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26 results for “cumulative impact”
Quantifying the magnitude of storm events that have impacted the Virginia Coast Reserve (2009-2024) using the Cumulative Storm Impact Index (CSII)
This dataset contains a record of storm events along with quantified magnitudes that have impacted the Virginia Coast Reserve between 2009- 2024, minus 2010. We retrieved hourly water level data and monthly datums from the NOAA Tides and Currents database (tidesandcurrents.noaa.gov) for the tide station located in Wachapreague, VA (Station 8631044) to quantify the magnitude of storms using 1) the Storm Erosion Potential Index (SEPI; Zhang et al. 2001), and 2) the Cumulative Storm Impact Index (CSII; Fenster and Dominguez 2022). CSII incorporates the timing and magnitude of previous storms as a measure of cumulative impact, or "storminess". We identified storm events based on storm surge that exceeded two standard deviations (> 2SD) of the average surge and storm tide that exceeded the annual average Mean High Water (MHW) of a semi-diurnal tide (12 hours; SEPI). We then calculated the CSII for each storm as the sum of the SEPI and an exponentially decaying weighting factor (delta) from the previous storm's CSII that accounts for beach recovery that may have occurred between storm events. Here we use delta = 0.3 to best capture storm clustering during the 15 year period (Fenster and Dominguez 2022). Years missing >10% of data were excluded. For detailed methods on the data retrieval process, identifying storms, and quantifying storm magnitude, see Fenster and Dominguez (2022) and Dominguez et al. (2024). We identified a total of 208 storm events with an average of 14.3 events per year +/- 2.3 (SD) and an average annual CSII of 428.1 (m2hr) +/- 196.1 (SD).
Cumulated dam impact in France and the Iberian Peninsula (SUDOANG project)
<h2><strong>1. SUDOANG PROJECT</strong></h2><p>The SUDOANG project has provided common tools and assessment methods to managers to support the eel conservation in the SUDOE zone (Southern France, Spain and Portugal). One of the goals of the project was to develop an eel abundance and distribution <a href="https://zenodo.org/record/7546419">atlas</a> in the three countries, based on the results of the implementation of Eel Density Analysis (<a href="https://sudoang.eu/wp-content/uploads/2022/02/E411_Briand_et_al_2022_EDA_report_opt-1.pdf">EDA</a>). This model extrapolates eel abundance from a range of river segments sampled by electrofishing, to the whole river and lake network, by considering how eel abundance, size and sex vary according to different parameters related to eel habitat. To do this, we have created a dataset of "cumulated dam impact" which compiles different ways of calculating cumulated height from the sea.</p><h2><strong>2. SUDOANG DATABASE</strong></h2><p>The dataset on cumulated impact was first derived from information on obstacles collected by the SUDOANG project. Obstacles data for the three countries were imported in the SUDOANG database (<a href="https://sudoang.eu/wp-content/uploads/2020/11/E221_data_collection_storage-1.html#3_data_import_on_physical_obstacles_in_spain_and_portugal">deliverable 2.2.1</a>), whose structure is inherited from the DataBase for EEl (DBEEL), developed during a European research project (POSE - Pilot projects to estimate potential and actual escapement of silver eel, Walker et al., 2011). This database is designed to contain all data relative to eel biology and anthropogenic pressures applying to eel. During the course of SUDOANG, this database was used and ameliorated. </p><p>In France the obstacles were compiled from three pre-existing different databases:</p><ul><li>the Referential of flow obstacles (<a href="https://professionnels.ofb.fr/fr/node/367">ROE</a>) ,</li><li>the Information of Ecological Continuity (<a href="https://professionnels.ofb.fr/en/node/731">ICE</a>) and</li><li>the Flow Obstruction Database (Base de Données des Obstacles à l'Ecoulement, (BDOE).</li></ul><p>The data we have integrated into the SUDOANG 1.0.4. database came from a database dump of the 12th September 2019. The inventory includes bridges that have a significant impact on river continuity.