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7,849 results for “Conservancy”
Supplementary material 3 from: Bongard C, Butler K, Fulthorpe R (2013) Investigation of fungal root colonizers of the invasive plant Vincetoxicum rossicum and co-occurring local native plants in a field and woodland area in Southern Ontario. Nature Conservation 4: 55-76. https://doi.org/10.3897/natureconservation.4.3578
Supplementary material 3 from: Bongard C, Butler K, Fulthorpe R (2013) Investigation of fungal root colonizers of the invasive plant Vincetoxicum rossicum and co-occurring local native plants in a field and woodland area in Southern Ontario. Nature Conservation 4: 55-76. https://doi.org/10.3897/natureconservation.4.3578
Raw data of the study: Categorizing urban avoiders, utilizers, and dwellers for identifying bird conservation priorities in a northern Andean city
<p>This datasheet contains raw data on bird count records made from 2016 and 2019. Data were taken in urban and adjacent non-urban areas of Medellín, Colombia. It was part of a collaborative sampling effort during environmental assessments and personal research, summarizing systematic information on 139 sampling points (124 within the city and 15 in adjacent non-urban areas). All points were sampled under the same protocol in order to facilited data for research; in all cases, sampling was in charge of ornithologist with at least 4 years of previous experience in bird surveys. This protocol consisted in sampling during 10 minutes, four times per point (i.e., repetitions), using a fixed radius of 25 m. </p> <p>Information on bird surveys (Count_Data within the corresponding datasheet tab) contains the ID of each site; whether corresponded to a urban or non-urban site; in what category of urban development the site was located, based on 1000, 500 and 200 m buffers (from the observer during bird counts: moderate, low or high); the taxonomic information of each species (order, family, scientific name); the number of recorded individuals; the repetition or number of the visit (1, 2, 3, or 4); the name of the project; the name of the observer, and the date of sampling. </p> <p>Information on categorization of bird species (Categorization within the corresponding datasheet tab) represents additional information on altitudinal ranges, trophic guilds, distribution, and others. In addition, information on frequency for each bird species is given, according to the location of each sampling site and the way it was grouped. This information was the base for categorizing bird species as urban avoider, utilizer, or dweller, under the calculations and decision rules that are also given within the corresponding cells of the datasheet.</p> <p>Any further information or questions about this data could be ask directly, writing to the e-mails: jgarizabal@unal.edu.co or njmacer@unal.edu.co.</p> <p> </p>
D-PLACE dataset derived from Jenkins et al. 2013 'Global patterns of terrestrial vertebrate diversity and conservation'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Jenkins CN, Pimm SL, Joppa LN. Global patterns of terrestrial vertebrate diversity and conservation. Proc Natl Acad Sci. 2013;110: E2602–E2610.</p> </blockquote>
Short-term trends in great ape density in a community-based conservation area in the eastern Democratic Republic of the Congo
<p>Provided are the following supporting data and R script for "Short-term trends in great ape density in a community-based conservation area in the eastern Democratic Republic of the Congo":</p> <ol> <li>A dataset with summarized transect-based ape sign data, <br>SupportingInformation_ApeSigns.xlsx, with coordinates made approximate.</li> <li>A randomized dataset, <br>RandomizedData_INLA.csv, derived from the original, used as INLA-modeling input. </li> <li>A reproducible R script as used to generate the INLA models: SSupporting_Information_exampleINLA_Rscript_new.doc</li> </ol>
Datasets for "Turbulent magnetic decay controlled by two conserved quantities"
<pre>This directory contains an index.html file with links to the run directories and idl plotting routines with secondary data for the other figures for the paper "Turbulent magnetic decay controlled by two conserved quantities" by A. Brandenburg, & A. Banerjee. If anything turns out to be incomplete, please email brandenb@nordita.org.</pre>
Direct molecular evidence for an ancient, conserved developmental toolkit controlling post-transcriptional gene regulation in land plants
<p>In plants, miRNA production is orchestrated by a suite of proteins that control transcription of the pri-miRNA gene, post-transcriptional processing and nuclear export of the mature miRNA. Post-transcriptional processing of miRNAs is controlled by a pair of physically-interacting proteins, HYL1 and DCL1. However, the evolutionary history and structural basis of the HYL1-DCL1 interaction is unknown. Here we use ancestral sequence reconstruction and functional characterization of ancestral HYL1 <em>in vitro</em> and in <em>Arabidopsis thaliana </em>to better understand the origin and evolution of the HYL1-DCL1 interaction and its impact on miRNA production and plant development. We found the ancestral plant HYL1 evolved high affinity for both double-stranded RNA (dsRNA) and its DCL1 partner before the divergence of mosses from seed plants (~500 Ma), and these high-affinity interactions remained largely conserved throughout plant evolutionary history. Structural modeling and molecular binding experiments suggest that the second of two double-stranded RNA-binding motifs (DSRMs) in HYL1 may interact tightly with the first of two C-terminal DCL1 DSRMs to mediate the HYL1-DCL1 physical interaction necessary for efficient miRNA production. Transgenic expression of the nearly 200 Ma-old ancestral flowering-plant HYL1 in <em>A. thaliana</em> was sufficient to rescue many key aspects of plant development disrupted by HYL1<sup>-</sup> knockout and restored near-native miRNA production, suggesting that the functional partnership of HYL1-DCL1 originated very early in and was strongly conserved throughout the evolutionary history of terrestrial plants. Overall, our results are consistent with a model in which miRNA-based gene regulation evolved as part of a conserved plant ‘developmental toolkit’.</p>
