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13,064 results for “predictions”
Data from: Combining climate, land use change and dispersal to predict the distribution of endangered species with limited vagility
<p><b>Aim: </b>Many rare species are dispersal-limited and minimal land use and climate changes can impact their colonization capacity. Most ecological niche models predict the distribution of species under future climate and land use change scenarios without incorporating specie-specific dispersal abilities. Here we investigated the effect of climate and land use change on low vagile species accounting for their dispersal capacity and defined accessible areas in the future.</p> <p><b>Location:</b> Europe.</p> <p><b>Taxon:</b> Saproxylic beetles.</p> <p><b>Methods:</b> We used the current (2007-2012) occurrences of six endangered saproxylics to develop ecological niche models using current climate and land use conditions. We projected species distributions under four future climate and land use change scenarios to estimate their potential occurrences. Finally, accounting for species-specific dispersal, we limited their distributions to accessible areas in 2040-50.</p> <p><b>Results:</b> Without accounting for dispersal abilities we found a strong and positive impact of climate change on the distribution of <i>Cerambix cerdo, Cucujus cinnaberinus, Morimus funereus</i> and <i>Rosalia alpina</i> and a positive effect of land use change on the distribution of <i>Lucanus cervus</i> and <i>Osmoderma eremita.</i> When species-specific dispersal was included, we found a strong and positive impact of land use change on the distribution of all the species. In this case climate change had a lower but positive effect on the distribution of <i>C. cerdo, C. cinnaberinus, L. cervus</i> and <i>R. alpina,</i> and a negative effect on the distribution of O.<i> eremita</i>.</p> <p><b>Main conclusion:</b> We found that climate change would promote the expansion of saproxylic beetles only in the unrealistic case of unlimited dispersal. Accounting for dispersal abilities, the expansion of our species would be mainly conditioned by the effect of land use change. Thus, we encourage researchers to combine climate and land use change with dispersal when projecting species distribution under future scenarios to accurately identify areas with fundamental species-specific resources.</p>
Calibration of probability predictions from machine-learning and statistical models
<p>This data set describes the occurrence (yes/no) of a bird, the Southern Whiteface (<i>Aphelocephala leucopsis)</i> in Australia. A suite of environmental variables is provided, which are used in the paper to illustrate a statistical problem. The data are meant to allow reproduction of the analysis in this paper. They are not intended for actual ecological analysis. The data come as .Rdata-file, i.e. as an R-dataset (described technically here: https://www.loc.gov/preservation/digital/formats/fdd/fdd000470.shtml).</p> <p>Here is the paper's abstract:</p> <p><span>Aim: Predictions from statistical models may be uncalibrated, meaning that the predicted values do not have the nominal coverage probability. This is easiest seen with probability predictions in machine-learning classification, including the common species occurrence probabilities. Here, a predicted probability of, say, 0.7 should indicate that out of 100 cases with these environmental conditions, and hence the same predicted probability, the species should be present in 70 and absent in 30.</span><br> <span>Innovation: A simple calibration plot shows that this is not necessarily the case, particularly not for over-fitted models or algorithms that use non-likelihood target functions. As a consequence, "raw" predictions from such model could easily be off by 0.2, are unsuitable for averaging across model types, and resulting maps hence be substantially distorted. The solution, a flexible calibration regression, is simple and can be applied whenever deviations are observed.</span><br> <span>Conclusion: "Raw", uncalibrated probability predictions should be calibrated before interpreting or averaging them in a probabilistic way.</span></p>
Data from: On the inconsistency of pollinator species traits for predicting either response to land-use change or functional contribution
The response and effect trait framework, if supported empirically, would provide for powerful and general predictions about how biodiversity loss leads to loss in ecosystem function. This framework proposes that species traits will explain how different species respond to disturbance (i.e. response traits) as well as their contribution to ecosystem function (i.e. effect traits). However, predictive response and effect traits remain elusive for most systems. Here, we use data on crop pollination services provided by native, wild bees to explore the role of six commonly used species traits in determining both species' response to land-use change and the subsequent effect on crop pollination. Analyses were conducted in parallel for three crop systems (watermelon, cranberry, and blueberry) located within the same geographical region (mid-Atlantic USA). Bee species traits did not strongly predict species' response to land-use change, and the few traits that were weakly predictive were not consistent across crops. Similarly, no trait predicted species' overall functional contribution in any of the three crop systems, although body size was a good predictor of per capita efficiency in two systems. Overall we were unable to make generalizable predictions regarding species responses to land-use change and its effect on the delivery of crop pollination services. Pollinator traits may be useful for understanding ecological processes in some systems, but thus far the promise of traits-based ecology has yet to be fulfilled for pollination ecology.
