Iowa State University Capstones, Theses and Retrospective Theses and Dissertations Dissertations
1-1-2006
The patch and landscape characteristics related to the occupancy of host-plant patches by the phlox moth, Schinia indiana (Lepidoptera: Noctuidae)
Danielle Marie DeBruyne Iowa State University
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Recommended Citation DeBruyne, Danielle Marie, "The patch and landscape characteristics related to the occupancy of host-plant patches by the phlox moth, Schinia indiana (Lepidoptera: Noctuidae)" (2006). Retrospective Theses and Dissertations. 19388. https://lib.dr.iastate.edu/rtd/19388
This Thesis is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University Digital Repository. It has been accepted for inclusion in Retrospective Theses and Dissertations by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. The patch and landscape characteristics related to the occupancy of host-plant patches by the phlox moth, Schinia Indiana (Lepidoptera: Noctuidae)
by
Danielle Marie DeBruyne
A thesis submitted to the graduate faculty
in partial fulfillment of the requirements for the degree of
MASTER OF SCIENCE
Major: Ecology and Evolutionary Biology
Program of Study Committee: Brent Danielson, Major Professor Diane Debinski Sue Fairbanks
Iowa State University Ames, Iowa 2006 ii
Graduate College Iowa State University
This is to certify that the master's thesis of
Danielle Marie DeBruyne has met the thesis requirements of lovva State University
Signatures have been redacted for privacy iii
TABLE OF CONTENTS
CHAPTER 1. GENERAL INTRODUCTION 1 Introduction 1 Thesis Organization 2 References 2
CHAPTER 2. THE PATCH AND LANDSCAPE CHARACTERISTICS RELATED TO THE OCCUPANCY OF HOST-PLANT PATCHES BY THE PHLOX IUiOTH, SCI-~INIA INDIANA (LEPIDOPTERA: NOCTUIDAE) 4 Abstract 4 Introduction 4 lMethods 7 Results 22 Discussion 25 Conclusions 28 Tables 32 Figures 41 Appendix A. Sample Datasheets 45 Appendix B. Field, GIS, and Calculated Data with a Summary 49 Appendix C. Correlations Between Predictor Variables 80 References 83
CHAPTER 3. GENERAL CONCLUSIONS 86 General Discussion 86 References 87
ACKNOVI/LEDGEIUIENTS 88 1
CHAPTER 1. GENERAL INTRODUCTION
Introduction
The preservation of large areas of contiguous native habitat is crucial to the conservation of many species (Vitousek et al., 1997). However, in the human-dominated landscapes that make up much of the earth's land surface area, few undisturbed native habitats of sufficient size exist (Vitousek et al., 1997). In such human-dominated regions, conservation efforts typically focus on small isolated habitat patches that support isolated populations or metapopulations of species (Saunders et al., 1991). Many of these conservation efforts are directed at specialized species that cannot persist in highly altered landscapes. In particular, many Lepidopteran species are at risk of extinction, and are thus the targets of conservation (Erlich, 1984; Thomas, 1991; Warren, 1993). Lepidopteran species are frequently rare in human-altered landscapes because many have larvae that specialize on a single species or family of host-plant (Janzen, 1988). Butterflies and moths are particularly important to the conservation of ecosystems because they comprise approximately 1/6th of the known diversity of life on earth (Janzen, 1988; Young, 1997). Lepidopterans are important to the functioning of many ecosystems due to their roles as pollinators, herbivores, and prey (Janzen, 1988; Young, 1997). Theoretical research has been focused on developing metapopulation models that can be re- parameterized for many different species in different situations to accurately predict the persistence of a population (Hanski, 1991). Many common species of butterflies and moths have been the focus of studies that have tested metapopulation and biogeography theory (Hanski &Gilpin, 1991; Nieminen & Hanski, 1998). As the basic tenets of these theories have held true for many common species, the theories have been increasingly applied to research on the conservation needs of rare species (for example, Hanski et al., 1994; Hill et al., 1996; Ravenscroft &Young, 1996; Menendez &Thomas, 2000}, and to the management of endangered populations (for example, Wisconsin Department of Natural Resources, 1999). Most butterfly and moth conservation efforts have been focused on federally listed species, such as the Karner blue butterfly and the regal fritillary in the Midwestern U.S. (U.S. Fish and Wildlife Service, 2006). Less attention has been given to species that are listed at lower levels of government or are decreasing in number but not yet listed. Although conservation efforts for highly endangered species are important, efforts to prevent endangerment are also necessary. Preventative measures are likely to be less expensive and less risky to population persistence than efforts to salvage a population. In Wisconsin, oak savanna and prairie habitats are among the rarest, and harbor some of the largest numbers of rare species. Less than 1% of the original acreage of oak savanna and prairie habitats currently exists. Due to the small size of most remaining habitat patches, the isolation of 2
many of these patches, and the alteration of forces that historically shaped these habitats, such as large grazing animals and wildfires, these habitats are no longer able to support many of the species that originally inhabited them. As a result, many of the obligate inhabitants of oak savannas and prairies are declining in number and are listed as rare. one of these species, the phlox moth (Schinia inc~iana, IVoctuidae), is listed as state endangered. .Although Wisconsin supports some of the largest known populations of the moth, it has not been listed federally, and therefore has not been the target of intensive research, conservation, or restoration activities. As a result, comparatively little is known about the moth. (Henderson &Epstein, e006; 1►Nisconsin Department of Rlatural Resources, 2006) because of its strong flight and the patchy nature of its host-plant, the phlox moth is believed to exist in a metapopulation structure (Swengel ~ ~wengel, 1999). However, due to the rarity of moths within host-plant patches, traditional metapopulation studies are not possible. The population sizes at patches of host-plants are exceedingly diffcult to assess, however, the presence or absence of the moth can be established, and metapopuiation concepts can be applied to the prediction of moth presence. The goal of this thesis was to examine many potentially important patch- and landscape-level characteristics to determine which characteristics are most important to patch occupancy by the phlox moth, to aid in conservation efforts, and to compare the roosting sites of the moth with non-roosting sites to determine whether moths are more likely to be found in certain areas within a patch.
Thesis ®rga~i~ation
This thesis is organized into three chapters. The first chapter is a general introduction, the second chapter is the research project itself, and the third chapter is a general conclusion. The general introduction puts the research project into a larger context, as it applies to the conservation of other Lepidopteran species. The second chapter presents the results of the research project, as formatted for submission to Ecological Entomology. The third chapter is a general conclusion, which generalizes the results of the study to the larger context of Lepidopteran conservation.
I~efere~ces
Erlich, P.R. (19$4) The biology of butterflies. Academic Press, London, UK. Hanski, 1. (1991) Single-species metapopulation dynamics: concepts, models and observations. Biological Journal of the Linnean Society, ~Z, 17-38. Hanski, 1. & Gilpin, f~. (1991} Il~etapopulation dynamics: brief history and conceptual domain. Biological Journal of the Linnean Society, 42, 3-16. 3
Hanski, I., Kuussaari, IUI. & Nieminen, M. (1994) Metapopulation structure and migration in the butterfly l~lelitaea cinxfa. Ecology, 73, 747-762. Henderson, R.A. &Epstein, E.J. (2006) Oak savannas in Wisconsin. Wisconsin Department of Natural Resources, Madison, WI Hill, J.K., Thomas, C.D. &Lewis, O.T. (1996} Effects of habitat patch size and isolation on dispersal by Hesperia comma butterflies: implications for metapopulation structure. Journal of Animal Ecology, 65, 725-735. Janzen, D.H. (1988) Ecological characterization of a Costa Rican dry forest caterpillar fauna. Biotropica, ~0, 120-135. ~llenendez, R. &Thomas, C.D. (2000} Metapopulation structure depends on spatial scale in the host-specific moth Vl/heeleria spilodactylus (Lepidoptera: Pterophoridae). Journal of Animal Ecology, fib, 935-951. Nieminen, M. ~ Hanksi, I. (1998) Metapopulations of moths on islands: a test of two contrasting models. Journal of Anirr~a! Ecology, fi7, 149-160. Ravenscroft, N.O.M. &Young, M.R. (1996) Habitat specificity, restricted range and metapopulation persistence of the slender scotch hornet moth ~`ygaena loti in western Scotland. Journal of Applied Ecology, 33, 993-1000. Saunders, D.A, Hobbs, R.J. & Margules, C.R. (1991} Biological consequences of ecosyster~n fragmentation: a reviev~r. Conservation Biology, ~, 18-32. Swengel, A. & Swengel, S. (1999) Observations of Schinia Indiana and Schinia lucens in the Midwestern United States (Lepidoptera: Noctuidae}. Holarctic Lepidoptera, fi, 11:21. Thomas, J.A. (1991) dare species conservation: case studies of the European butternies. British Ec©logical Society, London, UK. U.S. Fish and Wildlife Service. (2006) the endangered species program. v~nrvww.fws.gov/endangered, accessed 1 Apri12006. Warren, M.S. (1993) A review of butterfly conservation in central southern Britain. I. Protection, evaluation and extinction on prime sites. Biological Conservation, S4, 25-35. Vitousek, P.M., Mooney, H.A., Lubchenco, J. ~ Melillo, J.I'lll. {199?) Human domination of Earth's ecosystems. Science, 2~'7, 494-499. Wisconsin Department of Natural Resources. (1999} Kanner blue butterfly habitat conservation plan. wvvw.dnr.~rvi.govlong/land/forestry/karnerfhcptext, accessed 1 March 2006. Young, r~l. (1997) the natural history of moths. Ti &A Poyser, London, UK. 4
CHAPTER ~. THE ~'e4TCH ~,H® L~i~C3~~ApE CH/~i~CTERiST9GS ~E~ATE[3 TO THE OCCUPANCY OP HOST-P~~HT PATCHES ~Y THE iPHL®x MJOT`H, ~Ci~l~li~ Ii~~1~41VA ~~.EP~®t)P'TE6~: N~OCTUQL~AE~
A paper to be submitted to ~color~ica6 ~~toPnolog~ Danielle M. DeBruyne a~tP~~t
This study investigated the nr~icrosite, patch, and landscape level habitat requirements of the phlox moth (Schinia Indiana, PJoctuidae). Detection-nondetection and habitat data were collected at a 2 total of 121 host-plant patches spread across 60 km at the Fort McCoy military installation in ~/isconsin, USA,. The relationships between the probability of patch occupancy and the patch and landscape variables were modeled using program PI~ESE~ICE. A subset of patches was resampled to estimate the probability of detecting a moth when present, given imperfect detection. The subset of models which had the best predictive abilities were compared using a suite of model diagnostics including prediction error using the training set-test set approach, Hosmer-Lemeshow goodness of fit tests, and assessment of the standardized residuals. Patch size, distance to the nearest previously occupied patch, and the average number of buds per phlox stem within a patch were the most important predictors of troth occupancy, v~hile distance to the nearest patch, bordering road type, and the average number of blooms per stem were less important predictors. Cloud cover and precipitation influenced the probability of detecting moths during surveys. Frost-plant stem spacing, canopy cover, size of the nearest patch, and the total area of patches on the surrounding landscape were not found to be important predictors of moth occupancy in this study. Microsite habitat preferences were examined using MAI~lOVA. At the microsite level, moths were found more often stems with large numbers of buds and low canopy cover, located in areas with high stem density. Moths were not found more frequently in microsites Frith greater percentages of stems in bloom, or for stems with greater numbers of blooms than randomly selected sterns. Management recommendations include increasing patch sizes and connectivity, and mitigating habitat loss by replacing patches with patches equal or greater in occupancy probability, rather than simply considering patch size.
9ntrc~c~aactiort
Frequently in today's fragmented habitats, species exist as metapopulations, with small habitat patches supporting subpopulations that require occasional migrants to be maintained (F#anski ~ Gilpin, 2001 }. In these rnetapopulations, the patches must be large enough and provide the correct environment and resources to support the species of interest, and they must be close enough to other patches of sufficient size and quality to ensure a source of migrants (Hanski ~ Gilpin, 2001). The exact patch and landscape characteristics required to support subpopulations within a rnetapopulatian differ by species, but often there are commonalities between similar species' needs. Many butterfly and moth species have been the subjects of studies that have investigated the patch habitat requirements and landscape configurations needed to support metapopulations. The idea that in most lepidopteran metapopulations, larger patches that are closer together are more likely to be occupied and are more likely to receive migrants, compared to Smaller and more dispersed patches, is well-established (for example, Hanski et af., 199 ; Hill et al., 1996; i~ieminen, 1996; Brommer &Fred, 1999}. Likewise, the idea that in mainland-island populations, islands or patches that are closer to the stable mainland population are more likely to receive migrants than those located farther away is well-accepted (l~ieminen &Hanski, 1998). In mark-recapture studies, Pyror~ia tifho~us, fUlar~iola jurti~aa, Hesperia cornea, and Parnassius apc~llo butterflies have been observed moving mare than 1 km {Dover et al., 1992; Hill et al., 1996; Brommer &Fred, 1999}, and Solaria aquilanaris butterflies have moved up to 13 km (Baguette, 2003} bettiveen patches. l~fiost distances moved were typically much lovyer at about 50 m far Hesperia comma {Hill et al., 1996), 260 m in Pyronia tithonus, and 400 m in ~laniola jurtina (Dover et al., 1992). in similar studies of moths, including Choristoneura, Operophtera, Callirnarpha, Tyria, Lymantria, Paracolax, ~?usina, Hydraecia, Archanara, Spoa+optera, Diarsia, and Heliofhis species, distances moved ranged from maximums of 15G m to 5.8 km, and typically were below 2 km, while average distances moved were typically less than 100 m, although few moths were recaptured in many studies (summarized by l~ieminen, 1996}. l~~oths that are strong fliers or are oligophagous move longer distances than moths that are weak fliers or are polyphagous, when accounting for moth site (~lieminen, 1996; ~Vieminen &Hanski, 1998} Several studies have found that butterflies and moths are more likely to occupy a patch if the host plants are more robust. hatches with larger numbers of host plants and larger host plants are more likely to be occupied (11~enendez &Thomas, 2000; batter &Roland, 2002; fi~enendez et al. 2002). Similarly, patches with more nectar flowers are more likely to be occupied (Dover et al., '1992; Brommer &Fred, 1999; batter ~ Roland, 2002}. The sun exposure of the patch, measured by aspect, was also found to be an important positive predictor of occupancy in some cases (Ravenscroft & ~'oung, 1996; Forare t~ Solbreck, 1997}. ~Cnowledge of the patch and landscape characteristics required to support populations of rare species are of particular interest. The phlox moth (Schinia Indiana, ~loctuidae} is a rare, cryptic moth that was first described in 1908 in Indiana (Flardwick, 1958}. Since its discovery, it has been found in ~rk~nsas, Illinois, Indiana, Iowa, lvlichigan, liAinnesota, l~ebraska, Borth Carolina, Texas, and Wisconsin (Forbes, 1954; Hardwick, 1958; Balogh, 1987; Swengel ~ Swengel, 19►99}. Historically, 6 populations of the moth probably occurred throughout the range of its only host-plant, downy phlox (Phlox pilosa L., Polemoniaceae). Phlox occurs from the Atlantic coast west through the tallgrass prairie region in the I~SA, (Newcomb, 1977). Currently, phlox moth populations are known to exist in Iowa, Michigan, Minnesota, and Wisconsin (Swengel &Swengel, 1999). It is fisted as an endangered
Species !ii Michigan (Michigan iNatural Features Inventory, 2006), a species of special concern in Minnesota (Minnesota Department of Natural Resources, 2006), and an endangered species in Wisconsin (Wisconsin Department of Natural Resources, 2006). One of the reasons that the phlox moth is so rare is that its Life cycle is tied very closely to downy phlox (Hardwick, 1996; Swengel ~ Swengel, 1999). Phlox primarily inhabits prairies and savannas, two very rare habitats. Phlox also frequently can be found growing along roadsides, both in the mowed rights-of-way, and a short distance into bordering woodlands. Roadside patches of phlox may be mowed during spring, removing the buds, flowers, and seeds #hat the moths depend on (Wisconsin Department of Natural Resources, 1999). The adults emerge during late May and early June in Wisconsin, as phlox begins to bloom. They have multi-toned pink to violet wings and pale green hirsute heads and bodies, v~hich gives them a very similar appearance to the phlox flowers and buds. When the temperature is below 16°C, and at night, the moths roost on phlox flowers, and occasionally buds, apparently depending on their camouflage to protect them frorr~ predators. When the temperature is above 16°C, the diurnal adults are able to take flight. These strong fliers spend the daytime hours mating, feeding on nectar (phlox and possibly other species}, and if they are females, ovipositing. The females lay their eggs singly inside the sepals of flower buds. When the larvae hatch about a week later, they burrow into the flower buds, seal themselves inside, and feed on the buds. As the phlox goes to seed, the larvae begin to feed on the seeds as well. About three weeks later, the larvae are ready to pupate. They burrow into the soil, pupate, and remain as pupae until the following spring. (Hardwick, 1958; Hardwick, 1996; Swengel &Swengel, 1999) One of the largest known populations of the moth occurs at the Fort McCoy Military 2 Installation in west-central Wisconsin (Kirk, 1996). Fort McCoy encompasses about 243 km , comprised primarily of oak and pine woodlands, oak savannas, prairies, and marshes (Fort McCoy Public Affairs Office, 2006}. Scattered throughout the Fort, primarily along roadsides, are more than 300 known patches of downy phlox, ranging in size from a few stems to 3 ha (Kirk, 1996}. Since 1994, when the Mature Conservancy began mapping roadside phlox patches, ph{ox moths have been found at approximately 10% of the patches (Kirk, 1995; Wilder, 1999-2004; Wilder, pers. comet.). Phlox moth conservation efforts at Fort McCoy primarily involve monitoring the occupancy status of phlox patches, restoring oak savanna habitats, mitigating habitat loss, and protecting roadside patches from spring and early summer mowing (Wilder, pers. comet.). However, conservation efforts are hampered by lack of information (Wisconsin Department of [Natural Resources, 1999). In particular, the rarity of the moths has not been adequately quantified. 7
Only a few populations of moths have been found in each state, but many phlox patches have not been searched for moths, particularly those on private land, and few searches have been conducted in states other than 1~lichigan, llltinnesota, and Wisconsin (Wisconsin iDepartment of Natural Resources, ~ 990; Michigan Natural Features lnventor~~, 2006; il~innesota department of Natural Resources, 2006}. Knovv)edge of the characteristics related to patch occupancy is important to directing search efforts in new regions. Additionally, phlox moth searches are tedious (Swengel & Swengel, 1999). Therefore, the purpose of this study was two-fold. The f rst objective was to examine many potentially important patch- and landscape-level characteristics to determine which characteristics are most important to patch occupancy by the phlox moth. This information will aid in conservation efforts and the location of new populations. The second objective was to compare the roosting sites of the moth with non-roosting sites to determine whether moths are more likely to be found in certain areas within a patch. This information will allow future researchers and managers to have increased search efficiency.
nAeth®~~
PATCH OCCUPANCY AND ROOSTING SITE ,STUDIES
O ver~ie vW
This project is divided into two smaller projects. The first is an assessment of the local and landscape habitat variables that are related to the occupancy of a patch by the phlox moth, and is referred to as the Patch Occupancy Study. The second is an assessment of roosting site selection by the moth and is referred to as the Roosting Site Study. The data collection for both sub-projects occurred simultaneously, and some of the same data were used for both projects; therefore, the data collection methods for both projects are preser~#ed concurrently in the text. The Patch Occupancy Study was designed to assess the importance of local and landscape variables in predicting patch occupancy by the phlox math, while controlling for variables related to moth detection probabilities, such as weather conditions. Program PRESENCE (l'~IacKenzie &Hines, 2002}, based on logistic regression principles, along with standard stepwise and best-subsets regression procedures, were used to build candidate models. The models were evaluated using chi- square improvement and lack-of-fit tests, assessment of residuals, and prediction error using the training set-test set approach. The role of time-constant local and landscape variables in predicting patch occupancy and the role of time-varying and control variables in predicting detection probability were quantified. 8
The Roosting Site Study was designed to compare roosting sites (phlox sterns) selected by moths to randomly seiected roosting sites in the sanne patch. A paired I~A~®~/A followed by t-tests of significant variables were used to assess and quantify differences between moth-selected and ranc~ml}o seiected stems.
Patch Selection
during 1994- i 996, Katherine Kirk of the mature Conservancy mapped ail downy phlox populations visible from all roads and trails traversable by a two-wheel drive vehicle on the Fort McCoy Ivlilitary Reservation in Wisconsin (Kirk, 1996}. A list of patches to be surveyed in the current study was generated from Kirk's list of phlox patches, including the unnamed patches of less than 100 stems. To generate the fist, first, all unnamed patches and all sub-patches that Kirk mapped as a single patch were given individual patch names. Then, all patches south of interstate Highway 90 were excluded as inaccessible due to the placement of nearby firing ranges, and ail patches within 0.5 km of Interstate Highway 90, the Fort boundary, or the ~lorth impact Area were excluded to help ensure that the nearest neighboring patch of each survey patch would be accessible. The excluded patches were excluded from use as focal patches, but could be included in the study as nearest neighboring patches or as part of the total patch area surround'sng each focal patch. The remaining patches were randoml~,~ assigned to one of 3 groups: the training group, the test group, and the extra-patches group. The training group was used to build the models and the independent test data were used to assess the models' prediction accuracy. Training set-test set methods are generally recognized as more robust methods of model selection than jackknife or leave-one-out methods (1~ittinghoff et al., 2004). Training set-test set methods are Less likely to result in the selection of a model that is over-fit to the dataset and less easily generalized (~ittinghoff et af., 2004). The extra patches group was necessary in the event that a patch rio longer existed or was inaccessible due to military training; in either event, the nearest patch on the list of extra patches was substituted for the seiected patch. it was possible for patches in the training set to be nearest neighbors with patches in the test set, and vice versa. The patches in the training group ~~~ere further randomly divided into subgroups, with half the patches scheduled to be surveyed 3 times and the other half 1 tune, with each patch-survey combination assigned to 1 of 6 weeks. Six weeks is the maximum-recorded length of the flight period (Swenge~ ~ Swengel, 1999). Patches with 3 surveys were randomly assigned to the following schedules (each digit indicates a week): 1-2-4, 1-2-5, 1-3-4, 1-3-6, 1-4-5, 1-4-6, 2-3-5, 2-3-6, 2~-4-5, 2-5-6, 3-4-6, or 3-5-6. Ali test patches were scheduled to be surveyed 3 times, and were randomly assigned to the following schedules: 1-2-4, 1-3-5, 1-4-5, 2-3-6, 2-4--6, or 3-5-6. Some of the survey schedules for patches in the training set and in the test set were the same; some were not. The set 9 of survey schedules used for the training data and for the test data ensured that patches were not surveyed in 3 successive weeks, so that variability across the season would be sampled in each patch, and that all weeks were assigned equal numbers of patches. The patch-week combinations are important to the parameter estimation process, which will be explained below, while the patch- survey schedule combinations are not important. Within each week, the patches were divided into regionally proximal groups, so that 8 surveys were scheduled each day. Grouping the sites randomly would have made travel between sites on a given day logistically unfeasible. Successive surveys of the same patch and the same region were scheduled on the same day of following weeks, because the adult moths of many small species live about one week (Himmelman 2002 ~ Winter, 2000), and changes in the probability of detection between weeks, rather than within weeks, was of interest. If a given patch was surveyed more than once in a particular week, the survey results for that week would be biased slightly tov+rards the results of the surveys of that patch. Therefore, if multiple patches were surveyed more than once, the results could be highly skewed.
