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Future of Oak Forests experiment - DBH of trees in Black Rock Forest, New York, USA, 2007.
This data set contains the measurement of diameter at breast height (1.4 meters above ground) (dbh) for individual non-oak and oak trees at the Future of Oak Forests (FOF) experiment in Black Rock Forest (Cornwall, NY) during July and August of 2007. The North Slope site contains 12 square-shaped plots, each of 75 meters by 75 meters. Each plot was subdivided into nine equal-sized subplots, each of 25 meters by 25 meters. Within each plot, all individual oak trees with dbh of at least 2.4 centimeters were measured using metric DBH tapes (e.g. Lufkin executive thinline). Within each center subplot of a plot, all individual non-oak trees with dbh of at least 2.4 centimeters were measured using metric DBH tapes (e.g. Lufkin executive thinline). This data can be used to study the individual variation and metabolic scaling of oak and non-oak species in the Forest.
Time series of annual TAS 40-year trend from historical to future in CMIP5 model simulations
<p>Time series of 40-year linear trend for the annual near surface air temperature (tas) for the period 1979-2100 as simulated by the CMIP5 models on a 2 x 2 deg grid. The data for the period 1979-2005 are taken from the CMIP5 historical simulations, while data for 2006-2100 are from the CMIP5 future scenario rcp2.6, rcp4.5 and rcp8.5, respectively. The 40 year trend of 1979-2018 from the ERA-Interim reanalysis is also provided in a separate file. </p> <p>Each file contains the time series of the 40-year linear trend for tas at each grid point from historical to one of the three scenarios simulated by one model. The linear trend is calculated using cdo, and the year associated with each data point in a file corresponding to the last year in the 40-year time period, e.g, the associated year 2018 corresponds to the linear trend calculated for the period 1979-2018. Unit: C/year.</p>
Time series of Area mean TAS 40-year trend from historical to future in CMIP5 model simulations
<p>Time series of 40-year linear trend for the Arctic (ARC) and the Eastern Arctic (eARC) mean annual near surface air temperature (tas) for the period 1979-2100 as simulated by the CMIP5 models. The data for the period 1979-2005 are taken from the CMIP5 historical simulations, while data for 2006-2100 are from the future scenario rcp2.6, rcp4.5 and rcp8.5, respectively. The 40 year trend of 1979-2018 from the ERA-Interim reanalysis is also provided in separate files. The Arctic is defined as the area north of 70 N, and the eastern Arctic is defined as 0 - 180 E and north of 70 N. Each file contains the time series of the 40-year linear trend for the respective area mean tas from historical to one of the three scenarios simulated by one model. The linear trend is calculated using cdo, and the year associated with each data point in a file corresponding to the last year in the 40-year time period, e.g, the associated year 2018 corresponds to the linear trend calculated for the period 1979-2018. Unit: C/year.</p>
Distribution. Scattered localities from Colombia (Chocó and Putumayo departments) and adjacent Venezuela to Guyana, extending S to N & C Brazil (Para and Mato Grosso). Future research likely will extend distributional limits and establish occurrence in Peru and Ecuador. in Emballonuridae
Distribution. Scattered localities from Colombia (Chocó and Putumayo departments) and adjacent Venezuela to Guyana, extending S to N & C Brazil (Para and Mato Grosso). Future research likely will extend distributional limits and establish occurrence in Peru and Ecuador.
