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55 results for “spatial management”
Data from: Identifying priority areas for spatial management of mixed fisheries using ensemble of multi-species distribution models. Panzeri D. et al., 2023, Fish and Fisheries
<p>Panzeri D.<sup>1</sup>, Russo T., Arneri E., Carlucci R., Cossarini G., Isajlović I., Krstulović Šifner S., Manfredi C., Masnadi F., Reale M., Scarcella G., Solidoro C., Spedicato M.T., Vrgoč N., W. Zupa, Libralato S<sup>2</sup>.</p> <p><sup>1 </sup>dpanzeri@ogs.it<br> <sup>2 </sup>slibralato@ogs.it</p> <p>Spatial fisheries management is widely used to reduce overfishing, rebuild stocks, and protect biodiversity. However, the effectiveness and optimization of spatial measures depend on accurately identifying ecologically meaningful areas, which can be difficult in mixed fisheries. To apply a method generally to a range of target species, we developed an ensemble of species distribution models (e-SDM) that combines general additive models, generalized linear mixed models, random forest, and gradient-boosting machine methods in a training and testing protocol. The e-SDM was used to integrate density indices from two scientific bottom trawl surveys with the geopositional data, relevant oceanographic variables from the three-dimensional physical-biogeochemical operational model, and fishing effort from the vessel monitoring system. The determined best distributions for juveniles and adults are used to determine hot spots of aggregation based on single or multiple target species. We applied e-SDM to juvenile and adult stages of 10 marine demersal species representing 60% of the total demersal landings in the central areas of the Mediterranean Sea. Using the e-SDM results, hot spots of aggregation and grounds potentially more selective were identified for each species and for the target species group of otter trawl and beam trawl fisheries. The results confirm the ecological appropriateness of existing fishery restriction areas and support the identification of locations for new spatial management measures.</p> <p>Data (csv) for Panzeri et al. 2023</p> <p>1. <a href="https://zenodo.org/api/files/0b1b7af4-6a3b-481d-8d5f-57cf02d20eaa/Ensemble_density_F%26F_D.Panzeri_et_al_2023.csv">Ensemble_density_F&F_D.Panzeri_et_al_2023.csv: CSV file with density values (column pred) in terms of number of individuals (log N/km2) for each species (column sp) and life stage (column age) for each grid cell (X = longitude and Y = latitude).</a> </p> <p>2. <a href="https://zenodo.org/api/files/0b1b7af4-6a3b-481d-8d5f-57cf02d20eaa/Ensemble_density_F%26F_D.Panzeri_et_al_2023.csv">Getis_hotspot_F&F_D.Panzeri_et_al_2023.csv: CSV file with Getis ord Gi* values (column Gi) derived from the previous file 1, developed for each species and life stage for each grid cell (X = longitude and Y = latitude).</a></p> <p>3. <a href="https://zenodo.org/api/files/0b1b7af4-6a3b-481d-8d5f-57cf02d20eaa/Ensemble_density_F%26F_D.Panzeri_et_al_2023.csv">Multispecies_HotSpot_F&F_D.Panzeri_et_al_2023.csv: Frequency map expressed as the number of species for each grid cell (column freq) that has the hotspot (previous file 2) above the third quartile.</a></p> <p> </p> <p> </p>
Supplementary material 1 from: Pontoppidan M, Nachman G (2013) Spatial Amphibian Impact Assessment – a management tool for assessment of road effects on regional populations of Moor frogs (Rana arvalis). Nature Conservation 5: 29-52. https://doi.org/10.3897/natureconservation.5.4612
Full model description following the protocol suggested by Grimm et al. (2006, 2010) and model parameterisation. (doi: 10.3897/natureconservation.5.4612.app). File format: Adobe PDF document (pdf).:
Fig. 2 in Multi-Criteria Decision Analysis as a tool to extract fishing footprints: application to small scale fisheries and implications for management in the context of the Maritime Spatial Planning Directive
Fig. 2: Flowchart of steps and methods followed (AHP: Analytic Hierarchy Process, FM: Fuzzy Membership).
