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17 results for “management prioritization”
Figure 3 in America's Most Wanted Fishes: cataloging risk assessments to prioritize invasive species for management action
Figure 3. The proportion of risk statuses of fish families with four or more species assessed at the extent of Florida. Panel (A) shows the proportion of species with high, moderate, low, multiple, and undetermined risk statuses of assessed species (total number of species evaluated in a family). Panel (B) shows the ratio of assessed to unassessed species in a given family (total species in a family). Total species in a family were obtained from FishBase (Froese and Pauly 2023).
Figure 1 in America's Most Wanted Fishes: cataloging risk assessments to prioritize invasive species for management action
Figure 1. The proportion of risk statuses of fish families with four or more species assessed at the extent of the conterminous U.S. Panel (A) shows the proportion of species with high, moderate, low, multiple, and undetermined risk statuses of assessed species (total number of species evaluated in a family). Panel (B) shows the ratio of assessed to unassessed species in a given family (total species in a family). Total species in a family were obtained from FishBase (Froese and Pauly 2023).
Figure 2 in America's Most Wanted Fishes: cataloging risk assessments to prioritize invasive species for management action
Figure 2. The proportion of risk statuses of fish families with four or more species assessed at the extent of the Great Lakes region. Panel (A) shows the proportion of species with high, moderate, low, multiple, and undetermined risk statuses of assessed species (total number of species evaluated in a family). Panel (B) shows the ratio of assessed to unassessed species in a given family (total species in a family). Total species in a family were obtained from FishBase (Froese and Pauly 2023).
New indicators of ecological resilience and invasion resistance to support prioritization and management in the sagebrush biome, United States
<p>Ecosystem transformations to altered or novel ecological states are accelerating across the globe. Indicators of ecological resilience to disturbance and resistance to invasion can aid in assessing risks and prioritizing areas for conservation and restoration. The sagebrush biome encompasses parts of 11 western states and is experiencing rapid transformations due to human population growth, invasive species, altered disturbance regimes, and climate change. We built on prior use of static soil moisture and temperature regimes to develop new, ecologically relevant and climate-responsive indicators of both resilience and resistance. Our new indicators were based on climate and soil water availability variables derived from process-based ecohydrological models that allow predictions of future conditions. We asked: (1) Which variables best indicate resilience and resistance? (2) What are the relationships among the indicator variables and resilience and resistance categories? (3) How do patterns of resilience and resistance vary across the area? We assembled a large database (n = 24,045) of vegetation sample plots from regional monitoring programs and derived multiple climate and soil water availability variables for each plot from ecohydrological simulations. We used USDA Natural Resources Conservation Service National Soils Survey Information, Ecological Site Descriptions, and expert knowledge to develop and assign ecological types and resilience and resistance categories to each plot. We used random forest models to derive a set of 19 climate and water availability variables that best predicted resilience and resistance categories. Our models had relatively high multiclass accuracy (80% for resilience; 75% for resistance). Top indicator variables for both resilience and resistance included mean temperature, coldest month temperature, climatic water deficit, and summer and driest month precipitation. Variable relationships and patterns differed among ecoregions but reflected environmental gradients; low resilience and resistance were indicated by warm and dry conditions with high climatic water deficits, and moderately high to high resilience and resistance were characterized by cooler and moister conditions with low climatic water deficits. The new, ecologically-relevant indicators provide information on the vulnerability of resources and likely success of management actions and can be used to develop new approaches and tools for prioritizing areas for conservation and restoration actions.</p>
New indicators of ecological resilience and invasion resistance to support prioritization and management in the sagebrush biome, United States
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Data from: A proposed coupling framework of biological invasions: Quantifying the management prioritization in mealybugs invasion
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Prioritizing wildfire fuel management in California
<p><strong><span>The resources available for managing wildfire risk are insufficient and ultimately finite , while the risk of catastrophic fires is enormous and growing. Prioritization of responses is thus critical, but the basis for comparing the costs and societal benefits of alternative investments in wildfire mitigation is woefully inadequate. Here, we assess and compare the costs of landscape-scale fuel treatment to the benefits of avoided destruction of property and smoke-related health impacts, and identify areas where the net benefits are greatest statewide. We find that re-prioritizing treatment areas could increase net benefits by a factor of more than 7 relative to historical treatments, with average net benefits in the top decile of areas (i.e., 28,000 km<sup>2</sup>)<sup> </sup>of >$225k per km<sup>2 </sup>(as compared to an estimated $90k per km<sup>2</sup> of past treatments). By integrating physical, epidemiological, and economic methods, our results reveal large opportunities for improving the cost-effectiveness of wildfire fuel treatments, and demonstrate a general framework that can be applied by land managers in all wildfire-prone areas.</span></strong></p>
Table 1 in America's Most Wanted Fishes: cataloging risk assessments to prioritize invasive species for management action
<p><b>Table 1.</b> List of fish species with high-risk statuses in the conterminous U.S., in the Great Lakes region, and in the state of Florida. Horizontal lines separate families and species for clarity.</p><table><tbody><tr><th>Family Name</th><th>Fish species name</th><th>Conterminous U.S.</th><th>Great Lakes</th><th>Florida</th></tr></tbody><tbody><tr><th>Atherinidae</th><td><i>Atherina boyeri</i> (Risso, 1810)</td><td>X</td><td>X</td><td></td></tr><tr><th>Atherinopsidae</th><td><i>Menidia beryllina</i> (Cope, 1867)</td><td>X</td><td></td><td></td></tr><tr><td><i>Odontesthes bonariensis</i> (Valenciennes, 1835)</td><td>X</td><td></td><td></td></tr><tr><th>Callichthyidae</th><td><i>Hoplosternum littorale</i> (Hancock, 1828)</td><td></td><td></td><td>X</td></tr><tr><th>Centrarchidae</th><td><i>Lepomis microlophus</i> (Günther, 1859)</td><td>X</td><td>X</td><td></td></tr><tr><td><i>Micropterus dolomieu</i> (Lacepède, 1802)</td><td>X</td><td></td><td></td></tr><tr><td><i>Micropterus salmoides</i> (Lacepède, 1802)</td><td>X</td><td></td><td></td></tr><tr><th>Centropomidae</th><td><i>Lates niloticus</i> (Linnaeus, 1758)</td><td>X</td><td></td><td></td></tr><tr><th>Channidae</th><td><i>Channa argus</i> (Cantor, 