Data from: Species delimitation in endangered groundwater salamanders: implications for aquifer management and biodiversity conservation
Groundwater-dependent species are among the least-known components of global biodiversity, as well as some of the most vulnerable because of rapid groundwater depletion at regional and global scales. The karstic Edwards–Trinity aquifer system of west-central Texas is one of the most species-rich groundwater systems in the world, represented by dozens of endemic groundwater-obligate species with narrow, naturally fragmented distributions. Here, we examine how geomorphological and hydrogeological processes have driven population divergence and speciation in a radiation of salamanders (Eurycea) endemic to the Edwards–Trinity system using phylogenetic and population genetic analysis of genome-wide DNA sequence data. Results revealed complex patterns of isolation and reconnection driven by surface and subsurface hydrology, resulting in both adaptive and non-adaptive population divergence and speciation. Our results uncover new cryptic species diversity and refine the borders of several threatened and endangered species. The U.S. Endangered Species Act has been used to bring state regulation to unrestricted groundwater withdrawals in the Edwards (Balcones Fault Zone) Aquifer, where listed species are found. However, the Trinity and Edwards–Trinity (Plateau) aquifers harbor additional species with similarly small ranges that currently receive no protection from regulatory programs designed to prevent groundwater depletion. Based on regional climate models that predict increased air temperature, together with hydrologic models that project decreased springflow, we conclude that Edwards–Trinity salamanders and other co-distributed groundwater-dependent organisms are highly vulnerable to extinction within the next century.
Areas of global importance for conserving terrestrial biodiversity, carbon, and water
<p><strong>Content:</strong><br> This data repository contains the results of the NatureMap ( naturemap.earth/) conservation prioritization effort. The maps were created by jointly optimizing biodiversity and NCPs such as carbon and/or water.</p> <p><strong>Usage notes:</strong><br> Maps are supplied at both 10km and 50km resolution unless specified differently in the manuscript.<br> All maps that aim to find priority areas for all species considered in the analysis, utilize a series of representative sets.<br> The ranks for each layer are area-specific and can be used to extract summary statistics by simple subsetting.<br> For example:<br> To obtain the top 30% of land area for biodiversity and carbon, one needs to create a mask of all areas lower than a value of 30 from the respective ranked layers.</p> <p>For convenience two files are supplied that contain the fraction of land area per grid cell times 1000. Multiplying those with the cell area (100km2, respectively 2500km2) gives the exact amount of land area in a given grid cell.<br> These are labelled " globalgrid_mollweide_**km.tif " can be used to create masks for the priority maps.</p> <p><strong>Spatial resolution:</strong></p> <p>10 and 50 km</p> <p><strong>Geographic projection:</strong><br> World Mollweide Equal Area projection<br> PROJ4 ( +proj=moll +lon_0=0 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs )</p> <p><strong>Filename suffix description:</strong></p> <p><em>'minshort_speciestargets'</em><br> =- Problem formulation where targets were achieved by minimzing a shortfall</p> <p><em>'repruns10'</em><br> =- The number of representative that were used to create the ranked layer</p> <p><em>'biome.id'</em><br> =- Species distribution were split by biome, thus creating separate targets for subpopulation</p> <p><em>'withPA'</em><br> =- Fractions of current protected areas (Date: WDPA 2019) were locked in as baseline and starting budget. Approximately 15% of the globe. Note that not entire grid cells, but fractions were locked in and build opon!</p> <p><em>'carbon'</em><br> =- Carbon was included in the prioritization and jointly optimized together with the other assets by giving it equal weighting (see manuscript)</p> <p><em>'water'</em><br> =- Water was included in the prioritization and jointly optimized together with the other assets by giving it equal weighting (see manuscript)</p> <p><strong>License:</strong><br> CC-BY-SA 4.0</p> <p><strong>Citation:</strong><br> Jung, Martin, Andy Arnell, Xavier De Lamo, Shaenandhoa Garcia-Rangel, Matthew Lewis, Jennifer Mark, Cory Merow et al. (2021) "Areas of global importance for terrestrial biodiversity, carbon, and water." Nature Ecology & Evolution</p>
Tree mortality risks under climate change in Europe: assessment of silviculture practices and genetic conservation networks
