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opennotspecifiedJan 2020View details →

FIG. 2 in Nest-Site Fidelity and Sex-Biased Dispersal Affect Spatial Genetic Structure of Eastern Box Turtles (Terrapene carolina carolina) at Their Northern Range Edge

FIG. 2. Spatial genetic autocorrelograms of genetic correlation coefficients (r) as a function of distance for Eastern Box Turtles in northwestern Michigan. Plots represent (A) all individuals (n ¼ 165), (B) females only (n ¼ 104), and (C) males only (n ¼ 51). Dashed lines are permuted 95% confidence intervals across all data, and error bars are bootstrapped 95% confidence intervals within each distance class. Tables below graphs represent data for each distance class including the number of pairwise comparisons (n), the correlation coefficients (r), and the P-values (p) associated with bootstrap tests of significance for positive spatial genetic autocorrelation.

opennotspecifiedJan 2020View details →

FIG. 1 in Nest-Site Fidelity and Sex-Biased Dispersal Affect Spatial Genetic Structure of Eastern Box Turtles (Terrapene carolina carolina) at Their Northern Range Edge

FIG. 1. Scatterplot showing the matrix of pairwise genetic distances and matrix of pairwise geographic distances for box turtles sampled along the river corridor. Warmer colors within the kernel density indicate higher densities of points. The line (slope ¼ 1.074727e–05; R2 ¼ 0.002992) shows the correlation trend.

openOct 2024View details →

Dataset and code for A Multidimensional Error Verification Method for Weather Forecast Based on the Spatial Distribution Structural Similarity of Meteorological Elements

The dataset includes rainfall observations and mesoscale numerical prediction data for 16 July 2019, 13 June 2020 and 2024 rainy season. The code is the algorithm procedure of this paper. Among them, meiyutest.py is the rainfall scoring procedure for the Meiyu period, and 2019case is the forecast scoring and plotting procedure for two individual cases.

opencc-by-4.0Oct 2024View details →

Hollows on Mercury: A Comprehensive Analysis of Spatial Patterns and Their Relationship to Craters and Structures

Supporting Material Content   The raw data collected and produced in this paper are shown in the tables provided as supplementary information to the main text of the article. Specifically, the contents of each table are as follows:   1-    Matrix 1 This table shows the Boolean matrix in which all the data collected for each distinctive trait (header descriptions are reported in Table 1 in the main text) for each hollow location are collected. In Matrix 1 and 2, the ID progressive numbering used in Thomas et al., (2014a) have been maintained. When a new location was added to the list we used the same Id number of the closest identified location by Thomas et al., (2014a). For further clarity an univocal new progressive numbering has been assigned to each location. In addition, (i) the coordinates of the centroid of the mapped polygon for each location (latitude and longitude are provided in decimal degrees) and (ii) the automatically extracted minimum, maximum and mean elevations are given for each polygon. 2-    Matrix 2 This table shows the Boolean matrix in which the occurrences of degradation classes and geologic units are collected for all those hollows contained within craters. These data are reported both as single column cumulative data (e.g., for each location, when available, the degradation class code is reported) and as Boolean matrix. When data are not available for the given location the cells have been left empty. Crater diameters are also reported along with elevations related to crater morphologies. 3-    Matrix 3 This table shows the matrix that collects the results of equations 1, 2 (tab P) and 3 (tab I), described in the methods section, for the entire population of hollows. The data herein reported are the machine-readable version of the data reported in Table 2 in the main text. 4-    Matrix 4 This table shows the matrix that collects the results of equations 1, 2 (tab P) and 3 (tab I), described in the methods section, for the population of hollows contained within craters. This dataset also includes the results of the above equations by taking into account parameters such as degradation classes and geological units (names reported in the headers correspond to the ones used in Matrix 2 which are taken from geological mapping literature. The full literature list can be found in the main text in the methods section).   In addition to these tables, we also provided the GIS-ready shapefile containing all the polygons showing the areas where the hollows were observed, the attributes are the same as those included in Matrix 1.

