This file was created by scanning the printed publication. Errors identified by the software have been corrected; however, some errors may remain. Habitat Structure and the Distribution of Small Mammals in a Northern Hardwoods Forest1 In northern temperate forests small mammals are distributed unevenly across available habitat, even within a single forest type or age class (Dueser and Shugart 1978, Vickery 1981, Parren 1981, Seagle 1985a). Differential use of certain segments or microhabitats within a broader habitat type has most often been reported for sympatric species of small mammals, but intraspecific variation in microhabitat use has also been noted (Kitchingsand Levy 1981, Vickery 1981, Seagle 1985a). Differential use of microhabitats by small mammals may be a consequence of the ecological requirements of each species (i.e. habitat selection) or it may be the result of partitioning of habitat by competing species (Crowell and Pimm 1976, Porter and Dueser 1982). Another hypothesis is that the observed use of microhabitats by small mammals is primarily a function of the density of small mammal populations. Under this hypothesis, use of a certain microhabitat is determined more by the availability of animals to occupy the area than by structural 'Paper presented at symposium, Management of Amphibians, reptiles, and Small Mammals in North America. (Flagstaff,AZ, July 19-21, 1988.) 2JefferyA. Gore, formerly a graduate student, University of Massachueits, Bepartment of Forestry and Wildlife Managemen t is currently Nongame WildlifeBiologist, Florida Game and Fresh Water fish Commission, Rt. 4, Box 759, Panama City, Florida 32405. Abstract.-The influence of habitat structure on the distribution of small mammals was studied in an old-growth northern hardwoods forest in New Hampshire. Logistic regression equations developed with data from three live-trappinggrids were able to classify locations of just three of eight small mammal species better than expected by chance. For all species the regression models failed to correctly predict presence in an independent grid. At the scale tested, habitat structure had little effect on the distribution of small mammals within this forest type. characteristics of the microhabitat. Extrinsic factors, such as food availability, disease, or predation, could alter population levels and thus indirectly influence the distribution of small mammals among microhabitats. Observed microhabitat use might also be a function of some combination of habitat selection, competitive partitioning, and factors affecting population density. Thequestion of which mechanism most influences the distribution of small mammals is of more than academic importance. If small mammals select among microhabitats based on structural features, then disturbance such as timber harvesting may have a considerable impact on population density or species composition. Conversely, if the distribution of small mammals is primarily a function of population density, then habitat disturbance is likely to-have less effect, or at least a less direct effect, on local populations. Furthermore, if microhabitat requirements are known, it might be possible to manipulate population levels by altering structural components of the habitat. I measured use of microhabitat by small mammals in an old-growth northern hardwoods (Acer-Fagus- Betula) forest, a habitat that contains a variety of microhabitats (Bormann and Likens 1979)and supports several species of small mammals (Lovejoy 1970).In this paper I identify the small mammal-microhabitat associations observed, compare them to re- sul ts of previous studies, and suggest that the distribution of small mammals among microhabitats in the northern hardwoods forest is influenced little by structural features of the forest. Methods The study area was located in the White Mountain National Forest, New Hampshire in a topographically isolated site known as the Bowl (Martin 1977). All fieldwork was confined to the uncut, old-growth northern hardwoods forest that comprises about 210 ha in the lower (600-750 m) elevations of the Bowl. The oldgrowth forest is structurally heterogeneous; numerous treefalls and gaps in the canopy are present and the portion of the forest floor covered by rock, soil, water, or vegetation varies greatly across the stand (Gore 1986). Trapping In 1983, small mammals were livetrapped on three 60 x 105-m grids, each consisting of 40 trapping stations spaced at 15-m intervals along five rows. Two stations were added to each grid in 1984 to increase sampling at seeps and along streams. A fourth grid of 42 trapping stations was also established in 1984. This grid was used to evaluate the robust- ness of models of microhabitat use that were developed with data from the initial three grids. One Sherman (5 x 6 x 17 cm), Pymatuning (Tyron and Snyder 1973), and pitfall trap were located at each trapping station (Gore 1986). Traps were baited with sunflower seeds and set simultaneously for four consecutive days each month from August to October, 1983 and June to October, 1984. Each trapping period began 3-5 days after a new moon (to minimize variation in ambient light) and continued