Find research datasets worth reusing

Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.

645

datasets available to search

ShareScore release 0.7.1

Dataset results

645 results for “spatial distribution”

Learn how ShareScore rates datasets ↗

zenodo40/100

Dataset for: 'Patterns in the Plankton – Spatial distribution and long-term variability of copepods on the Agulhas Bank'

This dataset contains environmental data (in situ temperature and chlorophyll a) and integrated biomass (mg C m-2) data for a number of copepod taxa, as well as total copepod biomass and abundance, on the Agulhas Bank, South Africa, as predicted by a Generalized Additive Model (GAM), during late austral spring (October-December) from 1988 to 2011. Mean environmental and copepod biomass parameters for each area and year are also provided. Relevant information on sampling and statistical analysis of spatial distributions has been extracted from the paper. Please see paper for full details and figures, including supplementary data; <a href="https://doi.org/10.1016/j.dsr2.2023.105265">https://doi.org/10.1016/j.dsr2.2023.105265</a>. Please see the Word document Huggett_et_al_2023_README.docx for a list of the data files and descriptions of the contents.

opencc-by-4.0Jan 2023View details →

zenodo40/100

Codes in R for spatial statistics analysis, ecological response models and spatial distribution models

In the last decade, a plethora of algorithms have been developed for spatial ecology studies. In our case, we use some of these codes for underwater research work in applied ecology analysis of threatened endemic fishes and their natural habitat. For this, we developed codes in Rstudio® script environment to run spatial and statistical analyses for ecological response and spatial distribution models (e.g., Hijmans & Elith, 2017; Den Burg et al., 2020). The employed R packages are as follows: caret (Kuhn et al., 2020), corrplot (Wei & Simko, 2017), devtools (Wickham, 2015), dismo (Hijmans & Elith, 2017), gbm (Freund & Schapire, 1997; Friedman, 2002), ggplot2 (Wickham et al., 2019), lattice (Sarkar, 2008), lattice (Musa & Mansor, 2021), maptools (Hijmans & Elith, 2017), modelmetrics (Hvitfeldt & Silge, 2021), pander (Wickham, 2015), plyr (Wickham & Wickham, 2015), pROC (Robin et al., 2011), raster (Hijmans & Elith, 2017), RColorBrewer (Neuwirth, 2014), Rcpp (Eddelbeuttel & Balamura, 2018), rgdal (Verzani, 2011), sdm (Naimi & Araujo, 2016), sf (e.g., Zainuddin, 2023), sp (Pebesma, 2020) and usethis (Gladstone, 2022). It is important to follow all the codes in order to obtain results from the ecological response and spatial distribution models. In particular, for the ecological scenario, we selected the Generalized Linear Model (GLM) and for the geographic scenario we selected DOMAIN, also known as Gower's metric (Carpenter et al., 1993). We selected this regression method and this distance similarity metric because of its adequacy and robustness for studies with endemic or threatened species (e.g., Naoki et al., 2006). Next, we explain the statistical parameterization for the codes immersed in the GLM and DOMAIN running: In the first instance, we generated the background points and extracted the values of the variables (<a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code2_Extract_values_DWp_SC.R?versionId=c1ea0c61-53fe-4f95-ab88-0c1cb28399cb">Code2_Extract_values_DWp_SC.R</a>). Barbet-Massin et al. (2012) recommend the use of 10,000 background points when using regression methods (e.g., Generalized Linear Model) or distance-based models (e.g., DOMAIN). However, we considered important some factors such as the extent of the area and the type of study species for the correct selection of the number of points (Pers. Obs.).  Then, we extracted the values of predictor variables (e.g., bioclimatic, topographic, demographic, habitat) in function of presence and background points (e.g., Hijmans and Elith, 2017). Subsequently, we subdivide both the presence and background point groups into 75% training data and 25% test data, each group, following the method of Soberón & Nakamura (2009) and Hijmans & Elith (2017). For a training control, the 10-fold (cross-validation) method is selected, where the response variable presence is assigned as a factor. In case that some other variable would be important for the study species, it should also be assigned as a factor (Kim, 2009). After that, we ran the code for the GBM method (Gradient Boost Machine; <a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code3_GBM_Relative_contribution.R?versionId=1656bbae-66aa-409e-bb91-d8007dee8f95">Code3_GBM_Relative_contribution.R</a> and <a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code4_Relative_contribution.R?versionId=0e1d9352-e6b2-43da-984b-d6853a914258">Code4_Relative_contribution.R</a>), where