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13,064 results for “predictions”
Data from: Nest predation predicts infanticide in a cooperatively breeding bird
In cooperatively breeding species, social conflict is typically assumed to underlie destructive behaviours like infanticide. However, an untested alternative hypothesis in birds is that infanticide in the form of egg tossing may simply be a parental response to partial nest predation representing a life history tradeoff. We examined egg tossing behaviour in the colonial and cooperatively breeding grey-capped social weaver (Pseudonigrita arnaudi), a plural breeder in which pairs nest separately, often in the same tree. Using infrared nest cameras, we found that 78% of the tossing events from 2012 to 2017 were committed by parents, suggesting that social conflict is unlikely to be the main reason underlying egg tossing in this species. Instead, reductions in clutch size due to both natural and experimentally-simulated predation induced parental egg tossing. Our study suggests that destructive behaviour in cooperatively breeding birds can be shaped by a variety of mechanisms beyond social conflict and that alternative hypotheses must be considered when studying the adaptive significance of infanticide in group-living species.
Data from: Community- weighted mean plant traits predict small scale distribution of insect root herbivore abundance
Small scale distribution of insect root herbivores may promote plant species diversity by creating patches of different herbivore pressure. However, determinants of small scale distribution of insect root herbivores, and impact of land use intensity on their small scale distribution are largely unknown. We sampled insect root herbivores and measured vegetation parameters and soil water content along transects in grasslands of different management intensity in three regions in Germany. We calculated community-weighted mean plant traits to test whether the functional plant community composition determines the small scale distribution of insect root herbivores. To analyze spatial patterns in plant species and trait composition and insect root herbivore abundance we computed Mantel correlograms. Insect root herbivores mainly comprised click beetle (Coleoptera, Elateridae) larvae (43%) in the investigated grasslands. Total insect root herbivore numbers were positively related to community-weighted mean traits indicating high plant growth rates and biomass (specific leaf area, reproductive- and vegetative plant height), and negatively related to plant traits indicating poor tissue quality (leaf C/N ratio). Generalist Elaterid larvae, when analyzed independently, were also positively related to high plant growth rates and furthermore to root dry mass, but were not related to tissue quality. Insect root herbivore numbers were not related to plant cover, plant species richness and soil water content. Plant species composition and to a lesser extent plant trait composition displayed spatial autocorrelation, which was not influenced by land use intensity. Insect root herbivore abundance was not spatially autocorrelated. We conclude that in semi-natural grasslands with a high share of generalist insect root herbivores, insect root herbivores affiliate with large, fast growing plants, presumably because of availability of high quantities of food. Affiliation of insect root herbivores with large, fast growing plants may counteract dominance of those species, thus promoting plant diversity.
Data from: Seascape and life-history traits do not predict self-recruitment in a coral reef fish
The persistence and resilience of many coral reef species are dependent on rates of connectivity among sub-populations. However, despite increasing research efforts, the spatial scale of larval dispersal remains unpredictable for most marine metapopulations. Here, we assess patterns of larval dispersal in the angelfish Centropyge bicolor in Kimbe Bay, Papua New Guinea, using parentage and sibling reconstruction analyses based on 23 microsatellite DNA loci. We found that, contrary to previous findings in this system, self-recruitment (SR) was virtually absent at both the reef (0.4–0.5% at 0.15 km2) and the lagoon scale (0.6–0.8% at approx. 700 km2). While approximately 25% of the collected juveniles were identified as potential siblings, the majority of sibling pairs were sampled from separate reefs. Integrating our findings with earlier research from the same system suggests that geographical setting and life-history traits alone are not suitable predictors of SR and that high levels of localized recruitment are not universal in coral reef fishes.
Data from: Global gradients in vertebrate diversity predicted by historical area-productivity dynamics and contemporary environment
Broad-scale geographic gradients in species richness have now been extensively documented, but their historical underpinning is still not well understood. While the importance of productivity, temperature, and a scale-dependence of the determinants of diversity is broadly acknowledged, we here argue that limitation to a single analysis scale and data pseudoreplication have impeded an integrated evolutionary and ecological understanding of diversity gradients. We develop and apply a hierarchical analysis framework for global diversity gradients that incorporates an explicit accounting of past environmental variation and provides an appropriate measurement of richness. Due to environmental niche conservatism organisms generally reside in climatically defined bioregions, or "evolutionary arenas", characterized by in situ speciation and extinction. These bioregions differ in age and their total productivity and have varied over time in area and energy available for diversification. We show that, consistently across the four major terrestrial vertebrate groups, current-day species richness of the world's main 32 bioregions is best explained by a model that integrates area and productivity over geological time together with temperature. Adding finer-scale variation in energy availability as an ecological predictor of within-bioregional patterns of richness explains much of the remaining global variation in richness at the 110km grain. These results highlight the separate evolutionary and ecological effects of energy availability and provide a first conceptual and empirical integration of the key drivers of broad-scale richness gradients. Avoiding the pseudo-replication that impedes the evolutionary interpretation of non-hierarchical macroecological analyses, our findings integrate evolutionary and ecological mechanisms at their most relevant scales and offer a new synthesis regarding global diversity gradients.