regardless of weather conditions. Captured animals were marked and released at the capture site. In 1983,4,320 trap-nights (12 nights x 120 stations x 3 traps) were recorded on three grids. In 1984 four grids provided 10,080 trap-nights (20 nights x 168 stations x 3 traps). Microhabitat At each trapping station microhabitat was quantified by measures from within the 15 x 15-m plot in which the station was centered. The variables used to quantify the habitat are defined in table 1 and the methods for measuring them are described in detail by Gore (1986). The 26 habitat variables selected for analysis were a subset of a larger group of variables on which measures were made. The number was reduced in order to facilitate interpreta tion of results and to increase the ratio of sample cases to variables (Morrison 1985).The initial group was condensed by deleting one of each pair of highly correlated (n0.50) variables that were similar in ecological form or function. Some highly correlated variables, such as the number of dead trees and the number of logs, were retained because I felt they represented distinct ecological features. Variables were included regardless of whether their means or distributions varied between capture and no-capture stations. This allowed significant linear combinations of variables to be incorporated, but at the risk of complicating the interpretation of results. Data Analysis Chi-square tests (Snedecor and Cochran 1980) were used to identify statistically significant associations between capture locations of all possible pairs of species. Within species, differences between capture locations in different years and seasons were analyzed. The relative strength of associations was measured via the contingency correlation coefficient, Phi (Brown 1983). Habitat variables from plots with and without captures in 1984 were compared for each small mammal species. Capture locations from the entire trapping period were grouped even though some values, such as for vegetative cover, varied between seasons. If the habitat values within the two groups were normally distributed and had equal variances, significant differences between groups were determined via t-tests; if not, Mann-Whitney tests were used (Snedecor and Cochran 1980). Logistic regression (Bishop et a'l. 1975, Engelman 1983) was used to identify, for each mammal species, the microhabitat variables that accounted for statistically significant portions of the variation in capture success. The product of the analysis is a set of regression equations for predicting presence of each species at a station based upon quantitative measures of the station's habitat characteristics. Logistic regression was used instead of discriminant analysis because it does not assume that independent or explanatory variables are normally distributed or have homogeneous variances (Press and Wilson 1978). I used the BMDP-LR computer program (Engelman 1983) to construct regression equations, or models, for predicting microhabitat use as defined by captures. The program selected, in a stepwise manner, habitat variables that distinguished stations where animals were captured from those where they were not. Variables were entered into an equation if their F value was significant at P<0.10 and removed if P subsequen tly exceeded 0.15. Initial regression models were formed using data obtained in 1984 from Grids 1-3. The regression models for each mammal species were used to classify stations within the three grids as locations where the species was either present or absent. The models were then used to predict the presence of each species at stations in Grid 4. Finally, new regression models were developed for each species using data from all four grids. These were compared to the models from the initial three grids in order to assess the effect of different sites on the models. The Kappa sta- tistic (Fleiss 1973, Engelman 1983) determined the significanceof agreement between locations where a species was observed and the speciespresent locations predicted by the regression models. Results Trapping Thirteen species of small mammals were captured during the study, but only those captured more than ten times are considered. The number of captures varied widely among species and, for some species, between years (table 2). The smokey shrew (Sorex fumeus), pygmy shrew (S. hoyi), and eastern chipmunk (Tamias striatus) were captured infrequently in both 1983 and 1984, while the masked shrew (S. cinereus) and the southern red-backed vole (Clethrionomys gapped were common in both years. The northern short-tailed shrew (Blarina brevicauda), deer mouse (Peromyscus maniculatus) and woodland jumping mouse (Napaeozapus insignis) were captured more often in 1984 than in 1983. Captures from August through October at the 120 stations trapped in both years were compared for each species to determine whether 1983 and 1984 capture locations were associated. For all but two species, individuals were captured in both years at few (0-8 percent) of the stations with captures. No species showed a significant association in capture locations between years (X2 tests, P~0.05).Even for deer mice and jumping