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FIGURE 6 in Relationship between fish assemblage structure and predictors related to estuarine productivity in shallow habitats of a Neotropical estuary
FIGURE 6 | Abundance (n) relationship with the environmental variables that formed the most parsimonious linear model. Line represents the modeled values, and a gray area corresponds to the standard deviation. l.n = number of individuals in logscale. Temp = temperature; Sal = salinity; Time = succession of days from beginning to end of the sampling surveys; D = distance from the mouth of the estuary (see Material and Methods section for details).
FIGURE 3 in Relationship between fish assemblage structure and predictors related to estuarine productivity in shallow habitats of a Neotropical estuary
FIGURE 3 | Monthly variation in the mean historical rainfall data (monthly average between 1975 and 2015) and mean water temperature sampled from May 2000 to April 2001 at eight sites along the estuarine gradient of shallow areas of the northsouth axis of the PEC. For temperature, the values were averaged by month and bars represent standard deviation. Months were ordered according to the sequence of the sampling surveys.
FIGURE 5 in Relationship between fish assemblage structure and predictors related to estuarine productivity in shallow habitats of a Neotropical estuary
FIGURE 5 | Richness (S) relationship with the environmental variables that formed the most parsimonious GLM. Line represents the modeled values, and a gray area corresponds to the standard deviation. Temp = temperature; Transp = transparency; Sal = salinity; Time = succession of days from beginning to end of the sampling surveys (see Material and Methods section for details).
Figs 28–34 in Acrobasis khachella (Amsel, 1950): Little-known snout moth species and new data about its range and habitats (Lepidoptera, Pyralidae, Phycitinae)
Figs 28–34. Acrobasis khachella (Amsel, 1950),genitalia: 28–30 —male genitalia; 31–34 —female genitalia: 28 — Kazakhstan, Dzhungarsky Alatau Mts., Usek river valley; 29 — Kyrgyzstan, Moldo-Too Mts., near the Koro-Goo Pass; 30 — Tajikistan, Shakhdarinsky Mts., Vezdara river valley; 31 — Kazakhstan, Dzhungarsky Alatau Mts., Usek river valley; 32 — Kyrgyzstan, Fergansky Mts., southern shore of Toktogul reservoir near the settlement Imeni Chkalova; 33 — Kyrgyzstan, Moldo-Too Mts., near the Koro-Goo Pass; 34 — Tajikistan, Shakhdarinsky Mts., Vezdara river valley Рис. 28–34. Acrobasis khachella (Amsel, 1950), генитаΛии: 28–30 — генитаΛии самца; 31–34 — генитаΛии самки: 28 — Казахстан, хр. Δжунгарский АΛатау, ΑоΛина р. Усек; 29 — Киргизия, хр. МоΛΑо-Тоо, бΛиз пер. Коро-Гоо; 30 — ТаΑжикистан, ШахΑаринский хр., ΑоΛина р. ВезΑара; 31 — Казахстан, хр. Δжунгарский АΛатау, ΑоΛина р. Усек; 32 — Киргизия, Ферганский хр., южный берег ТоктогуΛьского вΑхр. бΛиз пос. имени ЧкаΛова; 33 — Киргизия, хр. МоΛΑо-Тоо, бΛиз пер. Коро-Гоо; 34 — ТаΑжикистан, ШахΑаринский хр., ΑоΛина р. ВезΑара
FIGURE 3 in Protection of spawning habitat for potamodromous fish, an urgent need for the hydropower planning in the Andes
FIGURE 3 | Correlations between geomorphological and physicochemical variables, and ichthyoplankton density. Basin: basin area, Sinuous: channel sinuosity index, Flood: floodplain area, Cond: conductivity, Trans: transparency, Temp: temperature. α = 0.05.
FIGURE 2 in Protection of spawning habitat for potamodromous fish, an urgent need for the hydropower planning in the Andes
FIGURE 2 | Spatial variation in the median density of ichthyoplankton among tributaries. (Kruskal-Wallis H = 208.29, df = 9, p-value <2.2e-16). T01: Samaná River; T02: Nare River; T03: Espíritu Santo River; T04: Carare River; T05: Opón River; T06: Sogamoso River; T07: Boque River; T08: Nechí River; T09: San Jorge River and T10: Cesar River.
FIGURE 4 in Protection of spawning habitat for potamodromous fish, an urgent need for the hydropower planning in the Andes
FIGURE 4 | Potential spawning grounds for 13 sampled potamodromous fish species of the Magdalena basin. A. Baseline (current) scenario, and B. Full hydroelectric projects development scenario.
