thought. are the as ratio in previously than lower which assumed than that occurrence shorter be species suggest species is cannot focal change also future turnover environmental equilibrium results our to high between metapopulation our response of The equilibrium However, in this, ecology new delay 2014). the a the trees. the fungi, before dead by al. challenges dependent years, decomposing et which explained hundred (Sverdrup-Thygeson slowly years, partly over reached or much 9-11 be is to just may environment decades during its rate high. from and place surprisingly lags, were metapopulation taken time study the long had this very in survey extinctions scales of another and plot making view and place. colonisations of patch take costs local the to scale the at Several changes spatial observed be the for the rates may necessary model to colonization-extinction span use mechanistically sensitive The time their less more the were of they and they Limitations that system, why realism, the resolution. explain higher of also resource may Their the the pattern, and which another. rate occupancy modelled during the to the reflect period to step they time reflect leading of time because long connectivity process models realistic one a more and over occupancy from are amount collected The change models current data of colonisation-extinction use the 2004). The have often than change. SDMs al. species rather may occupancy et these past environment more, (Pilgrim the of Even rarity reflect Many habitat. which with the increasing. patterns associated is land consequently distribution often amount forest are species habitat is the the sources if which all especially dispersal history across models, increases to life positive occupancy slow distances more with the to underestimated and leading thus decreasing land are production declines Future the in combined. declines future steep less .ferrugineofuscus P. ,smltn eald ml-cl edoddnmc a eiecet For inefficient. be may dynamics deadwood small-scale detailed, simulating ), .ferrugineofuscus P. 7 h rdce ouainices ythe by increase population predicted the .ferrugineofuscus P. iha with Posted on Authorea 21 Jan 2020 — CC BY 4.0 — https://doi.org/10.22541/au.157963812.23974358 — This a preprint and has not been peer reviewed. Data may be preliminary. lesn . .Dvm,A udto,adP .Wkeg 05 oetipc nlss21 K 15 SKA - 2015 analysis impact Forest Ecology Agency. 2015. Forest 20years? Swedish Wikberg. in . E. learned P. 2015). we and SKA have Lundstrom, - what konsekvensanalyser A. (Skogliga Duvemo, ecology: K. in S., scale Claesson, and pattern of problem The Letters 2013. J. Chave, true the cited detect may Literature we connectivity, spatial strong. and of are age) availability our they forest the in if and as bias trends colonisations (e.g. such and of for quality dynamics, patterns sources models habitat metapopulation future potential with these units, Nevertheless, al. The high driving resource et S1. a 2011). variables (Dietrich suitable Appendix key on al. change in for dependent et global detailed accounting will more (Johst are of extinctions occupancy persistence identified patches effects in we population habitat the that change of making addressing predictions connectivity of rates, when rate turnover good and e.g. habitat the to number increase of projections, required may future estimation data that the in Inaccurate 2012) fungi, bias (Evans lacking. deadwood-dependent severe species including still to the species, are lead of all models history most essentially life such for in detailed However, parameterise future, the biological were, the 2013). by of into probabilities driven predictions and al. is models, colonisation conditions precise et current Arguably, process-based This the beyond mechanistic probabilities. the extrapolating for elaborate same. occupancy if by require selected the especially the change, produced covariates for environmental usually selected to the occupancies was those responses that future change as same fact the in the of the change cases, direction by of the explained rate models, partly and colonisation-extinction probabilities and occurrence occupancy differing resource the on Despite discussion OCCUPANCY more FUTURE include for OF to S1 is PREDICTION influence Appendix allows, their RELIABLE quantity See as data analyses if age. or option, survey forest Another logs. ecology. the and cm 2015). species from size 5-10 and units al. common substrate resolution resource et very between small the (Loos interaction on the minor the occurs is exclude occasionally dynamics to especially only population justified which in species, on be but common this thus for 2008), very of may case is al. occupancy It the substrate et future be (Jonsson this the (old-growth may overestimate diameter if area This with to on and high-quality likely occur probability) areas. may is a low low-quality it choose one at in that in to then assuming (albeit logs) wise erroneously areas, areas diameter may If lower-quality be low-quality small species these effect. also in a mass may also from the (e.g. example, substrate it resulting For in quality However, such abundance) covariates subordinate analysis. species as 2009). high exploratory of with used initial substrate forest al. availability the a et resource during on (Hottola of quantities analysis change occur measures of deadwood also the resolution the covariate may resource-unit – inventoried, significant – the deadwood the deadwood projections of as of and volume trees) sizes models larger and minimum by density different mostly as With (influenced such volume For availability. deadwood resource reduced. having much of species was this decline for of the els preference them the including species of this when because that while conclusion land, the in production resulted rugineofuscus the units develop- in deadwood population smaller decline future the will of Excluding predictions data. the deadwood resolution on influence For considerable a ment. have can resolution Resource-unit SPECIES STUDY ECOLOGY THE THE OF ON DEPENDS RESOLUTION RESOURCE APPROPRIATE 16 :4-16. .viticola P. h ouainted ae ntecas n n edoddt eemr iia.Ti is This similar. more were data deadwood fine and coarse the on based trends population the h otsrkn ieec ntepoetoswsbtenuigtefieo coarse or fine the using between was projections the in difference striking most the .ferrugineofuscus P. .ferrugineofuscus P. o agrdaee edtesadcneunl h mod- the consequently and trees dead larger-diameter for 8 hs ooiainpoaiiyincreases probability colonization whose .fer- P. Posted on Authorea 21 Jan 2020 — CC BY 4.0 — https://doi.org/10.22541/au.157963812.23974358 — This a preprint and has not been peer reviewed. Data may be preliminary. enCre,J . .J .Cwe,adC .Toa.19.Dtcigdciei omrywidespread formerly a in decline Ecography Detecting icarus? Polyommatus 