<p>General context: Climate change can positively or negatively affect abiotic and biotic drivers of tree mortality. Process-based models integrating these climatic effects are only seldom used at species distribution scale.</p> <p>Objective: The main objective of this study was to investigate the multi-causal mortality risk of five major European forest tree species across their distribution range from an ecophysiological perspective, to quantify the impact of forest management practices on this risk and to identify threats on the genetic conservation network.</p> <p><br> Methods: We used the process-based ecophysiological model CASTANEA to simulate the mortality risk of \textit{Fagus sylvatica}, \textit{Quercus petraea}, \textit{Pinus sylvestris}, \textit{Pinus pinaster} and \textit{Picea abies} under current and future climate conditions, while considering local silviculture practices. The mortality risk was assessed by a composite risk index \textit{(CRIM)} integrating the risks of carbon starvation, hydraulic failure and frost damage. We took into account extreme climatic events with the \textit{CRIM$_{max}$}, computed as the maximum annual value of the \textit{CRIM}.</p> <p><br> Results: The physiological processes' contributions to \textit{CRIM} differed among species: it was mainly driven by hydraulic failure for \textit{P. sylvestris} and \textit{Q. petraea}, by frost damage for \textit{P. abies}, by carbon starvation for \textit{P. pinaster}, and by a combination of hydraulic failure and frost damage for \textit{F. sylvatica}. Under future climate, projection showed an increase of \textit{CRIM} for \textit{P. pinaster} but a decrease for \textit{P. abies}, \textit{Q. petraea} and \textit{F. sylvatica}, and little variation for \textit{P. sylvestris}. Under the harshest future climatic scenario, forest management decreased the mean \textit{CRIM} for \textit{P. sylvestris}, increased it for \textit{P. abies} and \textit{P. pinaster} and had no major impact for the two broadleaved species. By the year 2100, 38\% to 90\% of the conservation units are at extinction threat (\textit{CRIM$_{max}$}=1), depending on the species.</p> <p><br> Conclusions: Using a process-based ecophysiological model allowed us to disentangle the multiple drivers of tree mortality under current and future climate. Taking into account the positive effect of increased CO$_2$ on fertilization and water use efficiency, the average risks may increase or decrease in the future depending on species and sites. However, considering extreme climatic events, future projections are as pessimistic than those obtained with bioclimatic niche models.</p> <p> </p> <p>Abbreviation for column:</p> <p>X Longitude<br> Y Latitude<br> LAImax Leaf area index max reach<br> Nha Density per hectar<br> Vha Volume per hectar<br> NEE Net ecosystem exchange<br> NPP net primary production<br> Reco Respiration ecosystem<br> GPP Gross primary production<br> Etveg Evapotranspiration canopy<br> Etsol Evapotranspiration sol<br> TR tree transpiration<br> ETP evapotranspiration potentiel<br> BiomassOfReserves Biomass of reserve<br> rw ring width<br> dbh diameter at breast heast<br> height height<br> BBday Budburst date<br> rFD risk of frost<br> CRIM_max Maximum combined risk index of mortality reach<br> rNSC risk of carbon starvation<br> rPLC risk of embolism<br> rPLC_max Maximum risk of embolism reach<br> CRIM combined risk index of mortality<br> Climate Climatic model<br> rNSC_max maximum risk of carbon starvation reach<br> rFD_max Maximum risk of frost reach<br> Scenario_Sylvicol null means no silvulcture simulated<br> species species<br> Country Country<br> alt_watch altitude of climate simulated<br> grid_watch number of the pixel point of WATCH<br> grid_eurocordex number of the pixel point of Eurocordex<br> Pinus_sylvestris 0 abscence ; 1 presence<br> Fagus_sylvatica 0 abscence ; 1 presence<br> Quercus_petraea 0 abscence ; 1 presence<br> Picea_abies 0 abscence ; 1 presence<br> Pinus_pinaster 0 abscence ; 1 presence</p> <p> </p>
Embeddings from protein language models predict conservation and variant effects
<p>For this work, we used protein language model representations (embeddings) to predict sequence conservation without multiple sequence alignments (MSAs). Embeddings alone predicted residue conservation almost as accurately from single sequences as ConSeq using MSAs (two-state Matthew Correlation Coefficient – MCC - for ProtT5 embeddings of 0.596±0.006 vs. 0.608±0.006 for ConSeq).</p> <p><strong><em>ConSurf10k</em>- Dataset for the development of ProtT5cons:</strong> The method (ProtT5cons) predicting residue conservation used <em>ConSurf-DB </em>(Ben Chorin et al. 2020). This resource provided sequences and conservation for 89,673 proteins. For all, experimental high-resolution three-dimensional (3D) structures were available in the Protein Data Bank (PDB) (Berman et al. 2000). As standard-of-truth for the conservation prediction, we used the values from ConSurf-DB generated using HMMER (Mistry et al. 2013), CD-HIT (Fu et al. 2012), and MAFFT-LINSi (Katoh and Standley 2013) to align proteins in the PDB (Burley et al. 2019). For proteins from families with over 50 proteins in the resulting MSA, an evolutionary rate at each residue position is computed and used along with the MSA to reconstruct a phylogenetic tree. The ConSurf-DB conservation scores ranged from 1 (most variable) to 9 (most conserved). The PISCES server (Wang and Dunbrack 2003) was used to redundancy reduce the data set such that no pair of proteins had more than 25% pairwise sequence identity. We removed proteins with resolutions >2.5Å, those shorter than 40 residues, and those longer than 10,000 residues. The resulting data set (ConSurf10k) with 10,507 proteins (or domains) was randomly partitioned into training (9,392 sequences), cross-training/validation (555) and test (519) sets.</p> <p>Uploaded data:</p> <ul> <li>ConSuf10k_PDBid_seq_cons.fasta: fasta file with PDBid, sequence and conservation annotation</li> <li>consurf10k_test_ids.txt: txt file with id's of test set</li> <li>consurf10k_train_ids.txt: txt file with id's of train set</li> <li>consurf10k_val_ids.txt: txt file with id's of cross-validation set</li> </ul>