Meld Data
Moth detection-nondetection surveys for the training dataset began on 27 N1ay 2005, the day after adult moths were first observed during the season. Surveying Started about 2 weeks later than in typical years, in which surveys can be conducted during the third week of Nday (Wilder, 1999-2004; Vvilder pets. Comm.), because the late April and early to mid-Ivlay weather was about 5°C cooler than normal (weather Underground, 2006) and the phlox emerged late. Surveys were not conducted before adc.alt moths were known to have emerged to avoid violating the closed-season assumption of Program PRESENCE, which will be discussed in greater detail below. The assumption requires that the species of interest is available to be surveyed throughout the sampling period. If there are surveys during which the species cannot be found, due to migration, hibernation, etc., the estimates of detection probability, and the mode) as a whole, will be inaccurate. Surveying continued through 22 June 2005, during which the weather was about 5°C warmer than normal (Weather Underground, 2006} and Trost phlox plants lost all their blooms. Because of the weather conditions during the actual survey period, which also included awarmer-than-typical week 2 and 5 cm less precipitation than normal, fewer surveys than planned were comple#ed. The 3 1/2 week survey period included data collected at 13~ training set patches and 32 test set patches. Qf the surveyed patches, only 121 training patches (87 surveyed once and 34 surveyed twice} and 30 test patches (22 surveyed once and 8 surveyed twice} were used for analyses because 1 } 6 surveys were completed when temperatures were too high, and 2} no moths were found during week 4, violating the closed season assumption of Program PRESENCE, so all week 4 surveys (19-22 June) were excluded. 10
Surveys were conducted each day beginning at 05:00 (when it was light enough to conduct surveys) and continuing until the temperature reached 16°c in the shade or until ail 8 surveys scheduled for that day were completed. Kirk (1996) and Swengel and Swengei (1999) showed that adult phlox moths remain inactive, resting on phlox biossorns, in the mornings when the temperature is below 16°C, and are consequently available to be surveyed. Surveys for phlox moths Cann©t be conducted above 16°c because the moths are extremely swift fliers, and are rarely identifiable in flight. !f a sunoey was not completed on its scheduled day due to high temperatures, it was surveyed as soon as possible, but not within 6 days {including the survey day} of a previous or upcoming survey. Although cloud cover and precipitation were known to have an effect on detection probabilities, they were recorded as control variables, rather than used as survey constrainfs, so that surveys could be conducted on a majority of days. vvind speed was recorded during each survey, however, no prior evidence has been collected that would indicate that moths are more or less active during windy conditions; therefore, it was collected as a control variable and not used as a precondition for surveying. All phlox blooms on up to 50Q~ flowering stems, or ail the stems in a patch if there were fewer than 500 flowering, were searched far adult moths. Searching stopped when 500 stems were searched or a moth was found. Only 500 stems were searched because searching all stems would take most of the day for very large patches, and 500 was estimated to be a reasonable number to survey in half an hour, allowing 8 surveys to be completed each morning. The proportion of stems surveyed was estimated and was used as a control variable in PI~BSB~lcE. Patches were generally somewhat long and narrow, because they frequently parallel roadwa~rs, so searching began at either end or the center of each patch. The starting location was determined randomly, and different starting locations were used for each successive survey. Por each moth survey, the following data were recorded: patch name (1994-1996 training area, patch name with modifications detailed above, and 2005 training area), observers, date, start time, stop time, number of stems searched (using aclick-counter) and whether all stems in the patch were surveyed, temperature tat stop time}, wind speed tmis), cloud cover percentage tto the nearest 25%), precipitation {none, drizzle, rain, earlier in day, or last night), and number of moths found (Table 1 & Appendices A ~ B). If moths were found, the following data als© were recorded for the specific stem where the moth was found: stem number, canopy cover (number of cells on a spherical densi®meter with more than 50°lo canopy cover, converted t® canopy cover percentage), cloudy day, stem in sun, or stem in shade, number of blooms, number of buds, approximate stem spacing 2 (m /stem by estimating the Theissen polygon surrounding the stem [the area surrounding a point that is closer to the point than to any surrounding points]), percent of stems flowering nearby (of 10 nearest ster~~s), and distance to patch edge (m} {Table 1 ~ Appendices A ~ B}. Detection-nondetection surveys for the test dataset began on 27 May 2005. Three surveys each day, rather than 8 as for the training data, were carried out by members of the seasons! endang-eyed species crew at Port iVlccoy between 05:00 and 07:30, when the temperature was below 11
16°C. To avoid observer bias in data cofiection, different crew members were scheduled to survey the same patch during successive surveys. Unlike the training dataset, all flowering phlox stems in the patch were searched for adult moths, not just the f rst X00 stems, otherwise the methods were the same. A11 sterns were searched beca~:se the purpose of the test data was to conf rm presence if possible. For each survey, the same data as for the training dataset were recorded, with the exception that the number of stems searched was not recorded because the proportion of the site surveyed was 100%, and did not need to be estimated, and variables related to the specific stem where the moth was found were not recorded to avoid introducing observer bias into a small dataset (Table 1 & Appendices A & g}. within ~ days of each moth survey, the following patch data were recorded: patch type (open grassland, woodland, savanna, woodiandlgrassland edge, or grassland corridor through woodland), aspect (direction from which the patch receives the most sunlight), bordering road type (none, two-track, sand, gravel, seal-coated gravel, or paved, more than one road type could be 2 recorded), survey date, approximate stem spacing (m /stem by estimating the average Theissen polygon surrounding stems), the number of blooms and the number of buds on 10 stems, and the canopy cover percentage (using a spherical densiometer) above 10 stems (Table 1 &Appendices A ~ B). canopy cover data were recorded as the number of cells on the densiometer (out of 24) with greater than 50% canopy cover, and was converted to canopy cover percentage. 1=or the latter 3 variables, an approximate survey grid of 1 o survey points was calculated based on the length and width of each patch as measured by pacing, the grid was paced out through the patch, and at each point, the blooms and buds on the nearest stem were counted and canopy cover was measured. If there were fewer than 10 stems in a patch, the number of blooms and buds and the canopy cover above ail stems were recorded. The averages and standard deviations of the number of blooms, number of buds, and canopy cover v~rere then calculated. batch size, approximate stem spacing, and number of stems searched were used to estimate the proportion of the patch that was surveyed. The total number of stems in the patcl^~ was estimated by multiplying patch size by stem spacing, and the proportion surveyed was estimated as the number searched divided by the estimated total number of stems. l~ext, the nearest neighboring patch was found and was identified as one of Kirk's patches or was given a new name. IVew patches were named for the 1994-1996 military training area in which they were found. The patches were assigned levers in alphabetical order as they were found, starting with the next patch letter available in the training area. For each of the nearest neighboring 2 patches, patch name, stem spacing (m , by releve} and percentage of stems flowering (to the nearest 10%, by releve) were recorded (Table 1 ~ Appendices A ~ B). Two of the categoriea! variables were re-categorized to better reflect their relationships to moth presence (Appendix B}. The 8 aspect categories were regrouped into two categories: patches facing the sun while surveys were being conducted (east through southwest), and patches facing away (west through northeast}, to better reflect differences in microclimate. The road types were 12
grouped into 3 categories: dusty gravel roads, formerly dusty (pre-2002} sealcoated roads, and non- gravel roads. The categories reflect decreasing effects of roadside dust, respectively. Gravel roads may cause phlox patches to be dusty and uninhabitable, sealcoated roads may be in a state of recolonization, while non-gravel roads would be unaffected. Each patch and its nearest neighbor were mapped by hand on 1:10,000 scale orthophotos. Gaps of at least 10 m between phlox stems were used to delineate individual patches. Ten meters was a large enough gap to be mapped on the orthophotos, and seemed to be a natural break between most patches. The distance between each patch and its nearest neighbor was measured by hand to the nearest 10 rn, using the maps {Table 1 ~ Appendices A & B). Several missing values, errors, and inconsistencies were found in the dataset. Imputations and related explanations were reported only for data used in analyses. No survey had more than 1 value imputed. Also, no moths were found during any survey for which variable values were imputed. Because moths were not found during a r~najority of surveys (138 of 155 surveys), it 'rs unlikely that the imputed values affected the results of the study. Several minutes were subtracted from the start or stop times of 3 surveys. Two wind speed values were missing far the week 1 surveys of patches B15C and At~MOC3. No other surveys were conducted that day, so the missing values were imputed as 0 m/s, the most common wind speed recorded during the survey period {$5% of the wind speeds recorded were 0 mis). Also, wind speeds recorded that day at the Sparta-Fort McCoy airport averaged 0 m/s, with occasions! gusts averaging 0.7 m/s (V~eather Underground, 2006).
P~4 TC~ OCCUPAIVGY STUD ~'
G/S Data
The patches mapped on the orthophotos and the patches mapped by Kirk (1996) on Fort McCoy road reaps were digitized using ArcGIS 9 (ESRI, 200) into 2 separate shapefiles (.shp). The shapefiles were used to calculate patch area, area of the nearest patch, the total area of phlox patches in a 0.~ km radius, and the distance to the nearest previously occupied patch for each focal patch (Appendix B}. The area of each patch and the area of its nearest neighbor were calculated automatically by ArcGiS. The total area of ail patches within a 0.5 km radius of each focal patch, but not including the area of the focal patch, were calculated as follows: the center point of each focal patch was determined by using the centroid function in ArcGiS, constraining the center to fail within the patch boundary, because some of the patches were concave (for example a long linear patch along a road turning a corner at an intersection). The differences between the centroids constrained to be within patch boundaries and those not so constrained were all less than 50 m. Then, the 1994-1990 and 13
2005 total patch areas within 0.5 km were calculated as the total areas of ail 1994-1996 mapped and 2005 mapped patches within 0.5 km of each centroid minus the area of the central or focal patch, respectively. Ahalf-kilometer radius was used because many butterflies and moths are known to be able to fly 1 km between patches, but on average move about 1/3 of that distance (®over et al., 1992; Hill et al., 1996; IVieminen, 1996; Sror~mer ~ Fred, 1999), so 0.5 km was decided on as a reasonable distance that would include patches within range of a majority of individuals in each patch, and therefore potentially reflective of the largest home range size of the moths. Finally, the average total patch area in a 0.5 km radius was Calculated by averaging the areas of the 1994-1996 and 2005 mapped patches within 0.5 km of the patch centroid. The average was used because not all patches mapped in 1994-1996 were mapped in 2005, and most patches were similar in size and shape when mapped in 1994-1996 and 2005, so using the average of the total patch areas within 0.5 km helped to account for the area of patches unmapped in 2005. Ideally, the area of patches mapped in 1994- 1996, but not mapped in 2005, would have been added to the area of patches mapped in 2005, in the 0.5 km radius. Unfortunately, there was no way to do this using the GIs software available, and computing these numbers by hand would have been extremely time-consuming and error-prune. Finally, the distance from each focal patch to the nearest patch in which the moth's presence was confirmed at least once between 1994 and 2004 was determined. To calculate the distance to the nearest previously occupied patch, moth detection-nondetection data for surveys conducted between 1997 and 2004 were obtained from Tim Milder, head of the endangered species program at Fort McCoy (Milder, 1999-2004, Milder pars. Comm.). (~eBruyne participated in the 2001-2003 surveys as a member of the Forfi McCoy endangered species crew.) The 1997-2004 data had been Collected in a manner similar to the data collected for this study, by searching all phlox stems in each patch for adult moths between 07:00 and the time at which the temperature reached 16°C, however, cloudy and rainy mornings were favored for conducting these surveys. in ArcG1S, each patch mapped in 1994-1996 was classified into 1 of 3 categories: moths found, moths not found, or patch not surveyed during 1994-2004. The centroid of each 1994-1996 patch in which moths had been found was determined (again, constrained to lie within the patch}. Then, the distance from the centroid of each focal patch to the centroid of the nearest 1994-1996 patch where moths had been f®und between 1994-2004 was calculated.
Preliminary ~r~algeses
Most of the preliminary analyses for the Patch Gccupancy Study were carried out using JMP 6.0.0 (S,~S institute, Inc., 2005). Mhen an analysis was not conducted in JMP, the program that was used was noted in the text. 14
A,s a rule of thumb, logistic regression analyses, in order to be valid, require at least 5 observations per combination of categorical predictor variables (Glantz ~ Blinker, 1990; Vittinghoff et al., 2005). The variable patch type contained fewer than 20 patches per category for the woods, edge, open, and savanna categories, with only 5 observations for the open and savanna categories. However, patch type and canopy cover appeared to be associated with one another. A one-way analysis of variance revealed that patch type categories could be distinguished from one another based on canopy cover (F=11.9, p<0.0001). In the analysis, average canopy cover values for multiple surveys of a single patch were weighted according to how many surveys had been conducted at the patch. The Tukey-t4ramer l-lonestly Significant difference (HST) test was used to compare pairs of categories (Table 2). Open patches were significantly different from all other patch types. Savanna and edge habitats could not be distinguished from one another based on canopy cover. Likewise, edge, grass corridor, and wooded habitats, could not be distinguished. After considering the partially significant pattern of increasing canopy cover through the 5 categories, the low number of observations per category, and the subjectivity involved in classifying patches by patch type, patch type was eliminated from the list of variables being considered in the analyses. Canopy cover was not a perfect surrogate, but was more objective than patch type. Two variables, cloud cover and precipitation, had few or no observations for rriany categories when examined together in bivariate space (1=figure '! ). Visually, the variables appeared to be associated, but the association between the 2 variables could not be tested because there were too few observations per cell in the contingency table. The apparent association was an obvious result of the dependence of rain on cloud cover. To remedy the problems, cloud cover percentage and precipitation were collapsed into a single variable with 3 categories: no rain within 12 hours prior to the survey stop time and 0 to 50% cloud cover (sunny and dry), no rain within 7 2 hours prior to the survey stop time and 75% or greater cloud cover (cloudy and dry}, and rain within ~ 2 hours prior to the survey stop time (cloudy end wet}. There were no surveys for which rain occurred within 12 hours prior to the stop time and the cloud cover percentage was less than 75%. The middle category, 50% cloud cover, was assigned to the sunny and dry category because there were no surveys with 50% cloud cover in the cloudy and wet category. lVlultiple logistic regression analyses require that only a few assumptions about the data hold true. One assumption is that the continuous predictor variables do not contain extreme outliers (Vittinghoff et al., 2004). Extreme outliers can result in biased or inestimable model coefficients (Vi~ringhoff et a~., 2004}. Several of the patch variables -patch size, nearest patch size, distance to nearest patch, total patch area in a 0.5 krn radius, distance to the nearest previously Qccupied patch, and wind speed -contained several large outliers (the variables were right-skewed}. Each of these variables was log ~ p-transformed (Table ~). The transformations reduced the number of outliers and shortened the tails of the univariate distributions. Although transformed variables are more difficult to interpret, trial runs of Program PI~ESEI'~CE using the un-transformed variables indicated that 15
transformation was necessary; in the models that were fit using un-transformed variables, the coefficients were either inestimable or obviously erroneous. However, the coefficient for wind speed was inestimable by PRESENCE, regardless of any transformations used to reduce the number of outliers, so wind speed was not included in the analyses. There were 31 surveys with wind speeds greater than 0 mis, nnost of which were recorded on days with highly variable, gusty winds. As a result, wind speed was highly right-skewed. Examination of the data revealed that about 20% of the surveys had wind speeds greater than 0 m/s and about 20% of the moths were found when the wind speeds were greater than 0 m/s, so it is unlikely that there was a strong relationship between wind speed and moth detection probability. Another important assumption is that the outcomes are independent of one another (Vittinghoff et al., 2005). The biggest threat to the validity of this assumption in the phlox moth data was spatial autocorreiation among the outcomes. To assess spatia! autocorrelation, the spatial distribution of detections and nondetections was examined using the spatial autocorrelation tool in ArcGIS 9. This tool statistically tested the data for spatial clustering or spatial dispersion using f~lorans I test. A third important assumption related to the phlox moth dataset was that no muiticollinearity existed between the predictors (Glantz &Slinker, 1990; Vifitinghoff et al., 2005). Potential multicoliinearities were examined both before and after the logistic regression (Glantz &Slinker, 1990), and checks for multicollinearity are discussed here and in the following section. To screen for r~nulticoilinearit~,,~, the correlation matrix of continuous predictor variables was examined for the dataset, subsetted by week. Correlations above o.7 indicate potential multicollinearity (Glantz & Slinker, 1990}. Regressions of each variable against all other variables also can be used to check for 2 multicollinearity. Regressions with R values greater than 0.9 indicate probable multicollinearity (Glantz &Slinker, 1990). Because the proportion of stems surveyed was calculated from 3 other variables -patch size, number of sterns searched, and stem spacing -the proportion surveyed was regressed against the other remaining variables to check for rnulticollinearity. If the correlations or the regression indicated multicollinearity between 2 variables, the variable of less interest or importance to the study was eliminated. The associations involving categorical variables were investigated using graphical methods, and were assessed using contingency tables and ANQVA's.
Logistic Regression anr~ 11~odel Selection
The relationship between the predictor variables and the outcome variable was assessed using the multiple logistic regression techniques in Program PRESENCE, which was developed by the U.S. Geological Survey (Hines, 2005). PRESENCE eras developed to assess the probability of patch occupancy given imperfect detection of a species during multiple survey occasions throughout 16 a single or multiple seasons. if a species is present, it either can be detected or undetected during a given survey. If undetected, the species either was not present or was present but not detected. The aim of most presence-absence surveys is to determine if the species is using the patch. Studies which assume that undetected and absent are equivalent terms will underestimate the actual proportion of patches occupied. in order to correctly estimate the proportion of patches occupied, detection probability must be accounted for, To this end, the models in P1~ESEi~CE are divided into 2 parts: the probability that a patch is occupied (4') and the probability that an anima! is detected (p}. Each of these probabilities can be related to ccvariates. The occupancy probability is related to time- constant covariates, and the detection probability is related to time-varyin covariates. Each probab~l~ty is related to its covariates via the logit Ink f ~ or p 1/(1 exp 0 ~ ), where Ro is the intercept, R are the estimated coefficients for each variable, and ~; are the values of each variable}. lasing ~ and p, the probability of a given result (1 or 0) for a particular survey, or the probability of a patch history (e.g. 1-0-0-1) can be calculated using a series of probabilistic arguments, which are detailed in I'~acKen~ie et al. (2002}. The probability that a patch is occupied is calculated as the estimated probability that the patch is occupied (~) times the product of the probabilities of detecting (p) or not detecting (1-p) the animal during each successive survey, and the probability that a patch is not occupied is the estimated probability that the patch is occupied (~) times the product of the probabilities of not detecting (1-p) the animal during each successive survey, plus the estimated probability that the patch is not occupied. So, for example, if there were 3 surveys of a patch, and the animal ~►•as detected only during the first survey, the probability argument would be: ~*p*(1-p}*(1-p}, and if the animal was not detected during any survey, the probability argument would be ~4~~(1-p)*(1- p)*(1-p}}+(1- ~}. (~11acKenzie e~ a~., 2©OZ} for each model, the estimated coefficients for each variable represent the change in log odds of moths being found associated with a 1-unit increase in the variable, while holding all other variables constant. The coefficient for the intercept represents the baseline log odds, i.e. the log odds for observations in the baseline categories of categorical variables (for example, sunny and dry was coded as o, the baseline category for the cloud cover and precipitation variable). V'Vhen no categorical predictors are included, the coefficient for the intercept necessarily becomes 0, because the log odds must be 0 when all of the continuous predictors in the model are 0 (Hawkins, 1980). Vvhen the baseline is 0, comparisons of different values of one variable are made when holding ail other variables constant at their mean values. To be interpretable, the log odds are converted into odds ratios by raising a to the power of the coefficient. The odds ratio is the change in the odds (for example 3:1) of moths being found associated with a 1-unit increase in the predictor variable, calculated as the predicted probability divided by 1 minus the predicted probability. Or, for the intercept, the odds ratio is the baseline odds that moths will be found. Because some of the variables were logl,g transformed, the interpretation of coefificients is more complicated. Eor these variables, 17
the odds ratio is the change in the probability of moths being found associated with, in this case, a patch that is 10 times larger or less distant, white holding all other variables constant. In addition to the estimated variable coeffcients, the program output includes the naive Estimate of the proportion of patches occupied, the estimated proportion of patches occupied, and the detection probability for each sampling period. The naive estimate of the proportion of patches occupied is the proportion of patches where moths were found during at least one survey, and does not take into account detection probabilities of less than one. The program also provides the model likelihood estimate to assist in determining the best model. (~Ilac~Cenzie et ai., 2002) This project used the single season model vvith site (time--constant) and sample (time- varying) covariates. Each survey week was considered a separate sampling period for the phlox moth data because detection probabilities were expected to be fairly constant within a week. This expectation v~ras based on the knowledge that many adult moths of different species live for about a week (hinter, 2000; Hiri~meiman, 2002}, and peak abundances occur immediately after phlox begins to bloom (Swengel ~ Swengel, ~ 999). Successive weeks are expected to have fewer adult moths present, and therefore, each successive survey of a given patch should have a lower probability of detecting moths and concluding that the patch is occupied. Using days as separate sampling periods would be ideal, but it was not feasible to sample enough sites each day for a viable analysis. The categorical predictors with more than 2 categories, cloud cover and precipitation, and road type, were coded as ordinal variables, rather than using dummy variables. Coding the categories with dummy variables vrould have resulted in an additional variable being added to the model for each category beyond the second category (the first category is represented by the baseline probability of occupancy or detection). Due to the low number of detections and therefore, f®w number of estimable coefficients, this was not ideal. Coding the variables as ordinal makes the assumptions that the categories are ordered and each category is one unit greater than the previous category, which is only approximately true, but the ordinal coding allowed for the estimation of fewer coefficients compared to using dummy variables. For cloud cover and precipitatian, the categories are clearly ordered, both in terms of biology and the ratios of detections to nondetections per category; there was the smallest ratio of detections to nondetections for surveys conducted during sunny and dry conditions and the largest ratio for surveys conducted during cloudy and wet conditions. Likewise, for road type, the categories are ordered; previously dusty but now sealcoated roads come between dusty gravel roads and never dusty road types. However, the ratio of detections to nondetec#ions was the smallest for surveys along gravel roads and greatest for surveys along sealcoated roads, so gravel was coded as 0, non-gravel was coded as 1, and sealcoated was called as 2. P~odel selection followed the procedure outlined by Glantz and Stinker (1990, pg. 548-560). Frequently, researchers use an ad hoc method of selecting the best model and do not follow a specific protocol, primarily because protocols are rarely found in statistics texts, and when they are, 18
they are not able to accomodate the complexities cf most biologics! datasets. The procedure used here is designs-d for studies in which there are many predictor variables that may be related to the outcome, and the goal is to choose among those variables to determine which are important. The procedure is meant for datasets that have many nondetections (0's} relative the number of detections (1's}, many variables relative to the number of successes, and potentially complex relationships, such as interaction, mediation, and confounding, between predictor variables, control variables, and the outcome variable. This procedure was chosen for this study because it v►ras the only one found whicfi~ accommodated the complexities and nuances of the dataset, and because following an established procedure was deemed favorable to choosing a model ad hoc. (Glantz & Blinker, 1990) First, the number of variables being considered for inclusion in the final mode! was reduced using both forward and backward stepwise regressions. Separate regression procedures were carried out for the time-constant and time-varying variables. Vlihen the time-constant variables were being tested, the time-varying portion of the mode! was held constant, and vice versa. For the regressions, each successive model was fit in PRESENCE, and then the maximum likelihood values were exported to Microsoft Excel (Microsoft Corporation, 2000). Excel was used to calculate the 2 2 improvement chi-square associated with adding or removing each variable (Gj =21n(L/L_~), where G~ is the improvement chi-square associated with including the variable, L is the likelihood associated with the fuller model, and L J is the likelihood associated with the model excluding the variable} (Glantz ~ Blinker, 1990}. The improvement chi-square is compared to achi-square distribution with 1 degree of freedom (Glantz & Slinker, 1990). Both the fo~vard and backward stepwise regression procedures used an inclusion or removal criterion of p=0.15. The criterion of p=0.15 was used to help ensure that no important variables were excluded (Glantz &Slinker, 1990). The calculated improvement chi-square, rather than the z-test (z=b~ls~i, where bi is the coefficient of variable i, and sit is its associated standard error}, was used to test the significance of each variable because both procedures yield c®mparable results and calculation of the improverr~ent chi-square statistic was more straightforward given the output provided by Program PRESENCE. Second, a best subsets procedure was used to further reduce the number of variables and create a list of candidate models. Glantz and Slinker (1990} advise following the first stepwise regressions with another forward and backward stepwise regression process with a more stringent p- value of 0.05. however, best subsets procedures are generally recognized as being more likely to find the best model than stepwise regression procedures, and are preferred when they are passible (Glantz ~ Slinker, 1990; Vittinghoff et al., 2004). Because there were only 17 surveys during which moths were found (at 2 patches, meths were found twice}, the number of variables that could be included in the m©del was fimited, so the best subsets procedure was logistically feasible, and therefore the better alternative for finding the best model. ,approximately 5 detections are required per variable (Glantz &Slinker, 1990; Vittinghoff et a1., 2004). Since PRESENCE estimates the tir~e- 19
constant and time-varying portions of the model separately, the coefficients for a maximum 3 variables per portion were estimable. All subsets of 6 or fewer variables, with a maximum of 3 variables per portion of the model, were fitted using PRESENCE. The two best models, based on maximum likelihood estimates, with each number of variables were retained for further analyses. For the stepwise regression and best subsets procedures, intercept terrns were included in both portions of the model. The intercepts were included so that a comparison between models with the same number of variables would be a straightforenrard comparison of the maximum likelihood values. After the best models were selected, the unneeded intercepts were removed in the interest of model parsimony. Normally, the next step in the rnodel selection procedure would be to examine interactions. The interactions with prior research support or theoretical backing would be investigated. However, interactions were not investigated primarily because: 1) there were few detections, limiting the number of estimable coefficients, and main effects were deemed the more important purpose of modeling and 2) most ®f the variables in this study have not been investigated in prior phlox moth studies, so there is little research support for including interactions. The overall ability of each model to predict the outcome was assessed by using the likelihood ratio test to compare each model to the null. model. The likelihood ratio test is achi-square test, calculated by comparing the likelihood values of the fuller and null (intercept-only) models 2 ~ 2 (G =2*In[L/Lp], with k-1 degrees of freedom, where G is the chi-square test statistic, L is the likelihood value of the fuller model, Lp is the likelihood value of the null model with only an intercept term, and k is the number of estimated parameters}. If a fuller model, compared to the null model with only an intercept term, is a significantly better predictor of the outcome, the individual Coefficients can be tested for significance. The likelihood ratio test is analogous to the F-test, which compares the mean square values of a fuller model and a nut! model. The F-test was not used in this project simply because the mean square values are not provided in Program PRESENGE's output. To determine which of the models was the best descriptor of the data, several diagnostics were run. lJsing the z-test, the significance of each variable in each model was assessed at the p=0.05 significance level. Then, each model was assessed for lack of fit using the Hosmer- Lemeshow goodness of fit chi-square test (Glantz &Blinker, 1990), The Hosmer-Lemeshow goodness of fit test was designed for models with many variables, for which a standard goodness of ft test would be inappropriate. Goodness of fit tests only provide accurate results when there are sufficient numbers of observations per multivariate category (usually a minimum of 5 for most categories). The Hosmer-Lemeshow test eliminates the problem of too few observations per category by grouping the observations for analysis, so that there are fewer cells in the contingency table, and therefore more observations per cell. In the Hosmer-Lemeshow test, the probability of success fo~~ each observation is calculated. Then the probabilities are arranged in increasing order and divided into "deciles." For the phlox moth data, 3 "decilesn were used due to the low number of 20 successes. In each "decile," the probabilities are summed, and the successes are counted. Finally, - 2- the chi square statistic {X ~([O-E]2) /E } is calculated com pgrin g the observed and ex pected (summed probabilities) numbers of successes per "decile.n The chi-square statistic is compared to a chi-square distribution with g-2 degrees of freedom, where g equals the number of "deciles." The final diagnostic used to compare the models was prediction error. The probability of presence was calculated for each observation in the test dataset, for each of the best models. To calculate the prediction error, the predicted probability of finding a moth was calculated for each patch in the test dataset. Then the data were binned so that the 10 patches with the lowest, middle, and highest predicted pr©babilities were grouped in 3 bins. Then, the absolute value of the difference between the sums of the actual outcomes (1 or 0) and the sums of the predicted probabilities for each bin were calculated. Finally, the prediction error for each model was calculated as the sum of the differences divided by the number of observations. The best model was expected to include only significant variables, have anon--significant lack-of-fit, have the fev~est large residuals, and have the lowest prediction error of all the models. The models were examined for robustness, using 2 diagnostics. The first diagnostic used was an assessment of the standardized residuals of each model. The standardized residual for each observation was calculated as the difference between the observed outcome and the predicted success probability divided by tl~ie square root of the product of the predicted success probability and 1 minus the predicted success probability {t~bserved-Predicted/(~1Predicted*[1-Predicted])}. The number of residuals greater than 2.58 or less than -2.58 was counted for each model. The residuals were expected to follow a normal distribution, where few or no residuals would fall outside the range of --2.58 to 2.58, indicating they are outliers at the 0.01 significance level (Vittinghoff et al., 2004}. !n addition to indicating poor model fit, large residuals also indicate potentially influential observations, which must be examined to determine if they dictate the coefficient estimates. To examine whether the observations with large residuals were influential, the observations were removed from the dataset, independently for each model, and the models were refitted. Changes in the direction or large changes in the magnitude of the coefficients would indicate that the observations are influential. The final diagnostic run on each r~odei was apost-hoc check for r~nulticollinearity. The coefficient covariance matrix and Standard errors provided by PRESENCE were used to calculate the correlation matrix in Excel. Correlations above 0.9 indicated potential multicollinearity. To determine if the multicollinearities were causing inaccurate estimation of the model coefficients, the variance inflation factors (VIF) for the variables in question were calculated (VIF=1/[1-R2 ], where R2. is the regressi®n coefficient of the variable in question against all other variables included in the model). VIPs above 10 indicate that problematic multicollinearity exists in the model, and variables should be transformed, combined, or deleted. After the models were determined to be robust, the role of each variable in predicting the outcome was quantified. 21
ROOSTING SITE STUflY
For each patch where moths were found, one stem was randomly selected from the patch dataset Table ~). The number of blooms, number of buds, and canopy cover above that stem were paired with the number of blooms, number of buds, and canopy cover above the stern where the moth was found. The randomly selected stem data were frorr~ the patch survey that was conducted during the same week the moth was f®und. The randomly selected stems were required to have at least one bloom, because only stems with blooms had been searched for moths. Data on the average stem spacing of the patch and the average flowering percentage (calculated as the number of stems with at least one bloom divided by 'i 0, from the .patch data) were paired with the data on the stem spacing and flowering percentage of the '10 stems nearest to the stem where the moth was found. r'reliminary checks for violations of model assumptions were followed by a multivariate analysis of variance ~61lIANOVA). Bivariate, univariate, and multivariate graphs of the data revealed that the multivariate distributions of the means were approximately normal. Then the covariance matrix was examined and revealed that the variances of the variables were not approximately equal; the variables were standardized for the analysis. A paired MANOVA was conducted, using 2 Hotelling's T statistic to test if there were differences between the multivariate means of the stems with and without moths. The MAl'~OVA was followed by one-sided Bonferroni-c®rrected paired t-tests comparing the means of each variable between the groups of stems with and without moths. It was possible that moths were found more frequently on sterns that had more blooms or buds simply because they were choosing a bud at random, and therefore had a higher probability of choosing a stem with more buds. A paired t-test was used to determine whether moths were choosing stems with more buds by comparing the actual difference between stems with and without moths to the maximum possible difference of 6.7 . Another paired t-test was used to determine whether maths were choosing buds at random by comparing the actual difference between stems with and without moths to the expected difference based on the proportion of buds on the stems with more buds, a difference of 4.~. Significant differences between the means were then quantified. 22
Res u Its
PATCH OCCUPANCY sTUDY
Oata Collected
moths were found during 17 of 155 total surveys of 121 patches (34 patches were surveyed twice). Moths were found during 2 successive surveys of 2 patches; therefore, moths were found at 15 of the 121 patches. A total of 27 moths were found during the surveys; 1 or 2 moths were found per survey. Details of the locations where maths were found are listed along with the other data collected during the surveys and a surtr~mary of the data, including ranges of the data, and the mean and median values (Appendix B).