Data from: Predicting peatland carbon fluxes from non-destructive plant traits
1. Determining the plant traits that best predict carbon (C) storage is increasingly important as global change drivers will affect plant species composition and ecosystem C cycling. Despite the critical role of peatlands in the global C cycle, trait-flux relationships in peatlands are relatively unknown. 2. We assessed the ability of four non-destructive plant traits to predict carbon dioxide (CO2) and methane (CH4) fluxes over two growing seasons in a temperate peatland in Ontario, Canada. We examined relationships between C-fluxes and leaf area, leaf persistence (deciduous, evergreen), growth form (woody, herbaceous), and aerenchyma tissue. To explore potential inconsistencies between different scales of data aggregation, traits were analysed at the level of plots, species and microsites. 3. CO2 fluxes showed a positive relationship with leaf area and leaf persistence, and a negative relationship with proportion of woody species. CH4 fluxes showed a positive relationship with aerenchyma and leaf area. The significance of trait-flux relationships differed based on whether data were averaged at the level of plot, species or microsite. 4. We recommend applying leaf area as a non-destructive trait to other systems where it is not ideal to measure traits destructively. A better understanding of the relationships between above and belowground traits is likely needed to further explain variation in ecosystem respiration and CH4 fluxes from plant traits.
Data from: Use of simulation-based statistical models to complement bioclimatic models in predicting continental scale invasion risks
Invasive species represent one of the greatest risks to global biodiversity and economic productivity of agroecosystems. The development of certain novel crops—e.g., herbaceous perennial biomass crops—may create a risk of novel invasions by these crops. Therefore, potential benefits and risks need to be weighed in making decisions about their introduction and subsequent management. Ideally, such a weighing will be based on good estimates of invasion risks in realistic scenarios pertaining to actual landscapes of concern regarding invasion. Most previous large-scale analyses of invasion risk have used species distribution models and their established methods. Unfortunately, these approaches are unable to incorporate local scale biotic and spatial factors that influence invasion risk. Here we present a case study for how such factors can be efficiently incorporated in large-scale analyses of invasion risk, by extending simulation models with statistical modeling tools. By these means, we predict invasion risk at the scale of the entire United States for a major biomass crop, Miscanthus × giganteus. We then combine invasion risk predictions for this method with those from bioclimatic methods, producing a map of aggregated invasion risk that can offer more nuanced predictions of invasion risk than either approach alone. Lastly, we evaluate potential risks for invasive crops that differ in invasiveness traits, to examine how geographic patterns of invasion risk vary among invaders as a result of their particular constellation of traits.
Data from: Allometric scaling of indirect effects: body size ratios predict non-consumptive effects in multi-predator systems
1. Non-consumptive effects (NCES) frequently lead to non-independent effects of multiple predators. While such emergent predator effects are ubiquitous in natural communities, the strength of these effects varies among studies and systems, making it difficult to predict a priory how changes in predator diversity influence prey suppression. Thus, identifying general scaling rules which can explain this variation of non-independent effects is vital for modeling natural communities and how they respond to biodiversity loss. 2. Body size is a key trait determining the nature and strength of ecological interactions. While great progress has been made using allometric relationships to predict the interaction strength of predator-prey pairs, it is unknown whether similar relationships explain variation in the strength of NCEs, and how they are related to consumptive effects. 3. Here we experimentally manipulate the relative size difference of multiple predators to determine whether NCEs follow general allometric scaling relationships in an aquatic multi-predator system. 4. Results demonstrate that the presence and strength of NCEs can vary dramatically across predator combinations. However, this variation scaled predictably with the size ratio of predators; increasing the size difference among predators increased NCEs. This pattern was driven by a size-mediated shift in "food web motif" from competition to intraguild predation and a positive correlation of NCEs and intraguild predation rate. 5. Results indicate that models which assume that consumers have independent effects are particularly likely to make erroneous predictions when predators differ substantially in size, but simple allometric relationships of NCEs could be used to correct this bias.
