A graph-based approach to investigating the influence of the landscape on population spread processes

Modelling of landscape connectivity is a key point in the study of the movement of populations within a given landscape. For studies focused on the preservation of biodiversity, graph-based methods provide an interesting framework to investigate the landscape influence on population spread processes. Such an approach is described here, based on the mapping of landscape categories in habitat patches, including a diachronic data set describing the population spread within the habitat patches. A minimum planar graph was built by computing spatial distances between all pairs of neighbouring patches. From this structure, two types of analysis are proposed: one focused on the links of the graph and consists in correlating spatial distances and gap indicators computed from the diachronic data. The other was based on the correlations between population data and connectivity metrics at the patch level. As an example, this approach was applied to the spread of the fossorial water vole on the Jura plateau (France), with annual population data covering eleven years from 1989 to 2000. Link analysis allowed to find an optimal set of resistance values used in the least-cost distances computations, and thus to build a relevant graph. From this graph, patch analysis displayed a cyclic correlation between a metric based on potential dispersal flux and the population density, outlining the strong role of landscape connectivity in the population spread. The present study clearly shows that landscape modelling and graph-based approach can produce parameters which are consistent with field observations and thus pave the way to simulating the effect of landscape modification on population dynamics.     

Quantifying island isolation – insights from global patterns of insular plant species richness

Isolation is a driving factor of species richness and other island community attributes. Most empirical studies have investigated the effect of isolation measured as distance to the nearest continent. Here we expanded this perspective by comparing the explanatory power of seventeen isolation metrics in sixty‐eight variations for vascular plant species richness on 453 islands worldwide. Our objectives were to identify ecologically meaningful metrics and to quantify their relative importance for species richness in a globally representative data set. We considered the distances to the nearest mainland and to other islands, stepping stone distances, the area of surrounding landmasses, prevailing wind and ocean currents and climatic similarity between source and target areas. These factors are closely linked to colonization and maintenance of plant species richness on islands. We tested the metrics in spatial multi‐predictor models accounting for area, climate, topography and island geology. Besides area, isolation was the second most important factor determining species richness on the studied islands. A model including the proportion of surrounding land area as the isolation metric had the highest predictive power, explaining 86.1% of the variation. Distances to large islands, stepping stone distances and distances to climatically similar landmasses performed slightly better than distance to the nearest mainland. The effect of isolation was weaker for large islands suggesting that speciation counteracts the negative effect of isolation on immigration on large islands. Continental islands were less affected by isolation than oceanic islands. Our results suggest that a variety of immigration mechanisms influence plant species richness on islands and we show that this can be detected at macro‐scales. Although the distance to the nearest mainland is an adequate and easy‐to‐calculate measure of isolation, accounting for stepping stones, large islands as source landmasses, climatic similarity and the area of surrounding landmasses increases the explanatory power of isolation for species richness.     

Habitat quality of source patches and connectivity in fragmented landscapes

Because spatial connectivity is critical to dispersal success and persistence of species in highly fragmented landscapes, the way that we envision and measure connectivity is consequential for biodiversity conservation. Connectivity metrics used for predictive modeling of spatial turnover and patch occupancy for metapopulations, such as with Incidence Function Models (IFM), incorporate distances to and sizes of possible source populations. Here, our focus is on whether habitat quality of source patches also is considered in these connectivity metrics. We propose that effective areas (weighted by habitat quality) of source patches should be better surrogates for population size and dispersal potential compared to unadjusted patch areas. Our review of a representative sample of the literature revealed that only 12.5% of studies incorporated habitat quality of source patches into IFM-type connectivity metrics. Quality of source patches generally was not taken into account in studies even if habitat quality of focal patches was included in analyses. We provide an empirical example for a metapopulation of a rare wetland species, the round-tailed muskrat (Neofiber alleni), demonstrating that a connectivity metric based on effective areas of source patches better predicts patch colonization and occupancy than a metric that used simple patch areas. The ongoing integration of landscape ecology and metapopulation dynamics could be hastened by incorporating habitat quality of source patches into spatial connectivity metrics applied to species conservation in fragmented landscapes.     

