Evaluating expert-based habitat suitability information of terrestrial mammals with GPS-tracking data.

Aim: Macroecological studies that require habitat suitability data for many species often derive this information from expert opinion. However, expert-based information is inherently subjective and thus prone to errors. The increasing availability of GPS tracking data offers opportunities to evaluate and supplement expert-based information with detailed empirical evidence. Here, we compared expert-based habitat suitability information from the International Union for Conservation of Nature (IUCN) with habitat suitability information derived from GPS-tracking data of 1,498 individuals from 49 mammal species. Location: Worldwide. Time period: 1998-2021. Major taxa studied: Forty-nine terrestrial mammal species. Methods: Using GPS data, we estimated two measures of habitat suitability for each individual animal: proportional habitat use (proportion of GPS locations within a habitat type), and selection ratio (habitat use relative to its availability). For each individual we then evaluated whether the GPS-based habitat suitability measures were in agreement with the IUCN data. To that end, we calculated the probability that the ranking of empirical habitat suitability measures was in agreement with IUCN's classification into suitable, marginal and unsuitable habitat types. Results: IUCN habitat suitability data were in accordance with the GPS data (> 95% probability of agreement) for 33 out of 49 species based on proportional habitat use estimates and for 25 out of 49 species based on selection ratios. In addition, 37 and 34 species had a > 50% probability of agreement based on proportional habitat use and selection ratios, respectively. Main conclusions: We show how GPS-tracking data can be used to evaluate IUCN habitat suitability data. Our findings indicate that for the majority of species included in this study, it is appropriate to use IUCN habitat suitability data in macroecological studies. Furthermore, we show that GPS-tracking data can be used to identify and prioritize species and habitat types for re-evaluation of IUCN habitat suitability data.

Response of barren-ground caribou to advancing spring phenology.

Phenological shifts are occurring in many ecosystems around the world. The capacity of species to adapt to changing phenology will be critical to their success under climate change scenarios. Failure to adjust migratory and reproductive timing to keep pace with the earlier onset of spring has led to negative demographic effects for populations of species across a variety of taxa. For caribou, there have been concerns that earlier spring green-up on calving areas might not be matched by earlier migration and parturition, potentially leading to a trophic mismatch with nutritional consequences for parturient and lactating caribou cows. However, there is limited evidence supporting these concerns. Here, we investigate the response of barren-ground caribou to changing spring phenology using data from telemetry and satellite imagery. From 2004 to 2016, we found that the average start of green-up on the calving area advanced by 7.25 days, while the start of migration advanced by 13.64 days, the end of migration advanced by 6.02 days, and the date of peak calving advanced by 9.42 days. Despite the advancing onset of green-up, we found no evidence for the development of a trophic mismatch because the advancing green-up coincided with earlier migration and calving by caribou. Changing snow cover on the late winter and migratory ranges was the most supported driver of advancing migratory behavior. The ability of caribou to adjust the timing of migratory and reproductive behavior in response to changing environmental conditions demonstrates the potential resilience of the species to some aspects of climate change.

Beaver (Castor canadensis) use of borrow pits in an industrial landscape in northwestern Alberta.

Borrow pits, dug by industry to provide substrate for infrastructure such as roads and well sites, are prevalent throughout the boreal forest of western Canada yet little is known about their use by wildlife. During field surveys in Rainbow Lake, Alberta, we found that beavers (Castor canadensis) used inundated borrow pits extensively for foraging and over wintering, suggesting that borrow pits increase beaver populations above their natural carrying capacity in industrial landscapes. We visited a random sample of 90 borrow pits in the field and categorized them as having active beaver lodges with caches (n = 16), inactive lodges (n = 13), cutting (n = 29), no activity (n = 13), or as not inundated or not a borrow pit (n = 19). We then used this sample to model where beavers established lodges versus where they were inactive or were only cutting. We found that borrow pits with active lodges had unique characteristics from other pits in our sample and were closer to streams, marshes, and swamps and had greater vegetation concealment from roads. These models can be used by managers to develop methods for increasing or decreasing the abundance of beavers living at borrow pits. Wildlife managers might want to use borrow pits to increase beaver abundance because of the positive effects beavers can have on ecosystems and because beavers are an important source of food for boreal carnivores. For example, we found that wolverines (Gulo gulo) used borrow pits as hunting grounds for beavers and one wolverine denned within a beaver lodge at a borrow pit. Conversely, managers might want to reduce beaver populations living at borrow pits because of the damage that beavers can cause to infrastructure and because subsidized beaver populations might alter predator-prey dynamics. To accomplish this, we suggest reducing available vegetation around borrow pits, isolating new borrow pits from natural beaver habitats, digging borrow pits where there is less chance of having surface water, filling in borrow pits with substrate, or not creating borrow pits and using a centralized gravel pit or mine for substrate.

