Tracking animal movement by comparing trace element signatures in ...

Science of the Total Environment 468–469 (2014) 699–705

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Tracking animal movement by comparing trace element signatures in claws to spatial variability of elements in soils Danielle M. Ethier a,⁎, Christopher J. Kyle b, Joseph J. Nocera c a b c

University of Guelph, 50 Stone Road East, Guelph, Ontario N1G 2W1, Canada Natural Resources & DNA Profiling Forensic Centre, Trent University, DNA Building, 2140 East Bank Dr., Peterborough, Ontario K9J 7B8, Canada Ontario Ministry of Natural Resources, Trent University, DNA Building, 2140 East Bank Dr., Peterborough, Ontario K9J 7B8, Canada

H I G H L I G H T S • • • •

Trace element signatures in soils vary locally and broadly. Chemical profiles in claw keratin can be linked to the surrounding environment. Results provide evidence that movement can be discerned from claw chemistry. Element profiles in tissues could be used to assess geographic origin of animals.

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Article history: Received 5 June 2013 Received in revised form 23 August 2013 Accepted 27 August 2013 Available online 21 September 2013 Editor: Filip M.G. Tack Keywords: Animal movement Trace elements Element base-map sPCA American badgers Taxidea taxus jacksoni

a b s t r a c t Biogeochemical markers in ecology have provided a useful means for indicating geographic origin and movement patterns of species on various temporal and spatial scales. We used trace element analysis to resolve spatial and habitat-specific environmental gradients in elemental distributions that could be used to infer geographic origin and habitat association in a model terrestrial carnivore: American badger (Taxidea taxus jacksoni). To accomplish this, we generated element base-maps using spatial principal component analysis, and assessed habitat-specific signatures using multivariate statistics from soil element concentrations in southwestern Ontario, Canada. Using canonical correlation analysis (CCA) we also test whether element variability in the claw keratin of a terrestrial carnivore could be explained by the chemical variability in the soils of the local environment. Results demonstrated that trace element signatures in soils vary locally with land use practices and soil texture type and broadly with the underlying geology. CCA results suggest that chemical profiles in claws can be linked to the surrounding chemical environment, providing evidence that geographic patterns in mammalian movement can be discerned on the basis of claw chemistry. From this, we conclude that geographic assignment of individuals based on element profiles in their tissues and referenced against soil elemental distributions would be coarse (at a spatial scale of 100–1000 km, depending on the chemical heterogeneity of the landscape), but could be used to assess origin of highly mobile animals or habitat association of individuals. Compared to stable isotope analysis, the assessment of trace elements can provide a much greater level of detail in backcasting animal movement pathways. © 2013 Elsevier B.V. All rights reserved.

1. Introduction The movement of animals over space and time is a crucial component of almost all ecological and evolutionary processes (Nathan, 2008). Movement defines an individual's interaction with its surrounding environment, thus influencing the resources it encounters and the space it occupies. However, researchers are often limited in their ability to study movement patterns, especially for species that are rare, cryptic, or long-distance migrants (Chadès et al., 2008). Therefore, our understanding of movement by such species is typically indirect and incomplete. Various extrinsic (e.g., mark-recapture and radio⁎ Corresponding author. Tel.: +1 226 923 9206. E-mail addresses: [email protected] (D.M. Ethier), [email protected] (C.J. Kyle), [email protected] (J.J. Nocera). 0048-9697/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.scitotenv.2013.08.091

telemetry) and intrinsic (e.g., genetics) makers have provided valuable insight on ecological relationships and movement histories of species, but are often burdened by low sample sizes, prohibitive financial costs, or significant logistical barriers (Webster et al., 2002). These methods also carry their own set of limitations regarding their assumptions and temporal signatures, which can therefore be used jointly to get a more complete picture of a species movement ecology. Because of this, there has been increasing interest in alternative methods for tracking animal movement, such as using intrinsic chemical indicators. Stable isotopes have been used to determine migratory connectivity in a variety of taxa, including birds, mammals, fishes, and insects (e.g., Kelly et al., 2002; McCarthy and Waldron, 2000; Rubenstein and Hobson, 2004; Vogel et al., 1990; Wassenaar and Hobson, 1998), where movement is reflected in differences in the isotopic composition

