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43 results for “species recovery”
N fertilization and recovery experiment (2-4-6) plant species composition data for East of Tvan from 1997 to 2017, yearly
The realization that anthropogenic nitrogen (N) deposition is causing significant environmental change in many ecosystems has led to lower emissions of reactive N and deposition rates in many regions. However, the impacts of N deposition on terrestrial ecosystems can be long-lasting, with significant inertia in the return of the biota and biogeochemical processes to baseline levels. To better understand patterns of recovery and the factors that may contribute to slow or no responses following declines in N deposition, we followed plant species composition, microbial abundance, N cycling rates, soil pH, and pools of NO3- and extractable cations in an impacted alpine ecosystem following cessation of 12-year experiment increasing N deposition rates by 0, 20, 40, and 60 kg N/ha/yr. Simulated N deposition had resulted in a tripling in the cover of the nitrophilic species Carex rupestris, while the dominant sedge Kobresia myosuroides had decreased by more than half at the highest N input level. In addition nitrification rates were elevated, soil extractable magnesium (Mg2+) and pH decreased, and aluminum (Al3+) and manganese (Mn2+) were elevated at the highest N treatment inputs. Over the nine years following cessation of N additions to the impacted plots only the cover of the nitrophilic C. rupestris showed any recovery to prior levels. Abundances of both bacteria and fungi were lower with N addition in both treatment and recovery plots. Rates of nitrification and pools of NO3- remained elevated in the recovery plots, likely contributing to the lack of biotic response to the cessation of N inputs. In addition, nutrient base cations (Ca2+ and Mg2+) and soil pH remained depressed, and the toxic metal cations (Al3+ and Mn2+) remained elevated in recovery plots, also potentially influencing biotic recovery. These results emphasize the importance of considering long-term environmental impacts of N deposition associated with legacy effects, such as elevated N cycling and losse
Physiological information in Canadian species at risk recovery strategies (animals)
<p>Summary of physiological information contained in Canadian (animal) species at risk recovery strategies (SARA). For each recovery strategy, the dataset includes the species covered, publication dates, taxonomic classification, the number of times physiology was mentioned, the types of physiological traits included, and the threats the associated organism faces.</p>
Fig. 1. Samples were identified within a in Diversity of Cryptosporidium in brush-tailed rock-wallabies (Petrogale penicillata) managed within a species recovery programme
Fig. 1. Samples were identified within a phylogenetic framework with the tree constructed using neighbour-joining with bootstrap test (1,000 replicates, displayed at nodes) using the 18S rRNA locus (878 bp). KV denotes Kangaroo Valley.
Short-term effects of the control of an invasive plant Asclepias syriaca: secondary invasion of other neophytes instead of the recovery of native species
<p>Data sets to article: "Short-term effects of the control of an invasive plant <em>Asclepias syriaca</em>: secondary invasion of other neophytes instead of the recovery of native species".</p> <p>We studied the impact of <em>Asclepias syriaca</em>, a non-native herb species, on basic soil attributes and vegetation composition in sandy grasslands and the effect of mechanical control of this species. The <em>Asclepias </em>invasion changed the vegetation composition, but not the studied soil attributes. The shot-term cutting suppressed <em>Asclepias</em>, but instead of the recovery of native species, secondary invasion by other alien species occurred.</p>
Data from: Grassland restoration drives strong multitrophic biodiversity recovery, but climate extremes jeopardize drought-sensitive species
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Data from: Raising the bar: recovery ambition for species at risk in canada and the US
<p>Routinely crossing international borders and/or persisting in populations across multiple countries, species are commonly subject to a patchwork of endangered species legislation. Canada and the United States share numerous endangered species; their respective acts, the Species at Risk Act (SARA) and the Endangered Species Act (ESA), require documents that outline requirements for species recovery. Although there are many priorities for improving endangered species legislation effectiveness, species recovery goals are a crucial component. We compared recovery goal quality, as measured by goal quantitativeness and ambition, for species listed under SARA and ESA. By comparing across ESA and SARA, the intent of the study was to identify differences and similarities that could support the development of stronger species' recovery goals under both legislations. Our results indicated that: (1) overall, only 38% of recovery goals were quantitative, 41% had high ambition, and 26% were both quantitative and high ambition; (2) recovery goals had higher quantitativeness and ambition under ESA than SARA; (3) recovery goals for endangered species had higher ambition than threatened species under ESA and SARA, and; (4) no recovery goal aimed to restore populations to historic levels. Combined, these findings provide guidance to strengthen recovery goals and improve subsequent conservation outcomes. In particular, species at risk planners should seek to attain higher recovery goal ambition, particularly for SARA-listed species, and include quantitative recovery goals wherever possible.</p>
Drought timing and species growth phenology determine intra-annual recovery of tree height and diameter growth