we obtained the relative contribution of the variables used in the model. We parameterized the code with a Gaussian distribution and cross iteration of 5,000 repetitions (e.g., Friedman, 2002; kim, 2009; Hijmans and Elith, 2017). In addition, we considered selecting a validation interval of 4 random training points (Personal test). The obtained plots were the partial dependence blocks, in function of each predictor variable. Subsequently, the correlation of the variables is run by Pearson's method (<a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code5_Pearson_Correlation.R?versionId=275f8dd4-b056-44d2-bfe5-f6264bc3298b">Code5_Pearson_Correlation.R</a>) to evaluate multicollinearity between variables (Guisan & Hofer, 2003). It is recommended to consider a bivariate correlation ± 0.70 to discard highly correlated variables (e.g., Awan et al., 2021). Once the above codes were run, we uploaded the same subgroups (i.e., presence and background groups with 75% training and 25% testing) (<a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code6_Presence&backgrounds.R?versionId=d797b528-782f-4a19-bd61-cfb197f38513">Code6_Presence&backgrounds.R</a>) for the GLM method code (<a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code7_GLM_model.R?versionId=e4aca276-d601-49ec-a62c-a9223b05a7ed">Code7_GLM_model.R</a>). Here, we first ran the GLM models per variable to obtain the p-significance value of each variable (alpha ≤ 0.05); we selected the value one (i.e., presence) as the likelihood factor. The generated models are of polynomial degree to obtain linear and quadratic response (e.g., Fielding and Bell, 1997; Allouche et al., 2006). From these results, we ran ecological response curve models, where the resulting plots included the probability of occurrence and values for continuous variables or categories for discrete variables. The points of the presence and background training group are also included. On the other hand, a global GLM was also run, from which the generalized model is evaluated by means of a 2 x 2 contingency matrix, including both observed and predicted records. A representation of this is shown in Table 1 (adapted from Allouche et al., 2006). In this process we select an arbitrary boundary of 0.5 to obtain better modeling performance and avoid high percentage of bias in type I (omission) or II (commission) errors (e.g., Carpenter et al., 1993; Fielding and Bell, 1997; Allouche et al., 2006; Kim, 2009; Hijmans and Elith, 2017). Table 1. Example of 2 x 2 contingency matrix for calculating performance metrics for GLM models. A represents true presence records (true positives), B represents false presence records (false positives - error of commission), C represents true background points (true negatives) and D represents false backgrounds (false negatives - errors of omission). <table align="center"> <tbody> <tr> <td>                                      </td> <td> Validation set </td> </tr> <tr> <td> Model </td> <td> True </td> <td> False </td> </tr> <tr> <td> Presence </td> <td> A </td> <td> B </td> </tr> <tr> <td> Background </td> <td> C </td> <td> D </td> </tr> </tbody> </table> We then calculated the Overall and True Skill Statistics (TSS) metrics. The first is used to assess the proportion of correctly predicted cases, while the second metric assesses the prevalence of correctly predicted cases (Olden and Jackson, 2002). This metric also gives equal importance to the prevalence of presence prediction as to the random performance correction (Fielding and Bell, 1997; Allouche et al., 2006). The last code (i.e., <a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code8_DOMAIN_SuitHab_model.R?versionId=d951a8f2-d3a4-4804-b862-1b2762061876">Code8_DOMAIN_SuitHab_model.R</a>) is for species distribution modelling using the DOMAIN algorithm (Carpenter et al., 1993). Here, we loaded the variable stack and the presence and background group subdivided into 75% training and 25% test, each. We only included the presence training subset and the predictor variables stack in the calculation of the DOMAIN metric, as well as in the evaluation and validation of the model. Regarding the model evaluation and estimation, we selected the following estimators: 1) partial ROC, which evaluates the approach between the curves of positive (i.e., correctly predicted presence) and negative (i.e., correctly predicted absence) cases. As farther apart these curves are, the model has a better prediction performance for the correct spatial distribution of the species (Manzanilla-Quiñones, 2020). 2) ROC/AUC curve for model validation, where an optimal performance threshold is estimated to have an expected confidence of 75% to 99% probability (De Long et al., 1988).

