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.

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.

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.

Housing land transaction data and structural econometric estimation of preference parameters for urban economic simulation models.

This paper describes a dataset of 6284 land transactions prices and plot surfaces in 3 medium-sized cities in France (Besançon, Dijon and Brest). The dataset includes road accessibility as obtained from a minimization algorithm, and the amount of green space available to households in the neighborhood of the transactions, as evaluated from a land cover dataset. Further to the data presentation, the paper describes how these variables can be used to estimate the non-observable parameters of a residential choice function explicitly derived from a microeconomic model. The estimates are used by Caruso et al. (2015) to run a calibrated microeconomic urban growth simulation model where households are assumed to trade-off accessibility and local green space amenities.