1999. butterfly Thomas. blue D. common C. the and is Ecosystem Cowley, common R. Natural how J. on species: M. Based Ambio L., Op- Conservation J. Challenge. P. Complexity Leon-Cortes, Biodiversity and The Watzold, and Europe: F. Management Northern Wissel, Forest in S. Dynamics and Vos, 2009. connectivity C. Ecology number, T. Applied between C. Kuuluvainen, of trade-offs Hartig, Journal F. landscapes: patches. Teeffelen, dynamic habitat van in of of conservation A. diversity turnover Biodiversity and J. volume 2011. Ecology A. number, of Drechsler, dam. the Journal M. of communities. measure K., fungal unified Johst, of A response colonization- 2009. the the Hanski. and and I. trees models and dead metapopulation Ovaskainen, plant O. realistic J., Spatially Hottola, Ecology 2002. of What Cain. Journal L. trade-off. 2007. M. York. competition Ecological and New Peterson. I., Press, University S. T. characteristics? Oxford Higgins, A. species’ Ecology. and or Metapopulation Phillips, data, 1999. Techniques, I. S. Hanski, Graham, trees: H. of C. conservation R. occurrences Elith, for M. the Monographs J. distributions Ferrier, predicting S. Zimmermann, species for Austin, E. Predicting matters M. N. J. 2013. Broennimann, A., T. Maggini, O. Guisan, Tulloch, Buckley. R. Wintle, T. M. A. Rhodes, I. Y. Letters B. R. Ecology Schwartz, A. and J. W. decisions. Possingham, Martin, Sutcliffe, M. P. G. R. Elith, H. P. T. Mantyka-Pringle, J. Kearney, Naujokaitis-Lewis, C. Setterfield, I. McDonald-Madden, A. 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Andean J. range-restricted and of V., Avalos, Rosario del 77 :615-630. 16 :1424-1435. 217 280 :351-368. . 90 :616-626. 10 . 9 48 :1227-1235. 38 :309-315. 97 :1320-1328. 22 :643-650. 4 :459-469. 232 Posted on Authorea 21 Jan 2020 — CC BY 4.0 — https://doi.org/10.22541/au.157963812.23974358 — This a preprint and has not been peer reviewed. Data may be preliminary. aklc .B,J .Ncos .Ri,adR e.21.T rdc h ih,mdlclnzto and colonization model niche, the predict To 2015. Der. R. and Reid, J. Nichols, Ecology D. extinction. J. B., C. University Yackulic, Princeton Synthesis. Theoretical/Empirical A Princeton. Dynamics. Conservation Press, Population and Complex Biodiversity 2003. R. conser- P. perspectives. for Turchin, from relevant and scales WinBUGS status temporal current running and for Spatial species: :513-535. package associated 2014. A dead-wood Kouki. J. of R2WinBUGS: and vation Gustafsson, 2005. L. A., Sverdrup-Thygeson, Gelman. A. 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J., Loos, 27 120 24 :757-766. :33-46. :161-170. 96 7 :16-23. :368-378. 12 61 :1-16. :285-295. 28 :3049-3067. 103 22 10 :583-592. :566-578. 5 :99-110. 52 :1293-1310. 84 133 :2200-2207. 101 :442-454. :701- 23 Posted on Authorea 21 Jan 2020 — CC BY 4.0 — https://doi.org/10.22541/au.157963812.23974358 — This a preprint and has not been peer reviewed. Data may be preliminary. 10bsdo 00smltosfo h ulpseirdsrbtoso h te oes hw r also are Shown models. fitted the of distributions posterior full the from simulations 1000 on based 2110 2. Table cola viti- P. cus fus- neo- rugi- fer- P. Species was patch observed the the events in that of occupied number units means are modelling ‘10’ of Rates proportion of second. the history the is occupancy not a and but patch whereas possible event survey. the events survey event, second that of first means number survey the the ‘11’ each by at of divided at occupied history A be occupied to resolutions. be observed resource to and scales observed modelling was spatial varying community the and across richness species 1. - Table diversity epiphyte vascular Sampling 2011. Bader. Ecotropica Y. structure. M. and G., Zotz, auecl 0383 .809 .90.08 0.09 0.91 0.08 30 0.04 338 0.05 10 1.00 cut 1 clear- 0.05 5 cell 17 mature 357 cut 5 clear- 0 5 cell mature class Age ubr eoddfrec yeo ooiainetnto itr o ieetfrs g classes age forest different for history colonisation-extinction of type each for recorded Numbers encag nocpnyars l oetln n npouto adbten22 and 2020 between land production in and land forest all across occupancy in change Mean ac 5281 .008 .30.13 0.06 0.13 0.04 0.80 0.02 0.88 0.29 0.10 0.02 0.22 0.04 0.53 0.92 0.31 0.46 0.02 0.67 0.10 0.25 2 0.03 0.21 1 0.06 0.00 18 0.67 0.00 26 2 0.83 0.22 0.04 20 0.00 8 0.14 0.00 18 124 7 0.32 1.00 0.16 61 2 69 11 0.64 0.00 1 0.80 6 0.20 15 1 6 1 0.13 5 0 patch 7 23 plot 3 5 29 0 15 17 cell 4 5 12 127 patch 5 66 plot 2 79 10 1 7 15 4 0 5 patch 4 plot 1 5 15 cell 5 patch plot scale elling mod- Spatial 17 :103-112. 5161 .808 .90.10 0.02 0.09 0.00 0.86 0.24 0.75 0.08 0.43 0.00 0.09 0.50 0.12 0.00 0.21 0.75 1 0.00 0.00 0.09 0 12 0.00 1.00 38 0.14 1.00 0.00 15 6 0.18 0.00 0.07 3 16 55 1 0.80 0.08 168 1 3 0.14 15 1.00 0 3 15 0.08 0 3 15 1 38 15 10 5 15 4 14 61 0 170 0 4 15 4 15 1 0 15 15 (cm) limit eter (diam- tion resolu- Resource 11 001 00 10 11 history extinction Colonisation- history extinction Colonisation- 11 history extinction Colonisation- history extinction Colonisation- rate Colonisation rate Extinction aeOccupancy rate rate/extinction Colonisation Posted on Authorea 21 Jan 2020 — CC BY 4.0 — https://doi.org/10.22541/au.157963812.23974358 — This a preprint and has not been peer reviewed. Data may be preliminary. .viticola P. type Model fuscus ferrugineo- P. Species the in in increase decrease an be of would probability there and that 1.00. land predicted of forest resolutions probability resource all a and on with types increase set-asides model of All probability forest. and production interval credible Bayesian 95% cuac elcl deadwood 5 extinction colonisation- cell cell occupancy extinction colonisation- ltpo est of density 5 of density 0.57 age stand plot 0.51 age stand 5 plot 15 cell 5 patch connectivity 0.46 cell plot age stand patch 15 plot deadwood 5 patch 5 plot patch cell plot cell scale modelling Spatial 12 ? value) [?] cm in (diameter resolution Resource ? value) [?] cm in (diameter resolution Resource 5connectivity connectivity 15 15 1.02 age stand deadwood 15 15 1.25 age stand deadwood 15 15 rdbeinterval) credible Bayesian (95% estimates parameter associated and Covariates (-0.06-1.05) 0.45 (-0.11-1.48) 0.58 (0.75-6.58) 0.47 trees dead (-0.02-1.01) 2.91 (0.13-2.19) 0.99 connectivity and (0.48-2.20) 1.20 trees dead (0.06-1.14) (-0.10-1.18) (0.44-2.32) 1.23 volume (-0.19-1.21) (0.47-2.54) 1.32 volume (0.17-2.02) (-0.22-1.82) 0.66 volume (0.12-3.42) (-0.18-1.82) 0.68 volume * ++ ++ + Posted on Authorea 21 Jan 2020 — CC BY 4.0 — https://doi.org/10.22541/au.157963812.23974358 — This a preprint and has not been peer reviewed. Data may be preliminary. pce oe type Model Species cuac elcl est of density 5 cell cell occupancy ac ac 5dniyof density 15 of density patch 5 patch plot plot of density 15 patch patch scale modelling Spatial 13 ? value) [?] cm in (diameter resolution Resource 5connectivity 15 connectivity 15 value) [?] cm in (diameter resolution Resource (-0.05-1.28) 0.56 (-0.98-6.40)] 2.05 [connectivity (0.47-4.58) 2.02 (0.45-1.38) 0.90 trees dead (0.16-1.30)] 0.71 [connectivity (0.08-1.03) 0.54 trees dead (0.50-2.35)] 1.31 [connectivity (0.04-1.63) 0.77 connectivity and (0.49-1.79) 1.08 trees dead (0.46-2.03) 1.14 trees dead rdbeinterval) credible Bayesian (95% estimates parameter associated and Covariates ++ ++ + ++ + + Posted on Authorea 21 Jan 2020 — CC BY 4.0 — https://doi.org/10.22541/au.157963812.23974358 — This a preprint and has not been peer reviewed. Data may be preliminary. uvydars h hl ac.