The assessment of mangrove biomass and carbon in West Africa: a spatially explicit analytical framework

Mangrove forests are highly productive and have large carbon sinks while also providing numerous goods and ecosystem services. However, effective management and conservation of the mangrove forests are often dependent on spatially explicit assessments of the resource. Given the remote and highly dispersed nature of mangroves, estimation of biomass and carbon in mangroves through routine field-based inventories represents a challenging task which is impractical for large-scale planning and assessment. Alternative approaches based on geospatial technologies are needed to support this estimation in large areas. However, spatial data processing and analysis approaches used in this estimation of mangrove biomass and carbon have not been adequately investigated. In this study, we present a spatially explicit analytical framework that integrate remotely sensed data and spatial analyses approaches to support the estimation of mangrove biomass and carbon stock and their spatial patterns in West Africa. Forest canopy height derived from SRTM and ICESat/GLAS data was used to estimate mangrove biomass and carbon in nine West African countries. We developed a geospatial software toolkit that implemented the proposed framework. The spatial analysis framework and software toolkit provide solid support for the estimation and relative comparisons of mangrove-related metrics. While the mean canopy height of mangroves in our study area is 10.2 m, the total biomass and carbon were estimated as 272.56 and 136.28 Tg. Nigeria has the highest total mangrove biomass and carbon in the nine countries, but Cameroon is the country with the largest mean biomass and carbon density. The resulting spatially explicit distributions of mangrove biomass and carbon hold great potential in guiding the strategic planning of large-scale field-based assessment of mangrove forests. This study demonstrates the utility of online geospatial data and spatial analysis as a feasible solution for estimating the distribution of mangrove biomass and carbon at larger or smaller scales.     

Interactions between landcover pattern and geospatial processing methods: Effects on landscape metrics and classification accuracy

Remote sensing data is routinely used in ecology to investigate the relationship between landscape pattern as characterised by land use and land cover maps, and ecological processes. Multiple factors related to the representation of geographic phenomenon have been shown to affect characterisation of landscape pattern resulting in spatial uncertainty. This study investigated the effect of the interaction between landscape spatial pattern and geospatial processing methods statistically; unlike most papers which consider the effect of each factor in isolation only. This is important since data used to calculate landscape metrics typically undergo a series of data abstraction processing tasks and are rarely performed in isolation. The geospatial processing methods tested were the aggregation method and the choice of pixel size used to aggregate data. These were compared to two components of landscape pattern, spatial heterogeneity and the proportion of landcover class area. The interactions and their effect on the final landcover map were described using landscape metrics to measure landscape pattern and classification accuracy (response variables). All landscape metrics and classification accuracy were shown to be affected by both landscape pattern and by processing methods. Large variability in the response of those variables and interactions between the explanatory variables were observed. However, even though interactions occurred, this only affected the magnitude of the difference in landscape metric values. Thus, provided that the same processing methods are used, landscapes should retain their ranking when their landscape metrics are compared. For example, highly fragmented landscapes will always have larger values for the landscape metric “number of patches” than less fragmented landscapes. But the magnitude of difference between the landscapes may change and therefore absolute values of landscape metrics may need to be interpreted with caution. The explanatory variables which had the largest effects were spatial heterogeneity and pixel size. These explanatory variables tended to result in large main effects and large interactions. The high variability in the response variables and the interaction of the explanatory variables indicate it would be difficult to make generalisations about the impact of processing on landscape pattern as only two processing methods were tested and it is likely that untested processing methods will potentially result in even greater spatial uncertainty.     

Transmission of Leptosphaeria maculans from a cropping season to the following one

Current modelling of inoculum transmission from a cropping season to the following one relies on the extrapolation of kernels estimated on data at short distances from punctual sources, because data collected at larger distances are scarce. We estimated the dispersal kernel of Leptosphaeria maculans ascospores from stubble left after harvest in the summer previous to newly sown oilseed rape fields, using phoma stem canker autumn disease severity. We built a dispersal model to analyse the data. Source strengths are described in the spatial domain covered by source fields by a log‐Gaussian spatial process. Infection potentials in the following season are described in the space consisting of the target fields, by a convolution of sources and a power‐exponential dispersal kernel. Data were collected on farmers' fields considered as sources in 2009 and 2011 (72 and 39 observation points) and as targets in 2010 and 2012 (172 and 200 points). We applied the Bayesian approach for model selection and parameter estimation. We obtained fat tail kernels for both data sets. This estimation is the first from data acquired over distances of 0 to 1000 m, using several non‐punctual inoculum sources. It opens the prospect of refining the existing simulators, or developing disease risk maps.     