Data from: Predicting the maximum earthquake magnitude from seismic data in Israel and its neighboring countries
This paper explores several data mining and time series analysis methods for predicting the magnitude of the largest seismic event in the next year based on the previously recorded seismic events in the same region. The methods are evaluated on a catalog of 9,042 earthquake events, which took place between 01/01/1983 and 31/12/2010 in the area of Israel and its neighboring countries. The data was obtained from the Geophysical Institute of Israel. Each earthquake record in the catalog is associated with one of 33 seismic regions. The data was cleaned by removing foreshocks and aftershocks. In our study, we have focused on ten most active regions, which account for more than 80% of the total number of earthquakes in the area. The goal is to predict whether the maximum earthquake magnitude in the following year will exceed the median of maximum yearly magnitudes in the same region. Since the analyzed catalog includes only 28 years of complete data, the last five annual records of each region (referring to the years 2006–2010) are kept for testing while using the previous annual records for training. The predictive features are based on the Gutenberg-Richter Ratio as well as on some new seismic indicators based on the moving averages of the number of earthquakes in each area. The new predictive features prove to be much more useful than the indicators traditionally used in the earthquake prediction literature. The most accurate result (AUC = 0.698) is reached by the Multi-Objective Info-Fuzzy Network (M-IFN) algorithm, which takes into account the association between two target variables: the number of earthquakes and the maximum earthquake magnitude during the same year.
Data from: Evolutionary relationships can be more important than abiotic conditions in predicting the outcome of plant-plant interactions
Positive and negative plant–plant interactions are major processes shaping plant communities. They are affected by environmental conditions and evolutionary relationships among the interacting plants. However, the generality of these factors as drivers of pairwise plant interactions and their combined effects remain virtually unknown. We conducted an observational study to assess how environmental conditions (altitude, temperature, irradiance and rainfall), the dispersal mechanism of beneficiary species and evolutionary relationships affected the co-occurrence of pairwise interactions in 11 Stipa tenacissima steppes located along an environmental gradient in Spain. We studied 197 pairwise plant–plant interactions involving the two major nurse plants (the resprouting shrub Quercus coccifera and the tussock grass S. tenacissima) found in these communities. The relative importance of the studied factors varied with the nurse species considered. None of the factors studied were good predictors of the co-ocurrence between S. tenacissima and its neighbours. However, both the dispersal mechanism of the beneficiary species and the phylogenetic distance between interacting species were crucial factors affecting the co-occurrence between Q. coccifera and its neighbours, while climatic conditions (irradiance) played a secondary role. Values of phylogenetic distance between 207–272.8 Myr led to competition, while values outside this range or fleshy-fruitness in the beneficiary species led to positive interactions. The low importance of environmental conditions as a general driver of pairwise interactions was caused by the species-specific response to changes in either rainfall or radiation. This result suggests that factors other than climatic conditions must be included in theoretical models aimed to generally predict the outcome of plant–plant interactions. Our study helps to improve current theory on plant–plant interactions and to understand how these interactions can respond to expected modifications in species composition and climate associated to ongoing global environmental change.
Data from: Plasticity-led evolution: evaluating the key prediction of frequency-dependent adaptation
Plasticity-led evolution occurs when a change in the environment triggers a change in phenotype via phenotypic plasticity, and this pre-existing plasticity is subsequently refined by selection into an adaptive phenotype. A critical, but largely untested prediction of plasticity-led evolution (and evolution by natural selection generally) is that the rate and magnitude of evolutionary change should be positively associated with a phenotype's frequency of expression in a population. Essentially, the more often a phenotype is expressed and exposed to selection, the greater its opportunity for adaptive refinement. We tested this prediction by competing against each other spadefoot toad tadpoles from different natural populations that vary in how frequently they express a novel, environmentally induced carnivore ecomorph. As expected, lab-reared tadpoles whose parents were derived from populations that express the carnivore ecomorph more frequently were superior competitors for the resource for which this ecomorph is specialized—fairy shrimp. These tadpoles were better at utilizing this resource both because they were more efficient at capturing and consuming shrimp and because they produced more exaggerated carnivore traits. Moreover, they exhibited these more carnivore-like features even without experiencing the inducing cue, suggesting that this ecomorph has undergone an extreme form of plasticity-led evolution––genetic assimilation. Thus, our findings provide evidence that the frequency of trait expression drives the magnitude of adaptive refinement, thereby validating a key prediction of plasticity-led evolution specifically and adaptive evolution generally.