Data from: Predicting the effect of urban noise on the active space of avian vocal signals
Urbanization changes the physical environment of non-human species, but also markedly changes their acoustic environment. Urban noise interferes with acoustic communication in a range of animals including birds, with potentially profound impacts on fitness. However, a mechanistic theory to predict which species of birds will be most affected by urban noise is lacking. We develop a mathematical model to predict the decrease in the active space of avian vocal signals when moving from quiet forest habitats to noisy urban habitats, and find that the magnitude of the decrease is largely a function of signal frequency. However, this relationship is not monotonic. A meta-regression of observed increases in the frequency of birdsong in urban noise supports the model's predictions for signals with frequencies between 1.5 and 4 kHz. Using the results of the meta-regression and the model described above, we show that the expected gain in active space following observed frequency shifts is up to 12%, and greatest for birds with signals at the lower end of this frequency range. Our generally-applicable model, along with three predictions regarding the behavioral and population-level responses of birds to urban noise, represents an important step towards a theory of acoustic communication in urban habitats.
Data from: Habitat niche breadth predicts invasiveness in solitary ascidians
A major focus of invasion biology is understanding the traits associated with introduction success. Most studies assess these traits in the invaded region, while only few compare non-indigenous species to the pool of potential invaders in their native region. We focused on the niche-breadth hypothesis, commonly evoked but seldom tested, which states that generalist species are more likely to become introduced since they are capable of thriving under a wide set of conditions. Based on the massive introduction of tropical species into the Mediterranean via the Suez Canal (Lessepsian migration), we defined ascidians in the Red Sea as the pool of potential invaders. We constructed unique settlement plates, each representing six different niches, to assess ascidian niche breadth, and deployed them in similar habitats in the native and invaded regions. For each species found on plates, we evaluated its abundance, relative abundance across successional stages and niche breadth, and then compared (1)species in the Red Sea known to have been introduced into the Mediterranean (Lessepsian species) and those not known from the Mediterranean (non-Lessepsian); and (2)non-indigenous and indigenous species in the Mediterranean. Lessepsian ascidians identified on plates in the Red Sea demonstrated wider niche breadth than non-Lessepsian ascidians, supporting the niche-breadth hypothesis within the native region. No differences were found between Lessepsian and non-Lessepsian species in species abundance and successional stages. In the Mediterranean, non-indigenous species numerically dominated the settlement plates. This precluded robust comparisons of niche breadth between non-indigenous and indigenous species in the invaded region. In conclusion, using Red Sea ascidians as the pool of potential invaders, we found clear evidence supporting the niche-breadth hypothesis in the native region. We suggest that such patterns may often be obscured when conducting trait-based studies in the invaded regions alone. Our findings indicate that quantifying the niche breadth of species in their native regions will improve estimates of invasiveness potential.
Data from: Prediction of phylogeographic endemism in an environmentally complex biome.
Phylogeographic endemism, the degree to which the history of recently evolved lineages is spatially restricted, reflects fundamental evolutionary processes such as cryptic divergence, adaptation and biological responses to environmental heterogeneity. Attempts to explain the extraordinary diversity of the tropics, which often includes deep phylogeographic structure, frequently invoke interactions of climate variability across space, time and topography. To evaluate historical versus contemporary drivers of phylogeographic endemism in a tropical system, we analyse the effects of current and past climatic variation on the genetic diversity of 25 vertebrates in the Brazilian Atlantic rainforest. We identify two divergent bioclimatic domains within the forest and high turnover around the Rio Doce. Independent modelling of these domains demonstrates that endemism patterns are subject to different climatic drivers. Past climate dynamics, specifically areas of relative stability, predict phylogeographic endemism in the north. Conversely, contemporary climatic heterogeneity better explains endemism in the south. These results accord with recent speleothem and fossil pollen studies, suggesting that climatic variability through the last 250 kyr impacted the northern and the southern forests differently. Incorporating sub-regional differences in climate dynamics will enhance our ability to understand those processes shaping high phylogeographic and species endemism, in the Neotropics and beyond.
