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Habitat selection by American during Summer is related to hotspots of prey

Authors: Wolff, Patrick J., Taylor, Christopher A., Heske, Edward J., and Schooley, Robert L. Source: Wildlife Biology, 21(1) : 9-17 Published By: Nordic Board for Wildlife Research URL: https://doi.org/10.2981/wlb.00031

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Downloaded From: https://bioone.org/journals/Wildlife-Biology on 10 Jun 2020 Terms of Use: https://bioone.org/terms-of-use Wildlife Biology 21: 9–17, 2015 doi: 10.2981/wlb.00031 © 2015 Th e Authors. Th is is an Open Access article Subject Editor: Simone Ciuti. Accepted 27 August 2014

Habitat selection by American mink during summer is related to hotspots of crayfi sh prey

Patrick J. Wolff , Christopher A. Taylor , Edward J. Heske and Robert L. Schooley

P. J. Wolff (pwolff 2@.edu) and R. L. Schooley, Dept of Natural Resources and Environmental Sciences, Univ. of Illinois, 1102 S. Goodwin Avenue, Urbana, IL 61801, USA. – C. A. Taylor and E. J. Heske, Illinois Natural History Survey, Research Inst., 1816 S. Oak Street, Champaign, IL 61820, USA

Habitat selection by mammalian may be driven by prey availability, physical characteristics of the habitat, and landscape context. However, the cues used by carnivores to select habitat are often unclear. We examined the seasonal diet of American mink vison and determined if the abundance of a primary prey, crayfi sh, was an important driver of habitat use during summer in an agricultural landscape in Illinois. We also evaluated eff ects of stream size, water depth, riparian buff er width, and urbanization on occupancy of stream segments by mink. We collected mink scats during three seasons and tested for seasonal diff erences in the percentage of occurrence and volume percentage of prey classes in the diet of mink. Crayfi sh remains were the dominant component of mink scats during summer. In summer 2012, we performed occupancy surveys for mink and concurrently measured crayfi sh densities and habitat features in 59 stream segments. Site occupancy by mink was related positively to presence of local areas with high crayfi sh concentrations (hotspots) instead of local habitat characteristics that might indicate high prey densities. Mink also were associated negatively with degree of urbanization and stream size. Our study highlights the eff ectiveness of integrating data on diets and occupancy modeling to obtain insights on cues used by carnivores to select habitat.

Habitat selection by mammalian carnivores may be driven have seasonally variable diets due to temporal changes in by prey availability, physical characteristics of the habitat, relative availabilities of prey (Gerell 1967, Dunstone and and landscape context. However, the cues used by carnivores Birks 1987, Brzezi ń ski 2008). Th e diet of mink has been to select habitat are often unclear. Within a home range, examined within its native range (Dearborn 1932, Sealander carnivores should select habitat patches that maximize the 1943, Korschgen 1958, Racey and Euler 1983, Arnold and probability of encountering prey. Th ey may choose locations Fritzell 1987, Ben-David et al. 1997, Hoff man et al. 2009), for based on habitat characteristics that should and some studies have examined habitat use by mink in its indicate high prey abundance (Irwin et al. 2007, Slauson et al. native range (Burgess and Bider 1980, Arnold and Fritzell 2007), or based directly on prey abundance (O ’ Donoghue 1990, Ben-David et al. 1995, Stevens et al. 1997, Loukmas et al. 1998, Fukui et al. 2006). Because prey habitat does not and Halbrook 2001). However, studies linking diet with necessarily guarantee actual occurrence of prey at any given habitat selection behavior of mink are scarce. Crayfi sh often time, using direct measures of prey abundance could be occur in the diet of American mink where crayfi sh are avail- more informative than using the amount of prey habitat as a able both in the native (Dearborn 1932, Burgess and Bider surrogate for prey abundance (Keim et al. 2011). Moreover, 1980) and invasive range (Gerell 1967, Day and Linn 1972, habitat selection studies should be paired with mechanistic Ward et al. 1986, Melero et al. 2008, Fischer et al. 2009). data such as diet metrics (Keim et al. 2011) to understand In the invasive range, mink population density is correlated behavior more fully. positively with the proportion of crayfi sh in the diet, and One such carnivore that may select habitat based on prey home range size is correlated negatively with the crayfi sh abundance is the American mink Neovison vison . Th e spe- contribution (Melero et al. 2014). Spatial variability in abun- cies is an opportunistic, generalist carnivore native to North dance of this aquatic prey could explain habitat selection by America and invasive in , and Asia mink during seasons when crayfi sh are plentiful. (Macdonald and Harrington 2003, Bonesi and Palazon Mink may select habitat in relation to crayfi sh abundance 2007). Th is semiaquatic mustelid is closely associated with in two general ways. Mink could use habitat cues to decide watercourses, but is capable of spending extended periods of where to hunt for crayfi sh, or they could respond directly time away from water. For example, in central Illinois, USA, to relative crayfi sh densities. More specifi cally, because cray- radio-marked mink were located Ͼ 100 m away from the fi sh are patchily distributed within streams (DiStefano et al. water ’ s edge 14% of the time (A. Ahlers pers. comm.). Mink 2003), mink may respond to presence of prey hotspots instead

