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736 results for “habitat distribution”
Map 5 in The scolopendromorph centipedes (Chilopoda, Scolopendromorpha) of Tunisia: taxonomy, distribution and habitats
Map 5. Distribution of C. trisulcatus in Tunisia.
Map 4 in The scolopendromorph centipedes (Chilopoda, Scolopendromorpha) of Tunisia: taxonomy, distribution and habitats
Map 4. Distribution of O. spinicaudus in Tunisia. The Matmata record is marked with an arrow.
Map 3 in The scolopendromorph centipedes (Chilopoda, Scolopendromorpha) of Tunisia: taxonomy, distribution and habitats
Map 3. Distribution of C. gervaisianus in Tunisia.
Map 1 in The scolopendromorph centipedes (Chilopoda, Scolopendromorpha) of Tunisia: taxonomy, distribution and habitats
Map 1. Distribution of S. canidens in Tunisia.
Data from: Differential persistence favors habitat preferences that determine the distribution of a reef fish
A central focus of population ecology is understanding what factors explain the distribution and abundance of organisms within their range. This is a key issue in marine systems, where many organisms produce dispersive larvae that develop offshore before returning to settle on benthic habitat. We investigated the distribution of the neon goby, Elacatinus lori, on sponge habitat and evaluated whether variation in the persistence of recently settled individuals (i.e., settlers) among different sponge types can result in habitat preferences and establish their observed distribution. We found that E. lori settlers were more likely to occur on large yellow tube sponges (Aplysina fistularis) than on small yellow sponges or brown tube sponges (Agelas conifera). An experiment seeding settlers onto multiple species and sizes of sponge habitat revealed that settlers persist longer on large yellow sponges than on small yellow sponges or brown sponges. Habitat preference experiments also indicated that settlers prefer large yellow sponges over small yellow sponges or brown sponges. Settlers achieved these preference behaviors using visual, but not chemical, cues. Finally, new settlers arriving from the water column were more likely to occur on large yellow sponges than on small yellow sponges or brown sponges, indicating that the observed habitat preferences existed independent of prior experience. These results support the hypothesis that E. lori have evolved behavioral preferences for sponge habitats that will maximize their post-settlement persistence, and that decisions at settlement will shape the population level pattern of settler distribution on coral reefs.
Spatially explicit habitat selection: testing contagion and the ideal free distribution with culex mosquitoes
<p>Since its inception, attempts have been made to improve Ideal Free Distribution (IFD) Theory in order make it better fit real-world data. Spatial contagion is a newer ecological concept that suggests the perceived quality of a patch can be affected by the quality of its neighbor patches. Here, we present a series of experiments testing for potential contagion effects, examining how contagion can interact with the IFD, and determining whether spatial context affects assessment of habitat quality. First, we tested whether the presence of conspecific competitors negatively impacts oviposition habitat selection by female mosquitoes (<em>Culex restuans</em>). We then used a more complex spatial landscape to determine whether competition can create a spatial contagion effect. Finally, we examined whether the density of conspecifics can adjust the contagion effect of nutrient availability. We found that while females avoided patches containing conspecifics, there was no effect of competition/density on neighboring patches. Additionally, we found that resource availability was a significant predictor of where egg rafts were laid, but resource availability did not have a contagion effect. These results provide further support for the utility of the IFD, as individuals were able to accurately assess patch-level habitat quality.<br> </p>
Distribution, response to human disturbance, habitat preferences, and acoustic communication of tree hyraxes of Mt. Kilimanjaro, Tanzania
<p><span>This data consists data from recordings done in Mt. Kilimanjaro. Hourly calls of tree hyraxes have been calculated between 19.00 until 06:00. Dataset also has variables collected by other research groups.</span></p> <p><span>We combined our data of cue count per hour with data to analyse tree hyrax density with explanatory variables to model occupancy of tree hyraxes in Kilimanjaro. Dataset was combined from several research projects conducted within the Kili-Project (Hemp et al. 2018) (Table 1). Variables included forest type, temperature (Appelhans et al. 2015) precipitation (Appelhans et al. 2016). diameter breast height (DBH), leaf density, max vegetation height, and leaf area index (LAI) (Rutten et al., 2015). We also included land use index (LUI) (Peters et al. 2019) to the dataset, which included four different variables (percentage plant biomass removal, agricultural inputs, modification of the vegetation and percentage of agricultural area in the surroundings). </span></p> <p><span>Abstract</span></p> <p><span>Limited knowledge exists of the distribution, habitat selection, behavior