Data from: Geologic and geomorphic controls on rockfall hazard: how well do past rockfalls predict future distributions?
To evaluate the geospatial hazard relationships between recent (contemporary) rockfalls and their prehistoric predecessors, we compare the locations, physical characteristics, and lithologies of rockfall boulders deposited during the 2010-2011 Canterbury earthquake sequence (CES) (n=185) with those deposited prior to the CES (n=1093). Population ratios of pre-CES to CES boulders at two study sites vary spatially from ~5:1 to 8.5:1. This is interpreted to reflect (i) variations in CES rockfall flux due to intra- and inter-event spatial differences in ground motions (e.g. directionality) and associated variations in source cliff responses, (ii) possible variations in the triggering mechanism(s), frequency, flux, record duration, boulder size distributions, and post-depositional mobilization of pre-CES rockfalls relative to CES rockfalls, and (iii) geological variations in the source cliffs of CES and pre-CES rockfalls. On interfluves, CES boulders traveled approximately 100 to 250 m further downslope than prehistoric (pre-CES) boulders, interpreted to reflect reduced resistance to CES rockfall transport due to preceding anthropogenic hillslope de-vegetation. Volcanic breccia boulders are more dimensionally equant, rounded, larger, and traveled further downslope than coherent lava boulders, illustrating clear geological control on rockfall hazard. In valley bottoms, the furthest-traveled pre-CES boulders are situated further downslope than CES boulders due to (i) remobilization of pre-CES boulders by post-depositional processes such as debris flows, and (ii) reduction of CES boulder velocities and travel distances by collisional impacts with pre-CES boulders. A considered earth-systems approach is required when using preserved distributions of rockfall deposits to predict the severity and extents of future rockfall events.
Data from: Exploiting Poisson additivity to predict fire frequency from maps of fire weather and land cover in boreal forests of Québec, Canada
Predictive models of fire frequency conditional on weather and land cover are essential to assess how future cover-type distributions and weather conditions may influence fire regimes. We modelled the effects of bottom-up variables (e.g. land cover) and top-down variables (e.g. fire weather) simultaneously with data aggregated or interpolated to spatial and temporal units of 100 km2 and 1yr in the boreal forest of Québec, Canada. For models of human-caused fires, we used road density as a surrogate for human access and behaviour. We exploited the additive property of Poisson distributions to estimate cover-type specific fire count rates, which would normally not be possible with data of this spatial resolution. We used piecewise linear functions to model nonlinear relations between fire weather and fire frequency for each cover-type simultaneously. The estimated conditional rates may be considered as expected mean counts per unit area and time. It follows that these rates can be rescaled to arbitrary spatial and temporal extents. Our results showed fire frequency increased nonlinearly as aridity increased and more quickly in disturbed areas than other types. Road density exerted the strongest influence on the frequency of human-caused fires, which were positively correlated with road density. The estimates may be used to parameterize the fire ignition component of spatial simulation models, which often have a resolution different from that at which the data were collected. This is an essential step in incorporating biotic and abiotic feedbacks, land-cover dynamics, and climate projections into ecological forecasting. The insight into the power of Poisson additivity to reveal high-resolution ecological processes from low-resolution data could have applications in other areas of ecology.
Data from: Integrated modeling predicts shifts in waterbird population dynamics under climate change
Climate change has been identified as one of the most important drivers of wildlife population dynamics. The in-depth knowledge of the complex relationships between climate and population sizes through density dependent demographic processes is important for understanding and predicting population shifts under climate change, which requires integrated population models (IPMs) that unify the analyses of demography and abundance data. In this study we developed an IPM based on Gaussian approximation to dynamic N-mixture models for large scale population data. We then analyzed four decades (1972-2013) of Mallard (Anas platyrhynchos) breeding population survey, band-recovery, and climate data covering a large spatial extent from North American prairies through boreal habitat to Alaska. We aimed to test the hypothesis that climate change will cause shifts in population dynamics if climatic effects on demographic parameters that have substantial contribution to population growth vary spatially. More specifically, we examined the spatial variation of climatic effects on density dependent population demography, identified the key demographic parameters that are influential to population growth, and forecasted population responses to climate change. Our results revealed that recruitment, which explained more variance of population growth than survival, was sensitive to the temporal variation of precipitation in the southern portion of the study area but not in the north. Survival, by contrast, was insensitive to climatic variation. We then forecasted a decrease in Mallard breeding population density in the south and an increase in the northwestern portion of the study area, indicating potential shifts in population dynamics under future climate change. Our results implied that different strategies need to be considered across regions to conserve waterfowl populations in the face of climate change. Our modelling approach can be adapted for other species and thus has wide application to understanding and predicting population dynamics in the presence of global change.
