Combining a generic process-based productivity model and a statistical classification method to predict the presence and absence of tree species in the Pacific Northwest,U.S.A.

Although long-lived tree species experience considerable environmental variation over their life spans, their geographical distributions reflect sensitivity mainly to mean monthly climatic conditions. We introduce an approach that incorporates a physiologically based growth model to illustrate how a half-dozen tree species differ in their responses to monthly variation in four climatic-related variables: water availability, deviations from an optimum temperature, atmospheric humidity deficits, and the frequency of frost. Rather than use climatic data directly to correlate with a species’ distribution, we assess the relative constraints of each of the four variables as they affect predicted monthly photosynthesis for Douglas-fir, the most widely distributed species in the region. We apply an automated regression-tree analysis to create a suite of rules, which differentially rank the relative importance of the four climatic modifiers for each species, and provide a basis for predicting a species’ presence or absence on 3737 uniformly distributed U.S. Forest Services’ Forest Inventory and Analysis (FIA) field survey plots. Results of this generalized rule-based approach were encouraging, with weighted accuracy, which combines the correct prediction of both presence and absence on FIA survey plots, averaging 87%. A wider sampling of climatic conditions throughout the full range of a species’ distribution should improve the basis for creating rules and the possibility of predicting future shifts in the geographic distribution of species.     

Building statistical models to analyze species distributions.

Models of the geographic distributions of species have wide application in ecology. But the nonspatial, single-level, regression models that ecologists have often employed do not deal with problems of irregular sampling intensity or spatial dependence, and do not adequately quantify uncertainty. We show here how to build statistical models that can handle these features of spatial prediction and provide richer, more powerful inference about species niche relations, distributions, and the effects of human disturbance. We begin with a familiar generalized linear model and build in additional features, including spatial random effects and hierarchical levels. Since these models are fully specified statistical models, we show that it is possible to add complexity without sacrificing interpretability. This step-by-step approach, together with attached code that implements a simple, spatially explicit, regression model, is structured to facilitate self-teaching. All models are developed in a Bayesian framework. We assess the performance of the models by using them to predict the distributions of two plant species (Proteaceae) from South Africa's Cape Floristic Region. We demonstrate that making distribution models spatially explicit can be essential for accurately characterizing the environmental response of species, predicting their probability of occurrence, and assessing uncertainty in the model results. Adding hierarchical levels to the models has further advantages in allowing human transformation of the landscape to be taken into account, as well as additional features of the sampling process.     

What does ecological modelling model? A proposed classification of ecological niche models based on their underlying methods

Species distribution model is the term most frequently used in ecological modelling, but other authors used instead predictive habitat distribution model or species-habitat models. A consensual ecological modelling terminology that avoids misunderstandings and takes into account the ecological niche theory does not exist at present. Moreover, different studies differ in the type of niche that is represented by similar distribution models. I propose to use as standard ecological modelling terminology the terms “ecological niche”, “potential niche”, “realized niche” models (for modelling their respective niches), and “habitat suitability map” (for the output of the niche models). Therefore, the user can understand more easily that models always forecast species’ niche and relate more closely the different types of niche models.     

Sensitivity of species-distribution models to error,bias, and model design: An application to resource selection functions for woodland caribou

Models that predict distribution are now widely used to understand the patterns and processes of plant and animal occurrence as well as to guide conservation and management of rare or threatened species. Application of these methods has led to corresponding studies evaluating the sensitivity of model performance to requisite data and other factors that may lead to imprecise or false inferences. We expand upon these works by providing a relative measure of the sensitivity of model parameters and prediction to common sources of error, bias, and variability. We used a one-at-a-time sample design and GPS location data for woodland caribou (Rangifer tarandus caribou) to assess one common species-distribution model: a resource selection function. Our measures of sensitivity included change in coefficient values, prediction success, and the area of mapped habitats following the systematic introduction of geographic error and bias in occurrence data, thematic misclassification of resource maps, and variation in model design. Results suggested that error, bias and model variation have a large impact on the direct interpretation of coefficients. Prediction success and definition of important habitats were less responsive to the perturbations we introduced to the baseline model. Model coefficients, prediction success, and area of ranked habitats were most sensitive to positional error in species locations followed by sampling bias, misclassification of resources, and variation in model design. We recommend that researchers report, and practitioners consider, levels of error and bias introduced to predictive species-distribution models. Formal sensitivity and uncertainty analyses are the most effective means for evaluating and focusing improvements on input data and considering the range of values possible from imperfect models.     