and response to human disturbance of many mammalian species from mountains of Africa. This is especially true for nocturnal mammals. We studied acoustically very active tree hyraxes (<em>Dendrohyrax validus validus</em>) from Mt. Kilimanjaro National Park, Tanzania mainly with bioacoustical methods. To gain understanding of the habitat preferences of tree hyraxes we combined bioacoustical data with botanical and meteorological data collected earlier by <span>KiLi Project</span>. According to GLMM analysis, disturbance caused by logging or forest fires significantly reduced tree hyrax calling activity. In Mt. Kilimanjaro, highest density of tree hyraxes was found from 2750 m a.s.l. It seems that extensive hunting in the past and selective logging below elevation 2500 m caused tree hyraxes to move up the mountain. Calls of tree hyraxes in Mt. Kilimanjaro resemble calls emitted by hyraxes in Taita Hills, Kenya; however, there are clear differences in their calling cultures. In Mt. Kilimanjaro tree hyraxes also sing songs, and their acoustic communication is very active and diverse. In most preferred habitats, groups of tree hyraxes may call 4500–5500 times during one night. Calling seem to have elements of turn taking and individual signatures. Future of tree hyraxes in large, 650 km<sup>2</sup>, Mt. Kilimanjaro National Park seems promising and perhaps in the future tree hyraxes will recolonize the whole park area again.</span></p>
Fig.1 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance
Fig.1. Sampling sites for Vestia turgida in Ukraine (photo by O. Baidashnikov).
Fig. 5 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance
Fig. 5. Partial dependence plot for terrain roughness index (tri).
Fig. 7 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance
Fig. 7. Partial dependence plot for silt content (SLT).
Fig. 6 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance
Fig. 6. Partial dependence plot for pH water (phh2o).
Data from: Occurrence-habitat mismatching and niche truncation when modelling distributions affected by anthropogenic range contractions
<p><strong>Aims: </strong>Human-induced pressures such as deforestation cause anthropogenic range contractions (ARCs). Such contractions present dynamic distributions that may engender data misrepresentations within species distribution models. The temporal bias of occurrence data—where occurrences represent distributions before (past bias) or after (recent bias) ARCs—underpins these data misrepresentations. Occurrence-habitat mismatching results when occurrences sampled before contractions are modelled with contemporary anthropogenic variables; niche truncation results when occurrences sampled after contractions are modelled without anthropogenic variables. Our understanding of their independent and interactive effects on model performance remains incomplete but is vital for developing good modelling protocols. Through a virtual ecologist approach, we demonstrate how these data misrepresentations manifest and investigate their effects on model performance.</p> <p><strong>Location:</strong> Virtual Southeast Asia</p> <p><strong>Methods:</strong> Using 100 virtual species, we simulated ARCs with 100-year land-use data and generated temporally biased (past, recent) occurrence datasets. We modelled datasets with and without a contemporary land-use variable (conventional modelling protocols) and with a temporally dynamic land-use variable. We evaluated each model's ability to predict historical and contemporary distributions.</p> <p><strong>Results:</strong> Greater ARC resulted in greater occurrence-habitat mismatching for datasets with past bias and greater niche truncation for datasets with recent bias. Occurrence-habitat mismatching prevented models with the contemporary land-use variable from predicting anthropogenic-related absences, causing overpredictions of contemporary distributions. Although niche truncation caused underpredictions of historical distributions (environmentally suitable habitats), incorporating the contemporary land-use variable resolved these underpredictions, even when mismatching occurred. Models with the temporally dynamic land-use variable consistently outperformed models without.</p> <p><strong>Main conclusions:</strong> We showed how these data misrepresentations can degrade model performance, undermining their use for empirical research and conservation science. Given the ubiquity of anthropogenic range contractions, these data misrepresentations are likely inherent to most datasets. Therefore, we present a three-step strategy for handling data misrepresentations: maximise the temporal range of anthropogenic predictors, exclude mismatched occurrences, and test for residual data misrepresentations.</p>
Modelling the potential global distribution of suitable habitat for the biological control agent Heterorhabditis indica