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of their tissues. More recently, the application of trace element analysis has provided an additional geographic marker (Burger et al., 2001; Donovan et al., 2006; Gómez-Diaz and González-Solis, 2007; Kelsall and Calaprice, 1972; Kelsall and Burton, 1979; Hanson and Jones, 1976; Parrish et al., 1983; Norris et al., 2007; Ramos et al., 2009; Szép et al., 2003; Torres-Dowdall et al., 2012). Similar to stable isotopes, trace elements are incorporated into animal's tissues during growth, in proportion to local environmental concentrations (Driessens and Verbeeck, 1990; Gartner, 1989; Szép et al., 2003). These environmental concentrations are influenced by the surface geology, soil, vegetation, and anthropogenic chemical inputs in a particular area (Bortolotti et al., 1989; Szép et al., 2003). Therefore, information on environmental condition and habitat association are integrated into animal's tissues over the period the tissues are synthesized (Bearhop et al., 2002; Chamberlain et al., 1997; Hobson and Clark, 1992; Hobson and Wassenaar, 1997). If a tissue is selected that is chemically inert once formed (e.g., shells, otoliths, hair, claws) and is grown incrementally (i.e., deposited in layers on a daily, seasonal, or annual basis), shifts in an individual's foraging environment could be captured in the chemical changes in these tissues, acting as a time-integrated indicator of geographic origin. The blade horn keratin of mammalian claws has been identified as a likely reliable tissue for such time-series analysis, as this portion of the claw is deposited linearly and is uncomplicated by the mixing of old and new keratin layers along its length (Ethier et al., 2010). Trace element analysis presents an advantage over stable isotopes because it allows us to retrieve precise element archival information from biological samples (Outridge et al., 1995) due to its fine-scale sampling resolution (e.g., a 30–100 μm diameter beam for laser ablation), low detection limits (conservatively, b1 ppm), and multi-elemental analytical capabilities (N40 elements, Szép et al., 2003). In addition, stable isotopes typically vary regionally (N 1000 km; Marra et al., 1998), whereas trace element signatures can vary in distinct patterns at relatively fine spatial scales (10–1000 km; Szép et al., 2003), allowing for a higher degree of spatial resolution for geographic assignment. Despite these advantages, some research has suggested that trace element signatures show no geographic gradients in their distribution (Donovan et al., 2006), unlike those of stable isotopes (e.g., δD; Hobson and Wassenaar, 1997), possibly limiting our ability to assess geographic origin of samples transported across large spatial scales. Having little a priori information on trace element variability in terrestrial systems, it is not surprising that these markers have not been used to study movement history in terrestrial mammals. In terrestrial systems, soil is the major source of biologically available elements (Kabata-Pendias and Mukherjee, 2007), yet spatial patterns in soil element concentrations are poorly defined. Although there is a considerable amount of literature on element behavior in soils (e.g., Sauvé et al., 2003) and the impact of point-source contamination (e.g., Nriagu et al., 1998), there is comparatively little information on ambient background trace element concentrations (McKeague and Wolynetz, 1980; Sheppard et al., 2009) and their relative spatial distributions (Atteia et al., 1994; Facchinelli et al., 2001; McGrath et al., 2004; Saby et al., 2009). How element concentrations vary with biogeographical gradients is unclear and data regarding patterns of variation are lacking (Gómez-Diaz and González-Solis, 2007). Free of human interference, the element content of the soil is largely dependent on the parental rocks from which the soil was derived through weathering (Kabata-Pendias and Mukherjee, 2007). In highly altered agro-ecosystems, other factors, including the input of fertilizers, pesticides, sewage effluents, and biosolids (e.g., animal wastes, paper pulp sludge) can affect element and macronutrient concentrations (Kabata-Pendias and Mukherjee, 2007). Ideally, to indicate an animal's origin and track movement, a map of elemental signatures should be developed for the area in which the tissue was synthesized (Donovan et al., 2006). Mapping of multiple chemical signatures has been used to interpret natural and anthropogenic factors affecting their distribution (e.g., Fong et al., 2008; Saby et al., 2009; Tao, 1996), but such effects have yet to be applied to biogeochemical application in wildlife ecology.

