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1. movedesign: Shiny R app to evaluate sampling design for animal movement studies

   https://doi.org/10.1111/2041-210X.14153  

   Silva, Inês ; Fleming, Christen H. ; Noonan, Michael J. ; Fagan, William F. ; Calabrese, Justin M. ( June 2023 , Methods in Ecology and Evolution)

   Projects focused on movement behaviour and home range are commonplace, but beyond a focus on choosing appropriate research questions, there are no clear guidelines for such studies. Without these guidelines, designing an animal tracking study to produce reliable estimates of space‐use and movement properties (necessary to answer basic movement ecology questions), is often done in an ad hoc manner.

   We developed ‘movedesign’, a user‐friendly Shiny application, which can be utilized to investigate the precision of three estimates regularly reported in movement and spatial ecology studies: home range area, speed and distance travelled. Conceptually similar to statistical power analysis, this application enables users to assess the degree of estimate precision that may be achieved with a given sampling design; that is, the choices regarding data resolution (sampling interval) and battery life (sampling duration).

   Leveraging the ‘ctmm’Rpackage, we utilize two methods proven to handle many common biases in animal movement datasets: autocorrelated kernel density estimators (AKDEs) and continuous‐time speed and distance (CTSD) estimators. Longer sampling durations are required to reliably estimate home range areas via the detection of a sufficient number of home range crossings. In contrast, speed and distance estimation requires a sampling interval short enough to ensure that a statistically significant signature of the animal's velocity remains in the data.

   This application addresses key challenges faced by researchers when designing tracking studies, including the trade‐off between long battery life and high resolution of GPS locations collected by the devices, which may result in a compromise between reliably estimating home range or speed and distance. ‘movedesign’ has broad applications for researchers and decision‐makers, supporting them to focus efforts and resources in achieving the optimal sampling design strategy for their research questions, prioritizing the correct deployment decisions for insightful and reliable outputs, while understanding the trade‐off associated with these choices.

    

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2. Estimating encounter location distributions from animal tracking data

   https://doi.org/10.1111/2041-210X.13597  

   Noonan, Michael J. ; Martinez‐Garcia, Ricardo ; Davis, Grace H. ; Crofoot, Margaret C. ; Kays, Roland ; Hirsch, Ben T. ; Caillaud, Damien ; Payne, Eric ; Sih, Andrew ; Sinn, David L. ; et al ( May 2021 , Methods in Ecology and Evolution)

   Ecologists have long been interested in linking individual behaviour with higher level processes. For motile species, this ‘upscaling’ is governed by how well any given movement strategy maximizes encounters with positive factors and minimizes encounters with negative factors. Despite the importance of encounter events for a broad range of ecological processes, encounter theory has not kept pace with developments in animal tracking or movement modelling. Furthermore, existing work has focused primarily on the relationship between animal movement and encounterrateswhile the relationship between individual movement and the spatiallocationsof encounter events in the environment has remained conspicuously understudied.

   Here, we bridge this gap by introducing a method for describing the long‐term encounter location probabilities for movement within home ranges, termed the conditional distribution of encounters (CDE). We then derive this distribution, as well as confidence intervals, implement its statistical estimator into open‐source software and demonstrate the broad ecological relevance of this distribution.

   We first use simulated data to show how our estimator provides asymptotically consistent estimates. We then demonstrate the general utility of this method for three simulation‐based scenarios that occur routinely in biological systems: (a) a population of individuals with home ranges that overlap with neighbours; (b) a pair of individuals with a hard territorial border between their home ranges; and (c) a predator with a large home range that encompassed the home ranges of multiple prey individuals. Using GPS data from white‐faced capuchinsCebus capucinus, tracked on Barro Colorado Island, Panama, and sleepy lizardsTiliqua rugosa,tracked in Bundey, South Australia, we then show how the CDE can be used to estimate the locations of territorial borders, identify key resources, quantify the potential for competitive or predatory interactions and/or identify any changes in behaviour that directly result from location‐specific encounter probability.

   The CDE enables researchers to better understand the dynamics of populations of interacting individuals. Notably, the general estimation framework developed in this work builds straightforwardly off of home range estimation and requires no specialized data collection protocols. This method is now openly available via thectmm Rpackage.

    

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3. Integrated population models: Model assumptions and inference

   https://doi.org/10.1111/2041-210X.13195  

   Riecke, Thomas V. ; Williams, Perry J. ; Behnke, Tessa L. ; Gibson, Daniel ; Leach, Alan G. ; Sedinger, Benjamin S. ; Street, Phillip A. ; Sedinger, James S. ; Freckleton, ed., Robert ( May 2019 , Methods in Ecology and Evolution)

   Integrated population models (IPMs) have become increasingly popular for the modelling of populations, as investigators seek to combine survey and demographic data to understand processes governing population dynamics. These models are particularly useful for identifying and exploring knowledge gaps within life histories, because they allow investigators to estimate biologically meaningful parameters, such as immigration or reproduction, that were previously unidentifiable without additional data. AsIPMs have been developed relatively recently, there is much to learn about model behaviour. Behaviour of parameters, such as estimates near boundaries, and the consequences of varying degrees of dependency among datasets, has been explored. However, the reliability of parameter estimates remains underexamined, particularly when models include parameters that are not identifiable from one data source, but are indirectly identifiable from multiple datasets and a presumed model structure, such as the estimation of immigration using capture‐recapture, fecundity and count data, combined with a life‐history model.

