Alexandre H. Hirzel

ECOLOGICAL-NICHE FACTOR ANALYSIS: HOW TO COMPUTE HABITAT-SUITABILITY MAPS WITHOUT ABSENCE DATA?

We propose a multivariate approach to the study of geographic species dis- tribution which does not require absence data. Building on Hutchinsons concept of the ecological niche, this factor analysis compares, in the multidimensional space of ecological variables, the distribution of the localities where the focal species was observed to a reference set describing the whole study area. The first factor extracted maximizes the marginality of the focal species, defined as the ecological distance between the species optimum and the mean habitat within the reference area. The other factors maximize the specialization of this focal species, defined as the ratio of the ecological variance in mean habitat to that observed for the focal species. Eigenvectors and eigenvalues are readily interpreted and can be used to build habitat-suitability maps. This approach is recommended in situations where absence data are not available (many data banks), unreliable (most cryptic or rare species), or meaningless (invaders). We provide an illustration and validation of the method for the alpine ibex, a species reintroduced in Switzerland which presumably has not yet recolonized its entire range.

Presence-absence versus presence-only modelling methods for predicting bird habitat suitability

Habitat suitability models can be generated using methods requiring information on species presence or species presence and absence. Knowledge of the predictive performance of such methods becomes a critical issue to establish their optimal scope of application for mapping current species distributions under different constraints. Here, we use breeding bird atlas data in Catalonia as a working example and attempt to analyse the relative performance of two methods: the Ecological Niche factor Analysis (ENFA) using presence data only and Generalised Linear Models (GLM) using presence/absence data. Models were run on a set of forest species with similar habitat requirements, but with varying occurrence rates (prevalence) and niche positions (marginality). Our results support the idea that GLM predictions are more accurate than those obtained with ENFA. This was particularly true when species were using available habitats proportionally to their suitability, making absence data reliable and useful to enhance model calibration. Species marginality in niche space was also correlated to predictive accuracy, i.e. species with less restricted ecological requirements were modelled less accurately than species with more restricted requirements. This pattern was irrespective of the method employed. Models for wide-ranging and tolerant species were more sensitive to absence data, suggesting that presence/absence methods may be particularly important for predicting distributions of this type of species. We conclude that modellers should consider that species ecological characteristics are critical in determining the accuracy of models and that it is difficult to predict generalist species distributions accurately and this is independent of the method used. Being based on distinct approaches regarding adjustment to data and data quality, habitat distribution modelling methods cover different application areas, making it difficult to identify one that should be universally applicable. Our results suggest however, that if absence data is available, methods using this information should be preferably used in most situations.

Assessing habitat-suitability models with a virtual species

This paper compares two habitat-suitability assessing methods, the Ecological Niche Factor Analysis (ENFA) and the Generalised Linear Model (GLM), to see how well they cope with three different scenarios. The main difference between these two analyses is that GLM is based on species presence/absence data while ENFA on presence data only. A virtual species was created and then dispatched in a geographic information system model of a real landscape following three historic scenarios: (1) spreading, (2) at equilibrium, and (3) overabundant species. In each situation, the virtual species was sampled and these simulated data sets were used as input for the ENFA and GLM to reconstruct the habitat suitability model. The results showed that ENFA is very robust to the quality and quantity of the data, giving good results in the three scenarios. GLM was badly affected in the case of the spreading species but produced slightly better results than ENFA when the species was overabundant; at equilibrium, both methods produced equivalent results. The use of a virtual species proved to be a very efficient method, allowing one to fully control the quality of the input data as well as to accurately evaluate the predictive power of both analyses.

Which is the optimal sampling strategy for habitat suitability modelling

Abstract Designing an efficient sampling strategy is of crucial importance for habitat suitability modelling. This paper compares four such strategies, namely, ‘random’, ‘regular’, ‘proportional-stratified’ and ‘equal-stratified’—to investigate (1) how they affect prediction accuracy and (2) how sensitive they are to sample size. In order to compare them, a virtual species approach (Ecol. Model. 145 (2001) 111) in a real landscape, based on reliable data, was chosen. The distribution of the virtual species was sampled 300 times using each of the four strategies in four sample sizes. The sampled data were then fed into a GLM to make two types of prediction: (1) habitat suitability and (2) presence/absence. Comparing the predictions to the known distribution of the virtual species allows model accuracy to be assessed. Habitat suitability predictions were assessed by Pearsons correlation coefficient and presence/absence predictions by Cohens κ agreement coefficient. The results show the ‘regular’ and ‘equal-stratified’ sampling strategies to be the most accurate and most robust. We propose the following characteristics to improve sample design: (1) increase sample size, (2) prefer systematic to random sampling and (3) include environmental information in the design.