1842)</td><td>X</td><td></td><td>X</td></tr><tr><td><i>Channa marulius</i> (Hamilton, 1822)</td><td></td><td></td><td>X</td></tr><tr><th>Characidae</th><td><i>Pygocentrus nattereri</i> (Kner, 1858)</td><td>X</td><td></td><td></td></tr><tr><th>Cichlidae</th><td><i>Amatitlania nigrofasciata</i> (Günther, 1867)</td><td>X</td><td></td><td>X</td></tr><tr><td><i>Astronotus ocellatus</i> (Agassiz, 1831)</td><td>X</td><td></td><td></td></tr><tr><td><i>Cichla kelberi</i> (Kullander & Ferreira, 2006)</td><td>X</td><td></td><td></td></tr><tr><td><i>Cichla ocellaris</i> (Bloch & Schneider, 1801)</td><td>X</td><td></td><td>X</td></tr><tr><td><i>Cichlasoma uropthalma</i> (Günther, 1862)</td><td>X</td><td></td><td>X</td></tr><tr><td><i>Coptodon rendalli</i> (Boulenger, 1897)</td><td>X</td><td></td><td></td></tr><tr><td><i>Coptodon zilli</i> (Gervais, 1848)</td><td>X</td><td></td><td>X</td></tr><tr><td><i>Hemichromis letourneuxi</i> (Sauvage, 1880)</td><td>X</td><td></td><td></td></tr><tr><td><i>Herichthys cyanoguttatus</i> (Baird & Girard, 1854)</td><td>X</td><td></td><td></td></tr><tr><td><i>Oreochromis aureus</i> (Steindachner, 1864)</td><td>X</td><td></td><td>X</td></tr><tr><td><i>Oreochromis mossambicus</i> (Peters, 1852)</td><td>X</td><td></td><td>X</td></tr><tr><td><i>Oreochromis niloticus</i> (Linnaeus, 1758)</td><td>X</td><td></td><td>X</td></tr><tr><td><i>Parachromis managuensis</i> (Günther, 1862)</td><td>X</td><td></td><td>X</td></tr><tr><td><i>Pelmatotilapia mariae</i> (Boulenger, 1899)</td><td>X</td><td></td><td></td></tr><tr><td><i>Sarotherodon melanotheron</i> (Rüppell, 1852)</td><td>X</td><td></td><td></td></tr><tr><td><i>Serranochromis robustus</i> (Günther, 1864)</td><td>X</td><td></td><td></td></tr><tr><th>Clariidae</th><td><i>Clarias batrachus</i> (Linnaeus, 1758)</td><td>X</td><td></td><td>X</td></tr><tr><td><i>Clarias fuscus</i> (Lacepède, 1803)</td><td>X</td><td></td><td></td></tr><tr><td><i>Clarias gariepinus</i> (Burchell, 1822)</td><td>X</td><td></td><td></td></tr><tr><th>Clupeidae</th><td><i>Alosa aestivalis</i> (Mitchell, 1814)</td><td>X</td><td></td><td></td></tr><tr><td><i>Alosa pseudoharengus</i> (Wilson, 1811)</td><td>X</td><td>X</td><td></td></tr><tr><th>Cobitidae</th><td><i>Misgurnus anguillicaudatus</i> (Cantor, 1842)</td><td>X</td><td></td><td>X</td></tr><tr><th>Cyprinidae</th><td><i>Abramis brama</i> (Linnaeus, 1758)</td><td>X</td><td></td><td></td></tr><tr><td><i>Alburnus alburnus</i> (Linnaeus, 1758)</td><td>X</td><td></td><td></td></tr><tr><td><i>Barbus barbus</i> (Linnaeus, 1758)</td><td>X</td><td></td><td></td></tr><tr><td><i>Carassius auratus</i> (Linnaeus, 1758)</td><td></td><td></td><td>X</td></tr><tr><td><i>Carassius gibelio</i> (Bloch, 1782)</td><td>X</td><td></td><td></td></tr><tr><td><i>Ctenopharyngodon idella</i> (Valenciennes, 1844)</td><td>X</td><td>X</td><td>X</td></tr><tr><td><i>Culter alburnus</i> (Basilewsky, 1855)</td><td>X</td><td></td><td></td></tr><tr><td><i>Cyprinella lutrensis</i> (Baird & Girard, 1853)</td><td>X</td><td></td><td></td></tr><tr><td><i>Cyprinus carpio</i> (Linnaeus, 1758)</td><td>X</td><td>X</td><td>X</td></tr><tr><td><i>Gobio gobio</i> (Linnaeus, 1758)</td><td>X</td><td></td><td></td></tr><tr><td><i>Hypophthalmichthys molitrix</i> (Valenciennes, 1844)</td><td>X</td><td></td><td></td></tr><tr><td><i>Hypophthalmichthys nobilis</i> (Richardson, 1845)</td><td>X</td><td></td><td>X</td></tr><tr><td><i>Leuciscus idus</i> (Linnaeus, 1758)</td><td>X</td><td></td><td></td></tr><tr><td><i>Mylopharyngodon piceus</i> (Richardson, 1846)</td><td>X</td><td></td><td></td></tr><tr><td><i>Opsariichthys uncirostris</i> (Temminck & Schlegel, 1846)</td><td>X</td><td></td><td></td></tr><tr><td><i>Pimephales promelas</i> (Rafinsque, 1820)</td><td></td><td></td><td>X</td></tr><tr><td><i>Phoxinus