<p class="MsoNoSpacing">Entomopathogenic nematode (EPN) <em>Heterorhabditis indica</em> is a promising biocontrol candidate. Despite the acknowledged importance of EPN in pest control, no extensive data sets or maps have been developed on their distribution at global level. This study is the first attempt to generate Ecological Niche Models (ENM) for <em>H. indica</em> and its global Habitat Suitability Map (HSM) to generate biogeographical information and predicts its global geographical range of prospective areas for its exploration and to help identify the suitable release areas for biocontrol purpose. The aim of the modelling exercise was to access the influence of temperature and soil moisture on the biogeographical patterns of <em>H. indica</em> at the global level. CLIMEX software was used to model the distribution of <em>H. indica</em> and access to the influence of environmental variable on its global distribution. In total, 162 records of <em>H. indica</em> occurrence from 27 countries over 25 years was combined to generate the known distribution data. The model was further fine-tuned using the direct experimental observations of the <em>H. indica</em>'s growth response to temperature and soil moisture. Model predicts much of the tropics and subtropics has suitable climatic conditions for <em>H. indica</em>. It further predicts that <em>H. indica</em> distribution can extends into warmer temperate climates. Examination of the model output, predictions maps at a global level indicate that <em>H. indica</em> distribution may be limited by cold stress, heat stress and dry stresses in different areas. However, cold stress appears to be the major limiting factor. This study, highlighted an efficient way to construct HSM for EPN potentially useful in the search/release of target species in new locations. The study showed that <em>H. indica</em> which is known as warm adapted EPN generally found in tropics and subtropics can potentially establish itself in warmer temperate climates as well. The model can also be used to decide the release timing of EPN by adjusting with season for maximum growth. The model developed in the current study clearly identified the value and potential of Habitat Suitability Map (HSM) in planning of future surveys and application of <em>H. indica.</em></p>
The effects of habitat modification on the distribution and feeding ecology of Orthoptera 2015
<b>Description: </b><p>Postdoctoral project</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/4"><b>The effects of habitat modification on the distribution and feeding ecology of Orthoptera</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>Australian Research Council (ARC Discovery Project, DP140101541)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=7011354">here</a></p><p><b>Files: </b>This consists of 1 file: Hardwick_Orthoptera_220811.xlsx</p><p><b>Hardwick_Orthoptera_220811.xlsx</b></p><p>This file contains dataset metadata and 1 data tables:</p><ol><li><p><b>Orthoptera assemblage composition data 2015</b> (described in worksheet OrthopteraAssem)</p><p>Description: Orthoptera assemblage composition data collected at the SAFE Project in 2015. Worksheet contains a site by morphospecies abundance matrix. Orthoptera were collected by sweep netting along a 100m transect at each location. Orthoptera were identified to family and seperated into morphospecies using identification guides. </p><p>Number of fields: 95</p><p>Number of data rows: 48</p><p>Fields: </p><ul><li><b>Date1</b>: Date of the first collection (Field type: date)</li><li><b>Date2</b>: Date of the second collection (Field type: date)</li><li><b>Location</b>: SAFE Project location (2nd order) (Field type: location)</li><li><b>Type</b>: Disturbance gradient (Field type: ordered categorical)</li><li><b>Collector</b>: First initial and last name of person who collected the sample (Field type: categorical)</li><li><b>ACRI01_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>ACRI02_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>ACRI03_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>ACRI04_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>ACRI05_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>ACRI06_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>ACRI07_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>ACRI08_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>ACRI09_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>ACRI10_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>ACRI11_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>ACRI12_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>ACRI13_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>ACRI14_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>ACRI15_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>ACRI16_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>ACRI17_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>ACRI18_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>ACRI19_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>ACRI20_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TETR01_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TETR02_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TETR03_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TETR04_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TETR05_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TETR06_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TETR07_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TETR08_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TETR09_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TETR10_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