GIS-based modeling of the geographic distribution of Quercus emoryi Torr. (Fagaceae) in México and identification of significant environmental factors influencing the species’ distribution

The greatest concentration of oak species in the world is believed to be found in Mexico. These species are potentially useful for reforestation because of their capacity to adapt to diverse environments. Knowledge of their geographic distribution and of species–environment relations is essential for decision-making in the management and conservation of natural resources. The objectives of this study were to develop a model of the distribution of Quercus emoryi Torr. in Mexico, using geographic information systems and data layers of climatic and other variables, and to determine the variables that significantly influence the distribution of the species. The study consisted of the following steps: (A) selection of the target species from a botanical scientific collection, (B) characterization of the collecting sites using images with values or categories of the variables, (C) model building with the overlay of images that meet the habitat conditions determined from the characterization of sites, (D) model validation with independent data in order to determine the precision of the model, (E) model calibration through adjustment of the intervals of some variables, and (F) sensitivity analysis using precision and concordance non-parametric statistics applied to pairs of images. Results show that the intervals of the variables that best describe the species’ habitat are the following: altitude from 1650 to 2750 amsl, slope from 0 to 66°; average minimum temperature of January from −12 to −3 °C; mean temperature of June from 11 to 25 °C; mean annual precipitation from 218 to 1225 mm; soil units: lithosol, eutric cambisol, haplic phaeozem, chromic luvisol, rendzina, luvic xerosol, mollic planosol, pellic vertisol, eutric regosol; type of vegetation: oak forest, oak–pine forest, pine forest, pine–oak forest, juniperus forest, low open forest, natural grassland and chaparral. The resulting model of the geographic distribution of Quercus emoryi in Mexico had the following values for non-parametric statistics of precision and agreement: Kappa index of 0.613 and 0.788, overall accuracy of 0.806 and 0.894, sensitivity of 0.650 and 0.825, specificity of 0.963, positive predictive value of 0.945 and 0.957 and negative predictive value of 0.733 and 0.846. Results indicate that the variable average minimum temperature of January, with a maximum value of −3 °C, is an important factor in limiting the species’ distribution.     

Cross-validation of species distribution models: removing spatial sorting bias and calibration with a null model

Species distribution models are usually evaluated with cross-validation. In this procedure evaluation statistics are computed from model predictions for sites of presence and absence that were not used to train (fit) the model. Using data for 226 species, from six regions, and two species distribution modeling algorithms (Bioclim and MaxEnt), I show that this procedure is highly sensitive to "spatial sorting bias": the difference between the geographic distance from testing-presence to training-presence sites and the geographic distance from testing-absence (or testing-background) to training-presence sites. I propose the use of pairwise distance sampling to remove this bias, and the use of a null model that only considers the geographic distance to training sites to calibrate cross-validation results for remaining bias. Model evaluation results (AUC) were strongly inflated: the null model performed better than MaxEnt for 45% and better than Bioclim for 67% of the species. Spatial sorting bias and area under the receiver-operator curve (AUC) values increased when using partitioned presence data and random-absence data instead of independently obtained presence-absence testing data from systematic surveys. Pairwise distance sampling removed spatial sorting bias, yielding null models with an AUC close to 0.5, such that AUC was the same as null model calibrated AUC (cAUC). This adjustment strongly decreased AUC values and changed the ranking among species. Cross-validation results for different species are only comparable after removal of spatial sorting bias and/or calibration with an appropriate null model.     