phoxinus</i> (Linnaeus, 1758)</td><td>X</td><td></td><td></td></tr><tr><td><i>Protochondrostoma genei</i> (Bonaparte, 1839)</td><td>X</td><td></td><td></td></tr><tr><td><i>Pseudorasbora parva</i> (Temminck & Schlegel, 1846)</td><td>X</td><td></td><td></td></tr><tr><td><i>Rhodeus amarus</i> (Bloch, 1782)</td><td>X</td><td></td><td></td></tr><tr><td><i>Rutilus aula</i> (Bonparte,1841)</td><td>X</td><td></td><td></td></tr><tr><td><i>Rutilus rutilus</i> (Linnaeus, 1758)</td><td>X</td><td>X</td><td></td></tr><tr><td><i>Scardinius erythrophthalmus</i> (Linnaeus, 1758)</td><td>X</td><td>X</td><td></td></tr><tr><td><i>Tinca tinca</i> (Linnaeus, 1758)</td><td>X</td><td>X</td><td></td></tr><tr><th>Esocidae</th><td><i>Esox lucius</i> (Linnaeus, 1758)</td><td>X</td><td></td><td></td></tr><tr><td><i>Esox masquinongy</i> (Mitchill, 1824)</td><td>X</td><td></td><td></td></tr><tr><th>Gasterosteidae</th><td><i>Gasterosteus aculeatus</i> (Linnaeus, 1758)</td><td>X</td><td>X</td><td></td></tr><tr><th>Gobiidae</th><td><i>Neogobius melanostomus</i> (Pallas, 1814)</td><td>X</td><td>X</td><td></td></tr><tr><td><i>Ponticola platyrostris</i> (Pallas, 1814)</td><td>X</td><td></td><td></td></tr><tr><td><i>Proterorhinus semilunaris</i> (Heckel, 1837)</td><td>X</td><td></td><td></td></tr><tr><th>Heteropneustidae</th><td><i>Heteropneustes fossilis</i> (Bloch, 1794)</td><td>X</td><td></td><td></td></tr><tr><th>Ictaluridae</th><td><i>Ictalurus furcatus</i> (Valenciennes, 1840)</td><td>X</td><td></td><td></td></tr><tr><td><i>Pylodictis olivaris</i> (Rafinesque, 1818)</td><td>X</td><td></td><td></td></tr><tr><th>Loricariidae</th><td><i>Hypostomus plecostomus</i> (Linnaeus, 1758)</td><td>X</td><td></td><td>X</td></tr><tr><td><i>Pterygoplichthys anisitsi</i> (Eigenmann & Kennedy, 1903)</td><td></td><td></td><td>X</td></tr><tr><td><i>Pterygoplichthys disjunctivus</i> (Weber, 1991)</td><td>X</td><td></td><td>X</td></tr><tr><td><i>Pterygoplichthys multiradiatus</i> (Hancock, 1828)</td><td>X</td><td></td><td>X</td></tr><tr><td><i>Pterygoplichthys pardalis</i> (Castelnau, 1855)</td><td>X</td><td></td><td></td></tr><tr><th>Moronidae</th><td><i>Morone americana</i> (Gmelin, 1789)</td><td>X</td><td>X</td><td></td></tr><tr><th>Notopteridae</th><td><i>Chitala ornata</i> (Gray, 1831)</td><td>X</td><td></td><td></td></tr><tr><th>Odontobutidae</th><td><i>Perccottus glenii</i> (Dybowski, 1877)</td><td>X</td><td></td><td></td></tr><tr><th>Osmeridae</th><td><i>Hypomesus nipponensis</i> (McAllister, 1963)</td><td>X</td><td></td><td></td></tr><tr><td><i>Osmerus eperlanus</i> (Linnaeus, 1758)</td><td>X</td><td>X</td><td></td></tr><tr><td><i>Osmerus mordax</i> (Mitchill, 1814)</td><td>X</td><td></td><td></td></tr><tr><th>Osphronemidae</th><td><i>Trichogaster lalius</i> (Hamilton, 1822)</td><td>X</td><td></td><td></td></tr><tr><td><i>Trichopodus trichopterus</i> (Pallas, 1770)</td><td>X</td><td></td><td></td></tr><tr><th>Percidae</th><td><i>Gymnocephalus cernua</i> (Linnaeus, 1758)</td><td>X</td><td>X</td><td></td></tr><tr><td><i>Perca flavescens</i> (Mitchill, 1814)</td><td>X</td><td></td><td></td></tr><tr><td><i>Perca fluviatilis</i> (Linnaeus, 1758)</td><td>X</td><td>X</td><td></td></tr><tr><td><i>Sander lucioperca</i> (Linnaeus, 1758)</td><td>X</td><td></td><td></td></tr><tr><td><i>Sander vitreus</i> (Mitchill, 1818)</td><td>X</td><td></td><td></td></tr><tr><th>Petromyzontidae</th><td><i>Petromyzon marinus</i> (Linnaeus, 1758)</td><td></td><td>X</td><td></td></tr><tr><th>Poeciliidae</th><td><i>Belonesox belizanus</i> (Kner, 1860)</td><td>X</td><td></td><td></td></tr><tr><td><i>Gambusia affinis</i> (Baird & Girard, 1853)</td><td>X</td><td></td><td>X</td></tr><tr><td><i>Gambusia holbrooki</i> (Girard, 1859)</td><td>X</td><td></td><td></td></tr><tr><td><i>Poecilia latipinna</i> (Lesueur, 