Ensemble projections elucidate effects of uncertainty in terrestrial nitrogen limitation on future carbon uptake
<p>Simulation output as described in Meyerholt, J., Sickel K., and Zaehle, S., (2020), Ensemble projections elucidate effects of uncertainty in terrestrial nitrogen limitation on future carbon uptake, Global Change Biology, doi:10.1111/gcb.15114</p> <p>Data in the file <a href="https://zenodo.org/api/files/62e7c4a2-96e9-46b8-810f-c4c3c24b06c0/ocn4magicc_carbon_model.nc?versionId=cb25734e-52ce-42fa-b238-9b7192baf4dd">ocn4magicc_carbon_model.nc</a> describe the carbon-only version of the model, <a href="https://zenodo.org/api/files/62e7c4a2-96e9-46b8-810f-c4c3c24b06c0/ocn4magicc_carbon_model.nc?versionId=cb25734e-52ce-42fa-b238-9b7192baf4dd">ocn4magicc_nitrogen_models.nc </a>describe the carbon-nitrogen model outputs.</p>
Natural potential for future cropland expansion
<p><strong>Natural potentials for future cropland expansion </strong></p> <p>The potential for the expansion of cropland is restricted by the availability of land resources and given local natural conditions. As a result, area that is highly suitable for agriculture according to the prevailing local biophysical conditions but is not under cultivation today has a high natural potential for expansion. Policy regulations can further restrict the availability of land for expansion by designating protected areas, although they may be suitable for agriculture. Conversely, by applying e.g. irrigation practices, land can be brought under cultivation, although it may naturally not be suitable. Here, we investigate the potentials for agricultural expansion for near future climate scenario conditions to identify the suitability of non-cropland areas for expansion according to their local natural conditions.</p> <p>We determine the available energy, water and nutrient supply for agricultural suitability from climate, soil and topography data, by using a fuzzy logic approach according to Zabel et al. (2014). It considers the 16 globally most important staple and energy crops. These are: barley, cassava, groundnut, maize, millet, oil palm, potato, rapeseed, rice, rye, sorghum, soy, sugarcane, sunflower, summer wheat, winter wheat. The parameterization of the membership functions that describe each of the crops’ specific natural requirements is taken from Sys et al. (1993). The considered natural conditions are: climate (temperature, precipitation, solar radiation), soil properties (texture, proportion of coarse fragments and gypsum, base saturation, pH content, organic carbon content, salinity, sodicity), and topography (elevation, slope). As a result of the fuzzy logic approach, values in a range between 0 and 1 describe the suitability of a crop for each of the prevailing natural conditions at a certain location. The smallest suitability value over all parameters finally determines the suitability of a crop. The daily climate data is provided by simulation results from the global climate model ECHAM5 (Jungclaus et al. 2006) for near future (2011-2040) SRES A1B climate scenario conditions. Soil data is taken from the Harmonized World Soil Database (HWSD) (FAO et al. 2012), and topography data is applied from the Shuttle Radar Topography Mission (SRTM) (Farr et al. 2007). In order to gather a general crop suitability, which does not refer to one specific crop, the most suitable crop with the highest suitability value is chosen at each pixel.</p> <p>In addition the natural biophysical conditions, we consider today’s irrigated areas according to (Siebert et al. 2013). We assume that irrigated areas globally remain constant until 2040, since adequate data on the development of irrigated areas do not exist, although it is likely that freshwater availability for irrigation could be limited in some regions, while in other regions surplus water supply could be used to expand irrigation practices (Elliott et al. 2014). However, it is difficult to project where irrigation practices will evolve, since it is driven by economic investment costs that are required to establish irrigation infrastructure.</p> <p>In principle, all agriculturally suitable land that is not used as cropland today has the natural potential to be converted into cropland. We assume that only urban and built-up areas are not available for conversion, although more than 80% of global urban areas are agriculturally suitable (Avellan et al. 2012). However, it seems unlikely that urban areas will be cleared at the large scale due to high investment costs, growing cities and growing demand for settlements. Concepts of urban and vertical farming usually are discussed under the aspects of cultivating fresh vegetables and salads for urban population. They are not designed to extensively grow staple crops such as wheat or maize for feeding the world in the near future. Urban farming would require one third of the total global urban area to meet only the global vegetable consumption of urban dwellers (Martellozzo et al. 2015). Thus, urban agriculture cannot substantially contribute to global agricultural production of staple crops.