Spending degrees of freedom in a poor economy: A case study of building a sightability model for moose in northeastern Minnesota

A phenology of fear: Investigating scale and seasonality in predator-prey games between wolves and white-tailed deer

Predators and prey engage in games where each player must counter the moves of the other, and these games include multiple phases operating at different spatiotemporal scales. Recent work has highlighted potential issues related to scale-sensitive inferences in predator-prey interactions, and there is growing appreciation that these may exhibit pronounced but predictable dynamics. Motivated by previous assertions about effects arising from foraging games between white-tailed deer and canid predators (coyotes and wolves), we used a large and year-round network of trail cameras to characterize deer and predator foraging games, with a particular focus on clarifying its temporal scale and seasonal variation. Linear features were strongly associated with predator detection rates, suggesting these play a central role in canid foraging tactics by expediting movement. Consistent with expectations for prey contending with highly mobile predators, deer responses were more sensitive to proximal risk metrics at finer spatiotemporal scales, suggesting that coarser but more commonly used scales of analysis may miss useful insights into prey risk-response. Time allocation appears to be a key tactic for deer risk management and was more strongly moderated by factors associated with forage or evasion heterogeneity (forest cover, snow and plant phenology) than factors associated with the likelihood of predator encounter (linear features). Trade-offs between food and safety appeared to vary as much seasonally as spatially, with snow and vegetation phenology giving rise to a "phenology of fear." Deer appear free to counter predators during milder times of year, but a combination of poor foraging state, reduced forage availability, greater movements costs, and reproductive state dampen responsiveness during winter. Pronounced intra-annual variation in predator-prey interactions may be common in seasonal environments.

Harvest and density-dependent predation drive long-term population decline in a northern ungulate

The relative effect of top-down versus bottom-up forces in regulating and limiting wildlife populations is an important theme in ecology. Untangling these effects is critical for a basic understanding of trophic dynamics and effective management. We examined the drivers of moose (Alces alces) population growth by integrating two independent sources of observations within a hierarchical Bayesian population model. We used one of the largest existing spatiotemporal data sets on ungulate population dynamics globally. We documented a 20% population decline over the period examined. There was negative density-dependent population growth of moose. Although we could not determine the mechanisms producing density-dependent suppression of population growth, the relatively low densities at which we documented moose populations suggested it could be due to density-dependent predation. Predation primarily limited population growth, except at low density, where it was regulating. After we simulated several harvest scenarios, it appeared that harvest was largely additive and likely contributed to population declines. Our results highlight how population dynamics are context dependent and vary strongly across gradients in climate, forest type, and predator abundance. These results help clarify long-standing questions in population ecology and highlight the complex relationships between natural and human-caused mortality in driving ungulate population dynamics.

Conceptual and methodological advances in habitat-selection modeling: guidelines for ecology and evolution

Habitat selection is a fundamental animal behavior that shapes a wide range of ecological processes, including animal movement, nutrient transfer, trophic dynamics and population distribution. Although habitat selection has been a focus of ecological studies for decades, technological, conceptual and methodological advances over the last 20 yr have led to a surge in studies addressing this process. Despite the substantial literature focused on quantifying the habitat-selection patterns of animals, there is a marked lack of guidance on best analytical practices. The conceptual foundations of the most commonly applied modeling frameworks can be confusing even to those well versed in their application. Furthermore, there has yet to be a synthesis of the advances made over the last 20 yr. Therefore, there is a need for both synthesis of the current state of knowledge on habitat selection, and guidance for those seeking to study this process. Here, we provide an approachable overview and synthesis of the literature on habitat-selection analyses (HSAs) conducted using selection functions, which are by far the most applied modeling framework for understanding the habitat-selection process. This review is purposefully non-technical and focused on understanding without heavy mathematical and statistical notation, which can confuse many practitioners. We offer an overview and history of HSAs, describing the tortuous conceptual path to our current understanding. Through this overview, we also aim to address the areas of greatest confusion in the literature. We synthesize the literature outlining the most exciting conceptual advances in the field of habitat-selection modeling, discussing the substantial ecological and evolutionary inference that can be made using contemporary techniques. We aim for this paper to provide clarity for those navigating the complex literature on HSAs while acting as a reference and best practices guide for practitioners.

