Find research datasets worth reusing

opencc-by-4.0Apr 2018View details →

Figure 1 in Analysis of the spatial organization of Vallonia pulchella (Muller, 1774) ecological niche in Technosols (Nikopol manganese ore basin, Ukraine)

Figure 1. Research Centre of the Dnipro Agrarian and Economic University in Pokrov (Ukraine). A – satellite image of the study area (1 – reclaimed land; 2 – mining quarry); B – technosoils profile; C – quarry panorama view.

opencc-by-4.0Jul 2024View details →

Raw data for: Spatial and temporal variation in farmland bird nesting ecology: Implications for effective Corn Bunting Emberiza calandra conservation

These are raw data accompanying the study "Spatial and temporal variation in farmland bird nesting ecology: Implications for effective Corn Bunting Emberiza calandra conservation". All information on data origin, data analysis, and derived implications will be available with the original publiation.

Fig. 4 in Ontogenetic, spatial and temporal variations in the feeding ecology of Deuterodon langei Travassos, 1957 (Teleostei: Characidae) in a Neotropical stream from the Atlantic rainforest, southern Brazil

Fig. 4. MDS on the different sizes, sites and seasonal independent groups. The total length (Lt) categories compared were Juveniles (J = Lt <3 cm), Semi-adults (S = 3 8 cm). The three sites sampled along the catchment were: site 1 (P1), an upstream first order section of the basin near its spring; site 2 (P2), a second order middle section of the basin; and site 3 (P3), a downstream third order section of the basin. Seasons compared were: VER=summer (December, January and February), OUT=Autumn (March, April and May), INV=Winter (June, July andAugust) and Spring=PRI (September, October and November).

Fig. 5 in Ontogenetic, spatial and temporal variations in the feeding ecology of Deuterodon langei Travassos, 1957 (Teleostei: Characidae) in a Neotropical stream from the Atlantic rainforest, southern Brazil

Fig. 5. MDS on the different sizes, sites and seasonal groups. Total length (Lt) categories compared were Juveniles (J = Lt <3 cm), Semi-adults(S = 3 8 cm) and seasons were: VER=summer (December, January and February), OUT=Autumn (March, April and May), INV=Winter (June, July andAugust) and Spring=PRI (September, October and November).

Fig. 2 in Ontogenetic, spatial and temporal variations in the feeding ecology of Deuterodon langei Travassos, 1957 (Teleostei: Characidae) in a Neotropical stream from the Atlantic rainforest, southern Brazil

Fig. 2. Geographical location of the rio Ribeirão basin, with indication of the three sampling sites (P1, P2, and P3).

Fig. 3 in Ontogenetic, spatial and temporal variations in the feeding ecology of Deuterodon langei Travassos, 1957 (Teleostei: Characidae) in a Neotropical stream from the Atlantic rainforest, southern Brazil

Fig. 3. UPGMA cluster analysis using Morisita-Horn similarity coefficient on the different sizes, sites and seasonal groups. Total length (Lt) categories compared were Juveniles (J = Lt <3 cm), Semi-adults (S = 3 8 cm). The three sites sampled along the catchment were: site 1 (P1), an upstream first order section of the basin near its spring; site 2 (P2), a second order middle section of the basin; and site 3 (P3), a downstream third order section of the basin. The seasons are: VER=summer (December, January and February), OUT=Autumn (March, April and May), INV=Winter (June, July and August), and Spring=PRI (September, October, November). A (> 70 % similarity) = with a predominance of Autochthonous Insects, Allochthonous Insects and Non Identifiable Arthropod Parts; B (> 80 % similarity) = with a predominance of Detritus and Allochthonous and Autochthonous Insects; and C (> 80 % similarity)= with a predominance of Allochthonous plants and Detritus.