Conservation Reserve Program is a key element for managing white-tailed deer populations at multiple spatial scales.

Understanding the underlying mechanisms driving population demographics such as species-habitat relationships and the spatial scale in which these relationships occur is essential for developing optimal management strategies. Here we evaluated how landscape characteristics and winter severity measured at three spatial scales (1 km2, 9 km2, and hunting unit) influenced white-tailed deer occurrence and abundance across North Dakota by using 10 years of winter aerial survey data and generalized linear mixed effects models. In general, forest, wetland, and Conservation Reserve Program (CRP) lands were the main drivers of deer occurrence and abundance in most of the spatial scales analyzed. However, the effects of habitat features vary between the home-range scale (9 km2) and the finer spatial scale (1 km2; i.e., within home ranges). While escape cover was the main factor driving white-tailed deer occurrence and abundance at broad spatial scales, at a fine spatial scale deer also selected for food (mainly residual winter cropland). With CRP appearing in nearly all top models, here we had strong evidence that this type of program will be fundamental to sustaining populations of white-tailed deer that can meet recreational demands. In addition, land managers should focus on ways to protect other escape covers (e.g., forest and wetland) on a broad spatial scale while encouraging landowners to supply winter resources at finer spatial scales. We therefore suggest a spatial multi-scale approach that involves partnerships among landowners and government agencies for effectively managing white-tailed deer.

Coexistence with Large Carnivores Supported by a Predator-Compensation Program.

Compensation programs are used globally to increase tolerance for and help offset economic loss caused by large carnivores. Compensation program funding comes from a variety of sources, and in Wyoming and Idaho, USA and Alberta, Canada this includes revenue from hunting and fishing license sales. We review the patterns of livestock depredation and compensation costs of Alberta's predator-compensation program, and compare Alberta's program to compensation programs in neighboring Canadian and American jurisdictions. Current compensation costs in Alberta are well below historic levels, but have been rapidly increasing in recent years due to an increase in depredation events coupled with increased cattle prices. That increase has caused push back from Alberta's hunting and fishing community that finances the compensation program, although less than 3.6% of Alberta's license levy dollars are used for predator compensation. Hunting effort in Alberta is highest on the same privately owned lands with livestock depredation problems, suggesting that private lands support habitats for hunted ungulate species as well as carnivores. Although compensation programs do not prevent depredation events themselves, compensation programs effectively can support the maintenance of wildlife habitats on private lands.

REVIEW: Can habitat selection predict abundance?

Habitats have substantial influence on the distribution and abundance of animals. Animals' selective movement yields their habitat use. Animals generally are more abundant in habitats that are selected most strongly. Models of habitat selection can be used to distribute animals on the landscape or their distribution can be modelled based on data of habitat use, occupancy, intensity of use or counts of animals. When the population is at carrying capacity or in an ideal-free distribution, habitat selection and related metrics of habitat use can be used to estimate abundance. If the population is not at equilibrium, models have the flexibility to incorporate density into models of habitat selection; but abundance might be influenced by factors influencing fitness that are not directly related to habitat thereby compromising the use of habitat-based models for predicting population size. Scale and domain of the sampling frame, both in time and space, are crucial considerations limiting application of these models. Ultimately, identifying reliable models for predicting abundance from habitat data requires an understanding of the mechanisms underlying population regulation and limitation.

Cattle grazing management affects soil microbial diversity and community network complexity in the Northern Great Plains.

Soil microbial communities play a vital role in the biogeochemical cycling and ecological functioning of grassland, but may be affected by common land uses such as cattle grazing. Changes in microbial diversity and network complexity can affect key ecosystem functions such as nutrient cycling. However, it is not well known how microbial diversity and network complexity respond to grazing in the Northern Great Plains. Consequently, it is important to understand whether variation in grazing management alters the diversity and complexity of grassland microbial communities. We compared the effect of intensive adaptive multi-paddock (AMP) grazing and conventional grazing practices on soil microbial communities using 16S/ITS amplicon sequencing. Samples were collected from grasslands in 13 AMP ranches and 13 neighboring, conventional ranches located across the Canadian prairies. We found that AMP grazing increased fungal diversity and evenness, and led to more complex microbial associations. Acidobacteria, Actinobacteria, Gemmatimonadetes, and Bacteroidetes were keystone taxa associated with AMP grazing, while Actinobacteria, Acidobacteria, Proteobacteria, and Armatimonadetes were keystone taxa under conventional grazing. Besides overall grazing treatment effects, specific grazing metrics like cattle stocking rate and rest-to-grazing ratio affected microbial richness and diversity. Bacterial and fungal richness increased with elevated stocking rate, and fungal richness and diversity increased directly with the rest-to-grazing ratio. These results suggest that AMP grazing may improve ecosystem by enhancing fungal diversity and increasing microbial network complexity and connectivity.

How landscape traits affect boreal mammal responses to anthropogenic disturbance.