Sex determination using the scapula in New Kingdom skeletons from Tell El-Amarna

Both forensic and archaeological sciences use metric analysis of human skeletal remains for sex estimation of unknown individuals. Thomas Dwight first reported the utility of scapula metrics for sex estimation in 1894, and subsequent years have produced several techniques for sex estimation using scapula metrics. Levels of sexual dimorphism vary across time and space, making these methods not universally applicable. Novel discriminant functions for unique populations are thus necessary. The present study establishes metric standards for sex estimation for a New Kingdom Egyptian skeletal sample from Tell El-Amarna using scapular measurements. The sample for this research consists of 27 individuals (14 males; 13 females) whose sex estimate based on pelvic morphology is unambiguous. The five measurements showing the highest degree of sexual dimorphism (p ≤ 0.001) are used in the discriminant functions reported here: maximum length of the scapula, maximum length of the scapular spine, breadth of the infraspinous body, height of the glenoid fossa, and breadth of the glenoid fossa. The overall leave one out, cross-validated accuracy for the five reported discriminant functions ranges from 84.0 to 88.0%; similar to accuracies reported for the femur and humerus. Functions combining multiple variables produce higher accuracies than those based on single measurements. The unique population of Amarna, being comprised of emigrants from throughout Egypt, suggests these discriminant functions will have utility for Amarna period sites across the spatial distances of Egypt, and possibly the temporal range of the New Kingdom as a whole.     

Assessing phylogenetic signal with measurement error: a comparison of mantel tests, blomberg et Al.'s k, and phylogenetic distograms

In macroevolutionary studies, different approaches are commonly used to measure phylogenetic signal-the tendency of related taxa to resemble one another-including the K statistic and the Mantel test. The latter was recently criticized for lacking statistical power. Using new simulations, we show that the power of the Mantel test depends on the metrics used to define trait distances and phylogenetic distances between species. Increasing power is obtained by lowering variance and increasing negative skewness in interspecific distances, as obtained using Euclidean trait distances and the complement of Abouheif proximity as a phylogenetic distance. We show realistic situations involving "measurement error" due to intraspecific variability where the Mantel test is more powerful to detect a phylogenetic signal than a permutation test based on the K statistic. We highlight limitations of the K-statistic (univariate measure) and show that its application should take into account measurement errors using repeated measures per species to avoid estimation bias. Finally, we argue that phylogenetic distograms representing Euclidean trait distance as a function of the square root of patristic distance provide an insightful representation of the phylogenetic signal that can be used to assess both the impact of measurement error and the departure from a Brownian evolution model.     

A comparison of individual‐based genetic distance metrics for landscape genetics

A major aim of landscape genetics is to understand how landscapes resist gene flow and thereby influence population genetic structure. An empirical understanding of this process provides a wealth of information that can be used to guide conservation and management of species in fragmented landscapes and also to predict how landscape change may affect population viability. Statistical approaches to infer the true model among competing alternatives are based on the strength of the relationship between pairwise genetic distances and landscape distances among sampled individuals in a population. A variety of methods have been devised to quantify individual genetic distances, but no study has yet compared their relative performance when used for model selection in landscape genetics. In this study, we used population genetic simulations to assess the accuracy of 16 individual‐based genetic distance metrics under varying sample sizes and degree of population genetic structure. We found most metrics performed well when sample size and genetic structure was high. However, it was much more challenging to infer the true model when sample size and genetic structure was low. Under these conditions, we found genetic distance metrics based on principal components analysis were the most accurate (although several other metrics performed similarly), but only when they were derived from multiple principal components axes (the optimal number varied depending on the degree of population genetic structure). Our results provide guidance for which genetic distance metrics maximize model selection accuracy and thereby better inform conservation and management decisions based upon landscape genetic analysis.     