opencc-by-4.0Feb 2023View details →

zenodo40/100

FIGURE 1 in RANDOM SPATIAL DISTRIBUTION OF SCHISTOSOMA MANSONI AND HOOKWORM INFECTIONS AMONG SCHOOL CHILDREN WITHIN A SINGLE VILLAGE

FIGURE 1. Spatial distribution of different infection intensity levels of Schistosoma mansoni among school children living in the village of Fagnampleu, western Côte d'Ivoire.

opencc-by-4.0Aug 2003View details →

zenodo40/100

FIGURE 2 in RANDOM SPATIAL DISTRIBUTION OF SCHISTOSOMA MANSONI AND HOOKWORM INFECTIONS AMONG SCHOOL CHILDREN WITHIN A SINGLE VILLAGE

FIGURE 2. Spatial distribution of different infection intensity levels of hookworm among school children living in the village of Fagnampleu, western Côte d'Ivoire.

opencc-by-4.0Aug 2003View details →

zenodo40/100

Mars Watershed boundary data for "Global Spatial Distribution of Hack's Law Exponent on Mars Consistent with Early Arid Climate "

This is the watershed boundary data for Mars (along with Hack's Law exponent) as described in Luo et al. "Global Spatial Distribution of Hack’s Law Exponent on Mars Consistent with Early Arid Climate" accepted for publication in Geophysical Research Letters on 3/10/2023. The attributes are as follows: Id, gridcode = ID of basin Shape_Length = perimeter of the basin Shape_area = area of the basin geoArea = geodesic area of the basin geoLength = geodesic perimeter of the basin areaR = geoArea / Shape_area LengthR = geoLength / Shape_Length n_exponent = Hack’s Law Exponent (h in L = k A^h) n_coefficient = Hack’s Law exponent  (k in L = k A^h) n_r2 = r^2 of the nonlinear fit (optimize.curve_fit function in SciPy)

opencc-by-4.0Mar 2023View details →

dryad40/100

Niche suitability and spatial distribution patterns of anurans in a unique Ecoregion mosaic of Northern Pakistan

The lack of information regarding biodiversity states hampers designing and implementation conservation strategies and future targets. Northern Pakistan consists of a unique ecoregion mosaic which supports a myriad of environmental niches for anuran diversity to flourish in comparison to the deserts and xeric shrublands throughout the rest of the country. In order to study the niche suitability, overlap and distribution patterns in Pakistan, we collected observational data for nine amphibian species across several distinct ecoregions by surveying 87 randomly selected locations from 2016 to 2018 in District Rawalpindi and Islamabad Capital Territory. Our model showed that the precipitation of the warmest and coldest quarter, distance to rivers and vegetation were the greatest drivers of anuran distribution, expectedly indicating that the presence of humid forests and proximity to waterways greatly influences the habitable range of anurans in Pakistan. Sympatric overlap between species occurred at significantly higher density in tropical and subtropical coniferous forests than in other ecoregion types. We found species such as Minervarya spp., Hoplobatrachus tigerinus and Euphlyctis spp. showed preference for the lowlands in proximal, central and southern parts of the study area proximal to urban settlements, little vegetation and higher average temperatures. The toads Duttaphrynus bengalensis and D. stomaticus had scattered distributions throughout the study area with no clear preference for elevation. Sphaerotheca pashchima showed a patchy distribution in the midwestern extent of the study area as well as the foothills to the north. Microhyla nilphamariensis also showed a wide distribution throughout the study area with a preference for both lowlands and montane terrain. Endemic frogs (Nanorana vicina and Allopaa hazarensis) were observed only in locations with higher elevations, higher density of streams and lower average temperatures as compared to the other seven species sampled.

opencc-zeroJun 2023View details →

zenodo40/100

Global 10-m spatial distribution of Water-surface photovoltaics (2019-2021)

The recent boom in solar photovoltaics has intensified global competition for land use. Water-surface photovoltaics (WSPV) has also increased globally as an efficient alternative to land-based photovoltaics. Determining the spatio-temporally distribution of WSPVs is essential for estimating renewable energy capacity, evaluating the associated socio-environmental impacts, and managing and planning WSPV projects. However, a comprehensive inventory of WSPV locations and extent on the global scale is still lacking. To address these issues, we developed a workflow for identifying WSPVs using time-series optical satellite images and generated the first global-scale WSPV inventory map, with overall accuracy exceeding 96%.

