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Could habitat selection pattern be deformed in environment with low variability?

Could habitat selection pattern be deformed in environment with low variability?


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It is possible that strong (clearly visible) pattern of selection towards particular parameter is detectable only in environment with high variability (difference between used (red) and non-used (grey) space will be significant) whereas in sub-optimal environment used and non-used space do not differ due to low variability.

And if this is possible, is there any relevant study which focuses on such issue?

P.S. It seems to be very basic idea, however I could not find any relevant sources on this topic.


If I understand your question correctly, I've seen this idea in many papers, sometimes stated clearly and sometimes in more implicit terms. After a quick look I found a paper which should be relevant as a starting point for you: Mayor et al. 2007. Spectrum of selection: new approaches to detecting the scale-dependent response to habitat. Ecology 88(7).

In my mind, there are two questions here. First, to what extent will low population size in sub-optimal environments (leading to a smaller sample to work with) lead to more uncertain inferences of preferred habitat/substrate; basically saying that noise might swamp signal in sub-optimal environments. Second, to detect selection for a particular habitat/substrate, you must sample a wide enough range of environments so that you capture both preferred and unpreferred habitats and transitions between them. In the paper I linked to this issue is touched upon in the discussion:

Second, evidence of habitat selection depends on the scale of analysis. To understand scale dependence, investigations must encompass a spatial scope sufficiently wide to uncover scale domains of selection and the thresholds or transitions between them. In both cases, spatial scale is the empowering principle, and scaling continua are the means of application.

I've seen similar ideas as this show up in papers on habitat choice in birds living in agricultural landscapes as well, where some species are often found in rather poor environments (because these environments are common). Looking at habitat preferences only in these environments might then give a misleading view, since individual choices are constrained. Also relevant, even if not mentioned in your question, is the concept of maladaptive habitat selection (sometimes called ecological traps), where you can have situations where species use environmental cues that used to indicate high fitness habitats, but might now result in maladaptive choices, see e.g. Pärt el al. (2007). However, in this case you will still have a clear preference/selection (as in your high variability environment), the problem being that the organism is selecting suboptimal habitats from a fitness perspective.


Ecological dynamics in habitat selection of reindeer: an interplay of spatial scale, time, and individual animal's choice

While striving for “global” species models of habitat selection, spatiotemporal variation in utilization patterns within a particular habitat and intraspecies variation in space use are still poorly understood. We addressed these challenges by exploring habitat use of domesticated reindeer (Rangifer tarandus tarandus), focusing on factors that underlie ecological dynamics in habitat selection. We analyzed habitat selection of 15 (±2) female reindeer in southern Norway separately for (a) region and home range, (b) seasonality, and (c) each Global Positioning System (GPS)-collared reindeer. We explicitly evaluated spatiotemporal and intraspecies variability in habitat selection by applying multivariate ordination techniques based on the niche concept. In contrast to global assumptions, our results reveal a considerable and partly unpredictable amount of variation in habitat selection resulting from the interplay of spatial scale, time, and individual animal choice. Thus, we conclude that across-scale approaches describing animal space use facilitate better understanding of habitat selection instead of finding a single “best” model that indicates the strongest species–habitat relationships.

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Abstract

Like many species, movement patterns of southern elephant seals (Mirounga leonina) are being influenced by long-term environmental change. These seals migrate up to 4,000 km from their breeding colonies, foraging for months in a variety of Southern Ocean habitats. Understanding how movement patterns vary with environmental features and how these relationships differ among individuals employing different foraging strategies can provide insight into foraging performance at a population level. We apply new fast-estimation tools to fit mixed effects within a random walk movement model, rapidly inferring among-individual variability in southern elephant seal environment–movement relationships. We found that seals making foraging trips to the sea ice on or near the Antarctic continental shelf consistently reduced speed and directionality (move persistence) with increasing sea-ice coverage but had variable responses to chlorophyll a concentration, whereas seals foraging in the open ocean reduced move persistence in regions where circumpolar deep water shoaled. Given future climate scenarios, open-ocean foragers may encounter more productive habitat but sea-ice foragers may see reduced habitat availability. Our approach is scalable to large telemetry data sets and allows flexible combinations of mixed effects to be evaluated via model selection, thereby illuminating the ecological context of animal movements that underlie habitat usage.


Introduction

The annual cycle for a migratory species is characterized by long-distance movement of individuals between multiple geographically, and often ecologically, disparate locations (Newton 2008 , Åkesson and Hansson 2014 ). In each location, habitat heterogeneity drives individual settlement decisions and habitat selection processes often have individual fitness consequences (Hutto 1985 , Mayor et al. 2009 ). Migratory birds, despite their small size, travel some of the longest distances of any animal throughout their annual cycle (Alerstam et al. 2003 ). Although empirical studies of habitat selection have been conducted within breeding (Orians and Wittenberger 1991 , Mitchell et al. 2001 , Lee et al. 2002 ), migration (Buler et al. 2007 , Mccabe and Olsen 2015 , Lafleur et al. 2016 ), and winter (Chandler and King 2011 , Fraser et al. 2017 ) periods, few species have been assessed across multiple seasons. Moreover, studies rarely use the same individuals or populations (but see Beatty et al. 2014 , Pickens et al. 2017 ). As such, we know relatively little about the breadth of suitable habitats and how individuals select habitats throughout the annual cycle for migratory birds (Marra et al. 2015 , McGarigal et al. 2016 ).