</p><p>In Spain, data came from:</p><ul><li>the MITECO Ministry</li><li>the Basque Water Agency (URA) - Basque Country</li><li>the Catalan Water Agency (ACA) - Catalonia</li><li>the University of Girona - Catalonia</li><li>the University of Córdoba - Andalusia</li><li>Xunta de Galicia, Consellería de Medio Ambiente, Territorio e Vivenda - Galicia</li><li>the <a href="https://amber.international">AMBER </a>project</li></ul><p>In Portugal the data came from:</p><ul><li>the Portuguese Water Agency (APA)</li><li>MARE-ULisboa (University of Lisbon)</li><li>CIIMAR, the University of Porto</li><li>the <a href="https://amber.international">AMBER</a> project.</li></ul><p>In the case of the transboundary river Minho, the data came from:</p><ul><li>CIIMAR, the University of Porto (Portuguese area) (<a href="https://www.dgrm.mm.gov.pt/documents/20143/0/PGE+TIRM+Vers%C3%A3o+Portuguesa+Revis%C3%A3o+Novembro+2011.pdf/3c9d8b50-e5cc-2ed8-5714-90a115d4a6a5">report</a>)</li><li>EHEC, the University of Santiago de Compostela (Spanish area) (<a href="https://www.dgrm.mm.gov.pt/documents/20143/0/PGE+TIRM+Vers%C3%A3o+Portuguesa+Revis%C3%A3o+Novembro+2011.pdf/3c9d8b50-e5cc-2ed8-5714-90a115d4a6a5">report</a>)</li></ul><h2><strong>3. DATA DESCRIPTION</strong></h2><h3><strong>3.1. Data collected on artificial obstacles</strong></h3><p>Artificial obstacles were classified into 10 types according to the Adaptive Management of Barriers in European Rivers (<a href="https://amber.international">AMBER</a>) project. Some additional types (e.g., penstock pipes) were added to identify other obstacles in national databases that did not fit the AMBER classification (see the list below). Sometimes dams from different branches are connected, creating a dam-network. In those cases, we have only kept the dam(s) in the main course and use a hierarchical classification of the dams to only consider the cumulated height from the sea to the reference dam. We included only obstacles that are presently standing, <i>i.e.,</i> not planned, under construction, or destroyed. Dikes, longitudinal control structures and grates were excluded.</p><p><i>Obstacle classification according to the data collected and the AMBER project:</i></p><ul><li>BR - Bridge: A structure that is built over a river to allow people or vehicles to cross</li><li>CU - Culvert: A tunnel or pipe carrying a stream or open drain under a road or railway</li><li>DI - Dike: An embankment used to hold back water</li><li>DA - Dam: Structure that blocks the river and extends across the river bed to the flood plain</li><li>FO - Ford: A shallow crossing-place in a river</li><li>PP - Penstock: pipe Group of pipes that transport pressurised water from a reservoir (dam) to the turbines installed in a hydro-electric power plant</li><li>RR - Rock ramp: A weir made of rocks</li><li>WE - Weir: Structure across a river that does not extend to the flood plain</li><li>OT - Other: Structure that is not covered by previous definitions</li><li>UN - Unknown: Unknown</li></ul><p>We have projected obstacles on the SUDOANG river network at the nearest point within 300 m. To avoid projecting large obstacles in the wrong location in the southwestern France, SUDOANG experts have reviewed and corrected this information. We have also used an algorithm that extracts the best obstacle height data from the three existing databases in France. In the Iberian Peninsula, data providers validated and corrected obstacle location and height using a Shiny application developed by the project, in which they could directly correct the height of obstacles.</p><p>The variables in the <strong>obstacles </strong>table (csv delimiter ",") are:</p><ul><li><i>op_id</i>: Identifier of the observation place name</li><li><i>op_gis_layername</i>: Original data source</li><li><i>op_placename</i>: Name of the dam</li><li><i>op_op_id</i>: If the dam is linked within a complex (e.g. when there are multiple channels for the same river) the name of the parent dam</li><li><i>id_original</i>: Original id of the dam (in the raw table)</li><li><i>country</i>: Country code ('SP', 'ES' or 'FR')</li><li><i>dp_name</i>: Name of the data provider</li><li><i>obstruction_type_code</i>: Type of obstruction (see table obstruction type code)</li><li><i>obstruction_type_name</i>: Name of the dam</li><li><i>po_obstruction_height</i>: Difference of level of water between the downstream and the upstream part of the dam</li><li><i>po_presence_eel_pass</i>: Presence of a pass suitable for eel (see paper)</li><li><i>po_date_presence_eel_pass</i>: Date of construction of the eel (or eel compatible) pass</li><li><i>fishway_type_code</i>: Code of the fishway