Prelirninar~ Analyses
Spatial autocorrelation between the outcomes was not significant in the dataset (!'Vloran's 1=0.03, z=1.73, p=0.09}. There were however, some minor multicollinearity problems. Three rnulticoilinearities were found between variables in the dataset, after patch type had been removed and cloud cover and precipitation merged into a single category. These multicollinearities were between the average and 2 standard deviation of the number of blooms (R =0.79, 0.69, 0.76 for weeks 1, 2, and 3, respectively), 2 the average and standard deviation of the number of buds (R =0.76, 0.84, 0.53 for weeks 1, 2, and 3, res pectivel y), and the av era ga and standard deviation of canopy cover ( R2=0 81, 0 88, 0.7 2 f or weeks 1, 2, and 3, respectively}. The standard deviations were not included in the analyses because the averages were of primary interest in this study. The correlations between the other continuous variables were all less than 0.5 (Appendix C). The regression of proportion surveyed on patch size, 2 number of stems searched, and stem spacing also did not indicate multicollinearity (R =0.08, 0.13, 0.17 for weeks 1, 2, and 3, respectively).
Logistic f~egression and lilt'odel selection
The forward and backward stepwise regressions in i+'RESEl~10E, fit to the training data, using p=0.15 as the variable addition or removal criterion, resulted in the following 13 variables (of 17 original variables) being retained in the model: patch size, distance to nearest patch, nearest patch size, distance to the nearest previously occupied patch, road type, average number of blooms, 23 average number of buds, percentage of stems flowering in the nearest patch, cloud cover and precipitation, stop time, temperature, canopy cover percentage, and proportion surveyed (Table 5). The foilouving variables were excluded: stem spacing, stem spacing of the nearest patch, total area of patches within a 0.5 km radius, and aspect. The program was unable .to estimate the coefficients for aspect, regardless ofi the model, so aspect was removed from the list of variables being considered, even th®ugh it signifcantly improved the time-constant portion of the model. The inability of PI~ESEI~CE to estimate the coefficient was probably due to a lack of observations for cer#ain combinations of aspect with other variables in multivariate space. l~ext, the best subsets procedure was used to select the 2 best models with each number of variables (Table 6), by comparing the likelihood values of models with the same numbers t~f variables. The models compared in the best subsets procedure could have no more than 2 variables in the time-constant potion of the model and no more than 3 variables in the time-varying portion of the m~adel; including larger numbers of variables resulted in inestimable coefficients. The 2 best 6- variable models are therefore not presented. All 4 of the top models were significantly better predictors of the outcome than the null model, according to the likelihood ratio test (Table 6}. The z-tests of the model coefficients revealed that all but one of the variables in one cf the models, were significant predictors ofi the outcome (Table 6). load type in the first 4-variable model was not significant. Ali of the top models included 3 core variables: patch size, average number of buds, and cloud cover percentage and precipitation. In the 4-variable models, distance to the nearest previously occupied patch or road type was the fourth variable included. In the 5-variable models, the average nurr~ber of blooms and either distance to the nearest patch or road type were the additional variables included. Dearest patch size, stop time, temperature, percent stems flowering in the nearest patch, canopy cover, and proportion surveyed were not included in any of the best models. Examination of the standardized residuals indicated that the second 4-variable model was better than the others. All of the models had some residuals greater than 2.58 or less than --2.5$ (Table 6); the second 4-variable model had only 2 large residuals compared to the first 4-variable model with 9 large residuals, the first 5-variable model with 7 large residuals, and the second 5- variable model with 6 large residuals. There were no changes in the directions (positive or negative) of the relationships between the predictors and the outcome and there were no major changes in the magnitudes of the coefficients when the models were refitted without the observations with large residuals (Table 0), indicating that the observations with large residuals were not responsible for the relationships between the predictors and the outcome, and the models were robust. Prediction error (Table ~), likewise, indicated that the second 4-variable rr~odel was better than the others. Prediction errors ranged from 15.7% to 24.0°l©. Gnly one mode! adequately fit the data, according to the Nosmer-Lemeshow Goodness of Fit test (Table 6}. The model with adequate ft (i.e. anon-significant lack of fit) was the second 4-variable 24
model. The other models ail were poor fits to the dataset. However, only 213 of the cells in the contingency table (8 of 12 cells) for any given mode! contained more than 5 observations. The I°~osmer-Lemeshow test assumes that most of the cells contain at least 5 observations, although mast is not clearly defined. The results may be somewhat inaccurate, however, all of the other model diagnostics offer supporting evidence indicating that the second 4-variable model is the best. No problematic multicollinearity was found between variables in any of the 4 best models. All correlations between the estimated model coefficients were below 0.9, except the correlation between the coefficients of patch size and distance to the nearest previously occupied patch in the second 4- variable model (Table 7). To determine whether the correlation was problematic, the variable inflation factors for patch size and distance to the nearest previously occupied patch were calculated, and both were below 10, which is the minimum VIF value that indicates problematic multicollinearity 2 2 (patch size R =0.13, VIF=1.1, distance to the nearest previously occupied patch R =0.17, VIF=1.2}.
~"he Best tVlodels
There was one model that clearly outperformed the other models: the second 4-variable m©del, which included patch size, distance to the nearest previously occupied patch, average number of buds, and cloud cover and precipitation. It was the only model that adequately fit the data. It had the 1®west number of large residuals and the lowest prediction error of all the models. And, all of the included variables were significant predictors of the outcome (although this was also true of two other models). The best model estimated that 35% of the patches were occupied, and that detection probabilities averaged 13% across the 3 weeks. The naive estimate of the proportion of patches occupied was 13%. There was no clear distinction between the 3 other best models that would indicate which models were the second, third, and fourth best. Rather than presenting each of the 3 "second-best" models in detail, the coefficient estimates and predictions of the models were averaged, and the a~~erage results are discussed alongside the results for the best model (Table 8 &Table 9). The averaging approach is commonly used whan no single model appears better than the others, and allows for a generalized interpretation of how each variable affects the outcome ([MacKenzie et al., 2006)~ The estimated probabilities of occupancy and detection for the second ~-variable (best) model, the 3 other top models, and the averaged model were similar (Table 8). The relationships between the variables and the outcome in the best model and in the averaged model were somewhat less similar (Figure 2). 25
ROOSTING S1TE STUDY
Hotelling ' s T2 i'n d icated that there were sig nificant differences between the stems where moths were found and those randomly selected {T 2=73 .4 F=11.23, p-0.000)- 2 . Whe n controllin g for multiple comparisons by using a corrected p-value of 0.01, the numbers of buds on stems with moths were significantly higher (t=3.33, p=0.002), the canopy cover was significantly lower above stems with moths (t=3.33, p=0.002), and the stem spacing around stems with moths was significantly denser {t=5.00, p=0.0001}than stems without moths. The number of blooms and the percentage of stems flowering did not significantly differ between the two groups {t=41.32, p=0.10; t=1.27, p=0.11, respectively). The results of the t-tests used to determine whether moths were choosing stems with more buds or were randomly choosing buds were inconclusive (t = -0.007, p = 0.50; t = -0.95, p = 0.18; respectively). Phlox stems on which moths were found averaged 11.9 buds, while stems without moths averaged 7.7 buds. Stems with moths averaged 29.9% canopy cover, while those without moths 2 averaged 53.5% canopy cover. Phlox stems with moths were surrounded by 1.8 stems perm , 2 compared to a patch average of 1.0 stem perm . Phlox stems on which moths were found averaged 11.3 blooms, while stems without moths averaged 9.2 blooms. Phlox stems with rr~oths were located in areas with 62.8% of the stems flowering, compared to an average of 58.3%. (Table 4)
discussion
The low number of detections in the dataset did not allow for the assessment of a large number of predictor variables. however, the consistently high significance of the three core predictor variables in the bes# models indicates that the relationships between the core predictors and the outcome are very strong. With so few detections, the statistical tests would not be powerful enough to pick out weak relationships or to overfit the dataset. Additionally, the statistics used in the analyses were commonly-used epidemiological tests designed for low numbers of detections. Additionally, the three core variables were all expected to be among the top predictors of math presence or detection, on biological grounds. According to metapopulation and biogeography theories, larger patches support larger, more permanent populations {Hanski &Gilpin, 1991); theref©re, maths are more likely to be found in larger patches. The total number of buds available in a patch contributes to the amount of habitat present. Moths are very closely tied to phlox buds because more buds mean more larval food, and eventually more flowers for adult nectar and more seeds for larval food (F~ardwick, 1958; Kirk, 1990). Additionally, the moths are camouflaged to look like phlox buds or flowers {Swengel ~. Swengel, 1999}, and should be found where more cover exists for them to hide in. Cloud cover and precipitation were expected to be important variables because 26
they control, in part, the probability of detection at a patch. Swengel & Swengel (1999) and Kirk (1996} found that moths were more likely to be roosting on wet, cloudy days. The data on cloud cover and precipitation were collected so that they could be used as control variables in the model, and it would have been surprising if they were found to be insignificant. Similarly, the non-core set of variables that appeared in at least one of the best models also had significant support for their importance in predicting the outcome, due to the low number of detections. The repeated appearance ©f two of the non-core variables gives these variables the support of consistency. Likewise, the seeming interchangeability of distance to the nearest patch and distance to the nearest previously occupied patch, #ends support to the consistency of the model results because both variables are similar measures of landscape connectivity. The distance to the nearest patch was expected to be an important predictor due to its role in metapopulation dynamics. Patches that are closer to other patches have a higher probability of being occupied than patches that are widely dispersed (Hanski &Gilpin, 1991 }. Similarly, distance to the nearest previously occupied patch was also a reasonable variable to be included (Hanski &Gilpin, 1991). The endangered species program at Fort lilicCoy requires that each patch be surveyed 3 times, or until a moth detection occurs, during a single season, and each patch is resurveyed every several years (Wilder, pers. Comm.). Patches that are occupied in most years should have a greater probability of nnoths being found in a given year than patches which are intermittently occupied. According to metapopulation theory, patches that are located close to large, stable populations are more likely to have populations sustained or recolonized by immigrants, and are therefore more likely to be occupied at any given point in time than patches that are widely dispersed (Hanski &Gilpin, 1991 }. Therefore, distance to the nearest previously occupied patch probably closely represents distance to a stable sub-population. The average number of blooms was expected to be an important indicator of the amount of habitat available in the patch, similar to the number of buds, mentioned above. Phlox flowers provide nectar and cover for adults, and will produce seeds for larval food (Hardwick, 1958; Kirk, 1996}. road type was expected to be an important variable if dustiness was important to occupancy, either due to current dustiness or recolonization following dust reduction. The dustiness of patches growing alongside gravel roads may be unfavorable to the phlox moth because dust might interfere with breathing, although no journal articles related to this t©plc could be found. Historically, seafcoated roads were dusty gravel roads. The road types in the non-gravel category were never dusty. if gravel dust causes habitat to be unfavorable, and there is a recolonization period following sealcoating, patches a{ong gravel roads are expected to be largely unoccupied, follov~ed by sea{coated roads and then non-gravel roads in terms of increasing proportions of patches occupied. However, the proportions of detections per category were not ordered accordingly. The ordering of road types by the proportion of moths found in each, with the lowest number of moths first, was gravel, non-gravel, and sea{coated. This may indicate that gravel dust creates unfavorable 27
conditions, but recolonization occurs fairly quickly (within the 3-4 years since sealcoating of the gravel
roads began at Fort McCoy}, or that some other variable related to road type is responsible for the differences befinreen the groups, such as roadside maintenance activities or the concurrent central location of patches with high detection rates and high traffic volumes. Temperature and stop time were not important predictor variables, as expected. There was not much variability in the stem spacing of the focal or nearest patch, finding a trend is difficult with low variability unless a relationship is very strong. Most patches had stem 2 spacing values of 1 stem per 0.5-1.5 m , while a few patches had stem spacing values as dispersed as 1 s to m per 3. 5 m2. Also the ratios of detections to nondetections were a pp roximatel y a q ual among the levels of stem spacing. Had there been more variability between the patches, this variable may have been an important predictor, especially considering that stem spacing was important within the patch, according to the Roost Site portion of the study. Additionally, the stem spacing of the nearest patch was expected to have less predictive ability in determining moth detection than the stem spacing of the focal patch because the stem spacing nearby was less directly relevant. canopy cover was not a significant predictor of moth detection because no moths were found in wooded or open habitats, which made up the extremes in the range of canopy cover values. This result suggests that the relationship between canopy cover and the odds of detection or nondetection is non-lineal. A future study could investigate polynomial relationships between canopy cover and detection. The lack of moths in open and woodland sites may indicate that moths are more likely to be found in patches with medium canopy cover levels, and the probability of finding moths may increase as canopy cover increases to a threshold value and then decrease as canopy cover decreases. Strangely, variables related to the amount of habitat on the surrounding landscape —size of the nearest patch and total patch area in the 0.5 km radius —were not important predictors in any of the models. Size of the nearest patch is one of the key components of many metapopulation models (Hanski &Gilpin, 1991). Far this dataset, at least, the size of the focal patch and connectivity between patches seemed to be more important than the amount of habitat area in the surrounding landscape. This may indicate that most moths remain in their natal patch, with few moths colonizing nearby patches, that moths disperse Breater distances, despite available nearby habitat, or that moths may be using several nearby patches as a single patch. Similarly, the percentage of stems flowering in the nearest patch was not a significant predictor. This variable is also a measure of the amount of habitat available on the landscape surrounding the patch. It was not as clearly related to moth presence as distance to the nearest patch or total patch area in the 0.5 km radius, and rnoas expected to be unimportant if the other variables related to amount of surrounding habitat were unimportant. The proportion of the patch surveyed was thought to be an important predictor variable, but was n®t significant. There is a good chance that moths are clustered within patches due to the 28 attraction of males to female pheromones, so skipping portions of a patch could mean missing moths. In fact, during the survey of one very large patch ~Cfi~.}, the endangered species crew spent about 2.5 hrs searching for moths, and certainly searched considerably more than 500 sterns before finding finding a single moth. ~n the other hand, in most cases, surveying 500 sterns meant surveying the entire patch, so moths were not likely to have been overlooked. The results of the assessment of variables related to within patch habitat selection were an interesting complement to the results of the patch-level study. Just as the average number of buds per stem was one of the most important patch-level characteristics, it was also one of the most important within patch characteristics. The importance of this variable at both spatial scales underscores the overriding importance of buds in the life cycle of the phlox moth. The other variables significantly related to the stems that moths were found on, canopy cover and stem spacing, were not important in the larger-scale analysis. Average canopy cover may not matter as long as there are enough stems that have low canopy cover in each patch so that the moths can find appropriate roosting sites. Likewise, stem spacing may not matter in patch occupancy because there is a minimum number of stems required to support a subpopulation, not a minimum density, but the moths may prefer denser areas within patches due to the higher concentration of resources. The percentage of stems flowering and the number of blooms were not statistically significant, but their associated p-values were not very large. The significance of blooms in some of the models indicates that perhaps the amount of flowers is more important at a larger spatial scale.
C®nc~us~c~ns
The results of this study indicate that phlox moths are more likely to be found on stems that have larger numbers of buds, lower canopy cover, and higher surrounding stem density than the average stern within a patch. Phlox moths are likely to occupy phlox patches that are larger, closer to their nearest neighbors, closer to patches that were occupied in the past 11 years, and have more blooms and buds per stem than the typical patch, and may be negatively impacted by dust from nearby gravel roads. Additionally, m©ths are more likely to be detected when the weather is rainy or recently rainy rather than dry, and are more likely to be detected when the weather is cloudy rather than sunny. The most important variables in predicting whether moths will be found in a patch appear to be patch site, the average number of buds per stem, and cloud cover and precipitation. Distance to the nearest patch, distance to nearest previously occupied patch, the average number of blooms, and road type are less important variables, and when coupled with the 3 core variables, different combinations of the Tess important variables can be used to predict whether moths will be found with approximately equal accuracy. 29
The fact that the 3 core variables consistently appeared in the top models indicates that the relationships were fairly strong and there were sufficient data to assess the relationships between these predictors and the outcome. A larger sample of occupied patches might a#low other predictor variables to be identified. In particular, more repeat surveys of the same patches, which were planned but did not happen due to poor weather, and data collected over several field seasons, to account for year~to-year variability and to increase the chance of the moths having an average year population-wise, would improve this study. More data would not only strengthen the conclusions regarding the relationships between the ~ non-core variables and the outcome, but would also al{ow for the assessment of interactions or other non-linear relationships between the important variables in this study and the outcome, the predictive abilities of other variables considered in this study, the temporal variability in the relationships between the predictors and the outcome, and the threshold values of the variables necessary for temporary occupancy and for sustaining moth populations in a given patch. The results of the second 4-variable model, the best of the 4 top models, indicate that large patches that are close to patches that were occupied during the last 11 years are the patches tha# are most likely to be occupied. such patches then, should logically be the focus of management efforts, and management that increases patch sizes or increases connectivity should increase the math population. if patches are enlarged, they should be enlarged at least beyond the 0.5 occupancy probability threshold, to help ensure that the patch is usable, or the 0.9 occupancy probability threshold, to help ensure a stable sub-population, taking into account the distance to the nearest previously occupied patch when calculating the occupancy probability. For a patch that is the average distance to the nearest previously occupied patch (528 m), a patch must be enlarged beyond 2 2 794 m or 3,162 m to surpass a 0.5 or 0.9 occupancy probability threshold, respectively. To increase connectivity, large patches with a high occupancy probability should be established between patches that appear to support stable sub-populations and areas that have patches with low occupancy probabilities. The questions of exactly how large and how close together the patches should be to sustain the population have not been answered by this study; however, if the population is assumed to be stable, a very rough guideline can be used to assess the effects of changes in the size and arrangement of the patches. This guideline has its basis in studies of ®that rare species that exist in metapopulations. These studies have indicated that occupancy probability can be used as a surrogate fior population size to assess the effects of changes in landscape configuration on rare species, which exist in low numbers in any given patch (Boyle &Nichols, 2003; Mackenzie et al., 2005; Boyle et a1., 2005). Assuming that the population is stable and the management goal is to maintain the population, efforts should be made to maintain the same average occupancy probability, which was estimated as 35% by the model. The tradeoff between patch size and distance to the nearest previously occupied patch is that for each 10-fold increase or decrease iri patch size, the 30
patch must be 0.59 Iog10(10m) farther away or closer to, respectively, the nearest previously occupied patch in order to maintain a given occupancy probability (Figure 3). ®r conversely, for each 10-fold increase or decrease in the distance to the nearest previously occupied patch, the patch must 2 be 1.69 iog(m } larger or smelter, respectively, in size. So for example, if a patch is lost due to the encroachment of invasive plants, the occupancy probability of that patch can be calculated based on its size and distance to the nearest previously occupied patch, and a nearby patch could be expanded through habitat improvements until its probability of occupancy has been increased by the same amount as was lost. The increase in occupancy could be caused simply by the increase in patch size, or by enlarging the patch by a lesser amount, but enlarging it in a direction that makes it closer to the nearest previously occupied patch. The low detection rates estimated by this study indicate that 2 or 3 successive surveys of a patch during a single season are not sufficient to confirm moth presence. If a 13°~o detection rate is typical of most seasons, approximately 7 surveys are necessary to achieve a g0% probability of detecting moths, provided they are present. The Roosting Site portion of the study indicated that moths are more likely to be found in areas of the patch that have high stem densities, high numbers of buds per stem, and Idw canopy cover. Obviously then, detection-nondetection survey strategies should focus on searching the portions of the patch with these characteristics first to increase search efficiency. With the backing of the results and conclusions from this study, other future studies could encompass a wide range of possibilities, since so little is known about the phlox moth. Future studies could investigate the relationships of other variables not included in this study to patch occupancy. One set of variables that could be included in future studies are those related to the habitat requirements of larvae and pupae. For example, the amount of leaf litter, soi! moisture, and snow cover may affect whether larvae can find sites in which to pupate and whether the pupae can survive the winter. The apparent relationship between road type and patch occupancy could be investigated in more detail. As mentioned in the discussion, road type was expected to affect occupancy because patches bordering gravel roads might be dusty and uninhabitable. ®ust might play a role in inhibiting breathing by the moths by clogging spiracles. Or, dust might coat plant leaves to such an extent that ovipositing females cannot detect the appropriate phytochemicals. The proportion of occupied patches in the road type categories, however, did not follow the expected trend. it is possible that patches along sealcoated roads were colonized fairly quickly (3-4 years) after sealcoating occurred, particularly those patches located near large patches bordering non-dusty roads. Or, the real differences between patches bordering the different road types may be related to the amount of mowing or brush cutting, the age of the road and hence the successional stage of the roadside vegetation, or the width ®f the road corridor. 31
Knowing that patch size and the distance to the nearest patch or the nearest fairly stable subpopulation are important predictors of occupancy, the ideal landscape configuration of patches for sustaining the phlox moth population could be estimated. ~n understanding of the ideal landscape configuration could be used to direct activities related to landscape management. The results of this study may be applicable to the management of other Lepidopteran species, particularly those that are small, non-migratory, and highly specialized to a species of host plant. These moths and butterflies may require host plant patches of similar size and connectivity, and habitat improvement projects must account for the limits of distance and patch size on colonization. additionally, the fact that the average number of buds per stem u~as an important predictor of moth detection in all of the top models indicates that quality of the host plant, not just quantity, is important to the habitability of a patch. If the habitat area cannot be increased, but is not sufficient to support a population, perhaps the health of the host plants could be unproved through different management practices. 32
Table 1. List of variables, and units of measurement or coding type, for which data was collected in the field and using GiS for the P_ etch Occupancy and the Roosting Site datasets. --~ Data units or Details Coding Patch Name names follow Kirk {1996) with modifications detailed above Observers people who conducted moth survey Moth Survey Date dd/mm/yy date of mottl survey Start and Stop Times time at beginning and end of moth survey Number of Stems Searched number of stems searched for moths; maximum of 500 Complete Patch Survey category Yes =all stems were searched, No ~ not all stems were searched Temperature ~C temperature at end of moth survey, thermometer in the shade Wind Speed m/s wind speed at end of moth surrrey, hand-held digital wind meter Cloud Cover estimated to the nearest 25% during moth survey Precipitation category none, drizzle, rain, earlier in day, overnight Number of Moths number found, 0 if none found Study Patch Type category open grassland, woodland, savanna, edge, corridor Aspect category patch receives the most sun from the N, NE, E, SE, S, SW, W, or NW -track, sand, gravel, seal-coated gravel, paved, railroad Road Type category none, 2 Occupancy Patch Survey Date dd/mm/yy date of patch data collection Nearest Patch Name names follow Kirk {1996) with modifications detailed above Patch Nearest Patch Stem Spacing m2/stem estimated average spacing of stems throughout the patch Nearest Patch Flowering % estimated percentage of stems blooming in the nearest patch Nearest Patch Size m2 area of the nearest neighboring patch, calculated using GIS Distance to Nearest Patch m distance from patch edge to its nearest neighbor, measured by hand Patch Area in 0.5 km Radius m2 area of other phlox patches within 0.5 km of the patch, using GIS Proportion Surveyed proportion number of stems searched divided by est. total number of stems Distance to Nearest m distance to center of neares# patch with > 1 moths found 1994-2004 Previously Occupied Patch Standard Deviation Blooms standard deviation of number of blooms on 10 sterns evenly spaced Standard Deviation Buds standard deviation of number of buds on 10 stems evenly spaced Stem Spacing m~lstem estimated average spacing of stems throughout the patch Average Blooms average number on 10 stems evenly spaced across the patch
Studaes Average Buds average number on 10 stems evenly spaced across the patch Average Canopy Cover average above 10 stems evenly spaced, using densiometer Both 2 Patch Size m drawn by hand, digitized and measured using GIS Stem Number number of the stem on which the mothls was/were found
Canopy Cover % above the stem where the moth/s was/were found, using densiometer Sun or Shade category whether the moth/s was/were in the sun or shade when found Study Blooms count number on the stem where the mothls was/were found Site Suds count number on the stem where the mothls was/were found Stem Spacing m2lstem estimated from the 10 stems nearest where moth/s was/were found Nearest Patch Percentage of Roosting estimated percentage of stems in bloom, to nearest 10% Stems Flowering Distance to Patch Edge m from the stem where a moth was found, measured by pacing 33
Table 2. Results of the Tukey-Kramer HS® test examining the relationship between patch type and canopy cover. There is a general, partiaffy significant trend o~ increasing canopy cover from open habitats to Wooded and corridor habitats. r ~ .t ~ ~. , significantly Positive values indicate pairs of means that are different. Mean Canopy Different letters Cover (cells) indicate significant Category Woods Corridor Edge Savanna Open differences. Woods -7.83 -0.86 -0.30 1.72 8.18 16.43 A Corridor -0.86 -1.81 -1.94 0.18 5.68 11.62 A Edge -0.30 -1.94 -3.80 -1.72 4.12 10.59 A B Savanna 1.72 0.18 -1.72 -3.50 2.29 $.65 B Open 8.18 5.68 4.12 2.29 -7.00 0.83 C 34
Table 3. The variables used in the Patch occupancy Study, their uni#s or coding, and modifications made to the variables as the were on finally collected.