<p>These are the data reported in van Kampen et al. (2022) "Drought timing and species growth phenology determine intra-annual recovery of tree height and diameter growth" published in AoB Plants. They describe patterns of height and diameter growth for saplings of six tree species undergoing experimental drought conditions at different times of year. </p>
Banding and recovery data on several game bird species to study hunting selectivity
<p><span>Selective hunting has various impacts that need to be considered for the conservation and management of harvested populations. The consequences of selective harvest have mostly been studied in trophy hunting and fishing, where selection of specific phenotypes is intentional. Recent studies however show that selection can also occur unintentionally.</span> <span>With at least 52 million birds harvested each year in Europe, it is particularly relevant to evaluate the selectivity of hunting on this taxon. Here we considered </span><span>211 806</span><span> individuals belonging to 7 hunted bird species to study unintentional selectivity in harvest. Using linear mixed models, we compared morphological traits (mass, wing and tarsus size) and body condition at the time of banding between birds that were subsequently recovered from hunting during the same year as their banding, and birds that were not recovered. We did not find any patterns showing systematic differences between recovery categories, among our model species, for the traits we studied. Moreover, when a difference existed between recovery categories, it was so small that its biological relevance can be challenged. Hunting of birds in Europe therefore does not show any form of strong selectivity on the morphological and physiological traits that we studied, and should hence not lead to any change of these traits either by plastic or evolutionary response. </span></p>
Data from: Climatically robust multi-scale species distribution models to support pronghorn recovery in California
<p>We combined two climate-based distribution models with three finer-scale suitability models to identify habitat for pronghorn recovery in California now and into the future.</p> <p>Location: California, United States </p> <p>Methods: We used a consensus approach to identify areas of suitable climate now (1980-2010) and future (2031-2060) for pronghorn in California. We compared the results of models from two separate hypotheses about their historical ecology in the state, specifically the migration hypothesis and the niche reduction hypothesis. We combined occurrences from GPS collars distributed across three populations of pronghorn in the state to create three distinct habitat models: (1) an ensemble model using Random Forests, Maxent, Classification and Regression Trees, and a Generalized Linear Model; (2) a step selection function; and (3) an expert-driven model. We evaluated consensus among both the climate models and the suitability models to prioritize areas for, and evaluate the prospects of, pronghorn recovery. </p> <p>Results: Climate suitability for pronghorn in the future depends heavily on model assumptions. Under the migration hypothesis, our model predicted that there will be on suitable climate in California in the future. Under the niche reduction hypothesis, by contrast, suitable climate will expand. Habitat also depended on the methods used, but areas of consensus among all three exist in large patches throughout the state.</p> <p>Main Conclusions: Identifying habitat for a species which has undergone extreme range collapse, and which has very fine scale habitat needs, presents novel challenges for spatial ecologists. Our multi-method, multi-hypothesis approach can allow habitat modelers to identify areas of consensus and, perhaps more importantly, critical knowledge gaps that could resolve disagreements among the models. For pronghorn, a better understanding of their upper thermal tolerances and whether historical populations migrated will be crucial to their potential recovery in California and throughout the arid Southwest.</p>
A big data–model integration approach for predicting epizootics and population recovery in a keystone species
<p>Infectious diseases pose a significant threat to global health and biodiversity. Yet, predicting the spatiotemporal dynamics of wildlife epizootics remains challenging. Disease outbreaks result from complex non-linear interactions among a large collection of variables that rarely adhere to the assumptions of parametric regression modeling. We adopted a non-parametric machine learning approach to model wildlife epizootics and population recovery, using the disease system of colonial black-tailed prairie dogs (BTPD, <em>Cynomys ludovicianus</em>) and sylvatic plague as an example. We synthesized colony data between 2001–2020 from eight USDA Forest Service National Grasslands across the range of BTPD in central North America. We then modeled extinctions due to plague and colony recovery of BTPD in relation to complex interactions among climate, topoedaphic variables, colony characteristics, and disease history. Extinctions due to plague occurred more frequently when BTPD colonies were spatially clustered, in closer proximity to colonies decimated by plague during the previous year, following cooler than average temperatures the previous summer, and when wetter winter/springs were preceded by drier summer/falls. Rigorous cross-validations and spatial predictions indicated that our final models predicted plague outbreaks and colony recovery in BTPD with high accuracy (e.g., AUC generally > 0.80). Thus, these spatially-explicit models can reliably predict the spatial and temporal dynamics of wildlife epizootics and subsequent population recovery in a highly complex host-pathogen system. Our models can be used to support strategic management planning (e.g., plague mitigation) to optimize benefits of this keystone species to associated wildlife communities and ecosystem functioning. This optimization can reduce conflicts among different landowners and resource managers, as well as economic losses to the ranching industry. More broadly, our big data–model integration approach provides a general framework for spatially-explicit forecasting of disease-induced population fluctuations, for use in natural resource management decision-making.</p>
