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229 results for “habitat ecology”
Data from: An environmental habitat gradient and within-habitat segregation enable co-existence of ecologically similar bird species
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Data from: The temporal window of ecological adaptation in postglacial lakes: a comparison of head morphology, trophic position and habitat use in Norwegian threespine stickleback populations
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Karuk ecological fire management practices promote elk habitat in Northern California
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Breaking ecological barriers: anthropogenic disturbance leads to habitat transitions, hybridization, and high genetic diversity
<p>Genetic diversity is expected to erode in disturbed habitats through strong selection, local extinctions, and recolonization associated with genetic bottlenecks and restricted gene flow. Despite this general prediction and over three decades of population genetics studies, our understanding of the long-term effect of environmental disturbance on local and regional genetic diversity remains limited. We conducted a population genetic survey of the microcrustacean <i>Daphnia</i> across a landscape subject to anthropogenic stressors from a century of industrial mining. At the local scale we found moderate genetic diversity (i.e., low clonal diversity), characteristic of habitat-specific selective sweeps and local extinctions, but high diversity and strong genetic structure at the regional scale despite the shared watershed of many lakes and exceptional dispersal ability of daphniids. Many habitats experienced changes in species assemblages, with the obligate asexual <i>Daphnia pulex</i> lineages—known only to inhabit ponds—dominating disrupted urban lakes. This habitat transition (pond to lake) was likely facilitated by the disruption of ecological barriers maintaining the genomic separation of these young species. Thus, disrupted habitats can exhibit complex and unexpected genetic patterns of local extinctions and recolonizations, followed by habitat transitions, hybridization and potential speciation events that are difficult to predict and should not be underestimated.</p>
Data from: Genomics meets applied ecology: characterizing habitat quality for sloths in a tropical agroecosystem
Understanding how habitat quality in heterogeneous landscapes governs the distribution and fitness of individuals is a fundamental aspect of ecology. While mean individual fitness is generally considered a key to assessing habitat quality, a comprehensive understanding of habitat quality in heterogeneous landscapes requires estimates of dispersal rates among habitat types. The increasing accessibility of genomic approaches, combined with field-based demographic methods, provides novel opportunities for incorporating dispersal estimation into assessments of habitat quality. In this study, we integrated genomic kinship approaches with field-based estimates of fitness components and Approximate Bayesian Computation (ABC) procedures to estimate habitat-specific dispersal rates and characterize habitat quality in two-toed sloths (Choloepus hoffmanni) occurring in a Costa Rican agricultural ecosystem. Field-based observations indicated that birth and survival rates were similar in a sparsely-shaded cacao farm and adjacent cattle pasture-forest mosaic. Sloth density was threefold higher in pasture compared to cacao, whereas home range size and overlap were greater in cacao compared to pasture. Dispersal rates were similar between the two habitats, as estimated using ABC procedures applied to the spatial distribution of pairs of related individuals identified using 3,431 SNP and 11 microsatellite locus genotypes. Our results indicate that crops produced under a sparse overstory can, in some cases, constitute lower quality habitat than pasture-forest mosaics for sloths, perhaps because of differences in food resources or predator communities. Finally, our study demonstrates that integrating field-based demographic approaches with genomic methods can provide a powerful means for characterizing habitat quality for animal populations occurring in heterogeneous landscapes.