1821)</td><td>X</td><td></td><td></td></tr><tr><td><i>Poecilia reticulata</i> (Peters, 1859)</td><td>X</td><td></td><td>X</td></tr><tr><td><i>Poecilia sphenops</i> (Valciennes, 1846)</td><td>X</td><td></td><td>X</td></tr><tr><td><i>Xiphophorus helleri</i> (Heckel, 1848)</td><td>X</td><td></td><td></td></tr><tr><td><i>Xiphophorus variatus</i> (Meek, 1904)</td><td>X</td><td></td><td></td></tr><tr><th>Polypteridae</th><td><i>Polypterus delhezi</i> (Boulenger, 1899)</td><td></td><td></td><td>X</td></tr><tr><th>Pomacentridae</th><td><i>Neopomacentrus cyanomos</i> (Bleeker, 1856)</td><td>X</td><td></td><td></td></tr><tr><th>Potamotrygonidae</th><td><i>Potamotrygon falkneri</i> (Castex & Maciel, 1963)</td><td>X</td><td></td><td></td></tr><tr><td><i>Potamotrygon motoro</i> (Müller & Henle, 1841)</td><td>X</td><td></td><td></td></tr><tr><td><i>Potamotrygon schuhmacheri</i> (Castex, 1964)</td><td>X</td><td></td><td></td></tr><tr><th>Salmonidae</th><td><i>Coregonus albula</i> (Linnaeus, 1758)</td><td>X</td><td></td><td></td></tr><tr><td><i>Coregonus lavaretus</i> (Linnaeus, 1758)</td><td>X</td><td></td><td></td></tr><tr><td><i>Coregonus maraena</i> (Bloch, 1779)</td><td>X</td><td></td><td></td></tr><tr><td><i>Coregonus peled</i> (Gmelin, 1789)</td><td>X</td><td></td><td></td></tr><tr><td><i>Hucho hucho</i> (Linnaeus, 1758)</td><td>X</td><td></td><td></td></tr><tr><td><i>Oncorhynchus kisutch</i> (Walbaum, 1792)</td><td></td><td>X</td><td></td></tr><tr><td><i>Oncorhynchus mykiss</i> (Walbaum, 1792)</td><td></td><td>X</td><td></td></tr><tr><td><i>Oncorhynchus tshawytscha</i> (Walbaum, 1792)</td><td></td><td>X</td><td></td></tr><tr><td><i>Salmo trutta</i> (Linnaeus, 1758)</td><td></td><td>X</td><td></td></tr><tr><th>Scorpaenidae</th><td><i>Pterois miles</i> (Bennett, 1828)</td><td>X</td><td></td><td></td></tr><tr><td><i>Pterois volitans</i> (Linnaeus, 1758)</td><td>X</td><td></td><td></td></tr><tr><th>Siluridae</th><td><i>Silurus glanis</i> (Linnaeus, 1758)</td><td>X</td><td></td><td></td></tr></tbody></table>
Prioritizing terrestrial invasive alien plant species for management in urban ecosystems
<p>1. Invasive alien plant species (IAPs) in urban areas can have detrimental effects on biodiversity, ecosystem services and human well-being. Urban areas are complex social management mosaics with high land-use diversity, complex land tenure patterns, and many different stakeholder groups, some of which derive benefits from invading species. Urban conservation practitioners face complex decisions about which IAPs require management. Yet most IAPs prioritization frameworks have been designed for and implemented in natural or rural areas and are generally inadequate for guiding effective and sustainable interventions in urbanized areas.</p> <p>2. We modified an existing prioritization scheme to develop a framework for prioritizing terrestrial IAPs in urban areas which applies evidence-based (data-driven) and stakeholder-based (local knowledge) assessments to score and rank alien plant species in terms of their priority for management using an objective set of criteria.</p> <p>3. The framework consists of forty-six criteria, grouped into eight modules which assess invasion status, habitat requirements, biological characteristics, dispersal ability, distribution, impact (positive and negative), and potential for control for each alien plant species under consideration.</p> <p>4. We use the city of Toronto, Canada as a case study to test our framework – a list of 50 IAPs were effectively scored and ranked in order of high to low priority for control. Species with the highest <i>total prioritization scores</i> were <i>Vincetoxicum</i> <i>rossicum</i> (Dog Strangling Vine), <i>Convolvulus</i> <i>arvensis</i> (Field Bindweed) and <i>Taraxacum</i> <i>officinale</i> (Common Dandelion) (ranked 1, 2 and 3, respectively).