</p> <p>Protected areas or dense forested areas are not excluded from the calculation, in order not to lose any information in the further combination with the biodiversity patterns (see chapter 2.3). We use data on current cropland distribution by Ramankutty et al. (2008) and urban and built-up area according to the ESA-CCI land use/cover dataset (ESA 2014). From this data, we calculate the ‘natural expansion potential index’ (I<sub>exp</sub>) that expresses the natural potential for an area to be converted into cropland as follows:</p> <p>I<sub>exp</sub> = S * A<sub>av</sub></p> <p>The index is determined by the quality of agricultural suitability (S) (values between 0 and 1) multiplied with the amount of available area (A<sub>av</sub>) for conversion (in percentage of pixel area). The available area includes all suitable area that is not cultivated today, and not classified as urban or artificial area. The index ranges between 0 and 100 and indicates where the conditions for cropland expansion are more or less favorable, when taking only natural conditions into account, disregarding socio-economic factors, policies and regulations that drive or inhibit cropland expansion. The index is a helpful indicator for identifying areas where cropland expansion could take place in the near future.</p> <p><strong>Further information</strong></p> <p>Detailled information are available in the following publication:</p> <p>Delzeit, R., F. Zabel, C. Meyer and T. Václavík (2017).<strong> Addressing future trade-offs between biodiversity and cropland expansion to improve food security</strong>. Regional Environmental Change 17(5): 1429-1441. DOI: 10.1007/s10113-016-0927-1</p> <p><strong>Contact</strong></p> <p>Please contact: Dr. Florian Zabel, <a href="mailto:f.zabel@lmu.de">f.zabel@lmu.de</a>, Department für Geographie, LMU München (<a href="http://www.geografie.uni-muenchen.de">www.geografie.uni-muenchen.de</a>)</p>
CoDEC Dataset - Data underlying the paper "A high-resolution global dataset of extreme sea levels, tides and storm surges including future projections "
<p>The world’s coastal areas are increasingly at risk of coastal flooding due to sea-level rise (SLR). We present a novel global dataset of extreme sea levels, the Coastal Dataset for the Evaluation of Climate Impact (CoDEC), which can be used to accurately map the impact of climate change on coastal regions around the world. The third generation Global Tide and Surge Model (GTSM), with a coastal resolution of 2.5 km (1.25 km in Europe), was used to simulate extreme sea levels for the ERA5 climate reanalysis from 1979 to 2017, as well as for future climate scenarios from 2040 to 2100. The validation against observed sea levels demonstrated a good performance, and the annual maxima had a mean bias (MB) of -0.04 m, which is 50% lower than the MB of the previous GTSR dataset. The CoDEC-ERA5 dataset is the successor of GTSR <a href="https://www.nature.com/articles/ncomms11969">(Muis et al., 2016)</a> and is based on the next generation climate and hydrodynamic models. The main improvements are summarized in Table 2 of the accompanying paper <a href="https://www.frontiersin.org/articles/10.3389/fmars.2020.00263/abstract">(Muis et al., 2020)</a>.</p> <p><br> </p>
High-resolution future climate data for species distribution models in Europe
<p><strong>Description</strong></p> <p>This dataset contains a set of 13 climatological variables (<code>Variable</code>, <code>VariableName</code>) at a spatial resolution of 1x1km for Europe (nx = 13147, ny = 6071) for historical (<code>ClimatePeriod</code>) and future climate conditions. These variables are a subset of the so-called bioclimatic variables that are often part of global gridded datasets (e.g. <a href="https://worldclim.org/data/bioclim.html">WorldClim</a>, <a href="http://chelsa-climate.org/bioclim/">CHELSA</a>) that have been specifically developed for species distribution modelling and ecological applications.</p> <p>The climatological data correspond to 35-year (<code>Startyear_Endyear</code> = <code>1971_2005</code>) and 30-year (<code>Startyear_Endyear</code> = <code>2041_2070</code>) mean values representing respectively historical and future climate conditions. To account for the future climate conditions, three possible emission scenarios of greenhouse gases as defined by the <a href="https://www.ipcc.ch/">Intergovernmental Panel on Climate Change (IPCC)</a> are used (<code>ClimatePeriod</code> = <code>rcp26</code>, <code>rcp45</code>, <code>rcp85</code>).</p> <p>The complete set of variables (var[1-13]) for which historical and future climate data layers are produced are given below.</p> <p>The source data for the climate layers were assembled from the <a href="https://cordex.org/data-access/">EURO-CORDEX archive</a> (Kotlarski et al., 2014). More specifically, we have used the regional climate model simulations for Europe at a spatial resolution of 12.5x12.5km on which a three-step statistical downscaling approach has been applied:</p> <ol> <li><strong>Processing</strong> (averaging, totals, …) of all available time series of the EURO-CORDEX model experiments (<code>ClimatePeriod</code> = evaluation, historical, rcp) for the climatological variables.