Variable Units or Coding NOodif°ications for o~nalysis
Stop Time fractional time the proportion of the day that has passed since midnight
Temperature °C 0) no rain 12 hr before stop time &clouds <50%, 1) no rain 12 hr Cloud Cover and Precipitation category before stop time &clouds >50%, 2) rain in 12 hr before stop time ll~oth Detection category 0 = no moths found, 1 = at least 1 moth found
Aspect category 0 =west to r+ortheast, 1 =east to southwest
Road Type category 0 = o#her, 1 =gravel, 2 = sealcoated gravel
Nearest Patch Stem Spacing m~/stem Nearest Patch Percentage of proportion proportion is the flowering percent divided by 100 Stems Flowering
Nearest Batch Size Iog10{m~) the base-10 logarithm of the nearest patch size
Distance to Nearest Patch Iog10{m} the base-10 logarithm ofi distance to nearest patch
Pasch Area in 0.5 km Radius 1o910(m2) the base-10 logarithm of patch area in Q.5 km radius
Proportion Surveyed proportion Distance to the Nearest the base-10 logarithm of the distance to the nearet previously Io910(10m) Previously occupied Patch occupied patch divided by 10 Stem Spacing mZ/stem
Average Blooms average count
Average Buds average count
Average Canopy Cover average proportion average canopy cover percent divided by 100 Io910{m2) Patch Size the base-10 logarithm of the patch size 35
0co 0r~ 0a~ 0t` 0~ 0cfl 0co 0cfl 0co 0~ 0rn 0o 0 ~ 0ti 0r- 0oo 0~ 0ti
O O O O O O O O O O O O O O O O O O Cfl t:n ti f` ~ QO O O N to en f` ~ ~ ~ ti CO to O ~ __ ~ ~ ~~ C O ~ ~ O W ~
t!7 ~ ~ ~ ~ tf) t- r In r- Ln tf~ r ~- r- ~- ~ ~ r N r- O O ~- ~- ~ O O O ~ ~ ~ ~U O (6 ~^L t^ Q ` V J W 7 Q to ~ ~ to ~ to t.f) ~ to ti') ~ t.[) ~ ~ ~ to ~ ~ ...... O O O O O O O O ~- O O O O O O O O O m .~ _C ~ ~U ~.~..~ ~Q.
N CO O M to O ~ ~' QO 00 ~ 00 Cfl O ti O ~ ~ M O r- r r- r- r-
m Buds
O C Random
I` O N 't7' 00 ~- d' O t(') O ti N M ~ O tf') t~ d' N (V r ~- r r r M r r-- r
m Buds O No.
O Moth O N t` O 00 t~ 1`- M r- t1') O I` M r- DO N I~ I~ O r ~-- r ~- r ~- e~ r r No.
O _O m Random
d' ~ ~ CD r- Cfl ~- N Cfl ~ CO ~ ~ t.f) ti O CO O r (y ~-- r r r r N N r O cn Z ~ o ~ ~ m
Study. L n , r' ~ d' O O o0 N ti N 0 ~ ~f' ~- ~' r 00 tt O M W N ~-- <-~- N r - r - r - N N r r N Site -ov U~, cc~ Q ~ c
Roost m U mot' N O ~- O O ct O H O t` ti' N t~ N O mot' r - the ~ r r r— r r Q. fl in ~ t. ~ ~ U o .c U used 0 Data Name U X ~- m m o~ Q U O C7 m Q U~~~ c W C'~ O M d - ~t et ~-- N N N N ~t ~ ~ ~ (U
Patch t~ 00 ~- ~ ~ ~-- r- N N N N N N N N N CO m m m m m m m m m Cn m m m m Q Q O 36
Table 5. The models selected by the forward and backward stepwise regressions of the time- constant and time-varying variables include all variables in boldface, which significantly improved each model at the p=0.15 significance level. Variables not in boldface were added to or subtracted from the models in boldface, but did not significantly improve the models. Likelihood Model Model Significantly Forward Stepwise Regression of Time-Constant Variables Likelihood Ratio G2 df p-value Improved? Null Model 106.15 All Above Plus Lo910(Patch Size) 88.65 17.51 1 <0.001 Yes All Above Plus Log10{Distance to Past Presence) 85.48 3.16 1 0.076 Yes All Boldface Plus Lo910(Distance to Nearest Patch) 84.35 1.13 1 0.288 No All Boldface Plus Lo910(Nearest Patch Size) 85.20 0.28 1 0.597 No All Boldface Plus Road Type 85.47 0.01 1 0.920 No All Boldface Plus Log 10(Area in 0.5 km Radius) 85.48 0.01 1 0.920 No Likelihood Model 2 Model Significantly Backward Stepwise Regression of Time-Constant Variables Likelihood Ratio G df p-value Improved? AI! Time-Constant Variables 83.78 All Above Minus Log10(Area in 0.5 km Radius) 83.78 0.00 1 NA Equal All Boldface Minus Road Type 83.94 -0.16 1 NA No All Boldface Minus Lo910(Nearest Patch Size) 84.35 -0.57 1 NA No All Boldface Minus Log10(Distance #o Nearest Patch) 85.48 -1.70 1 NA No All Boldface Minus Log10(Distance to Prev. Occupied Patch) 88.64 -4.86 1 NA No All Boldface Minus Lo910(Patch Size) 98.52 -14.74 1 NA No Likelihood Model 2 Model Significantly Forward Stepwise Regression of Time-Varying Variables Likelihood Ratio G df p-value Improved? Null Model 106.15 All Above Plus Cloud Cover and Precipitation 94.84 11.31 1 0.001 Yes All Above Plus Average Number of Buds 86.09 8.75 1 0.003 Yes All Above Plus %Stems Flowering in Nearest Patch 78.90 7.19 1 0.007 Yes All Above Plus Proportion Surveyed 70.20 8.70 1 0.003 Yes All Above Plus Temperature 64.19 6.01 1 0.014 Yes All Boldface Plus Blooms 63.45 0.74 1 0.390 No All Boldface Plus Average Canopy Cover 63.45 0.74 1 0.390 No All Boldface Plus Time 64.19 0.00 1 NA No All Boldface Plus Stem Spacing 64.92 -0.73 1 NA No All Boldface Plus Nearest Patch Stem Spacing 64.92 -0.73 1 NA No Likelihood Model 2 Model Significantly Backward Stepwise Regression of Time-Varying Variables Likelihood Ratio G df p-value Improved? All Above All Time-Varying Variables 83.78 All Above Minus Stem Spacing 60.28 23.50 1 <0.001 Yes All Above Minus Nearest Patch Stem Spacing 60.28 0.00 1 NA Equal All Boldface Minus Blooms 64.92 -4.64 1 NA No All Boldface Minus Average Canopy Cover 66.33 -6.05 1 NA No All Boldface Minus Proportion Surveyed 67.59 -7.31 1 NA No All Boldface Minus Time 70.07 -9.79 1 NA No All Boldface Minus Average Number of Buds 73.11 -12.83 1 NA No All Boldface Minus Temperature 73.14 -12.86 1 NA No All Boldface Minus %Stems Flowering in Nearest Patch 79.55 -19.27 1 NA No All Boldface Minus Cloud Cover and Precipitation 84.26 -23.98 1 NA No 37
L ~ ~ O 0 a 0r 0O ~ ~ :.:., ~- N N N (0 U ~ UO ''"' ~
~L W ^ {..L ~ ~ ~ ~A\ M ~ M M ~ (/1 t` M ~ 00 O 07 M r ~- M r- 00 N M O r r M r U Cfl CO '~t ~- I~ O N ►~ (~ O r -- N Cfl O O f~ N O r ~ ~ ~ ~ ~ O N r- d' O O M N M O O M O N r I~ O N O t` O N r r ~ ~ ~ ~ ~ ~
~ `"~ ~ Coefficients ~ ~ ~ U Q 0 rn N "~ N +~'+ ~ ~ ~ +-~tai N ~ ~ ~ ~ N -O (n ~ O '~~ ~,~ Z ~ Q. ~ r r ~t U -+~ 0 0 to X ' — 0 0 0 ~ N ~ cv O 0 0 0 V V Q ~O ~ t[3
j _UO ~(L3 t~ N N O r N ~ ~ ~ ~ X r r -~ o a~ 0 ~ ~ 0 4-0 "~"~ i . C7 r Ln r Lf') ~ N I` C~ r- M ~ I` ~- O O ~ I~ ~ M M O O O O ~' O oo'~t N O O ~t O O M r ~tO O N ~- O ~ M O ~ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ~ o 0 0 0 0 0 ~ 0 0 0 0 0 0 0 0 0 0 0 0 0 co 0 0 0 o co 0 0 0 0 0 O ~c6 cn . U ~- a 00 O O N 0p 1` r O O N M~ O r- N ti ~ ~ O ~ ~ ~ O I~ O N O N O ti M M r- M 00 d' O r ~ ~?' ~_ .V t` N to O O r (n L- ~ c'7 N N M r- N N N N M N N N N N ~ N N N r- N M N ~ ~ ~-.+ to ~ ~ N ~ (~ ..~tB ~--+ ~ ~ ~ ~ U ~ N t~ I~t1') ~- 00 QO M M M CD t` In Cfl CO In GO O O ~ t` CU 00 t~ CO t` t` f` N N f~ CO t.f) ~ N N CO M O tQ f~ N f` 07 r O ~- M ~ ~ ~ L N O O N O O O O r N O O O M r-- O r- O O M r L r' O O ~ ~ O 0L f^ W vJ •v^ ,` ~`` ''~"~ ' /l VJ VW V J L" ' ^ ' W O O CO ~ N CO ~ N O N O O O M ~- ~t O N O r Ln (~ r tf') tf') r C M O f~ f~ ~' ~ ap M 07 O ~ CO d7 Cfl N '~' O ~ ~ ~ O 0p N ~- 00 O O N r' d' 00 O O r O N O to O r- M Cfl ~ O O (A ~ ~ ~ ~ ~ ~ ~ (~ Q1 '~"' ~U O ~ ~ CCS U ~ ~ ~
~ ~ ~ ~ ~ O .-. ~ N +-~ _~ ~ N~ ~ cn °_' ° ~- cn N N N & .0~ _~ ~ U ~ (b m ~ ~ ~ U m m m '~"rO ~~~ L ~ — .Q -+-• U ..-. 4.- ~.-. tU ~ U ~ ..~ N ~ ~.- y- O 0 O Q ...~, m m a. o 0 ~ •~ ~ U c~ ~ 0 ~ ~ ~ — ~ >' v o o ~j ~~ ~ -~ v o o ~j a Cover ~ c~ v o U O > ~ L O .n 0 0 ~ ~ Z Z ~ ,~ O O~ ~ Z Z -p ,Q 0 Z Q 0 0 ~- ~ z -o ,~- +-~ r r C r r +-+ ~N ~ .~ O N U C O ~ O O O U ~
3' .__I ~~ Q Q (~ d. J J Z~ Q Q V~ Cloud ~ ~ ~ ~ J Q a ~ ~~ ~ ~ r r ac 0 0 ~ O c~i~ 0 Q 0 0 <0.0001 O ~ ~ ~ v <0.0001 v ~ •~ ~ o r r N r ~ ~ ~ O M ~ ~ ~ ~ 42.02 M 36.23 M ~ '~ ~ O 0 0 ~
a~~°~~ O N O O r ~ '~ Q a~ o 0 ~ ~ ~ -a .~ ~ '~ ~ c~ °~ > ~ ~ Y_ a~ ~t o J ~ (~ (~ ~ ~"'~ ~;_- IapoW L alge~~en-5 Z aigel~en-~ ~ algeiaen-~ Z aigeiaen-~ 38
r N N f'7 O ~- r O N f~ M 00 00 `~t' C.O O r r ~ M O i~ ~ M ~~N O O O O O O O O & ~ },,~ ~ (Q ~ O O c~ T O ~ U U •Q Cover O ~ ~ ~ ~ ~ ~U ~ :~.+ "4~ U N ~ ~
O N U Clouds :+~, p- ~ U ~ ~ j .~ r (~. ~- r r QO r M ~ N ('7 M r O ~ p ~ M O O O N (t3 ~- ~- r C~ O O I` N M O O O O O r' M O O O & ~-+ -+-+ O O & O O O O O O ~ O O O Buds
Buds :~ o°~ ~ ~v of Cover of ~ c~ Cover ~ ~ a~ .Q ~U 'v No.
~--+ ~ No. U_ ~ Clouds ~ ~ ~ Clouds Avg. ~ t[S N Avg.
N N O r r M r r ~ d7 r M CO M r C~ Cfl C'7 0 ~ ~ O M O 0 0 ~ r r r' ~ O O N ~ O O O O O O V. O O O O O O O O Buds ~ Q Q1 Buds Blooms of Blooms of of ~O O ' RSs. of No. -~ (~ No.
~ ~ ~ No. No.
O .V N Avg. Avg. Avg. ~ ~ ~ Avg.
o r - N r ~— Cfl M r 1~- O r O N DO r O C'7 CO O ~ M N ~ ti ~ O O O r ~- O ~-' O O N U L' ~+--O O O O O O ~ O O O 0~ O O O O O O O O ~ ~ ~ ~ ~ '+'' ~ ~ ~ .-~ Q U
~ (~ U o_ O y-- o >, N ~ ~ c~ o Z rn ~ ~ ~ O ~ o ~O J .~ ~ O ~t~ ~ 0 a~ :~ T ~ a v ca ~ :~_~ r r In O r O r r ~ ~ U Cfl r ~ O o O N r r N O O O O O M ~ O ~ ~ O r O O U o O o 0 O O O O U ~ N O O 0 0 Size) Size) Size)
O ~ ~ ~U Size) .Q ~-' > ;~ O ~~~ ~ s ~ OU p(Patch p(Patch p(Patch O_ -p 1 ~ :+~ 1 }, ~ to
O Log1 Log Log10(Patch U (6 Log N ~ ~ ~ O ~ ~ ~ ~ ~ ~ ~ ~ ~ cn ~ `~ U Occ.) O .. -~ O 0 0 0 2 1
~ ~ O ~ 2
N ~ ~ ~ .~ .~ Prev. ~U (6 N Nearest) ~ a~ N
~ .~ t0 ....~~ ~, a to 0" Q. N
~ ~ . N N ~ -variable ' L -variable Size) ~,,, -variable ~ oZS 't3 0~ 4
4 . Cn O ~ Size} Nearest 5 Size)
Blooms m Q~ m ~ m ~ ~"'~ ~ Cfl O O O of 0 ~ ~ ~ ~ o o o fl.. U U U p(Dist. ~a~ -ao p(Patch Z No. p(Distance p(Patch o ~ Z ~ 1 p{Patch ~ z z N r 1 N °~ °~ o -~~ . ~ z . ~ ~ ~ U ~ ~ ~0 Lo91 o > > ° Log Log1 Log Avg. ~ ~ Q U Q ~ ~ ~ ~ Log1 ~ ~ Q U Q U 39 fable 8. estimated probabilities of occupancy and detection for the best models, and the average of the 3 second-best models. The nave estimate of the average probability that a patch was occupied was 0.1322.
Estimated Probability Estim2~ted Probability of Detection lVlodel of Occ►~pancy Week 1 week 2 Week 3
5-variable 1 0.3629 0.1228 0.1462 0.1172
5-variable 2 0.3656 0.1155 0.1367 0.1074
4-variable 1 0.4975 0.0915 0.0960 0.0716
Avg. of 3 Preceding 0.3594 0.1228 0.1439 0.112fi
4-variable 2 tSest) 0.3496 0.1302 0.1488 0.1132 40
o M ~ ~ ~ ~
~' ~ o ~ rne } ^ t- Q O V ~./ ~~ ~~ tom O Q r~ N , m N
.a ~t O c~ C d7 ~ N .~ a~ M CO '~t' O O ~U d' N O U
O N ON O O M ~t CO •~ L O O t~ M I~ N O C N O ~ O O I` a0 ~t N ~ ~ ~ ~ O r ~- CD J O I` O N ~ cNt3 0 ~U CX] 0 to '~t' N N ~ ~ ti M -F-~ ~' ~ O ~ f~ O d: O C f~ O O ~ O 00 N d' U_ i m
Q N UO
r o cfl C O N I~ CO ~ O C ~ O ~ O ~ ~ ~ ~ O O N O 1 '~U ~ m O
O N C M O O -variable O N O ~ 4 ~U
O U
O O O M O ~ ti '~ ^^ Q r~ r t.1./ 2
o~ a~~ a~~ aoM
-variable ~_ 00 O O 5 ~U
N UO
N M N t- ~ O ti L O •^~' M O ~ ~ M W O ^~ O V'J M O ~' ~ W 'W O r- t[S -a ~ 0 , a~ Ulm cts .~ ~ ~ ~ ~ ~ C M O ~_ 00 N ~- 00 O O N ~U
U Patch) Occ. Nearest) to Prev. &Precipitation Size) Buds Blooms to of of Cover No. No. p(Dist. p(Patch ~ Intercept Intercept Clouds P Avg. Avg. Log Log10(Distance Lo91 ~ 41
~I~~~ ~~~~ P'et~cent~~e
~~ ~ ~~
Figure 1. Bivariate distribution of surveys by cloud cover and precipitation. Many cells have fewer than 10 surveys, and some bivariate categories are not represented at all. 42
0 ~ cfl N U N ~j Or -N .~ ~ t~
100,000 L
(~ ~ ~
~ Q~ 31,623 ~ ~ ~ ;+,:r ~ L ~ p > [m]) 10,000 c~ .~ vi .—. 0 ~ U _N n (m) ~ N N rO Cfl N~ !~~ ~ ~ M .~. Patch M d (m2) N_ Patch Q~ '~ ^ ~ i!1 O -~ O A Q) t ti CO M O V Size t1'~ O Nearest ~.~:~ ~ ~ ~ ~ r to Nearest O p '~ .S~ ~ Patch O r to o N -;--- ~ M N U ~ Q J (~ .QU Dist. ~ O ~Q~ ~ .V j ~
O Log~p(Dist. N O r Q) ~ ~ ~ ~ ~ ~ Q.. Q •~ ~ ~' ~ to U ~ p~ Q) ' L ~ ~ O ;._.•~ ~ ~ ~ ~ __ O O ~ N .Q V1 Ai U .~ ~ rn ~ ti cfl ~n mot; c~ N T 0 t- rn ti c~ M N t— U .Q O O O O O O O ^ ' O !- O O O O O O O O O O O ~~ ~ Q. ~(auednaap ~o l~i~igegoad pa~aipaad ~auednaap ~o /(~~~~gegoad pa~~ipaad ~ ~ ~ ~ o a? — ~ ~ .0 (a ~ ~ 0 0 O ~ ~ ~ ~n o ~M~, N O ~ ~ ~ ~ O N ~ ~ ~ r-0 ~ ~ M 0 ~ a--• ~ ~~ O 0 n ;+= ~ O v~ cflN ~ Z N M 0 'i'' r ~ ~ M u (m) O .Q ~ ~ ~ O S
Odds ~L ~ O V
0 in r cv ~- ~ ~ U_
N ~ Patch N ~ ~ ~ ~'~ N r .0 0 ~ ~ ~ O
[m2]) 0 ~ M M 'a. ~ Change O ~ ~ O V (m2) N 0 V Size 0 0 Occupied O M 0 ~ ~ O 0 Size O M ~ N N ti M O ~n ~ ~ ~ ~ Patch a~L Qo ~ ~ a Previously Log~p(Patch
to .L ~n co U~~ ~ ~V O ~ 0 c~ ..r O ~ N O r - O ~ ~ ~-' ~ ~~ .~ Distance ~ u~ N ~ U ~(0 ~ r ..J e-~ M ~ ~ ~ ~ U 0 0 ~. ~ ~ ~~ r- o ~ ~ tf~ C'') N N ~ O M 1` cfl tf') ~t c~ N r - O r O ~ ti CO N ~ O 0 ~ ~ ~ ~ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ~ ~ ~ ~ ~Cauednaap ~o I(~i~igego~d pa~a~paad ~(auednaap ~o ~~~igegoad pa~a~pa~d ~ > •t6 O LL (Lf > U 43
O ~ N O ~ N O r
- O
~ ,Q Q. O ~ ~ _O n- O d. ~~ U ~S U oZS ti Buds ~~ 1 Blooms of 1 of 1 Number 1 Number 1 t ~- Average 1.83 1 Average 1.78
= M
= 1 1
Odds N Odds 1 in
in ~ ' 1 Change
Change O r ~ ~ ~ r ~p ti') ~" C''1 N r r O ~ O t,L") ~- M N O O rn ~ ~ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ~!!!gegoad pa;a!pa.~d uo!;aa;aa ;o ~!!!gegoad pa;a!paad uo!;aa;aQ ;o Buds of Number Average
a~ :.:.. 0 U
N
~ O ~ ti cD ~ ~' O O O O O O O uo!;oa;aa ~o IC;!i!gegoad pa;~!pa~d t% 44
~ r-, ~ 10, 000 3 Occupancy ~_.~ .~ Probability 3,162 2.5 - - - - a a. 35 ® 50% ~ ~ ~~ ~~ 1, 000 2 ® 90%
v ~ ~ ~ 316 1.5 - ~ ~ ~ N ~ ~ •° '~ 100 1 L ~. e- .° 32 0.5 -
.N ~ ~ 10 0 o .N a9 0 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 0 —~ 1 3 10 32 100 316 1, 000 3,162 10, 000 31, 623 100,000
Lo910 Patch Size ~m 2~)
Patch Size[ m 2l
Figure 3. The probability that a patch is occupied is a tradeoff between the size of the patch and the distance to the nearest previously occupied patch, according to the best model. An occupancy probability of 35% corresponds to the estimated proportion of patches occupied. 50% and 90% occupancy probabilities are approximate thresholds that indicate whether a patch may be temporarily or more permanently occupied. 45
Appendix A. ~arnpte Datasheets 46 in i 3 L filled
S been has datasheet
i The •}-+ i i ~ X '~L~ -♦-'~ ~7 ~ _~ ~ .~ ~ ~ ;,~
~ ~ ~~ ~ ~ L ~ O ~ O ttZ ~ tts ~ ~ a ~ ~ ~ T ~ 4) ~ ~
o _o U -,,-. 4) ~ O O ~ -~...O Q ~ `L7 ~ 11! ~ ~ -~ m
~~ U-+-'
~C[f ~~- r-~ 4- CCT ~
60 ~ ~ ~- ~ a ~ ~ 4 .-- U o ~ s~..a~ ca
~ ~U ~ ~X ~ ~~ ~~, '-' O / ~ ~ ~ ~ s
g F-- ~ (cells)
Day . ~ to Ede % Spacing # -~.,, ~
/Shade N Moth Blooms Buds )
2 N of of of ~ ~ Stem Flowering Canopy Sun Cloudy Paces Stem (m Patch Stem # # # .~-.~t~t~ ~~
~ .~ 47 been
00 has ~~ Flowering Stem datasheet
c ~ ~ The
,~N ~ ~ -~ ~ ~ ~ to ..~ .`~(tS X tII -+-~N `O N ~ ~ ~ ~ ~ ~
Survey ~- tV M t6 ~ ~ ~ ~ O ~~ ~ ~~ ~ ~ ~ O ~ ~ ~ ~.-.O -~-~-, O O ~N N N O~ ~- `n ~- N ~ ~ ~ c~ ~ Cover tv ~ ~ O ty r-3 N ~ ~ ~ M N M ~- O U r-~ N N T--4 i-1 N N N
Canopy ~ O ~ U
.`~> Q ~tII ~- 0 0 0 0 0 0 0 0 U O O ~ U O m
Buds ~oo~a>a~00000
# a ~ ~ a~ -~a~ o~ X 0 0 0 0 i~ O O N O ~ ~ ~ ~ co ~ ~ ~ ~Oo `~ O~ ~- ~ O O~ N M 00 ~ ~ ~ ~ o ~ a~ ~ ~~ ~ ~ ~ ~ ~ X ~ U ~t r{ tU T ~ '``'Ct3 Ct3 C!) (!) ~ ~ ~
) .~ N O O F-- ~ ~ ~ ~ ~ N~ ~~_ M ~ ~ ,~ Date Survey (dd/mm/y M ~-1 ~~ •- ~ ~ ~ 48
a~ ~~~ j-+ o 0 0 0 0 0 0 0 ~ o survey
z o with in ~ a~ ~ o ~ ~ .~,
o o~ , filled ~~ o ~.~' 2 2 •v o N~ •~ z~ 2 2 2 2 2 2 2 2 2 ~ N L ~ been L ~ •~~~~ o ~ .~ ~U~~W~Z has
~~~o -~, o U~ n~ o O O O O O O O `~ ~' ' n O ~ o oh o ~ ,~ N N N ~-~ datasheet NNE U U ~ o t~ The
a~ a~ ~L c~ ~ ~U -~ a~ O ~o ~ ►~. O ~- ~ m O ~ N O N m ~- ~- ~ M m o O O N L O O O O O O O O O O O 0 c~ -;-; z-+ ~ ~ ~ ►~ 00 O oo ►~. c~ O O ~ ~ .~ ~ ~ ~. . N M ~ N ~ ~ ~ O N O ~ ~ •~ O O O O O O O O O O O (n E— ~. c U '— SO/90/IO so/9o/zo _N ~ ~ ~ ~ ~ ~ O ~ ~' U • (p ~ ~ ~ ~ ~ O ~
~ ~ /05/05 ~ ~ ~ ~ 31 zT/os/os 3I/OS/05 p~ N ~ O O O 03/06/05
'I ~ ~ ~ o
~ ~ ~ ~ ~ ~ ~ -~ c0
~ ~ ~ ~ U ~ ~ ~ ~ ~,- ~ ~~ , O ~ ~ ~ ~ O Q., fCS ` ~ ~ ~ ~ ~ (~ U U ~ X ~ N ~
~ C ~, Observers ~ ~ ~ ti5 ~
'~. _ ~cn ~- (a ~-u ~-~- ~ ~ X U X ~ ~ X .~ ~ ~ ~ ~ ~- m ~ 2 ~.n ~ N ~,rj U t~ ~ ~ ~ ~ ~ ~ ~ v v ~ a~ ~~ U ~tMMOC3 ~ 0 ~ ~~ ~ ~ ~ ~ 49
Appendix B. Field, GIS, and ~alcuiated [data with a Summary
All of the collected during field surveys are reported in this appendix. If corrections were made to the field data, as detailed below or in the Results and Discussion sections of the text, only the corrected values are listed in the appendix. The last column in the table lists details of why specific surveys were not included in the analyses. The data generated using GIS and values calculated from the original data are also reported in the appendix. If transformed variables were used in the analyses, the transformed values are reported alongside the original variables. A summary of the data can be found on the last page of the appendix. The stop time for the survey of C7F during week 2 was 06:05 and the start time of the survey of C7B2 during week 2 was 06:02, an overlap of 3 minutes. Two minutes were subtracted from each survey so that they no longer overlapped. The start time for the week 3 survey of B12D was missing. The start time was imputed by adding 5 minutes to the stop time of the survey immediately before it, resulting in an imputed value of 06:20. 50
N N O N N O O~ M O 0 N (` O d' to ti ti N M r r r r r~ N 0 304 918 982 594 ~- ~ N ~' 702