Data from: Predicting spatial patterns of plant species richness: a comparison of direct macroecological and species stacking modelling approaches
PLEASE NOTE, THESE DATA ARE ALSO REFERRED TO IN TWO OTHER PUBLICATIONS. PLEASE SEE http://dx.doi.org/10.1111/j.1365-2486.2008.01766.x AND http://dx.doi.org/10.1111/2041-210X.12222 FOR MORE INFORMATION. Aim: This study compares the direct, macroecological approach (MEM) for modelling species richness (SR) with the more recent approach of stacking predictions from individual species distributions (S-SDM). We implemented both approaches on the same dataset and discuss their respective theoretical assumptions, strengths and drawbacks. We also tested how both approaches performed in reproducing observed patterns of SR along an elevational gradient. Location: Two study areas in the Alps of Switzerland. Methods: We implemented MEM by relating the species counts to environmental predictors with statistical models, assuming a Poisson distribution. S-SDM was implemented by modelling each species distribution individually and then stacking the obtained prediction maps in three different ways – summing binary predictions, summing random draws of binomial trials and summing predicted probabilities – to obtain a final species count. Results: The direct MEM approach yields nearly unbiased predictions centred around the observed mean values, but with a lower correlation between predictions and observations, than that achieved by the S-SDM approaches. This method also cannot provide any information on species identity and, thus, community composition. It does, however, accurately reproduce the hump-shaped pattern of SR observed along the elevational gradient. The S-SDM approach summing binary maps can predict individual species and thus communities, but tends to overpredict SR. The two other S-SDM approaches – the summed binomial trials based on predicted probabilities and summed predicted probabilities – do not overpredict richness, but they predict many competing end points of assembly or they lose the individual species predictions, respectively. Furthermore, all S-SDM approaches fail to appropriately reproduce the observed hump-shaped patterns of SR along the elevational gradient. Main conclusions: Macroecological approach and S-SDM have complementary strengths. We suggest that both could be used in combination to obtain better SR predictions by following the suggestion of constraining S-SDM by MEM predictions.
Data from: Specific leaf area predicts dryland litter decomposition via two mechanisms
1. Litter decomposition plays important roles in carbon and nutrient cycling. In dryland both microbial decomposition and abiotic degradation (by UV light or other forces) drive variation in decomposition rates, but whether and how litter traits and position determine the balance between these processes is poorly understood. 2. We investigated relationships between litter quality and their decomposition rates among diverse plant species in a desert ecosystem in vertically contrasting positions representing distinct decomposition environments driven by different relative contributions of abiotic and microbial degradation. Thereto, leaf litter samples from 17 desert species were sealed into litterbags and placed on the soil surface under strong solar exposure versus shade conditions, or buried in the soil at 10 cm depth, for a whole year. 3. Litter decomposition rates were 21 and 17 % higher in burial and light-exposed treatments, respectively, than those in shade. Leaf traits, i.e. specific leaf area (SLA), litter C:N ratio and lignin concentration could predict litter decomposition to some degree, but their predictive power was dependent on litter position. However, multiple linear regression showed that SLA, litter C and P significantly affected k values for leaf litter decomposition besides litter position, with SLA standing out as a strong determinant of litter decomposition rate as related either to solar radiation or the environment below the soil surface. Furthermore, the interspecific differences in litter decomposition rate decreased over time, implying that afterlife effects of leaf traits on decomposition were attenuated. 4. Synthesis. These findings suggest that abiotic photodegradation and soil burial mediated microbial decomposition could be responsible for higher than expected litter turnover in dryland. They point to a dual role of specific leaf area as a promotor of decomposition rates: via relative exposure of the leaf surface to abiotic factors such as UV light versus to soil moisture and microbes under soil burial.