Data from: Predicting ecological and phenotypic differentiation in the wild: a case of piscivorous fish in a fishless environment
Environmental variation drives ecological and phenotypic change. How predictable is differentiation in response to environmental change? Answering this question requires the development and testing of multifarious a priori predictions in natural systems. We employ this approach using Gobiomorus dormitor populations that have colonized inland blue holes differing in the availability of fish prey. We evaluated predictions of differences in demographics, habitat use, diet, locomotor and trophic morphology, and feeding kinematics and performance between G. dormitor populations inhabiting blue holes with and without fish prey. Populations of G. dormitor independently diverged between prey regimes, with broad agreement between observed differences and a priori predictions. For example, in populations lacking fish prey, we observed male-biased sex ratios, a greater use of shallow-water habitat, and larger population diet breadths as a result of greater individual diet specialization. Furthermore, we found predictable differences in body shape, mouth morphology, suction generation capacity, strike kinematics, and feeding performance on different prey types, consistent with the adaptation of G. dormitor to piscivory when coexisting with fish prey and to feeding on small invertebrates in their absence. The results of the present study suggest great potential in our ability to predict population responses to changing environments, which is an increasingly important capability in a human-dominated, ever-changing world.
Data from: Survival of the fattest? Indices of body condition do not predict viability in the brown anole (Anolis sagrei)
1. Measures of body mass and length are commonly used to derive indices of condition, which are often assumed to reflect the energetic state of an animal and, by extension, to predict its fitness. However, the relationship between condition and fitness is rarely quantified, and the appropriate method(s) for deriving indices of condition are frequently debated. 2. Data from a decade of mark-recapture studies involving over 4,600 individual lizards (Anolis sagrei) and 41 replicates of selection across seven populations were used to test the common assumption that condition, as estimated from body mass and length, is a strong predictor of adult survival, an important component of fitness. Inferences about natural selection were compared between two alternative indices of condition: the popular residual index (Ri), and the scaled mass index (Mi), which was recently proposed as a more appropriate method. 3. Linear, quadratic, and correlational selection gradients obtained using Ri and Mi were highly correlated with one another. Relative to variance in selection among replicates and to error associated with the estimation of selection gradients, variance due to the use of alternative condition indices was minor and effectively negligible. 4. Contrary to the intuitive prediction that individuals in better condition should exhibit higher survival, there was no evidence for strong or consistent linear (directional) selection for higher condition indices in either males or females. Significant quadratic (stabilizing or disruptive) selection on condition was similarly rare in both sexes. Correlational selection favored combinations of large size and high condition in males, but not females. 5. Collectively, these results indicate that inferences about natural selection may be robust to the choice between indices of condition, but that indices of condition can be unreliable as proxies for fitness, particularly when relationships between fitness components and condition are contingent upon interactions with other traits.
Data from: Comparing the prediction of joint species distribution models with respect to characteristics of sampling data
Biotic interactions have been rarely included in traditional species distribution models, wherein Joint Species Distribution Models (JSDMs) emerge as a feasible approach to incorporate environmental factors and interspecific interactions simultaneously, making it a powerful tool for analyzing the structure and assembly processes of biotic communities. However, the predictability and statistical robustness of JSDMs are largely unknown because of the lack of research efforts for those newly developed models. This study systematically evaluated the performances of five JSDMs in predicting the occurrence and biomass of multiple species, with a particular focus on diverse characteristics of sampling data, including type of response variables, number of sampling sites, and the number of species included in models. In general, most models yielded satisfactory performances on fitting to observed data and on the estimation of environmental effects; however, they showed less well performances in evaluating species associations, and their predictability had large variations. The JSDMs showed inconsistent performances between the goodness-of-fit and predictability in cross-validation, and the Boral model was relatively robust than others. The predictability of JSDMs was less influenced by sample sizes and substantially improved by incorporating rare species. This study contributes to an appropriate model selection and application of JSDMs.