Maximum entropy modeling of species geographic distributions

The availability of detailed environmental data, together with inexpensive and powerful computers, has fueled a rapid increase in predictive modeling of species environmental requirements and geographic distributions. For some species, detailed presence/absence occurrence data are available, allowing the use of a variety of standard statistical techniques. However, absence data are not available for most species. In this paper, we introduce the use of the maximum entropy method (Maxent) for modeling species geographic distributions with presence-only data. Maxent is a general-purpose machine learning method with a simple and precise mathematical formulation, and it has a number of aspects that make it well-suited for species distribution modeling. In order to investigate the efficacy of the method, here we perform a continental-scale case study using two Neotropical mammals: a lowland species of sloth, Bradypus variegatus, and a small montane murid rodent, Microryzomys minutus. We compared Maxent predictions with those of a commonly used presence-only modeling method, the Genetic Algorithm for Rule-Set Prediction (GARP). We made predictions on 10 random subsets of the occurrence records for both species, and then used the remaining localities for testing. Both algorithms provided reasonable estimates of the species’ range, far superior to the shaded outline maps available in field guides. All models were significantly better than random in both binomial tests of omission and receiver operating characteristic (ROC) analyses. The area under the ROC curve (AUC) was almost always higher for Maxent, indicating better discrimination of suitable versus unsuitable areas for the species. The Maxent modeling approach can be used in its present form for many applications with presence-only datasets, and merits further research and development.     

Species distribution modelling: Does one size fit all? A phytogeographic analysis of Salix in Ontario

Empirical models for predicting the distribution of organisms from environmental data have often focused on principles of ecological niche theory. However, even at large scales, there is little agreement over how to represent the dimensions of a species’ niche. The performance of such models is greatly affected by the nature of species distributional and environmental data. Regional scale distribution models were developed for 30 willow species in Ontario to examine (i) the predictive ability of logistic regression analysis, and (ii) the effects of using different distributional and environmental data sets. Two original measures of model accuracy and over-prediction were employed and evaluated using independent data. Models based on unique combinations of monthly climate data predicted distributions most accurately for all species. Models based on a fixed set of variables, while generating the highest average probabilities of occurrence for certain species with limited ranges, resulted in the greatest under- and over-estimates of willow distributions. Comparisons of models demonstrated climatic patterns among willows of differing habit and habitat. The distribution of dwarf willow species, present only in the Ontario arctic, followed gradients of summer maximum temperatures. The distribution of the tree species in the southerly portions of the province followed gradients of fall and winter minimum temperatures. Regardless of distributional and environmental data input, no algorithm maximized model performance for all species. Individual species models require individual approaches; i.e., the variable selection technique, the set of environmental factors used as predictors, and the nature of species distributional data must be carefully matched to the intended application. An understanding of evolutionary processes enhances the meaningful interpretation of individual species models. Unless sampling bias and species prevalence can be accounted for, models based on collection point data are best used to guide field surveys. While inferred range data may be better suited to determine potential ecological niches, overestimation of species prevalence and environmental tolerance must be recognized. A combination of available distributional data types is recommended to best determine species niches, an important step in developing conservation strategies.     

Landscape-level impact of tropical forest loss and fragmentation on bird occurrence in eastern Guatemala

Tropical forest destruction and fragmentation of habitat patches may reduce population persistence at the landscape level. Given the complex nature of simultaneously evaluating the effects of these factors on biotic populations, statistical presence/absence modelling has become an important tool in conservation biology. This study uses logistic regression to evaluate the independent effects of tropical forest cover and fragmentation on bird occurrence in eastern Guatemala. Logistic regression models were constructed for 10 species with varying response to habitat alteration. Predictive variables quantified forest cover, fragmentation and their interaction at three different radii (200, 500 and 1000 m scales) of 112 points where presence of target species was determined. Most species elicited a response to the 1000 m scale, which was greater than most species’ reported territory size. Thus, their presence at the landscape scale is probably regulated by extra-territorial phenomena, such as dispersal. Although proportion of forest cover was the most important predictor of species’ presence, there was strong evidence of area-independent and -dependent fragmentation effects on species presence, results that contrast with other studies from northernmost latitudes. Species’ habitat breadth was positively correlated with AIC model values, indicating a better fit for species more restricted to tropical forest. Species with a narrower habitat breadth also elicited stronger negative responses to forest loss. Habitat breadth is thus a simple measure that can be directly related to species’ vulnerability to landscape modification. Model predictive accuracy was acceptable for 4 of 10 species, which were in turn those with narrower habitat breadths.     

