The simulated effects of timber harvest on suitable ...

The simulated effects of timber harvest on suitable habitat for Indiana and northern long-eared bats B. P. PAULI,1,3, P. A. ZOLLNER,1 G. S. HAULTON,2 G. SHAO,1 1

AND

G. SHAO1

Department of Forestry and Natural Resources, Purdue University, West Lafayette, Indiana 47907 USA 2 Division of Forestry, Indiana Department of Natural Resources, Indianapolis, Indiana 46204 USA

Citation: Pauli, B. P., P. A. Zollner, G. S. Haulton, G. Shao, and G. Shao. 2015. The simulated effects of timber harvest on suitable habitat for Indiana and northern long-eared bats. Ecosphere 6(4):58. http://dx.doi.org/10.1890/ES14-00336.1

Abstract. Bat conservation in the eastern United States following the onset of white-nose syndrome necessitates the conservation and production of important summer habitat required by bats for both diurnal roosting and nocturnal foraging and commuting. Forest management via silvicultural applications can purposely direct forest succession so important habitat features are retained and developed. The effects of timber harvest on habitat at large spatiotemporal scales for a species are not always readily apparent but can be investigated with spatially explicit forest dynamic modeling. Here we used a forest succession model (LANDIS-II) to simulate future forest conditions under different harvest regimes. We simulated nine harvest scenarios on Indiana State Forests that ranged from a complete cessation of timber harvest to intensive timber extraction. We then applied previously created models of nocturnal and diurnal habitat occupancy for both Indiana and northern long-eared bats. We found that suitable nocturnal habitat was maximized for Indiana bats but minimized for northern long-eared bats under low intensity timber harvest scenarios. Among moderate intensity timber harvest scenarios, both species exhibited the greatest amount of suitable nocturnal habitat when timber harvest applications focused on regenerative openings. The quantity of suitable diurnal habitat trended in the opposite direction of nocturnal habitat with selection harvests favoring suitable roosting habitat. Finally, both species displayed a trend in which overall suitable habitat was primarily driven by the degree of suitable diurnal habitat rather than nocturnal habitat. These results highlight the complex nature of managing multiple habitat needs for more than one species. Furthermore, our research illustrates the importance of understanding the distinct habitat requirements associated with different life history needs that can occur within a single species. Despite such complexities, our results can help guide forest management to preserve and encourage suitable habitat for multiple imperiled bat species. Key words: forest; habitat; harvest; Indiana; Indiana bat; LANDIS; Myotis septentrionalis; Myotis sodalis; northern longeared bat; simulation; suitability. Received 17 September 2014; revised 9 November 2014; accepted 5 December 2014; final version received 13 February 2015; published 21 April 2015. Corresponding Editor: D. P. C. Peters. Copyright: Ó 2015 Pauli et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. http://creativecommons.org/licenses/by/3.0/ 3

Present address: Department of Biological Sciences, Boise State University, Boise, Idaho 83725 USA. E-mail: [email protected]

INTRODUCTION

habitat loss and disturbance during hibernation (Thomson 1982, Johnson et al. 1998, Carter et al. 2002, O’Shea and Clark 2002, Sparks et al. 2005), and the emergence of white-nose syndrome (WNS) which has already killed millions of bats

Many bat species in North America face considerable threats of population decline and local extirpation due to historic threats, such as v www.esajournals.org

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(Blehert et al. 2009, Turner et al. 2011, Thogmartin et al. 2012a, b, 2013, U.S. Fish and Wildlife Service 2012). Efforts are in place to mitigate the effects of population declines from WNS including minimizing WNS spread by humans (Shelley et al. 2013), modifying hibernation sites (Boyles and Willis 2009) and utilizing artificial hibernacula for bats (Slider and Kurta 2011). A complementary approach is the minimization of other stressors through the maintenance of important summer habitat for affected species (U.S. Fish and Wildlife Service 2007). The aim of such efforts is to identify and conserve habitat used by bats during the summer for roosting and foraging. Habitat conservation for more than one species can be difficult (Nicholson et al. 2006) but often is mandated by management objectives (Smith and Zollner 2005). Even closely related species can have different habitat requirements or responses to habitat management which complicates joint conservation efforts (Carey 2000). Such is the case for the WNS-affected Indiana bat (Myotis sodalis) and northern long-eared bat (M. septentrionalis). In an effort to delineate important habitat, models of the likelihood of habitat use by Indiana and northern long-eared bats have been created for Indiana State Forests and the region within 8km of property boundaries (Pauli 2014). These models predict the probability of occupancy of either species at a site for nocturnal foraging or diurnal roosting. These predictions allow for the identification of areas that are most likely to be important for both species. Such areas would be considered important for conservation by land managers, though few areas of this nature exist (Pauli 2014). Habitat conservation is often further complicated because many species have distinct habitat needs based upon life history characteristics. Some birds, for instance, may choose nest sites based upon protective cover but select foraging areas based upon the availability of other resources (Orians and Wittenberger 1991). Conservation efforts would need to identify and maintain all required habitat in order for conservation to be effective. This is likely the case for bat species of conservation concern in which suitable foraging and roosting habitat differs (U.S. Fish and Wildlife Service 2007). Identifying and protecting currently important habitat is insufficient for long-term species v www.esajournals.org

conservation. Landscapes are dynamic and naturally change. Areas that were once high quality habitat may not be so in the future, especially when ephemeral resources, such as snags used as roosts, are involved (Carter and Feldhamer 2005). Therefore, it is necessary for conservation efforts to be forward-thinking and encourage the continuous production of quality habitat into the future (Carter and Feldhamer 2005). There are a plethora of silvicultural techniques at the disposal of land managers that can be employed to direct forest succession and the resulting spatial and temporal habitat patterns. Foresters have used and adapted timber harvest regimes for decades to aid in the conservation and proliferation of wildlife, and a great deal of research has been conducted on the effects of timber harvest prescriptions on a wide variety of species (Sallabanks et al. 2000, Thompson et al. 2003, Semlitsch et al. 2009). Silviculture is often focused at the forest stand level, however, and the effects of landscape-level timber management applications on species of interest are not always apparent. In such cases, simulation modeling can relatively easily serve as a way to investigate the effects of various forest management approaches on wildlife over large areas and long time frames. Insights gained from such exercises can, in turn, inform forest management so important habitat for wildlife can be conserved. Several studies have investigated the effects of silvicultural treatments on bat activity. Generally, nocturnal bat activity increases immediately following timber harvest (Loeb and O’Keefe 2011), though this effect can vary greatly by species (Patriquin and Barclay 2003, Owen et al. 2004) and by spatial scale (Grindal and Brigham 1999). Overall, Indiana and northern long-eared bats are thought to utilize harvested areas, particularly forest edges, during foraging but may avoid large open areas created by clearcuts (Hogberg et al. 2002, Loeb and O’Keefe 2011). Silvicultural applications such as shelterwood or single-tree selection cuts that remove midstory clutter may also benefit foraging Indiana and northern long-eared bats (Sheets et al. 2013b), though the impacts of such methods may vary in their persistence. Little research exists on the effects of silvicultural treatments on Indiana and northern long2


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eared bat roosting habitat, and most focuses on correlates between past harvest and roost occurrence (Gardner et al. 1991, MacGregor et al. 1999, Lacki and Schwierjohann 2001, Menzel et al. 2001, 2002, Perry et al. 2007). Timber harvests have the potential to damage or remove roost trees, though roost sites can also be maintained and created during harvest via snag retention and tree girdling (Sheets 2010, Sheets et al. 2013b). Timber harvests may degrade the quality of roost sites for many bat species, but such effects are likely species-specific (Loeb and O’Keefe 2011). Shelterwood harvests that retain snags, cavity trees and shagbark hickories (Carya ovata) can also increase the number of suitable roost sites (MacGregor et al. 1999, O’Keefe 2009), and small forest openings from single-tree selection cuts may encourage roosting (Loeb and O’Keefe 2011). Despite such knowledge, little is known about how landscape-level changes via timber harvest affect roosting (Menzel et al. 2001). The long-term effects of forest harvests on bat communities are mostly unknown (Sheets et al. 2013b). Though long-term experiments have been recently established (Sheets 2010, Caylor 2011, Sheets et al. 2013a, b), conclusive results on the future impacts of silvicultural prescriptions on wildlife will take time. Thus, simulation models that can replicate forest dynamics may be crucial in predicting the long-term effects of timber harvest on bats of conservation interest. Landis-II is a forest simulator that models forest succession and disturbance at various spatial scales over time. Landis-II has been developed and applied extensively (Mladenoff et al. 1996, Mladenoff 2004, Scheller et al. 2007, He 2009); it simulates drivers of succession and disturbance that act at the local and landscape scales. Because Landis-II is not burdened with simulating individual trees and can incorporate large-scale disturbance events, particularly forest harvest (Gustafson et al. 2000), it is well suited for simulations of large tracts of land (Mladenoff 2004). Output from Landis simulations have been linked to wildlife models as a means of predicting impacts of forest succession on wildlife species (Zollner et al. 2005, 2008, He 2009, Longru et al. 2010, Rittenhouse et al. 2011). Our overall objective was to use forest successional modeling to evaluate the effects of varying v www.esajournals.org