()Terpae uvydt olce n14frs ace cosboreal across patches forest models. 174 colonization-extinction in and collected data occupancy survey the repeated build The to (B) used patch. Finland whole forest the across a surveyed in m) 20 x 1. Fig. FIGURES A h he pta clso aacleto n oeln ihasample a with modelling and collection data of scales spatial three The (A) patch ml oswr nysree ihntesml ltwietelrelg were logs large the while plot sample the within surveyed only were logs Small . 14 plot ihfive with cells (20 Posted on Authorea 21 Jan 2020 — CC BY 4.0 — https://doi.org/10.22541/au.157963812.23974358 — This a preprint and has not been peer reviewed. Data may be preliminary. itiuin ftefitdmodels. fitted the of distributions ( ( models colonisation-extinction the on neofuscus 2. Fig. Occ .Tepoetosaebsdo vrgn h eut ae n10 iuain rmtefl posterior full the from simulations 1000 on based results the averaging on based are projections The ). rjcin fma curnepoaiiyadrltv hnei curnefor occurrence in change relative and probability occurrence mean of Projections vrtepeetcnuyi epnet oetmngmn.Pnl - r o rjcin based projections for are A-B Panels management. forest to response in century present the over Col-ext n aesCDfrtoebsdo h cuac models occupancy the on based those for C-D panels and ) 15 hliu ferrugi- Phellinus Posted on Authorea 21 Jan 2020 — CC BY 4.0 — https://doi.org/10.22541/au.157963812.23974358 — This a preprint and has not been peer reviewed. Data may be preliminary. .Tepoetosaebsdo vrgn h eut ae n10 iuain rmtefl posterior full the from simulations 1000 on the based on results Statement based the Accessibility projections averaging Data for on models. are fitted based the A-B are of Panels projections distributions management. The forest ). to ( response models in colonisation-extinction century present the over 3. Fig. rjcin fma curnepoaiiyadrltv hnei curnefor occurrence in change relative and probability occurrence mean of Projections Col-ext n aesCDfrtoebsdo h cuac oes( models occupancy the on based those for C-D panels and ) 16 hliu viticola Phellinus Occ Posted on Authorea 21 Jan 2020 — CC BY 4.0 — https://doi.org/10.22541/au.157963812.23974358 — This a preprint and has not been peer reviewed. Data may be preliminary. nyapr fec oetpth h ug r oavrigetn pcaie ihrsett resource-unit to respect with specialised extent varying a cover to usually are that text). data fungi main survey the The i.e. in patch. size, 1 forest varying (Fig. each of patches plots of forest sample part structured in in the hierarchically a located collected in in only trees) been occur few by (dead usually fungi and limited units have these area resource data be Furthermore, Empirical to to in 2012). restricted appear al. small are and et typically they populations Norros are systems: 2004, local forests al. small set-aside have models et rich fungi (Edman colonization-extinction deadwood dispersal wood-decomposer of of As instead species smaller. models many occupancy be smaller, landscape, using to be to when assumed assumed projection be be in can can differences forests bias the natural-like the species, and common therefore production deadwood more between and In less rates 2013). extinction much (Nord´en al. and have et species colonisation forests specialised in of production requirements the because rates meet and forests, lower not be natural-like to in expected size than are In colonisation tree ( higher local 2002). species, extinction of Gourbiere resource rates tree local and species, from on (Gourbiere rare of for persistence (depending extinctions especially metapopulation centuries and balance ensure forests, even therefore production to and elsewhere must decades dynamics colonisations fungi the to colonization-extinction with in years Wood-decomposer units on dynamic of are dataset fungi conditions). fungi lifespan large environmental wood-decomposer wood-decomposer a a the and on have of utilize units they data Wood- also resource that The occupancy we 2002, performance. gradient. sense al. study Snapshot ecological et wide projection current 2014). a (Gu the over delay species al. collected in a et on with but change Sverdrup-Thygeson abundant, resolution environmental 2006, are to resource and respond al. may and modelling, et that colonisation-extinction scale Paltto species performance versus sessile modelling are the occupancy fungi spatial testing including decomposer of for models, system effects predictive excellent of the an kinds are different fungi of decomposer deadwood-dependent Forest-living, FUNGI FOREST DEADWOOD-DEPENDENT SYSTEM: STUDY THE introduction Supplementary S1. APPENDIX and APPENDICES and TS JN to and 268624) TS (project Norway to of 2013-1096 Council grant Research Formas with the proposals, second by and research the funded (2016-01949) JN. for Formas GreenFutureForest) while were funders JS (project Forestry, TS call national to and COFUND and the programme Agriculture BiodivERsA research PH of 2015-2016 Focus Ministry acknowledged by the Forest Finnish is contributions through EU the Moor the the by Helen survey. and funded and Environment first was survey the of survey in first Ministry part The Finnish took 1B. the that Figure assistants the field making and for experts Karppinen, other Miika several Karvonen, and Juha Pennanen, Jorma N Olli-Pekka Ala-Risku, Terhi Oja, the Mari Ottosson, data of Elisabet many writing publication. the the for the thank designed approval We led final TS TS gave and and and PH drafts JS decomposition; JN, the deadwood Acknowledgments JN, to management; simulate critically and to methodology; contributed software dynamics authors the designed forest All wrote of and OK manuscript. simulations data; ideas the the the conducted analysed JN conceived AL and TS LM PH, and collection; JS PH, JN, contributions report. Authors’ to interests competing no are There https://snd.gu.se/en. Service, Data Statement National Interests Swedish Competing the in archived are data The < 0)ta aua-iefrss(oso ta.21)adteqaiyo h edodpeetuulydoes usually present deadwood the of quality the and 2016) al. et (Jonsson forests natural-like than 10%) ¨ as ¨ ar n an ahannwoto ati h aacleto ntescn survey, second the in collection data the in part took who Jauhiainen Hanna and ¨ o 17 Posted on Authorea 21 Jan 2020 — CC BY 4.0 — https://doi.org/10.22541/au.157963812.23974358 — This a preprint and has not been peer reviewed. Data may be preliminary. esrda nMi ta.