</p> <p>5. Many of the identified high priority species align with the those previously flagged as of management concern by conservation practitioners, but also include those that are not actively managed due to their perceived lower ecological impacts. These species still require high resource investment for other objectives such as aesthetics. This highlights the complexity of alien plant species management in urban areas.</p> <p>6.<i> Synthesis and applications. </i>Prioritizing invasive alien plants for management in urban areas is particularly challenging due to often conflicting ecological, economic, and social objectives. We use available evidence and local stakeholder knowledge to develop an objective and systematic prioritization tool which can assist conservation practitioners in selecting priority species for management action in complex urban landscapes.</p>
Planning for Resilience: Incorporating scenario and model uncertainty and trade-offs when prioritizing management of climate refugia
<p>Climate change has become the greatest threat to the world's ecosystems. Locating and managing areas that contribute to the survival of key species under climate change is critical for the persistence of ecosystems in the future. Here we identify "Climate Priority" sites as coral reefs exposed to relatively low levels of climate stress that will be more likely to persist in the future. We present the first analysis of uncertainty in climate change scenarios and models, along with multiple objectives, in a marine spatial planning exercise and offer a comprehensive approach to incorporating uncertainty and trade-offs in any ecosystem. We first described each site using environmental characteristics that are associated with a higher chance of persistence (larval connectivity, hurricane influence and acute and chronic temperature conditions in the past and the future). Future temperature increases were assessed using downscaled data under four different climate scenarios (SSP1 2.6, SSP2 4.5, SSP3 7.0 and SSP5 8.5) and 57 model runs. We then prioritized sites for intervention (conservation, improved management or restoration) using robust decision-making approaches that select sites that will have a benign climate under most climate scenarios and models. The modeling work is novel because it solves two important issues. 1) It considers trade-offs between multiple planning objectives explicitly through Pareto analyses; and 2) It makes use of all the uncertainty around future climate change. Priority intervention sites identified by the model were verified and refined through local stakeholder engagement including assessments of local threats, ecological condition and government priorities. The workflow is presented for the Insular Caribbean and Florida, and at the national level for Cuba, Jamaica, Dominican Republic and Haiti. Our approach allows managers to consider uncertainty and multiple objectives for climate smart spatial management in coral reefs or any ecosystem across the globe.</p>
Prioritizing terrestrial invasive alien plant species for management in urban ecosystems
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Planning for Resilience: Incorporating scenario and model uncertainty and trade-offs when prioritizing management of climate refugia
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Data from: Prioritizing land management efforts at a landscape scale: a case study using prescribed fire in Wisconsin