An Innovative Scheme to Confront the Trade‐Off Between Water Conservation and Heat Alleviation With Environmental Justice for Urban Sustainability: The Case of Phoenix, Arizona
<p><em><strong>The manuscript for this dataset is accepted by AGU Advances and can be accessed here: <a href="https://doi.org/10.1029/2022AV000816">link</a>. Please cite the literature when using the datasets.</strong></em></p> <p><strong>How to cite this article: Yuanhui Zhu, Soe Myint, Xin Feng, Yubin Li. An Innovative Scheme to Confront the Trade‐Off Between Water Conservation and Heat Alleviation With Environmental Justice for Urban Sustainability: The Case of Phoenix, Arizona. AGU Advances, 4, e2022AV000816. <a href="https://doi.org/10.1029/2022AV000816">https://doi.org/10.1029/2022AV000816</a></strong></p> <p>This study aims to develop a practical and integrated framework to tackle the tradeoff between land surface temperature (LST) reduction and water conservation for heat mitigation and resilience planning in Phoenix, Arizona. We developed a multi-objective framework of spatial optimization for priority areas that considers environmental justice. We employed the priority areas (i.e., residential districts, socio-economically disadvantaged neighborhoods, hotspot regions, and opportunity areas), ECOSTRESS-based LST, actual evapotranspiration (ETa, as a proxy to water use), Landsat-based LST and ETa changes (2000–2020), and the evaporative stress index (ESI). These datasets are used to identify the priority areas in which environmental conditions need to be improved seriously and (2) spatially optimize the placement of new green space (tree %, grass %) in the priority areas to realize the most significant LST reduction and minimum OWU. We provide the results of the new green space configurations with the scenarios for the percentage of new vegetation coverage (including trees and grass) overall increased to 25%, 35%, and 45% within the entire study areas, residential districts, socio-economically disadvantaged neighborhoods, and hotspot regions.</p> <table> <caption>The dataset summarization</caption> <tbody> <tr> <td>Category</td> <td>Dataset</td> <td>Resolution</td> <td>Source/method</td> <td>Time</td> </tr> <tr> <td>Environmental database</td> <td>Summer daytime LST</td> <td>70m</td> <td>ECOSTRESS</td> <td>2019</td> </tr> <tr> <td>Environmental database</td> <td>Summer nighttime LST</td> <td>70m</td> <td>ECOSTRESS</td> <td>2019</td> </tr> <tr> <td>Environmental database</td> <td>Summer ETa</td> <td>70m</td> <td>ECOSTRESS</td> <td>2019</td> </tr> <tr> <td>Environmental database</td> <td>Summer ESI</td> <td>70m</td> <td>ECOSTRESS</td> <td>2019</td> </tr> <tr> <td>Environmental change database</td> <td>Trends of summer LST changes</td> <td>30m</td> <td>Landsat-based Statistical Mono-Window algorithm</td> <td>2000-2020</td> </tr> <tr> <td>Environmental change database</td> <td>Trends of summer ETa changes</td> <td>30m</td> <td>Landsat-based Simplified Surface Energy Balance</td> <td>2000-2020</td> </tr> <tr> <td>The results of new green space configurations</td> <td>The spatial distributions of new green space</td> <td>--</td> <td>Spatial optimization</td> <td>--</td> </tr> </tbody> </table> <p>note: LULC: Land use and land cover; LST: Land Surface Temperature; ETa: Actual Evapotranspiration; ESI: Evaporative Stress Index</p> <p>We provide the different scenarios in shapefile format for spatial distributions of new space configurations. The naming convention for attribute tables in shapefile is :</p> <p>VV_new_perNN_LSTWW</p> <p>where:</p> <ul> <li>VV = New vegetation for tree or grass</li> <li>NN = The scenarios with new vegetation increased to 25%, 35%, or 45% (unit: %)</li> <li>WW = The weight values of land surface temperature range from 0 to 1 (unit: %) when executing spatial optimization for the tradeoff between land surface temperature reduction and outdoor water use conservation with vegetation coverage. The weight of 0 represents that our spatial optimization models only focus on outdoor water use conservation, and the weight of 1 denotes that we only consider land surface temperature reduction. </li> </ul> <p>Example: grass_new_per25_LST65 means -- new vegetation for grass; the scenario is set up by new vegetation increased to 25%; the weight of land surface temperature is 0.65. </p> <p> </p>
Carbon data for: Evidence for the Multiple Benefits of Wetland Conservation in North America
<p>These data were synthesized as part of a rapid evidence assessment of the scientific literature on a wide range of benefits associated with wetland conservation and restoration. Our synthesis emphasized data from North America and especially the United States, although many of the high priority meta-analyses and reviews we incorporated were global in scope. The data in these files represent a range of metrics related to carbon sequestration, storage, or flux compiled from multiple sources for the purposes of summarizing the range of observed values and how they vary across wetland classes or by restoration status. For more detail on the synthesis methods and each set of metrics, please see the full report: </p> <p>Conlisk E, Chamberlin L, Vernon M, Dybala KE. 2022. Evidence for the Multiple Benefits of Wetland Conservation in North America: Carbon, Biodiversity, and Beyond. Point Blue Conservation Science, Petaluma, CA.</p>