(07.Lnsaesaecvrae eecnetvt optnildispersal potential to connectivity were covariates Landscape-scale aspect, and (2017). slope wetness, years), al. 4-206 (range et stand Mair living (ha in the density of were as age covariates measured volume, unit spruce resource living The were covariates species. (m occupancies focal the volume explain the would and of scales dynamics modelling spatial colonization-extinction different and for collected covariates that hypothesised We DYNAMICS METAPOPULATION AND OCCUPANCIES EXPLAINING COVARIATES – species two the by patches 2013). per al. have evenviticola et bodies bodies years, (Norden fruit several fruit logs many is the thinner produce after 2013). period usually years) fruiting al. further to total et species (up the Both long that recognizable so detectability. 2013). well years high many also has during and also log large of species are 2012). because this they Kotiaho rates as died, and colonisation-extinction but (Halme and bodies (Berglund al., detectability occupancies trees fruit et high living differing are Moor and have to 2011), H. stumps to leading deadwood; on expected al. below). absent suitable (see were et are lack ecologies cover; and (Berglund forests differing not 2011), resource such did main (because data al. old empirical their et years our as which 20-60 deadwood data) patches spruce unpublished in have found species rarely very polypore tool focal support (2014). two decision al. Our and et Hynynen management dynamics in stand forest species detail for stand-level polypore in models Focal a presented Prediction the is are the mortality. on sets MOTTI between and based growth data in predicted 2014). predicting underlying was years for and (Salminen 2014 volume of al. simulator models in number spruce stand tree-level et stand the forest living includes Hynynen living MOTTI was variables which of 2005, the survey stand Volume area using of second inventory survey. forest Patch al. first the age first the et the management. the in text), recorded during of in age measured We main intensity age characteristics Stand the and stand the ha). survey. to years, in 1.1 first added 66-134 (mean 1 the surveys age, two ha in varying 4.3 age Fig. of forests stand to managed cells, cells) habitats and and ha key 5 5 set-asides; 0.2 natural-like woodland x x representing herb-rich from 65 years, and 16 ranged 64-163 plots, brook-side age plot, 65 survey; cells), 5 patches, second a x the (65 had 56 of plots, which time 56 the patches, of at (56 16 years 5-20 patches, trees (53 dominant ( trees spruce retention Norway by with deadwood dominated patch small-diameter were if whole patches possible the All covering been have threshold for patch, would diameter was than forest log area patches the a everywhere. patch of of used remaining number included rest but total the was deadwood greater procedure the in all a same In resolution reaching the inventoried of resource-unit for stage. followed lower presence we and we decay The the plot, cells and recorded the cm. length the as we outside [?]15 diameter, simply unit, area Within of log henceforth resource subdivided remaining text). spruce the to deadwood and species, in main referred spruce m a focal type; each the i.e. 100 For the in deadwood x in of the m. both m 1 on [?]1.3 bodies 20 deadwood length (depending fruit (Fig. size and base and m the cm or 20 fungi of height [?]5 was breast diameter) x surveyed at plot m we diameter survey 20 2014), a The with of area. and patch cells (2003-2005 remaining survey surveys the into in both and in plot patch, survey forest each In projections. population on 2013), COLLECTION resolution DATA al. resource (Nord´en et of methods decomposition effects and of the material stage test Supplementary and to type opportunity size, an gives species, this tree and deadwood as such characteristics, hliu viticola Phellinus srte eeaie nisrsuc s.Ti ieyepan h ieetpootoso occupied of proportions different the explains likely This use. resource its in generalised rather is hliu ferrugineofuscus Phellinus 3 ha -1 fsrc os hi endaee n tg fdcmoiin h patch-level The decomposition. of stage and diameter mean their logs, spruce of ) cuso ohsal n ag-imtrlg Juianne l 01 Norden 2011, al. et (Juutilainen logs large-diameter and small- both on occurs .ferrugineofuscus P. hliu viticola Phellinus hliu ferrugineofuscus Phellinus cusmr rqetyo agrlg u cainlyas nthe on also occasionally but logs larger on frequently more occurs ie abies Picea a arylreadesl eonzdprnilfutbodies fruit perennial recognized easily and large fairly has n1%and 17% in 18 n oee ag ffrs ye:clear-cuts types: forest of range a covered and ) hliu ferrugineofuscus Phellinus .viticola P. seooial eaieyseilsdwhile specialised relatively ecologically is n2%o h ace n2014. in patches the of 24% in > 0yas(vsanne al. et (Ovaskainen years 10 a annual-biennial has -1 P. ) Posted on Authorea 21 Jan 2020 — CC BY 4.0 — https://doi.org/10.22541/au.157963812.23974358 — This a preprint and has not been peer reviewed. Data may be preliminary. o ahseis efitdheacia aeinsaesaemdl otepeec-bec aao h species the of data presence-absence the to models state-space Bayesian hierarchical fitted we species, each For 0.9. COLONISATION-EXTINCTION probability AND detection OCCUPANCY patch-specific density we MODELLING a deadwood forest, mean used the therefore managed for we 0.9 typical across models, be a probability to density occupancy detection estimated in and the was mean forests, resurvey than The As intensive managed the forest models. in in surveys. detectability old-growth recorded the four the species this in polypore resurvey covariate of during in all a intensive team encountered overall as the a density species larger during deadwood in considerably polypore recorded detectability.included needed, estimating the is occupancies time for of the deadwood data the assuming occupancies of collect taking data to true possible, was these ‘intensive the resurvey in as to intensive represented the patch survey regular models the resurvey, a forest estimate a of detectability second out purpose thorough old-growth fit detectability carried the the We first as an the knew In experts do now in two hence principle resurvey. that to of m) and experts team The was second 25 survey, field subsequent task a 2014. x general the the 2014, polypore in m In the resurvey’, about two twice 25 below. knowledge in the resurveyed see size without as of cells) modelling, resurvey the the (15 similar bodies in of partly is used fruit cells was and collection the obtained (64 2002 data of in ha detectability probability surveyed detectability 4 detection for for first of accounting was the to