opencc-zeroDec 2014View details →

Data from: Non-equilibrium conditions explain spatial variability in genetic structuring of little penguin (Eudyptula minor)

Factors responsible for spatial structuring of population genetic variation are varied, and in many instances there may be no obvious explanations for genetic structuring observed, or those invoked may reflect spurious correlations. A study of little penguins (Eudyptula minor) in southeast Australia documented low spatial structuring of genetic variation with the exception of colonies at the western limit of sampling, and this distinction was attributed to an intervening oceanographic feature (Bonney Upwelling), differences in breeding phenology, or sea level change. Here, we conducted sampling across the entire Australian range, employing additional markers (12 microsatellites and mitochondrial DNA, 697 individuals, 17 colonies). The zone of elevated genetic structuring previously observed actually represents the eastern half of a genetic cline, within which structuring exists over much shorter spatial scales than elsewhere. Colonies separated by as little as 27 km in the zone are genetically distinguishable, while outside the zone, homogeneity cannot be rejected at scales of up to 1400 km. Given a lack of additional physical or environmental barriers to gene flow, the zone of elevated genetic structuring may reflect secondary contact of lineages (with or without selection against interbreeding), or recent colonization and expansion from this region. This study highlights the importance of sampling scale to reveal the cause of genetic structuring.

opencc-zeroDec 2017View details →

Data from: Impact of habitat fragmentation on the spatial structure of the Eastern Arc Forests in East Africa: implications for biodiversity conservation

The Eastern Arc Mountains in Tanzania and Kenya are one of 35 global biodiversity hotspots. The Eastern Arc forests are, as are many other tropical biodiversity hotspots, highly fragmented. Understanding the impact of habitat fragmentation (i.e., habitat loss and subdivision) on the spatial structure of the Eastern Arc forests is important because forest spatial structure highly influences species richness, persistence, and extinction debt. Here we examine the impact of habitat fragmentation on the spatial structure of the Eastern Arc forests at a patch scale using very high resolution aerial imagery having a spatial resolution of 0.5–1.5 m. Forest area across the 13 Eastern Arc Mountains is 405,852 ha and is distributed into 311 fragments ≥ 10 ha in size with a median fragment size of 84 ha. The 18 largest forest fragments in the Eastern Arc Mountains contain greater than three-quarters of total forest area. Average fragment isolation, as assessed by median distance to nearest fragment and median distance to the nearest larger fragment, is 867 and 1533 m, respectively. Of total forest area, 14% is < 100 m from the forest edge and 33% is < 300 m from the forest edge. Establishing forested linkages among the largest and closest forest fragments through forest regeneration and protection of secondary regenerating forest as well as providing protected area status to the remaining non-protected forest including unprotected smaller forest fragments are important to enhancing the long-term persistence of many plant and animal species here.

opencc-zeroDec 2017View details →

Data from: Phylogeography of African locust bean (Parkia biglobosa) reveals genetic divergence and spatially structured populations in West and Central Africa

The evolutionary history of African savannah tree species is crucial for the management of their genetic resources. In this study, we investigated the phylogeography of Parkia biglobosa and its modelled distribution under past and present climate conditions. This tree species is very valued and widespread in West Africa, providing edible and medicinal products. A large sample of 1 610 individuals from 84 populations, distributed across 12 countries in Western and Central Africa, were genotyped using eight nuclear microsatellites. Individual-based assignments clearly distinguished three genetic clusters, extreme West Africa (EWA), centre of West Africa CWA), and Central Africa (CA). Overall, estimates of genetic diversity were moderate to high, with lower values for populations in EWA (AR=6.4, HE=0.78 and HO=0.7) and CA (AR=5.9, HE=0.67 and HO=0.61) compared to populations in CWA (AR=7.3, HE=0.79 and HO=0.75). The overall population differentiation was found to be moderate (FST=0.09). A highly significant isolation-by-distance pattern was detected, with a marked phylogeographic signature suggesting possible effects of past climate and geographic barriers to migration. Modelling the potential distribution of the species showed a contraction during the last glaciations followed by expansion events. The exploratory Approximate Bayesian Computation conducted suggests a best-supported scenario in which the cluster CWA traced back to the ancestral populations and a first split between EWA and CWA took place about 160 000 years BP, then a second split divided CA and CWA, about 100 000 years BP. However, our genetic data do not enable to conclusively distinguish among a few alternative possible scenarios.