FIGURE 1 in Protection of spawning habitat for potamodromous fish, an urgent need for the hydropower planning in the Andes
FIGURE 1 | Location of the sampling tributaries (black circles) in the Magdalena River basin. T01: Samaná River; T02: Nare River; T03: Espíritu Santo River; T04: Carare River; T05: Opón River; T06: Sogamoso River; T07: Boque River; T08: Nechí River; T09: San Jorge River and T10: Cesar River. Magdalena River runs north.
Chronic wasting disease alters the movement behavior and habitat use of mule deer during clinical stages of infection
<p>Integrating host movement and pathogen data is a central issue in wildlife disease ecology that will allow for a better understanding of disease transmission. We examined how adult female mule deer (<em>Odocoileus hemionus</em>) responded behaviorally to infection with chronic wasting disease (CWD). We compared movement and habitat use of CWD-infected deer (<em>n</em> = 18) to those that succumbed to starvation (and were CWD-negative by ELISA and IHC; <em>n</em> = 8) and others in which CWD was not detected (<em>n</em> = 111, including animals that survived the duration of the study) using GPS collar data from two distinct populations collared in central Wyoming, USA during 2018–2022. CWD and predation were the leading causes of mortality during our study (32 of 91 deaths attributed to CWD and 27 of 91 deaths attributed to predation). Deer infected with CWD moved slower and used lower elevation areas closer to rivers in the months preceding death compared with uninfected deer that did not succumb to starvation. Although CWD-infected deer and those that died of starvation moved at similar speeds during the final months of life, CWD-infected deer used areas closer to streams with less herbaceous biomass than deer that died of starvation. These behavioral differences may allow for the development of predictive models of disease status from movement data, which will be useful to supplement field and laboratory diagnostics or when mortalities cannot be quickly retrieved to assess cause-specific mortality. Furthermore, identifying individuals that are sick before predation events could help to assess the extent to which disease mortality is compensatory with predation. Finally, infected animals began to slow down around four months prior to death from CWD. Our approach for detecting the timing of infection-induced shifts in movement behavior may be useful in application to other disease systems to better understand the response of wildlife to infectious disease.</p>
Fig. 1 in SHORT COMMUNICATION Monitoring a population of Cruziohyla craspedopus (Funkhouser, 1957) using an artificial breeding habitat
Fig. 1. Site map for ABHab points at LPS: dashed line is approximate separation of terra firma and flood plain forest.
Data from: Stochastic character mapping, Bayesian model selection, and biosynthetic pathways shed new light on the evolution of habitat preference in cyanobacteria
<p>Cyanobacteria are the only prokaryotes to have evolved oxygenic photosynthesis paving the way for complex life. Studying the evolution and ecological niche of cyanobacteria and their ancestors is crucial for understanding the intricate dynamics of biosphere evolution. These organisms frequently deal with environmental stressors such as salinity and drought, and they employ compatible solutes as a mechanism to cope with these challenges. Compatible solutes are small molecules that help maintain cellular osmotic balance in high-salinity environments, such as marine waters. Their production plays a crucial role in salt tolerance, which, in turn, influences habitat preference. Among the five known compatible solutes produced by cyanobacteria (sucrose, trehalose, glucosylglycerol, glucosylglycerate, and glycine betaine), their synthesis varies between individual strains. In this study, we work in a Bayesian stochastic mapping framework, integrating multiple sources of information about compatible solute biosynthesis in order to predict the ancestral habitat preference of Cyanobacteria. Through extensive model selection analyses and statistical tests for correlation, we identify glucosylglycerol and glucosylglycerate as the most significantly correlated with habitat preference, while trehalose exhibits the weakest correlation. Additionally, glucosylglycerol, glucosylglycerate, and glycine betaine show high loss/gain rate ratios, indicating their potential role in adaptability, while sucrose and trehalose are less likely to be lost due to their additional cellular functions. Contrary to previous findings, our analyses predict that the last common ancestor of Cyanobacteria (living at around 3180 Ma) had a 97% probability of a high salinity habitat preference and was likely able to synthesize glucosylglycerol and glucosylglycerate. Nevertheless, cyanobacteria likely colonized low-salinity environments shortly after their origin, with an 89% probability of the first cyanobacterium with low-salinity habitat preference arising prior to the Great Oxygenation Event (2460 Ma). Stochastic mapping analyses provide evidence of cyanobacteria inhabiting early marine habitats, aiding in the interpretation of the geological record. Our age estimate of ~2590 Ma for the divergence of two major cyanobacterial clades (Macro- and Microcyanobacteria) suggests that these were likely significant contributors to primary productivity in marine habitats in the lead-up to the Great Oxygenation Event, and thus played a pivotal role in triggering the sudden increase in atmospheric oxygen.</p>