thus plot Finland population estimating and survey process southern For the observation larger of the a 2011). state for “true” model species, a unobserved al. via the et state allows observed (Harrison modelling the Bayesian to linked in be when level increase hierarchical can to further predicted show but A we included. excluded, which units: subset were were diameter), resource units a units minimum Finally, resource all resource on smallest including the models. its based with all our when (e.g. compared forests in survey as production this might in the conclusion for decrease species in accounted opposite have the included we an species Secondly, but be in focal fruiting, may reliable. result our and units and However, at present resource meaningful fruiting fruiting is the tree. data to it be of colonisation dead body if mycelial not the fruit even from might inside makes delays non-detected species which mycelia time go 2013) short the as as present fungi, al. well is in et as Firstly, (Ovaskainen it prevalences model if fruit-body 2008). and in and even sampling mycelial survey it al. the similar the for et with Altwegg accounted of for design 2006, accounted we 2003, time sampling not as the or if al. al. projections trends behaviour et et our surveyor population (MacKenzie (Kery about characteristics, on design conclusions effect probabilities presences species incorrect minimal of species extinction in a result because unobserved and can likely detection for trends has account Imperfect population Non-detection to ways. of failing 2006). several is estimates al. data biased et survey in Rhodes on result based can models which unity. in of pitfall variance a potential and One zero of mean probability a detection have in spruce the to three threshold the standardised Estimating tested age and further replacing centred we the tested transformed, distances exceeding log dispersal also were forest species’ covariates We on of All available absence information years). limited or for connectivity is [?]120 values presence there the different or the As in the years with cells cell. from followed grid grid measure [?]100 km We each of connectivity 20 ages inclusion the to 2008). for stand in up age with al. cells volumes forest of et cells grid in (Tomppo estimates surrounding thresholds (grid different layers in satellite-based calculation two spruce cell using tested of grid We 2008, calculated volumes connectivity. m the Boddy was of 25 ( weighing byexp Connectivity and of (2017), patch Heilmann-Clausen form focal relationship. al. 2018). the the et unimodal (e.g. in Mair a al. of studies age et expected procedure forest of Norden we and number which 2017, volume probability. large spruce for extinction a al. wetness on by et and effects supported Abrego decomposition negative are large- of but hypotheses and probability patch- stage our These resource-, For colonization are all 2017). and of exceptions effects al. occupancy Two positive et hypothesised on (Mair we covariates precipitation species and scale focal temperature specialized annual and and rare landscape, relatively surrounding the in sources α 01 . n ) ersnigama ipra itneo 0 n m respectively. km, 1 and 5 10, of distance dispersal mean a representing 1), and 0.2 (0.1, − αd > . nfrsswt o est fda od nteclnsto-xicinand colonisation-extinction the In wood. dead of density low a with forests in 0.9 ,where ), d stedsac otefclpthadapai h eeatsailscale spatial relevant the is alpha and patch focal the to distance the is 19 .viticola P. a rdce to predicted was Posted on Authorea 21 Jan 2020 — CC BY 4.0 — https://doi.org/10.22541/au.157963812.23974358 — This a preprint and has not been peer reviewed. Data may be preliminary. n htcolg( cloglog that and that: m assumed 400 we of clear-cut area been an not and had surveys two cell the the between years ten with and years) of where m 20 x m (20 areas cell different the and that: surveys such the m), between 25 years x of m numbers 25 different or the for correct to where ψ the at period: following instead indexing The level period define finest survey model. we cell-scale the models the cell-scale with detail the model For we this and respectively. scales of scale, patch simplifications patch and and are plot- plot cell-, models the patch-scale of and each probability plot- at colonisation locations. models a such and we assumed separate at zero models, we fitted mean unity We cell-level projections with of the the distributed probability In In normally numbers extinction be fitting. differing and patch). to model zero the or assumed during of plot for was estimated spatial which (cell, was offsetting patches, a that areas by variance the and survey conducted a for varying time) was effect the through random This and change a surveys concerning included S1). the extinction (Appendix between and species. hectare years colonisation focal adjusted of one (only were the patches years extinction) of by these and 10 used scale in colonisation of seldom modelling wood occurrence, scale very dead of did time are (probabilities the We a data, models of to our the sources. most on in dispersal as variables based patches surrounding response which, clear-cut The to residues in connectivity logging quantity effect of of age deadwood up effect negative connectivity, of made a and positive effect (i) is age a the hypothesized stand (ii) We for of test and patches. effects not clear-cut cutting, the from tested since data in only time not resurvey surveyed we with and were clear-cuts, only first-survey in that therefore all occupancy and used (2003-2005) species and surveys survey of two modelling first the the between the In to cut from of relation were patches estimates that in 19 survey. reliable patches especially first from 22 more second the clear-cuts, and data obtaining the 2014, on for included from in surveys) and, further data re-surveyed two we 2014), used requiring We and age, colonization-extinction 2003-2005 clear-cut models. not in occupancy (but both comparison fitted surveyed occupancy the hypothesised also For patches we we species. (174 that focal models scales survey the modelling [?]5 colonisation-extinction of spatial (diameter dynamics the different resolutions colonization-extinction for with resource and collected deadwood occupancies covariates two the included and explain We patch) would and cm). plot [?]15 (cell, or scales cm modelling spatial three at rbblte r eylreo eysal(odn ta. npeaain.Det aalmttosw could we limitations data to the Due where preparation). cases in to al., function et link (Golding logistic small conventional very more or the large than very suited are better probabilities is it nature asymmetrical i,j,t (1 = n c i,j,t ∗ i,j,t − and ie h ubro er ewe h uvy iie y1 ie cldb h yia number typical the by scaled (i.e. 10 by divided surveys the between years of number the gives Z a t i,j,t easm that assume We . i,j,t e i,j,t ∗ − e i,j,t ie h elae iie y40(..sae ytetpclcl iei m in size cell typical the by scaled (i.e. 400 by divided area cell the gives 1 ) c ieteclnsto n xicinpoaiiis epciey hyhv enoffset been have They respectively. probabilities, extinction and colonisation the give = ) i,j,t ∗ lgo ( cloglog ε + 2 ecoet s h opeetr o-o ikfnto,lgo,a u oits to due as function,cloglog, link log-log complementary the use to chose We . Z i,j,t Z c − i,j,t i,j,t 1 Z i,j,t 1 = ) − ∼ c i,j,t ∗ steocpnysaeo h oa pce ncell in species focal the of state occupancy the as e e δ i,j,t ∗ i,j,t ∗ enul ( Bernoulli 2 + 1 =