Inferring landscape effects on dispersal from genetic distances: how far can we go?

Functional connectivity affects demography and gene dynamics in fragmented populations. Besides species‐specific dispersal ability, the connectivity between local populations is affected by the landscape elements encountered during dispersal. Documenting these effects is thus a central issue for the conservation and management of fragmented populations. In this study, we compare the power and accuracy of three methods (partial correlations, regressions and Approximate Bayesian Computations) that use genetic distances to infer the effect of landscape upon dispersal. We use stochastic individual‐based simulations of fragmented populations surrounded by landscape elements that differ in their permeability to dispersal. The power and accuracy of all three methods are good when there is a strong contrast between the permeability of different landscape elements. The power and accuracy can be further improved by restricting analyses to adjacent pairs of populations. Landscape elements that strongly impede dispersal are the easiest to identify. However, power and accuracy decrease drastically when landscape complexity increases and the contrast between the permeability of landscape elements decreases. We provide guidelines for future studies and underline the needs to evaluate or develop approaches that are more powerful.

Landscape genetics of the Alpine newt (Mesotriton alpestris) inferred from a strip-based approach

Habitat destruction and fragmentation are known to strongly affect dispersal by altering the quality of the environment between populations. As a consequence, lower landscape connectivity is expected to enhance extinction risks through a decrease in gene flow and the resulting negative effects of genetic drift, accumulation of deleterious mutations and inbreeding depression. Such phenomena are particularly harmful for amphibian species, characterized by disjunct breeding habitats. The dispersal behaviour of amphibians being poorly understood, it is crucial to develop new tools, allowing us to determine the influence of landscape connectivity on the persistence of populations. In this study, we developed a new landscape genetics approach that aims at identifying land-uses affecting genetic differentiation, without a priori assumptions about associated ecological costs. We surveyed genetic variation at seven microsatellite loci for 19 Alpine newt (Mesotriton alpestris) populations in western Switzerland. Using strips of varying widths that define a dispersal corridor between pairs of populations, we were able to identify land-uses that act as dispersal barriers (i.e. urban areas) and corridors (i.e. forests). Our results suggest that habitat destruction and landscape fragmentation might in the near future affect common species such as M. alpestris. In addition, by identifying relevant landscape variables influencing population structure without unrealistic assumptions about dispersal, our method offers a simple and flexible tool of investigation as an alternative to least-cost models and other approaches.

Modelling functional landscape connectivity from genetic population structure: a new spatially explicit approach

Functional connectivity between spatially disjoint habitat patches is a key factor for the persistence of species in fragmented landscapes. Modelling landscape connectivity to identify potential dispersal corridors requires information about those landscape features affecting dispersal. Here we present a new approach using spatial and genetic data of a highly fragmented population of capercaillie (Tetrao urogallus) in the Black Forest, Germany, to investigate effects of landscape structure on gene flow and to parameterize a spatially explicit corridor model for conservation purposes. Mantel tests and multiple regressions on distance matrices were employed to detect and quantify the effect of different landscape features on relatedness among individuals, while controlling for the effect of geographic distance. We extrapolated the results to an area‐wide landscape permeability map and developed a new corridor model that incorporates stochasticity in simulating animal movement. The model was evaluated using both a partition of the data previously set apart and independent observation data of dispersing birds. Most land cover variables (such as coniferous forest, forest edges, agricultural land, roads, settlements) and one topographic variable (topographic exposure) were significantly correlated with gene flow. Although inter‐individual relatedness inherently varies greatly and the variance explained by geographic distance and landscape structure was low, the permeability map and the corridor model significantly explained relatedness in the validation data and the spatial distribution of dispersing birds. Thus, landscape structure measurably affected within‐population gene flow in the study area. By converting these effects into spatially explicit information our model enables localizing priority areas for the preservation or restoration of metapopulation connectivity.