9 Downloaded From: https://bioone.org/journals/Wildlife-Biology on 10 Jun 2020 Terms of Use: https://bioone.org/terms-of-use of to average prey densities for sites. Studies across multiple a negative association between site occupancy by mink taxa indicate prey hotspots can be important determinants and degree of urbanization. of predator space use and foraging behavior (Th ompson et al. 2001, Davoren et al. 2003, Gende and Sigler 2006, Ló pez-Bao et al. 2011). Mink use some parts of their home Material and methods range more intensively than others (Yamaguchi et al. 2003), which could refl ect prey distributions and suitable hunting Study area places (Gerell 1970). In addition to prey abundance, local habitat and Our study was conducted in east– central Illinois, USA landscape characteristics may be important correlates of and was centered on Champaign-Urbana (40° 12′ N, habitat selection by mink. In the Midwestern , 88 ° 26′ W). Fifty-nine sites were randomly selected from agricultural intensifi cation, including drainage for row a previous stratifi ed random sample of 90 sites, 50% of crops, and urbanization have reduced the amount of suitable which were located within a 2-km radius of incorporated habitat for semiaquatic wildlife (Zucker and Brown 1998), towns (Ͼ 2500 people), and 50% of which were located and limited the terrestrial habitat available to mink surround- outside of this urban buff er (Cotner and Schooley 2011). ing the small streams and agricultural ditches in which they Sampling for crayfi sh density and site occupancy by mink occur. Th ese riparian buff ers vary considerably in character- occurred at these 59 study sites. Each site was a 200-m istics such as width and vegetation structure (Ahlers et al. stretch of wadeable stream, ranging from 1st to 5th order 2010). Larger riparian buff ers provide mink with increased in size (average site wetted width ϭ 0 – 23.3 m; average site foraging space and terrestrial alternatives to the aquatic depth ϭ 0 – 0.97 m). Sites represented potential resource prey located within the stream channel. Large streams have patches for mink so our measure of site occupancy corre- a greater diversity of aquatic prey than do small streams sponded to habitat use (Schooley et al. 2012). Th e median (Sheldon 1968, Osborne and Wiley 1992), and mink are distance between one site and the nearest site was 3.4 associated positively with water depth of streams (Schooley km (range ϭ 0.5 – 13.5 km). Sites were distributed across et al. 2012). Additionally, mink might avoid areas of human an urbanization gradient (proportion of impervious sur- development (Racey and Euler 1983, Brzeziń ski et al. 2012), face within a 500-m buff er around each site, Cotner and although information regarding eff ects of urbanization on Schooley 2011), and had a wide range of riparian buff er mink is limited (Gehrt et al. 2010). widths (0 – 466 m). We also collected mink scats at an Climate change is expected to increase variability in additional 60 locations to increase our sample size. precipitation in the Midwestern United States; climate models predict an increase in frequency of summer drought Scat collection and spring fl ooding events (Wuebbles and Hayhoe 2004, Trenberth 2011). Th ese changes will lead to increased tempo- To evaluate seasonal variation in composition of mink diets, ral fl uctuations in water depths and fl ow regimes of streams we collected scats during fall, winter and summer. Mink that could have consequences for predator– prey interactions scat was identifi ed by its unique, twisted appearance with in riparian ecosystems. Th e severe drought of 2012 (Illinois tapered ends. Mink scat is distinguishable from other mam- Department of Natural Resources 2013) presented an ideal mal scat in the area based on size and appearance (Rezendes opportunity to examine predator– prey interactions during 1999). However, if uncertainties in identifi cation existed, environmental conditions expected to occur more frequently the scat was not collected. Scat samples from a single site under climate change. were combined in plastic bags and stored at Ϫ 15 °C. In fall We examined the seasonal diet of American mink and 2011 (21 September – 10 November), our 59 study sites determined if a primary prey, crayfi sh, was an impor- were surveyed for mink scat by two trained searchers; scat tant driver of habitat selection by mink during summer was found at 18 sites. Th ere was uncertainty regarding the of a severe drought year in an agricultural landscape. We source of one collected scat sample, resulting in an analysis hypothesized that diet of mink would refl ect seasonal of 17 scats from 18 sites for fall. In winter 2012 (4 January availability of prey. We predicted crayfi sh would be most – 9 March), the 18 sites were revisited and searched, and all important in the diet during summer when they are most found scats were collected. To maximize effi ciency of our available, whereas consumption of crayfi sh would decrease searching, we did not revisit the 41 sites at which we did during winter when mink shift to prey such as not fi nd scat in fall. To increase our sample size, we instead and fi sh. We also hypothesized that habitat selection by searched an additional 60 locations within our study area. mink during summer would be related directly to prey Th ese locations were underneath and within 50 m of bridges abundance. Hence, we expected a higher probability of in areas that often included rock and other substrates where occupancy for mink at sites with crayfi sh hotspots or mink typically deposit scat. Th e collection locations were higher mean densities. We also hypothesized that stream Ն 1.5 km apart. Th is separation distance was based on an and landscape characteristics would infl uence habitat estimate of average length of mink home ranges in the study selection by mink. We predicted site occupancy by mink area (A. Ahlers pers. comm.), and increased the likelihood would be related positively to water depth and stream size of independence of scat samples. In summer 2012 (25 because larger streams contain more aquatic resources. June – 31 July), the 18 sites and 60 scat collection locations We also predicted a positive association between occu- were revisited. Scat was also collected opportunistically pancy and riparian buff er width because wider buff ers within the same three seasons during a concurrent study of provide more foraging opportunities. Finally, we expected radio-marked mink.