Population structure of red drum (Sciaenops ocellatus) in the northern Gulf of Mexico, as inferred from variation in nuclear-encoded microsatellites

Allelic variation at eight nuclear-encoded microsatellites was assayed among 967 red drum (Sciaenops ocellatus) sampled from four consecutive cohorts at seven geographic localities (=28 samples total) in the northern Gulf of Mexico (Gulf). Number of alleles per microsatellite ranged from 6 to 21; average direct-count heterozygosity values per sample (-SE) ranged from 0.560ǂ.018 to 0.903ǂ.009. Tests of Hardy-Weinberg equilibrium revealed significant departures from expected genotype proportions at one microsatellite, which was omitted from further analysis. Tests of genotypic equilibrium indicated that genotypes between pairs of microsatellites were randomly associated. Homogeneity tests of allele distributions across cohorts within localities were non-significant following correction for multiple tests executed simultaneously, and results from molecular analysis of variance indicated that the genetic variance component attributable to variation among cohorts did not differ significantly from zero. Homogeneity tests of allele distributions among localities (cohorts pooled) revealed significant differences both before and after correction for multiple tests. Neighbor-joining clustering of a pairwise matrix of Š values (an unbiased estimator of F_ST), spatial autocorrelations, and regression analysis revealed a pattern of isolation by distance, where genetic divergence among geographic samples increases with geographic distance between sample localities. The pattern and degree of temporal and spatial divergence in the nuclear-encoded microsatellites paralleled almost exactly those of mitochondrial (mt) DNA, as determined in a prior study. Stability of both microsatellite and mtDNA allele distributions within localities indicates that the small but significant genetic divergence among geographic samples represents true signal and that overlapping populations of red drum in the northern Gulf may be influenced by independent population dynamics. The degree of genetic divergence in microsatellites and mtDNA is virtually identical, indicating that genetic effective size of microsatellites and mtDNA in red drum are the same. This, in turn, suggests either that gene flow in red drum in the northern Gulf could be biased sexually or that red drum populations may not be in equilibrium between genetic drift and migration. If a sexual bias exists, the observation that divergence in mtDNA is considerably less than 4 times that of microsatellites could suggest female-mediated dispersal and/or male philopatry. The observed isolation-by-distance effect indicates a practical limit to dispersal. Approximate estimates of geographic neighborhood size suggest the limit is in the range 700-900 km. Although the genetic studies of red drum indicate significant genetic divergence across the northern Gulf, the genetic differences do not delimit specific populations or stocks with fixed geographic boundaries.     

Null models reveal preferential sampling, spatial autocorrelation and overfitting in habitat suitability modelling

Nowadays, species are driven to extinction at a high rate. To reduce this rate it is important to delineate suitable habitats for these species in such a way that these areas can be suggested as conservation areas. The use of habitat suitability models (HSMs) can be of great importance for the delineation of such areas. In this study MaxEnt, a presence-only modelling technique, is used to develop HSMs for 223 nematode species of the Southern Bight of the North Sea. However, it is essential that these models are beyond discussion and they should be checked for potential errors. In this study we focused on two categories (1) errors which can be attributed to the database such as preferential sampling and spatial autocorrelation and (2) errors induced by the modelling technique such as overfitting, In order to quantify these adverse effects thousands of nulls models were created. The effect of preferential sampling (i.e. some areas where visited more frequenty than others) was investigated by comparing model outcomes based from null models sampling the actual sampling stations and null models sampling the entire mapping area (Raes and ter Steege, 2007). Overfitting is exposed by a fivefold cross-validation and the influence of spatial autocorrelation is assessed by separating test and training sets in space. Our results clearly show that all these effects are present: preferential sampling has a strong effect on the selection of non-random species models. Crossvalidation seems to have less influence on the model selection and spatial autocorrelation is also strongly present. It is clear from this study that predefined thresholds are not readily applicable to all datasets and additional tests are needed in model selection.     

