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312 results for “ecological model”
Fig. 3 in Using Ecological Niche Modeling For Biodiversity Conservation Guidance In The Western Podillya (Ukraine): Amphibians
Fig. 3. Response of Triturus cristatus to the Human Footprint: x-axis — Human Footprint; y-axis — logistic output (probability of presence).
Fig. 2 in Using Ecological Niche Modeling For Biodiversity Conservation Guidance In The Western Podillya (Ukraine): Amphibians
Fig. 2. Response of Pelobates fuscus to Bio 3: x-axis — isothermality; y-axis — logistic output (probability of presence).
Data from: A hierarchical model for jointly assessing ecological and anthropogenic impacts on animal demography
<p>1. The management of sustainable harvest of animal populations is of great ecological and conservation importance. Development of formal quantitative tools to estimate and mitigate the impacts of harvest on animal populations has positively impacted conservation efforts.</p> <p>2. The vast majority of existing harvest models, however, do not simultaneously estimate ecological and harvest impacts on demographic parameters and population trends. Given that the impacts of ecological drivers are often equal to or greater than the effects of harvest, and can covary with harvest, this disconnect has the potential to lead to flawed inference.</p> <p>3. In this study, we used Bayesian hierarchical models and a 43-year capture-mark-recovery dataset from 404,241 female mallards (Anas platyrhynchos) released in the North American midcontinent to estimate mallard demographic parameters. Further, we model the dynamics of waterfowl hunters and habitat, and the direct and indirect effects of anthropogenic and ecological processes on mallard demographic parameters.</p> <p>4. We demonstrate that density-dependence, habitat conditions, and harvest can simultaneously impact demographic parameters of female mallards, and discuss implications for existing and future harvest management models.</p> <p>5. Our results demonstrate the importance of controlling for multicollinearity among demographic drivers in harvest management models, and provide evidence for multiple mechanisms that lead to partial compensation of mallard harvest. We provide a novel model structure to assess these relationships that may allow for improved inference and prediction in future iterations of harvest management models across taxa.</p>
Transformed crane data from: Balancing structural complexity with ecological insight in spatio-temporal species distribution models
<p>The potential for statistical complexity in species distribution models (SDMs) has greatly increased with advances in computational power. Structurally complex models provide the flexibility to analyse intricate ecological systems and realistically messy data, but can be difficult to interpret, reducing their practical impact. Founding model complexity in ecological theory can improve insight gained from SDMs. </p> <p>Here, we evaluate a marked point process approach, which uses multiple Gaussian random fields to represent population dynamics of the Eurasian crane (<em>Grus grus</em>) in a spatio-temporal species distribution model. We discuss the role of model components and their impacts on predictions, in comparison with a simpler binomial presence/absence approach. Inference is carried out using Integrated Nested Laplace Approximation (INLA) with inlabru, an accessible and computationally efficient approach for Bayesian hierarchical modelling, which is not yet widely used in SDMs. </p> <p>Using the marked point process approach, crane distribution was predicted to be dependent on the density of suitable habitat patches, as well as close to observations of the existing population. This demonstrates the advantage of complex model components in accounting for spatio-temporal population dynamics (such as habitat preferences and dispersal limitations) that are not explained by environmental variables. However, including an AR1 temporal correlation structure in the models resulted in unrealistic predictions of species distribution; highlighting the need for careful consideration when determining the level of model complexity.</p> <p>Increasing model complexity, with careful evaluation of the effects of additional model components, can provide a more realistic representation of a system, which is of particular importance for a practical and impact-focused discipline such as ecology (though these methods extend to applications for a wide range of systems). Founding complexity in contextual theory is not only fundamental to maintaining model interpretability, but can be a useful approach to improving insight gained from model outputs. </p>
FIGURE 1 in Comparisons of two cryptic Ampedus species (Coleoptera: Elateridae) by using classical systematics, ecological niche modeling, and DNA barcoding
FIGURE 1. Habitus photos and aedeagi drawings of examined species. A-B. Ampedus platiai, C-D. A. samedovi, E-F. A. pomonae (Aedeagi of A. platiai and A. samedovi are redrawn from Kabalak 2010 and aedeagus of A. pomonae is redrawn from Platia 1994.). BML: Basal struts of median lobe, BP: Basal piece, ML: Median Lobe, PDT: Paramere distal tooth, PR: Paramere.