Here we address the issue of geographic gradients in trace elements in relation to animal movement by examining the spatial distribution and habitat-specific patterns of soil trace element signatures across a terrestrial agro-ecosystem of southwestern Ontario to facilitate the determination of geographic origin of a model terrestrial carnivore. We approached this investigation by first quantifying the spatial variation in elements to generate “base-maps” of elemental occurrences in soil. Element base-maps have been generated in few ecological studies and have, until now, been limited to stable isotopes (e.g., Hobson and Wassenaar, 1997). We also used multivariate statistics to describe the relationships between soil element composition and various treatments (i.e., land use practice and soil texture type) since element profiles are suggested to be site-specific, reflecting habitat characteristics rather than reflecting broad-scale geographic gradients (Bortolotti et al., 1989; Donovan et al., 2006). Our objectives were to assay soils so as to: (1) quantify the relative variability among 11 elements (Ca, Mg, K, Al, Ba, Cr, Cu, Fe, Mn, Sr, and Zn), (2) determine how environmental factors influence their distribution, and (3) describe the spatial relationships of these elements. To determine if element base-maps can be useful for geographic assignment, we quantify the level of association between trace element signatures in the claws of a model terrestrial carnivore and those from the soils of the local environment. This work follows that of Ethier et al. (2013), where trace element signatures in the blade horn keratin of American badger (Taxidea taxus jacksoni) claws could be attributed, in part, to endogenous uptake of trace chemicals from the local environment. Given these research findings, the logical next step in this field of study is to determine if these claw chemical signatures are in fact associated with those of the local soils. We conducted this study in the summer of 2009 in the agriculturally dominated landscape of southwestern Ontario, Canada (Fig. 1). Our sampling structure was designed to create a trace element base-map to compare to the elemental constitution of claws from an endangered population of American badgers. Badgers are difficult to study using conventional methods due to their secretive nature, low population densities, and nomadic behavior. The application of trace element analysis, therefore, lends itself well to the study of this elusive carnivore. Badgers maintain large territories (range 13–513 km2; Newhouse, 1998), have a high dispersal capability (upwards of 100 km; Messick and Hornocker, 1981), and broad foraging niche (Azevedo et al., 2006) making it highly probable that an individual will encounter a variety of chemically distinct environments throughout the course of its daily, seasonal, or annual cycle. 2. Materials and methods 2.1. Soil sampling We selected study sites to facilitate corresponding element analysis of claw samples collected from American badgers. Therefore, we concentrated soil sampling around collection locations of eleven male (sampling radius = 10 km, area = 314 km2), nine female (sampling radius = 5 km, area = 78.5 km2), and five un-sexed (sampling radius = 10 km, area = 314 km2) archival badger specimens, with site centroids on the location of claw collection (Fig. 1). Sampling diameters were selected to approximate home-range sizes for male (292–513 km2) and female badgers (2–82 km2) based on published literature (Newhouse, 1998). Within each study site, we selected 21 points randomly, of which a range of 6–17 was sampled based on site access and landowner permission. At each sampling point, we collected 20 soil profiles from a 10 m × 10 m area, which were pooled to form a composite for each sampling point (de Zorzi et al., 2008). We sampled the subsoil (10–20 cm depth) to reduce the effects of surface contaminants and variable organic content in the topsoil on ambient element concentrations (Sastre et al., 2001). We collected and analyzed a total of 367 (pooled to 25 study sites) soil samples. At each sampling point, we recorded additional information on land use practice and soil texture. Land use was divided broadly into five categories: forest

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Fig. 1. Map of southwestern Ontario indicating soil sampling study sites and soil sampling points. Study site centroids are over the collection location for American badger claw samples, where large study sites (10 km diameter) and small study sites (5 km diameter) reflect projected home range size for male and female badgers, respectively.