Data from: The predictability of a lake phytoplankton community, over time-scales of hours to years
Forecasting changes to ecological communities is one of the central challenges in ecology. However, nonlinear dependencies, biotic interactions and data limitations have limited our ability to assess how predictable communities are. We used a machine learning approach and environmental monitoring data (biological, physical and chemical) to assess the predictability of phytoplankton cell density in one lake across an unprecedented range of time scales. Communities were highly predictable over hours to months: model R2 decreased from 0.89 at 4 hours to 0.75 at 1 month, and in a long-term dataset lacking fine spatial resolution, from 0.46 at 1 month to 0.32 at 10 years. When cyanobacterial and eukaryotic algal cell density were examined separately, model-inferred environmental growth dependencies matched laboratory studies, and suggested novel trade-offs governing their competition. High-frequency monitoring and machine learning can help elucidate the mechanisms underlying ecological dynamics and set prediction targets for process-based models.
Data from: Dominance rank and boldness predict social attraction in great tits
Social relationships can have important fitness consequences, and how well an individual is socially connected often correlates with other behavioral traits. Whether such correlations are caused by underlying individual differences in social attraction usually remains unclear, because to identify effects of individual traits on social attraction, it is essential to experimentally exclude the influence of the social partner. Using standardized high-definition video playback on captive great tits (Parus major), we effectively demonstrate the influence of individual traits on the motivation to be near a conspecific. We show that social attraction varied contrastingly with boldness and stimulus novelty. Shyer birds tended to show stronger social attraction when they were confronted with the stimulus bird for the first time. Lower ranked birds showed the overall strongest social attraction. This rank effect remained after experimentally changing dominance ranks by altering group compositions. Moreover, preference for social association tended to increase with a decrease in dominance rank, suggesting that birds plastically change their social preference in relation to their within-group dominance status. Our results provide insight into how social relations can form and change, processes that are key for understanding the long-term consequences of the social environment, and the role individuals might play in influencing this environment themselves.
Data from: Predicting forest insect flight activity: a Bayesian network approach
Daily flight activity patterns of forest insects are influenced by temporal and meteorological conditions. Temperature and time of day are frequently cited as key drivers of activity; however, complex interactions between multiple contributing factors have also been proposed. Here, we report individual Bayesian network models to assess the probability of flight activity of three exotic insects, Hylurgus ligniperda, Hylastes ater, and Arhopalus ferus in a managed plantation forest context. Models were built from 7,144 individual hours of insect sampling, temperature, wind speed, relative humidity, photon flux density, and temporal data. Discretized meteorological and temporal variables were used to build naïve Bayes tree augmented networks. Calibration results suggested that the H. ater and A. ferus Bayesian network models had the best fit for low Type I and overall errors, and H. ligniperda had the best fit for low Type II errors. Maximum hourly temperature and time since sunrise had the largest influence on H. ligniperda flight activity predictions, whereas time of day and year had the greatest influence on H. ater and A. ferus activity. Type II model errors for the prediction of no flight activity is improved by increasing the model's predictive threshold. Improvements in model performance can be made by further sampling, increasing the sensitivity of the flight intercept traps, and replicating sampling in other regions. Predicting insect flight informs an assessment of the potential phytosanitary risks of wood exports. Quantifying this risk allows mitigation treatments to be targeted to prevent the spread of invasive species via international trade pathways.
Data from: Mutation predicts 40 million years of fly wing evolution
Mutation enables evolution, but the idea that adaptation is also shaped by mutational variation is controversial. Simple evolutionary hypotheses predict such a relationship if the supply of mutations constrains evolution, but it is not clear that constraints exist, and, even if they do, they may be overcome by long-term natural selection. Quantification of the relationship between mutation and phenotypic divergence among species will help to resolve these issues. Here we use precise data on over 50,000 Drosophilid fly wings to demonstrate unexpectedly strong positive relationships between variation produced by mutation, standing genetic variation, and the rate of evolution over the last 40 million years. Our results are inconsistent with simple constraint hypotheses because the rate of evolution is very low relative to what both mutational and standing variation could allow. In principle, the constraint hypothesis could be rescued if the vast majority of mutations are so deleterious that they cannot contribute to evolution, but this also requires the implausible assumption that deleterious mutations have the same pattern of effects as potentially advantageous ones. Our evidence for a strong relationship between mutation and divergence in a slowly evolving structure challenges the existing models of mutation in evolution.