type</li><li><i>fishway_type_name</i>: Name of the fishway type</li><li><i>googlemapscoods</i>: Link to google map</li><li><i>x_espg_4326</i>: Longitude (with ESPG 4326)</li><li><i>y_espg_4326</i>: Latitude (with ESPG 4326)</li></ul><h3><strong>3.2. Modeling missing data and estimating the cumulative impact on obstacles</strong></h3><p>For those obstacles missing height information, we have calculated height using a Generalized Linear Models (GLM of log transformed height, <i>family = gaussian, link = identity</i>. In France the <a href="https://forgemia.inra.fr/pole-migrateurs/eda/dbeel/-/blob/main/eda2.3/report/Dams.Rmd">model</a> was based on river segment slope, river segment median flow and hydrographic basin. In the Iberian Peninsula, we have implemented a simpler <a href="https://forgemia.inra.fr/pole-migrateurs/eda/dbeel/-/blob/main/eda2.3/report/E221_data_collection_storage_sp_pt.Rmd">model</a> based on obstacle type, as information about flow or slope was not available for all river segments.</p><p>The cumulated impact of obstacles was assessed by creating a table joining each river segment with all the dams located in the downstream course. Using this, various metrics were computed using different assumptions concerning the effect of obstacles. The heights were power transformed to test for a different effect of obstacle's height (the cumulated effect of two obstacles of 1 m might be different than the cumulated effect of a single obstacle of 2 m), and functions were developed to calculate cumulated obstacle transformed variables. Other variables were also tested. In fact, tests in France have shown that factors such as presence of a fish pass, and eel passability did not improve the <a href="https://forgemia.inra.fr/pole-migrateurs/eda/eda_model/-/blob/main/S4/BaseEdaRiosRiversegmentsDam.R\#L624">model performance</a>. For this reason, but also because in the Iberian Peninsula this type of information was too limited, we used dam height to model the cumulative height of obstacles at a given river segment. </p><p>The variables in the <strong>cumulated_dam_impact_SUDOANG </strong>table (format Rdata - to be read with the R software, this will load as a data.frame called datadam) are:</p><ul><li><i>cs_height_08_n</i>: Cumulated height from the sea, dam height transformed with power 0.8, no prediction for missing values</li><li><i>cs_height_08_n</i>.: Same variable but truncated to 300</li><li><i>cs_height_08_p</i>: Cumulated height from the sea, dam height transformed with power 0.8, with prediction for missing values</li><li><i>cs_height_08_p</i>.: Same variable but truncated to 300</li><li><i>cs_height_08_pps </i>Cumulated height from the sea, dam height transformed with power 0.8, with prediction for missing values, the height of dam is set to zero if equiped with an efficient fishway for eel</li><li><i>cs_height_10_FR</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams from France are considered when building on a transnational water course</li><li><i>cs_height_10_n</i>: Cumulated height from the sea, no transformation, no prediction for missing values</li><li><i>cs_height_10_n</i>.: Same variable but truncated to 200</li><li><i>cs_height_10_p</i>: Cumulated height from the sea, no transformation, missing height are extrapolated from two different models in France and the Iberian Peninsula <i>cs_height_10_p</i>.: Same variable but truncated to 200</li><li><i>cs_height_10_pass0</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams without pass are used to build the cumulated value</li><li><i>cs_height_10_pass1</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams with pass are used to build the cumulated value</li><li><i>cs_height_10_pp</i>: Cumulated height from the sea, no transformation, with prediction for missing values, the height of dam is set to zero if equiped with an efficient fishway for eel</li><li><i>cs_height_10_ppass0</i>: Cumulated height from the sea, no transformation, including prediction for missing values, only the dams without pass are used to build the cumulated value</li><li><i>cs_height_10_ppass1</i>: Cumulated height from the sea, no transformation, including prediction for missing values, only the dams with pass are used to build the cumulated value</li><li><i>cs_height_10_pps</i>: Cumulated height from the sea, no transformation, with prediction for missing values, the height of dam is set to zero if a score of efficient passage was attributed