(woodlands with trees predominantly N15 years old), disturbed (e.g., fallow fields, field edges, and early successional forest with trees b15 years old), livestock, urban lawns, and agriculture (all crops and orchards). We determined soil texture in the field using a standardized soil characterization protocol based on particle size analysis (GLOBE®, 2005), which grouped soils into three texture types: clay, loam, and sand. 2.2. Soil analysis We analyzed soil samples for 11 elements of putative importance: Ca, Mg, K, Mn, Cu, Fe, Zn, Sr, Al, Ba, and Cr. We selected elements based on preliminary lab analysis of element variability in badger claws using nebulisation inductively coupled plasma mass spectrometry (ICP-MS) at Queen's University Facility for Isotope Research, Kingston, ON. The elements selected here described 99.1–99.9% of the chemical variability of the claw on the first principal axis (principal component analysis (PCA; n = 5), unpublished data). Inductively coupled plasma atomic emission spectroscopy and ICP-MS were used to quantify element concentrations (ug/g — dry weight) in soils by Exova Accutest Laboratories (Ottawa, ON). Method reporting limits (R; ug/g) and uncertainty estimations (U; %) for trace metals are reported as follows (R, U): Mn (1, 18), Cu (1, 20), Fe (5, 25), Zn (5, 19), Sr (2, 19), Al (5, 28), Ba (1, 12), and Cr (1, 20).

ultrasonic bath, and pre-ablation (laser ablation performed at a reduced power) was used to clean the claws prior to ablation for data collection. We used a Thermo Scientific ELEMENT XR high-resolution inductively coupled plasma mass spectrometer (ICP-MS) equipped with a Newwave 213 nm NdYAG laser system to make all elemental measurements. Three parallel ablation transects were run from the base of the claw along the lateral surface of the blade horn keratin towards the distal end to collect keratin for element analysis. Due to a lack of a keratin-based calibration standard on which to base absolute concentration data, it was necessary to use a semi-quantitative technique in which we normalize the analyte intensity to that of an internal standard. For claws, sulphur is an appropriate internal standard since cysteine (a sulphur-containing amino acid) forms a major component of claw keratin (7.1 wt.% in hoof–horn; Zoccola et al., 2009). Following laser optimization, ablation scans were performed on a NIST glass standard reference material (SRM) and the claws to measure the isotope ion intensities (Ba, Mg, Cr, Mn, Fe, Cu, Zn, Sr, and K). We determined the trace element concentrations based on the relative ion intensities using the following formula developed by Seltzer and Berry (2005): h  io
 n 32 ðC E Þclaw ¼ ðC S Þclaw  ðIE Þ=ðIIS Þclaw  ½ð F S Þ=ð F E Þ  AðEÞ=A S ; where C = concentration (ppm); I = intensity (cps); A = percent abundance of isotope; F = relative sensitivity factor, E = element analyte; S = internal standard = 32S; claw = claw sample.

2.3. Claw analysis 2.4. Statistical analyses A detailed description of the claw trace element analysis can be found in Ethier et al. (2013). In short, claw samples were obtained from archival and road-killed badger specimens in southwestern Ontario, Canada with known collection coordinates (Fig. 1). A combination of surface scraping, washing with an organic solvent in an

2.4.1. Pre-data treatment We ensured the data met the necessary analytical assumptions prior to all multivariate and spatial analyses. We applied a Box–Cox power transformation (Box and Cox, 1964) on claw and soil data to better