Lepidoptera genomics based on 88 chromosomal reference sequences informs population genetic parameters for conservation
<p>This repository contains (1) germline mutations called by the DeepVariant (v1.1.0) pipeline in VCF format; (2) rejected substitution scores calculated by the Genomic Evolutionary Rate Profiling (GERP++) software on each species and chromosome; and (3) the phylogenetic tree used as guide tree in the Cactus alignment.</p>
Data from: Maximizing the potential for living cell banks to contribute to global conservation priorities
<p><strong>Summary</strong></p> <p>This dataset accompanies the publication "<strong>Maximizing the potential for living cell banks to contribute to global conservation priorities</strong>" published in Zoo Biology. This study analyzed the representation of amphibian, bird, mammal and reptile species within the San Diego Zoo Wildlife Alliance (SDZWA) Frozen Zoo® living cell collection (as of April 2019) and implemented a qualitative framework for the prioritization of species for future sampling. We used global conservation assessment schemes (including the IUCN Red List of Threatened Species<sup>TM</sup>, CITES, the Alliance for Zero Extinction, the EDGE of Existence, and Climate Change Vulnerability), and opportunities for sample acquisition from the global zoo and aquarium community, to identify priority species for future cryobanking efforts.</p> <p>Two datasets accompany this publication. The first provides a complete list of all “<em>Threatened</em>” species not currently represented within the San Diego Zoo Wildlife Alliance (SDZWA) Frozen Zoo®, their representation under various conservation assessment schemes, and their presence in the global zoo and aquarium community, as represented by Species360 members (<strong>Supporting Information Data S1</strong>). The second provides a list of the 2,351 amphibian and bird species not currently represented in the SDZWA Frozen Zoo® or listed as “<em>Threatened</em>” under the IUCN Red List, but assessed by Foden <em>et al.</em> (2013) as highly vulnerable to climate change. This dataset also shows their presence in the global zoo and aquarium community, as represented by Species360 members (<strong>Supporting Information Data S2</strong>).</p> <p>Data provided by SDZWA are only available upon request. Data for each conservation assessment scheme were collected from each source individually (all freely available), and are accurate to April 2019. Zoo species holdings data were provided upon request by Species360 (https://www.species360.org/), which operates the real-time database ZIMS (Zoological Information Management System). ZIMS is the largest real-time database of comprehensive and standardized information spanning more than 1,200 zoological collections globally, and provides the number of institutions currently managing each species and their current population sizes. </p> <p> </p> <p><strong>Description of the Datasets</strong></p> <p> </p> <p><strong>Supporting Information Data S1 (accurate to April 2019):</strong> A complete list of all “<em>Threatened</em>” species not currently represented within the San Diego Zoo Wildlife Alliance (SDZWA) Frozen Zoo®, their representation under various conservation assessment schemes, and their presence in the global zoo and aquarium community, as represented by Species360 members. Provided in .csv format, variables include:</p> <ul> <li><strong>Class: </strong>The taxonomic class of the species</li> <li><strong>IUCN Name: </strong>The taxonomic name of the species (genus and epithet) according to the IUCN Red List</li> <li><strong>IUCN Status: </strong>The IUCN Red List status of the species (VU, EN, or CR)</li> <li><strong>ZIMS:</strong> The presence (1) or absence (0) of the species from Species360 member zoos and aquariums</li> <li><strong>AZE:</strong> The listing of the species on the Alliance for Zero Extinction assessment (listed = 1, not listed = 0)</li> <li><strong>AZA:</strong> The presence of an active <em>ex situ</em> management programme for the species in the Association of Zoos and Aquariums (active programme = 1, no programme = 0)</li> <li><strong>EAZA: </strong>The presence of an active <em>ex situ</em> management programme for the species in the European Association of Zoos and Aquaria (active programme = 1, no programme = 0)</li> <li><strong>CITES:</strong> The listing of the species on of the three CITES appendices (listed = 1, not listed = 0)</li> <li><strong>CC Vulnerability: </strong>The listing of the species as being highly vulnerable to climate change by Foden <em>et al</em>. 2013 (vulnerable = 1, not vulnerable = 0)</li> <li><strong>EDGE: </strong>The listing of the species on the Evolutionary Distinctiveness and Global Endangerment assessment (listed = 1, not listed = 0)</li> </ul> <p> </p> <p><strong>Supporting Information Data S2 (accurate to April 2019):</strong> A list of the 2,351 amphibian and bird species not currently represented in the SDZWA Frozen Zoo® or listed as “<em>Threatened</em>” under the IUCN Red List, but assessed by Foden <em>et al.