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138 results for “population coding”
Data and code from: Seasonal timing of ecosystem linkage mediates life-history variation in a salmonid fish population
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Data and code for: Failure to purge: Population and individual inbreeding effects on fitness across generations of wild Impatiens capensis
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Code and data for: Emergence of spatially structured populations by area-concentrated search
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Data and code for: Nonlinear life table response analysis: Decomposing nonlinear and nonadditive population growth responses to changes in environmental drivers
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Code: A model of wild bee populations accounting for spatial heterogeneity and climate induced temporal variability of food resources at the landscape level
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Data and R computer code from: Summer elk calf survival in a partially migratory population
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Codes and data from: The effect of habitat choice on evolutionary rescue in subdivided populations
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Dataset and R code: Genetic diversity of lion populations in Kenya: evaluating past management practices and recommendations for future conservation actions by Chege M et.al
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Data and code for: Multiple genetic impacts of immigration interact to shape local population persistence versus extinction
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Data and code from: Cooperation and coordination in heterogeneous populations
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Data and coding files for: Within population plastic responses to combined thermal-nutritional stress differ from those in response to single stressors, and are genetically independent across traits in both males and females
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Code and data for: Familiarity breeds success: pairs that meet earlier experience increased breeding performance in a wild bird population
<p>This is a Data package that contains three separate datasets and the analysis code for a manuscript 'Familiarity breeds success: pairs that meet earlier experience increased breeding performance in a wild bird population '. The first Dataset (pairs_data_2007_10) and the second dataset (pairs_data_2011_14) contain the data used to analyse the influence of meeting time of a pair of Great tits (i.e. month in the first dataset, week in the second dataset, when a pair was first detected in a flock together) on different components of breeding success of a pair. The third dataset (meeting_and_divorce) was used to analyse whether meeting time of a pair (in winter prior to the breeding season t) influence the probability of a pair to stay together or separate (divorce) to the following breeding season (t+1).</p>
Code and data for: Shining a light on elusive lynx: density estimation of three Eurasian lynx populations in Ukraine and Belarus
<p class="MsoNormal"><span>The Eurasian lynx is a large carnivore widely distributed across Eurasia. However, our understanding of population status is heterogeneous across their range, with some populations isolated that are at risk of reduced genetic variation and a complete lack of information about others. In many European countries, Eurasian lynx are monitored through demographic studies crucial for their conservation and management. Even so, there are only rough and fragmented population assessments from Ukraine and Belarus, despite strict protection in both countries and their importance for lynx connectivity across Europe. We monitored lynx from October 2020 to March 2021 and used camera-trapping in combination with spatial capture-recapture (SCR) methods in a Bayesian Framework to provide the first SCR density estimation of three lynx populations across Ukraine and Belarus, including the Ukrainian Chornobyl Exclusion Zone, Southern Belarus, and the Ukrainian Carpathians. Our density estimates varied within our study areas ranging from 0.45 to 1.54 individuals/100 km<sup>2</sup>. This work</span><span> <span>provides a substantial scientific component to the overall understanding of lynx conservation for a region where only broad information is available </span></span><span>and opens the doors for further large-scale monitoring and trend assessments. </span><span>The crucial information we provide can greatly enhance the range-wide assessments of the status of this protected species. We also discuss the implications for Eurasian lynx conservation, despite the geopolitical realities impacting species monitoring in the region. Our work serves as a baseline, not only for future conservation interventions but also to evaluate the effects of disturbance and threats to these protected populations.</span></p>