for eel on this structure</li><li><i>cs_height_10_pscore0</i>: Cumulated height from the sea, no transformation, including prediction for missing values, only the dams without score are used to build the cumulated value</li><li><i>cs_height_10_pscore1</i>: Cumulated height from the sea, no transformation, including prediction for missing values, only the dams with score (that have been expertised as no or small barrier for eel) are used to build the cumulated value</li><li><i>cs_height_10_PT</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams from Portugal are considered when building on a transnational water course</li><li><i>cs_height_10_score0</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams without score are used to build the cumulated value</li><li><i>cs_height_10_score1</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams with score (that have been expertised as no or small barrier for eel) are used to build the cumulated value</li><li><i>cs_height_10_SP</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams from Spain are considered when building on a transnational water course</li><li><i>cs_height_12_n</i>: Cumulated height from the sea, dam height transformed with power 1.2, no prediction for missing values</li><li><i>cs_height_12_n</i>: Same variable but truncated to 500</li><li><i>cs_height_12_p</i>: Cumulated height from the sea, dam height transformed with power 1.2, with prediction for missing values</li><li><i>cs_height_12_p.</i>: Same variable but truncated to 500</li><li><i>cs_height_12_pp</i>: Cumulated height from the sea, dam height transformed with power 1.2, with prediction for missing values, the height of dam is set to zero if equiped with an efficient fishway for eel</li><li><i>cs_height_12_pps</i>: Cumulated height from the sea, dam height transformed with power 1.2, with prediction for missing values, the height of dam is set to zero if a score of efficient passage was attributed for eel on this structure</li><li><i>cs_height_15_n</i>: Cumulated height from the sea, dam height transformed with power 1.5, no prediction for missing values</li><li><i>cs_height_15_n:</i> Same variable but truncated to 800</li><li><i>cs_height_15_p</i>: Cumulated height from the sea, dam height transformed with power 1.5, with prediction for missing values</li><li><i>cs_height_15_p.:</i> Same variable but truncated to 800</li><li><i>cs_height_15_pp</i>: Cumulated height from the sea, dam height transformed with power 1.5, with prediction for missing values, the height of dam is set to zero if equiped with an efficient fishway for eel</li><li><i>cs_height_15_pps</i>: Cumulated height from the sea, dam height transformed with power 1.5, with prediction for missing values, the height of dam is set to zero if a score of efficient passage was attributed for eel on this structure </li><li><i>cumnbdamp</i>: Cumulated number of dam from the sea</li><li><i>cumnbdamso</i>: duplicate of cumnbdamp</li><li><i>idsegment</i>: Unique identifier of the segment [data type: UUID]. Use the <a href="https://doi.org/10.5281/zenodo.7546419">Atlas</a> to link with spatial table in PostgreSQL</li></ul><h2><strong>4. VERSIONS</strong></h2><ul><li><a href="https://doi.org/10.5281/zenodo.7825552">10.5281/zenodo.7825552 </a>1.0.0 - 2023-04-15 - Initial Upload (closed access)</li><li><a href="https://doi.org/10.5281/zenodo.8348374">10.5281/zenodo.8348374</a> 1.0.1 - 2023-09-15 - Update provider and names (closed access)</li><li><a href="https://doi.org/10.5281/zenodo.8348374">10.5281/zenodo.8348374</a> 1.0.1 - 2023-11-08 - Final version (open access)</li></ul><h2><strong>5. READ MORE</strong></h2><ul><li>Atlas of European Eel Distribution (<i>Anguilla anguilla</i>) in Portugal, Spain and France (<a href="https://doi.org/10.5281/zenodo.7546419">10.5281/zenodo.7546419</a>)</li><li>Electrofishing data for eel in the Iberian Peninsula (SUDOANG project) (<a href="https://doi.org/10.5281/zenodo.8348353">10.5281/zenodo.8348353</a>)</li><li>Eel data (<i>Anguilla anguilla</i>) and associated environment variables used to fit the EDA model in the SUDOE area (SUDOANG project) (<a href="https://doi.org/10.5281/zenodo.6397009">10.5281/zenodo.6397009</a>)</li></ul><h2><strong>6. FUNDING</strong></h2><p>Project co-financed by the INTERREG SUDOE Programme through the European Regional Development Fund (ERDF).</p>
Cumulative effects doubled the drought impacts for one-fifth of ecosystems exposed to recurrent droughts during Australia's Millennium Drought
