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93 results for “Environmental Predictability”
Abundance Trend Indicator - Models, Prediction, Stacked Environmental Data and Training Set Similarity
<p># Readme</p> <p>These trained models can be used to predict the abundance trends of New Zealand's forest species and can be used together with the code in https://github.com/lnilya/abundance-trend-indicator</p> <p>Since the process of using the models requires coding expertise and some setting up, please make sure to reach out to ilya.shabanov@vuw.ac.nz for any questions. All files will require the code in the repository to be read and used. </p> <p>If you want to explore the results generated with these models, please visit https://ati-nz-predictions-7e6f3d514735.herokuapp.com/ for a user-friendly, interactive UI.</p> <p>## Contents</p> <p>_models: Contains the trained models (Artificial Neural Network (ANN), Random Forest (RF), SVMW (Support vector machine) and GLM (logistic regression)) at different degrees of noise filtering, different datasets and variable sets. The model files also contain test and training scores. To load the files please refer to the readme in the code repository: ttps://github.com/lnilya/abundance-trend-indicator</p> <p><br>_predictions/_environment: Contains the predictor variables for the study area (New Zealand, 1950-2019) that are needed by the models to make predictions. </p> <p>_predictions/_similarity: Contains the masks of areas that can be predicted by models and are similar to the training set.</p> <p>_predictions/_ati: Contain the predicted results for the abundance trend. These can be explored on https://ati-nz-predictions-7e6f3d514735.herokuapp.com/ </p> <p> </p>
Data from: Functional traits and environmental conditions predict community isotopic niches and energy pathways across spatial scales
1. Despite ongoing research in food web ecology and functional biogeography, the links between food-web structure, functional traits and environmental conditions across spatial scales remain poorly understood. Trophic niches, defined as the amount of energy and elemental space occupied by species and food webs, may help bridge this divide. 2. Here, we ask how the functional traits of species, the environmental conditions of habitats and the spatial scale of analysis jointly determine the characteristics of trophic niches. We used isotopic niches as a proxy of trophic niches, and conducted analyses at spatial scales ranging from local food webs and metacommunities to geographically distant sites. 3. We sampled aquatic macroinvertebrates from 104 tank bromeliads distributed across five sites from Central to South America, and compiled the macroinvertebrates' functional traits and stable isotope values (δ15N and δ13C). We assessed how isotopic niches within each bromeliad were influenced by the functional trait composition of their associated invertebrates and environmental conditions (i.e., habitat size, canopy cover, and detrital concentration). We then evaluated whether the diet of dominant predators and, consequently, energy pathways within food webs, reflected functional and environmental changes among bromeliads across sites. Finally, we determined the extent to which the isotopic niches of macroinvertebrates within each bromeliad contributed to the metacommunity isotopic niches within each site, and compared these metacommunity-level niches over biogeographic scales. 4. At the bromeliad level, isotopic niches increased with the functional richness of species in the food web and the detrital concentration in the bromeliad. The diet of top predators tracked shifts in prey biomass along gradients of canopy cover and detrital concentration. Bromeliads that grew under heterogeneous canopy cover displayed less trophic redundancy and therefore combined to form larger metacommunity isotopic niches. Finally, the size of metacommunity niches depended on within-site heterogeneity in canopy cover. 5. Our results suggest that the trophic niches occupied by food webs can predictably scale from local food webs to metacommunities to biogeographic regions. This scaling process is determined by both the functional traits of species and heterogeneity in environmental conditions.