Adaptive habitat selection has been theorized as a hierarchical decision-making process by which the factors that drive habitat suitability (based on fitness costs and benefits) and the mechanisms for assessing suitability occur at different spatial and temporal scales across the annual cycle (Fig. 1 Johnson 1980 , Wiens 1989 , Mayor et al. 2009 ). At each spatial or temporal scale, the decision-making process may involve novel and multiple interacting criteria (e.g., patch size, predation rate) and be constrained by the effects of criteria from higher scales (annual vs. diel Rettie and Messier 2000 , Mayor et al. 2009 ). When considering spatial scales, habitat selection decisions at the broadest scale will shape the species' geographic range and patterns of regional population distribution (hereafter referred to as “regional” Johnson 1980 , Meyer and Thuiller 2006 ). Hutto ( 1985 ) refined this idea for migratory birds and suggested nonhabitat factors are expected to drive decisions at the regional scale. For example, an individual’s genetically programmed and learned migration route (Hutto 1985 ), weather (Moore and Aborn 2000 , Buler and Moore 2011 ), and physiological condition (Studds et al. 2008 , Rushing et al. 2015 ) are factors expected to play a role in regional habitat selection. In contrast, as the spatial scale decreases, Hutto ( 1985 ) suggested that habitat selection will increasingly be driven by the costs and benefits associated with the habitat itself. At intermediate scales (hereafter referred to as “landscape”), selection drives the choice of a particular habitat type and home range. Finally, at finer spatial scales, selection of habitats within the home range (hereafter referred to as “local”) or microhabitat selection is most closely linked to the ultimate factors driving habitat suitability (e.g., food or nesting site availability, nest predation risk). Equally important to consider is how the decision-making process changes across temporal scales or levels (Cody 1985 , Wiens 1989 ). For example, factors limiting an individual’s fitness can vary between diel and seasonal decisions (scales) or equally across different seasons (levels Mayor et al. 2009 ). Therefore, habitat selection may be a hierarchical process that operates not only across multiple spatial scales ranging from regional to microhabitat but also across temporal scales.

Over the last thirty years, the multi-scale approach to habitat selection has been incorporated into many ecological studies across a range of vertebrate taxa, largely in terms of spatial scales (Wiens and Milne 1989 , Orians and Wittenberger 1991 , Rettie and Messier 2000 , Grand and Cushman 2003 ). However, the scope of this research has often been restricted to studies of single seasons on single populations, limiting the inferences that can be drawn (McGarigal et al. 2016 ). For migratory songbirds, most ecological studies have been conducted during the breeding season (Marra et al. 2015 ). When habitat use across seasons has been documented, many migratory songbirds, even those considered habitat specialists, appear to show shifts in habitat use during the nonbreeding season (Petit 2000 , Zuckerberg et al. 2016 ). This lack of consistency in habitat associations suggests that individuals can maximize their fitness across seasons through behavioral flexibility in their habitat settlement decision making (i.e., nesting vs. refueling sites). However, documenting behavioral flexibility in habitat selection has remained challenging due to the difficulty in tracking small mobile animals across large spatial and temporal scales. Therefore, two large information gaps related to behavioral flexibility in habitat selection need to be addressed: (1) What factors drive habitat selection decisions across seasons for individual birds, and (2) what factors underlying habitat selection vary across the species range?

Here, we evaluated the spatial and temporal patterns of habitat selection at three spatial scales (local, landscape, and regional) and across all seasons (breeding, fall migration, winter, and spring migration) of the annual cycle for wood thrush (Hylocichla mustelina), a Neotropical migratory songbird using fine spatial resolution archival GPS geolocators deployed across five distinct breeding populations. First, to test the hypothesis that factors driving habitat suitability change across seasons, we explored individual variation in habitat selection at the local and landscape scales. Since wood thrush are characterized as forest species that are tolerant of forest fragmentation (Evans et al. 2011 ), we predicted that if habitat suitability remains constant across seasons, thrushes will consistently select local and landscape feature characteristics of forest habitats. In contrast, if the factors that drive habitat suitability exhibit seasonal variation, we predicted a shift to more generalist habitat selection during migratory periods as nonhabitat features (e.g., temporal constraints on migration, energetic demands) drive the decision-making process and the mechanisms for accurately assessing unfamiliar habitats become difficult for birds. In addition, we predicted that wood thrush will exhibit local- and landscape-scale habitat selection during stationary (breeding and winter) periods, but selection will shift to landscape-scale features during migration, as they provide a quick visual cue that can be used in flight to assess habitat quality (Buler et al. 2007 , Beatty et al. 2014 ).

Second, to test for seasonal changes in regional habitat selection among five breeding populations we assessed the strength of migratory connectivity. Migratory connectivity is most commonly used to describe the strength of regional redistribution of migratory individuals across the annual cycle, but here we used it to identify the presence of population-specific regional habitat selection decisions as individuals move large distances throughout the year. Based on earlier work on wood thrush migratory connectivity with low-resolution tracking devices (Stanley et al. 2015 ), we predicted the strength of regional habitat selection would vary seasonally. Specifically, we predict regional habitat selection during fall migration driven by population-specific migration routes (Stanley et al. 2015 ). Wood thrush show moderate connectivity from breeding to winter sites (Stanley et al. 2015 ), suggesting weaker patterns of regional habitat selection on winter sites. Finally, we predict no regional habitat selection during spring migration due to the convergence of migratory routes at the Gulf of Mexico.