Data and code from: Disentangling the drivers of decadal body size decline in an insect population
<p>While climate warming is widely predicted to reduce body size of ectotherms, evidence for this trend is mixed. Body size depends not only on temperature but also on other factors, such as food quality and intraspecific competition. Because temperature trends or other long-term environmental factors may affect population size and food sources, attributing trends in average body size to temperature requires the separation of potentially confounding effects. We evaluated trends in the body size of the midge <em>Tanytarsus gracilentus</em> and potential drivers (water temperature, population size, and food quality) between 1977 and 2015 at Lake Mývatn, Iceland. Although temperatures increased at Mývatn over this period, there was only a slight (non-significant) decrease in midge adult body size, contrary to theoretical expectations. Using a state-space model including multiple predictors, body size was negatively associated with both water temperature and midge population abundance, and it was positively associated with <sup>13</sup>C enrichment of midges (an indicator of favorable food conditions). The magnitude of these effects were similar, such that simultaneous changes in temperature, abundance, and carbon stable isotopic signature could counteract each other in the long-term body size trend. Our results illustrate how multiple factors, all of which could be influenced by global change, interact to affect average ectotherm body size.</p>
Data and code from: Accounting for unobserved population dynamics and aging error in close-kin mark-recapture assessments
<p>Obtaining robust estimates of population abundance is a central challenge hindering the conservation and management of many threatened and exploited species. Close-kin mark-recapture (CKMR) is a genetics-based approach that has strong potential to improve monitoring of data-limited species by enabling estimates of abundance, survival, and other parameters for populations that are challenging to assess. However, CKMR models have received limited sensitivity testing under realistic population dynamics and sampling scenarios, impeding application of the method in population monitoring programs and stock assessments. Here, we use individual-based simulation to examine how unmodeled population dynamics and aging uncertainty affect the accuracy and precision of CKMR parameter estimates under different sampling strategies. We then present adapted models that correct the biases that arise from model misspecification. Our results demonstrate that a simple base-case CKMR model produces robust estimates of population abundance with stable populations that breed annually; however, if a population trend or non-annual breeding dynamics are present, or if year-specific estimates of abundance are desired, a more complex CKMR model must be constructed. In addition, we show that CKMR can generate reliable abundance estimates for adults from a variety of sampling strategies, including juvenile-focused sampling where adults are never directly observed (and aging error is minimal). Finally, we apply a CKMR model that has been adapted for population growth and intermittent breeding to two decades of genetic data from juvenile lemon sharks (<em>Negaprion brevirostris</em>) in Bimini, Bahamas, to demonstrate how application of CKMR to samples drawn solely from juveniles can contribute to monitoring efforts for highly mobile populations. Overall, this study expands our understanding of the biological factors and sampling decisions that cause bias in CKMR models, identifies key areas for future inquiry, and provides recommendations that can aid biologists in planning and implementing an effective CKMR study, particularly for long-lived data-limited species.</p>
Data and code for: Sea level rise causes shorebird population collapse before habitat drowns
<p>Sea level rise causes habitat loss and is considered to be a key threat to coastal species globally. Sea level rise also reduces habitat quality, potentially threatening populations already before habitat drowns and is lost. The extent and timing of changes in habitat quality for wildlife actively adapting to sea level rise, and how this affects population numbers under different emission scenarios, is unknown. Here, we combine long-term field data with models of sea level rise, marsh geomorphology, adaptive behaviour, and population dynamics to show that habitat quality is already declining on three islands due to increased flooding of shorebird nests. Also, population collapses are projected well before habitat drowns. Habitat loss, a widely used proxy, thus severely underestimates population impacts of sea level rise and coastal species will suffer much sooner than previously thought. Despite shorebirds adapting by moving to higher grounds, sea level rise will result in up to 79% fewer birds in a century, eventually leading to extinction in their prime habitat. Local gas mining exacerbates matters, as deep soil subsidence makes habitat even more vulnerable to sea level rise, effectively halving the window of opportunity for conservation action. Climate change ultimately jeopardizes the biodiversity value of this UNESCO World Heritage Area, and nature management needs to take this long-term perspective on board by in the short-term, boosting the accretion of tidal marshes or developing flood-safe alternative habitat elsewhere.</p>
