Validating graph-based connectivity models with independent presence-absence and genetic data sets.

Habitat connectivity is a key objective of current conservation policies and is commonly modeled by landscape graphs (i.e., sets of habitat patches [nodes] connected by potential dispersal paths [links]). These graphs are often built based on expert opinion or species distribution models (SDMs) and therefore lack empirical validation from data more closely reflecting functional connectivity. Accordingly, we tested whether landscape graphs reflect how habitat connectivity influences gene flow, which is one of the main ecoevolutionary processes. To that purpose, we modeled the habitat network of a forest bird (plumbeous warbler [Setophaga plumbea]) on Guadeloupe with graphs based on expert opinion, Jacobs' specialization indices, and an SDM. We used genetic data (712 birds from 27 populations) to compute local genetic indices and pairwise genetic distances. Finally, we assessed the relationships between genetic distances or indices and cost distances or connectivity metrics with maximum-likelihood population-effects distance models and Spearman correlations between metrics. Overall, the landscape graphs reliably reflected the influence of connectivity on population genetic structure; validation R2 was up to 0.30 and correlation coefficients were up to 0.71. Yet, the relationship among graph ecological relevance, data requirements, and construction and analysis methods was not straightforward because the graph based on the most complex construction method (species distribution modeling) sometimes had less ecological relevance than the others. Cross-validation methods and sensitivity analyzes allowed us to make the advantages and limitations of each construction method spatially explicit. We confirmed the relevance of landscape graphs for conservation modeling but recommend a case-specific consideration of the cost-effectiveness of their construction methods. We hope the replication of independent validation approaches across species and landscapes will strengthen the ecological relevance of connectivity models.

La conectividad entre hábitats es un objetivo fundamental de las políticas de conservación actuales y con frecuencia se modela con grafos de paisaje (conjuntos de teselas de hábitat [nodos] conectados por vías potenciales de dispersión [enlaces]). Estos grafos se construyen a menudo con opiniones de expertos y modelos de distribución de especies (MDE), por lo que carecen de la validación empírica a partir de datos que reflejan de mejor manera la conectividad funcional. Por consiguiente, analizamos si los grafos de paisaje reflejan cómo la conectividad de hábitats influye sobre el flujo genético, que es uno de los principales procesos evolutivos. Con este propósito, modelamos la red de hábitats de un ave forestal (Setophaga plumbea) en Guadalupe con grafos basados en la opinión de un experto, en el índice de especialización de Jacobs o en un MDE. Usamos datos genéticos (712 aves de 27 poblaciones) para computar los índices genéticos locales y las distancias genéticas entre pares de poblaciones. Por último, analizamos las relaciones entre los índices o distancias genéticas y las distancias de costo o las métricas de conectividad con modelos de distancias de tipo maximum-likelihood-population-effect y correlaciones de Spearman entre las métricas e índices. En general, los grafos de paisaje reflejaron de manera confiable la influencia de la conectividad sobre la estructura genética de las poblaciones; el R2 de validación llegó hasta 0.30 y los coeficientes de correlación llegaron hasta 0.71. Aun así, la relación entre la pertinencia ecológica de los grafos, los requerimientos de datos y los métodos de construcción y análisis no fue directa porque los grafos basados en el método de construcción el más complejo (modelado a partir de la distribución de la especie) a veces tuvieron menos pertinencia ecológica que los otros. Los métodos de validación cruzada y los análisis de sensibilidad nos permitieron hacer espacialmente explícitas las ventajas y limitaciones de cada método de construcción. Así, confirmamos la pertinencia que tienen los grafos de paisaje para la conservación, aunque recomendamos se considere caso por caso el ratio entre la complejidad y la calidad de los métodos de construcción. Esperamos que la replicación de estrategias de validación independiente por varios paisajes y especies fortalezcan la pertinencia ecológica de los modelos de conectividad.

Assessing the influence of the amount of reachable habitat on genetic structure using landscape and genetic graphs.

Genetic structure, i.e. intra-population genetic diversity and inter-population genetic differentiation, is influenced by the amount and spatial configuration of habitat. Measuring the amount of reachable habitat (ARH) makes it possible to describe habitat patterns by considering intra-patch and inter-patch connectivity, dispersal capacities and matrix resistance. Complementary ARH metrics computed under various resistance scenarios are expected to reflect both drift and gene flow influence on genetic structure. Using an empirical genetic dataset concerning the large marsh grasshopper (Stethophyma grossum), we tested whether ARH metrics are good predictors of genetic structure. We further investigated (i) how the components of the ARH influence genetic structure and (ii) which resistance scenario best explains these relationships. We computed local genetic diversity and genetic differentiation indices in genetic graphs, and ARH metrics in the unified and flexible framework offered by landscape graphs, and we tested the relationships between these variables. ARH metrics were relevant predictors of the two components of genetic structure, providing an advantage over commonly used habitat metrics. Although allelic richness was significantly explained by three complementary ARH metrics in the best PLS regression model, private allelic richness and MIW indices were essentially related with the ARH measured outside the focal patch. Considering several matrix resistance scenarios was also key for explaining the different genetic responses. We thus call for further use of ARH metrics in landscape genetics to explain the influence of habitat patterns on the different components of genetic structure.