opencc-zeroDec 2015View details →

Data from: Limited gene dispersal and spatial genetic structure as stabilizing factors in an ant-plant mutualism

Comparative studies of the population genetics of closely associated species are necessary to properly understand the evolution of these relationships because gene flow between populations affects the partners' evolutionary potential at the local scale. As a consequence (at least for antagonistic interactions), asymmetries in the strength of the genetic structures of the partner populations can result in one partner having a co-evolutionary advantage. Here, we assess the population genetic structure of partners engaged in a species-specific and obligatory mutualism: the Neotropical ant-plant, Hirtella physophora, and its ant associate, Allomerus decemarticulatus. Although the ant cannot complete its life cycle elsewhere than on H. physophora and the plant cannot live for long without the protection provided by A. decemarticulatus, these species also have antagonistic interactions: the ants have been shown to benefit from castrating their host plant and the plant is able to retaliate against too virulent ant colonies. We found similar short dispersal distances for both partners, resulting in the local transmission of the association and, thus, inbred populations in which too virulent castrating ants face the risk of local extinction due to the absence of H. physophora offspring. On the other hand, we show that the plant populations probably experienced greater gene flow than did the ant populations, thus enhancing the evolutionary potential of the plants. We conclude that such levels of spatial structure in the partners' populations can increase the stability of the mutualistic relationship. Indeed, the local transmission of the association enables partial alignments of the partners' interests, and population connectivity allows the plant retaliation mechanisms to be locally adapted to the castration behaviour of their symbionts.

opencc-zeroDec 2011View details →

Data from: Defining spatial and temporal patterns of phylogeographic structure in Madagascar's iguanid lizards (Genus Oplurus)

Understanding the remarkably high species diversity and levels of endemism found among Madagascar's flora and fauna has been the focus of many studies. One hypothesis that has received much attention proposes that Pleistocene climate fluctuations spurred diversification. However, while spatial patterns of distribution and phylogenetic relationships can provide support for biogeographic predictions, temporal estimates of divergence are required to determine the fit of these geospatial patterns to climatic or biogeographic mechanisms. We use multilocus DNA sequence data to test whether divergence times among Malagasy iguanid lizards of the subfamily Oplurinae are compatible with a hypotheses of Quaternary diversification. We estimate the oplurine species tree and associated divergence times under a relaxed clock model. In addition, we examine the phylogeographic structure and population divergence times within two sister-species of Oplurus primarily distributed in the northwest and southwest of Madagascar (O. cuvieri and O. cyclurus, respectively). We find that divergence events among oplurine lineages occurred in the Oligocene and Miocene and are thus far older and incompatible with the hypothesis that recent climate fluctuations are related to current species diversity. However, the timing of intraspecific divergences and spatial patterns of population genetic structure within O. cuvieri and O. cyclurus suggest a role for both intrinsic barriers and recent climate fluctuations at population-level divergences. Integrating information across spatial and temporal scales allows us to identify and better understand the mechanisms generating patterns diversity.

opencc-zeroDec 2013View details →

Data from: Population genetic structure of the tree-hole tick Ixodes arboricola (Acari: Ixodidae) at different spatial scales

The endophilic tick Ixodes arboricola infests cavity-nesting birds, and its dispersal strongly depends on the movements of its host. Population genetic structure of I. arboricola was studied with seven polymorphic microsatellite markers. We collected 268 ticks from 76 nest boxes in four woodlots near Antwerp, Belgium. These nest boxes are mainly used by the principal hosts of I. arboricola, the great tit Parus major and the blue tit Cyanistes caeruleus. As these birds typically return to the same cavity for roosting or breeding, ticks within nest boxes were expected to be highly related, and tick populations were expected to be spatially structured among woodlots and among nest boxes within woodlots. In line with the expectations, genetic population structure was found among woodlots and among nest boxes within woodlots. Surprisingly, there was considerable genetic variation among ticks within nest boxes. This could be explained by continuous gene flow from ticks from nearby tree holes, yet this remains to be tested. A pairwise relatedness analysis conducted for all pairs of ticks within nest boxes showed that relatedness among larvae was much higher than among later instars, which suggests that larvae are the most important instar for tick dispersal. Overall, tick populations at the studied spatial scale are not as differentiated as predicted, which may influence the scale at which host–parasite evolution occurs.