One challenge in the effort to conserve biodiversity is identifying where to prioritize resources for active land management. Cost-benefit analyses have been used successfully as a conservation tool to identify sites that provide the greatest conservation benefit per unit cost. Our goal was to apply cost-benefit analysis to the question of how to prioritize land management efforts, in our case the application of prescribed fire to natural landscapes in Wisconsin, USA. We quantified and mapped frequently burned communities, and prioritized management units based on a suite of indices that captured ecological benefits, management effort, and the feasibility of successful long-term management actions. Data for these indices came from LANDFIRE, Wisconsin's Wildlife Action Plan, and a nationwide Wildland Urban Interface assessment. We found that the majority of frequently burned vegetation types occurred in the southern portion of the state. However, the highest-priority areas for applying prescribed fire occurred in the central, northwest, and northeast portion of the state where frequently burned vegetation patches were larger and where identified areas of high biological importance area occurred. Although our focus was on the use of prescribed fire in Wisconsin, our methods can be adapted to prioritize other land management activities. Such prioritization is necessary to achieve the greatest possible benefits from limited funding for land management actions, and our results show that it is feasible at scales that are relevant for land management decisions.
Dendritic prioritization through spatial stream network modeling informs targeted management of Himalayan riverscapes under brown trout invasion
<p>With the concept of 'riverscapes' long pending to be acknowledged in the 'landscape-centric' legislative framework of Himalayan nations, conservation of native riverine species stays practically unheeded. This necessitates urgent prioritization of stream networks to conserve the lotic taxa under invasion pressures. Himalayan riverscapes are pervaded with the invasive-exotic brown trout <i>Salmo trutta</i>,<i> </i>posing serious threats to the co-occurring native, the snow trout <i>Schizothorax richardsonii</i>. Using intensive surveys (218.7km) and geostatistical stream network models (n=537), we contrasted snow trout in two stream networks with and without invasives, for assessing differences in their spatial distribution. Our models indicate invasion-induced relegations of natives from the river mainstem into headwaters, with large sections of mainstem occupied by invasives. Furthermore, a concerningly small percentage of potential habitat left for natives to occupy in the mainstem is threatened, where a 100% overlap of native and invasive trout distributions is predicted. With a higher presence probability for the natives in headwaters of invaded watershed as compared to the non-invaded watershed, we highlight the headwater streams as<b> </b>potential refugia for the natives under invasion.</p> <p><i>Synthesis and Applications: </i>Our approach of basin-scale dendritic prioritization provides immediate management solutions to tackle brown trout invasion threats in Himalaya. We inform decisions on delineation of headwaters as invasion refugia for native fish, with assisted recovery of their fragmented populations in the river mainstems through targeted management of invasives</p>
Dendritic prioritization through spatial stream network modeling informs targeted management of Himalayan riverscapes under brown trout invasion
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Data from: Prioritizing management actions for invasive populations using cost, efficacy, demography, and expert opinion for 14 plant species worldwide
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Data from: Prioritizing land management efforts at a landscape scale: a case study using prescribed fire in Wisconsin
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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.