3 r .. .. data 1,302 1, 7, 9,900 r r C~ 7,126 15,158 56,268 40, Week survey 0 0 0 0 0 0 0 0 O O Cfl O 0 O M o0 ti M ti CU ~-- M ~- r - r - r ~-- N M I~ M O N O 16 17 733 312 864 559 939 886 041 809 or
2 r O d' 00 1, 3, 7, r- cYj 19, 95,659 20,485 Week Data
C~ N ~t CU N `~ 4 O O r- O O 00 r - I~ ~ N 00 ~ M r 17 variables r r r r N M (~- M et' M Cfl r 131 123 876 702 926 211 634 Test 1 ~- CU I` M 1,211 6, 6,222 4, M 18, 95,659 49,266 40, include Week 0 O Cfl ~' 00 mot' d' 4 O O 'ti' O O 00 CO I` O ~-- ~ ~ M M N M M 17 r -' N M ~ M O 07 O 16 N r not 793 594 917 702 006 ~- ~ O 00 871 7, 5,063 4, r' C'7 13, do 95,659 40, 49,266 Overall
~ M N~ M N 2 O O M O N ~- to 0 00 t` I~ O O 16 25 ~ ~ ~ ~ ~- N M tf~ 41 r - c1' CU ~ N N r r r 702 813 r r 648 3 ~ (V 1,154 7,981 3,393 r- M 2,359 13, 87,496 40, 22,405 summaries Week
The N M O N M O O O d' O M t~ M f~ M O o0 mot' O 11 17 ~ d' ~ ~t ~- N M O ~' mot' Cfl 16 N N r - T- N 141 995 706 717 535
2 N ti ti r 1,299 9, 3,029 7,804 6,073 M 87,496 28, Data Week 9 `~ 00 N Cfl 00 N cfl O O M O d' Cfl ~- CO 9 I` ~- O O O C `d' ~ t?' d' ~-' N M O 41 Cfl t~ t~ M r- M N ~- 055 702 049 396
1 N CA O I~ 1, 1,506 7, 4,480 4,668
Training ~-- M 14,911 28,243 CCS 40, N Week O r Cf~ ~ ~ 00 I~ O O N O M 07 ~ Cfl 9 mot' f` Cfl ~- ~' 16 17 22 N O ~- to M ~-- r N M O 41 O Cfl M ~ Cfl M 'd' d' 702 544 -~--+ r r r r 083 N 384 CU 00 ~ 1, 1,745 3,470 6,460 M 10, 87,496 22,405 (Cf 40, (~ ~ ~ Overall ~ ~ ~ ~ ~ ~ ~ ~ ~_~ U W
~~W ~ w Nondetection Detection NW Nondetection Detection o ~ SE Gravel Sealcoated Other cn a- 1) 1) 1) 1) Breakdown Minimum Maximum Median Mean Median Mean Maximum Minimum Median Mean Maximum Minimum Median Mean Minimum Mean Maximum Minimum Maximum 0) Median 0) 0) 0) Total Total 2) a~ ~ Total ~~- ~ c~ ~ ~ ~ o ~~ ~ -~
a~ km a.i ~ of 0.5 ~~ ~ U of (No. Nearest Nearest Patches) in Patches Moths Surveys ~- ~ Occupied to of to of of of Patch (m2) (No. (m) (m) ~ Size
t~ Area Type (m2)
~ ~ Patch Radius Distance Previously Patch Nearest Road Number Patch Distance Patch Number {m2) Size (Number Surveys/Category) Surveys/Category) Number Variable (1) ~ Aspect 51
O~ M M O Cfl tip ~ Cfl tf~ O O tf~ r- ~ C'7 00 ~ Cfl N O N C'7 d' mot' to 0 07 X 0 0 C'7 O N~ ~ ~- ~ p ~ r- nj C`rj ~- M M Cfl ~ r : ~ ~ N tf~ ~ O June June ~ O O O 13 17
~ t` M cfl ~ CO ~ tf') O ~ O N O N t` tf') O ~ ti '~' O O 00 M t~ t.f) t.f) Cfl O O ~ M O ~- t,C) ~ ~ ~ ~ p ~ r- C'7 to N ~ d' d' ~ O O O ~- ~' Cfl Cfl O June June ~ CO O O 6 10
O O r- cfl tt ~ O cfl ~' O N ~ ~ O M~ ~ N O d7 ~-- O cfl O O mot- ~ O~~ O~ O O 'd' O M d- ~ ~- ~ r !- ~ ~ ~- r- to O O ~ 00 O O O ~- d' Cfl CO O May
June ~ M ~ O O 3 27
t` C~ M M ~-- O~ O M ~ 0 0 0 ~ t~ ~ O ~ O 'd' O N N tt O~ to o0 X 0 0 0 0 ~-- ~- ~ r-- ~ CV O ~ ~- M ~t M ~' ~' ~ a0 0 0 0 N N Cfl Cfl O May lf~ 00 June O O 27 17
N ~t M mot' N cfl 00 O P~ N O O O X 0 0 ~ r- 00 ~fi 07 O O N f` O Cfl O M~~ ao O O O ~ O ~ N r - ~ r - N N O O I` O O r - r- N C'7 ~- ~ M O 0 0 0 C'7 M~~ O ~ N O ~ O r - June June O ~- 11 17
c~ c~ N cfl ~ cfl N~ Cfl ~t O M O~ 0 0 tt~ ~~ t7' O O t1~ d' O O M c~ M A O O~ O O O M O ~- O ~ ~ ~ ~- r- (~! X 0 0 CO O ~~ ~- M ~ `~ mot' d' d' 0 0 ~- O N N Cfl Cfl O ~N ~~Or- June June O r- O 4 10
N o0 ~ N ~ O M N M O O O O tf7 O O tf~ O ~' '~t O O Cfl ~ ~ ~' I~ to ~~ 00 O tf') O O 00 O ~- r- r N mot' c`~ O O Cfl O 0 r- r- N ~ r" ~j ~ M d' O O O O M `~ C~ ~ O May tt) O O r- O ~ ~ June O O O 3 27
Cfl `~t C'7 `Cfi C'7 Cfl 0p c0 ~ d' O c~ O ~f? O O ~ O N ~ O O ~ d' ~ O r - M ~ ~ O OO In O O 07 O r- N t- ~ ~ M~ B O O C~ O ~ ~~ O O~ ~~ ~ Cfl ~ O ~ ~ C'7 ~ ~' ~' ~ ~ May ~ N ~ ~ O ~- June O ~ O 27 17 Wet &Dry ~ &Dry Cloudy Sunny Cloudy 1) First Last Earliest Latest Maximum Minimum Median Mean Maximum Median Maximum Mean Maximum Median Mean Maximum Minimum Median Mean Maximum Minimum Median Maximum Minimum Median Mean Minimum Mean Minimum Median Minimum Median Mean Maximum Minimum Mean 0) 2) of Stem Dates & (No. Times of Table. Surveyed Number Number Canopy Patch Patch m/stem) Stop Flowering (%) Cover Spacing Survey Temperatures Buds Blooms (square Precipitation Nearest Nearest Percentage Summary (2005) End Surveys/Category) Proportion (m2) Cover Spacing Stems Moth Cloud (°C) of Survey Stem of Average Average Average 52
65 abed uo panui~uop a~ge1 O Cfl o~ QO O O N O V o0 O N lt7 ~ r- I` ~ d' a0 ~ '~t O r O f` N O O O CO t` ~ ~ O~ O ti O ti r O (SOOZ)ea~t-/ o ~~ ~~ ~~ ~~ zzzz o ~ r fiuiu~ealyo}ed~sa~eaNmQQ Q~ QQ QQ~ UUVUmm mm m mmm m mm yo~ed~sareaNQUU Ul.i Caw xc~w mmot~c~= Q_ w C7wU C~ Uw (966 L -~66 ~) ea.~tf r ~ ~ ~ ~ ~ ~ ~ ~ z ~,, ,~- a, 6ululealyo;ed~sa~ea~mQQ QQ ~Q QQv mmmUmm mm m mmm m mm ~ N CSJ t~ O O O CO M 07 O M t,f) O ti ti ~ 'c?' (O o0 CO f` M O M (yoked •oop ~(~snoina~d ; o ~ n' M ~ ow ti v ~ `r `D rn °r° ic o~o ~ v ono .N o c° ~ ~ o . r . r . r ...... of aoue~slQ) 60~ N N . N N. N. N M M M N M N r^' N. N. N. N. r . N. M. N. N . f~ tf~ O O et' O ~!' 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O . O . O . ~ . M . O . N . o0. N . 01. 1`. O . to. t`. ~ . O . d'. CO. (O. O . ~'. N . ~-. (azrS yoked) 60~ M M M M N M M M M M N N N N M M N N N N M N N M t7 r N r C+~ LS') O CO t~ O 00 i` ti N N CO O r O C!J mot' ~~ M O ~- V' M O ~O ~ CO ~- to M to c0 t` ~ M ti O O ~ ~- to N ~1' C~ O O a0 N N (` O ~- O~~ N t.f) M d'Q1 N '~t COf~ rO-- OM C~M M r Cfl O r ~ N ~- r CD M t.f) 'Cr' ~-- `_'Ct' (Zw) az~s yoked e~ea (~l) sal ~o(dl)6u~u~e~1~~~ ~~ ~~ ~~~ ~~~~~~ ~~ ~ ~~~ ~ ~~ (SOOZ)~~~ ~~ ~~ ~~ zzzz r ea~yfiu~u~e~l ~ ~ ~ ~ ~ ~ g ~ ~ N Q Q Q Q v ~ v v ~ co co ca a~ rn r Q Q Q Q Q Q Q Q Q U U U U U m m m m m m m m m m m M ~- yo} e d `-U UN U Uv- D r-x xN xM ~•x Q m mN U xv Q m U o w m Q w Q m w (966 ~-b66 ~) ~ ~ ~ ~ ~ ~ ~ ~ ~ easy 6u~u~ea1 Q Q Q Q Q Q Q Q Q m m m m• m m m m~ m m m m m m m 53 pg afied uo panut~uo~ angel r- ~ O Imo- ~t O ti M mot' t1) M T~ CO CO O N M (D N M CO Cfl Cfl T- ~f ~ N t7' 00 O ti mot' r - I~ t~ N CD O O N ~ r-- 00 O ~ N O) ~?' M N CO N O ~ ~' O N O ~' M CO mot' t~ O a0 ~t M ~ LO (ails yo}ed ;sa}eaN) 60~ `- M~ ~"~ N N N N c'7 N N N r- T- N M N N N N N <- M N a0 t.f) I~ ~-- t.f) f~ O t'7 M M 00 r- M (.fl f` fl0 d' O ~t O M 'V 00 00 O a0 V O O O op N O ~' N 1` O O N O d' O `~t' M N (zU,l} aZls ~-- oo CD QO ~ 01 ~r ~ to O (fl M N N N ~ ~ N v yoked ;saaeaN N ,. eaJd (SOOZ) O O d' N M N M c7 C7 N d' ~t ~ M CD ~ ~ to u~ ~ ti 6uiu~e~1 yo;ed ;saaeaN m m m mmmm m m m m m m m m m m m m m m m m yo;ed;saaeaN = -~_ wmmc~ ~~ ~.~ taw-~C~C~w Q C x x w z easy (9661- 661) r- O O O O ~-- ~- ~- N M N N N N N v BuEu~e~l ya;ed ;saaeaN m m m mmmm m m m m m m m m m m m m m m m m ~f' c*J I~ !n I` ~ ~-- f~ i~' ti ~ ~ N ~ O ti CD ,.M O CO ~ ~ {'~ (yo;ed ~~p ~(~snoinald ~ .~ rn ~ ~ o o rn ~ ~ o ~, rn ~ rn M ~ o 00 00 ~ ~ rn of aoue;sla) 60~ N N A M c~ M M N N N M M N N N N N M N N N N r - tn t- M t1') N ~- O (~ ~ ~' CO O~~ M . t` N O O O N t~ 00 ~ (w01) yo}ed Paldn~p ~ ~ m ~ ~ ~ ~ cv ri ~ ~ ri ~ ~ ~ ri ~r r~ r` r~ cD r` o0 ~(~snoina~d o; aoue;siQ I` N rtt t-M Ot- O~- c~0 f~ ~t ~- ~-CO c0 CD O N r- .~'O 1~ f` i~ i` O ~ ~t ~ 00 00 N c0 07 M o~ CO M '~' ~- ~- O Imo- I` tf) O ~ N O CU t` N M to tt') CO. M . M . C~. O . ~ . N . M . O . 00. i` . GO. O . ~ . 'Ct'. 3.487 3.808 3.888 2.350 4.407 4.080 4.058 4.892 ~- ~ ~t . 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M M M N N N M N N N c7 t-- N N M M N N N N N (azls yo;ed) fio~ N ~t aO ~ O) M N QO M O N ~- O CO op e- In ap N '~ O M CD (~ 00 toM t- ~ Q~ N ~ QO O ~t d' CD ~- ~ ~-- O ~ t.f) O ~i' f~ O M ~ f~ t~ N t.f) CO t1~ ti CD M I` ~- N N OO M ~-' M Cfl M r- N N t~ ~- N ~ ~ N CD (Zua) az~s yo;ed N r e;e~ (~l) Sal .~0(~!1)6wuie.11 ~ ~~ ~~~~ ~~ ~~ ~~~~~~ ~ ~ ~ ~ ~ ~ EsOOZ) O O O N i N M M M M M M v zt' v v ~n ~ to ~ t!) to u~ CO ea~yfiuiuiell ao mm mmmm mm mm mmmmmm m m m m m m y~;~d ~ ~ M N N M d' ~ N ~t ~ ~ M ~ m C.1 Cfl (~ CO X tXl C.~ ~ 7G X ~ Q C~ Q'i X X X X X X X r X i (9661-x'661.) O O 0 0 O O r'- ~-- ~-- t'- N N N N N N N M ea~t/fiuiwe~l m m m m m m_m _m m _m m _m m m l m m m _m m ,m _m _m _m 54 ~ g abed uo panty}uo~ algal N M 00 I` M M CO mot' r - Cfl 00 O O O o0 ~ CD ~- r ~' CD ~'- N ~ 00 N 0 ti r- ~-- M O O ~-- N~ Q1 O O~ ti M . V'. CO. t`. 0 . ti. ~ . ~-. '~t. Q1. M . N . O . 0 . ~t. 00. 00. M . M . CD. 00. (azis yoked }saa>?aN) 60~ N M M N M N N M tt M M N N N A M N M M N N • 00 M ~ N ~- •- 0 r- O 07 O~ O N t` 6(') ~O 01 O N N ~t t` '~T' T- O N O O M N M Qi M M ~-- 1~ O O 0 i` in (zU1) aZ1s Cfl CD ~ t0 M LD N M 1` CO O r oo CO r` M 1~ O 0 ~ t` N ~?' O r lf') 00 N 00 CD r r- yo~ed ~sa~eaN N N 0 La.ly (SOOZ) cfl ►n ti ~ ~ ~ ~ ti ~ ~t t0 ao 00 00 M ~- N N ~ 0 r r r r r ~• r r- ~- r N r r r r r N N N~ N 6uiuleal. ya~ed ~saaeaN m m m m m m m m m t7] ~1 m fn m C0 m m m LY] Q m - ~ yo~ed~sa~eaNw IL u..w ot~0 ~ _ ~c~C~ LL. Uon om mQo ea.ly (966 ~-t~66 L) M N d - ~t ~t ~t mot- ~t ~t M ~n ~.n ~n ~ ~n m ~ rn ~ o fiulule~l yoked ~sa~eaN ~ r r r r ~t r r r r N r r ~-- r r r ~- ~- ~ cy m m m m mmm m m mmm m mmm m m m Q m CO 1~ r CO O 1~- ~ O r r M Cp O) O CD N 0 ~?' O CO M N ~ M d- !` ao 07 CU M t.C) 0 In M M N ao v 0 ~- M (y ~ ~e d ~~ p ~(snolna.l~ d i~O o0 0 ~t N t7' ~t ~ ~t O 0 tD tf) N ti N O~ O N O of aoue}sla) 60~ N r r N N O r- N N N N N N N ~- N M N t- M C'7 Cif t~ 0 M ~t M e- t~ 0 c'7 ~ O N O ~ (~ !` O CO l!7 O, Paldn~p ~ O M ui CO (w0 ~) yoked CNO O M N c07 N o0 00 ~t M ~ N M 0 ~ 0 ~(Isnoina~d o~ aoue#siQ r' `"' r" . r ~ 1~ O L1~ N ~- O O In (~ Cfl r"' t~ 0 N Cfl r O0 ~ ~- N M O0 '~ In 07 O f~ ~ CU r- N r M O N 1`~ 'd''d' ~~' CD~?' N ~ CO 0 ~f~' 3.589 4.684 N M d' d' M c7 v d' ~ v 4.227 v d - 4.588 `~ N C'M N M sn~ Pe ~I w}ISO w eaay yoked) 60~ ' mot' ~i' V' '~t ~ r C~ N M ~ O '~t' to to CO t` tf~ M 00 1~ ~- N M V O M d) 11~ O Cfl H M O t.f) M 0 r 0 CD I` M O~ sn 1 p8d w~ S~ p M ao M N M t` Cfl M N ao ao ti N N (~ O M O ao 0 r~ CD ~ f~ CO t` cD i` t~ 00 00 ~ '7 M N • ~' ~fr Cflr ~- 0 r t.f) N r N N `7 M r- ui easy yoled ~ J r r Cp r r Cfl Cfl O O ~- O ~-- (O 1~ 00 M M ti' 00 N M ~ sa.lea o o r o o r. ~ 0 0 0 0 0 1~ rn rn rn ~n ~ n o r (y ~ d~ N M M r- M M r r o o c~ o M ~- o M M cD a~ rn cn 00 o~ aoue~slp) 60~ ~ ~ ~ .~ ~ ~ .~ ~ .= ~ ~ ~ ~ c•; ~ ~ r ~ ~ ~ r a~ ~n o o ~n cn o 0 0 0 0 ~n ~n u~ ~n +n ~ ~n o ~n rn 0N 0N ~- N N •-- ~- r- ~- N r - N r - N N N 'V' o0 0 'c1' CD (~ (w) yoked ~sa~eaN o~ ~~sia r c 0 6uipo~ ad~l peon r r N O O O O O O O O N N N N O N r- ~- r- O C ;.... C O U ay .nc~ Gravel Gravel Sealed Gravel Gravel > -a -a -v -r3 -~ ~„ ~ ~ C C C C C -Track, -Track, -Track, ad~(1 peon Sealed Gravei Sand, Sealed Sealed Sand, 2 Gravel Gravel Sealed 2 Gravel 2 ~ i cv C7 C~ ~ ~ ~ ~ ~ 6uipo~ ~oadst/ ~.... r . ~ ~ r r ~-- .- r ,- o r .- r r .- r o r r r }oadsycwn cn cn~ ~cWnm cn w cnz~ cn mmm cwnz cwnwcn L.. L ~ ~ ~ ~ L L c ot- c~ oL oL c~ o 0L 0L 0 oL c c c c ~, o o c .L c ~ ~ c -o -o ~ ~ -o c c c c ~ -o ~ c c -o ~ 'L 'C N ~ 'L.~ 'L~ L 'L 'f.- ~ Rf QS ~ ~~ ~ 1r L > ~. L ~ > L L L ~ L > > > > Qp ~- O L- > ad~lyo~ed~ U ci~U Uwcnf° U ,v UUU v~ nc~nrn ~U wUin O ~ M M ~ 00 d7 r- t7' ~ CO 1~ O 1~ ~t M ti M N O CD N Q~ CO 1~ M tf~ O r- a-- O M O N ~- O I.f) N N M Cl~ e- ao LO M 0 ~ (O O) ~ r N 0 O O t~ M ~' N T- t- ' CO ~ ...... (ails ya~ed) 60~ M M N M N M N v M c'7 M M N M c~ '~' N c~ M N N ~t M tt ~-' N~ •- 0 r ti 0 to N M to f~ M t~ t` O O r ~ M 0 ~- ~' ~- M O N N t- M ~- 1~ r CD N tf3 O N O O N M CO lI~ 00 ti M O CO O 00 N M t~ t`- M M ~f' M CU '~7' 0 ~ ~ r r 00 r tC) Cfl M r r (Zw) azis yoked N N e~ea (~l) sal ~0(2~l)6wuie.11 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ \SODZ) O M ti f` ~ ti ti t` ti ti ti a0 00 M M a0 r N N O Cn ea.ly6uiuie.11m m mm mmm m m mmm m mmm mm mmm ~ `- r N rt r ~}~ ed m D U D W LL 2 ,_ r—~ J i m U O D D X Q U Q U (966 ~-tr66 L) M c~ ~ ~ ~t ~t ~t ~t ~t v ~r u~ ~n ~n ~n ~n ao rn o 0 0 m mm mmm m m mmm m mmm mm mmm 55 Zg a6ed uo panui~uo~ angel ~t op ~ CO d' O Cfl o0 N M O r- ~ t1~ pp (O tt~ In 00 N O f` ~ r , O M O M t~ t - M GO O M r O r- Cfl O O O M O i~ 'Ct' O (O N O o0 d' ~ r -- CD t.f) M 00 ~t O O N O ~t O r f` M i"- a0 M O CD ...... (azlS yo}ed ~sa~eaN} 60~ M N r - N ~r M N N M M M ~- N M N M N N M N M M M N r N In ~ r ~ ~ ~ ~ M M M '~T N to 00 1~ d' O 'r ~J' CO op QO ~t 1` N ~ ~- 1~ ~t ~t O M M CU CO M ~-- ~' t,p 'd' M O N N N r (zU1) aZls N f` M O O M N c0 O r r M M N ~- ~ O ~ O O N ~ ' ~- ~t '~t ~ M c0 1~ ~- N CD N O yoked }saaeaN r (SOOZ) ea~d ~ o o M M M M M ~ M M M r M ~t v ~t r` ~t u~ o cn co 00 6ulule~lyo}ed~sa~eaN Q m m mm mm m m mmmmmmromm m mm m m m yo~ed~sa~eaN v o ~ J1; ~~ m m OXZY_mQ=X c~ c~c~ = LL.. v easy (966 L-t~66 ~) ~ r r N N N N N N N N N N N M M M M M tt to ~ ~ O 6u~u~e~lyo~ed~sa~eaN ~ m m mm mm m m mmmmmmmmm m mm m m m O 1~ C~ t` t~ f~ OQ O N ~ CO 0p N ~ ~' ~ CO CD ~ N r ~f CD •- O d' O ~ r ~ N r !~ to N M ~r v- 'Q' co ~ r` r- ~ ~- M (y O ~e d '00 O /~ 1SnOlnaa d ~ r-O ~ ~r ~ rn co ~- v rn d- cfl ao cfl o o r a~ a~ M co ~ ao 0 of aoue}siQ) 60~ N. N . N. N . N . r -. N. N . N. N. N. N. N. N. M. M. M. N. N. M. N . r-. ~-. M. CO QO ~ O N O tf) CO cf' r - 07 ~ M N Cfl ~ O M N ~ M ~ ~ ~ (U.10 ~) yo}ed paidrr~0 N O M r M N M ~ ~ N oN0 N ~ CD V' ~ ~- ('~ ~ ~ uci ~ O O ~(~snoinaad o~ aoue~siQ ~" T- ~' N r to CO M N N N ~t M N CO C~ of O ~- t1') ~ r -- O O '~t' t~ I` O 07 1` r O ~- r~ tf> ~ Q7 O r - O 'Cr M O ti M O~ O ~ N O O mot' ~- '~f N M O M M ~- r ~t M o0 N N I~ O M O M ...... M ~r v ~t v ~t ~t v' ~ M v ~r ~t ~r ('~ 3.304 ~- d- ~r M d' ~!' M N (sn I ped w~ 5.0 w eaay yoked) 60~ 00 O O Cfl O r - O M Cfl ►S7 r M M N M 'C 0p CO N r M M Cfl O t~ O CD ~t to ~-- (O ~t 07 ~- M r O <-- CO r f~ ~ M Imo- C~ 00 r-- O U.t~ S'0 ao O o0 M o0 M O tl') ~' d7 t` O ~n CO r- O (D M ~- co N t` N sniped• O r to N~ CO •- N ti ti `Ct M lf) I` N N 00 I~ CU r O 07 w easy yo}ed r N ~- (y r r N oo N r r N ~- r r N CO CO r QO ~t CO cA ~t r - N ~-- f~ f` ~-- CO CO oO ~ (O O N CO t- O r` r` o r~ ~' r~ r~ v o 0 0~ ti o~ ti a~ v r` o o r` 0 0 (yo}ed ~sa.leaN ~- r M f~ ~ r - T` Lt') M CD M 'Cf ~ M ~- r M lf? e- O CO r- M O r r r ~- r r r r ~- r r ~- r r s- r ~- e-- s- r r ~- ~- r Cfl o~ aoue~siQ) 60~ LL1 ►. + ~ Q~ ~ ~ O O In In O In O 0 0 0 0 0 In 11~ In In O O to O O r - t-` N CO M ~-- CO M N ~t N M M N ~- ~- N M ~-- r - ~t r N ~- N (w) yoked lsa~eaN o~ '~siQ ci c0 6wpo~ ad~(1 peod I ~ ~ , t- O O O r e- r ~- O ~- N N N N O O ~- O O ~- r- r r- ~-- -n C C O U a~ Paved Gravel Sealed Sealed ~ m a~ ~ ~ ~ ~ ~ ~ t6 O ~ ad~(1 peod Railroad Paved Gravel Gravel Sealed Gravel Sand, Sealed Sealed Sealed Sealed Sealed Sand, Sealed, Gravel Sand, C~ [Gravel cn C~ cn w z ~ 6uipo~ }oads~/ O r ~-- t-- o r ~- o ~- o o r .- ~-- ~- r o o r ~- o ~-- 0 0 ~oadsdz cn cn ~nz c~nv~ z acn z~cnu~ cwnc~i~ c~nz~ cn cnz cn z z 0 a~ ~ ad~l yoked a n. ~ v o Savanna Corridor Corridor Corridor Savanna Corridor Corridor Corridor Corridor Corridor Corridor Corridor Corridor Savanna Corridor - Corridor Savanna O O O w Woods Woods v _ _ O N O O o~ O O ~t N ~t ~ oo O) O CO o0 ~ ~-- t1~ Cfl I~ 00 N ~t N O N tD QO ~' '~7' t~ 07 M CO f~ N r p7 O M ti M N M r- ~-- M O N O N M Cf~ N ~-- ~t ~ c0 m N O~ O V' O (D 1~ O M r- N ~t ...... (azls yoked) fio~ N M N N N M M ~r M M M M r M M N M N N mot' M N v M M N N N 117 O (D ~- ~ M N N 1~ M o0 to (D O ~ ~ r r r Q) ('~ O M a0 ~t CO M ~- O N M r r M ~F' r ~- CO '~7' O 1~ M r r a0 ~ M r N M~ O ~- 'V' M to •- N M N ~t ~ CO r •'- M 1~ r - t7' t - d' M M Imo- Q~ 00 r- r O N CO N (Zw) azis yoked r ~- ~-- e~ea (~l) Sal ~ ~ ~ ~~ ~~ ~ ~ ~~~~~~~~~ ~~.~.~ ~ ~ ~ .~o(dl)6wuiea1 Wt— ~SOOZ) O O O O M M M M M M M M M M V' ti' rt' I` ice- ~ CO (O CO t~ ea.ry6uiuie.l1 m m m mm mm m m mmmmmmmmm m mm m m m yoked Q v w _m coo i C7 =_-~c~xQmLL.X X mQ m c~ c~ (966 L-t~66 ~) r r r r N N N N N N N N N N M M M M M '~ ~ ~ ~ ~ eaa~r6uiuiea1 m m m mm mm m m mmmmmmmmm m mm m m m ~6 £9 abed uo panui~uo~ algel - - -- M C5~ (A tom• I` ~ ('~ N O M O N O~ M O 'd' ~- CA r C70 N ~ M O O) N~ O M M ~- N N op M N~ op CD M M 00 CD ~- O ti ~ I~ 1` N N O N CO M N M 07 to N M~ M to mot' Q1 ~ ti N O ti) ...... (azis ya~ed }sa~eaN) 60~ N N M M N M N M .- N N t- .- N N R- N N N N M M N N ~- (n M CO N e- O Cf~ ~ O CD 'Cr I` i~ f~ M ~ M CD ~ O N C1Q d' N N O O O N M O r r f~ M ~- N O N o0 O f` N O ~- ti (zw) azis CD (D CD o0 ~-- O d' O ~t O N M M M O M N' ~n ~- M yoked ~saaeaN i ~ ~ ~ F v ~ (SOOZ)eaay.-oo~-r- ~ r ~, r,z z zz zzz z 6u~uje~1 yoked ~saaeaN m m m m mmm m y v v v U U U v U U U U ~ v v U A ~ ~ i ~ i yo~ed~saaeaNUmLL.0 wmQ v_m Q _= XXXmC~U_o Qm ~ z (966 ~-ti66 L) easy ~ cD ~ ~ ~ r . ~ a, z z z z zzz z 6uiuie~lyo~ed}saaea~ymmmm mmm mU c~~ UU r UUUUUUUU UU U U ~ V M ~ M r ~ ti') 07 ~-- t- t~ Cep N tt t~ r ~-- N ~- ~ O Cn N CA .