Wildlife habitat selection on landscapes with industrial disturbance

Technological advancements in remote sensing and telemetry provide opportunities for assessing the effects of expanding extractive industries on animal populations. Here, we illustrate the applicability of resource selection functions (RSFs) for modelling wildlife habitat selection on industrially-disturbed landscapes. We used grizzly bears ( Ursus arctos ) from a threatened population in Canada and surface mining as a case study. RSF predictions based on GPS radiocollared bears (n during mining = 7 n post mining = 9) showed that males and solitary females selected areas primarily outside mineral surface leases (MSLs) during active mining, and conversely inside MSLs after mine closure. However, females with cubs selected areas within compared to outside MSLs irrespective of mining activity. Individual variability was pronounced, although some environmental- and human-related variables were consistent across reproductive classes. For males and solitary females, regional-scale RSFs yielded comparable results to site-specific models, whereas for females with cubs, modelling the two scales produced divergent results. While mine reclamation may afford opportunities for bear persistence, managing public access will likely decrease the risk of human-caused bear mortality. RSFs are powerful tools that merit widespread use in quantitative and visual investigations of wildlife habitat selection on industrially-modified landscapes, using Geographic Information System layers that precisely characterize site-specific conditions.


3 RESULTS

In classical ‘snapshot’ IFD models (no resource dynamics no autecological differences purely ratio-dependent functional response), the IFD, ln[ρi] – ln[ρj], depends on the ratio of resource densities, ln[Ri] – ln[Rj], but not on the absolute densities of either consumers or their resources (Křivan, 2003 Lessells, 1995 Van Der Meer & Ens, 1997 ). Hence, even under this simplest of models, the IFD shifts with absolute resource density if the latter is not proportional to the inter-habitat density difference (i.e. if (RiRj)/(Ri + Rj) is not a constant for all Ri's and Rj's). Resource-selection strength, βR ( Equation 4), always shifts with absolute resource density (Figure 1a).

More generally, out of the 12 different scenarios considered here (six types of between-habitat differences, and two functional responses), nine scenarios result in consumer density dependence, whereas eight scenarios result in resource density dependence in habitat selection strength (Figures 1–3-1–3). Only two scenarios result in no density dependence, an Arditi–Akcakaya model where the two habitats differ by search rate only (a[Ai] − a[Aj] = 1 Figure 3a), and a Beddington–DeAngelis model where the two habitats differ by consumer interference only (c[Ai] − c[Aj] = 1 Figure 3d). Note, however, that density-independent habitat selection strength is also expected under a Type I Arditi–Akcakaya model (i.e. in the absence of handling time b[Ai] − b[Aj] = 1) where the two habitats differ by resource assimilation efficiency only (s[Ai] − s[Aj] = 1 Figure 2c). That said, even in these density-independent scenarios, habitat selection strength varies with the magnitude of the autecological effect (a[Ai] + a[Aj], c[Ai] + c[Aj] and s[Ai] + s[Aj]).

Across all density-dependent scenarios, the response is always nonlinear, with habitat selection strength either exponentially increasing or decreasing with consumer density, resource density or both (Figures 1-3-1-3). In the case of the scenarios assuming inter-habitat difference in autecological fitness gain (g[Ai] − g[Aj] = 1), the response is also non monotonic, first decreasing and then increasing with both consumer and resource densities (Figure 1e,f). The shapes of these response curves are qualitatively insensitive to the absolute magnitude of parameters or variables, or to whether or not the inter-habitat difference is linearly proportional to the mean value (i.e. whether or not ). The only exception is a type I (b[Ai] − b[Aj] = 0) Arditi–Akcakaya model in the case of an inter-habitat difference in autecological fitness gain, which results in a monotonic increase in βg with consumer density and a monotonic decrease in βg with resource density.


Acknowledgments

We thank V. Berigan, L. Erickson, G. Zimmerman, L. Berkeley, J. Slaght, W. Block, M. Johnson, D. Dobkin, and an anonymous reviewer for helpful suggestions, and A. Chatfield for her dedication during vegetation mapping. For their continued support we are grateful to F. Schurr, B. Heald, and S. Rambeau from the University of California Berkeley Blodgett Experimental Station, and the staff in Sponsored Projects Administration and staff in the Department of Fisheries, Wildlife, and Conservation Biology at the University of Minnesota. We thank our many field assistants over the past 15 years for their diligence. This project was funded by the USDA Forest Service (contract #FS-53-9158-00-EC14 to RJG). The University of Minnesota Institutional Animal Care and Use Committee approved all survey, capture, and handling methods.


Materials and methods

Study area

Our study area was located in the Grande Cache, Yellowhead, Clearwater, Livingstone and Castle grizzly bear management units in Alberta, Canada, which have a combined area of nearly 111 000 km 2 (Fig. 1). Elevation in the study area ranges from 450 to 3500 m and increases from east to west. Habitat types include alpine and sub-alpine ecosystems, mixed-wood forests and wet-meadow complexes ( Stenhouse et al., 2005). Mean temperature ranges from 12°C in the summer to −7.5°C in the winter, and mean annual precipitation is 450–800 mm. An extensive road network built to service industrial activities also provides recreational access for a variety of activities, including hunting, fishing, trapping, hiking and trail-riding with all-terrain vehicles and snowmobiles. A network of federal and provincial parks and protected areas, which prohibit resource-extraction activities, are found throughout the study area.