</li> <li><strong>Interpolation</strong> of the data layers from the 12.5x12.5km EURO-CORDEX grid to a 1x1km spatial <a href="http://chelsa-climate.org/">CHELSA</a> (Karger et al., 2017) reference grid (see files <code>lat_1km.csv</code> and <code>lon_1km.csv</code>).</li> <li><strong>Calculate differences</strong> between the 1x1km-interpolated variables (<code>Variable</code> = only for var[1-9]) from the evaluation model experiments (or <code>ClimatePeriod</code>) and the corresponding reference bioclimatic CHELSA variables. In order to account for possible biases present in the EURO-CORDEX climate models, these differences (or biases) are then subtracted from the respective 1x1-km-interpolated variables for the historical and rcp model experiments (<code>ClimatePeriod</code>).</li> </ol> <p>The dimensions of the 1x1km grid (excl. the first row and column):</p> <ul> <li>y-dimension = number of columns = 6071</li> <li>x-dimension = number of rows = 13147</li> </ul> <p>The longitudes and latitudes of respectively the southwest and northeast corner of the grid are:</p> <ul> <li>longitude -44.592; latitude 21.991 (southwest corner)</li> <li>longitude 64.967; latitude 72.583 (northeast corner)</li> </ul> <p>The climatological variables are used as input data for the species distribution modelling of Invasive Alien Species for the <a href="https://osf.io/7dpgr/">Tracking Invasive Alien Species (TrIAS)</a> project.</p> <p><strong>Variables</strong></p> <ul> <li><strong>Variable</strong> (VariableName): Unit</li> <li><strong>var1</strong> (AnnualMeanTemperature): °C</li> <li><strong>var2</strong> (AnnualAmountPrecipitation): mm year<sup>-1</sup></li> <li><strong>var3</strong> (AnnualVariationPrecipitation): coefficient of variation</li> <li><strong>var4</strong> (AnnualVariationTemperature): stdev</li> <li><strong>var5</strong> (MaximumTemperatureWarmestMonth): °C</li> <li><strong>var6</strong> (MinimumTemperatureColdestMonth): °C</li> <li><strong>var7</strong> (TemperatureAnnualRange): °C</li> <li><strong>var8</strong> (PrecipitationWettestMonth): mm</li> <li><strong>var9</strong> (PrecipitationDriestMonth): mm</li> <li><strong>var10</strong> (30yrMeanAnnualCumulatedGDDAbove5degreesC): °C days</li> <li><strong>var11</strong> (AnnualMeanPotentialEvapotranspiration): mm day<sup>-1</sup></li> <li><strong>var12</strong> (AnnualMeanSolarRadiation): W m<sup>-2</sup></li> <li><strong>var13</strong> (AnnualVariationSolarRadiation): stdev</li> </ul> <p><strong>Files</strong></p> <ul> <li><strong>varX_VariableName_ClimatePeriod_Startyear_Endyear.csv</strong>: climatological data layers for the 13 variables listed above</li> <li><strong>lon_1km.csv</strong>: longitudes for the 1x1km grid</li> <li><strong>lat_1km.csv</strong>: latitudes for the 1x1km grid</li> </ul>
Database for "Accurate estimates of past spatiotemporal temperature variability could strongly constrain future warming"
<p>This repository contains data used to support findings of the study "Accurate estimates of past spatiotemporal temperature variability could strongly constrain future warming", which is currently under review.</p>
Past and future decline of tropical pelagic biodiversity
<p>A major research question concerning global pelagic biodiversity remains unanswered: when did the apparent tropical biodiversity depression (i.e., bimodality of latitudinal diversity gradient [LDG]) begin? The bimodal LDG may be a consequence of recent ocean warming or of deep-time evolutionary speciation and extinction processes. Using rich time-slice datasets of planktonic foraminifers, we show here that a unimodal (or only weakly bimodal) diversity gradient, with a plateau in the tropics, occurred during the last ice age and has since then developed into a bimodal gradient through species distribution shifts driven by postglacial ocean warming. The bimodal LDG likely emerged before the Anthropocene (here defined as ∼1950) and perhaps ∼15,000 y ago, indicating a strong environmental control of tropical diversity even before the start of anthropogenic warming. However, our model projections suggest future anthropogenic warming further diminishes tropical pelagic diversity to a level not seen in millions of years.</p>
Supplementary material 3 from: Petersen M, Pramann B, Toepfer R, Neumann J, Enke H, Hoffmann J, Mauer R (2020) Research Data Management - Current status and future challenges for German non-university research institutions. Research Ideas and Outcomes 6: e55141. https://doi.org/10.3897/rio.6.e55141
Praxisbericht: Entwicklung eines Maßnahmenkatalogs zur Verbesserung des Forschungsdatenmanagements am Herder-Institut für historische Ostmitteleuropaforschung
Scenario Input files for "The Domestic and International Implications of Future Climate for U.S. Agriculture in GCAM"
<p>The GCAM scenario input files needed for the experiments described in the paper "The Domestic and International Implications of Future Climate for U.S. Agriculture in GCAM".</p>