~ N r- rn 00 CD ~r O O O [` O Cn O O ti ~t N ~t ~ ~r v N co (y ~ ~e d O ~ Isnorna~ d OM r- N O M M M M M M M r O O M ~t v v M N O to ~ (D ...... of aaue~sia) 60~ N M M M M M M M M M M C7 M M C7 C'7 M M M M N N N N r - ~1' ~ c0 M M N ~ r - M ~t M tf~ •- O r C7~ M M t1~ N 'Cr M M (U,IO ~) yoked paidnaop N M CND N v M N O ~ ~ M N N N O ~ ~ ACD ~ ~ o~o uci M ~ ~(Isnoina~d o~ aoue~siQ `- r r N N N N N N N r - ~- r - N N N N N ~-- i, - CA C10 N e- ~ CD O (` M r ~ N CD CA ~' O ~ O lf~ f~ Cn O M O M N O V' CO a0 N 1` I.nM~ CD O to N (~ O . tt . O . M . C9. ~ . O . O . M . ti . N . M . . ~-. O . ~f. . CD. ~ . 3.722 3.261 2.354 M N M M M M 3.824 M ~' M N M N 3.652 M mot- ~t ~ M M M (sniped U.l~ 5.0 ui eaay yoked) 60~ N~ ~' d' M N ~-- ~- N M O O O to O M ti r CD N M~ ~ M t` •-- O C~ N t` M CD ~ to N O N N r- N o0 ~ O CA O [~ ~ M W ~ i~ M O .- CD N CD CD ~ CO ~t ~ t` ao N N t7' a0 ~- ~ ~- ~- N O s n •~ ped 5.0 ~ ~- N ~t ~ M CD (A O CD r - ~- ~t M t1') ,-- tt~ M 'tt' M ui ea.►d yo;ed ~ r r N '~t CA O ~- e- r f~ e- 00 0p r O O O N ti ~ M e-- t~ r- i~ O t- ~t 1~ 0 0 O O t` O d~ O Q N ~ V' O r O to O r~ O t~ ti N l y~o e d~sa~ea N ~n O O M M M d - M M M M O ti ~ CD N M CO M d' M ~t ~- O r' r r r r r r r- r r N r r ~-- r ~- r e-- ti o~ aoue~siQ) 60-~ r N r ~- ~' N X 0 0 0 0 0 0 O In ~ O~ ~ lf) O~ O~ 0 0 O O ~ ~ O C'7 N r N N N M N N N N o0 to tt~ ~t CD N ~t N M N M ~- O Ctf (w) yoked ~sa~eaN o~ •}sia r .-- r a. c 0 6uipo~ ad~(1 peod N N O N N N N N N N N N N N N O O N O r• ~- O r r C C O J a~ .nc~ Paved Sealed ~ a~ c ~i> c -Track -Track Paved Paved Paved ad~(1 peod Paved Paved Paved Paved Paved Paved Paved Gravel 2 Gravel Sand, Sealed Gravel Sealed Gravel, Sealed v~ 2 a z Gravel 6uipo~ ~oadsy ,~,._. ~- o o ~- ~- o r o r- ~- t- o 0 0 0 o r r- r r ~- ~ ~- ~- ~oadsy~~zz wcr~z c~'n~ v~ c~cn zzzzzcncncn c~'i~v~i ~ ~ L L (] 'v (~ Q~ 'D Q~ Q~ CU ~ Q~ L- ~ Q~ L ~ ~ ~ Q) ad~(1 yoked ~ -a -v o -v -n ~ -v , v -v ~ -v ~ Savanna Savanna Corridor Savanna Savanna Corridor Corridor Uw wU Woods Savanna w~ wwww Corridor Corridor ww w w ~-- ct t~ N ct O CA to M O C4 ~ M ~ O ~f' ~t N `ct O t` ~ ~ N CD 00 O~ CD CD ~ M ~ N N M M to N O O N 't7' CD N O r- O O 1~ C''7 O O ~ a0 O CD CD N O O M N M O N CA aO t` [~- M N O ...... (aZIS yoked) 60'~ N M M M N M N N N •-- v M N ~- N N N t-- N M N C7 M N d' ~ O d' r - M t.f) M O ~ ~ r ~ ~ r ~ d- ~ C70 d' r - CD CD N O O r d' ~ ap C70 1~ t~ r r- N f~ N ~- O N O oO N M O ~' O d• O O r ~ B O O mot' mot' C3) t.f) N N r -' ~-- 1~ M t.f) O ~ CD N ~-- ~-- t` Or . c0 ~ N r (Zua) az~s yoked .~ e~ea (~l) lsal ao(dl)6wuieal~~~~ ~~~ ~~ ~ ~~ ~t w-~~~~~~ w~ ~ ~ ~SOOZ) ~~ r . ~ r r ~„~ M Z Z z Z Z Z Z Z ea~y6uiuie~lmmmm mmm mU U UU UUUUUUUU vv U U yo~edwQQx XXm xQ m Utt.. XXx`r xUwQm mQ m U (966 ~-t~66 ~) ,r, cD ~ ti ti ,~ o Q, z z z z zzz z ealy6u!uiealmmmm mmm my v UU U~Uc a.~Uc~UU UU v U 57 tg a6ed uo p~nulluo0 algel M O ti 0 r- M O T~ t0 O O N O~ N O r ~1• Q1 CU CO t1') tf7 c') V' N M ~ N N O ~ r ~ to O O 00 CO ~ M ~ ~ t~ O O CD r !.f) l(7 O . . . CU. lf). f~. 1~. O . ~-. O . to. `~•. ~ . . 'C7'. T`. tf•). 00. CD. N . ~ . O . =t'. O . (ails yoled lsa.leaN) fio~ M M N N N M M N N N e- N N N M N N r N N N r r M r O r ~ M 1 r -` In M M ti ~ N ~- M ~ CO ~ QO ~- I` M O ~ r a0 a0 M M to cfl cO N ~ N M c0 O N to t~ ~ c0 to ~ ~t M N c0 (zU.l) aZls O M M t# M N N r r o0 N M M O ~ M ~ <-- M r waled lsaaeaN ear v cD co c~ cfl a~ r- ~ o o ~ rn o o ~ ~ ~ ~ ~ ~C7 ~ r r- fiulu~e~l• yoled~sa~eaN °0U U U U U V U U U U U U U U U U U U U U ~ V U f~ waled lsa.leaN c~ Q I-- i ~ C~ m cn O c~ -~ U _ Y = ~ Q ¢ m -, _ 5c ~ o eaay (966 ~-~66 ~) r r ~-- fiulule~l waled lsa~ea~ U U U U U U U U UUU U U U U U UUU U U U U o ~t c0 01 O 1~ CO ci' f~- O ~ `~ O N ~ M ~ Cfl O r r N N P~ r CSJ o ~ M r I~ d' f~ M ~ Q1 O et Cs7 O N O r- O ~ e '~'J /~ SnOIAa~ oo v co t` ao (y ~ d O I d rn. o . ~ . cfl. r~. •-. ~n. co. ~t. r~. t~. ~n. ~r. rn. o . ~--. rn. rn. o . o . ~. ao. r~. ~n. of aouelsio) fi0~ `- N N r N N N N N N N N N N M M N N M M N N N M N N r (~ r r O in N N t!7 O I~ r ~• O O (U O N M t1•) + M 00 paidn~p ai ~i oo v c~i ui ui r= o ~r psi oo r` o o Sri ai ui Sri o psi v ~ co (w0 ~) yoled ~- N tf) r (+} ~ M In l!7 M N 00 r'- 'Ct' O O O O N f` CO r - ~(Isnoina~d of aouels~Q r- ~' r r- M O '~ O M CD O CO o0 N O ~ ~ M In M ti (fl M O 'd' r N O N O ~t O r 1~ '~ O ~ M CD ~?' M O 00 d' ti r N ~►' N N op O M CO O d' CSC O ~-- r CO O Cfl CO In O I` 00 CD CO tf) 3.550 M 3.843 M c~ v M~ M M M N ~ M y M M M M M M N N M (snlpe~ w~ 5•p ui easy yoled) 60~ CQ N N N O ' O d• i.n 1~ r r r M t0 In f` N N (~ ti O r 1~ t7' 00 (D CO ~ I` 1~ ~' O ~ CO M `~' O r N r r CL? r N CO N M N snlpe~u~~S'p~ rn ornrrnN rn ~~ asrn r- cn mr- M oao~ rn ~- ~v c~ • ~-- C~ CO (` N '~T O N M QO r- M ~f r ~ ~ M ~-` t,f) CD M ui eaay yoled N r- .- r-- I` ct (.fl CO ~-- r- ~ r t~ O e- O M a0 ~ M O t0 O 'd' CO I` Cfl r `~ ~ rn o o ~r o ~ M o o ~n rn v- o~ I~ r~ o r rn ~~ o (y ol ed l Sa,leaN ~v lf> r M M M t[') M '~' N M O CO M to M r- s- O •- M ~t r M o} aouelslQ) 60~ ~- ~ c^ .r- r r ~- r r N e- ~-- r ~-- e- ~- r e- r N t- .- ~- r ~ O t.C) t.C) to O O ~.t') O 0 0 0 O ~ to ltd ~ t1') ~ O O t.C) O lf~ O M M r N N N C') N M I` N r ~ N M N ~- •'- r M N M r- N ([f (w) yoled lsa.leaN of •Isla ~- ~- ,i c 0 fiutpo~ ad/(1 pL'o~ N N e- N t- r N O O r r N O r t'- r N N N r N r N O• ~ ~ N C .~ C O _N Q Sealed Gravel Sealed Sealed _ ~ U "d ~ 'd ~ 'C C (~ C R3 C -Track -Track, -Track -Track -Track, -Track -Track ad~(1 peon -Track Sand, 2 2 Sealed Sand, 2 Gravei Sealed Sealed 2 2 2 Sealed Sealed Sealed 2 2 ~ Ca can c:, i~ iii Gravel 6uipo~ loadsy O ~- .•- r- o .-• o .-- .. o r- o r- r- ~- ~•- ' r 0 o r o 0 o r- r- ~oadsy z cwn ct~ ~ ~ cn ~ cn z cn z w ~ cn cn ~ z z ~ z ~ z w cwn L. ~ ~ ~ L L ~ L L L ~ L I L L ~ L L L L O [n C C C O O C O O O C O O O C O O O O C A~ ~ ~ C C ~ ~ C "D ~ Z3 C Z3 "U 'O C 'D ~~~ ~ ~ 'L Q ~ ~ ~ ~ 'ice •C ~ ~ L L ~f~ ~ ~1~ ~ ~{r ~ ~L~ W •C ~L ad~1yoledU O ~ ~ ~ ~ ~ 0 ~ ~ ~ ~ ~ ~ ~ ~ •~ ~ ~ ~ ~ Q .. ~v~wcncnU U wcnU U U tnU w UUtn U w UU U c0 C~ to M N M~ ~ M r 0 O N N Cfl CO r 0 00 CO N d1 M M QO M lf? ~ N O') N 1~ I~ O ~ M Ci0 ~t N f~ M to e-- i~ f~ N mot' t.C) r CO In to c0. ~ . . ~ . N . (~. [~. . O . . I`. O . M. O . ~ . ~ . M. . CSJ. CD. ~ . N . M. M. (ails yoled) 60~ M N M N M ~t N N M M N N N M M M r N N N N M N M 1 ~ ~ N ~ 00 07 I~ CD r 01 M O 01 t.C) M ti M ~ M t1') In ~ M (O O d' f~ V N 'd' cfl CO M ~' M ti M ~ cD ~?' ~ O CO ~ r f~ t~ O N ~ 00 M '~ ('7 cD N to f~ r- r tt~ O ti ti M O M '[t 'V' M c0 N N ~' r r r r 0v M CO M N N ~Zua) azis yoled eleC] (~l) lsal ao(?~1)6uiwe~l~ ~~~~~~ tw- ~~~ ~ ~ ~~ ~ r i ~ \ r ~ ~~~i ~ ~ ~~ ~ 1507 ' ) O O O O O N N N r r M M eaayfiuiu~e~lU UUUUUU U UUU U U VU U c~UU U U UU o `- N M N N M r r- M ~ f- yolad w C~C~2YJ0~ c] oww Q m Uo w Q~~ X X oC~ o (966 ~-t~661.) r ~.. eaay fiu~uie~l ~O cfl o co co c~ r~ r` r~ r~ r oo co co 0o co rn rn rn rn o ~- ~- ~- U UUUUUU U UUU U U U U U UUU U U U U o 58 5g aBed uo parul}uo~ aigel M O M O ~- O O O ~ '~ tD E N O ~t N to N M Q~ M M ~-- N ca to t1~ ~-- O N N N t~ ~-- ~-- ~- M r•- ~- O t- N O to O t~ CD M t` t` M M ~ O f~ tf) M to ~ ►` M t` M CO O M ~ M N M f~ 00 CO r- cf) ...... (ails yoked ;saJeaN) 60~ M N N •- N N M d' N M N N N t7 N N N N M N N r- N M r- ~T' ~7' N O S s- d' t~ O O ti cU to N O Co Cfl ~t O M O M t~ CO M tf) N c0 r - N M ~-- I`~ ~-- 1~ O O O O N `~t (D M O O O I~ N M M (ZLU) aZls N N tf) M ~ ~ ~ ~ N CO N O O N CD M N tD 'eP N O~ ~ yoked }saJeaN M v (5002) eaJy v ~ ~ v ~ N ~.r~ co co ~n c~ ~n o~ rn o ~ ~ ~ 6ululeJlyo~ed~saJeallo 0 000UVU ~vvU Vv v UU v c~vmUw' O O m yo~ed~saJeallo o OXo~Y_ m ww Q c7 ~ w (966 L -t~66 l) eaJ~-/ ~ O g ~ g ~, 6u~u~eJlyoled~saaeallo 0 00000n C~ocUoo o DD o o~ n0 O ~ m M O I` O 'Ct M M C4 ('7 r -• In N CD `~?' CO c- O O ~- M ~- CO ~' t1~ CO O N M 00 ~ N ti CO N~ 00 M ~-- CD ~ N O O ti oo t~ d7 O M (y O ~ e d O'J O /~I SnOInaJ d `~~ mot- to v ~t to M N oo Cfl oo CO ~t N r - O M a0 ~ ~ a0 00 O O O ...... of aoue~siQ) 60~ M ~ ~''~ M N N M M N N N r - N M M N M N M M N N N M N O ct• t~ M lt') V N O ~~ O~ M M t~ M M r - 1~ M~ ~-- N t0 N M (wOl)yo~edPa►dn~pc"'~I ~ c~~~c~i~ o ti v~` r N o r o~ ~ o~r o- r`r' rn o 00 ~IsnoinaJd o~ aoue~siQ M M M N N ~- ~-' r - N M M r ~t O 1` c0 ~ tI)' O O J ti ~- M M M~ r - 00 00 tf') CO CO f` M ~ N O N t~ O ~-- ~--- I~ O tD CO 07 O ~t M O M O t-- mot' ~ ~ O M M N d' ~ CO O ~- 1~ O~ M M O O H tf') O M N O f~ to O O O O tf) ~ t.C) ...... M O N M M M M M M ~t M M M v M ~r M M N N M M M M M (snlpe~ lug S•0 ui eaJy yoked) ~~ ~- r ~ M ~ ~ 1~ O d7 O t-- 00 00 N M r- ~ f~ ~ N Cfl 00 1` ~ M N M O~ O M mot• ~ ~- M ~t M ~7' ~ N fD O O O t~ N N CD CD uJ~ ~ ~' N ~t N N N N O ~n ~' N O aD v ~ ~ O r- to ~ M ~ to snlpe~• 5.0 d' to O M t~ 1` ~- O M M ~-- ~t r - O t!') 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Q ~ ~ ~ ~ 0 ~ ~ ~ ~ Q Q ~ ~ ~ 0 ~ C ~ ~ Q •L 'L 'L '~ '~ •L ~ CO •L t0 '.= 'i. '` '` 't.. 't:.. 's:,.. p N 's. '~ ttS Ll~ (~ ~ Q ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ O ~ ~ 0 0 ad~lyo~edU U Uwv~UcnU UUUUUU ~ U_w U UUtncn cn w U t.C) ~ O CO O ~t ~- O ~- ~ N O ~-- ~ O ~•- t0 ~ ~- M M M N ~- , N~ CO O M 'ct c0 ~- O 00 O M O ~' M CO r - O 1~ N ~- d' M 1~ I` M ~?' O ~ O O ~- f~ 00 O ~f' T- M O ~ O) CO N +~ ~t M N CSJ cA <-- cp O ...... (aZls yoked) f)01 M N N N M M M ~r M N N v M N ~t M M N ~t N N N M N N 1 ~- N cA r- ti M r- O M M c0 O N O~ M to O N c0 ~ cfl •- f~- r- V' rnf~ N M N t.f) ~tM t- r N O d1 CO CO a0ts~ ti M t!) 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(~a~s/Z~) yoked ~sa.~eaN aye ~0 6uioeds qua}S o ~ ao r u~ N ~ v o v o 0 0 0o co o cfl ao co ~ ~ a~ ~- t~ ~ ~ oo M a~ o M cfl o0 o a0 co r-- M t1~. O . N. I~ . O). O . O . ~ . ~ . ~ . ~ . 0p. CO. O . O . O. tt. ~--. I`. N. ~-. N. N. ~ . CO. N. O. ~-. O. to . to. M . O . ~ . 00. M d' ~7' N M tf) N N M t- 'c7 Cfl CO tf) ('7 ti et N N ~- t.f) Cfl M d' M N M to M ~ ~' ~' (%) afieaan~ ~t op ~- CO ~ ~t ~- O M O O rt ~ ti fit' t~ N M N to M N O CD o0 h- M O tr1 ~- cD N M N M CO ...... 00 O) O cfl Q~ O O M cD (~ a0 'c1' ~- `Ct C~ N c0 1~ ~- ~ ►f) M N ~ op O O f~ t` QO M 07 N r - ~- r r r r r r r r r r e- e-- r r r (sllao) a6e~any O N N 0 0 0 0 t~ CO I` O O r- ~t c0 O O, ~- ~~ M '~ O N (D f` t.f) O ~t O O 1` M A N ~t r r r r r r r r (~J (V r r r r-- (sllao) 0 t, 00 ~t N N t~ o o ~-- co ~-^ ao o N `a' CO ~-- o N rn o ~- o0 0~ t~ o 0 o M o o t~ ~ tr) ~t ~-- N r ~- r ~- N ~-- r- N N N ~-- N ~-' ~- N (sllao) 6 a0 t~ O O M O O N , CO t17 O ~- N ~ CO o0 O ~-- ~ O 1~ t7' I~ M ~- O O CO O O I` r ~t N ~ ~-- ~-- N r r N r N N N N ~- r (s{lao) g o0 O O N C'7 M M N~ CO O ~ O N O r r O N O O O r- ~--' N 11') O~ t` M Cover O CO M, N N CD CD r r N ~-- r r N r r N r r N r e-- (sll~) L 0 0 0 r - , O O O M LO N N O t7 O O N O CO t.f) O O O QO r 00 lf> ~ O I~ r - a0 O i` ~A N~ ~- r r N N N r N N ~- N r - Canopy 1sll~) 9 _ d' O 1` N O O O N O N 't7' aO M M O M ~t tt CO O ~t r~ 0 t7' QO O M' M ~t O ~- A N N ~-- r- r- N ~— N N ~— N N r- N ~-- r N ~-- ~— N N (sllao) 5 v O O f~ QO r O O M 0 0 0 r-- O N M ~- 1` M N O O O In ~O t!7 0 0 0 0 M r- ~-- N O CO N ~ ~-- N *- ~-- N N N r r r Cy r r r r (sllao) t~ n.i~ 0 0 0 0 "ct' O O O N O N N CO ~' N ti M CD N O to r M N CO O O P~- `M N O O N •- M C O r N <- r (1J (V e- r N N ~- ~- N r N N ~- (sll~) ~ O O N O t-- r - O O N O O O M M I~ M N t~ N O O to '~t O ~!' O O M O ~- N N N O O C N r r N r e- r N ~- r r N N N r e-- ~- N (sllao) Z O H O M O + CO O O ~f' O O N O ~- 00 tf) 00 t0 N o0 1 r O O N N O CD O M O 1~ 00 O N N tt~ ~U ~-- r ~-- N N ~-- ~ ~- r r r ~- N N N r ~- N r _~ (sll~) ~ SJ(LS a~ M M O M~ 0 0 ;- V' O N M ti N to O N r- ~.c) O O c0 d' ~ M~ M~ O N O M t` r- ...... t7 O~ ~- O V N '~7 t1~ N ~- O V O ~- O t0 N N CO N O O O r- N M ~' CO r 1` O N T O a6eaany f~ Imo- In O O d' ~t O N O N T- O O O ~ N ~- CO N N O ~- O t17 O d' mot' ~ M O~ 0 0 0 0 r r r Q~ 0 0o M o 0 0 ~- ~ o ti M ~-- o 0 0 0 0~ o~ o0 0 0 0 o M o o M o m r- co 0 o rn o r 6 ~ M M O O N O N O O O O '~ 0 0 0 to O M~ M r- O O O ~- M f~ tt N M r O In 0 0 N N M ~- ~-- O O to `~t' ~t r - O O ~t c0 O 1~ O O O ~t O O O M r- N O ~- O O N O M O Nr cfl M O O Buds L o~ N oo co ~ o 0 o v' rn o 0 o M o M o~ ao~ N o ~o ~ o~ o 0 o rn o 0 0 0 0 0 0 0 0 r ~- r ('V 9 ~- `~' M N M~ (D N 0 0 0 0 0 0 0 0 ('7 M O O T tt~ O O O M O h- to O (~ 0 0 d7 O r- N Number 5 O' O f` 0 0 CO O O~ t,f) O O O r~ 0 0 0 0 M 0 M N O 0 0 ~--' d' N M O O M 0 0 0 N N ~-- N M to O O O N N N O 0 0~ 0 0 0 1~ O O O O O O O~ N O N N (D O Cfl O~ ap O ('7 ~-- r r r 00 0 ~n o o ~r T- o M ~n M o ►n o o ~t ~n N o co r` 0 0 0 ~n o co ~t rn ►n o 0 0 ►` r- r r- M Z O CD O O O O O ~t c'~ N N O r M 0 0 tl) ~1' ~-- N O O~ O N ti [` O O r (p 0 0 0 ' r r r ~ . 4 . 1 yo~edv~v vo x~ xx¢ mmvxQm coo ~. mc~u~ Q mw X966 ~-t~66 ~) ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ g m m m m m ~r v- ~r ~t ~n ~n ~ ~ ~. eaay6uiwea1 67 AMMO AMMO Q Q 4 Q Q Q_Q~m _m m m m m_~ m_m _m _m m_m~ m m 74 sas~leuy a a s a a a a a a m uao~~ papnlox~ ~~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 0CO 0CO 0ef' 0ti 0t!') 0op 0o0 0f~ 0ti 0M 0f` 0~ 0to 0O 0O 0~ 007 0t~ 000 0CO 0M 0f~- 01~- 0c0 0~ 0M 0d' 0~0N o0 0~ 0d' 0Cfl 0Q1 0Cfl 1`0 fiuua~nold yoled }sa~eaN 0 0 0 ~n s o ~n o ~n ~n ~n o ~n ~n ~n ~ o o u~ ~n ~ ~n o ~n u~ ~n o o ~n ~n ~n ~n ~ 1.0 ~-^. ~--. ~-. ~--. o. ~-. 0.5 O . ~--. O . O. O. r . 0 . 0 . 0 . O. ~--. r -. r -.~ O. O . O. N . O . ~--. O . ~--. ~- . O. O . O . O. O . tuaa~s/Zw) yoked }saaeaN aye ,~o fiuloeds wa~S N tS~ O c0 M~ 00 M CO o0 00 ~ r- N O O O M 00 1~ 00 M N ~ M~ 01 a0 M O M O 00 O ti ...... O I~ O CO ~- f~ oO ~- O M CO N 1` ct O O f~ r 0 CD O M 01 N o~ N N to r O M I` O ti Cfl to ~ (fl M M T` t- N tt~ ~ tt') to tf~ ~- N ~t M M t- f~ Cfl 1` ~-- N M F~ Cfl M M QO t!7 ~t (~~o) afieaan . N t7' '~t M t.C) CO lid r- M O r O ti O '4' aD t.f) ~ ct O O N O O O Cfl lt') CO t- zJ' O r~ O N ...... '~7 r - d' O ti M d' ~t7 N N 't7' N M M N ct' •- 1~ f ~ d' I` tt~ O N N O t1~ c0 ice- cT 00 O O M ~- r r r r r r r r r r r r e-- r e'- r r ~— (sllao) afieaan . N r 0 d' M N N O O N oO t~ ~f' O 1`~ ~- N CD tt~ O '~t Q1 r - O O O O o0 N (D N t - M •- ~t ~-- .-- N N ~-- N ~- N N r- r- ~- ~-- .-- N ~ e- N N \SII~) ~ O I` O O O N N O r- ~O d7 N t7' T- N M r 00 r N N r to O O O O M to N N O In Ct~ ~-- ~-' r r r r N r r N (V r r (1J r ~- (sll~) 6 ~ N O N N ~t N Cfl N CD Cfl CC3 O N O O ~ ~' ~ T~ O 'Ci' ~}' 0 0 0 1~ r zJ' ~' ~' r M ~t `t7' ~- ~-- N N N r N ~- ~-- ~- N N N N N N N N (sll~) S et l!~ to Cover m O N 0~ ~- M O O O O O ~t O O N r - GO O r- O M N CSC M CD r - ~?' N d' M N •- N N ~-- T--' ~ N r- N N N ~- ~-- •- N N (sllao) L d' M r- ~-- Q M M 0 0 0~ CO O ~t 0 0~ ~- 00 O '~' CD ct' N N O ~- QO r~ O t1') I` O r N r- N N N N N N e- N ~- r- Y- ~- ~- r- N Canopy (sii~) 9 M O M O r r~ N ~- N DO N M t.f) O r CU N N O ti mot' ~ O Cfl O O O a0 Cfl CO O N N to N N ~" r- r r r r ~-' r N ~- ~- N N (sllao) S M O N t.C) ~ ~t a0 ~f' '~7' QO a0 ~- S O N c0 to N N ~' ~-- c70 ti' N O O M M mot' M M O O M •~- N N r r N r r r r r r e— e— r s— nJ r (y r r N ~- CJ>(CS r (sllao) 1 ~- r M 0 r- ~t ~' ~~ ~t N O M M O r f` N M N M M t- O O O CU ~ ~ct M oD f~ ~ci' t~ O C N r- N ~-- ~- N t- r - N N N ~ ~-- N O (sll~) £ 00 00 O N N M ~J' r - O O M O 1` op M M ~t M CO N d' N O O O O N ~f' N o0 N Q1 N CO M C r r r ~- N r N N N N N N ~- z- ~'- `~- ~-- N .~C (sll~) Z O t~ ~ M O V' N M O O r Q' M N ~ O ~- N CO '~ '~ d' ~ `Cp' O O O r- Op ~ O ~' M 'd' N ~-- ~ J N ~- .