Grizzly bear capture locations in Alberta, Canada. A total of 163 grizzly bears were captured from 1999 to 2010 between April and October using a combination of leg-hold snares, culvert traps and remote drug delivery from a helicopter. Note that multiple bears were captured at specific culvert trap locations during the study period. Body condition was determined at the time of capture, and each bear was fitted with a GPS radiocollar to allow assessment of spatial patterns of habitat use. The five bear management units represent an area of nearly 111 000 km 2 .

Grizzly bear capture locations in Alberta, Canada. A total of 163 grizzly bears were captured from 1999 to 2010 between April and October using a combination of leg-hold snares, culvert traps and remote drug delivery from a helicopter. Note that multiple bears were captured at specific culvert trap locations during the study period. Body condition was determined at the time of capture, and each bear was fitted with a GPS radiocollar to allow assessment of spatial patterns of habitat use. The five bear management units represent an area of nearly 111 000 km 2 .

Bear captures and body condition index

We assessed the body condition of 163 grizzly bears (n = 69 males and 94 females) captured between 1999 and 2010. Captures occurred from April until October in order to account for potential changes in body condition dynamics over the entirety of the non-denning period, although the majority of captures were made between April and June. Bears were captured by the Foothills Research Institute Grizzly Bear Project using a combination of leg-hold snares, culvert traps and remote drug delivery from a helicopter. Captures followed protocols accepted by the Canadian Council of Animal Care for the safe handling of bears (University of Saskatchewan Committee on Animal Care and Supply Protocol number 20010016). We fitted a VHF ear-tag transmitter (Advanced Telemetry Systems) and a GPS radiocollar from either Televilt Simplex, Tellus (Followit Lindesberg, Sweden) or Advanced Telemetry Systems (Isanti, MN, USA) to each bear. The GPS-based locations were obtained at 4 h intervals prior to 2004 and at 1–2 h intervals after 2004.

We determined the age of each bear using microscopic analysis of a premolar section ( Stoneberg and Jonkel, 1966). For each individual, we recorded the gender, reproductive status (e.g. with or without cubs, and the age of cubs), and the number of times the bear had been captured previously ( Cattet et al., 2008 Boulanger et al., 2013 Nielsen et al., 2013a). We classified individuals as adult (>5 years old) males (n = 47) or females (n = 55), juvenile (3–5 years old) males (n = 22) or females (n = 22), and females with cubs-of-year (COY n = 17). We distinguished females with COY from females with older cubs (which were grouped with adult females) because females with COY have greater energetic requirements ( Farley and Robbins, 1995) and tend to have smaller home ranges ( Dahle and Swenson, 2003 Smulders et al., 2012). We weighed and measured bears using a load-scale and a tape stretched from the tip of the nose to the last tail vertebrae. We used weight and length measurements from individuals captured in the field to obtain BCI values that represent the standardized residuals from a linear regression of log-transformed total body mass and straight-line body length ( Cattet et al., 2002). The BCI values used in this analysis ranged from −3 to +3.

Grizzly bear habitat use

We characterized grizzly bear habitat use using GPS-based positional data from the period (30–60 days) following the capture of each individual. The 30- to 60-day post-capture period was selected to quantify habitat use in order to avoid potential capture effects on grizzly bear movement rates ( Cattet et al., 2008). We used fixed-kernel density estimation to calculate utilization distributions (package adehabitatHR in R version 2.15.0 Calenge, 2013) from the GPS data with a bandwidth defined by least-squares cross-validation ( Worton, 1989). We contoured the utilization distributions at the 95th percentile isopleth in order to represent the area used by each individual. Areas of habitat use, which represent the spatial unit of analysis, were used to summarize anthropogenic, habitat and habitat net-energy demand variables. To assess the similarity between post-capture habitat use and potential pre-capture habitat use, we compared the areal extent of the area of habitat use of 50 bears captured in the spring with the area of habitat use for the same animal from the preceding autumn based on GPS data for 30 days prior to den entry. The coefficient of variation between the areal extent of pre-capture aumtumn habitat use and post-capture spring habitat use was 38% for the 50 bears considered. Furthermore, the mean change in the habitat-use area centroid co-ordinates of the pre-capture autumn and post-capture spring periods was minimal (∼4 km in both the Easting and Northing directions). As a result, the post-capture characterization of grizzly bear habitat use is a reasonable approximation of the area used prior to capture, giving us confidence that BCI values can be related to environmental variables based on post-capture GPS telemetry data.

Covariate data

We modelled and temporally matched covariate data representing anthropogenic features, habitat characteristics and habitat net-energy demand with grizzly bear habitat-use areas in a Geographic Information System (GIS see Table 1 for rationale and data sources). Anthropogenic disturbance features that we considered included all-weather roads, oil and gas well sites, seismic lines, power lines, pipelines and forest harvest blocks. We characterized the localized influence of roads and oil and gas well sites using an exponential distance decay function, e −ad , where d is the distance in metres to the feature and a is fixed at 0.002 ( Nielsen et al., 2009). We represented secondary linear features (e.g. seismic lines, power lines and pipelines), which provide access to grizzly bear habitat and contribute to landscape fragmentation ( Linke et al., 2005 Stewart et al., 2013), as a cumulative linear density (in kilometres per square kilometre) within each habitat-use area. We represented forest harvest blocks based on the areal density (in kilometres per square kilometre) of these features within habitat-use areas. We quantified the influence of parks and protected areas, which represent a noted contrast in land use compared with the surrounding landscape, based on the areal percentage of habitat use that occurred within parks and protected areas.