Fig. 7 in Multi-Criteria Decision Analysis as a tool to extract fishing footprints: application to small scale fisheries and implications for management in the context of the Maritime Spatial Planning Directive
Fig. 7: Spatial representation of the Fishing pressure index from the small scale coastal fishery (FPc).
Figure 3 in Spatial distribution of Ceratitis capitata in guava orchards and influences from orchard management
Figure 3. Estimates of the parameters of the regression analysis by the Taylor power model, adjusted by the F test to evaluate the spatial distribution and the t-test to compare the hypotheses of the aggregation index of Ceratitis capitata in guava orchards, Ivinhema-MS. Tests: ANOVA (F = 304.05; p <0.001; g.l = 20); t (5.17; p <0.001) for the Alpha hypothesis (h0: a = 1 vs h1 a ≠ 1, where: Alpha (a <0, a = 0 and a>0) and; t (17.44; p <0.001) for the Beta hypothesis (h0: b = 1 vs h1: b ≠ 1), where: Beta (b <1, b = 1 and b> 1).
Figure 2 in Spatial distribution of Ceratitis capitata in guava orchards and influences from orchard management
Figure 2. Number of fruit fly/trap/day (FTD) of Ceratitis capitata and range of negative binomial thresholds (Bn) with other distributions, establishing the levels of safety and control activities for the Mediterranean fly in three guava orchards, Ivinhema, MS, Brazil.
Data from: Spatial configuration matters when removing windfelled trees to manage bark beetle disturbances in Central European forest landscapes
<p>The published dataset contains the result of the paper titled <strong>Spatial configuration matters when removing windfelled trees to manage bark beetle disturbances in Central European forest landscapes, in Journal of Environmental Management.</strong></p> <p>Two zip files contain maps in ascii format for the total bark beetle and wind damage over 54 simulation year in our study region in Slovakia under reference climate and different climate change scenarios. No- salvaging and 95% salvaging scenarios are shown, just as in the paper.</p> <p>Excel file contains data of the other figures in the paper (main text and appendices as well).</p> <p>See more details about target area, and iLand model:</p> <p>https://www.sciencedirect.com/science/article/pii/S0168192318302946</p> <p>http://iland.boku.ac.at/startpage</p> <p>contact: Laura Dobor; dobor.laura@gmail.com</p> <p> </p>
Figure 2 in Spatial and temporal nesting pattern of Sea Turtles in Alas Purwo National Park, and its implications for conservation management practices
Figure 2. Trend of sea turtle nesting in the past 40 years at APNP: (A) L.olivacea, (B) C. mydas, (C) E. imbricata, and (D) D. coriacea.
Figure 1 in Spatial and temporal nesting pattern of Sea Turtles in Alas Purwo National Park, and its implications for conservation management practices
Figure 1. Study location in Alas Purwo National Park's Pancur-Cungur Coast with six hypothetical stations (dot: sector benchmark point).
Figure 4 in Spatial and temporal nesting pattern of Sea Turtles in Alas Purwo National Park, and its implications for conservation management practices
Figure 4. Number of four sea turtles nesting in each month during survey period: (A) L.olivacea, (B) C. mydas, (C) E. imbricata, and (D) D. coriacea.
Fig. 4 in Spatial genetic structure in the vulnerable smooth-coated otter (Lutrogale perspicillata, Mustelidae): towards an adaptive conservation management of the species
Fig. 4. Multidimensional Scaling (MDS) plot (stress: 0.0045) performed using average pairwise TN93 (Tamura & Nei, 1993) distances among investigated Lutrogale perspicillata groups created according to the country of origin of samples (modern + museum DNA and GenBank entries).