-- r r (~J <-- N ~-- N ~- ~-- N r e- N N N \SII~) ~ O ~ CO N f~ ~ N o0 O I~ CO CD N O f~ r ~ r- 1~ t.C) to 00 t~ M O CD 1~ ~- CO M O t~ t~ N O ~ 1 ...... O M B ~- O N CD d' N O~~ O ~t N r- ~ N~ ~- O W N t~ r r- N O t1~ O M O (O 1`~- d' afieaan . O N O d7 N O N O r O O N O N O O r- N 0 0 0 00 d' M M 0 0 0 00 O ~t O M mot' O r e-- r r r r r ~~ r- r O N O N O r to M r - O O O O O N O O O O M ~-- M ~- O ~t O 'd' O 1~- O O~ N r r r- r e-- 6 O O O O 0 0~ CO O O r CO O O M t1') O r - O O ~- M N i(~ O O O O CO O CO f~ r N <- r e- r ~- CO O O O O O O M O O M ~-- 0 0 0 CU ~- t7' O O O O N O O M O GO O O O O d' I` N ~-- r - Buds L of 00 O O M O r 0 0 0 0 N ti 0 t~ N O N O~ M t!7 O O r C~ ~-- CO O O O CO O I` 00 O r ~- ~- 9 co co 0 0 0 o r` r` cfl M M co 0 0 •- co v M ~- M N o o N o 0 0 0 0 0 ~'t o 0 o cfl ~— r r r Number S r O M 0 0 0 <-- M 0 0 0 0 0 0 mot' O M V O M O '[i' O ~— N O O d' CO O ~-- O M O O r r r r r u~000 000Mrn000tno~oor`or~v-ooM~toMcooc~000~r 000 r r r r ~- r ~+ G D O O M O O O t- to O N 0 0 ("~ O O CO N M N O M N 07 0 0 0 0 0 0 0 0 ~t O 01 r r r r r r Z 00 00 O O M ~- a0 O O O d' ct O tf~ O ~f O ~t Cfl O O CO r- O '~t M O N t1') 0 0 0 0 0 0 r r- r• yo}ed m UCH QXm~ XX XX mXXXmU ~ X X X X Q \966 ~-X661.) O O O O 0 0 ~— ~— ~-- ~— N N N N N N N M eaay6uiui~al m m 88 mmmm mm mm mmmmmm m m m m m m ~~ sas/(leud ~~ ~ a m a ~ a a s a s a ~ cu m m m m m m a~ m ~ a~ ~ wo~~ PaPnlo~ ~yM ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ,~ ~ ~ ~ 0 0 0 00'0M t~ 0cfl 0t` 0t~ 0Q~ 0O 0~ 0M 0'~' 0CO 0d' 0ti 0ti 0~ 0tt~ 0 0t1~ 0~ 0~O 0cD 0~ 0N 0e7 0O 0to 0~0u~ CD 0M CO Cfl cfl mot- co % 6uuannold ya~ad ~sa~aa~ ~.n ~n o o ~n o ►n o ~n ~n ~n ~n o ~n ~n ~n o ~n ~n u~ ~n o ~n ~n o o ~n~ ~n u~ ~n ~ 0.5 0 0 ~- r GO t-- O 0.5 r" 0 0 ~ ~- r 0 ~-- r- N O O O N ~-- 0 0 ~- ~- O O O ~-- O (wa~s2w) yoked ~sa.,eaN aye ~0 6woeds wa}S N °O 00 N 'ci' cfl t.f) 00 ~--- 00 V' 00 ~ GO CO 00 M QO op r -- r - to N O M ~ M O M aCJ O fl0 t1' CD ...... O O O O O O) N ►.C) N , CO tt') M to V' O t-- t17 c~U i~ f` tom- O) T` to tD o0 t` O O O GO . U~ O) ~ M t0 ~ N M Cfl N Cfl N M M o~ 00 to ~f' M M 1~ Cfl ~ ~' N (D r r r tf) N N M 1~ ~- (%) a6eaany N M Cfl N O~ tt') O N O Cn Ln ~- (O M N O (D t'7 4.f) ~- 00 GO (~ 00 O ~ M M O 00 t'7 ~-- t~ ...... d' O `~' ~T tt' O t.f) CU ~t CO °O a0 O O N O~ 00 O O (A M •-- CD ~ ~t ~t tt' mot' to ~i' O~ co '~' ~-- r r r r (~J (V r r r r r r r r (sllao) a6e~any I~ Q1 ~ ~ N CO ~t O `~t M o0 N O ~t `~T r ~ M ~t ~?' M Cfl ~ M O O ~ ~ r' O O ti r - r r r N N ~- N N N N N r N t - N ~ ~-' N ~ (sllao) 0 <- tt N M ~t O O N 1` T- M N N d' M tt ~ N `~ CU ~ `~t O ~t 0 0 d' O r O~ ~ M N O N r N ~-- N r r '~- N N N r - r N r ~- N r r r (y T- ~"' (sliao) 6 ., t.C) '~7' ~t M O O ~ M 00 mot' T" ~ ~ N o0 'Cr O r M 1~ ~ O ~-- ~' M O ti N ~' c0 (~ d' 'tt <-- N N ~- N N r - ~- N N r- N ~- ~- N ~- r - N N ~-- (sll~) 8 _ _ ~t o0 ~ r- O M ~- O tf) O op O ~t ~- N ti , ~ M ~t N O Q~ O M O O O~ O '~ M M ~t O Cover (V r ~- N N ~- ~- N N r r N ~-- ~- N ~-- N (slleo} L O cD t` ~i' O M 'ct O op O c0 O M M N ~- O N N d' O o0 O CO O O O I` O~ Cfl r- N ~- ~- ~- N r-' N N t- N N N r` r N Canopy (sll~) 9 M 'CP' O C~ O o0 N~ mot' In O N c0 M ~- O N O mot' N °O CO c~ O O O O M N oo 'tt r- ~- r r N N ~"' N N ~- N t- <'- N t- r - N r t- N \SII~) 5 (Dti ~t O ~t T- cfl N ~- to ti O O cD ~ ~t CO c0 M '~t c0 O op O N O O O O r- ~t O O N N N N r r r r ~- N N r ~r- r r N N ~-- r O (sll~) ~ to 00 O O o0 O M N O O QO O O t'7 ~- O C O O ~- r - d' O to O cfl o0 c0 N `~t O ~t O ~- O ~- N r ~- r ~- r (V r (V ~- ~- ~- N N (silao) E a~ f~ O `~l' M t~ 'mot N CO Co ~T N~ rt' N N N r - N O T- ~ ~!' O O N O r O~ 1` l.C) N N~ M C N r r r N r N N N r r r r {V r C (sllao) Z O O O ~t ~' O O t1') CO O M '~ M ('') t~ M O O t --- ~f mot' t~ ~- M~ O O O 'ct O~ ' N O~ r N r N N N r r r N r r ~-' N _~ \SII~) Cli h- CO M ~- r- 1~- 1` 00 ~- N mot' ~- N ~- N M O M CD M M C~ ~-` t` r- 00 ~i" ~' ~'- e- r CO N ~"' t~ cD t` O O ~ O O 1` ~ N O ~7 O CO r- M ~f '~t O o0 O ct' O t` N r- O d' O CV d' ~ O a6e~any `~ tf) 0 0 0 0 0 0 1~ O M O O r 0 00 O ~- d' C7 O r- O~ O O N 0 0 0 0 0 d' O ~-- ~- r ~~ i '~ 0 0 0 0 0 0 1~ f~ O 'd' ~- O O CO r - O to M O O O O O fl0 0 0 0 0 0 °O r 0 ~- r r ~- ~- r ~-- r ~- ~- 6 _ ~ ~~ r r O O N 0~ Cfl 0 0 ~- 0 0 d' O O M O tf O (D 0 0 0 lf) O 'd' O O M O r r r r r 8 to l~ t1') O O M O O O O M O O O O O O N ~- O 'ct O to O O~~ 0 0 0 0 ~-- N r r r Buds L of ~ M Cfl O O O O M ~t C~ O O O O M O 0 0~ 0 C~ ~ M O O N O N ~' 0 0 0 0 r r r N r 9 ct' M N O O M O <-- f~- N 1` O N O N O O N O O O ~ O O O O O O O O O M O r r ~--- N r Number 5 CO ~t t~ O O t0 O~ r-~ M~ c0 O O r-- O O O O O ~- c~ O ts~ O o0 0 0 0 1 f~ 0 0 0 0 r (V r ~- N tom- N N O O O O O N CO O O mot' O M O~ ~-- CO O O r-- O f` f~ N O~ N~ O O N O 0 0 c0 O O ~t O CO O O ~t O c0 O r 0 a0 ~7' O N ~'- 0 0 0 0 0 0 CD r N ~- O r r r... Z CD M N O ~-- Q) ~, d' ('O t[7 O O Cfl 0 0 a0 ~ O O Cfl O O O O O fit' 11~ 0~ 0~ 1~ O r r 0 e- r r (V ~- r L yo}adm o Uca w~,..z _ ~ ~~m U o0o xQ U¢U (966 L-t~66 ~) M c~ v v ~t v~ v ~r tt ~t ~t ~n u~ ~n ~ ~ o rn rn t-- r r- r r ~- .~-- r- r r- .- ~ ~- .- ~- .~- ,- r- ,- 620 easy 6wureal B20 • ~ m m m m m m - m m m - m m m m - m - m - m ~ m m m, 7'6 ~- ~ ~- sas~leub ~ ~ ~ ~ ~ ~ ~ m uto~d papnlox~ ~yM ~ ~ ~ ~ ~ ~ ~ ~ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0~0 0 0~0~0 0 0 0~0 0 0 0 0 0 0 0 0 0 0 0 0 CO t~ ~ to ~ to ~ t.f) 00 to ~ Cp ~ Cfl ~ cD Cfl t` c0 1~ N 1~ t.{) 00 t~ ~ to t` N ti tI) t0 CD CO CO !~ ~ i` 6uuannold yoked ~sa~eaN , to u~ ~n ~ ~n ~n ~n ~n o o ~n ~n ~n u~ to ~n o 0 o u~~ ~n o 0 0 ~n ~n ~n ~ o ~n ~n o 0 0 ~n o ~n o O. r-. O . O . O . O . O . O . r . r . 0 . 0 . r' . t-. O. O. ~-. ~-. r -. ~-. r . ~'-. N. ~'-. O . O . O . r . N . M. r . r. N. N. O . M. N . N . (wa~s/Zw) yoked ~sa~eaN aye }0 6uioedS u,a~s M O C4 r" N i~ •- CO ~ M ~ t~ t.C) O Cfl M M 00 O CU ~- ~ O M ~t O ~ QO CO t0 ('7 '~ r ~ N r M t~- ...... CU ~ O N d' ~-- I~ O f~ r-- t~ ~- t~ !` ~t M M M O 'V' M N N to O O Cfl t17 d' d7 r O I~ (~ ~' f` M r - M c~ N N M r- M M M M mot' '~T ~i' t7' ~t O M tt~ M to M ~'- CO Cfl i` tI) N t7' N r-' M N V' (~~~) a6eaanbr t`~ 'd' M ~ O ~' t` O CD ~ N CD O ~-- M t.f) ~ tt~ CO t~ M oq i~ LO r - N O oO to r M O M CD ~ O Cfl O ...... QO CO N O r Q r lf') (D I` t7' t` 07 O M O O O O O N~ N M N ti ~' ~.t~ S O N t7' ~- CD M 0p ~ O r r r r- (~) e- r r r r r r r (sllao) a6eaan~ O 07 O O O O M ~Y r- M O~ O O ►n M O 't7 ~ O ~ N N O CO N N E ~t S O N ~- O 1` a0 T- r r r N N r r N ~- r N ~-- N N {sllao) O L N~ 0 0 0 0~ ~t N N tt O N •-- N ~t O~ O O M O M o0 M 07 N lf~ tf') O r t!') O ~- O !` O CO (V r N ~- ~- r N N r r ~-- .-- N ~- N (sllao) 6 _ _ ~ 1` 0 0 0 0 0 d7 ~- ~ '~' O~ tt 't7' r' ~~ O O t` O ~T O Cfl ~- N d1 N M N N O O M O C.O O~ r N ~- ~-- r r r N r r N N r N ~-- r-- (sl{ao) g ~- O O O l.f~ 0 r - r - O O O O tf~ N oo CD ~?' O Q7 O N O N O ap d' O~ M O O N O Cove O ~' ~ t7 tt ~- N N N ~- N N r- t- ~- N ~- N N N N {s{I~) L _ O M 0 0 0 0 0~ 0 0 1~ (*) O Cfl N ~t M O M S ~t ~t I O + O `O ~t M r N M' M mot' O M ~t O O ~!' t- N r - N r - N N r r +e- , r r N ~- N Canopy {sll~) 9 0 0 0 0 0 0 0 0 ~t O O~ N op O V' ~- O' O 't7 •- N M 01 c0 O N to M O O t~ 0 0 0 r - <-- N r ~-- N N r N N N ~- r r N ~- r N {SIIa~) 5 ti .i i i ~-- N M t!') O O O r'- M ^~ O to M N O CD M O~ N `~t `~T N O tt mot' M O M O~ ~ a0 O M tf~ O~ t` M N N N N N N N N N •'- N N N N N r N r- O {sllao) ~ tiS d' M O O O O O O O N M r (fl ~- O M A N CD ti 'V' O N ~- ~~ O M M ~- CU CO 00 O 00 O O N c (V ~- r N r N r ~- N N r ~- ~-- O {silao) E N ~- ~- O O O O O N M op N CD O Cfl O '~ ~t O ~- ~ N O ~t O 1` •- N 1~ tom- oO t0 O CO 0 0~ 0 ~t C N N N N r r ~- ~- r ~- ::rc {sllao) Z 0 N N O O O O O O N a0 ~- N N O O r e- CO ~ M I~ N CO O N I~ ~- ~ 0p N ct' O O O M O r- N O SU .'- N r r r N N ~'.- ~-- .-- ~- N ~-- N ~- (~ {SII~) (~ t~ ~ mot' 07 / ~ N CU ~t ~ 00 M N M o0 O mot' r' O O `~ O ~'- (D 1~ N op M cp N tf~ ~ t1' O ~t N N f~ N ...... M O CO N r- N N M N t~ t.f) ~ r- ~ N mot' CO r N ~- ~T ~- M r (O O r 00 O 1~ CO ~ T'- N ~ CU N 1` a6eaany~ r CO O O O ~f O N O t- •- CO O CO r N t.f) M 0 0 0 ~.f~ d' O O 1~ tp ~t N O O CO O O O O O r r r r r r 0~ 0 0 , CD O ~'- O O r'- O DO O~ N N O ~- ~-- O O O t~ O~ O ti ~7' M CO O O a0 0 0 0 0 0 0 f` r (y r r r- 6 co o~ ~- o o M o 0 o v N ~n o r c~ ~o cfl o N r~ r o ~-- mot' cfl N o 0 0~ o cfl cfl o~ c~ o~ o c~ cfl r r r r r r r ~-- 8 ~ CO O O O 1~ O O O CA 0 0 0 0 0 0 iJ' O O ~- O t.t') O~ op ~ O N I~ ~J' CD O r N O O M I` CO O t.f) ~ r- r r r r r m L O O M W O CD O O ~t O f~ ~' O N O O r to O f` O N r M O N t,f) O O O N O f~ O O O CU r - O (y r (~j r r r n~ 9 .n A _ ~ O O O M N '~ O Cfl O d' O 07 0 0 CO to ~ 00 O O N~ N~ O M O mot' O to map 'd' O O N o0 O mot' Z r r ~- r- r r- 5 O N ~- M ~ mot' O O •-- Cfl t~ •- O O ~7 O O ~ ~ t.f) O '~t O O Cfl c0 ~ O d' O o0 O O O ~- t~ f~ O G~ r .- ~- r ~- (V r r ~' O O O N tip N O O O O O O M r O 0 0 N 0 Cfl O O O O O In O M O d1 N O OO O M M O O r (V r r r r r ~- O O O ~-- CO M A O O O OO O M O ~- O O M M d' N O O O N O 0 0 0 0 0~ 1~ O M M N O O r r r r r r r Z ti 0 a0 0 0 0 CO N ~-- O O O O 07 CO Cp eT ~-- O~ O O M O f~ O O O O ~- ~~ N t- O O 't7 O r- ~- ~- N e- r r r I Y yo}ed {966 ~-t~66 L) r r- .-- r N N N N N N N N N N M M M M d - ~ to ~ ~ eaay6u~u~eal m m m mm mm m m mmmmmmm 623 m m mm m m m 77 ~- sas~(leud ~ a m a m ~ wo~~ papnlox~ ~(yM ~ ~ ~ ~ ~ ~ 0M 0t.C) 0I` 0lf') 0CO 0N 0~0O ~ 0(O 0~0~0cfl CU lf~ 0N 0O 0 O 0t` 0CU 0CD 0~ 0~ 0O 0M 0tt) 0`~t 0M 0Cfl 0M 0CSC 0ISM !~0 0~f' 0C'~ 0t.f) 0'~ 0GO 0CO ~'o 6uiaannol~ 4o~ad }sa~aaN ~o ~n ~n1 0 0 o u~ 0 0 0 0 0 ~n ~n o ~n ~n ~n ~ ~n o ~n 0 0 0 0 ~n ~n 0 0 0 0 ►n ~n ~n ~n . .r...... M. O. O. r . ~-. r . 0 . r . r . ~-. r . r . 0 . 0 . ~- . Q O. . ~-. r 0.5 r . r . r . e-. r O O r r r r 0 0 0 0 (wa}s/Zw) yolad ~saaeaN aye ~o fiwoedS wads tee- O O~ O Cfl t~ M GO M 0p M O O lf~ M 0p O r O '~ M M O CA O t!') r CO r r t.f) 0p r - 0p a0 N N tcA ti N ~ ~ ~ t?' ~ ~ ~ N N ~ M (D ~- ~ c~ t~ N C~7 N OtD CO M 'Ct CN7 t~ ~ ~ C~ ~ CD ~ ( (%) afie~any r M M O ~- r -- 'c7 M ~ to M {~ I~ ~ CO CO N r - M o0 ~ ~ r - O N ~ r oO r r ~} t` fl0 M N O ltd ...... ~' ~t O In in M Cfl CO O ti to N O c0 M f` Cfl O ~ t1') ~t o0 r ti M to M O M 'V' M f` O r Ln ~ r ~-- r r r r r r t'- r r r r r r e- r ~c~- r (sllao) a6e~an~ O M M CD ~- N r O~ O M 0 0 CO ~' N N E t!') O d7 t~ O t'- O N d1 QO M M M O N O r r r r N r r r r N N N r ~- r r r (stlao) 0 i. _ ~ v O O ~-- N O ~- c0 a0 M O O O O N 1~ cfl N O t!7 O v 0 M '~ O O N t7' O~ N O O to O N ~- ~ ~- N r N ~-- r t- N r N N N N (Sllaa) 6 O t!7 ~- 'mot M O O) ~O 00 O O O O N M O r - CO N o0 ~ M ~t 00 ~T O O ~' `~t O O O 1~ O r N r' ~- ~-- N N N T- N N ~- ~- N N N (sll~) 8 O r- O ~f' N ~t N M Cfl t7 a0 N M N CD O O t~ ti r M1 O t.C) t1') O O r - ~ CO CD t~ O O O Cover `~T ' ~- r r r r (~j r (V (V ~--- r r r e- (y r (~J r (silao) L _ ~N N ~i' M QO r- O O ^~ N Q) O O M ~' O ~--' CD N In f~- oO N Cfl ~t O f~ N M O d' ~- N M O O r r ~- (V r r- N N N N t- ~- r ~- ~- ~-- N (V ~- Canopy (sll~) 9 N O M d' O M M 0 0 00 tf) O O M ct' 00 O d' O N t.f) ~t O O M mot' N `V' 'd' d' cO N O cfl ~- O r N ~-' N N N r ~" N r' r r- N N T- r N N N N r - r' ( lsll~) ti i~ ~ N M ~t 'mot ~f O W N CO N O O M M M tf~ M O (D ~t ~?' M CO ~ mot' ~- Q1 N ti N O M~ CD N ~- ~- N N N r ~- N N r N N r CV r r r ~-- r ~-- r cLi (sll~) ~' O M ~-- ~ N o0 O N O N d' O to N c4 O) o~ O O ~t ~i' O •- O ~' M ti CO O N O O ~- O N N C N r N ~- r N N N ~-- ~-- r r r r r (V O (sllao) E a~ r ~- CO x 0 0 0 'mot CO d' CD G O O N ~t t17 tt ~t ~ M op O ~- ~1' a~ 0 M 0 0 '~!' M O O N ~-- C r r r r r N r ~'- N N r - N 'r- ~- ~- N N N N N r c (sllao) Z 0 p r r~ ('7 QO M ~- O O O O O 1~ N~ tI1 O O 'ct M to O ~- O ~t o0 M O ~- O ~- QO M to O U r r r r r r (~J ~-- r r C~J r r t- f Q~ \Slt~`) Y v C~ lf) CO to 00 M CO t~ ~ M r CO ti (~ CO M i` M ~ ~f' tf') ~t O tl~ ~ O In N 00 1` In ~ O O O ~-' V i N t7 N t~ O O r-- ~ '~' ~t ~ CD O N O ~- 'V' N ~- M 0 0 0 DO O C~ N 'd' tf? 0 0~ CO O M 1~ M afie~an~ O O~ CO ~t 0 0 0~~~~ 0 CU O ~'- 1` N O 0 0 0 ~t O r ~t d7 r O O (~ O O O t1') r r r t~ r r N 0L O O ^O I~ O O CO O O O O~ In O CO ~- O O tl~ O O O O CO O O O r 0 0 0 0 0 0 r r ('7 r r ~- 6 O~ O M M r 0 0 't}' 0 0 0 01 ~- O O M O O ~ 0 0~ 0 ~T' O t- (fl t1~ 0 0 0 0 00 r' - r N ~- ~- r r ~- r 8 O N d' ~ ~- O M O O r - O 1~ t~ O CD O N O M O 0 0 0 O) O C~ O O CO 0 0 0 0 0 CO M r r r r r r Buds L of O O O~ N O O N r- O CO M to O O ti O M ~t M ~' 0 0 op O Cfl O N O d' O ^T- O O r- O r r r r 9 0 0 , 0 01 O O O o0 00 Cfl O O N 0 0 0 I~ O N M r-- O T- O O N O CU '~t N O t17 O O ~i' O r (y r N r Number 5 J O O N O O O O O N O O M O 0 0 0 0 ~- O cn t7' O ~t O O r'- M O O o0 O CO O N I~ ~- (V r N r r r r r M cD O 07 O M O to In 0 cfl O r'- O O O N t~ O cfl CQ O O Q) O tf~ t!~ O O O O M O o0 00 M r r r r r r M M O O O O O M to O M O t~ N O O OQ O 00 O O O 0 1 0 0 0 0 0 0 ~7 00 O O O O r r- r Z _ _ _ O O O O O O r~ M Cif O O r 0~ 0 M r , O 0 0~ ~- O H O 0 0 0 0 0 0 0 CO 0 0 0 0 CO r' N t-- N .. yo}adwQQX XXm XQ m caw xXXXUwQm mQ m U . ~ ~ - (966 !.-~66 L) ~, o ti ~ r . r . ~ a, z z z z z z z ea~~'/6uiuie~lmmmm mmm mc~.~ U U~ CAN yUVUc~Uy UU_ v v v 78 ------. ~- ~ ~- ~- sas~cleuy a s a a a ~ a woad papnlox~ ~yM ~ ~ 'S S ~ ~ ~ 0 0 0 0 '0 0 0 0 0 0 0 0 ~0 0 0 0 0 0 0~ o~a o 0 0 ~0 0 0 0 0 0 0 0 0 0 0 0 CD N M ~ ~7 t` to 'd' M Cfl M tf~ CSC O r- N O CD CD to to d' tD to 1~ 00 ~ Cfl to M CO to f~ 00 d' ~ 6uuannold yoked ~saaeaN o Ln o ~ ~n o ~n o o u~ ~n ~n ~n o ~n ~n ~n o o ~n ~n ~n ~n ~n ~n o ~o to ~n o o ~n ~-. O . 0.5 0.5 r-. o.s O. O. t--. O. r-. r-. r . O . O. O. N . O. O . r . e-. r . O . O. O. 0.5 0 . 0 . 0 . ~--. ~-. O . r-. r' . ~-. r-. (wa~s~w} yoked ~sa~eaN aye ~0 6woedS wa~S N QO M op M O Cfl M M~ N M N M CO r o0 CO r t` 00 M c?' M M M O N M d' r M a0 N C'7 Cfl O ~ M M ~- O C1~ r CO N ~ M `~T CO 00 t~ O ~ N ~-- O CO O OO M (O h- O Gp ~ f~ CO O d' (O t7' ~ CO ~ ~t ~ M r- cp ~ t` O N M ~' d' ~- M cfl ~ ~-- CO t~ f` ~ M ~i' ~ ~t M N CO ►n N N N Ci') (%) a6e~an~ ,O N o0 O t.f) M N f~ ~ O '~7' ~ cfl N •- t~ ~-- ~T to ~ c0 rt M rn O O •-- rn M N ~- ~- to O M to ...... mot' In `Ct O N ~ V' O d' t` ~.1~ f~- c0 e- r ~' t` 11') N N O M C~ ~T 00 r- C'7 r C~ (D CO M ~ In lf') Cfl Lf') r r r ~--- r r r ~- r r r r ~- r r r r ~- r r r r r ~- (sllao) a6eaany' ' tt CO M lf~ O H O `7 M~ CD M t!~ N r - O CU O O O M M N 01 O CO M 1~ r~ ~?' O O CD N N r r N N N r r r r r N r N N N r r N N ~- r (sii~) Q CD ti O M QO ~t t!7 '~ m N O ~' O ~ O c0 M '~7 T- N CD to et CO N `~!' ~- t!~ ~ 00 ~t ltd O r - 00 ~- r - N r N N t- N N ~- N <- r N r N N ~-- N ~-- N ~sll~) 6 ct O~ d' 1`- O 1` O M M (A M d' r - t-- '~t + O O d' O O M N CD O ` ~-- '[t r V' O ~t ~d' N N r- ~-- I M N r - ~-- r r - N N ~- ~-- N N N ~- N N N N ~- N (sll~) 8 Cover r CO o0 M O r- O i` ~t ~t r N O) O ~f' N O ~T ~' N M O) r- M N O ~T i~ N c~ O M t` O O ~- r r ~-- r N ~- ~-- ~- ~- N N N N N N ~- ~- (sllao) L _ O 1~ ~'t' N N O O CO N '~l t7 ~t Cfl O t1) t` O I` ~ ~O • d' M O ~- N In d - r lf) M ~1' M, ~ O O N N r N 'r- ~-- r ~-- N N N N r ~- r ~-~- N N N r N ~- N ~- N ~-- Canopy (sll~) 9 ~t r'- M r - N N O N O N O O 1~ N V' O M r tt O ~t N T- 00 N O N r r - O ~t r - O O N <-- r- ~- r ~- ~- N ~-- N N N N r - N N N N N N N N N (sll~) 5 ; N '~7 N f~ N M o0 t~ M r 07 ~f I~ O lf~ ~-- O O t7' M 00 N O ti Cfl + ~i' ~t M, CD N r- d' N N ~-- M r (y N ~-- r N N r ~- ~- r r N r- Y-- ~-- N ~- e- N N r N N ~1crs (sliao) ~ ~i ~t N M N M r 0 ~t O O ~T O N E ~-^ I~ O M M O 't7 r' N O N E ~f O O r' ~- O r'- f~ , 1` M ~t l C N N N r N N N N N r r r r r N N ~- r-- r (V r r N O (silt) E M O CO O ~t O ~' 't7' r N M o~ ~ d' CD aO c'~ N N M to ~' ~- O) O N to M O r ~- (p O ~-- O CD C N r r - N N N N r- r'- N N N r N t- ~- r r- c (Slfao) Z 0 ~}' ~' ~- O N O O `~ O CO ~ O) O ~ M o0 O O r N ~ a0 mot' N ~'d' c0 M N 11~ OO ~ r- s- O O O ~-- c0 N ~ N N N r N ~- r ~- r N N ~'- ~- r r N (slims) (. n r CO r ~t CO i` M C~ N CD In O r CD CO f~ O ~-- ~ M 0p r O In M r 0p Cfl ~-- M CO M M t~ In f~ f ...... tl) O r M~ CO Cfl ~ O N O~ ~- M r Op M M M Cf~ Cfl O In O M t?' ~f M M O !.C) M ~-^ O r- O a6e~any~ v o 0 0 0 0 o M o 0 o N o r` o co ca t` N o 0 0 ^rn r a0 <-- y ~~ ~o o . cfl ~-- r- M o o r r r I Y O ~ ~ / O O cfl O cfl f` 1~ •- O r O O M CO N CO O N O r ~-- O O O r- O O 00 cfl O t7' 00 O O r M ~ r- ~- N N N ~- 6 ~ 00 0 ►.c~ +o o ~n o ~n o 0 0 0 0 , o M ~t M o co N rn o t~ t+'~ ~n 0 0 0 0 0 ~ oo m o o N r 8 0 0 0 0 ~t ~ O ~- O O O N O O M r M I` 0 0 0 0 r- M O O M M o0 M ~- M ~- O O tf~ 0 r r r r- r Buds L of ~t ~n o o ~n o o N N co o ~n o 0 o ao 0 0 0 0 to o co 0 0 0 0 c~ 0 0 0 ~- o r- 0 0 N N r r ~- 9 ~t O O t,f) O O r- O O O M ~!'> 0 0 0 r- O O O M O 0 0 0 0 `t7' Cfl O O O CO O~ CD r O O r r r r '~-~ Number S ~ i O M 0 0 0 0 0 `cT O O O O O O N O N O O~ N O O O N op N `t7' CO O ~t tf> ~- O O O r r r r {., V a0 O O O O M d' 0 0 0 0 0 0 0 0 ~0 00 CD t7' M O O t - O o0 to ap '~ O N tf) O O O O r r r r r r to O O Cfl O O •- T- 0 0 0 0 0 CO O O M 0 0 0 0 0~ 0~ CU DO O N O, O ~- O O O N ~- r r Z 0 0 0 ~t ~- N rn o 0 0 o t~ ao N o rn ~-- co c~ a~ ao O O O N ~- O O O N c0 e~ O N O O .- r ~-- I• ~~ `- N M N N M r r M d- r y}d,~ C7C72Y~m o owe.,.. Q m Uri w Q~~ X X C'~C~ D (9661.- 66 ~) r . egad 6Wuieal cfl co cfl cO cfl cfl r~ r~ r~ r~ r~ ao oo co ao 0o a~ rn rn rn o r- T- r- U U U U U U U U UUU U U U V U U U U U U U U ~ 79 sas~(leud ~ ~ ~ ~ wo~~ papnlox~ ~(y/V1 ~ ~ ~ t~ 0t0 0~ 0d' 0M 0op 0M 0c1' 0CO 0zf 1`0 0M 0M 0~0~ M 0(fl 0ti 0O 0~0 O ~ 0Cfl + 0M 0t~ 0M 0~ 0Cfl I`0 0'[1' 0~ 000 0N 0'~t 0~ 000 0~ 0O ~~o 6uuannoid yoked ~sa~eaN o ~n ~n ~n ~n ~n ~n o ~n ~n u~ ~ ~n ~n ~n ~n ~n ~ ~n ~n ~n ~n ~n ~ ~n o o ~.r~ Ln ~n Ln ~n u~ o 0 0 N O O O N 1.0 O O 1.0 ~- O O O r- O O O ~- O O O O O O r r-- O O r- ~-- O O r-- r r (wads/Zu,i} yoked ~sa.~eaN aye ~0 6uioedS wads _ .. o M co c~ N co r r~ 1` M M M co o t` N o o co o r o ~ ~ cfl ~ oo ao ~ ao u7 rn M o ~ I` ~ . M to '~t O O N M r M M op_ M O r- ~t O O t.[) t,[) I` O I~ t~ 'd' ~ O t.C) r- M N 1` CD O CD. O O CD CO Cfl ('~ CU CD M !` CO V CD O) CO t,f) OO In ~ CO ~ 'd' M CU ~ N ~ M C'7 '~T M M d' ~ ~ (~~o) afie~an~ op d' 00 tf) LD ~ 07 O CO LO N Cfl M Cfl , 00 O N O N t0 M (D O N ~- r- N CO CO ~ QO ~- r O CO ...... N N~~ CO O mot' CO ti ~~ ~-- ~ ~- 'd' M O N a0 t~ .- O O CO M Cfl N a0 ti O ti O ~- N M N N r r r r r r r r r (11 r r r r r r r r r r e~- r r r (sllao) afieaany r Cp 00 r O~ M O O t` r- N In ~ r M N O~ N In M M d' O W N to O O O r M '~7' ~- N r- r- r -- ~- N N ~'- r N N ~- ~-- ~- N e.