An official website of the United States government
Abstract Harvest of wild organisms is an important component of human culture, economy, and recreation, but can also put species at risk of extinction. Decisions that guide successful management actions therefore rely on the ability of researchers to link changes in demographic processes to the anthropogenic actions or environmental changes that underlie variation in demographic parameters.Ecologists often use population models or maximum sustained yield curves to estimate the impacts of harvest on wildlife and fish populations. Applications of these models usually focus exclusively on the impact of harvest and often fail to consider adequately other potential, often collinear, mechanistic drivers of the observed relationships between harvest and demographic rates. In this study, we used an integrated population model and long‐term data (1973–2016) to examine the relationships among hunting and natural mortality, the number of hunters, habitat conditions, and population size of blue‐winged tealSpatula discors, an abundant North American dabbling duck with a relatively fast‐paced life history strategy.Over the last two and a half decades of the study, teal abundance tripled, hunting mortality probability increased slightly (), and natural mortality probability increased substantially () at greater population densities. We demonstrate strong density‐dependent effects on natural mortality and fecundity as population density increased, indicative of compensatory harvest mortality and compensatory natality. Critically, an analysis that only assessed the relationship between survival and hunting mortality would spuriously indicate depensatory mortality due to multicollinearity between abundance, natural mortality and hunting mortality.Our findings demonstrate that models that only consider the direct effect of hunting on survival or natural mortality can fail to accurately assess the mechanistic impact of hunting on population dynamics due to multicollinearity among demographic drivers. This multicollinearity limits inference and may have strong impacts on applied management actions globally.
more »
« less
A hierarchical model for jointly assessing ecological and anthropogenic impacts on animal demography
Abstract The management of sustainable harvest of animal populations is of great ecological and conservation importance. Development of formal quantitative tools to estimate and mitigate the impacts of harvest on animal populations has positively impacted conservation efforts.The vast majority of existing harvest models, however, do not simultaneously estimate ecological and harvest impacts on demographic parameters and population trends. Given that the impacts of ecological drivers are often equal to or greater than the effects of harvest, and can covary with harvest, this disconnect has the potential to lead to flawed inference.In this study, we used Bayesian hierarchical models and a 43‐year capture–mark–recovery dataset from 404,241 female mallardsAnas platyrhynchosreleased in the North American midcontinent to estimate mallard demographic parameters. Furthermore, we model the dynamics of waterfowl hunters and habitat, and the direct and indirect effects of anthropogenic and ecological processes on mallard demographic parameters.We demonstrate that density dependence, habitat conditions and harvest can simultaneously impact demographic parameters of female mallards, and discuss implications for existing and future harvest management models.Our results demonstrate the importance of controlling for multicollinearity among demographic drivers in harvest management models, and provide evidence for multiple mechanisms that lead to partial compensation of mallard harvest. We provide a novel model structure to assess these relationships that may allow for improved inference and prediction in future iterations of harvest management models across taxa.
more »
« less
- Award ID(s):
- 2209765
- PAR ID:
- 10520339
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Journal of Animal Ecology
- Volume:
- 91
- Issue:
- 8
- ISSN:
- 0021-8790
- Page Range / eLocation ID:
- 1612 to 1626
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Climate and land use change are two of the primary threats to global biodiversity; however, each species within a community may respond differently to these facets of global change. Although it is typically assumed that species use the habitat that is advantageous for survival and reproduction, anthropogenic changes to the environment can create ecological traps, making it critical to assess both habitat selection (e.g. where species congregate on the landscape) and the influence of selected habitats on the demographic processes that govern population dynamics.We used a long‐term (1958–2011), large‐scale, multi‐species dataset for waterfowl that spans the United States and Canada to estimate species‐specific responses to climate and land use variables in a landscape that has undergone significant environmental change across space and time. We first estimated the effects of change in climate and land use variables on habitat selection and population dynamics for nine species. We then hypothesized that species‐specific responses to environmental change would scale with life‐history traits, specifically: longevity, nesting phenology and female breeding site fidelity.We observed species‐level heterogeneity in the demographic and habitat selection responses to climate and land use change, which would complicate community‐level habitat management. Our work highlights the importance of multi‐species monitoring and community‐level analysis, even among closely related species.We detected several relationships between life‐history traits, particularly nesting phenology, and species' responses to environmental change. One species, the early‐nesting northern pintail (Anas acuta), was consistently at the extreme end of responses to land use and climate predictors and has been a species of conservation concern since their population began to decline in the 1980s. They, and the blue‐winged teal, also demonstrated a positive habitat selection response to the proportion of cropland on the landscape that simultaneously reduced abundance the following year, indicative of susceptibility to ecological traps.By distilling the diversity of species' responses to environmental change within a community, our methodological approach and findings will help improve predictions of community responses to global change and can inform multi‐species management and conservation plans in dynamic landscapes that are based on simple tenets of life‐history theory.more » « less