Data from: Modelling the current and future biodiversity distribution in the Chilean Mediterranean Hotspot. The role of protected areas network in a warmer future
Aim: Mediterranean Chile is part of the five recognized Mediterranean-type climates in the world and harbors a very rich floral diversity. Climate change has been reported as a significant threat to its biodiversity. We used the flora of Mediterranean Chile to analyze how biodiversity patterns, as measured by Phylogenetic Diversity, genus and species richness will respond to climate change scenarios and identify the areas that will harbor the greatest evolutionary potential and biodiversity richness. We also evaluated how these spatial patterns are depicted within the current network of protected areas. Location: Chilean Mediterranean climate-type Region, South America. Methods: Biodiversity metrics were evaluated for current and future climatic scenarios. Species distribution models were done using Maxent for 1.727 species and 571 genera. Relationships between species/genera gain, loss and turnover were evaluated. For Mediterranean endemic species, loss and gain was also related to life form. Finally, variation in species gain, loss and turnover was evaluated in future climate change scenarios within and outside Mediterranean Chile state protected areas. Results: We found a general decrease in species richness in the entire Region toward future climate change scenarios. Phylogenetic Diversity is predicted to be higher than expected by richness in the north and south of the area, and lower than expected by richness in the Andes mountain. The highest average species and genus loss is predicted to occur outside the protected areas, meanwhile species and genus gain is higher within them. Main conclusions: Future biodiversity patterns are reported here for the first time in the Chilean Mediterranean Region. Our findings enhance the importance of the current protected areas to harbor this future variation, despite their reduced number and size along the region.
DC2 High resolution future hydrological data for Sweden
<p>Hourly river flow and total runoff were computed for the southern part of Sweden using the hourly version of a high resolution hydrological model S-HYPE, which is operationally used by SMHI. The model was calibrated and validated using radar based hourly precipitation and an operationally used hourly reanalysis temperature data. Projection of the impact of climate change was performed by running the model with hourly forcing data from an ensemble of EURO-COREX climate model simulations over 1971 - 2100. Four GCM-RCM combinations were used under two emission scenarios, RCP4.5 and RCP8.5. The results can be used to assess the risk of riverine flooding in areas located along a small to meso-scale river basin. The results can, in particular, be used to assess the risk of flash flooding that can result from heavy precipitation of short duration.</p>
The future urban forest: a survey of tree planting programs in the Northeastern United States
<p>Cities around the world are pursuing tree planting as a way to increase tree cover. Despite the growing interest in planting trees as a way to offset climate change, counter the negative impacts of urbanization, and provide benefits to city dwellers, there has not been a recent effort to quantify the number of trees being planted nor the species composition of these plantings. Because ecosystem services and ecosystem threats can transcend municipal boundaries, understanding trends in tree planting at multiple spatial scales is critical. To overcome this knowledge gap, we used a survey to collate recent tree planting data from 52 cities with populations greater than 50,000 people in the Northeastern USA. The four largest cities in our study (New York, Boston, Philadelphia, Washington D.C.) planted over 87% of all the trees that were planted in the region. Smaller cities, which are numerous in region, planted proportionally fewer trees and, in over 40% of the small cities surveyed, planting palettes included invasive tree species, highlighting both a resource and knowledge gap in smaller cities as compared with larger ones. Regardless of city size, records also illuminated an overreliance on certain genera for specific ecosystem services; nearly 20% of all shade trees were <i>Quercus</i> species and over 40% of ornamental trees were either <i>Syringa</i> or <i>Prunus</i> species. As cities continue to rely on tree-planting as a form of green infrastructure, our results demonstrate that more consideration to establishing diverse planting palettes will be an important way to ensure that ecological resilience is maintained. Achieving this will depend on increased opportunities to collaborate across municipal boundaries and promoting cross-learning from the experiences of more innovative urbanized regions to urban regions with less infrastructure and expertise.</p>
Forest-Forward: Identifing future suitable areas for forestry in Indonesia, Spain, and Sweden.