Non-parametric smoothing of multivariate genetic distances in the analysis of spatial population structure at fine scale

Species dispersal studies provide valuable information in biological research. Restricted dispersal may give rise to a non-random distribution of genotypes in space. Detection of spatial genetic structure may therefore provide valuable insight into dispersal. Spatial structure has been treated via autocorrelation analysis with several univariate statistics for which results could dependent on sampling designs. New geostatistical approaches (variogram-based analysis) have been proposed to overcome this problem. However, modelling parametric variograms could be difficult in practice. We introduce a non-parametric variogram-based method for autocorrelation analysis between DNA samples that have been genotyped by means of multilocus-multiallele molecular markers. The method addresses two important aspects of fine-scale spatial genetic analyses: the identification of a non-random distribution of genotypes in space, and the estimation of the magnitude of any non-random structure. The method uses a plot of the squared Euclidean genetic distances vs. spatial distances between pairs of DNA-samples as empirical variogram. The underlying spatial trend in the plot is fitted by a non-parametric smoothing (LOESS, Local Regression). Finally, the predicted LOESS values are explained by segmented regressions (SR) to obtain classical spatial values such as the extent of autocorrelation. For illustration we use multivariate and single-locus genetic distances calculated from a microsatellite data set for which autocorrelation was previously reported. The LOESS/SR method produced a good fit providing similar value of published autocorrelation for this data. The fit by LOESS/SR was simpler to obtain than the parametric analysis since initial parameter values are not required during the trend estimation process. The LOESS/SR method offers a new alternative for spatial analysis.     

植物空间分布格局中邻体距离的概率分布模型及参数估计

最近邻体法是一类有效的植物空间分布格局分析方法,邻体距离的概率分布模型用于描述邻体距离的统计特征,属于常用的最近邻体法之一。然而,聚集分布格局中邻体距离(个体到个体)的概率分布模型表达式复杂,参数估计的计算量大。根据该模型期望和方差的特性,提出了一种简化的参数估计方法,并利用遗传算法来实现参数优化,结果表明遗传算法可以有效地估计的该模型的两个参数。同时,利用该模型拟合了加拿大南温哥华岛3个寒温带树种的空间分布数据,结果显示:该概率分布模型可以很好地拟合美国花旗松(P.menziesii)和西部铁杉(T.heterophylla)的邻体距离分布,但由于西北红柏(T.plicata)存在高度聚集的团簇分布,拟合结果不理想;美国花旗松在样地中近似随机分布,空间聚集参数对空间尺度的依赖性不强,但西北红柏和西部铁杉空间聚集参数具有尺度依赖性,随邻体距离阶数增加而变大。最后,讨论了该模型以及参数估计方法的优势和限制。     

Ecological niche modeling re‐examined: A case study with the Darwin's fox

Many previous studies have attempted to assess ecological niche modeling performance using receiver operating characteristic (ROC) approaches, even though diverse problems with this metric have been pointed out in the literature. We explored different evaluation metrics based on independent testing data using the Darwin's Fox (Lycalopex fulvipes) as a detailed case in point. Six ecological niche models (ENMs; generalized linear models, boosted regression trees, Maxent, GARP, multivariable kernel density estimation, and NicheA) were explored and tested using six evaluation metrics (partial ROC, Akaike information criterion, omission rate, cumulative binomial probability), including two novel metrics to quantify model extrapolation versus interpolation (E‐space index I) and extent of extrapolation versus Jaccard similarity (E‐space index II). Different ENMs showed diverse and mixed performance, depending on the evaluation metric used. Because ENMs performed differently according to the evaluation metric employed, model selection should be based on the data available, assumptions necessary, and the particular research question. The typical ROC AUC evaluation approach should be discontinued when only presence data are available, and evaluations in environmental dimensions should be adopted as part of the toolkit of ENM researchers. Our results suggest that selecting Maxent ENM based solely on previous reports of its performance is a questionable practice. Instead, model comparisons, including diverse algorithms and parameterizations, should be the sine qua non for every study using ecological niche modeling. ENM evaluations should be developed using metrics that assess desired model characteristics instead of single measurement of fit between model and data. The metrics proposed herein that assess model performance in environmental space (i.e., E‐space indices I and II) may complement current methods for ENM evaluation.     