<p><strong>Drought-induced vegetation declines have been reported across the globe and may have widespread implications for ecosystem composition, structure, and functions. Thus, it is critical to maximizing our understanding of how vegetation has responded to recent drought extremes. To date, most drought assessments have focused on the impact of a single drought event, emphasizing the importance of drought intensity for vegetation responses. However, drought timing, duration, and repeat exposure may all be important aspects of ecosystem response with the potential for non-linear effects. Cumulative effects are one such phenomenon, representing the additional decline due to repeated or prolonged exposure to drought, and indicating gradual loss of ecosystem resistance. This study quantifies the frequency and magnitude of cumulative effects among Australian ecosystems as they responded to the Millennium Drought. Three distinct biophysical variables derived from satellite remote sensing were analyzed, including fraction of photosynthetically absorbed radiation, photosynthetic vegetation cover, and canopy density derived from passive microwave data. While cumulative effects were detected in only 20% or less of the landscape exposed to repeat or long-duration drought, they approximately doubled drought impacts in the areas where they occurred. Cultivated lands and grasslands were the most susceptible to cumulative effects from recurrent droughts. Despite being relatively infrequent in forests and savannas, cumulative effects from repeated or long-duration drought caused larger additional declines in these ecosystems. Overall, our study demonstrates that the temporal sequence of drought events can strongly influence the magnitude of drought impacts, which provides important insights into modeling vegetation response to recurrent drought events</strong></p>
X-ray imaging of 30 year old wine grape wood reveals cumulative impacts of rootstocks on scion secondary growth and harvest index
<p><span></span></p> <p><span>Annual rings from vines in a 30 year old, California rootstock trial were measured to determine the effects of 15 different rootstocks on Chardonnay and Cabernet Sauvignon scions. Viticultural traits measuring vegetative growth, yield, berry quality, and nutrient uptake were measured at the beginning and end of the lifetime of the vineyard.</span></p> <p><span>X-ray Computed Tomography (CT) was used to measure ring widths in 103 vines. Ring width was modeled as a function of ring number using a negative exponential model. Early and late wood ring widths, cambium width, and scion trunk radius were correlated with 27 traits. </span></p> <p><span>Modeling of annual ring width shows that scions alter the width of the first rings but that rootstocks alter the decay thereafter, consistently shortening ring width throughout the lifetime of the vine. The ratio of yield to vegetative growth, juice pH, photosynthetic assimilation and transpiration rates, and stomatal conductance are correlated with scion trunk radius.</span></p> <p><span>Rootstocks modulate secondary growth over years, altering hydraulic conductance, physiology, and agronomic traits. Rootstocks act in similar but distinct ways from climate to modulate ring width, which borrowing techniques from dendrochronology, can be used to monitor both genetic and environmental effects in woody perennial crop species.</span></p>
X-ray imaging of 30 year old wine grape wood reveals cumulative impacts of rootstocks on scion secondary growth and harvest index
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Data for the paper "Addressing Uncertainties in Modelling Cumulative Impacts within Maritime Spatial Planning in the Adriatic and Ionian Region"
<p>Data for the paper "Addressing Uncertainties in Modelling Cumulative Impacts within Maritime Spatial Planning in the Adriatic and Ionian Region."</p>
Geospatial dataset for Cumulative Impact assessment, Sea Use conflict analysis and Marine Ecosystem Services assessment in the Adriatic Ionian Region
<p>Geospatial dataset for Cumulative Impact assessment, Sea Use conflict analysis and Marine Ecosystem Services assessment in the Adriatic Ionian Region (reference year 2014).</p> <p>The datasets are derived from ADRIPLAN Portal (http://data.adriplan.eu/).</p>
Evidence for synergistic cumulative impacts of marking and hunting in a wildlife species.