timber harvest regimes on the habitat of the Indiana bat and northern long-eared bat. Specifically, our aims were to quantify the overall and proportional changes in suitable area for both species for foraging, roosting and integrated use under nine potential timber harvest scenarios on the state forests of Indiana. We predicted that extreme scenarios (i.e., no timber harvest and the most intensive timber harvests) would decrease suitable habitat for both species. In addition, we anticipated that an increase in regeneration openings on state forests would increase the diversity of usable habitat and create quality habitat for both bat species, but that such effects would differ in magnitude between species.

METHODS Study areas Our study areas consisted of Indiana State Forest properties along with the areas surrounding these properties within an 8-km (5-mi ) buffer. Indiana has two state forest properties in the northern half of the state with the rest occurring in the southern portion of the state (Fig. 1). The northern areas occur within the central till plain natural region while the southern areas occur throughout a number of natural regions (southwestern lowlands, southern bottomlands, Shawnee hills, highland rim and bluegrass natural regions; Homoya et al. 1985). State forest properties are primarily composed of forested stands of oaks (Quercus spp.) and hickories (Carya spp.) or mixed hardwoods (Shao et al. 2014). The surrounding landscape includes large areas of cultivated crops, hay/pasture land, and developed areas along with oak/hickory and mixed hardwood forests (Shao 2012).

Forest successional modeling We used the Landis-II forest simulation model (Scheller et al. 2007) to simulate forest succession and harvest throughout our study area over a 50year time span. Output from Landis-II simulations was used to map Indiana bat and northern long-eared bat occupancy over time under different harvest scenarios. Landis-II models the changes in species-age cohort (presence and biomass of tree species of the same age) over a landscape subject to disturbance. Tree establishment, growth, competition and mortality occurs 3


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areas into five categories: dry ridge, dry slope, mesic ridge, mesic slope and lowlands (Shao et al. 2004). This categorization, however, overestimated the proportion of lowland areas as they would be treated by forest managers (e.g., core bottomlands and perennial riparian areas; Indiana Division of Forestry, unpublished data). Thus, we split the lowland category in two. Based on the species composition of mixed hardwood forests identified in lowland areas (Shao 2012), areas where these forest types occurred were considered ‘core bottomlands’ while lowland areas with any other forest type were deemed ‘peripheral bottomlands’ as these constituted areas of low elevation mesic or dry sites that are not true wet-mesic bottoms (Indiana Division of Forestry, unpublished data). Areas outside of the previously created ecoregions were grouped into a single category.

Initial conditions To simulate forest succession it is necessary to determine the current composition of the forest. To simplify the number of tree species modeled we combined tree species into representative groups based upon their basic biology and the way in which they are managed (Table 1). We used plot data from Indiana Division of Forestry’s Continuous Forest Inventory (CFI) as a means of assessing forest composition. This system-wide inventory is analogous to the national Forest Inventory Analysis (Bechtold and Patterson 2005) in that it is a comprehensive sampling of forest variables within an extensive array of circular plots at points across large landscapes. Because Landis-II models forests by species-age cohorts, it was necessary to convert CFI data into estimates of species ages. We used species growth curves along with tree height and site index to estimate the age of each tree measured (Appendix: Table A1; Carmean et al. 1989). Tree ages were then used to determine the forest composition by age at each site. All estimated ages were rounded to the nearest integer. When the estimated age could not be calculated (exceeded the site-index curve) it was set to 120 years. If the estimated age exceeded the longevity for that species, the age was set to the species’ maximum. A total of 2117 CFI plots sampled within state forests from 2008 to 2010 were used for analysis. Plots occurred in all

Fig. 1. Areas simulated using LANDIS-II forest succession model. Black areas designate state forest property, while gray regions are areas within 8 km buffer of state forests.

within a single site (cell of raster map) while disturbance events such as timber harvest or forest fire can act at the landscape scale.

Ecological land types In Landis-II, species establishment and growth as well as harvest prescriptions are dependent upon the ecological land type (ecoregion) in which it occurs. To classify the ecoregions in our study areas, we used previously created ecological land type classifications for much of our study region produced following the methods of Shao et al. (2004). This classification grouped v www.esajournals.org

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PAULI ET AL. Table 1. Modeled species groups with lists of species comprising each group. Group White pine Virginia pine Other yellow pine Eastern Red-cedar White oak Chestnut oak Red oak Black oak Intolerant oak Other intermediate oak Loose-bark hickory Tight-bark hickory Hard maple Soft maple Ash Beech Yellow poplar Black walnut Pioneer hardwood Dispersal-limited intolerant hardwood Opportunistic intolerant hardwood Other intermediate hardwood Long-lived tolerant hardwood Short-lived tolerant hardwood Tolerant understory

Species eastern white pine (Pinus strobus) Virginia pine (P. virginiana) shortleaf pine (P. echinata), pitch pine (P. rigida), red pine (P. resinosa), jack pine (P. banksiana) eastern red-cedar (Juniperus virginiana) white oak (Quercus alba) chestnut oak (Q. prinus) northern red oak (Q. rubra) black oak (Q. velutina) shingle oak (Q. imbricaria), post oak (Q. stellata), scarlet oak (Q. coccinea) chinkapin oak (Q. muehlenbergii ), pin oak (Q. palustris) shagbark hickory (Carya ovata), shellbark hickory (C. laciniosa) pignut hickory (C. glabra), bitternut hickory (C. cordiformis), mockernut hickory (C. tomentosa) sugar maple (Acer saccharum) red maple (A. rubrum), silver maple (A. saccharinum) white ash (Fraxinus americana), green ash (F. pennsylvanica), blue ash (F. quadrangulata) American beech (Fagus grandifolia) yellow poplar (Liriodendron tulipifera) black walnut (Juglans nigra) eastern cottonwood (Populus deltoides), bigtooth aspen (P. grandidentata) black locust (Robinia psuedoacacia), sassafras (Sassafras albidum) sweet gum (Liquidambar styraciflua), black cherry (Prunus serotina) American elm (Ulmus americana), American sycamore (Platanus occidentalis), hackberry (Celtis occidentalis), boxelder (Acer negundo) black gum (Nyssa sylvatica), slippery elm (Ulmus rubra) American basswood (Tilia americana), common persimmon (Diospyros virginiana), pawpaw (Asimina triloba), Ohio buckeye (Aesculus glabra) eastern hophornbeam (Ostrya virginiana), American hornbeam (Carpinus caroliniana), flowering dogwood (Cornus florida), eastern redbud (Cercis canadensis)

ecoregions, within each forest type and for most combinations of ecoregion and forest type. The initial conditions of forests for LANDIS-II simulations were created by populating each cell in the study area (30 3 30 m) with the conditions of one of the 2117 empirically measured CFI plots. For each forested cell within the entire study area, the forest composition (species-age cohorts) was determined by randomly selecting the composition of one CFI plot that occurred within the same ecoregion and the same forest type (Shao 2012) as the cell being populated. For sites without a classified ecoregion the forest composition was drawn from all possible sites with the same forest type. This process was repeated three times so that three alternate initial conditions were created for all study areas.

upon light conditions. When they are not, though, the likelihood of a seed establishing at a site is drawn from a preset probability. Determining the likelihood of a seed establishing at a site given optimal light conditions is especially difficult since this establishment probability can vary by ecoregion. Therefore we used the current composition of forests as a means of assessing establishment. For each species group, we determined the proportion of CFI sites that contained at least one tree of that group in each ecoregion ( p). This resulted in a proportional establishment value by ecoregion for each species group. The majority of CFI sites, however, occurred in areas with mature forests. Because such areas are shaded by mature trees, these sites do not constitute optimal light conditions, particularly for shade intolerant species, and therefore underestimated the establishment probabilities for such species. Thus it was necessary to modify the establishment values for species based upon their shade intolerance class. Therefore, we applied a weighted additive factor to species establishment values. First, we