<p>This dataset includes the original data and modelling results used to create the interactive platform <a href="https://forest-forward.com">Forest-forward</a>.</p> <p>These include; climatic variables (historical reanalysis ERA-5, and the future CMIP5 projections) derived from the <a href="https://cds.climate.copernicus.eu">Copernicus Climate Data Store</a> that have been converted to 19 standard <a href="http://www.worldclim.com/bioclim">bioclimatic variables</a>; species occurrence data for a limited number of tree species derived from <a href="https://www.gbif.org/">GBIF</a>; and, the results of ensemble Species Distribution Modelling (SDM) using historical and future bioclimatic variables as predictors, created using the R package <a href="https://github.com/biomodhub/biomod2">biomod2</a> that have been assigned to a hexagonal grid for display. Code used for the full processing pipeline is available at <a href="https://github.com/Vizzuality/vspt">GitHub</a>.</p> <p>Data files are organised by region; Indonesia (IDN), mainland Spain (ESP), and Sweden (SWE). Bioclimatic variables are GeoTIFF files grouped per region in zipped directories representing historical reanalysis (1980-2019) and future predictions (further divided into 10 y time-intervals between 2020 and 2090). SDM results (and resampled bioclimatic variables) are supplied as CSV files with WKT geometries ("<iso3>_zonal_spp_uuid.csv", and "<iso3>_zonal_bv_uuid.csv", respectively). Species occurrence point data is supplied as a single CSV file with WKT geometries ("all_spp_occurrence.csv"); the field 'iso3' can be used to filter by region. Summaries of the total proportion of area of a country with suitable habitat for each species, and the area weighted mean of each bioclimatic variable per country are supplied as CSV files ("<iso3>_spp_summary_stats.csv", and "<iso3>_bv_summary_stats.csv", respectively).</p>
Flexible habitat choice by aphids exposed to multiple cues reflecting present and future benefits
Mothers choose suitable habitats for laying offspring to maximize fitness. Since habitat quality varies in space and time, mothers gather information to choose among available habitats through multiple cues reflecting different aspects of habitat quality at present and in the future. However, it is unclear how females assess and integrate different cues associated with current rewards and future safety to optimize oviposition/larviposition decisions, especially across small spatial scales. Here we tested the individual and interactive effects of leaf surface, leaf orientation and leaf bending direction on larviposition site choice and fitness benefits of wheat aphids (<i>Metopolophium dirhodum</i>) within individual leaves. We found that females preferred upper over lower surfaces for gaining current food-related rewards, downward- over upward-facing surfaces for avoiding potential abiotic risks, and sunken over protruding surfaces for avoiding potential biotic risks. When facing conflicting cues during larviposition, females preferred downward-facing/sunken surfaces over upper surfaces, suggesting that females prioritize potential safety at the cost of current rewards during decision making. Most importantly, our combined-cue experiments showed females still assessed secondary cues (i.e. the upper surface) when first-ranked cues (i.e. the downward-facing/sunken surface) are available, even though females only gained relatively small fitness rewards through secondary cues, and females can integrate different cues associated with current rewards and potential safety in a multiplicative way to make flexible and complex larviposition decisions. Overall, our findings provide new insights into how animals collect and process multi-cue information associated with current rewards and potential safety to maximize fitness at small spatial scales.