<p>Non-additive effects from multiple interacting stressors can have unpredictable outcomes on wildlife. Stressors that initially have negligible impacts may become significant if they act in synergy with novel stressors. Wildlife markers can be a source of physiological stress for animals and are ubiquitous in ecological studies. Their potential impacts on vital rates may vary over time, particularly when changing environments impose new stressors.</p> <p>In this study, we evaluated the temporal changes in the combined impact of two stressors, one constant (collar-marking) and another one variable over time (hunting intensity), in greater snow geese (Anser caerulescens atlantica). Over a 30-year period (1990-2019), hunting regulations were liberalized twice, in 1999 and 2009, with the instauration of special spring and winter hunting seasons, respectively. We evaluated the effect of collars on goose survival through this period of changing hunting regulations. We compared annual survival of >20,000 adult females marked with and without neck collars using multievent capture-recapture models, and partitioned hunting from non-hunting mortality.</p> <p>Survival of geese marked with or without collars was similar in 1990-1998, before hunting regulations were liberalized (average survival[95% C.I.]: 0.87[0.84, 0.89]). However, absolute survival of collared geese was 0.05[0.03, 0.07] lower than that of non-collared geese between 1999 and 2009, and 0.12[0.09, 0.15] lower after hunting regulations were liberalized further in 2009. Hunting and non-hunting mortality were both higher in collared birds compared to those without collars.</p> <p>The interaction between the effects of collars and hunting was synergistic because collars affected survival only after the hunting pressure increased significantly. These cumulated stresses probably reduced goose body condition sufficiently to increase their vulnerability to multiple sources of mortality.</p> <p>Synthesis and applications: Researchers relying on long-term marking programs should evaluate the effect of markers periodically rather than solely in the beginning, as interactions with changing environmental conditions may eventually affect conclusions of studies based on marked animals. Here, we provide a rare demonstration in a natural setting that a combination of stressors can push animals beyond a threshold where vital rates are affected, even when one stressor applied alone initially had no detectable impact.</p>
Evidence for synergistic cumulative impacts of marking and hunting in a wildlife species.