opencc-zeroAug 2021View details →

Spatial structure and dispersal dynamics in a house sparrow metapopulation

1. The effects of spatial structure on metapopulation dynamics depend upon the interaction between local population dynamics and dispersal, and how this relationship is affected by the geographical isolation and spatial heterogeneity in habitat characteristics. 2. Our aim is to examine how emigration and immigration of house sparrows, Passer domesticus, in a Norwegian archipelagic metapopulation are affected by key factors predicted by classic metapopulation models to affect dispersal: spatial and temporal variation in population size, inter-island distance, local demography and habitat characteristics. 3. This metapopulation can be divided into two major habitat types: (1) islands closer to the mainland where sparrows breed in colonies on farms, and (2) islands without farms, situated farther away from the mainland where sparrows are exposed to harsher environmental conditions. 4. Dispersal was spatially structured within the metapopulation; there was proportionally and numerically less emigration and immigration involving farm islands, as compared to non-farm islands. Furthermore, emigration and immigration occurred mostly between nearby islands. Moreover, emigration in response to spatial differences in mean population size differed between the habitat types, but populations with large mean received more immigrants in both habitat types. The number of emigrants and immigrants was negatively related to long-term recruit production, which was not the case in non-farm islands. The proportion and number of emigrants was positively related to temporal increases in recruit production on farm islands, however, not on non-farm islands. 5. Our results demonstrate that spatial heterogeneity in environmental conditions influences how spatial variation in long-term mean population size, and temporal and spatial variation in recruit production, affects dispersal dynamics. The spatial structure of this metapopulation is therefore best described by a spatially explicit model in which the exchange of individuals within each habitat type is strongly affected by the degree of geographical isolation, population size and recruit production. However, these relationships differed between the two habitat types, with the non-farm islands showing similarities to a mainland-island model type of structure, whereas dispersal on the farm islands showed features more associated with source-sink or balanced dispersal models. Such differential dispersal dynamics between habitat types is expected to have important consequences for the ecological and evolutionary dynamics within this metapopulation.

opennotspecifiedOct 2013View details →

Figure 2 in Historical demography and spatial genetic structure of the subterranean rodent Ctenomys magellanicus in Tierra del Fuego (Argentina)

Figure 2. Bayesian inference trees of Ctenomys genus. A, tree derived from the D-loop marker. B, tree derived from Cyt b. Numbers next to branches are bootstrap support values and Bayesian posterior probabilities, respectively.

opennotspecifiedOct 2013View details →

Figure 1 in Historical demography and spatial genetic structure of the subterranean rodent Ctenomys magellanicus in Tierra del Fuego (Argentina)

Figure 1. Geographical distribution of Ctenomys magellanicus sampling sites along the study area. Squares show the two regions: north (steppe, chromosome form 2n = 34) and south (ecotone, chromosome form 2n = 36). Each region was subdivided into subpopulations: two for the north (subpopulations A and B) and four for the south (subpopulations C–F).

opencc-by-4.0Jan 2023View details →

Spatial structure and benefits to hosts allow plasmids with and without post-segregational killing (PSK) systems to coexist