10 Downloaded From: https://bioone.org/journals/Wildlife-Biology on 10 Jun 2020 Terms of Use: https://bioone.org/terms-of-use Diet analysis To quantify the diversity of prey found in the mink diet, we calculated the Shannon diversity index (Shannon 1948) Each scat was soaked in warm water to facilitate separation and dietary evenness in each season using both PO and VOL of prey remains, washed through a sieve (0.8-mm mesh), metrics. We also calculated food niche breadth using Levins and air-dried. We sorted remains into seven prey classes (cray- ϭ 2 Ϫ1 (1968) B index: B ∑ ( p i ) in which p i is the proportion of fi sh, , , fi sh, insect, reptile and unknown) under scats containing prey class i . Diff erences in Shannon diversity a dissecting microscope (10 ϫ ) based on hair, teeth, bones, index values between seasons were assessed using Student’ s feathers, scales and exoskeleton fragments. Th e unknown t-tests, with variances calculated according to Zar (1984). prey class was mostly comprised of unidentifi able bone frag- Statistical tests were performed in SAS 9.2 (SAS Inst.). ments. Th ese general prey classes were adequate for testing our main prediction regarding seasonal shifts for crayfi sh in Occupancy surveys and crayfi sh sampling mink diets. Diet composition was recorded for each of the three Occupancy surveys for mink and crayfi sh sampling were seasons (fall, winter, summer) using three metrics: percent- conducted at the 59 study sites from 18 May – 26 July 2012, age of occurrence (PO; the number of occurrences of each during the core months of the severe drought of 2012, which prey class divided by the total number of scat samples, times included the second driest January to July period on record 100), relative frequency of occurrence (RFO; the number of in Illinois (Illinois State Water Survey 2012a, Illinois Depart- occurrences of each prey class divided by the total number ment of Natural Resources 2013). Two trained observers of occurrences of identifi ed prey), and volume percentage independently surveyed each site once. Each observer walked (VOL; visually estimated as the percentage of each prey class along both sides of the 200-m stream segment and searched in each scat). PO and RFO were highly correlated positively for sign within 5 m of the water’ s edge (Schooley et al. 2012). for all prey classes (r Ͼ 0.80), so we used PO for analyses. A site was considered occupied by mink if scat or tracks Each metric comes with caveats. PO may overestimate less were detected, but we dealt with imperfect detection in our digestible prey and underestimate more digestible prey, and modeling. Sign was likely left by mink with established home overestimate the contribution of prey taken regularly but ranges because our sampling was not during the dispersal in small amounts. VOL does not account for variation in period (Larivi è re 1999). scat sizes and also could underestimate the contribution Concurrently, crayfi sh (Orconectes , Procambarus , Cambarus ) of highly digestible prey (Klare et al. 2011). To minimize density was measured in each of the 59 sites by sampling the infl uence of biases associated with a single metric, we 1-m 2 areas in 10-m segments of stream (20 samples per site). used both PO and VOL methods in our analyses (Zabala We divided the stream ’ s wetted width in half, randomly and Zuberogoitia 2003). However, extrapolations from scat selected either the left or right side to begin sampling, and samples to the actual relative amounts of each prey type alternated sides as we sampled upstream. A sample was taken consumed should be made with caution. When making at the fi rst ‘ high-quality ’ crayfi sh habitat encountered on the seasonal comparisons with PO data, it should be more selected area within each 10-m segment. In streams Յ 1 m diffi cult to detect diff erences among prey classes because the wide, the entire stream width was sampled. High-quality contribution of rare food items is exaggerated. VOL data are habitat consisted of in-stream gravel, cobble, and rocks; more likely to reveal diff erences in the relative amounts of anchored woody debris; or submerged vegetation. If there prey types in scats, and thus detect seasonal specialization was no high-quality habitat within a segment, we sampled even if the overall variety of diet items per scat changes little. the fi rst 1-m2 area at the downstream end of the segment. A Because we expect biases due to digestibility of prey types seine net was placed perpendicular to the stream fl ow, and to remain constant across time, and our analyses focus a 1-m 2 area of substrate upstream of the net was disturbed on relative diff erences among seasons, conclusions about so that all crayfi sh were washed into the seine net (Mather seasonal variation in mink diet should be robust. and Stein 1993, Flinders and Magoulick 2003, Taylor and For the PO metric, we tested for variation in mink diet Soucek 2010). When necessary in low-fl ow areas, we dragged among seasons using χ 2-tests and ’ s exact tests when the seine net through the sampling area while disturbing the Ն 20% of the expected frequencies were Ͻ 5 (Zar 1984). substrate to collect crayfi sh. Upon collection, crayfi sh were For the VOL metric, we tested for between-season diff er- classifi ed as juvenile (Ͻ 15 mm carapace length) or adult, ences in diet composition using multi-response permuta- and adults were identifi ed to species. All crayfi sh species were tion procedures (MRPP – Mielke and Berry 2001, Roberts pooled for analyses. and Taylor 2008). We employed a Bonferroni correction We quantifi ed three habitat covariates that could be when making multiple, pair-wise seasonal comparisons correlated with crayfi sh density (Riggert et al. 1999, ( α ϭ 0.0167). An eff ect size ‘ A ’ was calculated to measure DiStefano et al. 2003): substrate particle size, number of the overall dietary agreement among scat samples within crayfi sh burrows, and number of woody debris accumula- the same season (McCune and Grace 2002, Roberts and tions. At each sampling location, we created a transect per- Taylor 2008). Within-season homogeneity of scat samples pendicular to the stream fl ow. We dropped a metal rod at is greater than expected by chance when A Ͼ 0, equal when 1-m intervals for the entire wetted width of the stream and A ϭ 0, and less when A Ͻ 0 (Roberts and Taylor 2008). measured the substrate particle size that the rod landed on Th e reptile prey class occurred in only one scat sample using a gravelometer. Th ese particle sizes were averaged to during fall, and was excluded from analyses of VOL. Th e create one measure of substrate particle size per 10-m seg- MRPP analysis was conducted in PC-ORD 6.0 (McCune ment of stream. Th e number of active crayfi sh burrows was and Meff ord 2011). counted in each 10-m segment of stream. We also counted