The biological, anthropogenic and habitat-related covariates considered to explain body condition of grizzly bears in Alberta, Canada

Covariate . Rationale . References . Data source .
Reproductive class Based on gender, age and presence of cub(s)-of-year, which influence habitat selection patterns and energetic demands, individuals were classified as adult males or females (>5 years old), juvenile males or females (2–5 years old) or adult females with cub(s)-of-year Boulanger et al. (2013), Nielsen et al. (2013a) Grizzly bear capture data
Number of previous captures Multiple handlings may adversely influence body condition Cattet et al. (2008)
Capture date (Julian date) Seasonal changes in food availability and habitat selection during the non-denning period may influence body condition McLellan (2011)
Index of habitat net-energy demand Factors related to anthropogenic disturbance and habitat characteristics influence predicted hair cortisol concentrations in grizzly bears. Predicted hair cortisol concentration values are interpreted as a sex-specific indicator of net-energy demand Macbeth et al. (2010), Bourbonnais et al. (2013), Bryan et al. (2013) Bourbonnais et al. (2013)
Roads (distance decay) Provide human access to grizzly bear habitat contribute to landscape fragmentation herbaceous foods are present in areas adjacent to roads Munro et al. (2006), Berland et al. (2008), Roever et al. (2008), Graham et al. (2010) AESRD FRIGBP Landsat 5 TM Landsat 7 ETM +
Oil and gas well sites (distance decay) Localized areas of human activity create forest edges and contribute to landscape fragmentation Laberee et al. (2014)
Density of secondary linear features (km/km 2 ) Seismic lines, power lines and pipelines create forest edges and contribute to landscape fragmentation and provide access to grizzly bear habitat Linke et al. (2005), Stewart et al. (2013)
Density of forest harvest blocks (km/km 2 ) Disturbance features associated with presence and abundance of herbaceous foods Nielsen et al. (2004a, c) Munro et al. (2006), Berland et al. (2008)
Percentage of parks and protected areas Considered core refugia and represent a marked contrast in land use compared with the surrounding industrialized landscape Gibeau et al. (2002)
Elevation (variation) Influences vegetation composition, human access and potential habitat net-energy demand Nielsen et al. (2004b, c) Bourbonnais et al. (2013) Landsat 5 TM Landsat 7 ETM+ DEM
Crown closure (variation) Influences understory vegetation abundance and growth of herbaceous foods Franklin et al. (2002, 2003) Nielsen et al. (2013a)
Percentage of conifer tree cover Characterization of forest species distribution and correlated with berry abundance Franklin et al. (2002, 2003)
Percentage of mixed and broadleaf tree cover Influences distribution of herbaceous foods and correlated with presence of ungulates Nielsen et al. (2010), Stewart et al. (2013)
Percentage of regenerating forest Regenerating forests have greater availability of herbaceous foods Nielsen et al. (2004c, 2010)
Percentage of shrub and herbaceous landcover Correlated with availability of herbaceous foods and berries Franklin et al. (2002, 2003)
Forest age Younger seral forests have a greater abundance of herbaceous foods Nielsen et al. (2004c, 2010)
Vegetation productivity Total vegetation productivity (cumulative greenness) influences availability of herbaceous foods Coops et al. (2008), Fontana et al. (2012) AVHRR DHI
Vegetation seasonality Seasonal variability (coefficient of variation) in vegetation productivity influences timing and availability of herbaceous foods Coops et al. (2008), Fontana et al. (2012)
Covariate . Rationale . References . Data source .
Reproductive class Based on gender, age and presence of cub(s)-of-year, which influence habitat selection patterns and energetic demands, individuals were classified as adult males or females (>5 years old), juvenile males or females (2–5 years old) or adult females with cub(s)-of-year Boulanger et al. (2013), Nielsen et al. (2013a) Grizzly bear capture data
Number of previous captures Multiple handlings may adversely influence body condition Cattet et al. (2008)
Capture date (Julian date) Seasonal changes in food availability and habitat selection during the non-denning period may influence body condition McLellan (2011)
Index of habitat net-energy demand Factors related to anthropogenic disturbance and habitat characteristics influence predicted hair cortisol concentrations in grizzly bears. Predicted hair cortisol concentration values are interpreted as a sex-specific indicator of net-energy demand Macbeth et al. (2010), Bourbonnais et al. (2013), Bryan et al. (2013) Bourbonnais et al. (2013)
Roads (distance decay) Provide human access to grizzly bear habitat contribute to landscape fragmentation herbaceous foods are present in areas adjacent to roads Munro et al. (2006), Berland et al. (2008), Roever et al. (2008), Graham et al. (2010) AESRD FRIGBP Landsat 5 TM Landsat 7 ETM +
Oil and gas well sites (distance decay) Localized areas of human activity create forest edges and contribute to landscape fragmentation Laberee et al. (2014)
Density of secondary linear features (km/km 2 ) Seismic lines, power lines and pipelines create forest edges and contribute to landscape fragmentation and provide access to grizzly bear habitat Linke et al. (2005), Stewart et al. (2013)
Density of forest harvest blocks (km/km 2 ) Disturbance features associated with presence and abundance of herbaceous foods Nielsen et al. (2004a, c) Munro et al. (2006), Berland et al. (2008)
Percentage of parks and protected areas Considered core refugia and represent a marked contrast in land use compared with the surrounding industrialized landscape Gibeau et al. (2002)
Elevation (variation) Influences vegetation composition, human access and potential habitat net-energy demand Nielsen et al. (2004b, c) Bourbonnais et al. (2013) Landsat 5 TM Landsat 7 ETM+ DEM
Crown closure (variation) Influences understory vegetation abundance and growth of herbaceous foods Franklin et al. (2002, 2003) Nielsen et al. (2013a)
Percentage of conifer tree cover Characterization of forest species distribution and correlated with berry abundance Franklin et al. (2002, 2003)
Percentage of mixed and broadleaf tree cover Influences distribution of herbaceous foods and correlated with presence of ungulates Nielsen et al. (2010), Stewart et al. (2013)
Percentage of regenerating forest Regenerating forests have greater availability of herbaceous foods Nielsen et al. (2004c, 2010)
Percentage of shrub and herbaceous landcover Correlated with availability of herbaceous foods and berries Franklin et al. (2002, 2003)
Forest age Younger seral forests have a greater abundance of herbaceous foods Nielsen et al. (2004c, 2010)
Vegetation productivity Total vegetation productivity (cumulative greenness) influences availability of herbaceous foods Coops et al. (2008), Fontana et al. (2012) AVHRR DHI
Vegetation seasonality Seasonal variability (coefficient of variation) in vegetation productivity influences timing and availability of herbaceous foods Coops et al. (2008), Fontana et al. (2012)