opencc-by-4.0Aug 2023View details →

zenodo40/100

Fig. 6 in Ground beetles (Coleoptera: Carabidae) from the region of Cape Emine (central Bulgarian Black sea coast). Part III. Spatial distribution and gradient analysis

Fig. 6. PCA distribution of the sampling sites and the subclasses of life forms (according to Sharova 1981): Z_Phytob – Zoophagous phytobionts; Z_Strat – Zoophagous stratobionts; M_Strat – Mixophytophagous stratobionts; M_Short – Mixophytophagous stratohortobionts; M_Geoh – Mixophytophagous geobionts.

opencc-by-4.0Aug 2015View details →

zenodo40/100

Fig. 7 in Ground beetles (Coleoptera: Carabidae) from the region of Cape Emine (central Bulgarian Black sea coast). Part III. Spatial distribution and gradient analysis

Fig. 7. PCA distribution of the sampling sites and categories of life forms (according to SHAROVA 1981): Life form class 1. Zoophagous. Life form subclass: 1.1 – Phytobios; 1.2 – Epigeobios; 1.3 – Stratobios; 1.4 – Geobios. Life form groups: 1.1.2 – stemdwelling hortobionts; 1.1.3 – leaf-dwelling dendrohortobionts; 1.2.2 – large walking epigeobionts; 1.2.2(1) – large walking dendroepigeobionts; 1.2.3 – running epigeobionts; 1.2.4 – flying epigeobionts; 1.3(1) – series crevice-dwelling stratobionts; 1.3(1).1 – surface & litter-dwelling; 1.3(1).2 – litter-dwelling; 1.3(1).3 – litter & crevice-dwelling; 1.3(1).4 – endogeobionts; 1.3(1).5 – litter & bark-dwelling; 1.3(1).6 – bothrobionts; 1.3(2).1 – litter & soil-dwelling; 1.4.2(1) – large digging geobionts. Life form class 2. Mixophytophagous. Life form subclass: 2.1 – Stratobios; 2.2 – Stratohortobios; 2.3 – Geohortobios. Life form groups: 2.1.1 – crevice-dwelling stratobionts; 2.2.1 – stratohortobionts; 2.3.1 – harpaloid geohortobionts; 2.3.1(1) – crevice-dwelling harpaloid geohortobionts; 2.3.2 – zabroid geohortobionts; 2.3.3 – dytomeoid geohortobionts.

opencc-by-4.0Aug 2015View details →

zenodo40/100

Fig. 4 in Ground beetles (Coleoptera: Carabidae) from the region of Cape Emine (central Bulgarian Black sea coast). Part III. Spatial distribution and gradient analysis

Fig. 4. Ordination of the sampling sites in relation to the humidity and vegetation. The calculations were performed by the use of the results from all of the sampling sites and all of the catches, standardized through the recalculation of the data as number of specimens per 100 trapdays.

opencc-by-4.0Aug 2015View details →

zenodo40/100

Fig. 3 in Ground beetles (Coleoptera: Carabidae) from the region of Cape Emine (central Bulgarian Black sea coast). Part III. Spatial distribution and gradient analysis

Fig. 3. Distribution of the permanent species in relation to the humidity and vegetation. The analysis included only the permanent species – those with a frequency above 50% (see TEOFILOVA 2015): A aenea – Amara aenea; A anth – Amara anthobia; A famil – Amara familiaris; Ac megac – Acinopus megacephalus; Agon sp – Agonum (Europhilus) sp.; Br crep – Brachinus crepitans; Br expl – Brachinus explodens; C ambig – Calathus ambiguus; C cinct – Calathus cinctus; C fuscip – Calathus fuscipes; Car conv – Carabus convexus; Car cor – Carabus coriaceus; Car ullr – Carabus ullrichi; Chl nit – Chlaenius nitidulus; H dimid – Harpalus dimidiatus; H dist – Harpalus distinguendus; H flavic – Harpalus flavicornis; H rubrip – Harpalus rubripes; H tardus – Harpalus tardus; Laem ter – Laemostenus terricola; Lei ruf – Leistus rufomarginatus; M maurus – Microlestes maurus; M minut – Microlestes minutulus; Myas ch – Myas chalybaeus; N brevic – Nebria brevicollis; O azur – Ophonus azureus; Par mend – Parophonus mendax; Ps rufip – Pseudoophonus rufipes; Pt melas – Pterostichus melas; Tr q – Trechus quadristriatus.

opencc-by-4.0Aug 2015View details →

dryad40/100