Data from: Self-reported functional status predicts post-operative outcomes in non-cardiac surgery patients with pulmonary hypertension
BACKGROUND: Pulmonary hypertension (PHTN) is associated with increased post-procedure morbidity and mortality. Pre-procedure echocardiography (ECHO) is a widely used tool for evaluation of these patients, but its accuracy in predicting post-procedure outcomes is unproven. Self-reported exercise tolerance has not been evaluated for operative risk stratification of PHTN patients. OBJECTIVE: We analyzed whether self-reported exercise tolerance predicts outcomes (hospi-tal length-of-stay [LOS], mortality and morbidity) in PHTN patients (WHO Class I - V) under-going anesthesia and surgery. METHODS AND FINDINGS: We reviewed 550 non-cardiac, non-obstetric procedures per-formed on 370 PHTN patients at a single institution between 2007 and 2013. All patients had cardiac ECHO documented within 1 year prior to the procedure. Pre-procedure comorbidities and ECHO data were collected. Functional status (< or ? 4 metabolic equivalents of task [METs]) was assigned based on responses to standard patient interview questions during the pre-anesthesia clinic visit. Multiple logistic regression was used to develop a risk score model (Pul-monary Hypertension Outcome Risk Score; PHORS) and determine its value in predicting post-procedure outcomes. In an adjusted model, functional status <4 METs was independently associ-ated with a LOS >7 days (p < .003), as were higher ASA class (p < .002), open surgical approach (p < .002), procedure duration > 2 hours (p < .001), and the absence of systemic hypertension (p = .012). PHORS Score ?2 was associated with an increased 30-day major complication rate (28.7% vs. 19.2%; p < 0.001) and ICU admission rate (8.6% s 2.8%; p = .007), but no statistical difference in hospital readmissions rate (17.6% vs. 14.0%; p = .29), or mortality (3.5% vs. 1.4%; p = .75). Similar ECHO findings did not further improve outcome prediction. CONCLUSIONS: Poor functional status is associated with severe PHTN and predicts increased LOS and post-procedure complications in patients with moderate to severe pulmonary hyperten-sion with different etiologies. A risk assessment model predicts increased LOS with fair accura-cy. A thorough evaluation of underlying etiologies of PHTN should be undertaken in every pa-tient.
Data from: Sea buckthorn berries (Hippophae rhamnoides L.) predict size and composition of a great tit population (Parus major L.).
In seasonal environments variation in food abundance in the non-breeding season is thought to affect songbird population dynamics. In a unique tit-sea buckthorn berry system we can estimate the berry abundance and both the tit consumption and population dynamics. Six hundred nest boxes were available to great and blue tits (Cyanistes caeruleus) for breeding in spring and roosting in winter. We followed the dynamics including the recapture histories of individually marked great tits from 2008-2013. In each year we estimated 1) the winter sea buckthorn berry availability, 2) an index of berry consumption in December based on the colour of the faeces of roosting birds, 3) the number of breeding great and blue tits, 4) both recapture probability and the return rate of the great tits and 5) immigration rates. December berry abundance positively predicted the number of breeding pairs of both species in the subsequent season and great tit return rates in the second half of the winter. There was support for a sex specific berry effect on the adult return rate in the great tit: female return rate was associated less strongly to berry abundance than male return rate. This skewed the sex ratio of the local breeders in the following breeding season. Intriguingly, annual berry consumption in December was not related to berry abundance, and individuals consuming more berries tended to have slightly lower return rates. Reproductive rate was not related to berry abundance. There was hardly support for a relation between immigration rates of first year breeders and berry abundance. Taken together these results imply that berry stock not only affected population size but also the population composition through sex specific exchange with the surroundings. Since population density covaried with berry abundance, density dependent effects provide an alternative explanation for the patterns observed.
Data from: Environmental quality predicts optimal egg size in the wild
Parents can maximize their reproductive success by balancing the trade-off between investment per offspring and fecundity. According to theory, environmental quality influences the relationship between investment per offspring and offspring fitness, such that well-provisioned offspring fare better when environmental quality is lower. A major prediction of classic theory, then, is that optimal investment per offspring will increase as environmental quality decreases. To test this prediction, we release over 30,000 juvenile Atlantic salmon (Salmo salar) into eight wild stream environments, and we monitor subsequent growth and survival of juveniles. We estimate the shape of the relationship between investment per offspring (egg size) and offspring fitness in each stream. We find that optimal egg size is greater when the quality of the stream environment is lower (as estimated by a composite index of habitat quality). Across streams, the mean size of stream gravel and the mean amount of incident sunlight are the most important individual predictors of optimal egg size. Within streams, juveniles recaptured in stream subsections that featured larger gravels and greater levels of sunlight also grew relatively quickly, an association that complements our cross-stream analyses. This study provides the first empirical verification that environmental quality alters the relationship between investment per offspring and offspring fitness, such that optimal investment per offspring increases as environmental quality decreases.