Abbreviations: AESRD, Alberta Environment and Sustainable Resource Development AVHRR, Advanced Very High Resolution Radiometer DEM, digital elevation model DHI, Dynamic Habitat Index ETM+, Enhance Thematic Mapper Plus FRIGBP, Foothills Research Institute Grizzly Bear Project TM, Thematic Mapper.

The biological, anthropogenic and habitat-related covariates considered to explain body condition of grizzly bears in Alberta, Canada

Covariate . Rationale . References . Data source .
Reproductive class Based on gender, age and presence of cub(s)-of-year, which influence habitat selection patterns and energetic demands, individuals were classified as adult males or females (>5 years old), juvenile males or females (2–5 years old) or adult females with cub(s)-of-year Boulanger et al. (2013), Nielsen et al. (2013a) Grizzly bear capture data
Number of previous captures Multiple handlings may adversely influence body condition Cattet et al. (2008)
Capture date (Julian date) Seasonal changes in food availability and habitat selection during the non-denning period may influence body condition McLellan (2011)
Index of habitat net-energy demand Factors related to anthropogenic disturbance and habitat characteristics influence predicted hair cortisol concentrations in grizzly bears. Predicted hair cortisol concentration values are interpreted as a sex-specific indicator of net-energy demand Macbeth et al. (2010), Bourbonnais et al. (2013), Bryan et al. (2013) Bourbonnais et al. (2013)
Roads (distance decay) Provide human access to grizzly bear habitat contribute to landscape fragmentation herbaceous foods are present in areas adjacent to roads Munro et al. (2006), Berland et al. (2008), Roever et al. (2008), Graham et al. (2010) AESRD FRIGBP Landsat 5 TM Landsat 7 ETM +
Oil and gas well sites (distance decay) Localized areas of human activity create forest edges and contribute to landscape fragmentation Laberee et al. (2014)
Density of secondary linear features (km/km 2 ) Seismic lines, power lines and pipelines create forest edges and contribute to landscape fragmentation and provide access to grizzly bear habitat Linke et al. (2005), Stewart et al. (2013)
Density of forest harvest blocks (km/km 2 ) Disturbance features associated with presence and abundance of herbaceous foods Nielsen et al. (2004a, c) Munro et al. (2006), Berland et al. (2008)
Percentage of parks and protected areas Considered core refugia and represent a marked contrast in land use compared with the surrounding industrialized landscape Gibeau et al. (2002)
Elevation (variation) Influences vegetation composition, human access and potential habitat net-energy demand Nielsen et al. (2004b, c) Bourbonnais et al. (2013) Landsat 5 TM Landsat 7 ETM+ DEM
Crown closure (variation) Influences understory vegetation abundance and growth of herbaceous foods Franklin et al. (2002, 2003) Nielsen et al. (2013a)
Percentage of conifer tree cover Characterization of forest species distribution and correlated with berry abundance Franklin et al. (2002, 2003)
Percentage of mixed and broadleaf tree cover Influences distribution of herbaceous foods and correlated with presence of ungulates Nielsen et al. (2010), Stewart et al. (2013)
Percentage of regenerating forest Regenerating forests have greater availability of herbaceous foods Nielsen et al. (2004c, 2010)
Percentage of shrub and herbaceous landcover Correlated with availability of herbaceous foods and berries Franklin et al. (2002, 2003)
Forest age Younger seral forests have a greater abundance of herbaceous foods Nielsen et al. (2004c, 2010)
Vegetation productivity Total vegetation productivity (cumulative greenness) influences availability of herbaceous foods Coops et al. (2008), Fontana et al. (2012) AVHRR DHI
Vegetation seasonality Seasonal variability (coefficient of variation) in vegetation productivity influences timing and availability of herbaceous foods Coops et al. (2008), Fontana et al. (2012)
Covariate . Rationale . References . Data source .
Reproductive class Based on gender, age and presence of cub(s)-of-year, which influence habitat selection patterns and energetic demands, individuals were classified as adult males or females (>5 years old), juvenile males or females (2–5 years old) or adult females with cub(s)-of-year Boulanger et al. (2013), Nielsen et al. (2013a) Grizzly bear capture data
Number of previous captures Multiple handlings may adversely influence body condition Cattet et al. (2008)
Capture date (Julian date) Seasonal changes in food availability and habitat selection during the non-denning period may influence body condition McLellan (2011)
Index of habitat net-energy demand Factors related to anthropogenic disturbance and habitat characteristics influence predicted hair cortisol concentrations in grizzly bears. Predicted hair cortisol concentration values are interpreted as a sex-specific indicator of net-energy demand Macbeth et al. (2010), Bourbonnais et al. (2013), Bryan et al. (2013) Bourbonnais et al. (2013)
Roads (distance decay) Provide human access to grizzly bear habitat contribute to landscape fragmentation herbaceous foods are present in areas adjacent to roads Munro et al. (2006), Berland et al. (2008), Roever et al. (2008), Graham et al. (2010) AESRD FRIGBP Landsat 5 TM Landsat 7 ETM +
Oil and gas well sites (distance decay) Localized areas of human activity create forest edges and contribute to landscape fragmentation Laberee et al. (2014)
Density of secondary linear features (km/km 2 ) Seismic lines, power lines and pipelines create forest edges and contribute to landscape fragmentation and provide access to grizzly bear habitat Linke et al. (2005), Stewart et al. (2013)
Density of forest harvest blocks (km/km 2 ) Disturbance features associated with presence and abundance of herbaceous foods Nielsen et al. (2004a, c) Munro et al. (2006), Berland et al. (2008)
Percentage of parks and protected areas Considered core refugia and represent a marked contrast in land use compared with the surrounding industrialized landscape Gibeau et al. (2002)
Elevation (variation) Influences vegetation composition, human access and potential habitat net-energy demand Nielsen et al. (2004b, c) Bourbonnais et al. (2013) Landsat 5 TM Landsat 7 ETM+ DEM
Crown closure (variation) Influences understory vegetation abundance and growth of herbaceous foods Franklin et al. (2002, 2003) Nielsen et al. (2013a)
Percentage of conifer tree cover Characterization of forest species distribution and correlated with berry abundance Franklin et al. (2002, 2003)
Percentage of mixed and broadleaf tree cover Influences distribution of herbaceous foods and correlated with presence of ungulates Nielsen et al. (2010), Stewart et al. (2013)
Percentage of regenerating forest Regenerating forests have greater availability of herbaceous foods Nielsen et al. (2004c, 2010)
Percentage of shrub and herbaceous landcover Correlated with availability of herbaceous foods and berries Franklin et al. (2002, 2003)
Forest age Younger seral forests have a greater abundance of herbaceous foods Nielsen et al. (2004c, 2010)
Vegetation productivity Total vegetation productivity (cumulative greenness) influences availability of herbaceous foods Coops et al. (2008), Fontana et al. (2012) AVHRR DHI
Vegetation seasonality Seasonal variability (coefficient of variation) in vegetation productivity influences timing and availability of herbaceous foods Coops et al. (2008), Fontana et al. (2012)

