Using stable isotope mixing models (SIMMs) as a tool to investigate the foraging ecology of animals is gaining popularity among researchers. As a result, statistical methods are rapidly evolving and numerous models have been produced to estimate the diets of animals—each with their benefits and their limitations. Deciding which SIMM to use is contingent on factors such as the consumer of interest, its food sources, sample size, the familiarity a user has with a particular framework for statistical analysis, or the level of inference the researcher desires to make (e.g., population- or individual-level). In this paper, we provide a review of commonly used SIMM models and describe a comprehensive SIMM that includes all features commonly used in SIMM analysis and two new features. We used data collected in Yosemite National Park to demonstrate IsotopeR's ability to estimate dietary parameters. We then examined the importance of each feature in the model and compared our results to inferences from commonly used SIMMs. IsotopeR's user interface (in R) will provide researchers a user-friendly tool for SIMM analysis. The model is also applicable for use in paleontology, archaeology, and forensic studies as well as estimating pollution inputs.

Overhunting in tropical forests reduces populations of vertebrate seed dispersers. If reduced seed dispersal has a negative impact on tree population viability, overhunting could lead to altered forest structure and dynamics, including decreased biodiversity. However, empirical data showing decreased animal-dispersed tree abundance in overhunted forests contradict demographic models which predict minimal sensitivity of tree population growth rate to early life stages. One resolution to this discrepancy is that seed dispersal determines spatial aggregation, which could have demographic consequences for all life stages. We tested the impact of dispersal loss on population viability of a tropical tree species, Miliusa horsfieldii, currently dispersed by an intact community of large mammals in a Thai forest. We evaluated the effect of spatial aggregation for all tree life stages, from seeds to adult trees, and constructed simulation models to compare population viability with and without animal-mediated seed dispersal. In simulated populations, disperser loss increased spatial aggregation by fourfold, leading to increased negative density dependence across the life cycle and a 10-fold increase in the probability of extinction. Given that the majority of tree species in tropical forests are animal-dispersed, overhunting will potentially result in forests that are fundamentally different from those existing now.

Environmental stochasticity is an important concept in population dynamics, providing a quantitative model of the extrinsic fluctuations driving population abundances. It is typically formulated as a stochastic perturbation to the maximum reproductive rate, leading to a population variance that scales quadratically with abundance. However, environmental fluctuations may also drive changes in the strength of density dependence. Very few studies have examined the consequences of this alternative model formulation while even fewer have tested which model better describes fluctuations in animal populations. Here we use data from the Global Population Dynamics Database to determine the statistical support for this alternative environmental variance model in 165 animal populations and test whether these models can capture known population-environment interactions in two well-studied ungulates. Our results suggest that variation in the density dependence is common and leads to a higher-order scaling of the population variance. This scaling will often stabilize populations although dynamics may also be destabilized under certain conditions. We conclude that higher-order environmental variation is a potentially ubiquitous and consequential property of animal populations. Our results suggest that extinction risk estimates may often be overestimated when not properly taking into account how environmental fluctuations affect population parameters.time series | environmental variance | variance scaling | population viability analysis | stochastic model A key question for ecologists is determining how environmental fluctuations drive population variability. Stochastic models of population dynamics consider environmental fluctuations as temporal perturbations to the mean of the birth and death rates of individuals in a population (1, 2). Because specific information on environmental covariates is not required in these models, the approach allows ecologists to make significant progress understanding how environmental perturbations drive population variability. These models have also proven to be useful in empirical settings where a stochastic process model is combined with an observation model to construct a likelihood of the observed abundances that can then be used to make inferences about the processes underlying the population (3, 4). A potentially important aspect of these models is the specification of how environmental forces drive variation in model parameters.Current practice assumes that environmental variation occurs as an additive term in the log of the per-capita growth rate, defined as R t = ln ðN t =N t−1 Þ. This variation can be derived by assuming that the density-independent reproduction rate is a random variable, which leads to a population variance with the well-known quadratic scaling of the population variance on abundances (e.g., refs. 1, 5). This model will capture the effects of environmental factors, like temperature, that can affect the maximum reproductive rate of individuals (6). However, the additi...