1 = X , k − − β (1 k ( C (1 ψ − 20 ) i,j,t X − c i,j,t k,i,j,t ( e C .Frtefis uvyperiod survey first the For ). i,j,t ) ) n ) i,j,t + 2 n a , c i,j,t i,j,t X i,j,t ∗ a l i,j,t β = l ( P c ) i,j,t X l,j,t ( P and ) + γ e j,t i,j,t ∗ = ψ e i,j,t i,j,t i npatch in ftefrs in forest the If . = 2 ω .Fracell a For ). n nthe in and j during Posted on Authorea 21 Jan 2020 — CC BY 4.0 — https://doi.org/10.22541/au.157963812.23974358 — This a preprint and has not been peer reviewed. Data may be preliminary. ri oiso uhhgl eopsdwo.W ontdffrnit ewe tnig(ng)addowned and (snags) standing clas- between differentiate stage not decay do Swedish We wood. the al. decomposed on et highly (based such Harmon on decomposition in bodies of described fruit simulated stage method We fourth chronosequence 2008-2012. (Sandstr with the during one-time sification enters plots projections the NFI deadwood the on calculations the When based initialized connectivity on (2000). We deadwood our observed mortality. the in deadwood tree spruce of utilised age, of natural was decomposition stand properties by which included and volume generated data amounts spruce Tree production output the living factors. projection deadwood site modelling, forest spruce and pro- the The and mixtures management included. in species Different also used tree were covariate period. categories, felling a time owner programmes final each forest management at different in specified practices e.g., growth by retention for, controlled actual specified is the harvest – are and The time forest grammes activities) framework. five-year the simulation harvest of in rule-based and ingrowth development development a 2014), the (silviculture by layer al. consequently steered tree et and are Fahlvik simulating – actions (see level management together growth forest tree 2014) RegWise for (Elfving In models steps. mortality individual (Wikstr and empirical tools tool of 2004) support planning hectare. up decision (Wikberg made one forest forestry is of at multiple-use Heureka suite values Heureka of to the the core variables within using all component made software scaled a first were is we dynamics projections and and the are (Wikstr circular management all Heureka set-asides are for forest plots The but of follows. NFI m The what 10 Projections forestry. or in clear-cutting 7 patches. set-asides by of reserves, retention as dominated diameter state forestry, patches in a otherwise from non-production have protected landscapes aside these is forest Forest land set of ‘business in forest areas collection National a situated of Agency the or 16% under 17,383 Forest to landowners scenario, 2110 Swedish on this refer individual the and We In by species by 2015). 2010 protected Analysis al. focal between voluntarily et Scenario Sweden our patches Eriksson Forestry 2015, nationwide of of al. recent zone et resource the (Claesson boreal utilizing the the scenario of across usual’ distribution as spread plots the (NFI) of Inventory projections made bias We to wood- shown as been such has species variability CENTURYTHE CENTURY restricted COMING (Sn such COMING THE resource-unit some for species CONDITIONS for accounting the on FOREST especially fungi.PROJECTING Not either competitive absences PROJECTING 2009a), both. of True decomposer al. or or dynamics et inventories, the 2000) 2009b). (Berglund the in (Jonsson predictions al. stochasticity trees of model-based of host et time such consequence their (Berglund the a or include patches be at 2005) some to also habitat necessarily on covariates, can possible not unmeasured patches established not to suitable but already that due was establishment variation species instance patch it of for by between models, time patch) exclusion capture the will the and patch in it included at random (plot that covariates advantage differences a the scales the by include has modelling explained to term not spatial effect non-independence, is random higher statistical This to the effects. due At random necessary, was term. it effect models (see cell-scale study the control For intensive an on based estimated probability was that probability assume detection we This model 1), observation Table the (see respectively define 1, we and simulations. Finally, 0 the be in to probabilities observed these were used probabilities we extinction and colonisation where cases random patch-scale separate by estimated given the we are between Specifically, l probability or cases. survey colonisation colonisation these first the the in the for or used before model ( probabilities were (either cuts,cloglog extinction models clear-cut clear only the been for intercept had for parameters and that models events) patches the survey forest two in in effects cells random for probability or covariates include not ac-cl oaitsaegvnby given are covariates patch-scale bv)adavleo . a used. was 0.9 of value a and above) me l 01.FrtepoetosteRgiesfwr vrin22 a sd RegWise used. was 2.2) (version software RegWise the projections the For 2011). al. et ¨ om me l 07,i srmvdfo h iuain sorfclseisd o produce not do species focal our as simulations the from removed is it 2007), al. et ¨ om Y γ i,j,t j,t hc a sue ob omlydsrbtdwt enzr n variance and zero mean with distributed normally be to assumed was which steosre cuac tt fcell of state occupancy observed the as c i,j,t X = ) l,i,j,t ( Y P i,j,t ) δ 1 ihcrepnigprmtrvalues parameter corresponding with ncolg( andcloglog ∼ enul ( Bernoulli X 21 k,i,j,t ( C ) e i,j,t ihcrepnigprmtrvalues parameter corresponding with Z = ) i,j,t p i ε where ) 1 npatch in The . p k elsaecvrae sdi the in used covariates cell-scale ie h eeto probability. detection the gives j uigsre period survey during β siaigtedetection the Estimating k ( P ) eas nld a include also We . me l 01.The 2011). al. et ¨ om β k ( l tal. et ¨ all C ) t σ The . For . 