11 Downloaded From: https://bioone.org/journals/Wildlife-Biology on 10 Jun 2020 Terms of Use: https://bioone.org/terms-of-use the number of woody debris accumulations anchored in the within-stream and landscape covariates. We did not include streambed in each segment of stream. two covariates in the same model if they were correlated at r Ն 0.60. We ran this candidate set alone and with each of Detection and occupancy covariates the three measures of crayfi sh density (48 total models), and ranked models using Akaike ’ s information criterion (AIC; We recorded covariates that could infl uence detection (p) of Burnham and Anderson 2002). Akaike weights (wi ) were mink sign including observer, Julian date, number of days summed for models that contained each of the three mea- since rain, and recent rainfall – total rainfall (cm) for the sures of crayfi sh density to determine (using across-model seven days prior to each survey (Illinois State Water Survey, inference) which measure of prey abundance was the best station no. 118740, Urbana, Illinois). Recent rainfall could predictor of occupancy by mink. wash away sign or raise water levels to hide sign (Schooley et al. 2012). We measured covariates for occupancy including crayfi sh Results density, water depth (m), average riparian buff er width (m), degree of urbanization (0– 1), drainage area (km2 ), wetted Seasonal diet width (m), and stream order (1 – 5). Water depth (thalweg) and riparian buff er width were measured at 50-m intervals We analyzed 103 scat samples (fall ϭ 17, winter ϭ 43, sum- (fi ve values) and averaged for each site (Cotner and Schooley mer ϭ 43). Crayfi sh and mammals were the most common 2011). Drainage area, wetted width, and stream order were diet items for mink throughout the year, occurring in Ͼ 76% correlated positively (all r Ͼ 0.47), so we used principal of all scats and comprising Ͼ 83% of the diet by volume. components analysis (PCA) to create orthogonal principal However, percentage of occurrence of diet items diff ered components (PC). Th e fi rst PC (sizePC) explained 75.9% among seasons (χ 2 ϭ 23.7, DF ϭ 12, p ϭ 0.02). Cray- of the variation and was correlated positively with all three fi sh occurred most frequently in summer (Fisher ’ s exact variables (r ϭ 0.74 – 0.94), so we used sizePC as a measure test: winter – summer, p ϭ 0.003; fall – summer, p ϭ 0.016; of stream size in our models (Cotner and Schooley 2011). Fig. 1), when PO of mammals was lowest (Fisher’ s exact test, We excluded water depth from the PCA because small streams fall – summer, p ϭ 0.025; Fig. 1). PO of mammals in the diet have dynamic fl ow regimes tied to local precipitation events; was greatest during fall and winter (Fig. 1). occurred in they fl ood and subside faster than do large streams (Ahlers 35.9% of scats, 8.1% by volume. PO of fi sh increased from et al. 2010). Th us, small streams can have deep waters dur- fall to winter (Fisher’ s exact test p ϭ 0.076), and both PO ing a sampling period because water depth is infl uenced by (44.2%) and VOL (17.5%) were greatest in winter. more than stream size. In addition, water depth alone can occurred in 22.3% of scats, but only comprised 3% of the be a predictor of site occupancy and colonization by mink total scat volume. Similarly, insects occurred commonly in (Schooley et al. 2012). the diet (PO ϭ 48.5%) but made up a small percentage of Crayfi sh were patchily distributed within sites, so we the dietary volume (3.6%). Th e unknown prey class was evaluated three measures of crayfi sh density. For each site, most likely the remains of mammals and birds but could we calculated average density of adult crayfi sh (adults mϪ 2 ), have included other prey. Based on PO, the con- average density of total crayfi sh (adults ϩ juveniles mϪ 2 ), tribution of the unknown prey class to the diet did not diff er and presence– absence of a crayfi sh ‘ hotspot ’ . A site was con- between seasons (p Ͼ 0.72 for all seasonal comparisons). sidered to contain a prey hotspot if Ն 1 of the 20 kick-seine MRPP results for the VOL metric showed that diet samples had a total crayfi sh density in the top 15% (Ն 1 5 composition for summer diff ered from diet composition crayfi sh mϪ 2 ; n ϭ 171) of all sampled crayfi sh densities for fall and winter (fall– winter, A ϭ 0.010, p ϭ 0.15; winter – (median ϭ 2 crayfi sh m Ϫ2 , n ϭ 1,180; Supplementary mate- summer, A ϭ 0.098, p Ͻ 0.001; fall – summer, A ϭ 0.155, rial Appendix 1 Fig. A1). Although mink are likely to prey p Ͻ 0.001). MRPP results highlight the greater contribution only on larger, adult crayfi sh, we used total crayfi sh density to defi ne hotspots because mink could use the presence of juvenile crayfi sh as an indicator of where adults occur. B A 100 AB Fall A A Winter A Occupancy modeling 80 B A Summer