Species establishment A major component to forest succession in Landis-II is species establishment. During simulations, trees reproduce and produce seeds, and those seeds disperse throughout the landscape. Seeds may be precluded from establishing based v www.esajournals.org

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PAULI ET AL. Table 2. Harvest scenarios. Each of nine harvest scenarios illustrating the maximum area (in hectares) harvested by cutting method on state forest property. Scenarios 4–9 are designed for maximum harvest levels of 60–100% of annual forest growth with varying proportions of harvests (at end of harvesting rotation) in either single-tree selection cuts (‘‘select.’’) or regeneration openings (‘‘regen.’’). Scenario Timber harvest method

1

2

3

4

5

6

7

8

9

Single-tree selection Patch cut (, 4 ha) Shelterwood Clearcut (. 4 ha) Maximum hectares harvested annually

0 0 0 0 0

635 26 0 20 681

2023 81 20 40 2164

4047 162 61 40 4310

3291 108 243 108 3750

2347 94 472 162 3075

1619 135 647 162 2563

5059 202 76 51 5388

6758 270 101 68 7197

Scenario definitions: 1, no harvest; 2, historical state forest harvest levels (pre-2005); 3, current state forest harvest levels (since 2008); 4, mMax. 60% growth (75% select., 25% regen.); 5, max. 60% growth (60% select., 40% regen.); 6, max. 60% growth (40% select., 60% regen.); 7, max. 60% growth (25% select., 75% regen.); 8, max. 75% growth (75% select., 25% regen.); 9 , max. 100% growth (75% select., 25% regen.).

calculated the relative reduction in establishment probability for different shade class species groups relative to those most shade-tolerant by taking the mean difference in establishment for all ecoregions (r). For a particular species group we calculated the ratio of the maximum proportional establishment ( pmax) to the proportional establishment in each ecoregion ( peco). We then weighted the relative difference by the ratio establishment to derive final estimates of establishment probabilities (ES): peco : ESeco ¼ peco þ r 3 pmax

Timber harvest We simulated 9 timber harvest scenarios. These ranged from no harvest to intensive extraction of timber on state forest lands (Table 2). Because state forest property is divided into management units defined by ‘‘tract’’ boundaries, rather than as ecologically defined forest stands, it was necessary to model timber harvests at the tract level. For each harvest scenario, we randomized a list of all harvestable tracts. For each tract we applied a forest harvest prescription until the maximum harvestable area for that prescription was reached. Harvest areas never exceeded the maximum prescription. This was repeated for all prescriptions within a scenario. Once all tracts had been harvested the harvest order was repeated until 50 years of harvest had been completed. If the harvest included an opening larger than that of single-tree selection, that tract was removed from the pool of harvestable tracts for the remainder of the simulated 50 years. If a tract was designated as part of a state forest ‘‘backcountry area’’ (as defined by the Indiana Division of Forestry) it was eliminated from the pool of tracts eligible for regeneration openings but could be subject to single-tree selection harvests. A number of timber harvest prescriptions were applied to various tracts including both even and uneven-aged management. Harvest prescriptions on state forest property included varying amounts of regeneration openings (i.e., patch cuts, shelterwood and clearcuts) and single-tree selection (Table 2). Even-aged management prescriptions constituted the simplest timber harvests. For clearcuts,

This approach corrected for underestimated establishment probabilities for shade intolerant species by increasing the establishment probability of species groups in such classes based upon the observed disparity in occurrence between shade classes. This method also maintains relative disparity in establishment by ecoregion, however (Appendix: Table A2).

Forest succession Forest growth and succession in Landis-II is driven by species-specific characteristics such as age of reproduction, shade tolerance, and the ability to vegetatively sprout following harvest. Attributes for each species group were obtained by examining the characteristics of representative species via existing species accounts (Burns and Honkala 1990, Prasad et al. 2007) and consultation with forestry experts (Appendix: Table A3; Indiana Division of Forestry personnel, personal communication). v www.esajournals.org

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a random site within a selected tract was chosen and was cut so that all biomass was removed. This process spread throughout the tract until either the opening reached 10 ha or 95% of the tract had been harvested. Patch cuts (regeneration openings ,4 ha) proceed in a similar way except that opening could not exceed 4 ha but multiple patches within a tract could be harvested until 15% of the tract had been cleared. Both clearcuts and patch cuts required tracts to have a minimum stand age of 40 years before entry. Though few natural conifer stands exist on state forest property, within Clark State Forest there are areas which are harvested to encourage natural conifer regeneration. Therefore, tracts within Clark State Forest that were dominated by pines were given a unique harvest prescription. For these sites, patch cuts were applied with a maximum size of 4 ha with no more than 50% harvest of the entire tract. Shelterwood prescriptions were also applied to state forest tracts in some scenarios. Under this prescription tracts were harvested twice (establishment and overstory removal cuttings). First, undesirable understory and midstory trees were removed. All oak and hickories with ages that corresponded to diameters under 41 cm were left in the under/midstory but all other small diameter trees were removed. In the canopy (those .41 cm DBH) 70% of oaks and tight-bark hickories and 95% of loose-bark hickories were retained. All other canopy trees were removed. Seven years later the tract was harvested again, and the previously retained overstory oaks and hickories were removed allowing the regenerating trees below to be released. Single tree selection regimes were applied to state forest tracts based upon ecoregion and forest composition. Xeric ecoregions (i.e., dry ridge and dry slope) were harvested under one of two prescriptions. Tracts that were dominated by chestnut, black or intolerant oaks were harvested under the ‘dry oak’ prescription (Appendix: Table A4). All other harvested xeric sites were cut under the ‘upland mixed hardwood’ prescription (Appendix: Table A5). Similarly, mesic ecoregions (i.e., mesic slope, mesic ridge and peripheral bottomlands) were also subject to one of two single-tree selection prescriptions. In tracts where white oaks, red oaks, loose-bark hickories or tight-bark hickories predominated, the ‘good v www.esajournals.org

oak’ harvest prescription was applied (Appendix: Table A6). All other mesic tracts were harvested under the ‘upland mixed hardwoods’ prescription (Appendix: Table A5). Finally, only one single-tree selection prescription, ‘lowland mixed hardwoods’, was applied to selected core bottomland tracts (Appendix: Table A7). We also simulated timber harvests on sites outside of state forest boundaries. Private lands were harvested under a uniform scenario in which 5% of forests were harvested annually via a generic single-tree selection prescription in areas up to 15 ha in size (G. S. Haulton, personal communication). Harvests in the Hoosier National Forest were restricted so that annually only a single large opening was created with a maximum size of 15 ha. Harvests outside of state forests were only simulated for scenarios where state forest harvests did not occur. The forest composition outside of state forest property from these simulations was then merged with those for all scenarios within forest boundaries. This procedure was undertaken in order to maintain consistency between simulations, to speed up processing time and so that metrics including neighborhood effects outside of property boundaries could be calculated.

Simulations We simulated each buffered state forest region separately with the exception of the two northern regions which were simulated concurrently due to their small size. Within each region we simulated each of the nine harvest scenarios. For each scenario we modeled forest succession and harvest for three different sets of initial conditions and with three replicates in which the harvest sequence on state forest properties was randomized. The forest composition of each simulation was then merged with that of the private harvest simulations outside of forest boundaries. Thus, 81 combinations of output were created per region (nine scenarios, three initial conditions, three harvest sequences). Output for individual regions were aggregated for the entire study area. For each simulation we generated output every five simulated years from the beginning of the simulation to year 50.