Data & codes for "Changes in abundance and distribution of European forest bird populations depend on biome, ecological specialisation and traits"
<h1>1. Selection of European forest bird species and classification of their biome preferences</h1> <p>We selected all species that are related to forest and woodland based on two data sources: Storchová & Hořák (2018) and Tobias et al. (2022), resulting in 107 bird species studied (Data S1). We defined forest bird species as those using environments ranging from closed-canopy forests to more open-canopy woodlands (A. Lehikoinen & Virkkala, 2018; Storchová & Hořák, 2018; Tobias et al., 2022). We determined their biome specialisation using breeding distribution centroids and the overall breeding distribution of each of the species, using the global map of terrestrial ecoregions from Olson et al. (2001) and range data from European Breeding Bird Atlas 1 and 2 (Hagemeijer & Blair, 1997; Keller et al., 2020). We categorised species as Mediterranean, temperate, or boreal based on their predominant biogeographic region. We considered species commonly occurring over several biomes as “generalists”. For instance, we reclassified the two typically boreal species Glaucidium passerinum Linnaeus and Strix uralensis Pallas as “generalists” due to significant range expansions into central and southern Europe in recent decades, therefore no longer restricted to the boreal region. For the complete list of species, biome specialisation, traits, and specialisation indices, refer to Data S1.</p> <h1>2. Changes in abundance and distribution of European forest bird species</h1> <p>We assessed long-term changes in European forest bird populations through two approaches: (i) changes in estimated total European-level species abundance over a 40-year timeframe; and (ii) changes in species spatial distribution over a 30-year timeframe (Fig. 1).</p> <p>We utilized the estimated trends in European-level population size (i.e., the total number of individuals) for each common native European bird species from 1980 to 2017, as reported by Burns et al. (2021). Three species out of the 107 studied forest species were missing in the original manuscript and we used data generated with the same method from 1980 to 2018 from the European assessment, Article 12 (https://nature-art12.eionet.europa.eu/article12/). These abundance trends were calculated by Burns et al. (2021) using multi-sourced annual times series. For each species, they gathered population estimates and trends from each European country as well as European Union (EU)-level population trends. They analysed these data with a Bayesian hierarchical model to reconstruct EU-level smoothed species population time series. The model outputs include an average annual rate of abundance change and an associated 95% credible interval (Burns et al., 2021). Therefore, we did not directly use the average annual rate of abundance change, as this would have led us to consider species with low uncertainty as similar to those with high uncertainty. To account for the uncertainty, we categorised species as (i) declining, i.e., annual rates below one, (ii) increasing, i.e., annual rates above one and (iii) stable, i.e., annual rate whose 95% CI overlap one, i.e., no significant change. To better acknowledge the magnitude of the abundance change, significant changes with rates below 0.98 were labelled as “strongly declining” (i.e., 6.5% of the 107 species), while those above 1.02 were labelled as “strongly increasing” (i.e., 11% of the 107 species). To evaluate the sensitivity of the decision to categorised abundance change data, we also analysed abundance trend as continuous variable (see Supporting Information Fig. S8).</p> <p>To determine changes in species distributions, we used a comparison of species distributions between two periods (i.e., 1985-1988 and 2013-2017) using the European Breeding Bird Atlas 1 and 2 (EBBA 1 & 2; Hagemeijer & Blair, 1997; Howard et al., 2023; Keller et al., 2020). Howard et al. (2023) provided calculations of observed colonisation and extinction areas at a 50 x 50 km resolution across Europe. We measured changes in range as the difference between colonisations and extinctions of each species, with negative values indicating contracting ranges and positive values indicating expanding ranges. Additionally, we calculated the shift in the centre of gravity of the distribution range between the two periods, as a distance (km) along the south-north gradient for each species (Howard et al., 2023).