</em> (2013) as highly vulnerable to climate change. This dataset also shows their presence in the global zoo and aquarium community, as represented by Species360 members. Provided in .csv format, variables include:</p> <ul> <li><strong>Class: </strong>The taxonomic class of the species</li> <li><strong>IUCN Name: </strong>The taxonomic name of the species (genus and epithet) according to the IUCN Red List</li> <li><strong>CC Vulnerability:</strong> The listing of the species as being highly vulnerable to climate change by Foden <em>et al</em>. 2013 (all species are vulnerable = H)</li> <li><strong>IUCN Status: </strong>The IUCN Red List status of the species (excluding VU, EN, or CR)</li> <li><strong>ZIMS:</strong> The presence (1) or absence (0) of the species from Species360 member zoos and aquariums</li> </ul> <p> </p> <p><strong>Acknowledgements</strong></p> <p>We sincerely thank the San Diego Zoo Wildlife Alliance and Species360 member institutions for their support and data input. This research was funded by the Irish Fulbright Commission 2018/2019 to A.M., the Irish Research Council Laureate Awards 2017/2018 IRCLA/2017/60 to Y.M.B. D.A.C. and J.S. were funded by Species360, the University of Southern Denmark and the Species360 Conservation Science Alliance sponsors: the World Association of Zoos and Aquariums (WAZA), Mandai Wildlife Reserve, and Copenhagen Zoo. </p> <p> </p> <p><strong>Disclaimer</strong></p> <p>Despite our best efforts at screening the data for errors and inconsistencies, some information could be erroneous. Similarly, data collected from the various conservation assessment schemes, and those contained within ZIMS, could contain errors or reporting failures. For example, ZIMS data are are based on submitted records from individual institutions, and are not subject to editorial verification, potentially permitting errors or failure to update species holdings etc. Despite this, ZIMS represents the only global database of zoo collection composition records, and as a result, is used by the IUCN, Convention on International Trade in Endangered Species (CITES), the Wildlife Trade Monitoring Network (TRAFFIC), United States Fish and Wildlife Service (USFWS) and Department for Environment, Food and Rural Affairs (DEFRA). </p> <p> </p> <p><strong>Credit</strong></p> <p>If you use this dataset, please cite the corresponding publication:</p> <p>Mooney, A., Ryder, O. A., Houck, M. L., Staerk, J., Conde, D. A., & Buckley, Y. M. (2023). Maximizing the potential for living cell banks to contribute to global conservation priorities. <em>Zoo Biology</em>, 1– 12. <a href="https://doi.org/10.1002/zoo.21787">https://doi.org/10.1002/zoo.21787</a></p>
Datapackage for national high-resolution conservation prioritisation of boreal forests
<p>This data package concerns the following work:</p> <p>Ninni Mikkonen, Niko Leikola, Joona Lehtomäki, Panu Halme, Atte Moilanen,<br> National high-resolution conservation prioritisation of boreal forests,<br> Forest Ecology and Management, Volume 541, 2023, 121079<br> ISSN 0378-1127</p> <p><a href="https://doi.org/10.1016/j.foreco.2023.121079">https://doi.org/10.1016/j.foreco.2023.121079</a></p> <p>The overall objective of the work was to develop spatial prioritizations that can assist the forest conservation programme METSO (The Finnish Government 2008; 2014) to make well-informed decisions about acquisition of forests for protection. The results are also aimed to be useful for other actors interested in forest conservation or biodiversity friendly forest management. We focused the prioritization on the most threatened forest types and areas that display some or many elements of natural forests: more than one and preferably more than two tree species, forest structure that present else than even age structure or a history of clear-cut harvesting, and the amount of dead wood that exceed the volume of dead tree material in managed forests. From the perspective of connectivity, these areas should be situated close (varying from metres to a few kilometres) to other valuable forest areas. These kinds of forest areas represent the most threatened forest types and forest species in Finland (Hyvärinen et al. 2019; Kontula and Raunio 2019).</p> <p>This data package includes 3 folders (see details of the data in the article):</p> <p>1) Data folder<br> a) DWP features: the 20 input data layers of the modelled biodiversity surrogate: dead wood potential. These are combinations of 4 tree species and 5 forest site type classes. Not that these are not normalized.<br> (1) bir = birch, obl = other broad leaved tree, st = forest site type<br> b) Other data layers:<br> i) condition_layer.img where the magnitude of the penalty is defined<br> ii) ProtectedOrNot.img layer is used in hierarchical analysis to define whether the area is permanently protected or not<br> iii) WRSCR04_PA.img layer consists of permanently protected areas cut form weighted range size corrected richness output layer from analysis version 4 to execute the positive interaction between the forests and permanently protected areas.