Bat model application We applied previously developed bat suitability models to all simulation output (for full model 7


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details, see Pauli 2014). Nocturnal suitability for Indiana bat and northern long-eared bats was determined via occupancy analysis (MacKenzie et al. 2002, 2006). To characterize nocturnal occupancy, acoustic recordings of bat echolocation calls from state forests in Indiana were identified using automated call classification software and detection histories for the Indiana bat and northern long-eared bat were constructed. These data were analyzed using a Bayesian, hierarchical single-species occupancy approach (Dorazio and Royle 2005, Dorazio et al. 2006). Models were constructed using a suite of habitat covariates (e.g., proportion of area forested within 1km) and the effect of these features on the occupancy was determined for each species. Models were validated using both training and independent data. These results identified environmental features that predicted the occurrence of each species and quantified the effect of each covariate on species occupancy. Indiana bat nocturnal occupancy was greatest in areas near a hibernaculum, with a low proportion of forest within 1km and that had experienced either recent timber harvest or had not been harvested in 30 years or more. The nocturnal occupancy of northern long-eared bats was greatest when sites had approximately 12% forest edge within 1km, were either close to or very far from the nearest major road and had few streams within 1km. Models of diurnal suitability for Indiana and northern long-eared bats were constructed using MaxLike, a presence-only occupancy modeling approach (Royle et al. 2012), along with known roost sites for each species and habitat features thought to influence roost site selection. A forward, step-wise procedure was used to determine which habitat covariates best predicted roost occupancy for each species. Diurnal occupancy of Indiana bat maternity roosts was greatest in areas with high local forest cover within broader landscapes with less forest, near perennial streams but far from intermittent streams and in areas with poor foraging habitat (as determined by the nocturnal occupancy model). Northern long-eared roost occupancy, on the other hand, was greatest in areas with high regional but fragmented forest cover with greater forest edge at an intermediate distance from the nearest major road. Models of nocturnal and diurnal occupancy were applied to all study areas for every fifth v www.esajournals.org

simulated year (the diurnal occupancy model for Indiana bats was based upon reproductive females only). Variables that were invariant to timber harvest (e.g., distance to nearest stream) were applied uniformly for all applicable models. Model covariates impacted by simulation results included those relative to proportional forest cover, forest edge and time since last harvest. Simulated timber harvests were tracked annually so that date of last harvest was easily extracted. Other model covariates were dependent upon a binary classification of habitat as forested or nonforested. Because cells in LANDIS-II simulations did not explicitly change cover type, it was necessary to derive a threshold for forest classification. We used the fifth percentile of cell biomass from forest initial conditions as calculated by LANDIS-II as a cutoff for a forested cell (13.9 kg/m2). Therefore, if the biomass of any cell fell below this threshold it was considered nonforested for bat model application. In addition, we combined the maps of predicted nocturnal occupancy with those created for diurnal occupancy for both species in order to create a single suitability index for all summer habitat requirements. Bats are considered centralplace foragers where roosting habitat is a relatively local phenomenon, whereas foraging areas are chosen based upon resources surrounding a roost (i.e., bats require local suitability for roosting such as the presence of a roost tree but forage within a broader region around that roosting location (Broders et al. 2006, Rainho and Palmeirim 2011). Therefore, we averaged the nocturnal occupancy predictions within a 1km radius and multiplied that value by the predicted roosting occupancy at each cell in the study area. This produced a single map of bat habitat suitability for each species based upon local roosting conditions and regional foraging/commuting habitat. All suitability models (nocturnal, diurnal, and integrated) for both species were then discretized into binary maps in order to distinguish suitable from non-suitable habitat. Thresholds for each model were derived from scores from modelspecific data such that locations with scores above this threshold were considered suitable while those below the cutoff were deemed unsuitable. For the nocturnal model, we used the fifth percentile occupancy score (2 SD) of 8


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acoustic detection sites as a threshold of suitability (same data as used in model construction). Thus, for the nocturnal models the occupancy scores at nocturnal detection sites were used to delineate suitable foraging and commuting sites (thresholds [probability of nocturnal occupancy]: Indiana, 0.215; northern long-eared, 0.417; Pauli 2014). Similarly, the fifth percentile of diurnal occupancy scores at known roost sites (of which some were used in occupancy model construction) was used as a cutoff for diurnal models (thresholds [probability of diurnal occupancy]: Indiana, 7.50 3 104; northern long-eared, 4.15 3 106; USFWS, unpublished data; see Pauli 2014). For the overall integrated map we used the 32nd percentile score (1 SD) as a threshold since the empirical data (capture locations not used in any model building) were less suited to the particulars of the model prediction and so warranted a more liberal suitability threshold. Thus, scores at known mist netting capture sites for both species within our study area were used to derive cutoffs for the integrated suitability models (thresholds [multiplied nocturnal and diurnal occupancy]: Indiana, 5.27 3 104; northern long-eared, 1.24 3 106 ). Values were drawn from 168 Indiana bat and 293 northern long-eared capture locations from 2002 to 2012 (USFWS, unpublished data).

tests may not be appropriate for simulated data, we have focused on the relative magnitude of the effects (White et al. 2013) and comparison between simulated scenarios (Grimm and Railsback 2005, Scheller and Mladenoff 2008) rather than their statistical significance (White et al. 2013). To assess the uncertainty in model results as a consequence of uncertainty propagated through occupancy models, a preliminary uncertainty analysis was conducted. For a single, randomly selected simulation output (one initial condition and harvest order under scenario 5), predictive maps of nocturnal, diurnal and overall habitat for each species were created following 50 simulated years that incorporated the error inherent in covariate effect estimation of occupancy models. Maps were created by randomly sampling beta values for each significant covariate used in each model assuming a known coefficient distribution. For each species, five maps were created assuming beta coefficients followed a normal distribution (using coefficient mean and standard deviations from each model) and five maps were created by sampling beta coefficients from a uniform distribution of the 95% confidence/ credible interval of each covariate. The areas of suitable nocturnal, diurnal and overall habitat were then compared with those of all simulations for scenario 5 in order to determine the degree of uncertainty associated with error propagation.

Statistical analyses

The area of suitable habitat at the end of the simulation (year 50) was used to compare the effects of harvest scenario, while accounting for RESULTS harvest order and initial community composiWe ran a total of 891 LANDIS-II simulations of tion, on bat habitat (nocturnal, diurnal and overall). A mixed-effects analysis of variance state forest properties in Indiana along with an 8was conducted for each suitability model for km buffer around each forest property. Single tree both bat species using the lme4 package (Bates et selection harvests resulted in a patchwork of forest al. 2013) in program R (R Core Team 2012). and openings that persisted only a few years while Because the simulated timber harvest scenarios regeneration openings created larger contiguous constitute a finite set of potential approaches, areas of open habitat that persisted longer. The amount of suitable nocturnal habitat after scenario was considered a fixed effect. The order of tract harvest and the composition of the initial 50 years differed between the timber harvest forest community, on the other hand, were both scenarios for both the northern long-eared (F8,68 samples of a larger candidate pool and, thus, ¼ 36.2, p , 0.0001) and Indiana bats (F8,68 ¼ 3638, were treated as random effects. To determine if p , 0.0001). For Indiana bats, suitable nocturnal the initial conditions or harvest order had a habitat was greatest under scenarios with little to significant effect on simulation outcomes, likeli- no timber harvest (scenarios 1 and 2; Fig. 2A). hood ratio tests (with Bonferonni corrected a ¼ Among more intensive harvest scenarios, Indiana 0.0042) for models with and without each effect bat nocturnal habitat was maximized under were conducted. However, because statistical intermediate harvest pressure with a focus on v www.esajournals.org

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Fig. 2. Box and whisker plots of the amount of suitable (A) Indiana bat nocturnal habitat, (B) northern longeared bat nocturnal habitat, (C) Indiana bat diurnal habitat, (D) northern long-eared bat diurnal habitat, (E) Indiana bat overall habitat and (F) northern long-eared bat overall habitat relative to nine harvest scenarios on Indiana State Forests. The black bar indicates the median value, the box incorporates the interquartile range, and the whiskers illustrate 1.5 times the interquartile range.

regeneration openings over single tree selection (scenario 7). Such harvest effects were evident throughout the simulations. Indiana bat nocturnal habitat increased under scenarios 1 and 2 but v www.esajournals.org

decreased for all other scenarios (Fig. 3). Northern long-eared bat nocturnal habitat displayed a similar response to intensive forest harvest with the greatest habitat at year 50 resulting from a 10


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Fig. 3. The proportional change in suitable habitat under 9 simulated harvest scenarios. Squares designate nocturnal habitat, circles represent diurnal habitat and triangles signify overall habitat. Open red symbols denote Indiana bat (MYSO) habitat while filled blue symbols are for northern long-eared bat (MSYE) habitat. Error bars denote one standard deviation from the mean. Note that the scale for plot A differs from all other plots.