We estimated the probability of site occupancy (ψ ) by mink 60 A A B using single-season occupancy models that accounted for 40 A A B A imperfect detection (MacKenzie et al. 2006) in Program AB A A 20

PRESENCE 5.8 (Hines 2006). We fi rst selected the best Percentage of occurrence

model for detection (p), and then modeled occupancy. We 0 used a maximum of two covariates for p and four covari- Crayfish Mammal Fish Bird Insect Reptile Unknown ates for ψ in a single model to avoid over-parameterization. Prey class We developed a candidate set of 12 occupancy models that Figure 1. Mean percentage of occurrence ( ϩ 1 SE) of seven included within-stream covariates (stream size, water depth) prey classes in the diet of American mink in fall 2011 (n ϭ 17 scat and landscape-level covariates (riparian buff er width, degree samples), winter 2012 (n ϭ 43), and summer 2012 (n ϭ 43). of urbanization). Th e candidate set contained models that Within a prey class, bars with diff erent letters indicate diff erences tested each covariate separately, and also combinations of among seasons (Fisher ’ s exact tests).

12 Downloaded From: https://bioone.org/journals/Wildlife-Biology on 10 Jun 2020 Terms of Use: https://bioone.org/terms-of-use Fall Winter Summer 2% 2% 3% 2% 1% 1% 13% 3% 1% 4% 26% 35% 17% 2% 19% 2%

44% 49% 74%

Figure 2. Volume percentage of seven prey classes in the diet of American mink in fall 2011 (n ϭ 17 scat samples), winter 2012 (n ϭ 43), and summer 2012 (n ϭ 43).