Abbreviations: AESRD, Alberta Environment and Sustainable Resource Development AVHRR, Advanced Very High Resolution Radiometer DEM, digital elevation model DHI, Dynamic Habitat Index ETM+, Enhance Thematic Mapper Plus FRIGBP, Foothills Research Institute Grizzly Bear Project TM, Thematic Mapper.

We selected habitat variables that characterized forest conditions, landcover, topography and vegetation productivity, and which represented proxies of potential food availability (see Table 1 for rationale and data sources). We quantified forest composition and structure within habitat-use areas based on the variance in crown closure, the percentage of coniferous forest, the percentage of mixed and broadleaf tree cover, the percentage of regenerating forest, the mean forest age and the percentage of shrub and herb landcover ( Franklin et al., 2002, 2003). We characterized topography associated with habitat use based on the variance in elevation. We used the cumulative greenness (total annual productivity) and coefficient of variation (seasonality) indices from the Dynamic Habitat Index (DHI) to quantify vegetation productivity within the habitat-use area of each bear ( Coops et al., 2008). The two DHI indices are obtained by summarizing annual trends in monthly images of the fraction of photosynthetically active radiation derived from Advanced Very High Resolution Radiometer reflectance values ( Fontana et al., 2012). We used a data product representing a spatial index of predicted HCC levels to characterize the potential habitat net-energy demand associated with grizzly bear habitat-use patterns (see Bourbonnais et al., 2013 for details). Stratified by gender, we calculated a habitat net-energy demand index value for each habitat-use area to represent the potential energetic demands associated with the habitat characteristics of the area.

Statistical analyses

We used linear mixed-effects models (package nlme in R version 2.15.0 Pinheiro, 2013) to examine the relationships between the dependent grizzly bear BCI response and the independent biological, anthropogenic, habitat and habitat net-energy demand index variables ( Pinheiro and Bates, 2000). Continuous independent variables were centred and scaled due to the range in values and to aid interpretation of regression coefficients ( Schielzeth, 2010). We limited collinearity and redundancy in covariates by excluding those with a Pearson correlation coefficient ≥0.6 and a variance inflation factor ≥5. We found that elevation was strongly correlated with a number of covariates, including the DHI variables, crown closure, roads and the percentage of parks and protected areas. As a result, we excluded elevation from the models because the DHI metrics adequately represented the variability in vegetation productivity resulting from elevation gradients.