focus on larger regeneration openings (Fig. 2B). In contrast to Indiana bats, the nocturnal habitat for northern long-eared bats was substantially reduced under a scenario in which timber harvests ceased. Over the course of the simulations, northern long-eared bat nocturnal habitat dev www.esajournals.org

creased but by varying degrees (Fig. 3). The magnitude of effects of timber harvests on nocturnal habitat was much less pronounced than that of Indiana bats. Timber harvest scenarios also had a significant effect on the amount of diurnal habitat for both 11


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14239, p , 0.0001; northern long-eared, F8,68 ¼ 14.4, p , 0.0001). For both species, the effects of timber harvest on the overall suitable area closely matched the effects on the diurnal suitable habitat. Thus, Indiana bats had less area with overall suitable habitat under no- and lowharvest regimes at year 50 but the greatest amount of suitable habitat for scenarios involving mostly single tree selection (Fig. 2E). Changes in overall suitable area throughout the LANDISII simulations also show a strong relationship in which overall suitability tracks with changes in diurnal suitability particularly in cases when diurnal suitability declined dramatically (Fig. 3). The amount of overall suitable habitat for northern long-eared bats was less dramatically affected by timber harvest scenarios, though overall suitability was maximized under the noharvest scenario. All other scenarios exhibited overlapping degrees of effects (Fig. 2F). Furthermore, under most harvest scenarios northern long-eared suitable habitat remained relatively constant (Fig. 3). Finally, likelihood ratio tests illustrated that neither initial forest conditions nor harvest order had a significant effect on any of the response variables after 50 simulated years (Table 3). The effect of propagation of model uncertainty on suitable areas for both species, however, was substantial. The area of suitable nocturnal, diurnal and overall habitat for both species was much more sensitive to errors associated with variance of covariate effects than those due to the stochasticity inherent in forest succession simulations (Fig. 4).

Table 3. Likelihood ratio tests of random effects. Tests of the effects of initial forest conditions and harvest order on amount of suitable nocturnal, diurnal and overall habitat for Indiana bats (MYSO) and northern long-eared bats (MYSE). Values represent v2 with 1 degree of freedom (p-value of effect). Note: Bonferonni corrected a ¼ 0.0042. Species

Habitat type

Initial forest conditions

Harvest order

MYSO

Nocturnal Diurnal Overall Nocturnal Diurnal Overall

0.000 (1.000) 0.000 (1.000) 0.000 (1.000) 0.000 (1.000) 0.295 (0.587) 0.000 (1.000)

1.2948 (0.2552) 0.1502 (0.6983) 6.1484 (0.0132) 2.3205 (0.1277) 1.7475 (0.1862) 2.8191 (0.0932)

MYSE

species (Indiana, F8,68 ¼ 15935, p , 0.0001; northern long-eared, F8,68 ¼ 30.2, p , 0.0001). At year 50, the effect of timber harvests on the amount of suitable area was nearly opposite for diurnal habitat compared to nocturnal habitat for both species. For Indiana bats, diurnal habitat for maternity colonies was substantially lower for scenarios with little or no timber harvest (scenarios 1 and 2) and greatest under scenarios in which timber harvest was primarily focused on single tree selection (scenarios 3, 4, 8 and 9; Fig. 2C). Such trends were evident over time as diurnal habitat decreased substantially for scenarios 1 and 2. Interestingly, many of the more intensive timber harvest scenarios displayed an initial drop in diurnal habitat followed by a rebound and, in some cases, overall increase in Indiana diurnal habitat compared to the initial conditions (e.g., scenario 3; Fig. 3). For northern long-eared bats, the most diurnal habitat resulted from a complete cessation of timber harvest (scenario 1), but scenarios without a great deal of regeneration harvests also resulted in a greater area of suitable diurnal habitat (Fig. 2D). Over time, most scenarios displayed an increase in northern long-eared diurnal habitat. As with Indiana bat habitat, some scenarios initially exhibited a decline in overall suitable diurnal habitat for northern long-eared bats (Fig. 3). As with the nocturnal models, the effects of harvest on diurnal habitat were more pronounced for Indiana bat habitat than that of northern longeared bats. The area of overall suitable habitat also differed among scenarios (Indiana, F 8,68 ¼ v www.esajournals.org

DISCUSSION These results highlight the inherent difficulty often associated with managing forests for multiple wildlife species. Different species often require different habitat and management that creates quality habitat for one species of interest can reduce habitat for another (Smith and Zollner 2005). We have demonstrated such a situation with Indiana and northern long-eared bats. Despite belonging to the same genus, we determined that each species responds to silvicultural treatments quite differently. Complete cessation of timber harvests on state forest properties, for instance, resulted in the greatest 12


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Fig. 4. Box and whisker plots of the amount of suitable (A) Indiana bat nocturnal habitat, (B) northern longeared bat nocturnal habitat, (C) Indiana bat diurnal habitat, (D) northern long-eared bat diurnal habitat, (E) Indiana bat overall habitat and (F) northern long-eared bat overall habitat when covariate uncertainty is ignored (Mean), assumed to be normally distributed (Normal) or is drawn from a uniform distribution of the 95% confidence/credible interval (Uniform). The black bar indicates the median value, the box incorporates the interquartile range, and the whiskers illustrate 1.5 times the interquartile range.

v www.esajournals.org

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amount of overall suitable habitat for the northern long-eared bat but a major decline in suitable habitat for Indiana bats. The situation is further complicated by the fact that these species may use habitat of different composition for roosting during the day and for foraging/commuting at night. Simulation modeling of this kind relies upon a number of assumptions that must be met for predictions to be credible. In this study we assumed that we sufficiently captured the initial forest conditions of the simulated properties, that our simulated timber harvest prescriptions accurately reflected those conducted by foresters, that our variables for tree establishment and growth were suitable and that the grain of our simulations was adequate to encompass both the scale of timber harvests and bat habitat selection. Future projections from predictive models are impossible to validate (Rastetter 1996). Instead, by comparing the simulated effects of differing scenarios (within a particular context) we can gain insight into the relative effects of varying management approaches on a system (Scheller and Mladenoff 2008). Furthermore, we reduced the complexity of model predictions by discretizing suitability estimates from continuous values to binary outputs. Additional analyses are possible using the continuous suitability scores that could reveal additional insight though that was outside of the scope of this study. Despite such complexities, our results provide insight into the effects of timber harvest on habitat for both species and provide practical guidance for management.

Thus, many studies have detected extensive foraging by Indiana bats in mature forests with closed canopies (Humphrey et al. 1977, LaVal et al. 1977, Owen et al. 2004). Under more intensive silvicultural regimes, an increase in the proportion of regeneration openings in our simulations benefited Indiana bat nocturnal habitat. Such an effect reinforces research that suggests Indiana bats prefer regeneration openings rather than uneven-aged forest management for foraging (Caylor 2011) and that single tree selection cuts may be too small to produce openings and forest edges preferred by Indiana bats and other species (Grindal and Brigham 1999, Sheets 2010, Sheets et al. 2013b). Diurnal habitat for Indiana bats maternity colonies, on the other hand, benefitted most from silvicultural treatments that emphasized unevenaged management and single tree selection. Indiana bats preferentially utilize snags for roosting (Humphrey et al. 1977, Gardner et al. 1991, Callahan et al. 1997). Silvicultural approaches that retain or create suitable snags should benefit Indiana bat roosting habitat. Indiana bats continue to roost in areas that have received selection harvests (Gardner et al. 1991), though some evidence suggests they may avoid clearcut interiors for decades (MacGregor et al. 1999). Interestingly, uneven-aged management strategies appear to benefit Indiana bat roosting habitat. Single tree selection can preserve and even produce suitable snags for roosts by Indiana bats (Sheets 2010, Loeb and O’Keefe 2011, Sheets et al. 2013b). Our findings that silvicultural scenarios that emphasize uneven-aged timber management produce more suitable diurnal habitat for Indiana bats reinforce these known species characteristics. Overall suitable habitat for Indiana bats closely matched the trends exhibited by the effect of timber harvest on diurnal habitat. Perhaps most surprising was the fact that overall suitable habitat was more responsive to the type (selective vs. regenerative) rather than the spatial intensity of the harvests. We expected the most extensive harvest scenarios to negatively impact Indiana bat habitat through the removal of forested habitat. Instead we discovered that extensive harvesting could maintain quality habitat for Indiana bats under scenarios in which the majority of harvest was done using selection