Data from: Comparative analysis of the shape and size of the middle ear cavity of turtles reveals no correlation with habitat ecology
<p>The middle ear of turtles differs from other reptiles in being separated into two distinct compartments. Several ideas have been proposed as to why the middle ear is compartmentalized in turtles, most suggesting a relationship with underwater hearing. Extant turtle species span fully marine to strictly terrestrial habitats, and ecomorphological hypotheses of turtle hearing predict that this should correlate with variation in the structure of the middle ear due to differences in the fluid properties of water and air. We investigate the shape and size of the air-filled middle ear cavity of 56 extant turtles using 3D data and phylogenetic comparative analysis to test for correlations between habitat preferences and the shape and size of the middle ear cavity. Only weak correlations are found between middle ear cavity size and ecology, with aquatic taxa having proportionally smaller cavity volumes. The middle ear cavity of turtles exhibits high shape diversity among species, but we found no relationship between this shape variation and ecology. Surprisingly, the estimated acoustic transformer ratio, a key functional parameter of impedance-matching ears in vertebrates, also shows no relation to habitat preferences (aquatic/terrestrial) in turtles. We suggest that middle ear cavity shape may be controlled by factors unrelated to hearing, such as the spatial demands of surrounding cranial structures. A review of the fossil record suggests that the modern turtle ear evolved during the Early to Middle Jurassic in stem turtles broadly adapted to freshwater and terrestrial settings. This, combined with our finding that evolutionary transitions between habitats caused only weak evolutionary changes in middle ear structure, suggests that tympanic hearing in turtles evolved as a compromise between subaerial and underwater hearing.</p>
Data from: Historical field records reveal habitat as an ecological correlate of locomotor phenotypic diversity in the radiation of Neotropical Geophagini fishes
<p>Phenotypic macroevolutionary studies provide insight into how ecological processes shape biodiversity. However, the complexity of phenotype-ecology relationships underscores the importance of also validating phenotype-based ecological inference with direct evidence of resource use. Unfortunately, macroevolutionary scale ecological studies are often hindered by the challenges of acquiring taxonomically and spatially representative ecological data for large and widely distributed clades. The South American cichlid fish tribe Geophagini represents a continentally distributed radiation whose early locomotor morphological divergence suggests habitat as one ecological correlate of diversification, but an association between locomotor traits and habitat preference has not been corroborated. Field notes accumulated over decades of collecting across South America provide first-hand environmental records that can be mined for habitat data in support of macroevolutionary ecological research. In this study, we applied a newly developed method to transform descriptive field note information into quantitative habitat data, and used it to assess habitat preference and its relationship to locomotor morphology in Geophagini. Field note-derived data shed light on geophagine habitat use patterns and reinforced habitat as an ecological correlate of locomotor morphological diversity. Our work emphasizes the rich data potential of museum collections, including often overlooked material such as field notes, for evolutionary and ecological research.</p>
The evolution of plasticity in brain morphology following colonization of an ecologically divergent habitat in Trinidadian guppies
<p>Natural environments are constantly changing. To survive, organisms will either need to rapidly adapt to new conditions or colonize new habitats. Colonization has been hypothesized to select for increased plasticity as well as increased brain size, though empirical tests of these effects have proven difficult to evaluate. In particular, the degree to which plasticity of brain morphology can evolve, and its subsequent ecological consequences have rarely been explored. Trinidadian guppies (<em>Poecilia reticulata</em>) are known for their repeated adaptation to ancestral high-predation (HP) and derived low-predation (LP) environments. We used this system to examine the evolution and plasticity of brain morphology. We exposed second-generation offspring of individuals collected from HP and LP sites to two different kinds of environmental treatments: predation cues and conspecific social environment. We found that guppies descended from a colonized LP habitat showed greater plasticity in brain morphology than descendants of their ancestral HP population, supporting the hypothesis that plasticity of brain morphology may increase fitness after colonization of a novel habitat. Additionally, we show sexual dimorphism in brain morphology plasticity. Overall, these results suggest the evolution of brain morphology plasticity as an important mechanism that allows for ecological diversification and colonization of novel habitats.</p>
Ecological traits drive genetic structuring in two open-habitat birds from the morphologically cryptic genus Elaenia (Aves: Tyrannidae)
<p>Understanding the relative contributions of the many factors that shape population genetic structuring is a central theme in evolutionary and conservation biology. Historically, abiotic or extrinsic factors (such as geographic barriers or climatic shifts) have received greater attention than biotic or intrinsic factors (such as dispersal or migration). This focus stems in part from the logistical difficulties in taking a comparative phylogeographic approach that contrasts species that have experienced similar abiotic conditions during their evolution yet differ in the intrinsic attributes that might shape their genetic structure. To explore the effects of intratropical migration on the genetic structuring of Neotropical birds, we chose two congeneric species, the Lesser Elaenia (<em>Elaenia chiriquensis</em>) and the Plain-crested Elaenia (<em>E. cristata</em>), that are largely sympatric, and which have similar plumage, habitat preferences, and breeding phenology. Despite these many commonalities, they differ in migratory behavior: <em>E. chiriquensis</em> is an intratropical migratory species while <em>E. cristata</em> is sedentary. We used a reduced representation genomic approach to test whether migratory behavior is associated with increased gene flow and therefore lower genetic population structure. As predicted, we found notably stronger genetic structuring in the sedentary species than in the migratory ones. <em>E. cristata</em> comprises genetic clusters with geographic correspondence throughout its distribution, while there are no geographic groups within Brazil for <em>E. chiriquensis</em>. This comparison adds to the growing evidence about how intrinsic traits like migration can shape the genetic structuring of birds, and advances our understanding of the diversification patterns of the understudied, open habitat species from South America.</p>
Fig. 2 in A Pasture Of Big Ungulate Animals As Key Ecological Factor Influencing On The Fluctuation Of Natural Habitat Of Steppe Herbivorous Mammals
Fig. 2. The steppe marmot quantity dynamics in the 20–21th century (cattle vs the steppe marmot).