Model based grouping of species across environmental gradients

We present a novel approach to the statistical analysis and prediction of multispecies data. The approach allows the simultaneous grouping and quantification of multiple species’ responses to environmental gradients. The underlying statistical model is a finite mixture model, where mixing is performed over the individual species’ responses to environmental gradients. Species with similar responses are grouped with minimal information loss. We term these groups species archetypes. Each species archetype has an associated GLM that can be used to predict distributions with appropriate measures of uncertainty. Initially, we illustrate the concept and method using artificial data and then with application to real data comprising 200 species from the Great Barrier Reef (GBR) lagoon on 13 oceanographic and geological gradients from 12°S to 24°S. The 200 species from the GBR are well represented by 15 species archetypes. The model is interpreted through maps of the probability of presence for a fine scale set of locations throughout the study area. Maps of uncertainty are also produced to provide statistical context. The presence of each species archetype was strongly influenced by oceanographic gradients, principally temperature, oxygen and salinity. The number of species in each group ranged from 4 to 34. The method has potential application to the analysis of multispecies distribution patterns and for multispecies management.     

How decisions about fitting species distribution models affect conservation outcomes

Species distribution models (SDMs) are increasingly used in conservation and land-use planning as inputs to describe biodiversity patterns. These models can be built in different ways, and decisions about data preparation, selection of predictor variables, model fitting, and evaluation all alter the resulting predictions. Commonly, the true distribution of species is unknown and independent data to verify which SDM variant to choose are lacking. Such model uncertainty is of concern to planners. We analyzed how 11 routine decisions about model complexity, predictors, bias treatment, and setting thresholds for predicted values altered conservation priority patterns across 25 species. Models were created with MaxEnt and run through Zonation to determine the priority rank of sites. Although all SDM variants performed well (area under the curve >0.7), they produced spatially different predictions for species and different conservation priority solutions. Priorities were most strongly altered by decisions to not address bias or to apply binary thresholds to predicted values; on average 40% and 35%, respectively, of all grid cells received an opposite priority ranking. Forcing high model complexity altered conservation solutions less than forcing simplicity (14% and 24% of cells with opposite rank values, respectively). Use of fewer species records to build models or choosing alternative bias treatments had intermediate effects (25% and 23%, respectively). Depending on modeling choices, priority areas overlapped as little as 10–20% with the baseline solution, affecting top and bottom priorities differently. Our results demonstrate the extent of model-based uncertainty and quantify the relative impacts of SDM building decisions. When it is uncertain what the best SDM approach and conservation plan is, solving uncertainty or considering alterative options is most important for those decisions that change plans the most.     

Site specificity in advertisement calls and responses to playbacks of local and foreign call variants in satin bowerbirds, <Emphasis Type="Italic">Ptilonorhynchus violaceus</Emphasis>

Avian vocalisations often show patterns of geographic variation. Previous work on the satin bowerbird has shown that although spatial variation in this species’ advertisement calls is strongly associated with habitat structure, some variation is apparent within habitat types. Seventeen populations located throughout the species’ distribution were used to examine whether spatial call variation could be influenced by other processes such as random drift or the presence of fine-scale vocal traditions; if this were the case, differing call variants would be expected at geographically discrete sampling sites both within and among habitat types. There were population-specific call variants at each of the sites sampled, with different variants apparent even within habitat types. At most sites, individuals gave only a single variant of advertisement call, and the call variant at one site, sampled after a 5-year interval, appears to have been relatively stable. Playback experiments were conducted at three populations to examine whether local call variants invoked a greater response than several non-local variants differing in their degree of similarity to the local variant. Birds responded strongly to local call variants but not to either of two foreign variants, one of which was similar to their local variant and one of which was very different. A pattern of geographic variation across populations, the fact that local and non-local variants evoke different responses and circumstantial evidence indicating that individuals can learn new calls all suggest that factors affecting song learning and the ability of males to establish and defend a bower site may have contributed to the establishment of geographically variable vocal cultures in this species.     