of crayfi sh to the diet in summer than in fall and winter p ϭ 0.78). Total crayfi sh density was not related to sub- (Fig. 2). Repeating the analysis excluding the unknown prey strate (R2 Ͻ 0.001, p ϭ 0.52), but was related weakly and class did not qualitatively aff ect MRPP results. negatively to crayfi sh burrows (R2 ϭ 0.005, p ϭ 0.02) and Based on the PO metric, the diversity indices for woody debris accumulations (R2 ϭ 0.008, p Ͻ 0.01). Cray- mink diet did not diff er strongly among seasons (Table 1; fi sh hotspots were not associated with substrate (R2 ϭ 0.003, Shannon diversity index: fall– winter, t ϭ 0.45, DF ϭ 86, p ϭ 0.92) or crayfi sh burrows (R2 ϭ 0.007, p ϭ 0.64), but p ϭ 0.65; winter – summer, t ϭ 1.03, DF ϭ 211, p ϭ 0.31; were associated negatively with woody debris accumulations fall – summer, t ϭ 0.19, DF ϭ 70, p ϭ 0.85). Based on the (R 2 ϭ 0.164, p ϭ 0.056). VOL metric, summer diets of mink had the lowest evenness, Mink sign was detected at 18 of 59 sites (na ï ve occu- narrowest niche breadth, and lowest diversity (Table 1). Diet pancy ϭ 0.305). We decided the best model for detection of mink was least diverse during summer when they focused was the competitive model ranked second (Δ AIC ϭ 0.14; on crayfi sh (Shannon diversity index: fall-winter, t ϭ 0.19, Table 2), and we used this model for subsequent evaluation DF ϭ 91, p ϭ 0.85; winter – summer, t ϭ 4.00, DF ϭ 244, of occupancy covariates. Th e top detection model contained p Ͻ 0.001; fall – summer, t ϭ 3.23, DF ϭ 103, p Ͻ 0.01). observer alone, but adding rainfall to that model improved the log likelihood substantially (Table 2). Detection (p) was β ϭ ϭ Summer habitat selection related positively to rainfall ( rainfall 0.652, SE 0.501). Akaike weights (wi ) summed across occupancy models A total of 7798 crayfi sh of four species (Orconectes virilis , indicated that among the three measures of crayfi sh abun- Orconectes propinquus , Procambarus acutus , Cambarus spp.) dance, the best predictor of site occupancy by mink was were captured (2068 adults, 5730 juveniles). Average densities presence of a hotspot (hotspot w ϭ 0.821, total crayfi sh Ϫ i of adult crayfi sh per site ranged from 0 to 16.8 crayfi sh m 2 density w ϭ 0.100, adult crayfi sh density w ϭ 0.032). Ϫ i i (median ϭ 0.45 crayfi sh m 2 ), and total crayfi sh densities All competitive models ( Δ AIC Ͻ 2) contained crayfi sh Ϫ per site ranged from 0 to 41.8 crayfi sh m 2 (median ϭ 2.55 hotspot as a covariate (Table 3). Occupancy probability was Ϫ crayfi sh m 2 ). Crayfi sh hotspots were present at 20 of 59 sites related positively to the presence of a crayfi sh hotspot at a (33.9%). None of the three measures of crayfi sh abundance β ϭ ϭ site ( hotspot 1.721, SE 0.625; Fig. 3). Estimated occu- were strongly associated with the four habitat covariates used pancy from the hotspot model was 0.562 for sites with in occupancy modeling (all p Ͼ 0.05, Wolff 2013). Adult crayfi sh hotspots, and 0.187 for sites without hotspots. crayfi sh density was associated weakly and negatively with Site occupancy by mink was related negatively to stream substrate particle size (R2 ϭ 0.006, p ϭ 0.02) and the num- β ϭ ϭ size ( sizePC – 0.598, SE 0.463; Fig. 3) and urbanization ber of woody debris accumulations (R2 ϭ 0.004, p ϭ 0.03), but not with the number of crayfi sh burrows (R2 Ͻ 0.001, Table 2. Ranking of detection (p) models for American mink in Illinois based on Akaike ’ s information criterion (AIC). Detection covariates included observer, Julian date, days since rain, and Table 1. Three indices summarizing the diet of American mink rainfall for seven days prior to each survey (rainfall). Δ AIC ϭ AIC ( Neovison vison ) across three seasons: fall 2011, winter 2012, and for a given model minus AIC for the top model. K ϭ number of ϭ summer 2012, in Illinois, USA. Indices were calculated both using model parameters, w i Akaike weights, and LL is the log-likelihood. percentage of occurrence data and volume percentage data. Models better than the intercept-only model are presented. ϫ Percentage of Model Δ AIC wi K – 2 LL occurrence Volume ψ (.), p(observer) 0 0.2849 3 103.34 Index Fall Winter Summer Fall Winter Summer ψ (.), p(observer, rainfall) 0.14 0.2657 4 101.48 Shannon 1.65 1.60 1.68 1.30 1.27 0.81 ψ (.), p(observer, days since rain) 0.83 0.1881 4 102.17 diversity index ψ (.), p(observer, Julian date) 1.58 0.1293 4 102.92 Evenness 0.85 0.90 0.93 0.67 0.71 0.45 ψ (.), p(observer, Julian date, days 2.80 0.0703 5 102.14 Food niche 4.49 4.38 4.77 2.96 2.92 1.70 since rain) breadth ψ (.), p(.) 3.06 0.0617 2 108.40

13 Downloaded From: https://bioone.org/journals/Wildlife-Biology on 10 Jun 2020 Terms of Use: https://bioone.org/terms-of-use Table 3. Ranking of occupancy models for American mink in Illinois based on Akaike ’ s information criterion (AIC). Detection covariates included observer and rainfall for the seven days prior to each survey (rainfall). Occupancy covariates included presence-absence of a crayfi sh hotspot (hotspot), stream size (sizePC), water depth, and degree of urbanization. Δ AIC ϭ AIC for a given model minus AIC for ϭ ϭ Ͻ the best model. K number of model parameters, wi Akaike weights, and LL is the log-likelihood. Competitive models (Δ AIC 2) and the intercept-only model without occupancy covariates are presented. ϫ Model Δ AIC w i K – 2 LL ψ (hotspot, sizePC), p(observer, rainfall) 0 0.1533 6 91.28 ψ (hotspot), p(observer, rainfall) 0.12 0.1444 5 93.40 ψ (hotspot, urbanization), p(observer, rainfall) 0.62 0.1124 6 91.90 ψ (hotspot, sizePC, urbanization), p(observer, rainfall) 0.74 0.1059 7 90.02 ψ (hotspot, depth), p(observer, rainfall) 1.87 0.0602 6 93.15 ψ (hotspot, sizePC, depth), p(observer, rainfall) 1.87 0.0602 7 91.15 ψ (.), p(observer, rainfall) 6.20 0.0069 4 101.48