We considered separate anthropogenic and habitat linear mixed-effects models, as well as a global model combining covariates from the anthropogenic and habitat models. We assessed the support for the three models considered using Akaike weights (wi) based on Akaike information criterion (AIC) values ( Burnham and Anderson, 2002). We controlled for biological and capture effects on body condition by including in each of the models the reproductive class, the number of previous captures and the capture date. As habitat characteristics and anthropogenic activities influence habitat net-energy demand ( Bourbonnais et al., 2013), we included values from this index in the anthropogenic, habitat and global models. In order to examine the influence of habitat net-energy demand on body condition further, we used a factorial ANOVA to compare potential energetic demands associated with habitat use in each of the five reproductive classes considered.

Covariates representing biology, anthropogenic disturbance, forest characteristics, vegetation productivity and habitat net-energy demand were included as fixed effects, with a unique identifier for each bear as the random effect in the respective models. As suggested by Zuur et al. (2009), we refitted the models using restricted maximum likelihood estimation to limit bias in the regression coefficients. We found no evidence of correlation of predictor variables in the final anthropogenic, habitat and global models, and within-group residuals appeared to be normally distributed ( Pinheiro and Bates, 2000 Zuur et al., 2009). We assessed the normality of the random effects by plotting the best linear unbiased estimators for each model ( Pinheiro and Bates, 2000). These were acceptable for all three of the models considered. We quantified the variance explained by fixed effects in each model using a marginal r 2 and the cumulative variance explained by fixed and random effects using a conditional r 2 ( Nakagawa and Schielzeth, 2013).


Habitat selection by European badgers in Mediterranean semi-arid ecosystems

We studied the habitat selection patterns of badgers Meles meles (Linnaeus, 1758) in Mediterranean semi-arid ecosystems. Fifty-seven plots were sampled in two semi-arid regions of Spain. In each plot, badger latrines were located along 2.6 km transects. The number of badger latrines per km was used as a surrogate of badger abundance and as an index of habitat selection by badgers. For each plot, a series of environmental variables were measured at two spatial scales. These variables were related to land use and vegetation formation parameters that are considered potentially important for habitat requirements (i.e., food and shelter). The habitat selection model was carried out using generalised linear models (GLM) and an information-theoretic approach. Results indicated that badgers prefer fruit orchards, and shrub and rock-covered areas, which provide additional trophic and shelter resources, and avoid intensively cultivated fields and human settlements. We conclude that badger conservation in semi-arid environments of the Iberian Peninsula requires the existence of fruit orchards and the limitation of human development. Policies restraining agriculture intensification would encourage traditional or new non-intensive agricultural practices and increase shrub-patch availability, which would benefit this species.

Highlights

► We model the habitat selection patterns of badgers in Mediterranean semi-arid ecosystems. ► Badgers select fruit orchards, shrub and rock-covered areas, and avoid intensively cultivated fields and human settlements. ► Policies that encourage traditional agricultural practices would benefit badgers. ► Badgers would benefit policies that restrict human development.


Could habitat selection pattern be deformed in environment with low variability? - Biology

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Habitat Selection and Genetic Structure of the Endangered Frog Species Odorrana wuchuanensis (Anura: Ranidae)

Yongjie Huang, 1 Wei Zhao, 2 Li Ding, 2 Xinkang Bao, 2,* Jing Wang, 1 Yinghua Lin, 1,* Jingcheng Ran, 3 De Yang, 4 Hao Zou, 4 Jianxin Liu 5

1 1Institute of Wetland Research, Chinese Academy of Forestry, Beijing 100091, China
2 2Gansu Key Laboratory of Biomonitoring and Bioremediation for Environmental Pollution, School of Lif
3 3Guizhou Management Station of Wildlife, Guiyang 550000, China
4 4Mayang River National Nature Reserve, Yanhe 565300, China
5 5Wuchuan High School, Wuchuan 564300, China

* Corresponding author. E-mail: [email protected] (Y Lin) [email protected] (X Bao)

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Understanding the habitat selection and population genetic structure of an endangered species can play important roles in its protection. The Wuchuan odorous frog (Odorrana wuchuanensis) is endemic to the karst regions of southwest China. This frog is currently listed as “Critically Endangered” by the IUCN, but little is known about its habitat selection and population genetics. In this study, we conducted analyses of habitat selection with occurrence/absence sites and environmental data, and assessed the genetic structure between north and south populations in Guizhou provinces in China using three mitochondrial markers. The results revealed that the probability of this frog occupying cave habitats increased with higher average humidity in July and higher lowest temperature in January, but was negatively related to precipitation in January. Analyses of F statistics combined with analyses of median-joining haplotype networks and the phylogenetic tree showed low genetic differentiation between the two populations of O. wuchuanensis. Considering the small population size and geographic isolation because of the complex karst terrains, we suggest careful management practices are needed to protect this species.

© 2019 Zoological Society of Japan

Yongjie Huang , Wei Zhao , Li Ding , Xinkang Bao , Jing Wang , Yinghua Lin , Jingcheng Ran , De Yang , Hao Zou , and Jianxin Liu " Habitat Selection and Genetic Structure of the Endangered Frog Species Odorrana wuchuanensis (Anura: Ranidae)," Zoological Science 36(5), 402-409, (1 October 2019). https://doi.org/10.2108/zs180141

Received: 8 September 2018 Accepted: 11 March 2019 Published: 1 October 2019