Data from: Density-dependent habitat selection predicts fitness and abundance in a small lizard
Density-dependent habitat selection has been used to predict and explain patterns of abundance of species between habitats. Thermal quality, a density-independent component of habitat suitability, is often the most important factor for habitat selection in ectotherms which comprise the vast majority of animal species. Ectotherms may reach high densities such that individual fitness is reduced in a habitat due to increased competition for finite resources. Therefore, density and thermal quality may present conflicting information about which habitat will provide the highest fitness reward and ectotherm habitat selection may be density-independent. Using ornate tree lizards Urosaurus ornatus at 10 sites each straddling two adjacent habitats (wash and upland), we tested the hypothesis that habitat selection is density-dependent even when thermal quality differs between habitats. We first tested that fitness proxies decline with density in each habitat, indicating density-dependent effects on habitat suitability. We also confirmed that the two habitats vary in suitability (quantified by food abundance and thermal quality). Next, we tested the predictions that habitat selection depends on density with isodar analyses and that fitness proxies are equal in the two habitats within a site. We found that monthly survival rates decreased with density, and that the wash habitat had more prey and higher thermal quality than the upland habitat. Lizards preferred the habitat with more food and higher thermal quality, lizard densities in the two habitats were positively correlated, and fitness proxies of lizards did not differ between habitats. These patterns are consistent with density-dependent habitat selection, despite differences in thermal quality between habitats. We expect that density-dependent habitat selection is widespread in terrestrial ectotherms when densities are high and temperatures are close to their optimal performance range. In areas where thermal quality is low, however, we expect that depletable resources, such as food, become less limiting because assimilating resources is more difficult.
Data from: Stress hormones predict a host superspreader phenotype in the West Nile virus system
Glucocorticoid stress hormones, such as corticosterone (CORT), have profound effects on the behaviour and physiology of organisms, and thus have the potential to alter host competence and the contributions of individuals to population- and community-level pathogen dynamics. For example, CORT could alter the rate of contacts among hosts, pathogens and vectors through its widespread effects on host metabolism and activity levels. CORT could also affect the intensity and duration of pathogen shedding and risk of host mortality during infection. We experimentally manipulated songbird CORT, asking how CORT affected behavioural and physiological responses to a standardized West Nile virus (WNV) challenge. Although all birds became infected after exposure to the virus, only birds with elevated CORT had viral loads at or above the infectious threshold. Moreover, though the rate of mortality was faster in birds with elevated CORT compared with controls, most hosts with elevated CORT survived past the day of peak infectiousness. CORT concentrations just prior to inoculation with WNV and anti-inflammatory cytokine concentrations following viral exposure were predictive of individual duration of infectiousness and the ability to maintain physical performance during infection (i.e. tolerance), revealing putative biomarkers of competence. Collectively, our results suggest that glucocorticoid stress hormones could directly and indirectly mediate the spread of pathogens.
Data from: Rain shadow effects predict population differences in thermal tolerance of leaf-cutting ant workers (Atta cephalotes)
<p>Tests of hypotheses for the evolution of thermal physiology often rely on mean temperatures, but mounting evidence suggests geographic variation in temperature extremes is also an important predictor of species' thermal tolerances. Although the tropics are less thermally variable than higher latitude regions, rain shadows on the leeward sides of mountains can experience greater diel and seasonal variation in temperature than windward sites. Rain shadows provide opportunities to test predictions about the relationships of extreme temperatures with thermal physiology while controlling for latitude. We tested the hypothesis that populations of leaf-cutting ants (Atta cephalotes) in leeward, montane, and windward sites in Costa Rica would differ in upper thermal tolerances (CT<sub>max</sub>) of workers. As predicted from rain shadow effects via extreme high temperatures, the leeward rain-shadow site yielded the highest mean CT<sub>max</sub> (rain shadow site 42.1±0.3 °C, Montane site 38.2±0.5 °C, windward site 38.2±0.3 °C). This suggests that high-temperature extremes in tropical rain shadow forests can select for higher thermal tolerances. CT<sub>max</sub> increased with worker body size within sites, but CT<sub>max</sub> increased with body size more gradually at the two lowland sites, as predicted if local high temperatures selected more strongly on the most thermally vulnerable society members (small workers). This suggests that warmer lowland climates selected for colonies with less variation in heat tolerance than cooler high elevation climates.</p>
Data from: DNA methylation predicts immune gene expression in introduced house sparrows (Passer domesticus)
Populations undergoing range expansions are often faced with novel selective pressures, and to cope with such challenges, populations must either adapt quickly or exhibit phenotypic plasticity. This latter option allows for rapid phenotypic adjustments and persistence in novel environments, and thus could be advantageous at range‐edges. Our previous research on house sparrows in Kenya—a site of ongoing range expansion— and a growing literature suggests that invasion success is facilitated by epigenetic regulation of gene expression. Previously, we found (i) differences in the expression of a microbial surveillance gene (i.e. Toll‐like receptor 4—TLR4), and (ii) extensive variation in genome‐wide DNA methylation among house sparrows across Kenya. Here, our goal was to investigate whether these two observations are related, specifically whether DNA methylation within a target sequence upstream of the TLR4 transcription start site is associated with variation in TLR4 expression. We found that DNA methylation in the aforementioned region was quite variable among individuals, and variation at one CpG site predicted differences in TLR4 expression. Moreover, we found genetic variation within the same sequence upstream of the TLR4 exon, but this variation did not predict TLR4 expression. To our knowledge, this is the first study to demonstrate an association between DNA methylation and the expression of an ecologically relevant trait in a range‐expanding vertebrate.