Data associated with: Applying remote sensing for large-landscape problems: Inventorying and tracking habitat recovery for a broadly distributed Species At Risk
<ol> <li> <p><span>Anthropogenic habitat alteration is leading to the reduction of global biodiversity. Consequently, there is an imminent need to understand the state and trend of habitat alteration across broad areas. In North America, habitat alteration has been linked to the decline of threatened woodland caribou. As such, habitat protection and restoration are critical measures to support recovery of self-sustaining caribou populations. Broad estimates of habitat change through time have set the stage for understanding the status of caribou habitat. However, the lack of updated and detailed data on post-disturbance vegetation recovery is an impediment to recovery planning and monitoring restoration effectiveness. Advances in remote sensing tools to collect high-resolution data at large spatial scales are beginning to enable ecological studies in new ways to support ecosystem-based and species-based management.</span></p> </li> <li> <p><span>We used semi-automated and manual methodologies to fuse photogrammetry point clouds (PPC) from high-resolution aerial imagery with wide-area Light Detection and Ranging (LiDAR) data to quantify vegetation structure (height, density, class) on disturbances associated with caribou declines. We also compared vegetation heights estimated from the semi-automated PPC-LiDAR fusion to heights estimated in the field, using stereoscopic interpretation, and using multi-channel TiTAN LiDAR.</span></p> </li> <li> <p><span>Vegetation regrowth was occurring on many of the disturbance types, though there was local variability in the type, height, and density of vegetation. Heights estimated using PPC-LiDAR fusion were highly correlated (r ≥ 0.87 in all cases) with heights estimated using stereomodels, TiTAN multi-channel LiDAR, and field measurements. </span></p> </li> <li> <p>We demonstrated that PPC-LiDAR fusion can be operationalized over large areas to collect comprehensive and consistent vegetation data across landscape levels, providing opportunities to link fine-resolution remote sensing to landscape-scale ecological studies. Crucially, these data can be used to estimate rates of habitat recovery at resolutions that are not feasible using more commonly used satellite-based sensors, bridging the gap between resolution and extent. Such data are needed to achieve effective and efficient habitat monitoring to support caribou recovery efforts, as well as a myriad of additional forest management needs.</p> </li> </ol>
Data from: Incomplete recovery of tree community composition and rare species after 120 years of tropical forest succession in Panama
<p>Determining how fully tropical forests regenerating on abandoned land recover characteristics of old-growth forests is increasingly important for understanding their role in conserving rare species and maintaining ecosystem services. Despite this, our understanding of forest structure and community composition recovery throughout succession is incomplete, as many tropical chronosequences do not extend beyond the first 50 years of succession. Here, we examined trajectories of forest recovery across eight 1-hectare plots in middle and later stages of forest succession (40 – 120 years) and five 1-hectare old-growth plots, in the Barro Colorado Nature Monument (BCNM), Panama. We first verified that forest age had a greater effect than edaphic or topographic variation on forest structure, diversity and composition and then corroborated results from smaller plots censused 20 years previously. Tree species diversity (but not species richness) and forest structure had fully recovered to old-growth levels by 40 and 90 years, respectively. However, rare species were missing, and old-growth specialists were in low abundance, in the mid- and late secondary forest plots, leading to incomplete recovery of species composition even by 120 years into succession. We also found evidence that dominance early in succession by a long-lived pioneer led to altered forest structure and delayed recovery of species diversity and composition well past a century after land abandonment. Our results illustrate the critical importance of old-growth and old secondary forests for biodiversity conservation, given that recovery of community composition may take several centuries, particularly when a long-lived pioneer dominates in early succession.</p>
Data from: Incomplete recovery of tree community composition and rare species after 120 years of tropical forest succession in Panama
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Data from: The rapid recovery of a Caatinga dry forest is mediated by disturbance-adapted species
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A big data–model integration approach for predicting epizootics and population recovery in a keystone species
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Banding and recovery data on several game bird species to study hunting selectivity
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Drought timing and species growth phenology determine intra-annual recovery of tree height and diameter growth
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Data from: Climatically robust multi-scale species distribution models to support pronghorn recovery in California
Open the record for dataset details and reuse information.
Data associated with: Applying remote sensing for large-landscape problems: Inventorying and tracking habitat recovery for a broadly distributed Species At Risk
Open the record for dataset details and reuse information.
Data from: Raising the bar: recovery ambition for species at risk in canada and the US
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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.