</p> <h1>3. Trait and specialisation data for European forest bird species</h1> <p>We extracted data for six functional traits from several sources (Table 1). (i) The species temperature index (STI)represents the long-term average temperature within the species’ breeding range (A. Lehikoinen et al., 2021). (ii) Diet data during the breeding season were obtained from Storchová & Hořák (2018), classifying species into binary variables as vertebrate carnivorous, invertebrate carnivorous, and herbivores (combining the leaf and seed eaters). Storchová & Hořák (2018) classified species into a diet category when the corresponding food resource represented at least 10% of the species diet throughout the breeding season. Therefore, one species can be in several categories (i.e., omnivores). (iii) We obtained nesting site data from Pearman et al. (2014), classifying species into binary variables as ground nesters, tree hole nesters, or elevated nesters (> 1 m in a tree or shrub). We also included data on (iv) species dependence on old-growth forests (Data S1; mostly from Fraixedas et al. (2015) and Mönkkönen et al. (2014), if present on both references, we classified them as “1” and if only in one reference as “0.5”), (v) migration distance (Howard et al., 2023), and (vi) body mass (Tobias et al., 2022).</p> <p>Finally, we extracted and developed seven species specialisation indices. (i) We used an overall specialisation index based on multiple traits (i.e., temperature, diet, foraging behaviour and substrate, habitat, and nesting site), and (ii) a nesting specialisation index, both obtained from Morelli et al. (2019). Both indices represent species specialization based on the dispersion of trait preferences for each species: e.g., nesting specialism equal 0 for species that nest in all habitat type and equal 1 for species that nest in only one habitat type). They are both calculated using the Gini index of inequality, which measures overall dispersion across, e.g., all traits for the overall specialization, based on data from Pearman et al. (2014) and Storchová & Hořák (2018). For additional information, see Morelli et al. (2019). We also used (iii) the diet specialisation index, (iv) the species distribution range during the breeding season (hereafter “breeding range area”) and (v) the climatic niche breadth from Reif et al. (2016). The diet specialisation index was calculated as the coefficient of variation for diet preferences for each species, where high values denotes specialized species (Reif et al., 2016). The breeding range area was evaluated as the number of 50-km squares in the distribution maps in Europe occupied by each species during the reproduction period, and is based on EBBA 1 (Hagemeijer & Blair, 1997). The climatic niche breadth was calculated as the difference between the 5% hottest and the 5% coldest mean temperature between April and June in which each species occurs, using EBBA 1 (Hagemeijer & Blair, 1997; Reif et al., 2016).</p> <p>Additionally, (vi) we calculated a broadleaf forest specialisation index based on binary forest habitat preferences (Storchová & Hořák, 2018), assigning values of one for species found only in broadleaf forests; zero for those in coniferous forests, and 0.5 for those found in both. Lastly, (vii) we created a forest specialisation index based on the species habitat preferences (Storchová & Hořák, 2018). The forest specialisation index was calculated as the mean of species affinity across habitats. We used increasing habitat weights along a gradient of tree dominance: open habitats as 1, shrubland as 1.5, woodland as 2 (i.e., species associated with habitats structured by trees in lower density than in forest), forest generalist (found in both coniferous and broadleaf dense forests) as 3, and forest specialist (found only either in coniferous or broadleaf dense forests) as 4. For instance, the index value for species occurring either in shrubland, woodland or both broadleaf and coniferous forests is 2.167.</p> <h1>4. Data analysis</h1> <p>Data analyses were conducted with R software version 4.4.1. (R Core Team, 2024). Given the non-independence of species due to their genetic relatedness, we accounted for interspecific phylogenetic distance in all models. We constructed the phylogenetic tree for the 107 European forest bird species using ‘rotl’ and ‘ape’ R-packages (Michonneau et al., 2022; Paradis et al., 2023). We used rotl as an interface with the "Open Tree of Life", employing tol_induced_subtree R-function to generate the phylogenetic tree and compute.brlen R-function to set branch lengths using Grafen’s computation. We generated separate phylogenetic trees for boreal (17), temperate (15), Mediterranean (16) and “generalist” (59) species to perform biome-specific analysis (see Supplementary Information, Figs. S1 & S2).