-N N r r r- r N N (silt) O L d' T- O O N N M O~ O O N d1 ~t ~ 07 O• (D O E M ~t '~' O N CO N to t1~ O E M ~t M Z- N M N N r r N e-- ~- r ~- ~-- N N N N r r ~-- (V .-- N ~- lSll~) 6 M ~t N r - 07 O ~t `c7' C~ ~t ~' M ~ N ~t ~t ~t ~t a0 O M N ti ~ to M r- 't7 07 ~i- r - ~t O M N M N N r N ~- N N N N N N N r -- N N ~-- r r ~- r N ~- N (silt) 8 N ~t Cfl N N CO O N V' N O t7' 00 1` ~ 00 M N O O o0 O O Cfl M N N~ CO O d' ~i CD O Cover '~' '~7 N N N ~- r ~- N ~- r r r ~-- N N ~- r N r N N N r- N ~-- ~-- (sllao) L N O N ~t M ~' mot' O O r N N 0~ 0 op r 0~ ~'~ ct' M r mot' '~J' <- O GO t17 t4 r~ 0~ l~ M M N N N r- N N r N N N r r - N N N N N N r ~- ~--- ~- N Canopy (sllao) 9 ~ ~ ~ r O lC) r M ~ ~ O t.f) N t7 Cfl r- 'cf r - ~7' ~t N O 'd' ~1' N N ►1~ f~ ~t t` CO O O ti r - N N r r - N r N N N r - r N ~'- N N r N N r - N N r r t- r r (sliao) 5 _ r M r- O t7 N N CO ~fi ~t O N O 'V N M ~?' N C O O 00 r t~ •- N O 11') O M t.f) I` t.f) CO Cfl ~ N N N N N N N r ~- N N N T- N N T- ~-- ~-- r- •-- (silao) tr M '~7 V' O V' `~T ~- O N O Cfl M 't7' ~t W O ~a0 N O M N O O 'V' I~ M N~ N CU Cfl M M N 1~ N N r' N N ~- N r N N N '~- ~- r - r r r r r - ~- r N N (silt) E ~f' N ~t ~t ~t O O CO ~ r C7 oO r N L(') ~ ~ ~' Cfl O Cn '[r M O r- N ~ ~- ~- N r O M ~ ~ N N N N N •- N r - ~-- ~- N ~- r N N r N N ~- N r- ~- N t -- (sllao) Z '~t ~ O N "7 N ~ N t.f) ~?' 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Patch Stem Log10(Patch Stop Patch Proportion Log Standard Standard Temperature Log10(Nearest Log Standard Stem Nearest Nearest Average Average Stop Average Average Average Temperature 82 M O M to ~- ~ N r O ICdOUe~ O~ O O N~ 0 0 0 0~ 0 t~ N O O r 0000 uo~~e~naa'p~s00000 00000000000 . . CO r M r lf') tf') M ~- M C~ ~ t- N O S I` O M M ~O d' r' N O W N O s p n 8 r - M O r- O O ~-- N O O K a0 N ~-- N O 000 0 uoi}e~naa'p~S0ci0000000000000 0 O~ r M M ap M N~ u~ 0 0 0 N r O N 0 ~- ~t ~- N r N r S w 0018 Or O ~'- r ~- O r- O N ~-- 00 N N ~- N O 00 00 uo~~e~naQ'p~s00000000000000 00 . . r- r ~ r O r M lf') 00 CO ~ T` ~ N t - QO 00 N ~- 07 O O O ~- '~ N (aZIS M O N N to ~- ~ r ~' O r O ~-- r r (V 0 0 0 0 uo~ed}p ~ 60~ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 r r o co ~"~ Q r~ O M O oo M O o0 M O r N N M N O N O ~(doue ~ a6eaand 0 O r- N O M O N M~~ N ~- N N t~ ...... O O O O O O O O O O O O O O O O O O O O ~ i i ~ i ~ i i i ~ r M~ 00 ~ r s n a6eaan N N O to o N r . 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Cfl ao O M r- N O ( .S y}e d M O N M r-- N '~ M r M r 0 tt) r' O N 0 0 0 0 0 ~saaeaN)p ~ 60~ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 _ _ _ ~ 0 0 r N ~~p 6uiaaMol~ o~ o r M~ r' ,O ~-- O ~t N N N rN^' r- O 00000 uo~ed~saaeall000 0000000000000 rn M000~n uioedSwa~s°Oorc~Moo~tooaoMoot~►nMr- ~n N O r- `d' r fi r r- O N r-- N O N r M 0 ~- N ~- O O 00000. . . . . uo~ed~saaealloo. . 00000000000000...... r r o o (fl N M M oo O O N r- M ~oo M ~!' `On ~-o N ~ `t~- O~ aJn~ eaadwa1 M ~- ~- O O r O O r M mot' 0 0 0 C7 ~- ...... O O O O O O O O O O O O O O O O O O O O O O O O 47 O r r O~ N M ~t oo t~ oo N N CU r- ~- tf7 O M N r - r r awi.I dO~S M r- O M O O N O O r- r- O M O r O ...... O O O O O O O O O O O O O O O O O O O O O Patch) Size) Radius Spacing Blooms Buds Presence) Blooms Canopy M Canopy Y km Patch Flowering Stem Past Nearest 0.5 Size) Size) to to in Surveyed Deviation Deviation Buds Deviation Deviation Deviation Deviation Blooms Buds Canopy Canopy Patch Patch Area Spacing Time 10(Patch 10(Dist. Log Proportion Standard Nearest Nearest Log10(Nearest Log10(Dist. Standard Log Patch Standard Stem Log10(Patch Stop Average Average Temperature Standard Average Standard Standard Average 83 References landscape occupancy in a rnetapopu{anon of the Ba 9 uette, M. 2003} Long distance dispersal and cranberry fritillary butterfly. Ecography, 25, 153-150. t~aiough, t~..~. ~~ y8- ) New localities fiat Schinia Indiana (Smith} (Noctuidae). ~3hio Lepidopterist, 9, 15-16 Brommer, J.E. &Fred, M.S. (1999) Movement of the Apollo butterfly Parnassius apo/lo related to host plant and nectar plant patches. Ecological Entomology, 24, 125-131. Dover, J.W., Clarke, S.A., & Rew, L. (1992) Habitats and movement patterns of satyrid butterflies (Lepidoptera: Satyridae} on arable farmland. Entomologist's Gazette, 43, 29-44. Forare, J. & Solbreck, C. (1997} Population structure of a monophagous moth in a patchy landscape. Ecological Entomology, ~~, 255-263. Forbes, W.T.M. (1954} Lepidoptera of New York and neighboring states. Part 111. Noctuidae. Cornell University Agricultural Experiment Station Memoir 329, Ithaca, NY. Fort McCoy Public Affiairs Office. (2005} Fort McCoy, wiscor~sin. www. mccoy.army. mil, accessed 1 March 2005. Glantz, S. & Stinker, B. (1990) Primer of applied regression and analysis of variance. McGraw-Hill, Inc., New York. Hanski, I. &Gilpin, M. (1991} Metapo~pulation dynamics: brief history and conceptual domain. Biological Journal of the Linnean Society, 42, 3-16. Hanski, f., Kuussaari, M. ~ Nieminen, M. (1994) Metapopulation structure and migration in the butterfly Nlelitaea cinxia. Ecology, 75, 747-762. Hardwick, D. t=. (1958} Taxonomy, life history, and habits of the elliptoid-eyed species of Schinia (Lepidoptera: Noctuidae), with notes on the Heliothentinae. The Canadian Entomologist, Supplement 6. Insect Systematics and Biological Control Unit, Entomology Division, Ottawa, Canada. Hardwick, D.F. (1996) A monograph to the /North American Heliothentinae (Lepidoptera: Noctuidae). Center for Land and Biological Resources Research, Agriculture Canada, Ottawa, Canada. Hawkins (1980} A note on fitting a regression with no intercept term. The American Statistician, 34, 233. Hill, J.K., Thomas, C.D. 8~ Lewis, O.T. (199} Effects of habitat patch size and isolation on dispersal by Hesperia comma butterflies: implications for metapopuiation structure. Journa! of Animal Ecology, 65, 725-735. Himmelman, J. (2002) Discovering moths: Nighttime jewels in your own backyard. Down East Books, Maine. Hines, J. E. (2005) Program PRESENCE. U.S. Geol©gicai Survey. Patuxent vvildlifie Research Center, Laurel, Maryland. 84 Kirk, K. ~1996} Report on a survey of the distribution of downy phlox (Phlox pilos;~) and the phlox math {Schinia Indiana) {Lepidoptera: Noctuidae) on Fort McCoy, Monrae County, Wisconsin. The Nature Conservancy, Wisconsin Chapter, Visconsin. MacKenzie, D.L., Nichols, J.D., Lachman, G.B., Droege, S., Boyle, J.A., ~ Langti~nm, C.A. (2002} estimating site occupancy rates when selection progadiiities are less tna~n one. Ccoiogy, ~:3, 2248-2255. MacKenzie, D.L., Nichols, J.®., Sutton, N., Kawanishi, K., Bailey, L.L. (2005} Improving inferences in population studies of rare species that are detected imperfectly. Ecology, 86, 1101-1113. MacKenzie, D.L., Nichols J.D., Boyle J.A., Pollock K.A., Bailey L.L., &Hines J.E. (2006) occupancy estimation and modeling: inferring patterns and dynamics of species occurrence. Academic Press, Burlington, MA. Matter, 5.1=. &Roland J. (202) An experiments{ examination of the effects of l~abitat qua#ity on the dispersal and local abundance of the butterfly Parnassius smintheus. Ecological Entorr~ology, 27, 308-316. Menendez, R. &Thomas, C.D. (2000) Metapopulation structure depends on spatial scale in the host-specific moth ~/heeleria spilo~actylus (Lepidoptera: Pterophorida~e). Journal of Animal Ecology, 69, 935-951. Menendez, R.D. Gutierrez, &Thomas, C.D. (2002) Migration and Allee effect: in the six-spot burnet moth Zygaena filipendulae. Ecological Entomology, 27, 317-325. 1Vlichigan Natural Features inventory. (2x06) I~lichigan's Special Animals. w~nrw.web4.msue.msu.edulmnfi/data/specialanimals.cfm, accessed 1 March 2006. Minnesota Department of Natural Resources. (2006} Endangered species: L)ragontlies, butterflies, moths, caddis~ies. vvww.dnr.state.mn.uslets/dragonflies.html, acces~~ed 1 March 2006. lvewcomb, L. { 19 7 7) 1~iewcoma's wiidtiower guide. Little, Brown and Lompa~~y, Boston, Massachusetts. Nieminen, M. (199~i) Migration of moth species in a network of small islands. t~ecologia, '~ 0~, 643- 651. Nieminen, M. & Hanksi, I. (1998} Metapopulations of moths on islands: a test of two contrasting models. Journal of Animal Ecology, 67, 149-160. Ravenscroft, N.Q.M. &Young, M.R. (1996) Habitat specificity, restricted rarfge and metapopulation persistence of the slender scotch burnet moth Zygaena loti in western Scotland. Journal of Applied Ecology, 33, 993-1000. Boyle, J.A. & J.D. Nichols. (2003) Estimating abundance from repeated prE:sence-absence data or point counts. Ecology, 84, 777-790. Boyle, J.A., Nichols, J.D., Kery, M. (2005) Modelling occurrence and abundance of species when detection is imperfect. Oikos, 110, 353-359. 85 Swengel, A. ~ Swengel, S. (1999) Observations of Schinla indiana and Schinla lu+yens in the Midwestern United States (Lepidoptera: Noctuidae). Holanctic Lepidopter~i, fi, 11:21. Vittinghoff, E., Glidden, D., Shiboski, S. &McCullough, C. (2004) Statistics for biology and health. regression methods in biostatistics: Linear, logistic, survival, and repeated measures models. Springer Science and Business Media, Inc., New York, USA. Weather Underground. (2006} www.vvunderground.com, accessed 1 March 2006. Wi{der, T.T. (1999) Summary of surveys and management activities conducted fc►r state threatened and endangered species. Fort McCoy. Department of Training and Mobilization, Wisconsin. Wilder, T.T. (2000} Summary of surveys and management activities conducted for state threatened and endangered species. Fort McCoy. Department of Training and Mobilization, Wisconsin. Wilder, T.T. (2001) Summary of surveys and rr~anagement activities conducted f~~r state threatened and endangered species. Fart McCoy. Department of Training and Mobilization, Wisconsin. Wilder, T.T. (2002) Summary of surveys and management activities conducted for state threatened and endangered species. Fort McCoy. Department of Training and Mobilization, Wisconsin. Wilder, T.T. (2003) Summary of surveys and management activities conducted for state threatened and endangered species. Fort McCoy. Department of Training and Mobiliizatian, Wisconsin. Wilder, T.T. (2004) Summary of surveys and management activities conducted for state threatened and endangered species. Fort McCoy. Department of Training and Mobiifizatian, Wisconsin. Winter, W.D. Jr. (2000) Basic techniques fvr observing and studying moths and ~juttertlies. Memoirs of the Lepidopterists' Society No. 5. Natural History Museum, Los Angeles, California. Wisconsin Department of Natural Resources. (1099) Karner blue butterfly habita~f conservation plan. ~nrww.dnr.wi.gov/org/land/forestry/karner/hcptext, accessed 1 March 2006. Wisconsin Department of Natural Resources, Endangered Resources. (2006) Phlox flower moth Schinla indiana Smith). www.dnr.state.wi.us/ORGILANDIer/invertebrate~~l butterflies moths/ph{oxflower. htm, accessed 1 March 2006. 86 CHAPTER 3. GENERAL CONCLUSloNS General Discussi®n The phlox moth is an example of a species that currently exists in small ~~ubpopulations interconnected as a metapopulation in a highly fragmented landscape (Swengel ~~ Swengel, 1999}. This study is consistent with other, similar studies of rare Lepidopteran species (for example, Hanski et al., 1994; Hill et al., 1996; Ravenscroft &Young 1996; Menendez &Thomas, x'000}~ and lends additional evidence to the argument that the theoretical factors important to patch occupancy in a metapopulation, in fact, matter in real populations of species. ~Iot only does patch size and quality matter in supporting subpopulations, but the connectivity between patches is highly important, particularly the connectivity with large, stable subpopulations. For species like the phlox moth, which exist at law numbers wherever they are found, occupancy probability may closely track metapopulation size. For such species, quantifications of the effects of the tradeoff between patch size and connectivity on the proportion of patches occupied may be valuable to conservation efforts. The proportion of patches occupied and metapopulation persistence are closely related, and by focusing on maintaining the average probability of occupancy in a metapopulation, the population size likely can be maintained. Likewise, increasing the average probability of occupancy should increase population size. Therefore, small, well-placed habitat patches can potentially have a great deal of conservation importance, because they may increase the overall probability of occupancy for the entire metapopulation. Having at {east one large habitat that can support a stable population is preferable, but when large habitat patches 2tre destroyed or cannot be acquired or created, sma11, well-placed patches can be used to help sustain or increase a population. v~/hether a single large patch or several small patches are better for species persistence, however, may depend largely on the distance between the patches, and the tradeoff between size and distance for a given species. Future research projects related to this study could be focused on quantifying the relationship between patch size and distance in the probability of occupancy for rare species that exist at low numbers in a metapopulation or patchy population structure. Past research on Lepidopteran species has provided evidence that many rare butterflies and moths require similar landscape arrangements to persist (for example, Hanski et al., 1994; Hiil et al., 1996; Ravenscroft & Y~~ung, 1996; Menendez & Thomas, 2000}. Similarities may likewise exist within other taxonomic groups, particularly other insects. General guidelines for groups of species, based on studies of several species in the groups, may be particularly useful for rare species that are currently unprotected, or pare protected only locally, for which there insufficient funding to study every species in detail. Currently, land management activities are frequently directed at increasing the connectivity of isolated habitat patches. Stepping stones and corridors are considered as ways to increase 87 connectivity for rare species whose populations are declining in isolated habitat patches {Schultz, 1995; iUfonkkonen & Mutanen, 2003; Haddad &Tewksbury, 2005). Government ac~encies and non- profit organizations are interested in acquiring land that is located near their currenl: properties to allow for the greater movement of species between parcels, thereby buffering indiv~iduai parcels against species loss. The findings of this study support efforts to increase connectiivity between preserves, as well as increasing preserve size, to conserve rare species. References Haddad, N.M. &Tewksbury, J.J. (2005} Low-quality habitat corridors as movement conduits for two butterfly species. Ecological Applications, ~ 5, 250-257. Hanski, i., Kuussaari, M. ~ Nieminen, M. (1994) Metapopulation structure and migration in the butterfly Melitaea cinxia. Ecology, 75, 747-762. Hill, J.K., Thomas, C.D. &Lewis, G.T. (1996) Effects of habitat patch size and isoi~~tion on dispersal by Hesperia comma butterflies: implications for metapopuiation structure. Journal of Animal Ecology, 65, 725-735. Menendez, R. &Thomas, C.D. (2000} Metapopulation structure depends on spati~~i scale in the host-specific moth Wheeleria spilodactylus (Lepidoptera: Pterophoridae). ~~oumal of Animal Ecology, fig, 935-951. Monkkonen, M. & Mutanen, M. (2003} Occurrence of moths in boreal forest corridt~rs. Conservation Biology, 17, 468-475. Ravenscroft, N.D.M. &Young, M.R. (1996) Habitat specificity, restricted range and metapopulation persistence of the slender scotch burnet moth Zygaena loti in western Scotland. Journal of Applied Ecology, 33, 993-1000. Schultz, C.B. (1995) Corridors, islands and stepping stones: the role of dispersal bE~havior in designing reserves for a rare Gregon butterfly. Bulletin of the Ecological Society of America, 76, 240. Swengel, A. & Swengei, S. ('~ 999) Observations of Schinia Indiana and Schinia luc+ins in the Midwestern United States {Lepidoptera: Noctuidae). f~olarctic Lepidoptera, fi, 11:21. 88 ACKNOwLED~EN~ENTS My biggest thanks goes to Tim vVilder, who made the entire project possible and without whom 1 would have had neither the idea for the project nor the resources to complete it. Thanks also go to Fort McCoy, for allowing me to conduct my research there. Thank you Jason Beck, Ryan ~lytry, Staci James, Adam Kehoe, Amanda I'rochazka, and Matt Shuler, for spending many early mornings conducting moth surveys. Thank you Dan Frohberg, for your support on my project. And finally, thank you Bill Earthen, for helping me get through the last few months of work on this project.