Indiana bat We found that the amount of suitable Indiana bat nocturnal habitat was greatest under reduced levels of overall timber harvest. This was somewhat unexpected as research suggests that bat nocturnal activity increases following timber harvest (O’Keefe 2009, Loeb and O’Keefe 2011). Generally, Indiana bats are thought to utilize forest openings, in particular the forest edges created by harvests, for commuting and foraging (Murray and Kurta 2004, Sparks et al. 2005). Their short wings and great maneuverability, though, suggest that Indiana bats are a clutter adapted species that can forage successfully in fully forested areas (Norberg and Rayner 1987). v www.esajournals.org

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methods, matching the findings of another study using a coarser model of habitat suitability (Rittenhouse 2008). State forests in Indiana have long been managed primarily under unevenaged, selection harvests (Carman 2013, Haulton 2013). Our results suggest that a continuation of such a practice would create and maintain the greatest proportion of high-quality Indiana bat habitat. As a public forest system managed for multiple uses, Indiana State Forests must be managed for more than just the Indiana bat. Forest composition such as oak regeneration and habitat management for other species must also be taken into account (Haulton 2013). Our findings indicate that, while less beneficial to Indiana bat habitat suitability, greater use of regenerative openings can still be compatible with Indiana bat habitat conservation.

derson et al. 2008) with relatively moderate to high levels canopy cover (Menzel et al. 2002, Timpone et al. 2010). In general, such characteristics, and thus suitable roosting habitat, are associated with mature, intact forests (Lacki and Schwierjohann 2001, Carter and Feldhamer 2005, Loeb and O’Keefe 2006, Perry and Thill 2007, Perry et al. 2007). Northern long-eared bats will also roost in managed stands (Menzel et al. 2002), particularly those involving thinning or selection cutting (Perry et al. 2007) if the stand retains much of the characteristics of intact stands. Therefore it has been suggested that selection harvests that maintain a diversity of tree size and age could benefit northern long-eared roosting habitat (Lacki and Schwierjohann 2001, O’Keefe 2009). Our LANDIS-II simulations reinforce such assertions. We found roosting habitat to be maximized under scenarios in which timber harvest did not occur or when harvests were moderate and consisted of primarily unevenaged management. Overall suitable habitat for northern longeared bats generally followed a negative relationship with timber harvest intensity. A cessation of timber harvest on state forest properties resulted in the greatest amount of suitable habitat for the species. The likelihood of such a scenario in reality is extremely low given the necessity of silvicultural harvest for forest management and conservation of oak and other wildlife species (including the Indiana bat as our results demonstrate). The impact of other harvest scenarios on the overall habitat of the northern long-eared bats was relatively moderate and suggests that, within the range examined, timber harvest intensity likely has only limited impacts on northern long-eared bat habitat.

Northern long-eared bat Our result that northern long-eared bat nocturnal habitat was maximized under conditions with greater emphasis on regenerative openings was unexpected. A number of studies have shown that northern long-eared bats avoid areas of recent clearcut or regenerative openings (Owen et al. 2003, 2004, Patriquin and Barclay 2003). Instead, northern long-eared bats are considered to primarily forage in areas with intact forest (Owen et al. 2003, Henderson and Broders 2008) but will also exploit small openings (Loeb and O’Keefe 2006) and appear to be tolerant of harvests that reduce midstory clutter (Titchenell et al. 2011). Northern long-eared bats will also use forest edges, however, for nocturnal movement and foraging (Henderson and Broders 2008). The presence of forest edges was the main driver of northern long-eared bat nocturnal habitat in our models. Northern long-eared bats had the greatest nocturnal occupancy in areas with intermediate levels of forest edge. Singletree selection creates small pockets of forest edge that are ephemeral and quickly recolonized by neighboring trees. Regeneration openings, on the other hand, have greater persistence and, thus, allow for more permanent forest edge features. In our models, such edge permanency increased the amount of suitable nocturnal habitat for northern long-eared bats. Northern long-eared bats often roost in cluttered forest interiors (Broders et al. 2006, Henv www.esajournals.org

Overall A number of general conclusions can be drawn from this research. First, we observed nearly opposite effects of harvest scenario on nocturnal and diurnal habitat for both species. Roosting and foraging constitute somewhat disparate needs for both species, but the contrasting way in which the silvicultural scenarios impacted suitability was unexpected. Such a result highlights the need for a holistic approach to specieshabitat assessments. A study that only focused on one aspect of habitat selection by these 15


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species, such as nocturnal occupancy via mist netting or acoustic surveys, would omit an important aspect of habitat requirements and could, ultimately, provide incomplete or detrimental management guidance. Future research should investigate the potential for spatial pairing of timber harvest techniques so that quality roosting and foraging habitat can be produced in proximity for the greatest benefit of imperiled bat species. Second, while we examined the effect of timber harvest on both the nocturnal and diurnal habitat of two bat species, we discovered that the impact on roosting habitat had a greater effect on the overall habitat suitability than did that of commuting/foraging. The effect of the nocturnal occupancy was by no means negligible, however, and a number of areas with high roost suitability had resulting low composite suitability due to low predicted nocturnal values. Such a relationship is probably appropriate for both bat species. The requirements for roosting are restricted and habitat suitable for roosting is rare relative to foraging areas. Foraging behavior of the two species, on the other hand, is a bit more plastic and, thus, many areas could be good nocturnal habitat (Carter 2006, Henderson and Broders 2008). Third, we detected a difference in the magnitude of the effects of varying timber harvest regimes by species. Indiana bat habitat exhibited a stronger response to different silvicultural application for nocturnal, diurnal and integrated habitat. Such results suggest that Indiana bats may be more sensitive to variation in timber harvest while the effect on suitable habitat for northern long-eared bats was negligible. This could be because northern long-eared bats are more plastic in their selection of roosts compared to Indiana bats and, thus, are less sensitive to timber harvests (Lacki et al. 2009). Fourth, we found that the effects of timber harvest application differed substantially between Indiana bats and northern long-eared bats after 50 years of management. Particularly under extreme scenarios (e.g., no harvest or intense harvest), the species exhibited opposite responses relative to suitable habitat. Such results make managing for both species (not to mention other species of conservation interest) difficult. Under numerous intermediate scenarios we found that v www.esajournals.org

a high amount of suitable area can be maintained for both species. In particular, when timber harvest is moderate and primarily focused on selective harvests (e.g., scenarios 3, 4, 5, and 8) habitat for both species is best conserved. This conclusion should be interpreted cautiously, however. Our initial assessment of model uncertainty due to error propagation suggests high variance in the amount of suitable habitat predicted within a single scenario. Thus, the absolute estimates of suitable areas include high degrees of uncertainty and should not be treated as precise estimates. Instead, the relative comparisons of output between scenarios should be emphasized. Studies of habitat suitability rarely, if ever, consider the effects of parameter uncertainty on prediction (Roloff and Kernohan 1999, Van der Lee et al. 2006). Refinement of bat habitat models via an increase in the number of sampling locations used to construct the models could reduce their associated uncertainties, thus reducing the overall uncertainty of model predictions. Such an approach would require additional data on nocturnal and diurnal locations of both species, however. Our results indicate that additional investigation into the uncertainty associated with all model output is warranted. This study highlights ways in which forest management can be applied to best conserve habitat for Indiana and northern long-eared bats. The maintenance and promotion of such habitat is crucial due to the potential effects WNS may have on both species. It is possible, though, that population declines due to WNS may overwhelm any effect of habitat manipulation and make forest management irrelevant for species conservation. We are hopeful, however, that appropriate forest management, coupled with other conservation efforts, can aid in bat conservation. Our results highlight an approach for species conservation that can be applied beyond the regions and species described here. Preservation of habitat for multiple species must account for the inherent differences in habitat needs between species. Our approach highlights the further necessity of recognizing the potential for distinct habitat needs within a species based upon life history characteristics. When coupled with simulations of future conditions, such an approach can aid in guiding management of multiple species with various independent habitat needs. 16