Phylin 2.0: Extending the phylogeographical interpolation method to include uncertainty and user‐defined distance metrics

Estimating geographical ranges of intra‐specific evolutionary lineages is crucial to the fields of biogeography, evolution, and biodiversity conservation. Models of isolation mechanisms often consider multiple distances in order to explain genetic divergence. Yet, the available methods to estimate the geographical ranges of lineages are based on direct geographical distances, neglecting other distance metrics that can better explain the spatial genetic structure. We extended the phylogeographical interpolation method (phylin ) in order to accommodate user‐defined distance metrics and to incorporate the uncertainty associated with genetic distance calculation. These new features were tested with simulated and empirical data sets. Multiple distance matrices were generated including geographical, resistance, and environmental distances to derive maps of lineage occurrence. The new additions to this method improved the ability to predict lineage occurrence, even with low sample size. We used a regression framework to quantify the relationship between the genetic divergence and competing distance matrices representing potential isolation processes that are subsequently used in the interpolation process. Including uncertainty in tree topology and the different distance matrices improved the robustness of the variograms, allowing a better fit of the theoretical model of spatial dependence. The improvements to the method increase its potential application in other fields. Accurately mapping genetic divergence can help to locate potential contact zones between lineages as well as barriers to gene flow, which has a broad interest in biogeographical and evolutionary studies. Additionally, conservation efforts could benefit from the integration of genetic variation and landscape features in a spatially explicit framework.     

Visual representation of DNA sequences for exon detection using non-parametric spectral estimation techniques

This paper presents a new approach for modeling of DNA sequences for the purpose of exon detection. The proposed model adopts the sum-of-sinusoids concept for the representation of DNA sequences. The objective of the modeling process is to represent the DNA sequence with few coefficients. The modeling process can be performed on the DNA signal as a whole or on a segment-by-segment basis. The created models can be used instead of the original sequences in a further spectral estimation process for exon detection. The accuracy of modeling is evaluated evaluated by using the Root Mean Square Error (RMSE) and the R-square metrics. In addition, non-parametric spectral estimation methods are used for estimating the spectral of both original and modeled DNA sequences. The results of exon detection based on original and modeled DNA sequences coincide to a great extent, which ensures the success of the proposed sum-of-sinusoids method for modeling of DNA sequences.     

Its all about connections: hubs and invasion in habitat networks

During the early stages of invasion, the interaction between the features of the invaded landscape, notably its spatial structure, and the internal dynamics of an introduced population has a crucial impact on establishment and spread. By approximating introduction areas as networks of patches linked by dispersal, we characterised their spatial structure with specific metrics and tested their impact on two essential steps of the invasion process: establishment and spread. By combining simulations with experimental introductions of Trichogramma chilonis (Hymenoptera: Trichogrammatidae) in artificial laboratory microcosms, we demonstrated that spread was hindered by clusters and accelerated by hubs but was also affected by small‐population mechanisms prevalent for invasions, such as Allee effects. Establishment was also affected by demographic mechanisms, in interaction with network metrics. These results highlight the importance of considering the demography of invaders as well as the structure of the invaded area to predict the outcome of invasions.     

From compositional to functional biodiversity metrics in bioassessment: A case study using stream macroinvertebrate communities

While compositional diversity is a common metric for assessing human impacts on aquatic communities, functional diversity is scarcely employed, though highly desirable from the perspective of the European Water Framework Directive. Using abundance data from 99 minimally disturbed sites (i.e., no or very weak anthropogenic impact) from a national survey, we studied the spatial variability of compositional and functional biodiversity metrics across a predefined ecoregional classification. Metrics of compositional diversity comprised taxonomic and EPT richness and Simpson diversity. Functional diversity metrics were based on Rao's Quadratic Entropy (RQE), which described the differences among benthic invertebrate genera in eleven biological traits (e.g., size, life cycle, reproduction types, feeding habits). Using generalized linear models we show that taxonomic richness may vary greatly across ecoregions, contrasting with Simpson diversity and functional metrics that varied weakly in response to natural environmental variability. Functional diversity metrics, because of their stability in response to natural environmental variability, may be useful tools for assessing human impairment to ecosystem function. We further tested the response of functional diversity metrics to a specific human impact (sewage) and demonstrated significant modifications of functional diversity downstream of sewage pollution. Further investigations are required to test the ability of functional diversity metrics to precisely and accurately indicate different types of human impacts.     