Environmental mitigation hierarchy and biodiversity offsets revisited through habitat connectivity modelling.

Biodiversity loss is accelerating because of unceasing human activity and land clearing for development projects (urbanisation, transport infrastructure, mining and quarrying …). Environmental policy-makers and managers in different countries worldwide have proposed the mitigation hierarchy to ensure the goal of "no net loss (NNL) of biodiversity" and have included this principle in environmental impact assessment processes. However, spatial configuration is hardly ever taken into account in the mitigation hierarchy even though it would greatly benefit from recent developments in habitat connectivity modelling incorporating landscape graphs. Meanwhile, national, European and international commitments have been made to maintain and restore the connectivity of natural habitats to face habitat loss and fragmentation. Our objective is to revisit the mitigation hierarchy and to suggest a methodological framework for evaluating the environmental impact of development projects, which includes a landscape connectivity perspective. We advocate the use of the landscape connectivity metric equivalent connectivity (EC), which is based on the original concept of "amount of reachable habitat". We also refine the three main levels of the mitigation hierarchy (impact avoidance, reduction and offset) by integrating a landscape connectivity aspect. We applied this landscape connectivity framework to a simple, virtual habitat network composed of 14 patches of varying sizes. The mitigation hierarchy was addressed through graph theory and EC and several scenarios of impact avoidance, reduction and compensation were tested. We present the benefits of a habitat connectivity framework for the mitigation hierarchy, provide practical recommendations to implement this framework and show its use in real case studies that had previously been restricted to one or two steps of the mitigation hierarchy. We insist on the benefits of a habitat connectivity framework for the mitigation hierarchy and for ecological equivalence assessment. In particular, we demonstrate why it is risky to use a standard offset ratio (the ratio between the amount of area negatively impacted and the compensation area) without performing a connectivity analysis that includes the landscape surrounding the zone impacted by the project. We also discuss the limitations of the framework and suggest potential improvements. Lastly, we raise concerns about the need to rethink the strategy for biodiversity protection. Given that wild areas and semi-natural habitats are becoming scarcer, in particular in industrialised countries, we are convinced that the real challenge is to quickly reconsider the current vision of "developing first, then assessing the ecological damage", and instead urgently adopt an upstream protection strategy that would identify and protect the land that must not be lost if we wish to maintain viable species populations and ecological corridors allowing them the mobility necessary to their survival.

Spatial modelling of landscape aesthetic potential in urban-rural fringes.

The aesthetic potential of landscape has to be modelled to provide tools for land-use planning. This involves identifying landscape attributes and revealing individuals' landscape preferences. Landscape aesthetic judgments of individuals (n = 1420) were studied by means of a photo-based survey. A set of landscape visibility metrics was created to measure landscape composition and configuration in each photograph using spatial data. These metrics were used as explanatory variables in multiple linear regressions to explain aesthetic judgments. We demonstrate that landscape aesthetic judgments may be synthesized in three consensus groups. The statistical results obtained show that landscape visibility metrics have good explanatory power. Ultimately, we propose a spatial modelling of landscape aesthetic potential based on these results combined with systematic computation of visibility metrics.

Dispersal, landscape and travelling waves in cyclic vole populations.

Travelling waves (TW) are among the most striking ecological phenomena emerging in oscillating populations. Despite much theory, understanding how real-world TW arise remains a challenge for ecology. Herein, we analyse 16-year time series of cyclic vole populations collected at 314 localities covering 2500 km² in France. We found evidence for a linear front TW spreading at a speed of 7.4 km year(-1) along a north-west/south-east direction and radiating away from a major landscape discontinuity as predicted by recent theory. The spatial signature of vole dispersal was assessed using genetic data collected at 14 localities. Both data sets were handled using similar autocorrelation approaches. Our results revealed a remarkable congruence of the spatial extent and direction of anisotropy of both demographic and genetic structures. Our results constitute the first empirical evidence that effective dispersal is limited in the direction of TW while most of the individual exchanges occur along the wave front.

What can optimized cost distances based on genetic distances offer? A simulation study on the use and misuse of ResistanceGA.