Understanding mammalian responses to anthropogenic disturbance is challenging, as ecological processes and the patterns arising therefrom notoriously change across spatial and temporal scales, and among different landscape contexts. Responses to local scale disturbances are likely influenced by landscape context (e.g., overall landscape-level disturbance, landscape-level productivity). Hierarchical approaches considering small-scale sampling sites as nested holons within larger-scale landscapes, which constrain processes in lower-level holons, can potentially explain differences in ecological processes between multiple locations. We tested hypotheses about mammal responses to disturbance and interactions among holons using collected images from 957 camera sites across 9 landscapes in Alberta from 2007 to 2020 and examined occurrence for 11 mammal species using generalized linear mixed models. White-tailed deer occurred more in higher disturbed sites within lower disturbed landscapes (ß = -0.30 [-0.4 to -0.15]), whereas occurrence was greater in highly disturbed sites within highly disturbed landscapes for moose (ß = 0.20 [0.09-0.31]), coyote (ß = 0.20 [0.08-0.26]), and lynx (ß = 0.20 [0.07-0.26]). High disturbance sites in high productivity landscapes had higher occurrence of black bears (ß = -0.20 [-0.46 to -0.01]), lynx (ß = -0.70 [-0.97 to -0.34]), and wolves (ß = -0.50 [-0.73 to -0.21]). Conversely, we found higher probability of occurrence in low productivity landscapes with increasing site disturbance for mule deer (ß = 0.80 [0.39-1.14]), and white-tailed deer (ß = 0.20 [0.01-0.47]). We found the ecological context created by aggregate sums (high overall landscape disturbance), and by subcontinental hydrogeological processes in which that landscape is embedded (high landscape productivity), alter mammalian responses to anthropogenic disturbance at local scales. These responses also vary by species, which has implications for large-scale conservation planning. Management interventions must consider large-scale geoclimatic processes and geographic location of a landscape when assessing wildlife responses to disturbance.

A protocol for assessing bias and robustness of social network metrics using GPS based radio-telemetry data.

BACKGROUND: Social network analysis of animal societies allows scientists to test hypotheses about social evolution, behaviour, and dynamic processes. However, the accuracy of estimated metrics depends on data characteristics like sample proportion, sample size, and frequency. A protocol is needed to assess for bias and robustness of social network metrics estimated for the animal populations especially when a limited number of individuals are monitored. METHODS: We used GPS telemetry datasets of five ungulate species to combine known social network approaches with novel ones into a comprehensive five-step protocol. To quantify the bias and uncertainty in the network metrics obtained from a partial population, we presented novel statistical methods which are particularly suited for autocorrelated data, such as telemetry relocations. The protocol was validated using a sixth species, the fallow deer, with a known population size where ∼ 85 % of the individuals have been directly monitored. RESULTS: Through the protocol, we demonstrated how pre-network data permutations allow researchers to assess non-random aspects of interactions within a population. The protocol assesses bias in global network metrics, obtains confidence intervals, and quantifies uncertainty of global and node-level network metrics based on the number of nodes in the network. We found that global network metrics like density remained robust even with a lowered sample size, while local network metrics like eigenvector centrality were unreliable for four of the species. The fallow deer network showed low uncertainty and bias even at lower sampling proportions, indicating the importance of a thoroughly sampled population while demonstrating the accuracy of our evaluation methods for smaller samples. CONCLUSIONS: The protocol allows researchers to analyse GPS-based radio-telemetry or other data to determine the reliability of social network metrics. The estimates enable the statistical comparison of networks under different conditions, such as analysing daily and seasonal changes in the density of a network. The methods can also guide methodological decisions in animal social network research, such as sampling design and allow more accurate ecological inferences from the available data. The R package aniSNA enables researchers to implement this workflow on their dataset, generating reliable inferences and guiding methodological decisions.

Selection, use, choice and occupancy: clarifying concepts in resource selection studies.

1. During the last decade, there has been a proliferation of statistical methods for studying resource selection by animals. While statistical techniques are advancing at a fast pace, there is confusion in the conceptual understanding of the meaning of various quantities that these statistical techniques provide. 2. Terms such as selection, choice, use, occupancy and preference often are employed as if they are synonymous. Many practitioners are unclear about the distinctions between different concepts such as 'probability of selection,' 'probability of use,' 'choice probabilities' and 'probability of occupancy'. 3. Similarly, practitioners are not always clear about the differences between and relevance of 'relative probability of selection' vs. 'probability of selection' to effective management. 4. Practitioners also are unaware that they are using only a single statistical model for modelling resource selection, namely the exponential probability of selection, when other models might be more appropriate. Currently, such multimodel inference is lacking in the resource selection literature. 5. In this paper, we attempt to clarify the concepts and terminology used in animal resource studies by illustrating the relationships among these various concepts and providing their statistical underpinnings.