Data from: Historical mutation rates predict susceptibility to radiation in Chernobyl birds
Extreme environmental perturbations are rare, but may have important evolutionary consequences. Responses to current perturbations may provide important information about the ability of living organisms to cope with similar conditions in the evolutionary past. Radioactive contamination from Chernobyl constitutes one such extreme perturbation, with significant but highly variable impact on local population density and mutation rates of different species of animals and plants. We explicitly tested the hypothesis that species with strong impacts of radiation on abundance were those with high rates of historical mutation accumulation as reflected by cytochrome b mitochondrial DNA base pair substitution rates during past environmental perturbations. Using a dataset of 32 species of birds we show higher historical mitochondrial substitution rates in species with the strongest negative impact of local levels of radiation on local population density. These effects were robust to different estimates of impact of radiation on abundance, weighting of estimates of abundance by sample size, statistical control for similarity in the response among species due to common phylogenetic descent, and effects of population size and longevity. Therefore, species that respond strongly to the impact of radiation from Chernobyl are also the species that in the past have been most susceptible to factors that have caused high substitution rates in mitochondrial DNA.
Data from: Predicted effects of climate factors on mountain species are not uniform over different spatial scales
The selection of relevant factors and appropriate spatial scale(s) is fundamental when modelling species response to climate change. We evaluated whether the effects of climate factors on species distribution/occurrence are consistently modelled over different spatial scales in birds, and used a two-scale approach to identify species-climate correlations unlikely to represent causal effects. We used passerine birds inhabiting mountain grassland in the Apennines (Italy) as a model. We surveyed four grassland species at 400 sampling points, and built habitat selection models (territory scale) and distribution models (7 algorithms, landscape scale). We compared the effect of climatic predictors on occurrence/distribution highlighted by models over to the two spatial scales, and with the effects supposed a priori based on the climatic niche of each species. Models at the territory level included at least one climatic predictor for three species; the observed effect of climatic predictors was seldom consistent with supposed effects. At the broadest scale, distribution models for all species included climatic predictors, with varying consistence with supposed effects and findings at the finer scale. Despite the importance of climate for species distribution, occurrence could be more directly related to other factors, with important implications for understanding/predicting the impacts of climate/environmental changes. Our approach revealed key variables for grassland birds, and highlighted the scale-dependent perceived importance of climate. At the local scale, climate effects were weak or hard to interpret. We found a general lack of consistence between supposed and observed effects at the territory level, and between landscape and territory models. Our results show the importance of predicting the potential effect of climatic factors prior to the analyses, carefully selecting ecologically meaningful variables and scales, and evaluating the nature and scale of climate-species links. We call for caution when predicting under future climates, especially when mechanistic effects and consistency across scales lack.
Data from: Ecological resistance surfaces predict fine scale genetic differentiation in a terrestrial woodland salamander
Landscape genetics has seen tremendous advances since its introduction, but parameterization and optimization of resistance surfaces still poses significant challenges. Despite increased availability and resolution of spatial data, few studies have integrated empirical data to directly represent ecological processes as genetic resistance surfaces. In our study, we determine the landscape and ecological factors affecting gene flow in the western slimy salamander (Plethodon albagula). We used field data to derive resistance surfaces representing salamander abundance and rate of water loss through combinations of canopy cover, topographic wetness, topographic position, solar exposure, and distance from ravine. These ecologically-explicit composite surfaces directly represent an ecological process or physiological limitation of our organism. Using generalized linear mixed effects models, we optimized resistance using a non-linear optimization algorithm to minimize model AIC. We found clear support for the resistance surface representing the rate of water loss experienced by adult salamanders in the summer. Resistance was lowest at intermediate levels of water loss and higher when the rate of water loss was predicted to be low or high. This pattern may arise from the compensatory movement behavior of salamanders through suboptimal habitat, but also reflects the physiological limitations of salamanders and their sensitivity to extreme environmental conditions. Our study demonstrates that composite representations of ecologically-explicit processes can provide novel insight and can better explain genetic differentiation than ecologically-implicit landscape resistance surfaces. Additionally, our study underscores the fact that spatial estimates of habitat suitability or abundance may not serve as adequate proxies for describing gene flow, as predicted abundance was a poor predictor of genetic differentiation.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.