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1. Trade‐offs with telemetry‐derived contact networks for infectious disease studies in wildlife

   https://doi.org/10.1111/2041-210X.13355  

   Gilbertson, Marie L. J. ; White, Lauren A. ; Craft, Meggan E. ; McCrea, ed., Rachel ( February 2020 , Methods in Ecology and Evolution)

   Network analysis of infectious disease in wildlife can reveal traits or individuals critical to pathogen transmission and help inform disease management strategies. However, estimates of contact between animals are notoriously difficult to acquire. Researchers commonly use telemetry technologies to identify animal associations, but such data may have different sampling intervals and often captures a small subset of the population. The objectives of this study were to outline best practices for telemetry sampling in network studies of infectious disease by determining (a) the consequences of telemetry sampling on our ability to estimate network structure, (b) whether contact networks can be approximated using purely spatial contact definitions and (c) how wildlife spatial configurations may influence telemetry sampling requirements.

   We simulated individual movement trajectories for wildlife populations using a home range‐like movement model, creating full location datasets and corresponding ‘complete’ networks. To mimic telemetry data, we created ‘sample’ networks by subsampling the population (10%–100% of individuals) with a range of sampling intervals (every minute to every 3 days). We varied the definition of contact for sample networks, using either spatiotemporal or spatial overlap, and varied the spatial configuration of populations (random, lattice or clustered). To compare complete and sample networks, we calculated seven network metrics important for disease transmission and assessed mean ranked correlation coefficients and percent error between complete and sample network metrics.

   Telemetry sampling severely reduced our ability to calculate global node‐level network metrics, but had less impact on local and network‐level metrics. Even so, in populations with infrequent associations, high intensity telemetry sampling may still be necessary. Defining contact in terms of spatial overlap generally resulted in overly connected networks, but in some instances, could compensate for otherwise coarse telemetry data.

   By synthesizing movement and disease ecology with computational approaches, we characterized trade‐offs important for using wildlife telemetry data beyond ecological studies of individual movement, and found that careful use of telemetry data has the potential to inform network models. Thus, with informed application of telemetry data, we can make significant advances in leveraging its use for a better understanding and management of wildlife infectious disease.

    

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2. Does plant ecosystem thermoregulation occur? An extratropical assessment at different spatial and temporal scales

   https://doi.org/10.1111/nph.18632  

   Guo, Zhengfei ; Still, Christopher J. ; Lee, Calvin K. F. ; Ryu, Youngryel ; Blonder, Benjamin ; Wang, Jing ; Bonebrake, Timothy C. ; Hughes, Alice ; Li, Yan ; Yeung, Henry C. H. ; et al ( December 2022 , New Phytologist)

   To what degree plant ecosystems thermoregulate their canopy temperature (T_c) is critical to assess ecosystems' metabolisms and resilience with climate change, but remains controversial, with opinions from no to moderate thermoregulation capability.

   With global datasets ofT_c, air temperature (T_a), and other environmental and biotic variables from FLUXNET and satellites, we tested the ‘limited homeothermy’ hypothesis (indicated byT_c&T_aregression slope < 1 orT_c < T_aaround midday) across global extratropics, including temporal and spatial dimensions.

   Across daily to weekly and monthly timescales, over 80% of sites/ecosystems have slopes ≥1 orT_c > T_aaround midday, rejecting the above hypothesis. For those sites unsupporting the hypothesis, theirT_c–T_adifference (ΔT) exhibits considerable seasonality that shows negative, partial correlations with leaf area index, implying a certain degree of thermoregulation capability. Spatially, site‐mean ΔTexhibits larger variations than the slope indicator, suggesting ΔTis a more sensitive indicator for detecting thermoregulatory differences across biomes. Furthermore, this large spatial‐wide ΔTvariation (0–6°C) is primarily explained by environmental variables (38%) and secondarily by biotic factors (15%).

   These results demonstrate diverse thermoregulation patterns across global extratropics, with most ecosystems negating the ‘limited homeothermy’ hypothesis, but their thermoregulation still occurs, implying that slope < 1 orT_c < T_aare not necessary conditions for plant thermoregulation.