TETR11_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TETR12_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TETR13_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TETR14_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TETR15_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TETR16_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TETR17_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TETR18_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TETR19_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TETR20_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TETR21_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>GRYL01_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>GRYL02_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>GRYL03_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>GRYL04_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>GRYL05_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>GRYL06_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>GRYL07_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>GRYL08_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>GRYL09_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>GRYL10_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>GRYL11_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>GRYL12_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>GRYL13_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>GRYL14_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>GRYL15_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>GRYL16_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>GRYL17_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>GRYL18_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>GRYL19_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>GRYL20_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>GRYL21_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>MOGO01_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>MOGO02_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TRID01_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TRID02_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TRIG01_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TRIG02_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TRIG03_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TRIG04_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TRIG05_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TRIG06_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>UNID01_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>UNID02_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>UNID03_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>UNID04_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>UNID05_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>UNID06_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>UNID07_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>UNID08_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>UNID09_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>UNID10_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>UNID12_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>UNID13_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>UNID14_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>UNID15_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>UNID16_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>UNID17_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>UNID18_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>UNID19_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li></ul></li></ol><p><b>Date range: </b>2015-06-03 to 2015-08-14</p><p><b>Latitudinal extent: </b>4.6359 to 4.7509</p><p><b>Longitudinal extent: </b>116.9549 to 117.6257</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div> -  Animalia <br> -  -  Arthropoda <br> -  -  -  Insecta <br> -  -  -  -  Orthoptera <br> -  -  -  -  -  [UNID01] <br> -  -  -  -  -  [UNID02] <br> -  -  -  -  -  [UNID03] <br> -  -  -  -  -  [UNID04] <br> -  -  -  -  -  [UNID05] <br> -  -  -  -  -  [UNID06] <br> -  -  -  -  -  [UNID07] <br> -  -  -  -  -  [UNID08] <br> -  -  -  -  -  [UNID09] <br> -  -  -  -  -  [UNID10] <br> -  -  -  -  -  [UNID12] <br> -  -  -  -  -  [UNID13] <br> -  -  -  -  -  [UNID14] <br> -  -  -  -  -  [UNID15] <br> -  -  -  -  -  [UNID16] <br> -  -  -  -  -  [UNID17] <br> -  -  -  -  -  [UNID18] <br> -  -  -  -  -  [UNID19] <br> -  -  -  -  -  Gryllidae <br> -  -  -  -  -  -  [GRYL01] <br> -  -  -  -  -  -  [GRYL02] <br> -  -  -  -  -  -  [GRYL03] <br> -  -  -  -  -  -  [GRYL04] <br> -  -  -  -  -  -  [GRYL05] <br> -  -  -  -  -  -  [GRYL06] <br> -  -  -  -  -  -  [GRYL07] <br> -  -  -  -  -  -  [GRYL08] <br> -  -  -  -  -  -  [GRYL09] <br> -  -  -  -  -  -  [GRYL10] <br> -  -  -  -  -  -  [GRYL11] <br> -  -  -  -  -  -  [GRYL12] <br> -  -  -  -  -  -  [GRYL13] <br> -  -  -  -  -  -  [GRYL14] <br> -  -  -  -  -  -  [GRYL15] <br> -  -  -  -  -  -  [GRYL16] <br> -  -  -  -  -  -  [GRYL17] <br> -  -  -  -  -  -  [GRYL18] <br> -  -  -  -  -  -  [GRYL19] <br> -  -  -  -  -  -  [GRYL20] <br> -  -  -  -  -  -  [GRYL21] <br> -  -  -  -  -  Acrididae <br> -  -  -  -  -  -  [ACRI01] <br> -  -  -  -  -  -  [ACRI02] <br> -  -  -  -  -  -  [ACRI03] <br> -  -  -  -  -  -  [ACRI04] <br> -  -  -  -  -  -  [ACRI05] <br> -  -  -  -  -  -  [ACRI06] <br> -  -  -  -  -  -  [ACRI07] <br> -  -  -  -  -  -  [ACRI08] <br> -  -  -  -  -  -  [ACRI09] <br> -  -  -  -  -  -  [ACRI10] <br> -  -  -  -  -  -  [ACRI11] <br> -  -  -  -  -  -  [ACRI12] <br> -  -  -  -  -  -  [ACRI13] <br> -  -  -  -  -  -  [ACRI14] <br> -  -  -  -  -  -  [ACRI15] <br> -  -  -  -  -  -  [ACRI16] <br> -  -  -  -  -  -  [ACRI17] <br> -  -  -  -  -  -  [ACRI18] <br> -  -  -  -  -  -  [ACRI19] <br> -  -  -  -  -  -  [ACRI20] <br> -  -  -  -  -  Tridactylidae <br> -  -  -  -  -  -  [TRID01] <br> -  -  -  -  -  -  [TRID02] <br> -  -  -  -  -  Trigonidiidae <br> -  -  -  -  -  -  [TRIG01] <br> -  -  -  -  -  -  [TRIG02] <br> -  -  -  -  -  -  [TRIG03] <br> -  -  -  -  -  -  [TRIG04] <br> -  -  -  -  -  -  [TRIG05] <br> -  -  -  -  -  -  [TRIG06] <br> -  -  -  -  -  Tetrigidae <br> -  -  -  -  -  -  [TETR03] <br> -  -  -  -  -  -  [TETR04] <br> -  -  -  -  -  -  [TETR05] <br> -  -  -  -  -  -  [TETR06] <br> -  -  -  -  -  -  [TETR07] <br> -  -  -  -  -  -  [TETR09] <br> -  -  -  -  -  -  [TETR10] <br> -  -  -  -  -  -  [TETR11] <br> -  -  -  -  -  -  [TETR12] <br> -  -  -  -  -  -  [TETR13] <br> -  -  -  -  -  -  [TETR14] <br> -  -  -  -  -  -  [TETR15] <br> -  -  -  -  -  -  [TETR16] <br> -  -  -  -  -  -  [TETR17] <br> -  -  -  -  -  -  [TETR18] <br> -  -  -  -  -  -  [TETR20] <br> -  -  -  -  -  -  [TETR21] <br> -  -  -  -  -  -  <i>Eucriotettix</i> <br> -  -  -  -  -  -  -  [TETR01] <br> -  -  -  -  -  -  <i>Cladonotella</i> <br> -  -  -  -  -  -  -  [TETR19] <br> -  -  -  -  -  -  <i>Boczkitettix</i> <br> -  -  -  -  -  -  -  <i>Boczkitettix borneensis</i> <br> -  -  -  -  -  -  <i>Paratettix</i> <br> -  -  -  -  -  -  -  <i>Paratettix variabilis</i> (as homotypic_synonym: <i>Euparatettix variabilis</i>)<br> -  -  -  -  -  Mogoplistidae <br> -  -  -  -  -  -  [MOGO01] <br> -  -  -  -  -  -  [MOGO02] <br></div><p></p>
Future sea level rise in northwest Mexico is projected to decrease the distribution and habitat quality of the endangered Calidris canutus roselaari (Red Knot)
<p>Sea level rise (SLR) is one of the most unequivocal consequences of climate change, yet the implications for shorebirds and their coastal habitats is not well understood, especially outside of the north temperate zone. Here, we show that by the year 2050, SLR has the potential to cause significant habitat loss and reduce the quality of the remaining coastal wetlands in Northwest Mexico—one of the most important regions for Nearctic breeding migratory shorebirds. Specifically, we used species distribution modelling and a moderate SLR static inundation scenario to assess the effects of future SLR on coastal wetlands in Northwest Mexico and the potential distribution of <em>Calidris canutus roselaari </em>(Red Knot), a threatened long-distance migratory shorebird. Our results suggest that under a moderate SLR scenario, 55% of the current coastal wetland extent in northwest Mexico will be at risk of permanent submergence by 2050, and the high-quality habitat areas that remain will be 20% less suitable for <em>C. c. roselaari</em>. What is more, 8 out of the 10 wetlands currently supporting the largest numbers of <em>C. c. roselaari</em> are predicted to lose — on average — 17.8% of their highly suitable habitat areas, with two sites completely losing all their highly suitable habitat. In combination with increasing levels of coastal development and anthropogenic disturbance in Northwest Mexico, these predicted changes suggest that the potential future distribution of <em>C. c. roselaari</em> (and other shorebirds) will likely contract, exacerbating their ongoing population declines. Our results also make clear that SLR will likely have profound effects on ecosystems outside the north temperate zones, providing a clarion call to natural resource managers. Urgent action is required to begin securing sufficient space to accommodate the natural capacity of wetlands to migrate inland and implement local-scale solutions that strengthen the resilience of wetlands and human populations to SLR.</p>
Fig. 2 in Native Bugseed Species Corispermum Intermedium Schweigg And Alien Corispermum Pallasii Steven In Coastal Habitats Of Latvia - New Knowledges Of Distribution And Invasions
Fig. 2. Achenes of Corispermum intermedium (left) and C. pallasii (right). Image: I. Svilāne.
Fig. 4 in Habitat Distribution Of Dytiscus Latissimus Linnaeus, 1758 (Coleoptera: Dytiscidae) In The Ecosystem Of Ruģeļi Fish Ponds (Daugavpils, Latvia)
Fig. 4. RuĢeļi fish ponds (Control site S№ 2. (april)).
Fig. 3 in Habitat Distribution Of Dytiscus Latissimus Linnaeus, 1758 (Coleoptera: Dytiscidae) In The Ecosystem Of Ruģeļi Fish Ponds (Daugavpils, Latvia)
Fig. 3. RuĢeļi fish ponds (Control site S№ 3. (august)).
Fig. 6 in Habitat Distribution Of Dytiscus Latissimus Linnaeus, 1758 (Coleoptera: Dytiscidae) In The Ecosystem Of Ruģeļi Fish Ponds (Daugavpils, Latvia)
Fig. 6. RuĢeļu fish ponds (Control site S№ 4. (august)).
Fig. 1 in Habitat Distribution Of Dytiscus Latissimus Linnaeus, 1758 (Coleoptera: Dytiscidae) In The Ecosystem Of Ruģeļi Fish Ponds (Daugavpils, Latvia)
Fig. 1. The territory of RuĢeļi fish ponds (Daugavpils) (Daugavpils… 2015).
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.