PAULI ET AL. forest management. USDA Forest Service, Northern Research Station, Newtown Square, Pennsylvania, USA. Carmean, W. H., J. T. Hahn, and R. D. Jacobs. 1989. Site index curves for forest tree species in the eastern United States. General Technical Report NC-128. USDA Forest Service, North Central Forest Experiment Station, St. Paul, Minnesota, USA. Carter, T. C. 2006. Indiana bats in the Midwest: the importance of hydric habitats. Journal of Wildlife Management 70:1185–1190. Carter, T. C., S. K. Carroll, J. E. Hofmann, J. E. Gardner, and G. A. Feldhamer. 2002. Landscape analysis of roosting habitat in Illinois. Pages 160–164 in A. Kurta and J. Kennedy, editors. The Indiana bat: biology and management of an endangered species. Bat Conservation International, Austin, Texas, USA. Carter, T. C., and G. A. Feldhamer. 2005. Roost tree use by maternity colonies of Indiana bats and northern long-eared bats in southern Illinois. Forest Ecology and Management 219:259–268. Caylor, M. K. 2011. Impacts of different forest treeharvest methods on diets and populations of insectivorous forest bats. Thesis. Indiana State University, Terre Haute, Indiana, USA. Dorazio, R. M., and J. A. Royle. 2005. Estimating size and composition of biological communities by modeling the occurrence of species. Journal of the American Statistical Association 100:389–398. Dorazio, R. M., J. A. Royle, B. So¨derstro¨m, and A. Glimska¨r. 2006. Estimating species richness and accumulation by modeling species occurrence and detectability. Ecology 87:842–854. Gardner, J. E., J. D. Garner, and J. E. Hofmann. 1991. Summer roost selection and roosting behavior of Myotis sodalis (Indiana bat) in Illinois. Illinois Natural History Survey, Champaign, Illinois, USA. Grimm, V., and S. F. Railsback. 2005. Individual-based modeling and ecology. Princeton University Press, Princeton, New Jersey, USA. Grindal, S. D., and R. M. Brigham. 1999. Impacts of forest harvesting on habitat use by foraging insectivorous bats at different spatial scales. Ecoscience 1999:25–34. Gustafson, E. J., S. R. Shifley, D. J. Mladenoff, K. K. Nimerfro, and H. S. He. 2000. Spatial simulation of forest succession and timber harvesting using LANDIS. Canadian Journal of Forest Research 30:32–43. Haulton, G. S. 2013. Past is prologue: a synthesis of state forest management activities and hardwood ecosystem experiment pre-treatment results. Pages 339–350 in R. K. Swihart, M. R. Saunders, R. A. Kalb, G. S. Haulton, and C. H. Michler, editors. The Hardwood Ecosystem Experiment: a framework for studying responses to forest management.

ACKNOWLEDGMENTS This research was funded by the Indiana Department of Natural Resources, Division of Forestry. We would like to thank B. C. Pijanowski, J. Doucette, J.-M. Mulesa and the Purdue University Rosen Center for Advanced Computing for providing computational support. Many of the personnel at the IDNR-DOF provided expert opinions on forest management practices. R. K. Swihart, S. Fei, D. W. Sparks, A. J. Cohen, R. J. Spaul and S. H. Smith provided valuable feedback on earlier drafts of this manuscript. We would also like to thank the anonymous reviewers of this manuscript for their valuable suggestions.

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PAULI ET AL.

SUPPLEMENTAL MATERIAL APPENDIX Table A1. Values from growth curves used to calculate tree age. For each species group listed are the percentages of trees in the CFI dataset within this group, the five beta values used to calculate tree age, the species of tree from which the growth curve was determined and the figure number of the growth curve from Carmean et al. b5 1989. (The growth equation is H ¼ b1 Sb2 ð1 eb3 A Þb4 S where H is tree height, S is site index and A is tree age.) The species growth curve information includes the figure number from Carmean et al. (1989) in parentheses.

Group name

Percentage of trees in CFI dataset

b1

b2

b3

b4

White pine Virginia pine Other yellow pine Eastern red-cedar White oak Chestnut oak Red oak Black oak Intolerant oak Other intermediate oak Loose-bark hickory Tight-bark hickory Hard maple Soft maple Ash Beech Yellow poplar Black walnut Pioneer hardwood Dispersal-limited intolerant hardwood Opportunistic intolerant hardwood Other intermediate hardwood Long-lived tolerant hardwood Short-lived tolerant hardwood Tolerant understory§

2.24 2.70 2.03 2.17 8.86 7.99 2.39 3.86 0.62 0.64 1.84 4.15 17.69 7.30 2.46 6.45 7.09 1.08 0.57 5.87 1.93 2.97 3.13 0.87 2.70

3.2425 0.7716 1.8900 0.9276 4.5598 1.9044 1.5403 2.9989 1.6763 4.5598 1.8326 1.8326 6.1308 2.9435 4.1492 29.7300 1.2941 1.2898 1.3615 0.9680 7.1846 1.0370 1.3213 4.7633 4.7633

0.7980 1.1087 1.0000 1.0591 0.8136 0.9752 1.0006 0.8435 0.9837 0.8136 1.0015 1.0015 0.6904 0.9132 0.7531 0.3631 0.9892 0.9982 0.9813 1.0301 0.6781 1.1906 0.9995 0.7576 0.7576

0.0435 0.0348 0.0198 0.0424 0.0132 0.0162 0.0216 0.0200 0.0220 0.0132 0.0207 0.0207 0.0195 0.0141 0.0269 0.0127 0.0315 0.0289 0.0675 0.0468 0.0222 0.0030 0.0254 0.0194 0.0194

52.0549 0.1099 1.3892 0.3529 2.2410 0.9262 1.0616 3.4635 0.9949 2.241 1.4080 1.4080 10.1563 1.658 14.5384 16.7616 1.0471à 0.8546 1.5494 0.1639 13.9186 0.1391 0.8549 6.5110 6.5110

b5

Species growth curve used

0.7064 Eastern white pine (104) 0.5274 Virginia pine (125) 0.0000 Red pine (95) 0.3114 Eastern red-cedar (58) 0.1880 White oak (41) 0.0000 Chestnut oak (46) 0.0044 Northern red oak (47) 0.3020 Black oak (49) 0.0240 Scarlet oak (42) 0.1880 White oak (41) 0.0005 Hickories (10) 0.0005 Hickories (10) 0.5330 Sugar maple (3) 0.1095 Red maple (1) 0.5811 White ash (13) 0.6804 American beech (11) 0.0368 Yellow poplar (25) 0.0171 Black walnut (16) 0.0767 Cottonwood (28) 0.4127 Black locust (50) 0.5268 Black cherry (35) 0.2655 American elm (52) 0.0016 Swamp tupelo (27) 0.4156 American basswood (51) 0.4156 American basswood (51)

Used since growth of chinkapin oak is similar to that of white oak (Limstrom 1965). à Growth curve incorrectly reported this value as negative. § No growth curves available so set as equivalent to short-lived tolerant hardwoods.


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PAULI ET AL. Table A2. Seed establishment probability by species group and ecoregion. Values represent the probability of a seed establishing at a site given optimal light conditions. Values derived from forest composition with correction for shade intolerance. Species group

Peripheral bottomlands

Dry ridge

Mesic ridge

Mesic slope

Dry slope

Core bottomlands

Overall

White pine Virginia pine Other yellow pine Eastern red-cedar White oak Chestnut oak Red oak Black oak Intolerant oak Other intermediate oak Loose-bark hickory Tight-bark hickory Hard maple Soft maple Ash Beech Yellow poplar Black walnut Pioneer hardwood Dispersal-limited intolerant hardwood Opportunistic intolerant hardwood Other intermediate hardwood Long-lived tolerant hardwood Short-lived tolerant hardwood Tolerant understory

0.16 0.28 0.20 0.28 0.39 0.10 0.22 0.21 0.22 0.14 0.21 0.27 0.40 0.38 0.24 0.21 0.33 0.29 0.30 0.23 0.39 0.30 0.19 0.16 0.16

0.07 0.15 0.19 0.26 0.35 0.23 0.23 0.36 0.22 0.07 0.22 0.35 0.52 0.32 0.19 0.29 0.32 0.12 0.22 0.43 0.17 0.09 0.21 0.10 0.14

0.26 0.34 0.29 0.31 0.24 0.05 0.12 0.25 0.10 0.05 0.21 0.19 0.43 0.45 0.37 0.24 0.47 0.29 0.26 0.49 0.20 0.24 0.32 0.16 0.21

0.02 0.08 0.02 0.13 0.35 0.31 0.22 0.24 0.05 0.04 0.26 0.30 0.62 0.33 0.16 0.28 0.35 0.11 0.14 0.34 0.09 0.10 0.24 0.21 0.12

0.04 0.12 0.02 0.21 0.56 0.49 0.29 0.38 0.26 0.09 0.24 0.34 0.51 0.39 0.13 0.28 0.15 0.10 0.07 0.24 0.06 0.07 0.22 0.07 0.09

0.08 0.19 0.00 0.12 0.32 0.05 0.14 0.00 0.00 0.22 0.09 0.17 0.37 0.41 0.17 0.23 0.10 0.13 0.00 0.00 0.23 0.45 0.36 0.15 0.07

0.10 0.19 0.12 0.22 0.37 0.21 0.20 0.24 0.14 0.10 0.20 0.27 0.47 0.38 0.21 0.26 0.29 0.17 0.17 0.29 0.19 0.21 0.26 0.14 0.13

Table A3. Species group characteristics. Attributes of species groups for maximum tree age (long.), age of sexual maturity, shade tolerance class, effective (95%) seed dispersal distance (meters), maximum seed dispersal distance (meters) for species group, probability of vegetative sprouting and the minimum and maximum age of vegetative sprouting.