<br> iv) similarity matrix</p> <p>2) Input folder<br> a) example setup files for analysis version 7 (hierarchical analysis where permanently protected areas are forced to highest priorities, including information on dead wood potential of the forest stands, penalties followed by the forest management, connectivity within the forests, observations of red-listed forest species, and connectivity to forest key habitats and permanently protected areas)<br> i) .spp file for list of input features for the analysis<br> ii) .dat file for the analysis settings<br> iii) .bat file to run the analysis in command line<br> iv) conditionlayer.txt to define the used condition file in the analysis<br> v) groups file to define the use of the condition layer<br> vi) interact file to define the interactions between feature layers in connectivity calculations</p> <p>3) Output folder<br> a) includes folder for each analysis version. Each folder includes<br> i) rank file in .img format which is the actual spatial priority ranking result<br> ii) wrscr file which describes the weighted range size corrected richness of all input features<br> iii) curves file: the performance of each input feature within the cell removal<br> iv) jpg picture of the result</p> <p>The package DOES NOT include sensitive data. For species observations, ask for Finnish Biodiversity Info Facility https://laji.fi/en. For forest key habitats (small forest patches protected by the Forest Act, that are classified as “habitats of special importance to safeguard the biodiversity of forests”) on state owned land and land owned by companies, ask the data providers and owners.</p> <p>See Moilanen et al. (2014) for more technical information on the input and output files.</p> <p><br> Overview of the data</p> <p>The resolution of the spatial data is 96 m x 96 m. The study area covered the forested land area in Finland, excluding the autonomous Åland Islands.</p> <p>The data on forest stands are from year 2015, the forest management year 2017, and protected area network early winter 2018. See details of the data extraction in the article, Appendix A.</p> <p>The main source of biodiversity information were the modelled dead wood potential (DWP) indices. The DWP is an estimation of the potential of a stand for hosting dead wood dependent species. The potential is increased when the stand can be expected to produce more dead wood and more varied dead wood in terms of size and tree species composition. The modelling is based on forest growth and increase of dead wood calculated with Motti forest simulator 3.3 (Salminen et al., 2005; Hynynen et al., 2014; Hynynen et al., 2015) for 168 combinations of seven tree species, six forest site types, and four vegetation zones. See detailed information on the dead wood potential modelling in doi:10.3390/f11090913 (Mikkonen et al. 2020, Modeling of Dead Wood Potential Based on Tree Stand Data)</p> <p>The DWP was calculated for each stand or pixel based on the forest data (Finnish Forest Centre 2015; Metsähallitus 2015; Metsähallitus Parks & Wildlife Finland and Centres for Economic Development Transport and the Environment 2015; Natural Resources Institute Finland 2015b; 2015a): tree species and tree stock quantities (mean diameter at breast height and volume), soil fertility (Cajander, 1926), and location. In the DWP modelling the size information was combined with stand volume and forest site type. Eventually, the data were compiled to 20 input layers. See detailed information on the pre-processing of the input-data in the Appendix B.</p> <p>Spatial conservation prioritizations were made with the Zonation software 4.0 (Moilanen et al. 2005; Moilanen et al. 2009; Moilanen et al. 2011). With multiple analysis versions, the greatest interest is on those areas that repeatedly receive high ranks – these areas are important from all perspectives included in analysis.</p> <p>The ecological model of conservation value included seven analysis versions that start from a local perspective and then evolve towards regional and national levels (following Lehtomäki et al. 2009). Each new analysis version included everything that had been included in the previous simpler versions. The versions are 1) local estimation of the conservation potential of the forests based on tree stock alone, 2) local estimation with additional information about forest management and drainage, 3) landscape level (not local but not regional either) estimation with internal forest connectivity, 4) landscape level estimation with additional information about observations of red-listed forest species, 5) landscape-level estimation with added short distance connectivity to key forest habitats, 6) regional estimation with added long distance connectivity to permanently protected areas, and 7) regional estimation of the most appropriate addition to the present conservation network.</p> <p>These results do not replace in-depth ecological inventory assessment. They can be used as one source of information in land use planning.</p> <p><br> Literature</p> <p>Finnish Forest Centre. 2015. [dataset] Field and forest stand database AARNI.</p> <p>Hyvärinen, E., Juslén, A., Kemppainen, E., Uddström, A. & Liukko, U.-M. (Eds.). 2019. The 2019 Red List of Finnish Species. Helsinki, Ministry of the Environment & Finnish Environment Institute. 704 p.</p> <p>Kontula, T. & Raunio, A. (Eds.). 2019. Threatened Habitat Types in Finland 2018. Red List of Habitats – Results and Basis for Assessment. Helsinki, Finnish Environment Institute and Ministry of the Environment. The Finnish Environment 2/2019. 254 p. http://urn.fi/URN:ISBN:978-952-11-5110-1<br> http://hdl.handle.net/10138/308426.</p> <p>Lehtomäki, J., Tomppo, E., Kuokkanen, P., Hanski, I. & Moilanen, A. 2009. Applying spatial conservation prioritization software and high-resolution GIS data to a national-scale study in forest conservation. Forest Ecology and Management 258(11): 2439-2449.</p> <p>Metsähallitus. 2015. [dataset] SutiGIS 2015. Forestry resource and planning system for Metsähallitus Forestry Ltd and Protected Area Biotope Information System; biotope, and tree stock data on state-owned conservation areas, for Metsähallitus Parks & Wildlife Finland.