2 In . Posted on Authorea 21 Jan 2020 — CC BY 4.0 — https://doi.org/10.22541/au.157963812.23974358 — This a preprint and has not been peer reviewed. Data may be preliminary. rbblt nrae ihlgdaee.Drn h rtrudo oe eeto,w on oiieeffect positive a found we selection, model of for round first that extinction hand, the found and other During colonisation the (2008) for diameter. of whereas on log size, study densities, al. log with previous log of et increased regardless a Higher probability relative logs Jonsson In occupied cell, logs. level, to one units. large connectivity log at two with deadwood volume increased individual or smaller large one several the a by the of cells) at caused m up for events 20 be made models x often be fitted m will often (20 the our cells, will scale other of in modelling at spatial selected abundance that smallest covariates this to and cell-scale At characteristics the volume. the the Of to by STUDY uses. influenced primarily probability, THE are colonisation species the species OF units wood-decomposer resource ECOLOGY of dynamics THE Local ON DEPENDS RESOLUTION to SPECIES predicted RESOURCE was connectivity APPROPRIATE the 2050 measures After connectivity levels. the discussion initial set-asides. on Supplementary the This influence the exceeding strong forests. and or a younger land returning had of production land again, self-thinning production the increase the the to both the owing in coming whereas affected increase ages volumes, the still the which ( deadwood over would in old the land land decline of to initial production clear-cutting production both contribute abrupt the The would the at in that S4-2). in land logs logs age (Fig. of large production 2050 stand density fewer the and in declining the 2020 resulted in coarse a between forests in the decline of decline unprotected on increase to the because steepest based an the was predicted when of showed with This was decrease forecasts decades, logs a volume S4-1). of but the deadwood the data, density (Fig. and the of deadwood the resolutions However, text) resolution combination for fine main a data. Projections the from the on resolution S2-1). based resulted in (Table when species 1 land models polypore production (Table fitted two rates the the of colonization-extinction of covariates or occupancies occupancies the of general forecasts differing The PROJECTIONS FUTURE 2110. initialising and output for 2020 modelling used ( between results the pieces data results of Supplementary small the NFI inspection missing show the Visual the thus resolution, of cm. we effect finest [?]10 and modelling the diameter the minimal years with decomposition at ten deadwood cm and after of that [?]5 projections terms showed diameter in fitting, are with and model projections means deadwood area our compute the Finally, of to radii). Although terms used m values. was in 10 Sweden, and hectare-scale are across the colonisation (7 tracts here these simulate and plots NFI NFI of occupancy step, to of the densities the totals steps of time different on sizes for time different used first accounting the 10-year scaling, were for the factor used Offsets account colonisation- the values. to in we fitted probabilities final parameter years, species the extinction states estimated using and 10 polypore its 2110 occupancy until was with each plots the surveys NFI model For the extinction our initialise in 2010-2110. dynamics between extinction to for and time colonisation utilised species average subsequent was the focal As model our 2010. of occupancy dynamics fitted iterations. occupancy 3000 final visually after the assessed reached was projected was for Convergence convergence We run cases 100. CENTURY were all COMING of chains In THE thinning MCMC OCCUPANCIES chains. for a three POLYPORE three except PROJECTING and cases the 0.01, all iterations of of In 1000 results deviation five. of the standard of and a on burn-in density zero and based a the between zero with prior for of uniform iterations mean 0.64 (2017). a a 3000 and given with al. was wood priors et given which Mair dead were species. sigma in spruce parameters focal as model of our done the volume for were All NFI covariates the projections the the connectivity in for in Forest spruce) by 0.59 trees. dead quantities as all spruce deadwood of dead estimated (out the were logs multiply spruce multipliers to of much These proportion how the determine used to but data simulations the in deadwood (logs) .viticola P. epne otedniyo os whereas logs, of density the to responded 22 < 0c)o h edodpoetoswas projections deadwood the on cm) 10 .viticola, P. .ferrugineofuscus, P. .ferrugineofuscus P. ooiainprobability colonisation > colonisation responded 2 years) 120 Posted on Authorea 21 Jan 2020 — CC BY 4.0 — https://doi.org/10.22541/au.157963812.23974358 — This a preprint and has not been peer reviewed. Data may be preliminary. PEDXS3 APPENDIX α (see ++ years [?]120 forests in 2008) + Tmp ta.20) ?10yas(codn oaGSbsdlyrfrfrs g Tmp ta.20)and 2008) al. et (Tomppo age forest for layer GIS-based a to (according years [?]120 to an 2008)) standardised al. were et covariates square (Tomppo one. All in of patches. enclosed deviation cut Covariates standard on and * only. models zero probability of occupancy mean colonisation the a for the have selected for those are were covariates brackets for effects. the adverse climate models, be detect extinction to to power likely the is S2-1. lacked climate Table data future our Finally, if 2013). even S2 2018) al. APPENDIX et al. diameter Norden et log Fifthly, with 2008, (Mair increase species deadwood. al. probabilities of model et occurrence amounts our (Jonsson and intermediate colonization species or the 2017). this low that for shown with al. have cuts forests studies et clear from other forests (Ruete data from while deadwood-rich field fungi effects in on that probabilities For edge wood-decomposer assumed based occurrence sensitive we ignored are owners and 2100. we on models new colonisation year Thirdly, effect our and the the as negative misestimated 2006) until 2008). have a models. al. aside (Eriksson may have our et we set forest Fourthly, will by (Ingemarson oldest be which years expected the not set-asides 22.5 is disappear cutting is fact into what will landownership by in than sources private connectivity start may more of dispersal Firstly, often aside length rates as set mean colonization acting reasons. voluntarily the the populations several example, currently down Some for forest slow S4-2). biased the will Secondly, which be (Fig. time may 2020 that after trends during years population 20 future for the size. decreases of log decreasing predictions with Our increases PREDICTIONS disappearance MODEL log IN to BIAS due POTENTIAL extinction deterministic of rate The . nldn lo51 mlg.Tedffrnei ieybcuei u data our in because likely is difference P The than logs logs. fast-decomposing cm smaller, 5-15 in also fruiting in including the peak later At and significant. viticola period longer fruiting no ferrugineofuscus longer was the P. that reflecting effect probably found this logs, (2008) model similar-sized al. the et than in Jonsson risk included trees, extinction been dead had individual of volume level once but shown), for