Fig. 3. A in Spatial genetic structure in the vulnerable smooth-coated otter (Lutrogale perspicillata, Mustelidae): towards an adaptive conservation management of the species
Fig. 3. A, Lutrogale perspicillata network computed using haplotypes (h) from the 305 bp-long sequence alignment (modern + museum DNA and GenBank entries). A scale to infer the number of sequences for each pie (i.e., haplotype) was provided together with a length bar to compute the number of mutational changes. The colour of each country and the number of each haplotype are indicated. See Table S1 for more details. B, Mismatch Distributions (MD) of the mtDNA pairwise differences (dotted: observed; line: expected) calculated for South East Asia haplogroup (Fig. 3A). Estimates of FS and R2 statistics (with related P values), r (raggedness index) and the outcome of SSD and SSD* test under a model (H0) of sudden demographic and spatial population expansion, respectively, are provided.
Fig. 2 in Spatial genetic structure in the vulnerable smooth-coated otter (Lutrogale perspicillata, Mustelidae): towards an adaptive conservation management of the species
Fig. 2. Photos of MNHN-ZM-MO-2001-350, L. p. perspicillata holotype resident in the mammal collection of the National Museum of Natural History of Paris, France. A, right side, lateral view (bar length = 20 cm); B, left forelimb, lateral view; C, basement, in French "Lutra perspicillata = Lutra leptonix Horsf., loutre de Java par m Diard, mai 1821, la tête est au lab d'anatomie", which can be translated into and interpreted as: "Lutra perspicillata = Lutra leptonix (Horsfield, 1824), Java otter from M. Diard, May 1821, skull is in the lab of anatomy" (see also Material and Methods). Photos courtesy and copyright: © MNHN - RECOLNAT - Laura Flamme - 2014.
Fig. 1 in Spatial genetic structure in the vulnerable smooth-coated otter (Lutrogale perspicillata, Mustelidae): towards an adaptive conservation management of the species
Fig. 1. Lutrogale perspicillata distribution (in yellow; see insets for Iraq and Pakistan) including sampling localities of modern (white circles) and museum (green squares) individuals. As far as the latter are concerned, we reported only sites for which samples were successfully investigated (see Table S1 for the entire sample size of this study; symbol "?" stands for unknown locality). The white stars indicate, in Iraq, the locality (TaqTaq, Kurdistan) where the sample of Omer et al. (2012) was collected, in Cambodia/Thailand and Malaysia, the country/ies of origin of EF472348 and KY117557 GenBank sequence, respectively. In Iraq, Pakistan, and supposedly Java, Indonesia, the green squares indicate localities (when known) of L. p. maxwelli, L. p. sindica, and L. p. perspicillata museum holotypes, respectively. Finally, Naga Hills at the border between Myanmar and India as well as Bahoo-Kalat River Basin between Iran and Pakistan are indicated (see text for more details). The species' geographic range was adapted from IUCN (International Union for Conservation of Nature) 2015. Lutrogale perspicillata. The IUCN Red List of Threatened Species 2019-3 was modified using CorelDraw!12 (2003). Digital images (insets) were obtained from Google Earth 7.1.5.1557 (2015 Google Inc.) and Google Earth map data (Data SIO, NOAA, U.S. Navy, NGA, GEBCO - Image Landsat). Please note that thick dotted lines mark out new borders for L. p. sindica and L. p. perspicillata subspecies as established in this study (see text for more details).