Frontiers in Ecology and Evolution 01 frontiersin.org Why grazing and soil matter for dry grassland diversity: New insights from multigroup structural equation modeling of micro-patterns
<p>Grazing is recognized as a major process driving the composition of plant<br> communities in grasslands, mostly due to the heterogeneous removal of<br> plant species and soil compaction that results in a mosaic of small patches<br> called micro-patterns. To date, no study has investigated the differences in<br> composition and functioning among these micro-patterns in grasslands in<br> relation to grazing and soil environmental variables at the micro-local scale.<br> In this study, we ask (1) To what extent are micro-patterns different from each<br> other in terms of species composition, species richness, vegetation volume,<br> evenness, and functioning? and (2) based on multigroup structural equation<br> modeling, are those differences directly or indirectly driven by grazing and soil<br> characteristics? We focused on three micro-patterns of the Mediterranean dry<br> grassland of the Crau area, a protected area traditionally grazed in the South-<br> East of France. From 70 plant community relevés carried out in three micro-<br> patterns located in four sites with different soil and grazing characteristics,<br> we performed univariate, multivariate analyses and applied structural equation<br> modeling for the first time to this type of data. Our results show evidence<br> of clear differences among micro-pattern patches in terms of species<br> composition, vegetation volume, species richness, evenness, and functioning<br> at the micro-local scale. These differences are maintained not only by direct<br> and indirect effects of grazing but also by several soil variables such as fine<br> granulometry. Biological crusts appeared mostly driven by these soil variables,<br> whereas reference and edge communities are mostly the result of different<br> levels of grazing pressure revealing three distinct functioning specific to each<br> micro-pattern, all of them coexisting at the micro-local scale in the studied<br> Mediterranean dry grassland. This first overview of the multiple effects of<br> grazing and soil characteristics on communities in micro-patterns is discussed<br> within the scope of the conservation of dry grasslands plant diversity.</p>
Comparative ecological analysis and predictive modeling of tick-borne pathogens
<p>Tick-borne diseases constitute the predominant vector-borne health threat in North America. Recent observations have noted a significant expansion in the range of the black-legged tick (<em>Ixodes scapularis</em> Say, Acari: Ixodidae), alongside a rise in the incidence of diseases caused by its vectored pathogens: <em>Borrelia burgdorferi</em> (Spirochaetales: Spirochaetaceae), <em>Babesia microti</em> (Piroplasmida: Babesiidae), and <em>Anaplasma phagocytophilium</em> (Rickettsiales: Anaplasmataceae), the causative agents of Lyme disease, babesiosis, and anaplasmosis, respectively. Prior research identified environmental features that influence the ecological dynamics of <em>I. scapularis</em> and <em>B. burgdorferi</em> that can be used to predict the distribution and abundance of these organisms, and thus Lyme disease risk. In contrast, there is a paucity of research into the environmental determinants of <em>B. microti</em> and <em>A. phagocytophilium</em>. Here we use over a decade of surveillance data to model the impact of environmental features on the infection prevalence of these increasingly common human pathogens in ticks across New York State (NYS). Our findings reveal a consistent northward and westward expansion of <em>B. microti</em> in NYS from 2009 to 2019, while the range of <em>A. phagocytophilum</em> varied at fine spatial scales. We constructed biogeographic models using data from over 1000 site-year visits and encompassing more than 250 environmental variables to accurately forecast infection prevalence for each pathogen to future years that were not included in model training. Several environmental features were identified to have divergent effects on the pathogens, revealing potential ecological differences governing their distribution and abundance. These validated biogeographic models are immediately useful for disease prevention efforts.</p>
Figure 5 in Establishment of an expansion-predicting model for invasive alien cerambycid beetle Aromia bungii based on a virtual ecology approach
Figure 5. Map of predicted occurrence units for the whole of Saitama Prefecture using both the river density model and river single model. The degree of shading reflects the theoretical invasion number predicted by each simulation model.
Figure 4 in Establishment of an expansion-predicting model for invasive alien cerambycid beetle Aromia bungii based on a virtual ecology approach
Figure 4. (a) Map of occurrence records for A. bungii through 2019. (b–g) Predicted occurrence units based on our models for each habitat variable. The degree of shading reflects the theoretical invasion number predicted by each model.
Figure 2 in Establishment of an expansion-predicting model for invasive alien cerambycid beetle Aromia bungii based on a virtual ecology approach
Figure 2. Basic structure of the cellular automata model. (A) Two values are associated with each cell: 1) the cell ID "x," a unique ID for each cell, and 2) the expansion probability "ex" indicating four directional vectors into adjacent cells (described below). (B) Values e1, e2, e3, and e4 indicate the probability of dispersion using the path to the top, left, bottom, and right cells, respectively. If the dispersion path value is 1, the insect population in this cell can expand to the adjacent cell.