Figs 35–42 in Acrobasis khachella (Amsel, 1950): Little-known snout moth species and new data about its range and habitats (Lepidoptera, Pyralidae, Phycitinae)
Figs 35–42. Acrobasis khachella (Amsel, 1950), habitats: Kyrgyzstan: 35 — Talassky Mts., Kara-Buura river bank; 36 — Alai Mts., Kyzyl-Eshme valley; 37 — Moldo-Too Mts., Koro-Goo Pass environs; 38 — Dzhumgaltoo Mts., Sary-Kaiky gorge near Kojomkul; 39 — Kirghizsky Mts., Ala-Too settlement environs; 40 — Alai Mts., near Kara-Bulak; Kazakhstan: 41 — Dzhungarsky Alatau Mts., Usek river valley; Tajikistan: 42 — Shakhdarinsky Mts., Vezdara river valley (in the center: the author of this article with the local driver, Maziyo) Рис. 35–42. Acrobasis khachella (Amsel, 1950), биотопы: Киргизия: 35 — ТаΛасский хр., побережье р. Кара-Буура; 36 — АΛайский хр., ущ. КызыΛ-Эшме; 37 — хр. МоΛΑо-Тоо, окр. пер. Коро-Гоо; 38 — хр. ΔжумгаΛтоо, массив Сары-Кайкы бΛиз пос. КожомкуΛ; 39 — Киргизский хр., окр. пос. АΛа-Тоо; 40 — АΛайский хр., бΛиз пос. Кара-БуΛак; Казахстан: 41 — хр. Δжунгарский АΛатау, ΑоΛина р. Усек; ТаAжикистан: 42 — ШахΑаринский хр., ΑоΛина р. ВезΑара (в центре: автор настоящей работы и местный воΑитеΛь, Мазиё)
Figs 1–27 in Acrobasis khachella (Amsel, 1950): Little-known snout moth species and new data about its range and habitats (Lepidoptera, Pyralidae, Phycitinae)
Figs 1–27. Acrobasis khachella (Amsel, 1950), upper sides: 1 — Kyrgyzstan, Kirghizsky Mts., Ala-Too environs; 2 — Kazakhstan, Dzhungarsky Alatau Mts., Usek river valley; 3 — Tajikistan, Shakhdarinsky Mts., Vezdara river valley; 4 — Kyrgyzstan, Fergansky Mts., southern shore of Toktogul reservoir near Imeni Chkalova; 5 — Kyrgyzstan, Alai Mts., Kyzyl-Eshme valley; 6–8 — Kyrgyzstan, Alai Mts. near Kara-Bulak; 9–14 — Kyrgyzstan, Talassky Mts., Kara-Buura river valley; 15–18 — Kyrgyzstan, Fergansky Mts., Kara-Suu river valley; 19 — Kyrgyzstan, south shore of the Issyk Kul Lake near Kara-Talaa; 20–27 — Kyrgyzstan, Moldo-Too Mts., near the Koro-Goo Pass. 1, 9, 23, 24 — females, the remaining specimens — males. Scale bar: 1 cm Рис. 1–27. Acrobasis khachella (Amsel, 1950), виΑ сверху: 1 — Киргизия, Киргизский хр., окр. пос. АΛа-Тоо; 2 — Казахстан, хр. Δжунгарский АΛатау, ΑоΛина р. Усек; 3 — ТаΑжикистан, ШахΑаринский хр., ΑоΛина р. ВезΑара; 4 — Киргизия, Ферганский хр., южный берег ТоктогуΛьского вΑхр. бΛиз пос. имени ЧкаΛова; 5 — Киргизия, АΛайский хр., ущ. КызыΛ-Эшме; 6–8 — Киргизия, АΛайский хр. бΛиз пос. Кара-БуΛак; 9–14 — Киргизия, ТаΛасский хр., ΑоΛина р. Кара-Буура; 15–18 — Киргизия, Ферганский хр., ΑоΛина р. Кара-Суу; 19 — Киргизия, южный берег оз. Иссык-КуΛь бΛиз пос. Кара-ТаΛаа; 20–27 — Киргизия, хр. МоΛΑо-Тоо, бΛиз пер. Коро-Гоо; 1, 9, 23, 24 — самки, остаΛьные — самцы. Масштабная метка: 1 см
Data from: Home range and habitat selection of wolves recolonising Central European human-dominated landscapes
<p>Decades of persecution has resulted in the long-term absence of grey wolves (<em>Canis lupus</em>) from most European countries. However, recent changes in both legislation and public attitudes toward wolves has eased the pressure, allowing wolves to rapidly re-establish territories in their previous Central European habitats over the last 20 years. Unfortunately, these habitats are now heavily altered by humans. Understanding the spatial ecology of wolves in such highly modified environments is crucial, given the high potential for conflict and the need to reconcile their return with multiple human concerns. We equipped 20 wolves, originating from seven packs in six Central European regions, with GPS collars, allowing us to calculate monthly average home range sizes for 14 of the animals of 213.3 km2 using Autocorrelated Kernel Density Estimation. We then used ESA WorldCover data to assess the mosaic of available habitats used within each home range. Our data confirmed a general seasonal pattern for breeding individuals, with smaller apparent home ranges during the reproduction phase, and no specific pattern for non-breeders. Predictably, our wolves showed a general preference for remote areas, and especially forests, though some wolves within military training areas also showed a broader preference for grassland, possibly influenced by local land use and high availability of prey. Our results provide a comprehensive insight into the ecology of wolves during their re-colonisation of Central Europe. Though wolves are spreading relatively quickly across Central European landscapes, their permanent reoccupation remains uncertain due to conflicts with the human population. To secure the restoration of European wolf populations, further robust biological data, including data on spatial ecology, will be needed to clearly identify any management implications.</p>