mice, which were abundant in both years, stations with captures in both years comprised only 32-41 percent of all stations with captures of these species. A similar comparison was made between the capture locations of each species in summer (June-August)and fall (September-October)of 1984 on all four grids. No species exhibited a significant (P>0.05)association between summer and fall locations. For all trapping periods and species combined, each trapping station had at least two captures. In 1984, all 168 stations recorded at least one capture and 85 percent had more than four captures. The maximum number of captures at a single station was 22 for all species combined and 15 for a single species, the jumping mouse. Only for deer mice and jumping mice did stations with multiple captures outnumber stations with single captures. All other species were taken only once or not at all at the majority of trapping stations. The only pairs of species captured at the same locations more often than expected by chance (X2 tests, P<0.05) were jumping mouse:red-backed vole, short-tailed shrew:masked shrew, and short-tailed shrew:smokey shrew. The association between each pair was positive and weak (0.15<Phi<0.30). Some animals died after capture, but the effect upon local populations of each species was unknown. For abundant species that experienced low mortality, such as the deer mouse and jumping mouse, the effect was probably negligible. For the shrews, which had high mortality rates during capture, the effect may have been substantial. However, cap- ture rates indicated no adverse effects on shrew abundance. In 1984 more shrews of each species were captured in September than in the previous three months. Furthermore, of the four shrew species, only captures of pygmy shrews declined between years (table 2). Microhabitat Use Differencesbetween habitat values for the capture and no-capture stations of Grids 1-3 from 1984 were compared. For each species, at least one habitat variable differed significantly between stations with and without captures (table 3). Logistic regression is not useful if the number of cases of either of the dependent variable values (species presence or absence) is less than five percent of the total number of cases (D. Hosmer, University of Massachusetts, personal communication). Because the pygmy shrew was found at only two percent of the stations, it was deleted from the analysis. Deer mice and smokey shrews each also had widely disparate group sizes, 95 percent present and 95 percent absent respectively; therefore, results for these species should be considered cautiously. The first set of logistic regression models of microhabitat use were based on captures in 1984 from the 126 stations in Grids 1-3. The number of significant variables included in each model ranged from one, when presence of red-backed voles was the dependent variable, to 12, when presence of eastern chipmunks was used (table 4). For most species the variables included in the regression models were not the same as those whose means differed between capture and no-capture stations (table 3). This suggests that some linear combination of habitat variables was important in defining the microhabitat where a species was captured, even though individual variables alone were not. The habitat variables included in the logistic regression models (table 4) were selected because each was associated with a significant (P<0.10) portion of the variance in the capture data of a species. However, if these variables and their regression coefficients cannot be used to correctly predict the capture success of a species at a station, they are of limited practical value regardless of their statistical significance. To assess the utility of regression models as descriptors of microhabitat, they were independently used to classify each trapping station, based on habitat parameters, as one with the species present or absent (table 5). Tests of the agreement between predicted and observed capture success were not possible for the smokey shrew because no sites were classified as having the species present. The regression model was not able to identify, based upon the habitat variables measured, the eight sta- tions that captured smokey shrews. Conversely, nearly all stations were predicted to capture deer mice and jumping mice (table 5).The regression models for those two species were unable to distinguish those stations where the animals were not captured. For the other four species the numbers of stations with and without captures were more similar and, consequently, so were the number of species-present and species-absent classifications. For red-backed voles, however, only 47 percent of the classifications of present were correct and this was not significantlybetter than chance (table 5). Only for the eastern chipmunk, short-tailed shrew, and masked shrew were the regression models able to classify capture success at a level better than chance agreement. For these three species, the logistic regression models may be useful descriptors of the microhabitat used within Grids 1-3. Because of the large number of variables included in the regression model for the eastern