   To examine the behaviour of model parameter estimates, we simulated stable populations closed to immigration and emigration. We simulated two scenarios that might induce error into survival estimates: marker induced bias in the capture–mark–recapture data and heterogeneity in the mortality process. We subsequently fit capture–mark–recapture, state‐space and fecundity models, as well asIPMs that estimated additional parameters.

   Simulation results suggested that when model assumptions are violated, estimation of additional, previously unidentifiable, parameters usingIPMs may be extremely sensitive to these violations of model assumption. For example, when annual marker loss was simulated, estimates of survival rates were low and estimates of immigration rate from anIPMwere high. When heterogeneity in the mortality process was induced, there were substantial relative differences between the medians of posterior distributions and truth for juvenile survival and fecundity.

   Our results have important implications for biological inference when usingIPMs, as well as future model development and implementation. Specifically, using multiple datasets to identify additional parameters resulted in the posterior distributions of additional parameters directly reflecting the effects of the violations of model assumptions in integrated modelling frameworks. We suggest that investigators interpret posterior distributions of these parameters as a combination of biological process and systematic error.

    

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4. An r package and online resource for macroevolutionary studies using the ray‐finned fish tree of life

   https://doi.org/10.1111/2041-210X.13182  

   Chang, Jonathan ; Rabosky, Daniel L. ; Smith, Stephen A. ; Alfaro, Michael E. ; Silvestro, ed., Daniele ( April 2019 , Methods in Ecology and Evolution)

   Comprehensive, time‐scaled phylogenies provide a critical resource for many questions in ecology, evolution and biodiversity. Methodological advances have increased the breadth of taxonomic coverage in phylogenetic data; however, accessing and reusing these data remain challenging.

   We introduce the Fish Tree of Life website and associatedrpackagefishtreeto provide convenient access to sequences, phylogenies, fossil calibrations and diversification rate estimates for the most diverse group of vertebrate organisms, the ray‐finned fishes. The Fish Tree of Life website presents subsets and visual summaries of phylogenetic and comparative data, and is complemented by therpackage, which provides flexible programmatic access to the same underlying data source for advanced users wishing to extend or reanalyse the data.

   We demonstrate functionality with an overview of the website, and show three examples of advanced usage through therpackage. First, we test for the presence of long branch attraction artefacts across the fish tree of life. The second example examines the effects of habitat on diversification rate in the pufferfishes. The final example demonstrates how a community phylogenetic analysis could be conducted with the package.

   This resource makes a large comparative vertebrate dataset easily accessible via the website, while therpackage enables the rapid reuse and reproducibility of research results via its ability to easily integrate with otherrpackages and software for molecular biology and comparative methods.

    

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5. Disease's hidden death toll: Using parasite aggregation patterns to quantify landscape‐level host mortality in a wildlife system

   https://doi.org/10.1111/1365-2656.13343  

   Wilber, Mark Q. ; Briggs, Cheryl J. ; Johnson, Pieter T. J. ; Cattadori, ed., Isabella ( September 2020 , Journal of Animal Ecology)

   World‐wide, infectious diseases represent a major source of mortality in humans and livestock. For wildlife populations, disease‐induced mortality is likely even greater, but remains notoriously difficult to estimate—especially for endemic infections. Approaches for quantifying wildlife mortality due to endemic infections have historically been limited by an inability to directly observe wildlife mortality in nature.

   Here we address a question that can rarely be answered for endemic pathogens of wildlife: what are the population‐ and landscape‐level effects of infection on host mortality? We combined laboratory experiments, extensive field data and novel mathematical models to indirectly estimate the magnitude of mortality induced by an endemic, virulent trematode parasite (Ribeiroia ondatrae) on hundreds of amphibian populations spanning four native species.

   We developed a flexible statistical model that uses patterns of aggregation in parasite abundance to infer host mortality. Our model improves on previous approaches for inferring host mortality from parasite abundance data by (i) relaxing restrictive assumptions on the timing of host mortality and sampling, (ii) placing all mortality inference within a Bayesian framework to better quantify uncertainty and (iii) accommodating data from laboratory experiments and field sampling to allow for estimates and comparisons of mortality within and among host populations.

   Applying our approach to 301 amphibian populations, we found that trematode infection was associated with an average of between 13% and 40% population‐level mortality. For three of the four amphibian species, our models predicted that some populations experienced >90% mortality due to infection, leading to mortality of thousands of amphibian larvae within a pond. At the landscape scale, the total number of amphibians predicted to succumb to infection was driven by a few high mortality sites, with fewer than 20% of sites contributing to greater than 80% of amphibian mortality on the landscape.

   The mortality estimates in this study provide a rare glimpse into the magnitude of effects that endemic parasites can have on wildlife populations and our theoretical framework for indirectly inferring parasite‐induced mortality can be applied to other host–parasite systems to help reveal the hidden death toll of pathogens on wildlife hosts.

    

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