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approximate a normal distribution and diminish the extremity of outliers, while reducing the relative distribution of the data so that elements with higher abundances (e.g., macronutrients) are brought into the same range as elements with lower abundances (e.g., trace metals). To improve the statistical power of the soil data analysis, we identified and removed outliers and reduced the data set to decrease multicollinearity of variables. Multivariate outliers were identified using squared robust Mahalanobis distances (MDi) when we plotted the observed data against the empirical distribution. We used a principal component analysis (PCA) based on ‘duality diagram matrix decomposition’ (see Escoufier, 1987) to reduce the number of soil variables into a smaller set of dimensions. We opted for the duality diagram over traditional PCA since it allows the introduction of a spatially weighted matrix W for multivariate spatial correlation analysis. 2.4.2. Soil multivariate analysis We sought to determine whether systematic variation in elements could allow for reliable back-classification of samples to certain land use practices and soil texture types. We used MANOVA to determine if environmental factor variables (land use practice and soil texture types) were associated with particular element compositions. To evaluate models, we used a robust version of the Wilks' lambda statistic by replacing the classical estimators with minimum covariance determinant (MCD) estimator (Rousseeuw, 1985; Todorov and Filzmoser, 2009a), where a finite sample distribution of the test statistic (lambda) is computed by simulation, with 1000 replicates, and compared to the corresponding approximate Bartlett's distribution. If the MANOVA was significantly different, we used an ANOVA (Bonferroni corrected α = 0.005) and Tukey's HSD post-hoc test (95% family-wise error) to identify which elements contributed to group differentiation. For subsequent analyses, we pooled groups that were not chemically distinct. We then used a robust linear discriminant function analysis (DFA) using MCD estimators (Todorov and Filzmoser, 2009b) to derive standardized linear combinations of element concentrations that best classify objects into groups, by maximizing between-group variance relative to withingroup variance. To evaluate the linear discriminant function, we determined the ability of the function to correctly classify unknown observations by using a leave-one-out cross-validation method. The prior probability of a group was set to be proportionate to its sample size. 2.4.3. Soil spatial analysis Describing the spatial correlation of elements (i.e., whether data close in space are more similar than those more distant) is necessary if they are to provide an indication of a particular geographic location (Donovan et al., 2006). We used sPCA scores to map linear combinations of elements. As described by Dray et al. (2008), sPCA introduces a spatially weighting matrix W into the PCA of matrix X; where W contains spatial information corresponding to some measure of spatial proximity. We used an inverse distance squared weighting connection network to derive the spatial weights. The advantage of sPCA over a regular PCA for spatial analysis is that sPCA scores maximize spatial autocorrelation, and therefore shows strong spatial structure on the first few principal axes, whereas PCA scores show spatial structure on any axis. We used a Monte-Carlo test, with 10,000 iterations, to check the statistical significance of the observed global structure. The sPCA scores obtained for the first two axes were then examined for spatial correlation using empirical semivariograms. We selected the optimal fitted semivariogram using the ‘autofitVariogram’ function in R package automap, which iteratively searches through various model types and parameter estimates to minimize the sum-of-squares between the observed data and the fitted model. If elements were spatially correlated, we used ordinary kriging to map predicted element concentrations. We used leave-one-out cross-validation to assess the accuracy of

spatial kriging. The goal is to have a standardized mean prediction error near 0, a small root-mean-squared prediction error, an average standard error near the root-mean-squared prediction error, and a standardized root-mean-squared prediction error near 1. We evaluate the sensitivity of our results to model assumptions by altering the model structure (i.e.,global trends and anisotropy) to assess whether error could be decreased. 2.4.4. Linking trace element profiles in claws to soils A canonical correlation analysis (CCA) was used to investigate the relationships between the elemental composition of claws and that of the soils around the collection location for each claw. Local environmental element concentrations were calculated by taking the median concentration of the soil samples from within the specified radius of an individual (5 or 10 km, depending upon sex). Median element signatures at the base of the claw (newest growth) were used to determine if claw trace element signatures could be associated with the local soils. We first determined if there was a relationship by examining the canonical correlation coefficients (Rc). We used asymptotic and permutation tests (1000 replicates) to assign statistical significance to the Wilks' lambda F-approximations of Rc. If we observed a significant relationship, canonical loadings for each significant Rc were examined to discern which elements contributed most to their respective canonical variates. We also assessed canonical cross-loading to see how each variable correlated with the opposite canonical variate. We then calculated a redundancy index to express how much variation in the claw data could be attributed to variation in the local soils (Butts, 2009). Analyses were conducted in R statistical software version 2.12.0 (R Development Core Team, 2008) with the following packages: mvtnorm (Genz and Bretz, 2009), pcaPP, robustbase, rrcov (Todorov and Filzmoser, 2009b), ade4 (Chessel et al., 2004), spdep (Bivand et al., 2008), CCA (González and Déjean, 2009), CCP (Menzel, 2009), YACCA (Butts, 2009), and automap (Hiemstra, 2010). Mapping and spatial kriging were done in ArcMapTM 9.3.1 (Copyright © 1999–2009 ESRI Inc.). 3. Results Summary statistics for soil and claw element concentrations are given in Table 1. We report the median and the median absolute deviation (MAD) in addition to the mean, since these measures are inherently more stable against outliers and deviations, and in most cases, give a more realistic value for location and spread (Reimann and Filzmoser, 2000). Iron was the most abundant trace element in soils (median ± MAD: 13700 ± 6820 ppm), whereas Cu (11 ± 7.4 ppm) and Cr (13 ± 7.4 ppm) were the least abundant. In claw samples, Mg (median ± MAD: 4808 ± 4441 ppm) and Zn (2081 ± 1615 ppm) were the most abundant elements, and Cr (34 ± 47 ppm) and Mn (48 ± 52 ppm) were the least abundant. Table 1 Nine trace elements quantified in American badger claws (Taxidea taxus) and eleven trace elements quantified in local soils sampled in southwestern Ontario, including: mean, median, and median absolute deviation (MAD). All element concentrations in parts per million (ug/g — dry weight). Claw