not diameter log mean of onciiybsdo rsneasneo puefrss(codn oaGSbsdlyrfrsrc volume spruce for layer GIS-based a to (according forests spruce of presence/absence on based connectivity au f1 of value onciiybsdo oueo pue(codn oaGSbsdlyrfrsrc oue(opoe al. et (Tomppo volume spruce for layer GIS-based a to (according spruce of volume on based connectivity α onciiybsdo rsneasneo puefrss(see forests spruce of presence/absence on based connectivity au f0.1 of value a ihretnto ae than rates extinction higher had oaitsadascae aaee siae ntefia te oes o h colonisation- the For models. fitted final the in estimates parameter associated and Covariates .ferrugineofuscus P. Jnsne l 08 odne l 03 vsanne l 03.I u data, our In 2013). al. et Ovaskainen 2013, al. et Norden 2008, al. et (Jonsson .ferrugineofuscus P. .ferrugineofuscus P. * bv)adan and above) ooie l edteswt imtr[] mwt h aeprobability same the with cm [?]5 diameter with trees dead all colonizes ferrugineofuscus . .ferrugineofuscus P. wt nulmraiyrtso 31 n 96,rsetvl)on respectively) 29.6%, and 13.1% of rates mortality annual (with hnbsdo h ellvlfiesaedawo aa(results data deadwood scale fine cell-level the on based when α au f1 of value 23 htlast oefeun xicin in extinctions frequent more to leads that ttecl,po n ac cl seilywhen especially scale patch and plot cell, the at .viticola P. * bv)[]2 er (see years [?]120 above) a usatal oe log-level lower substantially a had .viticola P. cuso vrg on average on occurs * .viticola P. bv)adan and above) .viticola P. than P. Posted on Authorea 21 Jan 2020 — CC BY 4.0 — https://doi.org/10.22541/au.157963812.23974358 — This a preprint and has not been peer reviewed. Data may be preliminary. nices ntestaie rgt ae nteclnzto-xicinmdl for models colonization-extinction the on based (right) . set-asides the in increase an S3-1. Fig. rbblt fa nraeoe l ace lf) eraei h rdcinln mdl)and (middle) land production the in decrease a (left), patches all over increase an of Probability 24 hliu ferrugineofuscus Phellinus Posted on Authorea 21 Jan 2020 — CC BY 4.0 — https://doi.org/10.22541/au.157963812.23974358 — This a preprint and has not been peer reviewed. Data may be preliminary. i.S3-3. Fig. for models occupancy the on based (right) set-asides the in increase an S3-2. Fig. rbblt fa nraeoe l ace lf) eraei h rdcinln mdl)and (middle) land production the in decrease a (left), patches all over increase an of Probability and (middle) land production the in decrease a (left), patches all over increase an of Probability 25 hliu ferrugineofuscus Phellinus . Posted on Authorea 21 Jan 2020 — CC BY 4.0 — https://doi.org/10.22541/au.157963812.23974358 — This a preprint and has not been peer reviewed. Data may be preliminary. nices ntestaie rgt ae nteocpnymdl for models occupancy the on S4 based APPENDIX (right) set-asides the in increase an S3-4. Fig. for models colonization-extinction the on based (right) set-asides the in increase an rbblt fa nraeoe l ace lf) eraei h rdcinln mdl)and (middle) land production the in decrease a (left), patches all over increase an of Probability 26 hliu viticola Phellinus hliu viticola Phellinus . . Posted on Authorea 21 Jan 2020 — CC BY 4.0 — https://doi.org/10.22541/au.157963812.23974358 — This a preprint and has not been peer reviewed. Data may be preliminary. egud . .B ’aa n .G oso.20a uniyn aia eurmnso Tree-Living of Requirements Habitat Biology Quantifying Conservation 2009a. Methods. Jonsson. Bayesian fungal G. with Forests B. wood-inhabiting Boreal and Fragmented of in O’Hara, vulnerability Species B. extinction Conservation R. the Biological H., forests. Assessing Berglund, boreal 2008. Swedish Jonsson. northern fragmented G. in B. species imperfect and with Ecography H., vulnerability. species Berglund, extinction of regional their dynamics to range Penttil fungi R. the wood-inhabiting Hottola, and J. Climate H., 2008. Berglund, Erni. B. Letters and Biology Wheeler, detection. M. R., FungalAltwegg, models. habitat species-specific from reserves beech European Ecology in fungi B wood-inhabiting C. of distribution Christensen, M. N., Abrego, References all the forests give spruce in of 1. b-d age presence/absence Panel on stand based lines). the connectivity (dashed α (b) an gives set-asides and models: a and years final Panel lines); [?]120 the (dotted in forests S2-1). used measures Table production connectivity lines); (see (solid models land occupancy forest and colonisation-extinction fitted S4-2. Fig. spruce of proportion the used but spruce). simulations dead the dead- (based all in resolution decomposition of deadwood of coarse (out (logs) stage lines); (Sandstr the downed logs fourth (dotted and for classification the (snags) forests values reaches stage standing production corresponding deadwood decay between lines); when the Swedish that (solid give Note the land d forest cm). on and all [?]15 c density (diameter in the Panels data cm respectively wood [?]5 lines). give diameter b (dashed a and set-asides a with and Panels logs of S2-1). Table volume (see and models occupancy and colonisation-extinction S4-1. Fig. au f1 n d onciiybsdo rsneasneo puefrss[]2 er n an and years [?]120 forests spruce of presence/absence on based connectivity (d) and 1; of value 27 :168-174. rjcin fsadaeadcnetvt oaitsdiigteseispoetosuigthe using projections species the driving covariates connectivity and age stand of Projections rjcin ftesrc edodcvrae rvn h pce rjcin sn the using projections species the driving covariates deadwood spruce the of Projections α au f01 c onciiybsdo oueo puei oet ?10yasadan and years [?]120 forests in spruce of volume on based connectivity (c) 0.1; of value 4 :581-584. slr .Anwrh n .Himn-lue.21.Udrtnigthe Understanding 2017. Heilmann-Clausen. J. and Ainsworth, A. ¨ assler, ,adJ itnn 01 ikn usrt n aia eurmnsof requirements habitat and substrate Linking 2011. Siitonen. J. and ¨ a, me l 07,i secue.W ontdifferentiate not do We excluded. is it 2007), al. et ¨ om 27 34 :864-875. 141 :3029-3039. 23 :1127-1137. α au of value Posted on Authorea 21 Jan 2020 — CC BY 4.0 — https://doi.org/10.22541/au.157963812.23974358 — This a preprint and has not been peer reviewed. Data may be preliminary. ug nabra l-rwhPcaaisfrs.Junlo Ecology of Journal forest. abies Picea old-growth boreal a in fungi M J M. Ecology and Fungal Kotiranta, fungi. wood-inhabiting H. Ecology and Halme, Forest P. biodiversity. K., for implications Juutilainen, and outcomes Policy - Management Journal forests and forest. Swedish abies managed Picea Ekstr in old-growth availability M. boreal G., a in B. in owners Jonsson, debris forest woody private coarse Science small-scale of Vegetation Availability of of 2000. typology G. A B. 2006. Jonsson, Research Eriksson. Forest L. of Journal and Rum- Scandinavian Lindhagen, A. Sweden. A. Lehtonen, M. F., Siipilehto, Ingemarson, J. Resources. 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