β ϭ ϭ ( urban – 1.793, SE 1.606; Fig. 3). Although water depth during summer by American mink within a human- occurred in competitive occupancy models (Table 3), water dominated landscape. Mink seemed to select sites based depth did not substantially increase model fi t based on log directly on high prey concentrations instead of habitat char- likelihoods; the top two models were essentially unchanged acteristics that might indicate high prey densities. Mink by including depth as a covariate. In addition, the model shifted strongly to feeding mainly on crayfi sh during sum- with depth alone performed worse than the intercept-only mer, and mink were more likely to occupy stream segments model. Riparian buff er width also was not a good predictor that contained crayfi sh hotspots. Crayfi sh hotspots were a of site occupancy. far better predictor of occupancy by mink than were aver- age crayfi sh densities. Habitat occupancy by mink was also aff ected by stream and landscape characteristics; mink were Discussion associated negatively with both stream size and urbanization. Th e spatial variation in abundance of a common prey, To increase the chance of encountering prey and maxi- crayfi sh, was the primary predictor of habitat selection mizing energetic gains in a patchy environment (MacArthur and Pianka 1966, Charnov 1976), predators may either select prey habitat or select locations most used by prey (a) 1 (Flaxman and Lou 2009, Keim et al. 2011). Mink in our 0.9

) study appeared to select foraging habitat based on locations ψ 0.8 most used by prey – hotspots with high densities of adult 0.7 and juvenile crayfi sh. Given that crayfi sh density was not 0.6 highly correlated with any measured habitat variable, mink 0.5 0.4 likely cue directly on crayfi sh. Th is tactic may be particularly 0.3 eff ective in human-dominated landscapes in which stream 0.2 habitat structure is altered due to channelization, habitat 0.1 heterogeneity is low, and prey distribution might mostly 0 refl ect spatial population dynamics (i.e. crayfi sh distributions –1 0 1 2 3 4 may be patchy due to demographic processes instead of dif- Stream size (sizePC) ferences in habitat quality). In our study, habitat selection by (b) 1 mink also was infl uenced by urbanization. Mink had lower 0.9 occupancy probabilities in urban areas (Fig. 3). Th e ability of ) probability ( Occupancy

ψ 0.8 mammalian carnivores to adapt to urban areas is infl uenced 0.7 by characteristics such as body size, reproductive potential, 0.6 diet, behavior, and habitat requirements (Gehrt et al. 2010). 0.5 Urbanization fragments natural habitats (McKinney 2002), 0.4 which can cause the decline or local of carnivore 0.3 species (Crooks 2002). Roads and human development act 0.2

Occupancy probability ( as barriers to dispersal (Forman and Alexander 1998, Riley 0.1 et al. 2006) and increase mortality risk from vehicle 0 0 0.2 0.4 0.6 0.8 1 collisions (Tigas et al. 2002, Dickson et al. 2005). However, Urbanization little information exists regarding the eff ects of urbanization on mink (Gehrt et al. 2010). In , cottage develop- Figure 3. Relationships between probability of site occupancy by ment around lakes reduced habitat heterogeneity, decreased American mink and (a) stream size from the top occupancy model, and (b) urbanization from the 3rd best occupancy model (Table 3). mink activity, and altered diets of mink (Racey and Euler Gray triangles indicate estimated occupancy at sites with crayfi sh 1983). Radio-marked American mink around Polish lakes hotspots. Gray diamonds indicate estimated occupancy at sites also avoided areas near human settlements (Brzezi ń ski et al. without crayfi sh hotspots. Black circles indicate naï ve occupancy 2012). Th ese studies were not set in urban areas, but for sites (1 ϭ occupied, 0 ϭ unoccupied). they demonstrate the negative impact of human disturbance