Ba Mg Cr Mn Fe Cu Zn Sr K Ca Al

Soil

Mean

Median

MAD

Mean

Median

MAD

499 9718 4717 163 3178 284 2143 210 1191 – –

155 4808 34 48 461 181 2081 84 518 – –

152 4441 47 52 425 110 1615 86 433 – –

51 3481 15 431 14520 12 62 18 865 8714 8765

44 2370 13 376 13700 11 37 13 664 3760 664

28 2136 7 255 6820 7 21 9 391 3410 2136

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3.1. Soil multivariate results and geostatistical interpolation Using the Kaiser criterion (Kaiser, 1960) we retained the first two components from the PCA, which had eigenvalues N1 and explained 83% of the total variation in the soil data. We used loadings to describe how a variable and component are related. Examination of the first component loadings indicated that Mg, K, Ba, Cr, Cu, Fe, and Al carried the same information and explained 71% of the overall variation. The second component showed strong loading on Ca, Mg, Sr, and Al, which explained an additional 12% of the overall variation. Element composition in soils varied significantly between land use practices (robust Wilks' λ = 0.71, χ2 = 92.5, p b 0.0001), and soil texture types (λ = 0.51, χ2 = 159.7, p b 0.0001). All trace elements contributed to treatment differentiation with the exception of Al in defining land use practices (ANOVA; df362,4, F = 3.304, p = 0.011). Sr had the greatest number of pair-wise differences for land use practices, with disturbed (median ± MAD; 23 ± 14.8 ppm), livestock (21 ± 12.1 ppm), and lawns (20 ± 14.1 ppm) having significantly greater concentrations (all p b 0.0001) than agricultural (11 ± 5.93 ppm) and forests (7 ± 5.93 ppm). K, Al, Ba, and Cr had the greatest number of pair-wise differences for soil texture types, with decreasing concentrations in all elements from fine particle soils (clay) to coarse soils (sand). ANOVA identified chemical differences between all treatments except disturbed, lawns, and livestock, which were subsequently recategorized. The probabilities of correctly classifying observations using linear discriminant analysis are shown in Table 2, where the predicted classification using cross-validation from the robust DFA is compared to the apparent or actual observation. The overall probability of correctly classifying land use practice was 62%, and for soil texture type was 75%. High rates of misclassification occurred between forests and agriculture (72%; Table 2a), and between loam and sand (55%; Table 2b). The Monte-Carlo permutation test of the sPCA scores was significant (p = 0.001) indicating that the global spatial structures exhibited by elements could not be attributed to random variation alone. The Matérn model best suited sPCA scores, having the lowest sum-of-squares for the first and second sPCA axes. Both variograms exhibited marked increases, indicating a high degree of spatial autocorrelation. A secondorder polynomial was fitted to remove the global trend surface so that kriging could focus on short-range variation in the data (Fig. 2), where the accuracy of the spatial kriging was assessed using diagnostic statistics from cross-validation. The mean prediction errors for both the 1st and 2nd axes – hereafter reported in this order – approached zero (−0.008 and −0.002) indicating that measurement error was unbiased