A polygenic architecture with habitat-dependent effects underlies ecological differentiation in Silene
<p><span>Ecological differentiation can drive speciation but it is unclear how the genetic architecture of habitat-dependent fitness contributes to lineage divergence. We investigated the genetic architecture of cumulative flowering, a fitness component, in second-generation hybrids between <em>Silene dioica</em> and <em>S. latifolia</em> transplanted into the natural habitat of each species.</span></p> <p><span>We used reduced-representation sequencing and Bayesian Sparse Linear Mixed Models (BSLMMs) to analyze the genetic control of cumulative flowering in each habitat.</span></p> <p><span>Our results point to a polygenic architecture of cumulative flowering. Allelic effects were mostly beneficial or deleterious in one habitat and neutral in the other. Positive-effect alleles were often derived from the native species, whereas negative-effect alleles, at other loci, tended to originate from the non-native species.</span></p> <p><span>We conclude that ecological differentiation is governed and maintained by many loci with small, habitat-dependent effects consistent with conditional neutrality. This pattern may result from differences in selection targets in the two habitats and from environmentally-dependent deleterious load. Our results further suggest that selection for native alleles and against non-native alleles acts as a barrier to gene flow between species.</span></p>
Ecological drivers of avian diversity in a subtropical landscape: effects of habitat diversity, primary productivity and anthropogenic disturbance
<p>Understanding the roles of ecological drivers in shaping biodiversity is fundamental for conservation practice. In this study, we explored the effects of elevation, conservation status, primary productivity, habitat diversity, and anthropogenic disturbance (represented by human population density and birding history) on taxonomic, phylogenetic and functional avian diversity in a subtropical landscape in southeastern China. We conducted bird surveys using 1-km transects across a total of 30 sites, of which 10 sites were located within a natural reserve. Metrics of functional diversity were calculated based on six functional traits (body mass, clutch size, dispersal ratio, sociality, diet and foraging stratum). We built simultaneous autoregression models to assess the association between the ecological factors and diversity of the local avian communities. Local avian diversity generally increased with increasing habitat diversity, human population density and primary productivity. We also detected phylogenetic and functional clustering in these communities, suggesting that the avian assemblages were structured mainly by environmental filtering, rather than interspecific competition. Compared to sites outside the natural reserve, sites within the natural reserve had relatively lower avian diversity but a higher level of phylogenetic heterogeneity.</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>
FIGURE 1 in The Sea Slug Phanerophthalmus luteus (Gastropoda: Opisthobranchia) and its Habitat and Ecology at the Marine Jellyfish Lake (Ongeim'l Tketau), Palau, Western Pacific Ocean
FIGURE 1. The Palau Islands showing the location of Mecherchar Island and Jellyfish Lake.
Fig. 1 in Morphological and ecological features of peripherial local populations of Bufo raddei Str. within the north western part of their habitat
Fig. 1. Sites of registration of Mongolian toad on the border the North-West of the area.
Fig. 3 in Morphological and ecological features of peripherial local populations of Bufo raddei Str. within the north western part of their habitat
Fig. 3. The age structure of the population of Mongolian toad, N=42 (East Pribaikalje).
Fig. 2 in Morphological and ecological features of peripherial local populations of Bufo raddei Str. within the north western part of their habitat
Fig. 2. The age structure of the population of Mongolian toad, N=41 (West Pribaikalje).
Fig. 4 in Morphological and ecological features of peripherial local populations of Bufo raddei Str. within the north western part of their habitat
Fig. 4. Dimensions of juveniles after metamorphosis, N=152 (West Pribaikalje).
Dataset accompanying Riesch et al. 2019. Grazing by wild red deer: management options for the conservation of semi-natural open habitats. Journal of Applied Ecology
<p>This repository contains vegetation biomass and forage quality data used by Riesch et al. in an article accepted in Journal of Applied Ecology.</p> <p>Metadata are provided in the first excel worksheet ('explanation_overview'). For further details please see the original article.</p>
Figure 6 in Ecological and faunistic features of caddisflies (Insecta: Trichoptera) in different types of habitats in the Dinaric karst area (Central Croatia)
Figure 6. Dobra - spring (D1). A) before channelization (2010), B) after channelization (2020).
ScienceDex guides
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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.