Fig. 1. Monthly rainfall for 2002 in Ontogenetic, spatial and temporal variations in the feeding ecology of Deuterodon langei Travassos, 1957 (Teleostei: Characidae) in a Neotropical stream from the Atlantic rainforest, southern Brazil

Fig. 1. Monthly rainfall for 2002 (x) and 2003 (filled triangles) in the study region, and average temperature in 2002 (filled squares) and 2003 (open squares). Source: IAPAR.

opencc-by-4.0Aug 2013View details →

Fig. 2 in Insights Into The Spatial And Temporal Ecology Of The Sunda Clouded Leopard Neofelis Diardi

Fig. 2. Frequency distribution of hourly activity for Sunda clouded leopard based on 135 independent photo-captures.

opencc-by-4.0Aug 2013View details →

Fig. 1 in Insights Into The Spatial And Temporal Ecology Of The Sunda Clouded Leopard Neofelis Diardi

Fig. 1. Study site, showing the home-range of the Sunda clouded leopard derived from radio telemetry.

opencc-zeroSep 2021View details →

Data from: Fractal triads efficiently sample ecological diversity and processes across spatial scales

The relative influence of ecological assembly processes, such as environmental filtering, competition, and dispersal, vary across spatial scales. Changes in phylogenetic and taxonomic diversity across environments provide insight into these processes, however, it is challenging to assess the effect of spatial scale on these metrics. Here, we outline a nested sampling design that fractally spaces sampling locations to concentrate statistical power across spatial scales in a study area. We test this design in northeast Utah, at a study site with distinct vegetation types (including sagebrush steppe and mixed conifer forest), that vary across environmental gradients. We demonstrate the power of this design to detect changes in community phylogenetic diversity across environmental gradients and assess the spatial scale at which the sampling design captures the most variation in empirical data. We find clear evidence of broad-scale changes in multiple features of phylogenetic and taxonomic diversity across aspect. At finer scales, we find additional variation in phylodiversity, highlighting the power of our fractal sampling design to efficiently detect patterns across multiple spatial scales. Thus, our fractal sampling design and analysis effectively identify important environmental gradients and spatial scales that drive community phylogenetic structure. We discuss the insights this gives us into the ecological assembly processes that differentiate plant communities found in northeast Utah.

opencc-zeroOct 2021View details →

Species ecology explains the various spatial components of genetic diversity in tropical reef fishes

Generating genomic data for 19 tropical reef fish species of the Western Indian Ocean, we investigate how species ecology influences genetic diver- sity patterns from local to regional scales. We distinguish between the α, β and γ components of genetic diversity, which we subsequently link to six ecological traits. We find that the α and γ components of genetic diversity are strongly correlated so that species with a high total regional genetic diversity display systematically high local diversity. The α and γ diversity components are negatively associated with species abundance recorded using underwater visual surveys and positively with body size. Pelagic larval duration is found to be negatively related to genetic β diversity supporting its role as a dispersal trait in marine fishes. Deviation from the neutral theory of molecular evolution motivates further effort to understand the processes shaping genetic diversity and ultimately the diversification of the exceptional diversity of tropical reef fishes.

opencc-by-4.0Feb 2023View details →

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-zeroMar 2023View details →

Data from: The spatial patterns of community composition, their environmental drivers and their spatial scale dependence vary markedly between fungal ecological guilds

Aim How community composition varies in space and what governs the variation has been extensively investigated in macroorganisms. However, we have only limited knowledge for microorganisms, especially fungi, despite their ecological and economic significance. Based on previous research, we define and test a series of hypotheses regarding the composition of fungal communities, its most influential drivers and their spatial scale dependence. Location Czech Republic. Time period Present. Taxa studied Fungi. Methods We analyzed the distance decay relationships, community composition and its drivers (physical distance, litter and soil chemistry, tree composition, climate) in fungi, using multivariate analyses. We compared the results across three fungal ecological guilds (ectomycorrhizal fungi, saprotrophs and yeasts), two forest microhabitats (litter and bulk soil) and six spatial scales (from 5 m to 80 km) that comprehensively cover the Czech Republic. Results We found that, similar to macroorganisms, the ectomycorrhizal fungi and saprotrophs showed marked distance-decay relationships, and their community composition was driven mainly by vegetation and dispersal at local scales, but at regional scales, by environmental effects. In contrast, the third fungal guild, the unicellular yeasts, showed little distance decay, suggesting extraordinary spatial homogeneity, as often seen in microorganisms, such as bacteria. Main conclusions Our results underscore the remarkable variation in the community ecology of fungi, which seems to range well-known patterns both from the macro- and the microworld. Knowledge of these patterns advances our understanding of the ecology of fungi, rather understudied organisms of significant ecological and economic importance, which our findings identify as a potentially suitable model for bridging the gaps between the biogeography of micro- and macroorganisms.