Data from: Habitat selection predicts genetic relatedness in an alpine ungulate
Landscape heterogeneity plays an integral role in shaping ecological and evolutionary processes. Despite links between the two disciplines, ecologists and population geneticists have taken different approaches to evaluating habitat selection, animal movement, and gene flow across the landscape. Ecologists commonly use statistical models such as resource selection functions (RSFs) to identify habitat features disproportionately selected by animals, while population genetic approaches model genetic differentiation according to the distribution of habitat variables. We combined ecological and genetic approaches by using RSFs and step-selection functions (SSFs) to predict genetic relatedness across a heterogeneous landscape. We constructed sex and season-specific resistance surfaces based on RSFs and SSFs estimated using data from 102 GPS radiocollared mountain goats (Oreamnos americanus) in southeast Alaska. Based on mountain goat ecology, we hypothesized that summer and male surfaces would be the best predictors of relatedness. All individuals were genotyped at 22 microsatellite loci, which we used to estimate genetic relatedness. Summer resistance surfaces derived from RSFs were the best predictors of genetic relatedness, and winter models the poorest. Male and female specific surfaces were similar, except for winter where male habitat selection better predicted genetic relatedness. The null models of isolation-by-distance and barrier only outperformed the winter models. This study merges high-resolution individual locations through GPS telemetry and genetic data, that can be used to validate and parameterize landscape genetics models, and further elucidates the relationship between landscape heterogeneity and genetic differentiation.
Data from: Stream community richness predicts apex predator occupancy dynamics in riparian systems
Streams and adjacent riparian habitats represent linked terrestrial and aquatic ecosystems that exchange materials and energy. Recognized relationships among apex predators and ecosystem biodiversity led us to hypothesize that these predators in riparian-stream systems were more likely to be found in sites with high stream quality, defined as increased ecosystem function and integrity. In our freshwater study system, river otter (Lontra canadensis) and mink (Neovison vison) play critical roles as apex predators. We used multi-season occupancy modelling across 3 sampling years (2012-2014) to compare aspects of the stream communities that explain occupancy dynamics of river otter and mink, including their interactions with other semi-aquatic mammals. We surveyed for semi-aquatic mammals at 77 sites in 12 major watersheds in southern Illinois, USA (44,526 km<sup>2</sup>). Naïve occupancy differed among years but generally increased for river otter, and remained high (≥93.5%) for mink. Increasing substrate availability increased detectability of river otter, whereas mink detection varied by survey period. Occupancy of river otter during the initial survey period was higher in sites closer to reintroduction points. Probability of colonization of river otter was positively associated with macroinvertebrate index of biotic integrity, fish species richness, and beaver presence. Sites with high species richness of fish families preferred by river otter also had increased river otter persistence. Mink occupied sites with increased fish richness, muskrat presence, and mussel community index. Taken together, our results show occupancy of both mink and river otter were predicted by aspects of prey diversity and presence, indicating the importance of community composition in occupancy dynamics of riparian predators. Ultimately, these relationships suggest that habitat heterogeneity and system stability are important to apex predator site use. However, the relative role of bottom-up and top-down forcing in stream systems remains to be resolved.
ScienceDex guides
Understand access before you commit
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.