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Combining seascape connectivity with cumulative impact assessment in support of ecosystem-based marine spatial planning
<p>1. Cumulative impact assessment (CIA) is a promising approach to guide marine spatial planning (MSP) and management. One limitation of CIA is the neglect of seascape connectivity, which may spread the impact of localised pressures to ambient areas, e.g. through lost dispersal and recruitment of organisms.</p> <p>2. We here, for the first time, incorporate seascape connectivity into a traditional CIA model using a connectivity matrix, exemplified by dispersal of propagules estimated through biophysical modelling. Two <i>connectivity impacts</i>are identified: the <i>source impact</i> represents downstream areas losing recruits because of reduced larval dispersal from sites affected by the pressure, and the <i>sink impact</i> represents loss of recruits originating from upstream areas prevented from settlement in the site affected by the local pressure.</p> <p>3. By including seascape connectivity into the Swedish MSP-guiding CIA tool Symphony we demonstrate how to practically account for remote effects of local environmental impact. Our example on blue mussel shows how reducing mussel fitness in a given area may have impacts on mussels far from the acting pressures. Overall, results indicate that connectivity impact for blue mussels plays a minor role in most areas, less than 10% of the ordinary cumulative impact. However, in some smaller areas, e.g. on offshore banks and the Danish Straits, seascape connectivity may increase ordinary cumulative impact with 20-30%. In an example of scenario-based CIA analyses of MSP projections, we demonstrate how impacts of particular management actions, e.g. shipping rerouting and wind power developments, can be tracked far from the original area of influence.</p> <p>4. Depending on the dispersal ability of ecosystem components, a local pressure may impact a considerable area through seascape connectivity, transgressing management units and national borders. Although the mean connectivity impact may be modest for a single ecosystem component, the consideration of seascape connectivity across multiple ecosystem components may significantly alter the mapping of cumulative impact and the assessment of different MSP scenarios.</p> <p>5. Synthesis and applications.<span><span><span><span><span><span><span><span><span><span><span> Our extension of Cumulative Impact Assessment offers a new method for mapping and practically integrating seascape connectivity with ecosystem-based MSP and other spatial instruments for policy making, such as marine protected areas.</span></span></span></span></span></span></span></span></span></span></span></p>
Cumulative impacts of land cover change and dams on the land-water interface of the Tocantins River
<p>This dataset accompanies the manuscript entitled "Cumulative impacts of land cover change and dams on the land-water interface of the Tocantins River". It contains shapefiles and geoTIFF files.</p>
Data from: Assessing cumulative impacts of forest development on the distribution of furbearers using expert-based habitat modeling
Cumulative impacts of anthropogenic landscape change must be considered when managing and conserving wildlife habitat. Across the central-interior of British Columbia, Canada, industrial activities are altering the habitat of furbearer species. This region has witnessed unprecedented levels of anthropogenic landscape change following rapid development in a number of resource sectors, particularly forestry. Our objective was to create expert-based habitat models for three furbearer species: fisher (Pekania pennanti), Canada lynx (Lynx canadensis), and American marten (Martes americana) and quantify habitat change for those species. We recruited 10 biologist and 10 trapper experts and then used the analytical hierarchy process to elicit expert knowledge of habitat variables important to each species. We applied the models to reference landscapes (i.e., registered traplines) in two distinct study areas and then quantified the change in habitat availability from 1990 to 2013. There was strong agreement between expert groups in the choice of habitat variables and associated scores. Where anthropogenic impacts had increased considerably over the study period, the habitat models showed substantial declines in habitat availability for each focal species (78% decline in optimal fisher habitat, 83% decline in optimal lynx habitat, and 79% decline in optimal marten habitat). For those traplines with relatively little forest harvesting, the habitat models showed no substantial change in the availability of habitat over time. The results suggest that habitat for these three furbearer species declined significantly as a result of the cumulative impacts of forest harvesting. Results of this study illustrate the utility of expert knowledge for understanding large-scale patterns of habitat change over long time periods.
Data from: Current and projected cumulative impacts of fire, drought and insects on timber volumes across Canada
Canada's forests are shaped by disturbances such as fire, insect outbreaks and droughts that often overlap in time and space. The resulting cumulative disturbance risks and potential impacts on forests are generally not well accounted for by models used to predict future impacts of disturbances on forest. This study aims at projecting future cumulative effects of four main natural disturbances – fire, mountain pine beetle, spruce budworm and drought - on timber volumes across Canada's forests using an approach that accounts for potential overlap among disturbances. Available predictive models for the four natural disturbances were used to project timber volumes at risk under aggressive climate forcing up to 2100. Projections applied to the current vegetation suggest increases of volumes at risk related to fire, mountain pine beetle and drought over time in many regions of Canada, but a decrease of the volume at risk related to spruce budworm. When disturbance effects are cumulated, important changes in volumes at risk are projected to occur as early as 2011-2041, particularly in central and eastern Canada. In our last simulation period covering 2071 to 2100, nearly all timber volumes in most of Canada's forest regions could be at risk of being affected by at least one of the four natural disturbances considered in our analysis, a six-fold increase relative to the baseline period (1981-2010). Tree species particularly vulnerable to specific disturbances (e.g., trembling aspen to drought) could suffer disproportionate increases in their volume at risk with potential impacts on forest composition. By 2100, estimated wood volumes not considered to be at risk could be lower than current annual timber harvests in central and eastern Canada. Current level of harvesting could thus be difficult to maintain without the implementation of adaptation measures to cope with these disturbances.