    

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3. Comparing traditional and Bayesian approaches to ecological meta‐analysis

   https://doi.org/10.1111/2041-210X.13445  

   Pappalardo, Paula ; Ogle, Kiona ; Hamman, Elizabeth A. ; Bence, James R. ; Hungate, Bruce A. ; Osenberg, Craig W. ; O'Hara, ed., Robert B. ( July 2020 , Methods in Ecology and Evolution)

   Despite the wide application of meta‐analysis in ecology, some of the traditional methods used for meta‐analysis may not perform well given the type of data characteristic of ecological meta‐analyses.

   We reviewed published meta‐analyses on the ecological impacts of global climate change, evaluating the number of replicates used in the primary studies (n_i) and the number of studies or records (k) that were aggregated to calculate a mean effect size. We used the results of the review in a simulation experiment to assess the performance of conventional frequentist and Bayesian meta‐analysis methods for estimating a mean effect size and its uncertainty interval.

   Our literature review showed thatn_iandkwere highly variable, distributions were right‐skewed and were generally small (mediann_i = 5, mediank = 44). Our simulations show that the choice of method for calculating uncertainty intervals was critical for obtaining appropriate coverage (close to the nominal value of 0.95). Whenkwas low (<40), 95% coverage was achieved by a confidence interval (CI) based on thetdistribution that uses an adjusted standard error (the Hartung–Knapp–Sidik–Jonkman, HKSJ), or by a Bayesian credible interval, whereas bootstrap orzdistribution CIs had lower coverage. Despite the importance of the method to calculate the uncertainty interval, 39% of the meta‐analyses reviewed did not report the method used, and of the 61% that did, 94% used a potentially problematic method, which may be a consequence of software defaults.

   In general, for a simple random‐effects meta‐analysis, the performance of the best frequentist and Bayesian methods was similar for the same combinations of factors (kand mean replication), though the Bayesian approach had higher than nominal (>95%) coverage for the mean effect whenkwas very low (k < 15). Our literature review suggests that many meta‐analyses that usedzdistribution or bootstrapping CIs may have overestimated the statistical significance of their results when the number of studies was low; more appropriate methods need to be adopted in ecological meta‐analyses.

    

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4. Spatial and temporal patterns in Arctic mosquito abundance

   https://doi.org/10.1111/een.13198  

   DeSiervo, Melissa H. ; Finger‐Higgens, Rebecca A. ; Ayres, Matthew P. ; Virginia, Ross A. ; Culler, Lauren E. ( November 2022 , Ecological Entomology)

   Organisms that undergo a shift in ontogeny and habitat type often change their spatial distribution throughout their life cycle, but how this affects population dynamics remains poorly understood.

   We examined spatial and temporal patterns inAedes nigripesabundance, a widespread univoltine Arctic mosquito species (Diptera: Culicidae), hypothesizing that the spatial distribution of adults would be closely tied to aquatic habitat.

   We tracked adult densities ofA. nigripesnear Kangerlussuaq, Greenland using emergence traps, CO₂‐baited traps, and sweep‐nets.

   In back‐to‐back years of sampling (2017 and 2018) we found two‐fold variation in overall abundance.

   Adults were spatially patchy when first emerging from aquatic habitats but within a week, mean capture rates for host‐seeking adult females were similar across locations, even in places far from larval habitat.

   Daily variation in mosquito captures was primarily explained by weather, with virtually no mosquito activity when temperatures averaged less than 8°C or wind speeds exceeded 6 m/s. Gravid females (3% of resting adults) were spatially patchy on the landscape, but not always in the same places where most adults emerged.

   The spatial distribution of adults is quickly uncoupled from the spatial distribution of larvae becauseA. nigripesfemales may disperse far from their natal habitats in search of a blood‐meal and high‐quality oviposition habitat.

   8. This research highlights the value of studying ecological processes that act at disparate life stages for understanding the population biology of organisms with complex life cycles.

    

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5. 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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