An on-ice aerial survey of the Kane Basin polar bear (Ursus maritimus) subpopulation

There is an imminent need to collect information on distribution and abundance of polar bears (Ursus maritimus) to understand how they are affected by the ongoing decrease in Arctic sea ice. The Kane Basin (KB) subpopulation is a group of high-latitude polar bears that ranges between High Arctic Canada and NW Greenland around and north of the North Water polynya (NOW). We conducted a line transect distance sampling aerial survey of KB polar bears during 28 April–12 May 2014. A total of 4160 linear kilometers were flown in a helicopter over fast ice in the fjords and over offshore pack ice between 76° 50′ and 80° N′. Using a mark-recapture distance sampling protocol, the estimated abundance was 190 bears (95% lognormal CI: 87–411; CV 39%). This estimate is likely negatively biased to an unknown degree because the offshore sectors of the NOW with much open water were not surveyed because of logistical and safety reasons. Our study demonstrated that aerial surveys may be a feasible method for obtaining abundance estimates for small subpopulations of polar bears.

Monitoring sun bears and Asiatic black bears with remotely sensed predictors to inform conservation management

Asiatic black bear Ursus thibetanus and sun bear Helarctos malayanus populations are declining throughout South-east Asia as a result of habitat loss and human disturbance. Knowledge of the distribution and status of each species is limited and largely anecdotal. Range maps are coarse, compiled by expert opinion, and presence or absence is unknown over large portions of South-east Asia. These two species co-occur in Lao People's Democratic Republic and may be faring better there than in neighbouring countries. During 2010–2013 we searched for bear sign along 99 transects within eight study sites throughout Lao. To explore countrywide relative abundance and habitat suitability, we modelled bear sign as a log-linear function of biological and anthropogenic predictors that were associated with habitat assemblages and human disturbance. Bears favored higher elevations and rugged terrain in areas less accessible to humans, and were most abundant in the north and east of Lao. Suitable habitats were rare in the southern lowland plains where bear abundance was relatively low. Our model predicted that Nam Et–Phou Louey National Protected Area had the largest areas of suitable bear habitat, followed by the Nakai-Nam Teun and Nam Ha National Protected Areas. Using transects to survey for bear sign, we created a replicable geographical information system based assessment tool for bears in Lao that can be used to identify conservation opportunities and monitor changes in bear distribution over time.

Comparing survey and multiple recruitment-mortality models to assess growth rates and population projections

Estimation of population trends and demographic parameters is important to our understanding of fundamental ecology and species management, yet these data are often difficult to obtain without the use of data from population surveys or marking animals. The northeastern Minnesota moose (Alces alces Linnaeus, 1758) population declined 58% during 2006-2017, yet aerial surveys indicated stability during 2012-2017. In response to the decline, the Minnesota Department of Natural Resources (MNDNR) initiated studies of adult and calf survival to better understand cause-specific mortality, calf recruitment, and factors influencing the population trajectory. We estimated population growth rate (λ) using adult survival and calf recruitment data from demographic studies and the recruitment-mortality (R-M) Equation and compared these estimates to those calculated using data from aerial surveys. We then projected population dynamics 50 years using each resulting λ and used a stochastic model to project population dynamics 30 years using data from the MNDNR's studies. Calculations of λ derived from 2012 to 2017 survey data, and the R-M Equation indicated growth (1.02 ± 0.16 [SE] and 1.01 ± 0.04, respectively). However, the stochastic model indicated a decline in the population over 30 years (λ = 0.91 ± 0.004; 2014-2044). The R-M Equation has utility for estimating λ, and the supporting information from demographic collaring studies also helps to better address management questions. Furthermore, estimates of λ calculated using collaring data were more certain and reflective of current conditions. Long-term monitoring using collars would better inform population performance predictions and demographic responses to environmental variability.

Impact of prey occupancy and other ecological and anthropogenic factors on tiger distribution in Thailand's western forest complex

Despite conservation efforts, large mammals such as tigers (Panthera tigris) and their main prey, gaur (Bos gaurus), banteng (Bos javanicus), and sambar (Rusa unicolor), are highly threatened and declining across their entire range. The only large viable source population of tigers in mainland Southeast Asia occurs in Thailand's Western Forest Complex (WEFCOM), an approximately 19,000 km2 landscape of 17 contiguous protected areas.We used an occupancy modeling framework, which accounts for imperfect detection, to identify the factors that affect tiger distribution at the approximate scale of a female tiger's home range, 64 km2, and site use at a scale of 1-km2. At the larger scale, we estimated the proportion of sites at WEFCOM that were occupied by tigers; at the finer scale, we identified the key variables that influence site-use and developed a predictive distribution map. At both scales, we examined key anthropogenic and ecological factors that help explain tiger distribution and habitat use, including probabilities of gaur, banteng, and sambar occurrence from a companion study.Occupancy estimated at the 64-km2 scale was primarily influenced by the combined presence of all three large prey species, and 37% or 5,858 km2 of the landscape was predicted to be occupied by tigers. In contrast, site use estimated at the scale of 1 km2 was most strongly influenced by the presence of sambar.By modeling occupancy while accounting for imperfect probability of detection, we established reliable benchmark data on the distribution of tigers in WEFCOM. This study also identified factors that limit tiger distributions; which managers can then target to expand tiger distribution and guide recovery elsewhere in Southeast Asia.