Species group

Long.

Sexual maturity

White pine Virginia pine Other yellow pine Eastern Red-cedar White oak Chestnut oak Red oak Black oak Intolerant oak Other intermediate oak Loose-bark hickory Tight-bark hickory Hard maple Soft maple Ash Beech Yellow poplar Black walnut Pioneer hardwood Dispersal-limited intolerant hardwood Opportunistic intolerant hardwood Other intermediate hardwood Long-lived tolerant hardwood Short-lived tolerant hardwood Tolerant understory

250 125 250 200 400 325 250 125 250 150 250 200 325 120 200 325 210 175 100 100 150 200 200 100 100

10 5 20 10 20 20 25 20 25 20 40 30 25 10 20 40 15 6 15 10 15 20 10 10 15


Seed dispersal

Shade tolerance

Effective

3 2 2 2 3 3 3 3 2 3 4 3 5 4 3 5 3 2 1 2 2 3 4 4 5

200 100 125 100 50 50 50 50 50 50 50 50 200 150 200 50 100 50 1000 50 100 200 75 50 100

22

Veg. age

Max

Vegetative repro. prob.

Min

Max

400 200 225 2000 3000 3000 3000 3000 3000 3000 3000 3000 300 250 400 2000 200 2000 5000 200 250 1000 400 1000 1000

0 0 0 0 0.25 0.8 0.7 0.5 0.5 0.5 0.5 0.5 0.4 0.5 0.5 0.5 0.9 0.5 0.9 0.5 0.5 0.5 0.5 0.5 0.3

0 0 0 0 10 10 20 10 10 10 10 10 10 0 10 5 10 10 10 10 10 10 0 0 0

0 0 0 0 100 100 200 100 200 100 100 100 200 100 100 75 210 75 100 100 150 75 100 100 100


PAULI ET AL. Table A4. ‘‘Dry oak’’ harvest prescription. Values are percentages of species group harvested by size class under dry oak harvest. Parenthetical values denote age ranges of trees harvested corresponding to estimated tree diameter. Size class

Chestnut oak

Black and red oak groups

White oak

Tight-bark hickory

Loose-bark hickory

All other species

,12" 12–16" 17–24" 25–30" .30"

5 (,78) 40 (78–91) 75 (92–108) 90 (109–120) 100 (.120)

5 (,68) 12 (68–85) 50 (86–110) 90 (.110) 90

5 5 (,111) 20 (112–129) 75 (.129) 75

10 (,93) 12 (93–105) 50 (106–122) 75 (.122) 75

5 5 5 5 5

50 50 (,65) 80 (66–75) 100 (.75) 100

Species groups include: black oak, intolerant oak and red oak.

Table A5. ‘‘Upland mixed hardwood’’ harvest prescription. Percentage of species group harvested by size class under upland mixed hardwood harvest. Parenthetical values denote age ranges of trees harvested corresponding to estimated tree diameter. When age ranges vary by species group within a harvest column, ages associated with size of harvest trees are included as footnotes. Size class

Hard maple

Opportunistic intolerant hardwood

Other tier 1 species

Tier 2 speciesà

Ash

Loose-bark hickory

All other species

,12" 12–16" 17–24" 25–30" .30"

5 (,55) 10 (55–70) 30 (71–90) 80 (91–107) 95 (.107)

5 (,40) 10 (40–60) 30 (61–87) 75 (.87) 75

5 10 10 25 75

25 50 65 80 80

25 (,50) 50 (50–68) 90 (.68) 90 90

5 5 5 5 5

50 50 (,65) 80 (66–75) 100 (.75) 100

Species groups include: white oak (,80, 80–95, 96–110, 111–120, .120), red oak (,65, 65–75, 76–95, 96–110, .110) and black walnut (,40, 40–60, 61–80, 81–100, .100). àSpecies groups include: black oak (,60, 60–75, 76–95, .95), chestnut oak (,72, 72–85, 86–100, .100), yellow poplar (,38, 38–51, 52–72, .72) and tight-bark hickory (,78, 78–89, 90–103, .103).

Table A6. ‘‘Good oak’’ harvest prescription. Values are percentages of species group harvested by size class under good oak harvest. Parenthetical values denote age ranges of trees harvested corresponding to estimated tree diameter. When age ranges vary by species group within a harvest column, ages associated with size of harvest trees are included as footnotes. Oak Size class ,12" 12–16" 17–24" 25–30" .30"

White group

Black Chestnut groupà

Hickory Red

5 25 5 3 (,80) (,72) (,60) (,65) 12 50 12 7 (80–95) (72–85) (60–75) (65–75) 12 65 25 22 (96–110) (86–100) (76–95) (76–95) 40 80 55 50 (111–120) (.100) (.95) (96–110) 70 80 55 75 (.120) (.110)

Tight-bark Loose-bark 10 (,78) 12 (78–89) 50 (90–103) 75 (.103) 75

10 (,78) 10 (78–89) 10 (90–103) 10 (103–111) 10 (.111)

Black walnut

Yellow Maple All poplar group§ Ash other species

0 5 (,40) (,38) 0 75 (40–60) (38–44) 10 75 (61–80) (45–51) 50 100 (81–100) (.51) 100 100 (.100)

40

95

50

20

95

75

95

75

95

75

95

50 (,65) 80 (66–75) 100 (.75) 100

Species groups include: white oak and other intermediate oak. à Species groups include: black oak and intolerant oak. § Species groups include: hard maple (,55, 55–70, .70), soft maple (,43, 43–60, .60) and opportunistic intolerant hardwood (.40, 40–60, .60).


23


PAULI ET AL. Table A7. ‘‘Lowland mixed hardwood’’ harvest prescription. Values are percentage of species group harvested by size class under lowland mixed hardwood harvest. Parenthetical values denote age ranges of trees harvested corresponding to estimated tree diameter. [Note: when age ranges vary by species group within a harvest column, ages associated with size of harvest trees are included as footnotes]. Size class ,12" 12–16" 17–24" 25–30" .30"

Tier 1 species

Tier 2 speciesà

Ash

Loose-bark hickory

All other species

5 10 10 25 75

25 50 65 80 80

25 (,50) 50 (50–68) 90 (.68) 90 90

5 5 5 5 5

50 50 (,65) 80 (66–75) 100 (.75) 100

Species groups include: white oak (,80, 80–95, 96–110, 111–120, .120), other intermediate oak (,80, 80–95, 96–110, 111– 120, .120), chestnut oak (,72, 72–78, 79–85, 86–100, .100), black oak (,60, 60–67, 68–75, 76–95, .95), intolerant oak (,60, 60–67, 68–75, 76–95, .95), red oak (,65, 65–75, 76–95, 96–110, .110), tight-bark hickory (,78, 78–89, 90–103, 104–115, .115) and black walnut (.40, 40–60, 61–80, 81–100, .100). à Species groups include: hard maple (,55, 55–70, 71–90, .90), soft maple (,43, 43–60, 61–80, .80), opportunistic intolerant hardwood (,40, 40–60, 61–87, .87), pioneer hardwood (,45, 45–65, 66–75, .75) and other intermediate hardwood (,45, 45– 65, 66–75, .75).

SUPPLEMENT Consolidated LANDIS simulation output (Ecological Archives http://dx.doi.org/ES14-00336.1.sm).


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