Both real-time and long-term environmental data perform well in predicting shorebird distributions in managed habitat
<p>Highly mobile species, such as migratory birds, respond to seasonal and inter-annual variability in resource availability by moving to better habitats. Despite the recognized importance of resource thresholds, species distribution models typically rely on long-term average habitat conditions, mostly because large-extent, temporally-resolved, environmental data are difficult to obtain. Recent advances in remote sensing make it possible to incorporate more frequent measurements of changing landscapes; however, there is often a cost in terms of model building and processing and the added value of such efforts is unknown. Our study tests whether incorporating real-time environmental data increases the predictive ability of distribution models, relative to using long-term average data. We developed and compared distribution models for shorebirds in California's Central Valley based on high temporal resolution (every 16-days), and 17-year long-term average, surface water data. Using abundance-weighted boosted regression trees, we modeled monthly shorebird occurrence as a function of surface water availability, crop type, wetland type, road density, temperature, and bird data source. While modeling with both real-time and long-term average data provided good fit to withheld validation data (0.79 < AUC < 0.89 across taxa), there were small differences in model performance. The best models incorporated long-term average conditions and spatial pattern information for real-time flooding (e.g. perimeter-area ratio of real-time water bodies). There was not a substantial difference in the performance of real-time and long-term average data models within time periods when real-time surface water differed substantially from the long-term average (specifically during drought years 2013-2016) and in intermittently flooded months or locations. Spatial predictions resulting from the models differed most in the southern region of the study area where there is lower water availability, fewer birds, and lower sampling density. Prediction uncertainty in the southern region of the study area highlights the need for increased sampling in this area. Because both sets of data performed similarly, the choice of which data to use may depend on the management context. Real-time data may ultimately be best for guiding dynamic, adaptive conservation actions whereas models based on long-term averages may be more helpful for guiding permanent wetland protection and restoration. --</p>
Data from: Environmental quality predicts optimal egg size in the wild
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Species interactions limit the predictability of community responses to environmental change
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Environmental heterogeneity predicts global species richness patterns better than area
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Predicting barrier effects of transportation networks on Asian elephants: Implications for environmental impact assessment
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Data from: Measures of biologically relevant environmental heterogeneity improve prediction of regional plant species richness
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Data from: Environmental gradients predict the genetic population structure of a coral reef fish in the Red Sea
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Data from: Prediction of phylogeographic endemism in an environmentally complex biome.
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Data from: The evolution of vertebrate eye size across an environmental gradient: phenotype does not predict genotype in a Trinidadian killifish
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Data from: Functional traits and environmental conditions predict community isotopic niches and energy pathways across spatial scales
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Data from: Predicting genotypes environmental range from genome-environment associations
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Both real-time and long-term environmental data perform well in predicting shorebird distributions in managed habitat
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Data from: Accurate predictions of coexistence in natural systems require the inclusion of facilitative interactions and environmental dependency
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Data from : Environmental predictability drives adaptive within- and transgenerational plasticity of heat tolerance across life stages and climatic regions
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Data and code from: Network theory predicts ecosystem robustness across environmental conditions
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Data from: Increased transgenerational epigenetic variation, but not predictable epigenetic variants, after environmental exposure in two apomictic dandelion lineages
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Data from: Environmental transmission of a personality trait: foster parent exploration behaviour predicts offspring exploration behaviour in zebra finches
Consistent behavioural differences among individuals are common in many species and can have important effects on offspring fitness. To understand such 'personality' variation, it is important to determine the mode of inheritance, but this has been quantified for only a few species. Here, we report results from a breeding experiment in captive zebra finches, Taeniopygia guttata, in which we cross-fostered offspring to disentangle the importance of genetic and non-genetic transmission of behaviour. Genetic and foster-parents' exploratory type was measured in a novel environment pre-breeding and offspring exploratory type was assessed at adulthood. Offspring exploratory type was predicted by the exploratory behaviour of the foster but not the genetic parents, whereas offspring size was predicted by genetic but not foster-parents' size. Other aspects of the social environment, such as rearing regime (uni- versus biparental), hatching position, brood size or an individual's sex did not influence offspring exploration. Our results therefore indicate that non-genetic transmission of behaviour can play an important role in shaping animal personality variation.
An Interpretable 3D Multi-Hierarchical Molecular Hybrid Representation for Environmental, Health, and Safety Properties Prediction
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