A novel method for detecting extra-home range movements (EHRMs) by animals and recommendations for future EHRM studies
<p>Infrequent, long-distance animal movements outside of typical home range areas provide useful insights into resource acquisition, gene flow, and disease transmission within the fields of conservation and wildlife management, yet understanding of these movements is still limited across taxa. To detect these extra-home range movements (EHRMs) in spatial relocation datasets, most previous studies compare relocation points against fixed spatial and temporal bounds, typified by seasonal home ranges (referred to here as the "Fixed-Period" method). However, utilizing home ranges modelled over fixed time periods to detect EHRMs within those periods likely results in many EHRMs going undocumented, particularly when an animal's space use changes within that period of time. To address this, we propose a novel, "Moving-Window" method of detecting EHRMs through an iterative process, comparing each day's relocation data to the preceding period of space use only. We compared the number and characteristics of EHRM detections by both the Moving-Window and Fixed-Period methods using GPS relocations from 33 white-tailed deer (Odocoileus virginianus) in Alabama, USA. The Moving-Window method detected 1.5 times as many EHRMs as the Fixed-Period method and identified 120 unique movements that were undetected by the Fixed-Period method, including some movements that extended nearly 5 km outside of home range boundaries. Additionally, we utilized our EHRM dataset to highlight and evaluate potential sources of variation in EHRM summary statistics stemming from differences in definition criteria among previous EHRM literature. We found that this spectrum of criteria identified between 15.6% and 100.0% of the EHRMs within our dataset. We conclude that variability in terminology and definition criteria previously used for EHRM detection hinders useful comparisons between studies. The Moving-Window approach to EHRM detection introduced here, along with proposed methodology guidelines for future EHRM studies, should allow researchers to better investigate and understand these behaviors across a variety of taxa.</p>
Effects of future climate on coral-coral competition
As carbon dioxide (CO 2 ) levels increase, coral reefs and other marine systems will be affected by the joint stressors of ocean acidification (OA) and warming. The effects of these two stressors on coral physiology are relatively well studied, but their impact on biotic interactions between corals are poorly understood. While coral-coral interactions are less common on modern reefs, it is important to document the nature of these interactions to better inform restoration strategies in the face of climate change. Using a mesocosm study, we evaluated whether the combined effects of ocean acidification and warming alter the competitive interactions between the common coral Porites astreoides and two other mounding corals ( Montastraea cavernosa or Orbicella faveolata ) common in the Caribbean. After 7 days of direct contact, P. astreoides suppressed the photosynthetic potential of M. cavernosa by 100% in areas of contact under both present (~28.5°C and ~400 μatm p CO 2 ) and predicted future (~30.0°C and ~1000 μatm p CO 2 ) conditions. In contrast, under present conditions M. cavernosa reduced the photosynthetic potential of P. astreoides by only 38% in areas of contact, while under future conditions reduction was 100%. A similar pattern occurred between P. astreoides and O. faveolata at day 7 post contact, but by day 14, each coral had reduced the photosynthetic potential of the other by 100% at the point of contact, and O. faveolata was generating larger lesions on P. astreoides than the reverse. In the absence of competition, OA and warming did not affect the photosynthetic potential of any coral. These results suggest that OA and warming can alter the severity of initial coral-coral interactions, with potential cascading effects due to corals serving as foundation species on coral reefs.
Multi-dimensional biodiversity hotspots and the future of taxonomic, ecological, and phylogenetic diversity: a case study of North American rodents
<p>Aim: We investigate geographic patterns across taxonomic, ecological, and phylogenetic diversity to test for spatial (in)congruency and identify aggregate diversity hotspots in relation to present land-use and future climate. Simulating extinctions of imperiled species, we demonstrate where losses across diversity dimensions and geography are predicted.</p> <p>Location: North America</p> <p>Time period: Present-day, future</p> <p>Major taxa studied: Rodentia</p> <p>Methods: Using geographic range maps for rodent species, we quantified spatial patterns for eleven dimensions of diversity: taxonomic (species, range-weighted), ecological (body size, diet, habitat), phylogenetic (mean, variance, and nearest-neighbor patristic distances, phylogenetic distance, genus-to-species ratio) and phyloendemism. We tested for correlations across dimensions and used spatial residual analyses to illustrate regions of pronounced diversity. We aggregated diversity hotspots in relation to land-use and climate-change predictions and recalculated metrics following extinctions of IUCN-listed imperiled species.</p> <p>Results: Topographically-complex western North America hosts high diversity across multiple dimensions: phyloendemism and ecological diversity exceed predictions based on taxonomic richness and phylogenetic variance patterns indicate steep gradients in phylogenetic turnover. While an aggregate diversity hotspot emerges in the west, spatial incongruence exists across diversity dimensions at the continental scale. Notably, phylogenetic metrics are uncorrelated with ecological diversity. Diversity hotspots overlap with land-use and climate change, and extinctions predicted by IUCN status are unevenly distributed across space, phylogeny, or ecological groups.</p> <p>Main conclusions: Comparison of taxonomic, ecological, and phylogenetic diversity patterns for North American rodents clearly shows the multifaceted nature of biodiversity. Testing for geographic patterns and (in)congruency across dimensions of diversity facilitates investigation into underlying ecological and evolutionary processes. The geographic scope of this analysis suggests that several explicit regional challenges face North American rodent fauna in the future. Simultaneous consideration of multidimensional biodiversity allows us to assess what critical functions or evolutionary history we might lose with future extinctions and maximize the potential of our conservation efforts.</p>
ScienceDex guides
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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.