Clair K, Fieberg JR. Time series sightability modeling of animal populations

Logistic regression models—or “sightability models”—fit to detection/non-detection data from marked individuals are often used to adjust for visibility bias in later detection-only surveys, with population abundance estimated using a modified Horvitz-Thompson (mHT) estimator. More recently, a model-based alternative for analyzing combined detection/non-detection and detection-only data was developed. This approach seemed promising, since it resulted in similar estimates as the mHT when applied to data from moose (Alces alces) surveys in Minnesota. More importantly, it provided a framework for developing flexible models for analyzing multiyear detection-only survey data in combination with detection/non-detection data. During initial attempts to extend the model-based approach to multiple years of detection-only data, we found that estimates of detection probabilities and population abundance were sensitive to the amount of detection-only data included in the combined (detection/non-detection and detection-only) analysis. Subsequently, we developed a robust hierarchical modeling approach where sightability model parameters are informed only by the detection/non-detection data, and we used this approach to fit a fixed-effects model (FE model) with year-specific parameters and a temporally-smoothed model (TS model) that shares information across years via random effects and a temporal spline. The abundance estimates from the TS model were more precise, with decreased interannual variability relative to the FE model and mHT abundance estimates, illustrating the potential benefits from model-based approaches that allow information to be shared across years.

A SEROSURVEY OF DISEASES OF FREE-RANGING GRAY WOLVES (CANIS LUPUS) IN MINNESOTA, USA

We tested serum samples from 387 free-ranging wolves ( Canis lupus ) from 2007 to 2013 for exposure to eight canid pathogens to establish baseline data on disease prevalence and spatial distribution in Minnesota's wolf population. We found high exposure to canine adenoviruses 1 and 2 (88% adults, 45% pups), canine parvovirus (82% adults, 24% pups), and Lyme disease (76% adults, 39% pups). Sixty-six percent of adults and 36% of pups exhibited exposure to the protozoan parasite Neospora caninum . Exposure to arboviruses was confirmed, including West Nile virus (37% adults, 18% pups) and eastern equine encephalitis (3% adults). Exposure rates were lower for canine distemper (19% adults, 5% pups) and heartworm (7% adults, 3% pups). Significant spatial trends were observed in wolves exposed to canine parvovirus and Lyme disease. Serologic data do not confirm clinical disease, but better understanding of disease ecology of wolves can provide valuable insight into wildlife population dynamics and improve management of these species.

Model averaging and muddled multimodel inferences

Three flawed practices associated with model averaging coefficients for predictor variables in regression models commonly occur when making multimodel inferences in analyses of ecological data. Model-averaged regression coefficients based on Akaike information criterion (AIC) weights have been recommended for addressing model uncertainty but they are not valid, interpretable estimates of partial effects for individual predictors when there is multicollinearity among the predictor variables. Multicollinearity implies that the scaling of units in the denominators of the regression coefficients may change across models such that neither the parameters nor their estimates have common scales, therefore averaging them makes no sense. The associated sums of AIC model weights recommended to assess relative importance of individual predictors are really a measure of relative importance of models, with little information about contributions by individual predictors compared to other measures of relative importance based on effects size or variance reduction. Sometimes the model-averaged regression coefficients for predictor variables are incorrectly used to make model-averaged predictions of the response variable when the models are not linear in the parameters. I demonstrate the issues with the first two practices using the college grade point average example extensively analyzed by Burnham and Anderson. I show how partial standard deviations of the predictor variables can be used to detect changing scales of their estimates with multicollinearity. Standardizing estimates based on partial standard deviations for their variables can be used to make the scaling of the estimates commensurate across models, a necessary but not sufficient condition for model averaging of the estimates to be sensible. A unimodal distribution of estimates and valid interpretation of individual parameters are additional requisite conditions. The standardized estimates or equivalently the t statistics on unstandardized estimates also can be used to provide more informative measures of relative importance than sums of AIC weights. Finally, I illustrate how seriously compromised statistical interpretations and predictions can be for all three of these flawed practices by critiquing their use in a recent species distribution modeling technique developed for predicting Greater Sage-Grouse (Centrocercus urophasianus) distribution in Colorado, USA. These model averaging issues are common in other ecological literature and ought to be discontinued if we are to make effective scientific contributions to ecological knowledge and conservation of natural resources.