14 Downloaded From: https://bioone.org/journals/Wildlife-Biology on 10 Jun 2020 Terms of Use: https://bioone.org/terms-of-use typical of urban areas on the behavior of mink, and agree values indicate greater dietary specialization on crayfi sh with our result. during summer. Th is increased contribution of crayfi sh to Contrary to our prediction, site occupancy for mink the diet appears to drive the habitat selection behavior of was related negatively to stream size (Fig. 3). We expected mink. Some species of crayfi sh move into deeper water and mink to select larger streams because we assumed that larger severely reduce activity at low water temperatures (Aiken streams had more available resources, and we do not have an 1968). Th is behavior may make crayfi sh less available to ter- explanation for this surprising result. In the invasive range, restrial and semiaquatic predators during winter. Conversely, Zuberogoitia et al. (2006) found the use of small versus large fi sh increased in frequency and volume in the diet from fall streams by radiomarked mink was sex-dependent; females to winter. Other diet studies of mink have observed increased used small streams and males used large streams. Unfortu- fi sh consumption during winter as a result of increased vul- nately, we cannot assess sex-dependent habitat selection with nerability of fi sh (Gerell 1967, Magnusdottir et al. 2012). our occupancy surveys. Low water temperatures decrease the mobility of fi sh, We did not detect a strong, positive relationship between making them more vulnerable to predation (Parsons and occupancy probability for mink and water depth in contrast Smiley 2003, Brown et al. 2011). Our study area experienced to previous research in our study area (Schooley et al. 2012). an unseasonably mild winter during 2012. January and Our results may diff er because of much less variable water February temperatures in Illinois were 3.67° C and 2.61 ° C levels during our study, which took place during the severe warmer than average, respectively (Illinois State Water Survey drought of 2012. In contrast, Schooley et al. ’ s (2012) study 2012b, 2012c). Th us, many streams did not freeze over, but included a year with extreme fl ooding (2008 was 2nd wet- were still cold enough to decrease the mobility of fi sh, thus test year on record; Changnon and Black 2009). Diff erent allowing mink constant access to vulnerable fi sh prey. factors may infl uence mink habitat selection under diff erent Our results have implications for the effi cacy of habitat environmental conditions. Schooley et al. (2012) noted that models for species management. For instance, Loukmas and colonization of vacant sites was variable if water depths were Halbrook (2001) concluded that the poor performance of a Յ 0.4 m, but consistently high if water depths were Ͼ 0.4 habitat suitability model for mink was primarily due to lack m. Seventy-one precent (42 of 59) of sites during our study of habitat variables for key prey. If mink cue in on their prey had water depths below 0.4 m. Th us, we acknowledge that directly, then improving model performance will be diffi cult our results from this severe drought year may not extend to if habitat variables are used as surrogates for prey abundance. years of high precipitation. However, because climate mod- However, quantifi cation of prey abundance can be diffi cult, els for the Midwestern United States predict an increase in time-consuming, and expensive, especially for a generalist the frequency of summer drought and spring fl ooding events predator that consumes a variety of prey items. Our approach (Wuebbles and Hayhoe 2004, Trenberth 2011), our study may of using seasonal diet data to identify key prey and direct off er a glimpse into occupancy dynamics under climate change. resource measurement could be the most effi cient strategy for Mink and other semiaquatic mammals are likely to experience developing predictive habitat models for predators. increased environmental stochasticity that could create tempo- Our combined results for diet and occupancy modeling ral variability in factors that infl uence habitat selection. indicate the spatial distribution of mink during summer We measured a range of habitat variables typically is greatly infl uenced by the abundance patterns of their used for assessing crayfi sh abundance (Riggert et al. 1999, preferred prey. Our data support recent evidence from Europe DiStefano et al. 2003), but mink could have keyed in on suggesting that availability of crayfi sh, and an increased an unmeasured habitat factor. For example, submerged prevalence of crayfi sh in the diet of American mink, may vegetation provides protective cover for crayfi sh (Kershner alter the spatial distribution of mink and aid in expansion and Lodge 1995) and reduces predation risk from aquatic of mink across its invasive range (Melero et al. 2014, Rodri- and terrestrial predators (Wolff 2013). We did not measure gues et al. 2014). Further examination of this predator – prey submerged vegetation cover at crayfi sh hotspots in this study. interaction in could lead to a better under- However, we performed a post hoc analysis using submerged standing of mink ecology in places where the co-occurrence vegetation data collected between July and September 2012 of mink and crayfi sh is novel. More generally, the importance at our study sites (Wolff 2013) to examine the relationship of prey hotspots merits further investigation to understand between submerged vegetation cover, crayfi sh hotspots, the mechanisms underlying habitat selection by carnivores. and site occupancy (Supplementary material Appendix 2). Crayfi sh hotspots were associated positively with submerged vegetation cover, but submerged vegetation did not explain Acknowledgements – Th is research was funded by the Illinois Dept much variation (R2 Ͻ 0.17). Moreover, submerged veg- of Natural Resources, State Furbearer Fund. We appreciate help etation cover was a poor predictor of site occupancy (worse from B. Bluett with initiating the project. We thank A. Mathis, M. than intercept-only model) compared to crayfi sh hotspots Olsta, J. Osbourne, B. Berry, S. Beyer, J. Nawrocki, L. Pritchard, B. Hays, A. Deitch and A. Ahlers for fi eld and lab assistance. We also (Supplementary material Appendix 2 Table A1). Th us, we thank the many landowners that granted us access to their property. found little evidence that mink recognize areas with high cover of submerged vegetation as good locations to hunt. Th e diet of mink in our study refl ected the seasonal References availability of prey. Although crayfi sh and mammals were important year-round food sources for mink, crayfi sh Ahlers, A. A. et al. 2010. Eff ects of fl ooding and riparian buff ers occurred most frequently and in the highest volume in the on survival of (Ondatra zibethicus ) across a fl ashiness summer diet. Low diversity, evenness and niche breadth gradient. – Can. J. Zool. 88: 1011 – 1020.

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Supplementary material (available online as Appendix wlb.00031 at Ͻ www.wildlifebiology.org/readers/appendixϾ ). Appendix 1 – 2.

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