Data from: The importance of biotic interactions in distribution models of wild bees depends on the type of ecological relations, spatial scale and range
<p>Studies have found that biotic information can play an important role in shaping the distribution of species even at large scales. However, results from species distribution models are not always consistent among studies, and the underlying factors that influence the importance of biotic information to distribution models, are unclear. 2. We studied wild bees and plants, and cleptoparasite bees and their hosts in the Netherlands to evaluate how the inclusion of their biotic interactions affects the performance of species distribution models. We assessed model performance through spatial block cross-validation and by comparing models with interactions to models where the interacting species were randomized. Finally, we evaluated how, (i) spatial resolution, (ii) taxonomic rank (genus or species), (iii) degree of specialization, (iv) distribution of the biotic factor, (v) bee body size and (vi) type of biotic interaction, affect the importance of biotic interactions in shaping the distribution of wild bee species using generalized linear models. 3. We found that the models of wild bees improved when the biotic factor was included. The model performance improved the most for parasitic bees. Spatial resolution, taxonomic rank, distribution range of the biotic factor, and degree of specialization of the modelled species all influenced the importance of the biotic interaction to the models. 4. We encourage researchers to include biotic interactions in species distribution models, especially for specialized species and when the biotic factor has a limited distribution range. However, before adding the biotic factor we suggest considering different spatial resolutions and taxonomic ranks of the biotic factor. We recommend using single species or genus data as a biotic factor in the models of specialist species and for the generalist species, we recommend using an approximate measure of interactions, such as flower richness.</p>
Рис. 6. МоΔеΛирование экоΛогических ниш коΛораΔского жука ΔΛя ΔаΛьневосточного, европейского и североамериканского ареаΛов метоΔом метрического Δвухмерного шкаΛирования с применением коэффициента Жаккара Fig. 6. Models of ecological niches of the Colorado potato beetle for the Far Eastern, European, and North-American habitats (metric multidimensional scaling, Jaccard index) in Comparative characterization of the ecology of native (Henosepilachna vigintioctomaculata) and invasive (Leptinoatrsa decemlineata) species under the conditions of the monsoon climate in the southern part of the Russian Far East
Рис. 6. МоΔеΛирование экоΛогических ниш коΛораΔского жука ΔΛя ΔаΛьневосточного, европейского и североамериканского ареаΛов метоΔом метрического Δвухмерного шкаΛирования с применением коэффициента Жаккара Fig. 6. Models of ecological niches of the Colorado potato beetle for the Far Eastern, European, and North-American habitats (metric multidimensional scaling, Jaccard index)
Spread of the non-native anemone Anemonia alicemartinae Häussermann & Försterra, 2001 along the Humboldt-current large marine ecosystem: an ecological niche model approach
<p>Environmental variables and script</p>
Figure 1 in Climatic preferences and distribution of 6 evolutionary lineages of Typhlops vermicularis Merrem, 1820 in Turkey using ecological niche modeling
Figure 1. Important mountain chains of Anatolia and ecological niche modeling of T. vermicularis in Turkey under current climatic conditions.
Figure 3 in Climatic preferences and distribution of 6 evolutionary lineages of Typhlops vermicularis Merrem, 1820 in Turkey using ecological niche modeling
Figure 3. Predicted models of lineages G, H, and I according to Last Interglacial (LIG) and Last Glacial Maximum (LGM; CCSM and MIROC) (4, 4A, 4B, 4C for lineage G; 5, 5A, 5B, 5C for lineage H; 6, 6A, 6B, 6C for lineage I).
Figure 2 in Climatic preferences and distribution of 6 evolutionary lineages of Typhlops vermicularis Merrem, 1820 in Turkey using ecological niche modeling
Figure 2. Predicted models of lineages B, C, and E according to Last Interglacial (LIG) and Last Glacial Maximum (LGM; CCSM and MIROC) (1, 1A, 1B, 1C for lineage B; 2, 2A, 2B, 2C for lineage C; 3, 3A, 3B, 3C for lineage E).
Figure 6 in A contribution to the biogeography and taxonomy of two Anatolian mountain brook newts, Neurergus barani and N. strauchii (Amphibia: Salamandridae) using ecological niche modeling
Figure 6. Results of the identity tests (D and I). The bars with different colors are calculated as the significance threshold of the replicates with identity test mode. Arrows refer to actual niche overlaps between Neurergus barani and N. strauchii.
Figure 4 in A contribution to the biogeography and taxonomy of two Anatolian mountain brook newts, Neurergus barani and N. strauchii (Amphibia: Salamandridae) using ecological niche modeling
Figure 4. The range of current climate suitability predicted by MaxEnt model for A) N. barani and B) N. strauchii in the Anatolian Peninsula and Near East Asia.
Figure 3 in A contribution to the biogeography and taxonomy of two Anatolian mountain brook newts, Neurergus barani and N. strauchii (Amphibia: Salamandridae) using ecological niche modeling
Figure 3. Relative predictive power of the six bioclimatic variables predicted by the jackknife of regularized training gain in MaxEnt model for both species (Neurergus barani and N. strauchii).
The ECOLOPES Voxel Model: Multi-domain data integration for ontology-aided generative computational design of ecological building envelopes
<p>The research portrayed in this article is part of the research project ‘ECOlogical building enveLOPES: a game-changing design approach for regenerative ecosystems’ funded by Horizon 2020 Future and Emerging Technologies. The overall research project focuses on developing a multi-domain data-driven computational design framework for the design of ecological building enclosures that addresses humans, plants, animals and microbiota. This article focuses on the development of a key component of the computational workflow in which initial designs are computationally initiated generated and analyzed, namely the ECOLOPES Voxel Model that contains and correlates multi-domain spatialised data for the design process, and its interactions with other components of the ontology-aided generative computational design process for ecological building envelopes.</p> <p>This repository contains all relevant data produced in this paper. Extended technical description is available in the Appendix A to the published paper, containing listing and description of individual voxel data layers. Data were exported from the RDB server (PostgreSQL) in text-based, future-proof format (csv).</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)
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.