Table 2 Robust linear discriminant function analysis assignment results using leave-one-out cross validation. Values are the proportion of observations assigned to each land use practice (a) and soil texture type (b), where the predicted classifications are compared to the apparent or actual observation. (a) Predicted

Apparent

Prior probability of group Agriculture Disturbed Forest

Agriculture

Disturbed

Forest

0.50 0.88 0.49 0.72

0.34 0.12 0.49 0.24

0.16 0.00 0.00 0.04

Clay

Loam

Sand

0.22 0.70 0.39 0.05

0.15 0.00 0.61 0.28

0.63 0.31 0.55 0.92

(b) Predicted

Apparent

Prior probability of group Clay Loam Sand

703

(centered on the measurement values). The root-mean-squared prediction errors, which are small if predicted values are close to their true values, were 2.123 and 0.60 respectively. Since the average standard errors (±2.301 and 0.664) are close to the root-mean-square predictions, we assume that we correctly assessed the variability in the predictions. Lastly, the standardized root-mean-square prediction errors were close to 1 (0.92 and 0.90) indicating that the prediction standard errors are accurate. Changes in the model structure (e.g., anisotropy and global trends) did not decrease error. 3.2. Canonical correlation analysis results CCA revealed a significant relationship between the multivariate chemical profiles at the base of the claw and those of the local environment (Wilks' λ = 2.19e-05, F81,35 = 1.83, p = 0.02). Significant results could be attributed to the first canonical function. In general, loadings and cross-loadings for soil variables were much lower and more evenly dispersed relative to those associated with claws. In both claws and soils, Mn had the highest loading and cross-loading, indicating that this element contributed most substantially to the correlation of variable sets. Barium, Fe, and Zn also had relatively high loadings on the first canonical variate (N 0.40) for the claw data set. The redundancy index for the full CCA was 0.53, indicating that 53% of the variability at the base of an individual's claw could be attributed to element variation in the soils. For the first canonical function, the redundancy was 17%. 4. Discussion We have illustrated that the chemical profile of blade horn keratin of mammalian claws, in combination with element base-maps, may provide information on an animal's geographic location history. Element signatures in southwestern Ontario soils demonstrated marked variability, which could be partly attributed to habitat characteristics. In correctly assigning soil samples based on habitat characteristics, we achieved a success rate of 62–75% for the 11 elements we examined. Our success rate was modest, perhaps due to incomplete sampling design, indistinct boundaries between environmental factor variables, or the large amount of chemical variability due to agricultural practices in the province. Significant spatial autocorrelation of element profiles in soils could also have a masking effect on habitat specific variability. Spatial gradients in element concentrations were evident and were not a product of random variation. The first axis (Fig. 2a) indicated a high degree of association between element concentrations of Mg, K, Al, Ba, Cu, and Fe and the underlying physiography of southwestern Ontario, where low concentrations of the aforementioned elements were associated with the Norfolk sand plains. The second axis (Fig. 2b) had a more patchy spatial distribution, with elevated concentrations of Ca, Mg, Al, and Sr in the north-eastern portion of the study area. This spatial trend could also be attributed to the geology of the area, where the limestone plains of the Niagara escarpment, rich in CaCO3, appear to be driving chemical associations with the second principal axis. Even in this highly altered agro-ecosystem, it is apparent that element concentrations are, in part, influenced by the underlying geology of the landscape. There was a significant relationship between element profiles at the base of the claw and those in the surrounding environment, indicating that time-resolved chemical analysis of claw keratin could be used to assess movement pathways in terrestrial mammals. The canonical loadings and cross-loadings associated with the soil variables were low and more evenly distributed relative to those associated with the claws. This result indicates that soil elements contributed relatively equal to the significant canonical correlation. In claws, however, Mn, Ba, Fe, and Zn appear to drive the relationship between canonical variates. This result was expected given that claw chemical variability should be ascribable to element variability in the soils. The chemical which showed the greatest potential for assignment of American badgers to habitat type was Mn, since this element was