Bumble bee responses to climate and landscapes: Investigating habitat associations and species assemblages across geographic regions in the United States of America
<p><span>Bumble bees are integral pollinators of native and cultivated plant communities, but species are undergoing significant changes in range and abundance on a global scale. Climate change and land cover alteration are key drivers in pollinator declines; however, limited research has evaluated the cumulative effects of these factors on bumble bee<em> </em>assemblages. This study tests bumble bee assemblage (calculated as richness and abundance) responses to climate and land use by <span>modeling </span>species-specific habitat requirements, and assemblage-level responses across geographic regions. <span>We integrated species richness, abundance, and distribution data for 18 bumble bee species with site-specific bioclimatic, landscape composition, and landscape configuration data to evaluate</span> the effects of multiple environmental stressors <span>on bumble bee assemblages throughout</span> 433 agricultural fields in<span> Florida, Indiana, Kansas, Kentucky, Maryland, South Carolina, Utah, Virginia, and West Virginia from 2018 to 2020. Distinct east vs. west groupings emerged when evaluating species-specific habitat associations, prompting a detailed evaluation of bumble bee assemblages by geographic region. Maximum temperature of warmest month and precipitation of driest month had a positive impact on bumble bee assemblages in the Corn Belt/Appalachian/northeast, southeast, and northern plains regions, but a negative impact in the mountain region. Further, </span>forest land cover surrounding agricultural fields was highlighted as supporting more rich and abundant bumble bee assemblages<span>. Overall, climate and land use combine to drive bumble bee assemblages, but how those processes operate is idiosyncratic and spatially contingent across regions. From these findings, we suggested regionally specific management practices to best support rich and abundant bumble bee assemblages in agroecosystems. </span>Results from this study contribute to a better understanding of climate and landscape factors affecting bumble bees and their habitats throughout the USA. </span></p>
Fig. 10 in Can artificial retreat sites help frogs recover after severe habitat devastation? Insights on the use of "coqui houses" after Hurricane Maria in Puerto Rico
Fig. 10. Comparison of the relative abundance, measured as the number of adult Eleutherodactylus coqui observed per sampling night in the experimental transect where artificial coqui houses were made available, versus the control.
Fig. 6 in Can artificial retreat sites help frogs recover after severe habitat devastation? Insights on the use of "coqui houses" after Hurricane Maria in Puerto Rico
Fig. 6. Variation in operative temperatures measured by frog agar models in typical forest microhabitats after Hurricane Maria, showing a significant decrease during the cool-dry season (in blue) during midday (A), and nighttime (B).
Fig. 8 in Can artificial retreat sites help frogs recover after severe habitat devastation? Insights on the use of "coqui houses" after Hurricane Maria in Puerto Rico
Fig. 8. Bar graphs showing coqui house occupancy rate by Eleutherodactylus coqui during the length of this study by daytime (A), and by nighttime (B) surveys. The shaded area in (A) denotes sampling in months during the cool-dry season.
Fig. 7 in Can artificial retreat sites help frogs recover after severe habitat devastation? Insights on the use of "coqui houses" after Hurricane Maria in Puerto Rico
Fig. 7. Box plots showing variation in forest microhabitat temperature by day (A) and night (B) during the cool-dry season (February) of 2015 (a non-hurricane year), and in 2019, 17 months after Hurricane Maria hit Puerto Rico.
Fig. 5 in Can artificial retreat sites help frogs recover after severe habitat devastation? Insights on the use of "coqui houses" after Hurricane Maria in Puerto Rico
Fig. 5. Drastic changes in temperature at the transects in the Palo Colorado forest of El Yunque as a consequence of Hurricane Maria. (A) Ambient temperatures registered by HOBO data logger in the forest understory before, during, and shortly after Hurricane Maria. (B–C) Box plots showing variation in forest microhabitat temperature by day and at night during the month of September in 2015 (a non-hurricane year), and in 2017, the year that Hurricane Maria hit Puerto Rico.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.