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1. 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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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. Identifying latent behavioural states in animal movement with M4, a nonparametric Bayesian method

   https://doi.org/10.1111/2041-210X.13745  

   Cullen, Joshua A. ; Poli, Caroline L. ; Fletcher, Jr., Robert J. ; Valle, Denis ( October 2021 , Methods in Ecology and Evolution)

   Understanding animal movement often relies upon telemetry and biologging devices. These data are frequently used to estimate latent behavioural states to help understand why animals move across the landscape. While there are a variety of methods that make behavioural inferences from biotelemetry data, some features of these methods (e.g. analysis of a single data stream, use of parametric distributions) may limit their generality to reliably discriminate among behavioural states.

   To address some of the limitations of existing behavioural state estimation models, we introduce a nonparametric Bayesian framework called the mixed‐membership method for movement (M4), which is available within the open‐sourcebayesmoveR package. This framework can analyse multiple data streams (e.g. step length, turning angle, acceleration) without relying on parametric distributions, which may capture complex behaviours more successfully than current methods. We tested our Bayesian framework using simulated trajectories and compared model performance against two segmentation methods (behavioural change point analysis (BCPA) and segclust2d), one machine learning method [expectation‐maximization binary clustering (EMbC)] and one type of state‐space model [hidden Markov model (HMM)]. We also illustrated this Bayesian framework using movements of juvenile snail kitesRostrhamus sociabilisin Florida, USA.

   The Bayesian framework estimated breakpoints more accurately than the other segmentation methods for tracks of different lengths. Likewise, the Bayesian framework provided more accurate estimates of behaviour than the other state estimation methods when simulations were generated from less frequently considered distributions (e.g. truncated normal, beta, uniform). Three behavioural states were estimated from snail kite movements, which were labelled as ‘encamped’, ‘area‐restricted search’ and ‘transit’. Changes in these behaviours over time were associated with known dispersal events from the nest site, as well as movements to and from possible breeding locations.

   Our nonparametric Bayesian framework estimated behavioural states with comparable or superior accuracy compared to the other methods when step lengths and turning angles of simulations were generated from less frequently considered distributions. Since the most appropriate parametric distributions may not be obvious a priori, methods (such as M4) that are agnostic to the underlying distributions can provide powerful alternatives to address questions in movement ecology.

    

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4. Modelling effects of disturbance on population dynamics of generalized crayfish life history strategies

   https://doi.org/10.1002/aqc.3516  

   Bayer, Leah M. ; Fournier, Robert J. ; Magoulick, Daniel D. ( February 2021 , Aquatic Conservation: Marine and Freshwater Ecosystems)

   Crayfish play a crucial ecological role and are often considered a keystone species within freshwater ecosystems; however, North American crayfish species face disturbance and ecological threats including invasive species and intensified drought.

   Demographic models can allow examination of population dynamics of a targeted species under a wide variety of disturbance scenarios. In this study, crayfish population dynamics were modelled and their responses to simulated biological invasions and drought were assessed.

   As life history data on crayfish are relatively rare, models were used to explore the population viability of four generalized species with distinct life history strategies under 11 disturbance scenarios. RAMAS‐Metapop was used to construct stage‐based demographic metapopulation models parameterized using vital rates from established literature sources.

   Models indicated that populations respond differentially to disturbance based on life history. However, bothr‐ andK‐selected species appear to be highly susceptible to decline when faced with the additive effects of reduced carrying capacity resulting from invasion and reduced survival rates caused by drought.

   Constructing models that explore a broad array of life histories and disturbance regimes can provide managers with tools to develop generalized, widely applicable conservation strategies in data‐depauperate systems.

    

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5. Mitigating pseudoreplication and bias in resource selection functions with autocorrelation‐informed weighting

   https://doi.org/10.1111/2041-210X.14025  

   Alston, Jesse M. ; Fleming, Christen H. ; Kays, Roland ; Streicher, Jarryd P. ; Downs, Colleen T. ; Ramesh, Tharmalingam ; Reineking, Björn ; Calabrese, Justin M. ( February 2023 , Methods in Ecology and Evolution)

   Resource selection functions (RSFs) are among the most commonly used statistical tools in both basic and applied animal ecology. They are typically parameterized using animal tracking data, and advances in animal tracking technology have led to increasing levels of autocorrelation between locations in such data sets. Because RSFs assume that data are independent and identically distributed, such autocorrelation can cause misleadingly narrow confidence intervals and biased parameter estimates.

   Data thinning, generalized estimating equations and step selection functions (SSFs) have been suggested as techniques for mitigating the statistical problems posed by autocorrelation, but these approaches have notable limitations that include statistical inefficiency, unclear or arbitrary targets for adequate levels of statistical independence, constraints in input data and (in the case of SSFs) scale‐dependent inference. To remedy these problems, we introduce a method for likelihood weighting of animal locations to mitigate the negative consequences of autocorrelation on RSFs.

   In this study, we demonstrate that this method weights each observed location in an animal's movement track according to its level of non‐independence, expanding confidence intervals and reducing bias that can arise when there are missing data in the movement track.

   Ecologists and conservation biologists can use this method to improve the quality of inferences derived from RSFs. We also provide a complete, annotated analytical workflow to help new users apply our method to their own animal tracking data using thectmm Rpackage.

    

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