Modelling population connectivity is central to biodiversity conservation and often relies on resistance surfaces reflecting multi-generational gene flow. ResistanceGA (RGA) is a common optimization framework for parameterizing these surfaces by maximizing the fit between genetic distances and cost distances using maximum likelihood population effect models. As the reliability of this framework has rarely been studied, we investigated the conditions maximizing its accuracy for both prediction and interpretation of landscape features' permeability. We ran demo-genetic simulations in contrasted landscapes for species with distinct dispersal capacities and specialization levels, using corresponding reference cost scenarios. We then optimized resistance surfaces from the simulated genetic distances using RGA. First, we evaluated whether RGA identified the drivers of the genetic patterns, that is, distinguished Isolation-by-Resistance (IBR) patterns from either Isolation-by-Distance or patterns unrelated to ecological distances. We then assessed RGA predictive performance using a cross-validation method, and its ability to recover the reference cost scenarios shaping genetic structure in simulations. IBR patterns were well detected and genetic distances were predicted with great accuracy. This performance depended on the strength of the genetic structuring, sampling design and landscape structure. Matching the scale of the genetic pattern by focusing on population pairs connected through gene flow and limiting overfitting through cross-validation further enhanced inference reliability. Yet, the optimized cost values often departed from the reference values, making their interpretation and extrapolation potentially dubious. While demonstrating the value of RGA for predictive modelling, we call for caution and provide additional guidance for its optimal use.

Impact assessment of a high-speed railway line on species distribution: application to the European tree frog (Hyla arborea) in Franche-Comté.

The aim of the present work is to assess the potential long-distance effect of a high-speed railway line on the distribution of the European tree frog (Hyla arborea) in eastern France by combining graph-based analysis and species distribution models. This combination is a way to integrate patch-level connectivity metrics on different scales into a predictive model. The approach used is put in place before the construction of the infrastructure and allows areas potentially affected by isolation to be mapped. Through a diachronic analysis, comparing species distribution before and after the construction of the infrastructure, we identify changes in the probability of species presence and we determine the maximum distance of impact. The results show that the potential impact decreases with distance from the high-speed railway line and the largest disturbances occur within the first 500 m. Between 500 m and 3500 m, the infrastructure generates a moderate decrease in the probability of presence with maximum values close to -40%. Beyond 3500 m the average disturbance is less than -10%. The spatial extent of the impact is greater than the dispersal distance of the tree frog, confirming the assumption of the long-distance effect of the infrastructure. This predictive modelling approach appears to be a useful tool for environmental impact assessment and strategic environmental assessment. The results of the species distribution assessment may provide guidance for field surveys and support for conservation decisions by identifying the areas most affected.

Inferring landscape resistance to gene flow when genetic drift is spatially heterogeneous.

In connectivity models, land cover types are assigned cost values characterizing their resistance to species movements. Landscape genetic methods infer these values from the relationship between genetic differentiation and cost distances. The spatial heterogeneity of population sizes, and consequently genetic drift, is rarely included in this inference although it influences genetic differentiation. Similarly, migration rates and population spatial distributions potentially influence this inference. Here, we assessed the reliability of cost value inference under several migration rates, population spatial patterns and degrees of population size heterogeneity. Additionally, we assessed whether considering intra-population variables, here using gravity models, improved the inference when drift is spatially heterogeneous. We simulated several gene flow intensities between populations with varying local sizes and spatial distributions. We then fit gravity models of genetic distances as a function of (i) the 'true' cost distances driving simulations or alternative cost distances, and (ii) intra-population variables (population sizes, patch areas). We determined the conditions making the identification of the 'true' costs possible and assessed the contribution of intra-population variables to this objective. Overall, the inference ranked cost scenarios reliably in terms of similarity with the 'true' scenario (cost distance Mantel correlations), but this 'true' scenario rarely provided the best model goodness of fit. Ranking inaccuracies and failures to identify the 'true' scenario were more pronounced when migration was very restricted (<4 dispersal events/generation), population sizes were most heterogeneous and some populations were spatially aggregated. In these situations, considering intra-population variables helps identify cost scenarios reliably, thereby improving cost value inference from genetic data.

Analysing landscape effects on dispersal networks and gene flow with genetic graphs.

Graph-theoretic approaches have relevant applications in landscape genetic analyses. When species form populations in discrete habitat patches, genetic graphs can be used (a) to identify direct dispersal paths followed by propagules or (b) to quantify landscape effects on multi-generational gene flow. However, the influence of their construction parameters remains to be explored. Using a simulation approach, we constructed genetic graphs using several pruning methods (geographical distance thresholds, topological constraints, statistical inference) and genetic distances to weight graph links (FST , DPS , Euclidean genetic distances). We then compared the capacity of these different graphs to (a) identify the precise topology of the dispersal network and (b) to infer landscape resistance to gene flow from the relationship between cost-distances and genetic distances. Although not always clear-cut, our results showed that methods based on geographical distance thresholds seem to better identify dispersal networks in most cases. More interestingly, our study demonstrates that a sub-selection of pairwise distances through graph pruning (thereby reducing the number of data points) can counter-intuitively lead to improved inferences of landscape effects on dispersal. Finally, we showed that genetic distances such as the DPS or Euclidean genetic distances should be preferred over the FST for landscape effect inference as they respond faster to landscape changes.