Data and code: Assessing fish-fishery dynamics from a spatially explicit metapopulation perspective reveals winners and losers in fisheries management
<ol> <li><span>Sustainable management of living resources must reconcile biodiversity conservation and socioeconomic viability of human activities. In the case of fisheries, sustainable management design is made challenging by the complex spatiotemporal interactions between fish and fisheries.</span></li> <li><span>We develop a comprehensive metapopulation framework integrating data on species life-history traits, connectivity and habitat distribution to identify priority areas for fishing regulation and assess how management impacts are spatially distributed. We trial this approach on European hake fisheries in the north-western Mediterranean, where we assess area-based management scenarios in terms of stock status and fishery productivity to prioritize areas for protection. </span></li> <li><span>Model simulations show that local fishery closures have the potential to enhance both spawning stock biomass and landings on a regional scale compared to a status quo scenario, but that improving protection is easier than increasing productivity. Moreover, the interaction between metapopulation dynamics and the redistribution of fishing effort following local closures implies that benefits and drawbacks are heterogeneously distributed in space, the former being concentrated in the proximity of the protected site. </span></li> <li><span>A network analysis shows that priority areas for protection are those with the highest connectivity (as expressed by network metrics) if the objective is to improve the spawning stock, while no significant relationship emerges between connectivity and potential for increased landings.</span></li> <li> <span><em>Synthesis and applications</em> – </span><span>Our framework provides a tool for 1) assessing area-based management measures aimed at improving fisheries outcomes in terms of both conservation and socioeconomic viability and 2) describing the spatial distribution of costs and benefits, which can help guide effective management and gain stakeholder support. Adult dispersal remains the main source of uncertainty that needs to be investigated to effectively apply our model to fisheries regulation.</span> </li> </ol>
Spatial variation in current and historical management of Arabica coffee across forests in its indigenous distribution
Open the record for dataset details and reuse information.
Data and code: Assessing fish-fishery dynamics from a spatially explicit metapopulation perspective reveals winners and losers in fisheries management
Open the record for dataset details and reuse information.
Tutorial for the 2022 ACM SIGMOD Conference: Spatial Data Quality in the IoT Era: Management and Exploitation
<p>Within the rapidly expanding Internet of Things (IoT), growing amounts of spatially referenced data are being generated. Due to the dynamic, decentralized, and heterogeneous nature of the IoT, spatial IoT data (SID) quality has attracted considerable attention in academia and industry. How to invent and use technologies for managing spatial data quality and exploiting low-quality spatial data are key challenges in the IoT. In this tutorial, we highlight the SID consumption requirements in applications and offer an overview of spatial data quality in the IoT setting. In addition, we review pertinent technologies for quality management and low-quality data exploitation, and we identify trends and future directions for quality-aware SID management and utilization. The tutorial aims to not only help researchers and practitioners to better comprehend SID quality challenges and solutions, but also offer insights that may enable innovative research and applications.</p>
Data from: The role of predation, forestry and productivity in moose harvest at different spatial levels of management units
<p>Management of ungulate populations to the desired density and/or demographic composition are challenged by contrasting aims of different stakeholders. For example, hunters may want to maximize hunting opportunities whereas commercial forest owners may want to minimize moose densities to mitigate browsing damage. In addition, the return of large predators such as wolves (<em>Canis lupus</em>) affects the possible harvest yield of ungulates and influences the population composition through their selection of specific age classes. The aim of this study was to gain a better understanding of factors related to the variation in moose (<em>Alces alces</em>) harvest. We used moose harvest statistics from the period 2012-2020, wolf annual monitoring data, annual brown bear (<em>Ursus arctos</em>) density, proportion of young forest per management unit, and proportion of agricultural land per management unit (index for productivity and roe deer (<em>Capreolus capreolus</em>) density) to explain variation in moose harvest across different management units at two spatial levels in two bordering countries, Sweden and Norway. The results showed variable responses in total harvest to changes in wolf territory density both at the regional and local management level. The proportion of young forest was correlated with both increased total harvest and proportion of calves. Increased proportion of agricultural land was linked to both increased total harvest and proportion of calves, likely due to that increased roe deer densities re-directed wolf predation from moose to roe deer, and an inverse relationship with brown bear density. Differences between countries may be due to differences in the management regime of moose, both in an historical and present perspective. Improved monitoring for individual hunting areas over time will be important for both the understanding of how different ungulate populations are affected by various factors and for the desired management of wildlife populations shared across borders.</p>
Fig. 1 in Multi-Criteria Decision Analysis as a tool to extract fishing footprints: application to small scale fisheries and implications for management in the context of the Maritime Spatial Planning Directive
Fig. 1: Map of the study area.
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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)
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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.