</p> <p>To investigate the effects of functional traits and specialisation indices on abundance, range changes, and distribution shift, we used two regression methods. All methods were based on the relationships between a measure of change and a functional trait or specialisation index. Our sample unit is an individual forest bird species (i.e., one value for each species, either abundance or range change, or distribution shift). Abundance change was a categorical variable (i.e., strong decline – decline – stable – increase – strong increase), while range change (i.e., difference between colonisation and extinction) and distribution shift (i.e., south-north shift) were continuous variables. Therefore, to study abundance changes, we used proportional-odds linear mixed effects model using (Phylo)clmm R-function from the ‘ordinal’ R-package (Christensen, 2022). Interspecific phylogenetic relatedness was included as a random effect, reflecting the correlation between species based on phylogenetic distances (see also Hagge et al. (2021) and Seibold et al. (2015)). For distribution changes, we employed phylogenetic generalised least squares regression (PGLS) using the gls R-function from the ‘nlme’ R-package (Pinheiro et al., 2023). The phylogenetic correlation structure was integrated into PGLS using Pagel’s lambda parameter (λ; Pagel (1999)) a widely used measured of phylogenetic signal strength (see, e.g., Hagge et al., 2021; Triviño et al., 2013).</p> <p>Furthermore, we included latitude, a key driver of bird communities at broad scales (Luoto et al., 2007), as a fixed covariable (centroid latitude of the species’ breeding distribution) in all global models (i.e., species from all biomes together), except for the STI model due to strong correlation. For biome-specific analysis, we included latitude only in boreal species models for range change and distribution shift, as it significantly improved model fit (ΔAIC < -2). We did not add latitude for models specific to temperate, Mediterranean, and generalist species since it did not improve model fits (ΔAIC > -2). Additionally, we included breeding range area in range change and distribution shift models, assuming that species with larger ranges would exhibit larger shifts. We scaled predictors to a mean of 0 and standard deviation of 1 to facilitate effect size comparisons. We adjusted p-values using the Holm method (for n=3) to account for multiple testing of traits and specialisation indices on three response variables.</p>
Input data and code supporting the cod_v2 population estimates
<p>The <strong><em>model.zip</em></strong> file contains input data and code supporting the cod_v2 population estimates. The file<strong> <em>modelData.RData</em></strong> provides the input data to the JAGS model and the file<em> <strong>modelCode.R</strong></em> contains the source code for the model in the JAGS language. The files can be used to run the model for further assessments and as a starting point for further model development.</p> <p>The data and the model were developed using the statistical software <strong>R version 4.0.2</strong> (https://cran.r-project.org/bin/windows/base/old/4.0.2) and <strong>JAGS 4.3.0</strong> (https://mcmc-jags.sourceforge.io), a program for analysis of Bayesian graphical models using Gibbs sampling, through the R package <strong>runjags 2.2.0</strong> (https://cran.r-project.org/web/packages/runjags).</p>
Deriving population scaling rules from individual-level metabolism and life history traits - Code and Data
<p>Individual metabolism generally scales with body mass with an exponent around 3/4. From dimensional arguments it follows that maximum population growth rate (rmax) scales with a -1/4 exponent. However, the dimensional argument implicitly assumes that offspring size is proportional to adult size. Here we calculate rmax from metabolic scaling at the level of individuals within size-structured populations while explicitly accounting for offspring size. We identify four general patterns of how rmax scales with adult mass based on four empirical life-history patterns employed by groups of species. These life-history patterns are determined by how traits of somatic growth rate and/or offspring mass relate to adult mass. One life-history pattern -- constant adult:offspring mass ratio and somatic growth rate independent of adult mass -- leads to the classic -1/4 scaling of rmax. The other three life-history patterns lead either to non-metabolic population growth scaling with adult mass or do not follow a power-law relationship at all. Using life-history data of five marine taxa and terrestrial mammals, we identify species groups that belong to one of each case. We predict that elasmobranchs, copepods, and mammals follow standard -1/4 power-law scaling, whereas teleost fish and bivalves do not have a pure power-law scaling. Our work highlights how taxa may deviate from the classic -1/4 metabolic scaling pattern of maximum population growth. The approach is generic and can be applied to any taxa.</p>
ASreml code and Data from the study "Between-population differences in the genetic and maternal components of body mass in roe deer"
<p>This repository contains the source code (ASremL input files) and the data used to perform the QG analyses (univariate, bivariate, random regression animal models) in the study "Between-population differences in the genetic and maternal components of body mass in roe deer". Quéméré E et al.</p> <p> </p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.