Using cumulative impact mapping to prioritise marine conservation efforts in Equatorial Guinea
<p>Marine biodiversity is under extreme pressure from anthropogenic activity globally, leading to calls to protect at least 10% of the world's oceans within marine protected areas (MPAs) and other effective area-based conservation measures by 2020. Fulfilling such commitments, however, requires a detailed understanding of the distribution of potentially detrimental human activities, and their predicted impacts. One such approach that is being increasingly used to strengthen our understanding of human impacts is cumulative impact mapping; as it can help identify economic sectors with the greatest potential impact on species and ecosystems in order to prioritise conservation management strategies, providing clear direction for intervention. In this paper, we present the first local cumulative utilisation impact mapping exercise for the Bioko-Corisco-Continental area of Equatorial Guinea's Exclusive Economic Zone – situated in the Gulf of Guinea one of the most important and least studied marine regions in the Eastern Central Atlantic. This study examines the potential impact of ten direct anthropogenic activities on a suite of key marine megafauna species and reveals that the most suitable habitats for these species, located on the continental shelf, are subject to the highest threat scores. However, in some coastal areas, the persistence of highly suitable habitat subject to lower threat scores suggests that there are still several strategic areas that are less impacted by human activity that may be suitable sites for protected area expansion. Highlighting both the areas with potentially the highest impact, and those with lower impact levels, as well as particularly damaging activities can inform the direction of future conservation initiatives in the region.</p>
Data for Cumulative hydrological impacts of forest disturbance follow the Law of Diminishing Returns
<p>This dataset supports the analysis of the manuscript, entitled "<span lang="EN-US">Long-term Cumulative Hydrological Effects of Forest Disturbances Follow the Law of Diminishing Returns in British Columbia, Canada</span>". It includes forest disturbance history, i.e., cumulative equivalent clear-cut area (CECA), hydrological sensitivities in the post-threshold period in the study watersheds, and the estiamted CECA at the upper limit and environmental variables. </p>
Data from: Repeat disturbances have cumulative impacts on stream communities
(1) Climate change has altered disturbance regimes in many ecosystems, and predictions show that these trends are likely to continue. The frequency of disturbance events plays a particularly important role in communities by selecting for disturbance-tolerant taxa. (2) However, ecologists have yet to disentangle the influence of disturbance frequency per se and time since last disturbance, because more frequently disturbed systems have also usually been disturbed more recently. Our understanding of the effects of repeated disturbances is therefore confounded by differences in successional processes. (3) We used in-situ stream mesocosms to isolate and examine the effect of disturbance frequency on community composition. We applied substrate moving disturbances at five frequencies, with the last disturbance occurring on the same day across all treatments. Communities were then sampled after a recovery period of 9 days. (4) Macroinvertebrate community composition reflected the gradient of disturbance frequency driven by differential vulnerability of taxa to disturbance. Diversity metrics, including family-level richness, decreased, reflecting a likely loss of functional diversity with increasing disturbance frequency. In contrast, overall abundance was unaffected by disturbance frequency as rapid recovery of the dominant taxon compensated for strong negative responses of disturbance-vulnerable taxa. (5) We show that cumulative effects of repeated disturbances—not just the time communities have had to recover before sampling—alter communities, especially by disproportionately affecting rare taxa. Thus, the timing of past disturbances can have knock-on effects that determine how a system will respond to further change.
Data from: Current and projected cumulative impacts of fire, drought and insects on timber volumes across Canada
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Data from: Combining optimization and simulation modelling to measure the cumulative impacts of prescribed fire and wildfire on vegetation species diversity
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Data from: Repeat disturbances have cumulative impacts on stream communities
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Data from: Cumulative and partially recoverable impacts of nitrogen addition on a temperate steppe
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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.