chipmunk (table 4), it was difficult to concisely describe the microhabitat of this species. Briefly, the eastern chipmunk was associated with large trees (+BATWE),downed wood (+NLOGS, -LOGDIST, +AVGVOLB, +AVGVOLC), and dense vegetation taller than 0.5 m (+VEG52, +VEGT2, +NSHRB36). The microhabitat of the shorttailed shrew has fewer variables but is also difficult to characterize. Captures were negatively associated with numbers of medium-sized shrubs and with vegetative cover between 0.5 and 2 m above ground. The masked shrew was found at stations with numerous logs and dense vegetation <0.5 m tall. The positive association with slightly decayed logs and dense ground cover and the negative association with standing dead trees suggest that recent treefalls may provide good habitat for masked shrews. To be useful predictors of species microhabitat, regression models should be successful with data that are independent of those from which the models were formed. To test sitespecificity of regression models, they were applied to data from 42 stations in Grid 4. Unlike the other three grids, Grid 4 had a perennial stream running through it, two extensive canopy gaps from recent treefalls, and highly variable soil conditions. Regression models from each of the seven species were used to classify the stations in Grid 4 according to capture success. None of the classifications, even those of the eastern chipmunk, short-tailed shrew, and masked shrew were correct more often than expected due to chance (for all tests Kappa ~0.48,b0.05). Because none of the regression models were useful in predicting capture locations in Grid 4, data from all four grids were combined and new logistic regression models for predicting species presence were developed to determine the influence of data from Grid 4 (table 7). Deer mice, jumping mice, and smokey shrews again had widely disparate group sizes and the models could not correctly classify the stations in the less common group (table 7). Agreement be tween observed and predicted locations was significant for red-backed voles as well as eastern chipmunks, short-tailed shrews, and masked shrews (table 71, which had significant models earlier. Some of the variables included in the models of each species (table 6 ) were different from those included when only data from Grids 1-3 were used (table 4). For the masked shrew, eastern chipmunk, and red-backed vole the regression models created with and without the data from Grid 4 were similar, even though the predictions from Grids 1-3 for the redbacked vole were not better than expected by chance. The coefficients changed but most variables were the same. This suggests that for these three species the microhabitats in Grid 4 were similar to those identified in the other three grids. The relationship of species to microhabitat parameters may not be as sensitive as the regression models suggest. This would account for the poor performance of the models from Grids 1-3 in predicting captures on Grid 4. For short-tailed shrews the regression model changed greatly when data from Grid 4 were included. Four new variables were added, and the sign of the coefficient was reversed on the only variable, vegetation between 0.5 and 2 m, that was retained. This suggests that captures of short-tailed shrews or the measured habitat parameters poorly reflect the microhabitat requirements of the species, or that short-tailed shrews are not restricted by microhabitat within this forest. Discussion In an environment of limited resources, sympatric species are expected to partition resources as a means of coexisting, i.e. avoiding competitive exclusion (Schoener 1974). Since Brown (1973) first suggested that temperate forest rodents would be likely to partition habitat rather than seasonally variable food supplies, numerous studies in northern temperate forests have identified statistically significant associations between habitat structure and small mammal distributions (Dueser and Shugart 1978, Kitchings and Levy 1981, Parren 1981, Vickery 1981, Schloyer 1983, Seagle 1985a). Statistical significance, however, does not necessarily impart biological meaning to observed patterns of species distributions. Few authors have tested the biological relevance of their models of microhabitat use by using them to predict microhabitat use at independent locations or times. Parren and Capen (1985) found that capture locations of deer mice could not be accurately predicted using discriminant functions of rnicrohabitat use developed with data from similar habitats the previous year. Similarly, none of the logistic regression models I developed were useful in predicting capture locations at stations other than those from which the models were developed. One reason for the poor predictive capabilities of the multivariate models may be that trapping'does not accurately portray the relationship between species presence and habitat requirements. In addition, the way in which habitat features are measured may not depict the variability perceived by small mammals or the variation in microhabitat structure may be small relative to the niche breadth of each species. Unfortunately, these