The code for the numerical analysis and parametric walks for both the single strain and specialization model for the paper "Spatial structure and benefits to hosts allow plasmids with and without post-segregational killing (PSK) systems to coexist " Programs Need: Python, Jyupter notebooks and WinZip (to unzip the package). The Data files: The Data files are in .OUT meaning they can be overwritten if the code is run again, so if one wishes to examine the outputs, copy them into another folder or create a second copy of this folder to investigae   Numerical Analysis The numerical analysis was conducted using the ode() function and ’lsoda’ method in Scientific Python (vers. 0.19.0). The ’lsoda’ method was used because both the single-strain model and specialization model are numerically ’stiff’. Parameters for ’lsoda’ were set to their default values, except for runs that characterized all outcomes of the model (both coexistence and exclusion); here, numerical tolerance parameters atol and was lowered to 10−9. The default tolerance values (which are both equal to 10−5) were used for runs that specifically sought coexistence of PSK+ and PSK- plasmids. These larger tolerances may miss some points of coexistence, but allow for faster numerical simulation, which was necessary given that a large number of parameter sets needed to be studied to find points of coexistence. In all cases numerical solutions were checked that they successfully completed the full time interval, which in our case was 109 time steps. Numerical analysis also used the numpy library (vers. 1.12.1) and the Python environment was vers. 3.6.1. Python scripts of the numerical systems are provided as further Supplementary Materials, as well as Juypter Notebooks that allow for the examination of single parameter sets.   Parameteric Walks  To assess whether points of coexistence of PSK- and PSK+ are mutationally connected, we took one point of coexistence for each of the single-strain and specialization models and subjected them to a parameter walk. A parameter walk consist of starting at a point of coexistence and running the simulations again with parameters randomly perturbed from their initial value. In particular, the parameter walk was either unbiased or biased. For an unbiased walk, a parameter was perturbed uniformly to up to 10% above or 10% below its current value. For a biased walk, a parameter was uniformly perturbed 1% above and 10% below, or 10% above and 1% below its current value. If a random perturbation of parameter resulted in a new coexistence point, that set of parameters was taken as the current set and perturbed again.

Fig. 1 in Socio-spatial organization and kin structure in ocelots from integration of camera trapping and noninvasive genetics

Fig. 1.—Map of Barro Colorado Island, Panama, showing the locations of camera traps placed along trails and at ocelot (Leopardus pardalis) latrines.

Fig. 2 in Socio-spatial organization and kin structure in ocelots from integration of camera trapping and noninvasive genetics

Fig. 2.—Cumulative frequency distributions of association index values among pairs of male versus female ocelots (Leopardus pardalis) on Barro Colorado Island, Panama. Half-weight association index values represent the strength of spatiotemporal overlap between same-sex dyads based on how often they were photographed at the same camera trap within the same 30-day interval.

Fig. 4 in Socio-spatial organization and kin structure in ocelots from integration of camera trapping and noninvasive genetics

Fig. 4.—Relatedness of individual ocelots (Leopardus pardalis) on Barro Colorado Island, Panama, depending on sex and overlap of space use. Values shown are observed mean differences in relatedness between dyads of individual ocelots with overlapping space use (vertical bold lines) versus all dyads in the sampled population, along with the cumulative distribution of simulated differences from 1,000,000 randomly generated bootstrap replicates. Reference lines represent quantiles from the simulated distribution. A) All dyads, B) male-female dyads, C) male-male dyads, and D) female-female dyads.

Fig. 3 in Socio-spatial organization and kin structure in ocelots from integration of camera trapping and noninvasive genetics

Fig. 3.—Half-weight association index values between pairs of a) female and b) male ocelots (Leopardus pardalis) from Barro Colorado Island, Panama, shown in both matrix and graphical format. Values and line weights represent the strength (on a scale of 0–1) of spatiotemporal overlap between pairs of individuals based on how often they were photographed at the same camera trap during the same 30-day period. Asterisks represent associations and double asterisks represent dissociations that differed from random expectations (P <0.05).

opencc-by-4.0Aug 2023View details →

Research data supporting "Dynamic Local Structure in Caesium Lead Iodide: Spatial Correlation and Transient Domains"

This is research data associated with "Dynamic Local Structure in Caesium Lead Iodide: Spatial Correlation and Transient Domains", accepted to Small.  The repository contains the trajectories which support the conclusions of the paper, the MLIP parameters, and some additional visualizations.