-
Abstract Numerous modelling techniques exist to estimate abundance of plant and animal populations. The most accurate methods account for multiple complexities found in ecological data, such as observational biases, spatial autocorrelation, and species correlations. There is, however, a lack of user‐friendly and computationally efficient software to implement the various models, particularly for large data sets.We developed thespAbundance Rpackage for fitting spatially explicit Bayesian single‐species and multi‐species hierarchical distance sampling models, N‐mixture models, and generalized linear mixed models. The models within the package can account for spatial autocorrelation using Nearest Neighbour Gaussian Processes and accommodate species correlations in multi‐species models using a latent factor approach, which enables model fitting for data sets with large numbers of sites and/or species.We provide three vignettes and three case studies that highlightspAbundancefunctionality. We used spatially explicit multi‐species distance sampling models to estimate density of 16 bird species in Florida, USA, an N‐mixture model to estimate black‐throated blue warbler (Setophaga caerulescens) abundance in New Hampshire, USA, and a spatial linear mixed model to estimate forest above‐ground biomass across the continental USA.spAbundanceprovides a user‐friendly, formula‐based interface to fit a variety of univariate and multivariate spatially explicit abundance models. The package serves as a useful tool for ecologists and conservation practitioners to generate improved inference and predictions on the spatial drivers of abundance in populations and communities.more » « less
-
Abstract Estimating and monitoring plant population size is fundamental for ecological research, as well as conservation and restoration programs. High‐resolution imagery has potential to facilitate such estimation and monitoring. However, remotely sensed estimates typically have higher uncertainty than field measurements, risking biased inference on population status.We present a model that accounts for false negative (missed plants) and false positive (misclassified or double‐counted plants) error in counts from high‐resolution imagery via integration with ground data. We apply it to estimate the abundance of a foundational shrub species in post‐wildfire landscapes in the western United States. In these landscapes, plant recruitment is crucial for ecological recovery but locally patchy, motivating the use of spatially extensive measurements from unoccupied aerial systems (UAS). Integrating >16 ha of UAS imagery with >700 georeferenced field plots, we fit our model to generate insights into the prevalence and drivers of observation errors associated with classification algorithms used to distinguish individual plants, relationships between abundance and landscape context, and to generate spatially explicit maps of shrub abundance.Raw counts of plant abundance in high‐resolution imagery resulted in substantial false negative and false positive observation errors. The probability of detecting (p) adult plants (0.25 m tall) varied between sites within 0.52 < < 0.82, whereas the detection of smaller plants (<0.25 m) was lower, 0.03 < < 0.3. On average, we estimate that 19% of all detected plants were false positive errors, which varied spatially in relation to topographic predictors. Abundance declined toward the interior of previous wildfires and was positively associated with terrain roughness.Our study demonstrates that integrated models accounting for imperfect detection improve estimates of plant population abundance derived from inherently imperfect UAS imagery. We believe such models will further improve inference on plant population dynamics—relevant to restoration, wildlife habitat and related objectives—and echo previous calls for remote sensing applications to better differentiate between ecological and observational processes.more » « less
-
Abstract Spatial environmental heterogeneity maintains species diversity in ecological communities, but it is difficult to model and its effects on individuals are often ignored in empirical studies of species coexistence. This is problematic because many studies estimate plant competition at the neighbourhood level, and modelling individual demographic responses without neighbourhood‐level environmental data could lead to biased results. We aimed to understand how and when ignoring spatial heterogeneity produces biased estimates of the strength of intraspecific and interspecific competition and, therefore, incorrect predictions about species coexistence.First, we used a spatially explicit individual‐based model (IBM), with neighbourhood competition depending on neighbour size and distance, to simulate two plant populations that coexist via differing responses to spatial heterogeneity. Next, we mimicked the action of ecologists collecting data in observational studies or short‐term experimental studies and sampled from the simulated populations. Then, we fitted demographic models for each species including parameters for the intensity of intra‐ and interspecific competitionwithoutinformation about spatial heterogeneity. Finally, we simulated invasions of species at low density using the IBM and estimated parameters to compute invasion growth rates (IGR).Demographic modelling with data from simulated observational studies resulted in underestimates of intraspecific competition and overestimates of interspecific competition. When we simulated invasions with the IBM and those estimated competition coefficients, the IGRs were negative or around zero and incorrectly predicted competitive exclusion. In simulated experimental studies, the estimates of competition were more accurate, but the model still incorrectly predicted competitive exclusion.Synthesis: Our study demonstrates how biases can arise in neighbourhood competition studies and highlights the importance of explicitly incorporating spatial heterogeneity into empirical coexistence studies. Failure to account for environmental heterogeneity at the level of individual plants may lead to mistaken conclusions about coexistence outcomes.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Journal Article:
- https://doi.org/10.1111/1365-2656.13747