problems are not easily identified or solved. Ideally, the activity of many individual animals would be intensively monitored, but that is very difficult to accomplish. Another reason for the poor performance of the models is that problems in applying the mu1tivaria te analyses, such as disparate sizes of presence and absence groups and mu1ticollinearity of variables, make it difficult to interpret the results of habitat models (Noon 1984). The scale at which habitat and small mammals are sampled also greatly affects the relationship that can be defined (Morris 1984). Despite these potential limitations, I believe the inability of my models to predict species presence on a independent grid in the same forest stand suggests that structural features alone, at least at the microhabitat level, are not important to the distribution of small mammals. Comparisons of capture locations and review of habitat requirements for each species supports my argument. The locations where species were captured suggest that no interspecific segregation of microhabitats occurred. Overlap in capture sites was high among species and no inverse relationships were observed, even when data were examined by season. This suggests that habitat partitioning or microhabita t selection is absent or operating at a finer scale than my trap stations. The weak association I found among capture locations of each species between years and seasons suggests that individual species were not selecting particular trapping stations. It is possible that subtle shifts in the microhabitat used would not be per- ceived by examination of trapping locations alone. The microhabitat occupied by small mammals has been reported to shift with season (Kitchings and Levy 1981, Vickery 19811, population density (M'Closkey 1981, Adler 19851, and species composition (Seagle 1985b).This suggests that microhabita t use is dynamic, regardless of whether the shifting is deterministic or stochastic. Another argument against differential use of microhabitats by the small mammals I observed is the variety of habitats they occupy. The eight species 1found in the oldgrowth northern hardwoods forest have been found in other age- and size-classes of northern hardwoods forests as well as in other forest types (Lovejoy 1970, Richens 1974, Kirkland 1977, Miller and Getz 1977, Hill 1981, and others). Except for smokey shrews and pygmy shrews, the species are common in a variety of habitats comprising a wide range of structural characteristics. In fact, descriptions of the important habitat features associated with each species d o not always agree [e.g. see Hamil- ton (1941), Brower and Cade (1966), Lovejoy (1970), Vickery (19811, and Parren (1981).fordescriptions of jumping mouse habitat]. If each species is common under a wide range of habitat conditions, it seems unlikely that they would partition or select habitat based on the advantages of structural features alone. Fine discrimination of the forest habitat seems more improbable when the temporal variability of microhabitats is considered. Within the northern hardwoods forest of New Hampshire microhabitats are greatly modified in winter by deep snow cover, in summer by closed canopies and sparse ground cover, and in fall by deep leaf litter. Therefore, resident species must accommodate seasonally variable microhabitats as well as seasonally variable food supplies. The reasoning Brown (1973) used to suggest that temperate forest rodents could not specialize on seasonally variable food resources seems applicable also to seasonally variable microhabitats. In the forest I sampled, presence of most species at individual trapping stations could not be accurately predicted based on structural features of the habitat. If microhabitat structure does not greatly influence the distribution of small mammals within this forest type, disturbance of the habitat should not directly affect population levels. However, the scale at which the disturbance occurs may determine to what extent local populations are affected. Small scale disturbance of the habitat, such as harvesting by single-tree or small-group selection, would likely not affect species composition or density of the resident small mammals. More wide scale disturbance, such as clear-cutting of the entire forest stand, might alter the habitat so greatly that species abundance and distribution is affected (Kirkland 1977).Given the apparent wide range of habitat conditions in which these small mammals occur, even a large scale disturbance of the northern hardwoods forest would likely cause only temporary changes in species composition or population levels of small mammals. The relationship between small mammals and habitat structure within the northern hardwoods forest remains poorly understood. However, the data presented here, as well as comparisons at different scales (Morris 1984,1987),suggest that microhabitat features play only a minor role in the distribution of small mammals within the forest. A more important determinant of small mammal distribution may be population size and the factors that affect it, such as food, weather, and predators. 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