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Fig. 2. Kriging maps of Box–Cox transformed spatial principal component analysis scores for the first (a) and second (b) principal axes. Trace element concentrations of Mg, K, Al, Ba, Cr, Cu, and Fe load heavily on the first principal axis, whereas Ca, Mg, Al, and Sr load heavily on the second principal axis.

chemically distinct between treatments, was not affected by spatial autocorrelation (i.e., underlying geology), and had a high canonical loading in the claws. The chemicals which showed promise for assignment of badgers to broad-scale geographic origin based on underlying geology were Ba and Fe. These elements weighted heavily on the first principal axis in the sPCA and had a high canonical loading in the claws. Since chemical variability in soils is being driven by both habitat characteristics and underlying geology, it would be difficult in Ontario's highly altered agro-ecosystem to assign badgers unequivocally to geographic location given the data presented here. However, these results do highlight that given some refinement of this technique, chemical base-maps could be a valuable tool used to study animal movement in terrestrial systems. The question becomes whether the goal of the research is to assign individuals to location based on underlying geographic gradients or finer-scale investigation of habitat-use, which will dictate how soil samples are collected and which elements are evaluated. By adding additional trace elements to the soil analysis, model discriminatory ability for habitat type should be improved. For example, the inclusion of Pb, Cd, Sb, and Ti, which function as good urban contamination indicators (de Miquel et al., 1997), would likely better identify areas of high human population densities and industrial activities. The inclusion of Hg might be appropriate in regions where pulp and paper mills are common (Hakeem and Bhatnagar, 2010). In selecting elements for analysis, researchers need to consider the natural and anthropogenic sources, and weigh the benefits of adding elements with the analytical costs. Consideration should also be given to the behavior of the species for which movement patterns are sought and the potential source of elements that they will encounter. Soil sampling intensity can affect the degree of spatial resolution achieved with kriging for element base-maps (Saby et al., 2009), and should be dictated by the dispersal ability of the species under study, the ecological questions being asked, and the anticipated

degree of chemical complexity associated with the landscape (e.g., agro-ecosystem vs. boreal forests). As was observed in this study, large scale geographic gradients in trace element variability are often attributed to the chemistry of the underlying parental material (Imrie et al., 2008). These element gradients could be especially useful for indicating movement in long distance dispersers such as wolverines (Gulo gulo), mountain lions (Puma concolor), or caribou (Rangifer tarandus) in landscapes less chemically altered by human activity. Further quantification of spatial trends in element concentrations in terrestrial environments will improve such studies.

5. Conclusion Due to the novelty of this approach, our investigation worked towards developing baseline techniques for trace element applications in movement ecology of terrestrial mammals. We identified several elements that could be used to determine the origin of long-distance dispersers and better understand foraging behavior and habitat selection of individuals. With these data, geographic assignment of individuals based on element profiles in their tissue would be coarse (at the scale of 100–1000 km, depending on the chemical landscape), but could be used to indicate origin of long-distance dispersers or ascertain habitat associations. Future research should consider additional elements, clearly defined habitat boundaries, and a refined soil sampling scheme. It is also suggested that a species to be selected does not occupy highly chemically altered landscapes.

Conflicts of interest None.

D.M. Ethier et al. / Science of the Total Environment 468–469 (2014) 699–705

Acknowledgments We would like to thank Josh Sayers for his helpful comments and edits on earlier drafts of this manuscript. Robyn Reudink, Sophie Gibbs, Benjamin Lewis, and Josh Sayers assisted with soil sampling. We are in dept to the countless land owners who generously provided access to private lands. The Ontario Badger Recovery Team, Royal Ontario Museum, University of Western Ontario, and numerous private collectors donated claws for this study. Funding was provided in part by the Ministry of Natural Resources, Ontario Graduate Scholarship, Species at Risk Research Fund for Ontario/World Wildlife Fund, Species at Risk Stewardship Fund, and the Natural Sciences and Engineering Research Council of Canada. Thanks to Bill MacFarlane and personnel at the Queen's University Facility for Isotope Research for their technical assistance. References Atteia O, Dubois J, Webster R. Geostatistical analysis of soil contamination in the Swiss Jura. Environ Pollut 1994;86:315–27. 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