We used genetic and stable isotope analysis of hair from free‐ranging black bears (Ursus americanus) in Yosemite National Park, California, USA to: 1) identify bears that consume human food, 2) estimate the diets of these bears, and 3) evaluate the Yosemite human–bear management program. Specifically, we analyzed the isotopic composition of hair from bears known a priori to be food‐conditioned or non‐food‐conditioned and used these data to predict whether bears with an unknown management status were food‐conditioned (FC) or non‐food‐conditioned (NFC). We used a stable isotope mixing model to estimate the proportional contribution of natural foods (plants and animals) versus human food in the diets of FC bears. We then used results from both analyses to evaluate proactive (population‐level) and reactive (individual‐level) human–bear management, and discussed new metrics to evaluate the overall human–bear management program in Yosemite. Our results indicated that 19 out of 145 (13%) unknown bears sampled from 2005 to 2007 were food‐conditioned. The proportion of human food in the diets of known FC bears likely declined from 2001–2003 to 2005–2007, suggesting proactive management was successful in reducing the amount of human food available to bears. In contrast, reactive management was not successful in changing the management status of known FC bears to NFC bears, or in reducing the contribution of human food to the diets of FC bears. Nine known FC bears were recaptured on 14 occasions from 2001 to 2007; all bears were classified as FC during subsequent recaptures, and human–bear management did not reduce the amount of human food in the diets of FC bears. Based on our results, we suggest Yosemite continue implementing proactive human–bear management, reevaluate reactive management, and consider removing problem bears (those involved in repeated bear incidents) from the population. © 2012 The Wildlife Society.

We used carbon (δ13C) and nitrogen (δ15N) stable isotopes derived from the tissues of American black bears (Ursus americanus) to estimate the proportion of human‐derived foodstuffs and food waste (“human foods”) in the diets of human food‐conditioned bears over the past century in Yosemite National Park, located in central–eastern California. Our goal was to understand how the foraging ecology of bears responded to changing management strategies. We found that the proportion of human foods increased in bear diets when park personnel and visitors fed bears intentionally in 1923–1971, remained relatively high and constant after artificial feeding areas were closed, and declined drastically in 1999–2007, following a $500 000 annual government appropriation used to mitigate human–bear conflicts in the park. This reduction in the amount of human foods in bear diets suggests that Yosemite managers have been successful in reducing the availability of human foods to bears. Yosemite bears currently consume human foods in the same proportion as they did in 1915–1919. This result indicates a notable management achievement in the park, considering that thousands of people visited Yosemite annually in the early 1900s while about four million people visit each year today.

Long-distance seed dispersal (LDD) is considered a crucial determinant of tree distributions, but its effects depend on demographic processes that enable seeds to establish into adults and that remain poorly understood at large spatial scales. We estimated rates of seed arrival, germination, and survival and growth for a canopy tree species (Miliusa horsfieldii), in a landscape ranging from evergreen forest, where the species' abundance is high, to deciduous forest, where it is extremely low. We then used an individual-based model (IBM) to predict sapling establishment and to compare the relative importance of seed arrival and establishment in explaining the observed distribution of seedlings. Individuals in deciduous forest, far from the source population, experienced multiple benefits (e.g., increased germination rate and seedling survival and growth) from being in a habitat where conspecifics were almost absent. The net effect of these spatial differences in demographic processes was significantly higher estimated sapling establishment probabilities for seeds dispersed long distances into deciduous forest. Despite the high rate of establishment in this habitat, Miliusa is rare in the deciduous forest because the arrival of seeds at long distances from the source population is extremely low. Across the entire landscape, the spatial pattern of seed arrival is much more important than the spatial pattern of establishment for explaining observed seedling distributions. By using dynamic models to link demographic data to spatial patterns, we show that LDD plays a pivotal role in the distribution of this tree in its native habitat.

Scientific progress depends upon the accumulation of empirical knowledge via reproducible methodology. Although reproducibility is a main tenet of the scientific method, recent studies have highlighted widespread failures in adherence to this ideal. The goal of this study was to gauge the level of computational reproducibility, or the ability to obtain the same results using the same data and analytic methods as in the original publication, in the field of wildlife science. We randomly selected 80 papers published in the Journal of Wildlife Management and Wildlife Society Bulletin between 1 June 2016 and 1 June 2018. Of those that were suitable for reproducibility review (n = 74), we attempted to obtain study data from online repositories or directly from authors. Forty‐two authors did not respond to our requests, and we were further unable to obtain data from authors of 13 other studies. Of the 19 studies for which we were able to obtain data and complete our analysis, we judged that 13 were mostly or fully reproducible. We conclude that the studies with publicly available data or data shared upon request were largely reproducible, but we remain concerned about the difficulty in obtaining data from recently published papers. We recommend increased data‐sharing, data organization and documentation, communication, and training to advance computational reproducibility in the wildlife sciences. © 2020 The Authors. The Journal of Wildlife Management published by Wiley Periodicals, Inc. on behalf of The Wildlife Society.

Predicting population extinction risk is a fundamental application of ecological theory to the practice of conservation biology. Here, we compared the prediction performance of a wide array of stochastic, population dynamics models against direct observations of the extinction process from an extensive experimental data set. By varying a series of biological and statistical assumptions in the proposed models, we were able to identify the assumptions that affected predictions about population extinction. We also show how certain autocorrelation structures can emerge due to interspecific interactions, and that accounting for the stochastic effect of these interactions can improve predictions of the extinction process. We conclude that it is possible to account for the stochastic effects of community interactions on extinction when using single-species time series.