An Assessment Framework for Optimal FMS Effectiveness

Equipment failures in an FMS are significant to performance and can lead to costly, incorrect decisions. Fortunately, effectiveness measurement techniques can be mapped to clever modeling frameworks to help predict, track, and then improve upon the FMS performability or mission effectiveness, and improve maintenance. This article provides sources and guidelines for efficient and effective FMS modeling, a framework for applying the modeling to predict the impact on customers from their point of view, and a method for tying it all together for improving the FMS effectiveness. It is not enough to simply examine the working and failed states of an FMS or even to calculate common reliability metrics. It is necessary to consider the FMS as a whole, and that system includes the needs of the customer and the business. It is also necessary to be purposeful about the measures of performance selected and to support the measures of effectiveness. In this article, we present: a framework for considering customer needs in the measures of effectiveness for FMS; modeling approaches for solving for effectiveness measures; and an example to show how to apply it to an FMS, to improve it or plan for meeting specific customer needs.     

Microsatellite null alleles and estimation of population differentiation

Microsatellite null alleles are commonly encountered in population genetics studies, yet little is known about their impact on the estimation of population differentiation. Computer simulations based on the coalescent were used to investigate the evolutionary dynamics of null alleles, their impact on F(ST) and genetic distances, and the efficiency of estimators of null allele frequency. Further, we explored how the existing method for correcting genotype data for null alleles performed in estimating F(ST) and genetic distances, and we compared this method with a new method proposed here (for F(ST) only). Null alleles were likely to be encountered in populations with a large effective size, with an unusually high mutation rate in the flanking regions, and that have diverged from the population from which the cloned allele state was drawn and the primers designed. When populations were significantly differentiated, F(ST) and genetic distances were overestimated in the presence of null alleles. Frequency of null alleles was estimated precisely with the algorithm presented in Dempster et al. (1977). The conventional method for correcting genotype data for null alleles did not provide an accurate estimate of F(ST) and genetic distances. However, the use of the genetic distance of Cavalli-Sforza and Edwards (1967) corrected by the conventional method gave better estimates than those obtained without correction. F(ST) estimation from corrected genotype frequencies performed well when restricted to visible allele sizes. Both the proposed method and the traditional correction method have been implemented in a program that is available free of charge at http://www.montpellier.inra.fr/URLB/. We used 2 published microsatellite data sets based on original and redesigned pairs of primers to empirically confirm our simulation results.     

Population genetic structure and direct observations reveal sex‐reversed patterns of dispersal in a cooperative bird

Sex‐biased dispersal is pervasive and has diverse evolutionary implications, but the fundamental drivers of dispersal sex biases remain unresolved. This is due in part to limited diversity within taxonomic groups in the direction of dispersal sex biases, which leaves hypothesis testing critically dependent upon identifying rare reversals of taxonomic norms. Here, we use a combination of observational and genetic data to demonstrate a rare reversal of the avian sex bias in dispersal in the cooperatively breeding white‐browed sparrow weaver (Plocepasser mahali). Direct observations revealed that (i) natal philopatry was rare, with both sexes typically dispersing locally to breed, and (ii), unusually for birds, males bred at significantly greater distances from their natal group than females. Population genetic analyses confirmed these patterns, as (i) corrected Assignment index (AIc), F_ST tests and isolation‐by‐distance metrics were all indicative of longer dispersal distances among males than females, and (ii) spatial autocorrelation analysis indicated stronger within‐group genetic structure among females than males. Examining the spatial scale of extra‐group mating highlighted that the resulting ‘sperm dispersal’ could have acted in concert with individual dispersal to generate these genetic patterns, but gamete dispersal alone cannot account entirely for the sex differences in genetic structure observed. That leading hypotheses for the evolution of dispersal sex biases cannot readily account for these sex‐reversed patterns of dispersal in white‐browed sparrow weavers highlights the continued need for attention to alternative explanations for this enigmatic phenomenon. We highlight the potential importance of sex differences in the distances over which dispersal opportunities can be detected.