An official website of the United States government
Abstract Understanding migratory connectivity, or the linkage of populations between seasons, is critical for effective conservation and management of migratory wildlife. A growing number of tools are available for understanding where migratory individuals and populations occur throughout the annual cycle. Integration of the diverse measures of migratory movements can help elucidate migratory connectivity patterns with methodology that accounts for differences in sampling design, directionality, effort, precision and bias inherent to each data type.The R packageMigConnectivitywas developed to estimate population‐specific connectivity and the range‐wide strength of those connections. New functions allow users to integrate intrinsic markers, tracking and long‐distance reencounter data, collected from the same or different individuals, to estimate population‐specific transition probabilities (estTransition) and the range‐wide strength of those transition probabilities (estStrength). We used simulation and real‐world case studies to explore the challenges and limitations of data integration based on data from three migratory bird species, Painted Bunting (Passerina ciris), Yellow Warbler (Setophaga petechia) and Bald Eagle (Haliaeetus leucocephalus), two of which had bidirectional data.We found data integration is useful for quantifying migratory connectivity, as single data sources are less likely to be available across the species range. Furthermore, accurate strength estimates can be obtained from either breeding‐to‐nonbreeding or nonbreeding‐to‐breeding data. For bidirectional data, integration can lead to more accurate estimates when data are available from all regions in at least one season.The ability to conduct combined analyses that account for the unique limitations and biases of each data type is a promising possibility for overcoming the challenge of range‐wide coverage that has been hard to achieve using single data types. The best‐case scenario for data integration is to have data from all regions, especially if the question is range‐wide or data are bidirectional. Multiple data types on animal movements are becoming increasingly available and integration of these growing datasets will lead to a better understanding of the full annual cycle of migratory animals.
more »
« less
This content will become publicly available on April 1, 2027
Integrating multiple field measurements in a Bayesian parallel regression framework to estimate Tasmanian devil age
Abstract Understanding age structure in populations is foundational for ecological study, yet ascertaining the age of individuals in the wild can be problematic. Methods typically rely on the prior determination of a relationship between age and some morphological measurement specific to the species or animal group under investigation.We present a novel method to incorporate multiple measures of animal size and development to estimate age, using Bayesian parallel regression to integrate multiple regression relationships into predictions.We apply the method to Tasmanian devils, a carnivorous marsupial that is threatened by a transmissible cancer. We estimate devil age based on measures of mass, head width, canine over‐eruption and molar characteristics.Practical implication. The method provides a flexible framework with potential application to a range of species; however, the method is best suited to capture–mark–recapture style studies where repeated measures data have been collected for individuals across multiple points in time.
more »
« less
- PAR ID:
- 10704926
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Ecological Solutions and Evidence
- Volume:
- 7
- Issue:
- 2
- ISSN:
- 2688-8319
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
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 Droughts are increasing in frequency and severity globally due to climate change, leading to changes in resource availability that may have cascading effects on animal ecology. Resource availability is a key driver of animal space use, which in turn influences interspecific interactions like intraguild competition. Understanding how climate‐induced changes in resource availability influence animal space use, and how species‐specific responses scale up to affect intraguild dynamics, is necessary for predicting broader community‐level responses to climatic changes.Although several studies have demonstrated the ecological impacts of drought, the behavioural responses of individuals that scale up to these broader‐scale effects are not well known, particularly among animals in top trophic levels like large carnivores. Furthermore, we currently lack understanding of how the impacts of climate variability on individual carnivore behaviour are linked to intraguild dynamics, in part because multi‐species datasets collected at timescales relevant to climatic changes are rare.Using 11 years of GPS data from four sympatric large carnivore species in southern Africa—lions (Panthera leo), leopards (Panthera pardus), African wild dogs (Lycaon pictus) and cheetahs (Acinonyx jubatus)—spanning 4 severe drought events, we test whether drought conditions impact (1) large carnivore space use, (2) broad‐scale intraguild spatial overlap and (3) fine‐scale intraguild interactions.Drought conditions expanded space use across species, with carnivores increasing their monthly home range sizes by 35% (wild dogs) to 66% (leopards). Drought conditions increased the amount of spatial overlap between lions and subordinate felids (cheetahs and leopards) by up to 119%, but only lion‐cheetah encounter rates were affected by these changes, declining in response to drought.Our findings reveal that drought has a clear signature on the space use of multiple sympatric large carnivore species, which can alter spatiotemporal partitioning between competing species. Our study thereby illuminates the links between environmental change, animal behaviour and intraguild dynamics. While fine‐scale avoidance strategies may facilitate intraguild coexistence during periodic droughts, large carnivore conservation may require considerable expansion of protected areas or revised human‐carnivore coexistence strategies to accommodate the likely long‐term increased space demands of large carnivores under projected increases in drought intensity.more » « less
-
A hierarchical model for jointly assessing ecological and anthropogenic impacts on animal demographyAbstract 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
-
Abstract Missing values are ubiquitous in ecological time series. Methods like linear interpolation,k‐nearest neighbour (kNN) imputation or regression‐based imputation are commonly used to repair these gaps, but may be unsuitable when the data are infrequently sampled or have nonlinear dynamics.We introduce multiview cross‐mapping (MVCM), a novel method based in empirical dynamic modelling (EDM) that exploits shared information between dynamically coupled time series. Rather than using points nearby in time, MVCM uses similar system states on an attractor to estimate the value of a missing data point. MVCM works best where other dynamically coupled variables have been observed, but it can also predict into short gaps where all variables are missing (data void).Using model data from a coupled five‐species system, and observational data from a long‐term plankton survey in Lake Zurich, Switzerland, we show that MVCM is robust and performs significantly better than linear methods (linear interpolation, linear regression‐based imputation) and kNN imputation.Crucially, this approach differs from methods based on a purely statistical paradigm because it assumes that the time series are generated by underlying deterministic rules. This dynamical framework allows us to exploit information shared between time series from a mechanistically coupled system, making complexity an asset for the analysis of imperfect observational data.more » « less
