Find research datasets worth reusing

Fig. 5 in Ecomorphological patterns of the fish assemblage in a tropical floodplain: effects of trophic, spatial and phylogenetic structures

Fig. 5. Distribution of scores centroids of the 35 species grouped by habitat type on the first two axes of the Principal Components Analysis (PC 1 and PC 2), applied to the correlation matrix (Pearson) formed by 22 ecomorphological indices. Each polygon defines the morphological space occupied by the species that exploit the corresponding habitat type.

Fig. 3 in Ecomorphological patterns of the fish assemblage in a tropical floodplain: effects of trophic, spatial and phylogenetic structures

Fig. 3. Distribution of scores centroids of the 35 species on the first two axes of the Principal Components Analysis (PC 1 and PC 2), applied to the correlation matrix (Pearson) formed by 22 ecomorphological indices.

Fig. 2 in Ecomorphological patterns of the fish assemblage in a tropical floodplain: effects of trophic, spatial and phylogenetic structures

Fig. 2. Schematic representation of the linear morphometric measurements and the calculated areas: standard length (SL), maximum body height (MBH), body midline height (BMH), maximum body width (MBW), caudal peduncle length (CPdL), caudal peduncle height (CPdH), caudal peduncle width (CPdW), head length (HdL), head height (HdH), head width (HdW), length of snout with the mouth closed (LSC), length of snout with the mouth open (LSO), eye height (EH), mouth height (MH), mouth width (MW), dorsal fin length (DL), dorsal fin height (DH), caudal fin length (CL), caudal fin height (CH), anal fin length (AL), anal fin height (AH), pectoral fin length (PtL), pectoral fin height (PtH), pelvic fin length (PvL), pelvic fin height (PvH), eye area (EA), dorsal fin area (DA), caudal fin area (CA), anal fin area (AA), pectoral fin area (PtA), and pelvic fin area (PvA).

Fig. 1 in Ecomorphological patterns of the fish assemblage in a tropical floodplain: effects of trophic, spatial and phylogenetic structures

Fig. 1. Study area with sampling stations in the upper Paraná River floodplain: rivers: Paraná (1), Baía (2) and Ivinheima (3); channels: Cortado (4), Curutuba (5) and Ipoitã (6); connected lagoons: Garças (7), Guaraná (8) and Finado Raimundo (9); disconnected lagoons: Fechada (10), Ventura (11) and Zé do Paco (12).

opencc-zeroNov 2022View details →

dryad40/100

Code and data for: Emergence of spatially structured populations by area-concentrated search

The idea that populations are spatially structured has become a very powerful concept in ecology, raising interest in many research areas. However, despite dispersal being a core component of the concept, it typically does not consider the movement behavior underlying any dispersal. Using individual-based simulations in continuous space, we investigate the emergence of a spatially structured population in landscapes with spatially heterogeneous resource distribution and with organisms following simple area-concentrated search (ACS); individuals do not, however, perceive or respond to any habitat attributes per se but only to their foraging success. We investigated effects of different resource clustering patterns in landscapes (single large cluster vs. many small clusters) and different resource densities on spatial structure of populations and movement between resource clusters of individuals. As the results, we found that foraging success increased with increasing resource density and decreasing number of resource clusters. In a wide parameter space, the system exhibited attributes of a spatially structured population with individuals concentrated in areas of high resource density, searching within areas of resources, and 'dispersing' in a straight line between resource patches. 'Emigration' was more likely from patches that were small or of low quality (low resource density), but we observed an interaction effect between these two parameters. With the ACS implemented, individuals tended to move deeper into a resource cluster in scenarios with moderate resource density than in scenarios with high resource density. 'Looping' from patches was more likely if patches were large and of high quality. Our simulations demonstrate that spatial structure in populations may emerge if critical resources are heterogeneously distributed and if individuals follow simple movement rules (such as ACS). Neither the perception of habitat nor an explicit decision to emigrate from a patch on the side of acting individuals is necessary for the emergence of spatial structure.

Figure 4 in The spatial structure of а snow leopard population (Panthera uncia, Felidae, Carnivora) in east Kyrgyzstan

Figure 4. Relationship between the males of the Sarychat-Ertash Reserve (as inferred from the DNA microsatellite profiles).

Figure 3 in The spatial structure of а snow leopard population (Panthera uncia, Felidae, Carnivora) in east Kyrgyzstan

Figure 3. Relationship between the females of the Sarychat–Ertash Reserve (as inferred from the DNA microsatellite profiles).

Figure 2. Sections 1–3 in The spatial structure of а snow leopard population (Panthera uncia, Felidae, Carnivora) in east Kyrgyzstan

Figure 2. Sections 1–3 of different intensity of marking activity of the snow leopard. For a description, see text (polygons A, B, C).

Figure 1 in The spatial structure of а snow leopard population (Panthera uncia, Felidae, Carnivora) in east Kyrgyzstan

Figure 1. Areas of snow leopard study in the East Kyrgyzstan. Blue squares-surveyed areas; red points, traces of snow leopard activity.

figure 1 in Spatial genetic structure in the Eurasian otter (Lutra lutra) meta-population from its core range in Italy

figure 1 The area surveyed for collection of otter samples (40° 40' N, 39° 37' N). Red spots indicate the location of the collected samples. The blue lines highlight the main rivers (order 1) and their tributaries (order 2, 3 and 4 according to waterway hierarchy). The continuous red lines represent regional boundaries. In the inset, the current otter distribution (inferred from Balestrieri et al., 2016, modified) is reported in orange and the study area is defined by the black bold square.

figure 4 in Spatial genetic structure in the Eurasian otter (Lutra lutra) meta-population from its core range in Italy

figure 4 Principal Component Analysis (pca) performed on microsatellite genotypes (dots). Circles show the well-defined spatial groups. A) pca according to the belonging of genotypes to the six river basins: the Cilento basin (green dots); the Agri basin (pink dots); the Sinni basin (blue dots); the Lao basin (red dots); the Basento basin (orange dots); the Abatemarco basin (violet dots); black dots indicate the samples outside of the main river basins. Dashed line indicates geographically contiguous but genetically different genotypes. B) pca according to clusters inferred by STRUCTURE: genotypes assigned unambiguously to K2 (green dots), to K3 (yellow dots), to K5 (violet dots). Grey dots represent samples with mixed genotypes assignable to K1 and K4.

figure 3 in Spatial genetic structure in the Eurasian otter (Lutra lutra) meta-population from its core range in Italy

figure 3 Genetic structure and distribution of the Italian otter genotypes in the study area. A) Estimated population structure based on the analysis of 11 microsatellite loci according to STRUCTURE (K = 5). Each bar represents a sample analysed. B) Geographic visualisation of genotypes in the study area performed using QGIS 3.4.1 software with base layers acquired from http://www.pnc.miniambiente. it/. Each circle represents a sample analysed. The colours indicate the percentage of assignment of an individual to each cluster: in blue, K1; in green, K2; in orange, K3; in red, K4; in violet, K5. The bold blue lines highlight the main rivers, while the tiny blue lines show all other waterways.

figure 6 Mantel test for A in Spatial genetic structure in the Eurasian otter (Lutra lutra) meta-population from its core range in Italy

figure 6 Mantel test for A) the correlation between geographic distance (GGDsq) and genetic distance (LinGD) (Rxy = 0.264, P = 0.0001) and for B) the correlation between resistance distance (a measure of ecological distance) (ECO500) and LinGD (Rxy = 0.217, P = 0.0001).