HABITAT-QUALITY EFFECTS ON METAPOPULATION DYNAMICS IN GREATER WHITE-TOOTHED SHREWS, CROCIDURA RUSSULA

The effects of patch size and isolation on metapopulation dynamics have received wide empirical support and theoretical formalization. By contrast, the effects of patch quality seem largely underinvestigated, partly due to technical difficulties in properly assessing quality. Here we combine habitat-quality modeling with four years of demographic monitoring in a metapopulation of greater white-toothed shrews (Crocidura russula) to investigate the role of patch quality on metapopulation processes. Together, local patch quality and connectivity significantly enhanced local population sizes and occupancy rates (R2 = 14% and 19%, respectively). Accounting for the quality of patches connected to the focal one and acting as potential sources improved slightly the model explanatory power for local population sizes, pointing to significant source-sink dynamics. Local habitat quality, in interaction with connectivity, also increased colonization rate (R2 = 28%), suggesting the ability of immigrants to target high-quality patches. Overall, patterns were best explained when assuming a mean dispersal distance of 800 m, a realistic value for the species under study. Our results thus provide evidence that patch quality, in interaction with connectivity, may affect major demographic processes.

Inferring recent migration rates from individual genotypes

We present a novel and straightforward method for estimating recent migration rates between discrete populations using multilocus genotype data. The approach builds upon a two‐step sampling design, where individual genotypes are sampled before and after dispersal. We develop a model that estimates all pairwise backwards migration rates (mij, the probability that an individual sampled in population i is a migrant from population j) between a set of populations. The method is validated with simulated data and compared with the methods of BayesAss and Structure. First, we use data for an island model and then we consider more realistic data simulations for a metapopulation of the greater white‐toothed shrew (Crocidura russula). We show that the precision and bias of estimates primarily depend upon the proportion of individuals sampled in each population. Weak sampling designs may particularly affect the quality of the coverage provided by 95% highest posterior density intervals. We further show that it is relatively insensitive to the number of loci sampled and the overall strength of genetic structure. The method can easily be extended and makes fewer assumptions about the underlying demographic and genetic processes than currently available methods. It allows backwards migration rates to be estimated across a wide range of realistic conditions.

State of Emergency Medicine in Switzerland: a national profile of emergency departments in 2006

BackgroundEmergency departments (EDs) are an essential component of any developed health care system. There is, however, no national description of EDs in Switzerland. Our objective was to establish the number and location of EDs, patient visits and flow, medical staff and organization, and capabilities in 2006, as a benchmark before emergency medicine became a subspecialty in Switzerland.MethodsIn 2007, we started to create an inventory of all hospital-based EDs with a preliminary list from the Swiss Society of Emergency and Rescue Medicine that was improved with input from ED physicians nationwide. EDs were eligible if they offered acute care 24 h per day, 7 days per week. Our goal was to have 2006 data from at least 80% of all EDs. The survey was initiated in 2007 and the 80% threshold reached in 2012.ResultsIn 2006, Switzerland had a total of 138 hospital-based EDs. The number of ED visits was 1.475 million visits or 20 visits per 100 inhabitants. The median number of visits was 8,806 per year; 25% of EDs admitted 5,000 patients or less, 31% 5,001-10,000 patients, 26% 10,001-20,000 patients, and 17% >20,000 patients per year. Crowding was reported by 84% of EDs with >20,000 visits/year. Residents with limited experience provided care for 77% of visits. Imaging was not immediately available for all patients: standard X-ray within 15 min (70%), non-contrast head CT scan within 15 min (38%), and focused sonography for trauma (70%); 67% of EDs had an intensive care unit within the hospital, and 87% had an operating room always available.ConclusionsSwiss EDs were significant providers of health care in 2006. Crowding, physicians with limited experience, and the heterogeneity of emergency care capabilities were likely threats to the ubiquitous and consistent delivery of quality emergency care, particularly for time-sensitive conditions. Our survey establishes a benchmark to better understand future improvements in Swiss emergency care.