</p> <p>Metsähallitus Parks & Wildlife Finland & Centres for Economic Development Transport and the Environment. 2015. [dataset] SutiGIS 2015: Protected area biotope information system, biotope and tree stock data on private conservation areas.</p> <p>Mikkonen, N., Leikola, N., Lehtomäki, J., Halme, P. & Moilanen, A. 2023. National high-resolution conservation prioritisation of boreal forests. Forest Ecology and Management, Volume 541. <a href="https://doi.org/10.1016/j.foreco.2023.121079">https://doi.org/10.1016/j.foreco.2023.121079</a></p> <p>Mikkonen, N., Leikola, N., Halme, P., Heinaro, E., Lahtinen, A. & Tanhuanpää, T. 2020. Modeling of Dead Wood Potential Based on Tree Stand Data. Forests 11(913): 21.</p> <p>Moilanen, A., Franco, A. M. A., Early, R. I., Fox, R., Wintle, B. & Thomas, C. D. 2005. Prioritizing multiple-use landscapes for conservation: methods for large multi-species planning problems. Proceedings of the Royal Society B-Biological Sciences 272(1575): 1885-1891.</p> <p>Moilanen, A., Kujala, H. & Leathwick, J. 2009. The Zonation framework and software for conservation prioritization. In: Moilanen, A., Wilson, K. A. & Possingham, H. P. (Eds.). Spatial conservation prioritization - Quantitative Methods & Computational tools. New York, Oxford University Press Inc. p. 196-210.</p> <p>Moilanen, A., Leathwick, J. R. & Quinn, J. M. 2011. Spatial prioritization of conservation management. Conservation Letters 4(5): 383-393.</p> <p>Moilanen, A., Pouzols, F. M., Meller, L., Veach, V., Arponen, A., Leppänen, J. & Kujala, H. 2014. Zonation - Spatial conservation planning methods and software. Version 4. User Manual. 4. Helsinki, C-BIG Conservation Biology, Informatics Group, Department of Biosciences, University of Helsinki, Finland. 290 p.</p> <p>Natural Resources Institute Finland. 2015a. [dataset] Segmented multi-source national forest inventory data of Finland: estimates of mean diameter at breast height for tree species based on National Forest Inventory 2013. Unpublished. Date of datacut 19.8.2015.</p> <p>Natural Resources Institute Finland. 2015b. [dataset] The Multi-Source National Forest Inventory of Finland (MS-NFI) 2013, CC BY 4.0.</p> <p>The Finnish Government. 2008. Decision-in-Principle of The Finnish Government on the Forest Biodiversity Programme for Southern Finland for years 2008-2016 (in Finnish). 13.</p> <p>The Finnish Government. 2014. Decision-in-Principle of the Finnish Government on extension of the Forest Biodiversity Programme for Southern Finland (METSO) for years 2014-2025. 18.</p> <p> </p>
Dataset - Drying out fish ponds, for an entire growth season, as an agroecological practice: maintaining primary producers for fish production and biodiversity conservation
<p>This dataset is based on samples taken from fish ponds in the Dombes region between 2007 and 2014. It includes sediment and water physio-chemistry data, as well as primary producer diversity, benthic invertebrate density and fish yield for 85 different ponds. All these data are linked to the distance to the last dry-out, a major practice in extensive fish farming in this region.</p> <p>There are two .tab and .csv files:<br> One containing the dataset<br> One containing the description of the different variables (Metadata)</p>
Supplementary file 1 from: Moliner Cachazo L, Makati K, Chadwick MA, Catford JA, Price BW, Mackay AW, Guiry MD, Murray-Hudson M, Murray-Hudson F (2023) A review of the freshwater diversity in the Okavango Delta and Lake Ngami (Botswana): taxonomic composition, ecology, comparison with similar systems and conservation status. Aquatic Sciences
<p>Dataset with 2,204 freshwater species from the Okavango Delta and Lake Ngami (Botswana), with additional 355 species found in other areas of Botswana that are likely to be present in the study region. The dataset covers the following groups: amphibians, birds, fishes, macroinvertebrates, macrophytes, mammals, reptiles, phytoplankton, and zooplankton. The following information is given for each species: status in the Okavango Delta and Lake Ngami (present/potentially present); conservation status globally, Phylum, Class, Order, Family, Genus, species name, cited synonyms, common name, habitat, presence in high water, presence in low water, ecology, distribution in continental Africa, confirmed locations in the Okavango Delta, site coordinates, references, notes.</p>
Data of the publication "Hydrodynamics in long-range interacting systems with center-of-mass conservation"
<p>In systems with a conserved density, the additional conservation of the center of mass (dipole moment) has been shown to slow down the associated hydrodynamics. At the same time, long-range interactions generally lead to faster transport and information propagation. Here, we explore the competition of these two effects and develop a hydrodynamic theory for long-range center-of-mass-conserving systems. We demonstrate that these systems can exhibit a rich dynamical phase diagram containing subdiffusive, diffusive, and superdiffusive behaviors, with continuously varying dynamical exponents. We corroborate our theory by studying quantum lattice models whose emergent hydrodynamics exhibit these phenomena.</p>
Data from: Species delimitation in endangered groundwater salamanders: implications for aquifer management and biodiversity conservation
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Inferring the mammal tree: Species-level sets of phylogenies for questions in ecology, evolution, and conservation
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