Evaluating predictive errors of a complex environmental model using a general linear model and least square means

A general linear model (GLM) was used to evaluate the deviation of predicted values from expected values for a complex environmental model. For this demonstration, we used the default level interface of the regional mercury cycling model (R-MCM) to simulate epilimnetic total mercury concentrations in Vermont and New Hampshire lakes based on data gathered through the EPAs Regional Environmental Monitoring and Assessment Program (REMAP). The response variable for the GLM was defined as R-MCMs predictive error: the difference between observed mercury concentrations and modeled mercury concentrations in each lake. Least square means of the response variable are used as an estimate of the magnitude and significance of bias, i.e., a statistically discernable trend in predictive errors for a given lake type, e.g., acidic, stratified, or oligotrophic. Using our approach, we determined lake types where significant over-prediction and under-prediction of epilimnetic total mercury concentration was occurring, i.e., regions in parameter space where the model demonstrated significant bias was distinguished from regions where no significant bias existed. This technique is most effective for finding regions of parameter space where bias is significant. Drawing conclusions concerning regions that show no significant bias can be misleading. The significant interaction terms in the GLM demonstrated that addressing this problem using univariate statistical techniques would lead to a loss of important information.     

Heterogeneity in the photoprotective capacity of three Antarctic diatoms during short-term changes in salinity and temperature

The Antarctic marine ecosystem changes seasonally, forming a temporal continuum of specialised niche habitats including open ocean, sea ice and meltwater environments. The ability for phytoplankton to acclimate rapidly to the changed conditions of these environments depends on the species’ physiology and photosynthetic plasticity and may ultimately determine their long-term ecological niche adaptation. This study investigated the photophysiological plasticity and rapid acclimation response of three Antarctic diatoms—Fragilariopsis cylindrus, Pseudo-nitzschia subcurvata and Chaetoceros sp.—to a selected range of temperatures and salinities representative of the sea ice, meltwater and pelagic habitats in the Antarctic. Fragilariopsis cylindrus displayed physiological traits typical of adaptation to the sea ice environment. Equally, this species showed photosynthetic plasticity, acclimating to the range of environmental conditions, explaining the prevalence of this species in all Antarctic habitats. Pseudo-nitzschia subcurvata displayed a preference for the meltwater environment, but unlike F. cylindrus, photoprotective capacity was low and regulated via changes in PSII antenna size. Chaetoceros sp. had high plasticity in non-photochemical quenching, suggesting adaptation to variable light conditions experienced in the wind-mixed pelagic environment. While only capturing short-term responses, this study highlights the diversity in photoprotective capacity that exists amongst three dominant Antarctic diatom species and provides insight into links between ecological niche adaptation and species’ distribution.     

Assessing the Geographic Representativeness of Genebank Collections: the Case of Bolivian Wild Potatoes

Abstract: Genebank collection databases can be used for ecogeographical studies under the assumption that the accessions are a geographically unbiased sample. We evaluated the representativeness of a collection of wild potatoes from Bolivia and defined and assessed four types of bias: species, species-area, hotspot, and infrastructure. Species bias is the sampling of some species more often than others. Species-area bias is a sampling that is disproportionate to the total area in which a species is found. Hotspot bias is the disproportionate sampling of areas with high levels of diversity. Infrastructure bias is the disproportionate sampling of areas near roads and towns. Each of these biases is present in the Bolivian wild potato collection. The infrastructure bias was strong: 60% of all wild potato accessions were collected within 2 km of a road, as opposed to 22%, if collections had been made randomly. This analysis can serve as a guide for future collecting trips. It can also provide baseline information for the application of genebank data in studies based on geographic information systems.     

Stepping-stone gene flow in the solitary coral Balanophyllia elegans: equilibrium and nonequilibrium at different spatial scales

Limited dispersal should result in genetic differences between populations proportional to geographic distances of separation. This association between gene flow and distance can be disrupted by (1) continuing genetic exchange among distant populations, (2) historical changes in gene flow, and (3) physical barriers or corridors to dispersal. The movements of larvae are thought to determine dispersal capability in benthic marine invertebrates. The solitary scleractinian Balanophyllia elegans Verrill possesses crawling larvae capable of only limited dispersal. Paradoxically, however, inferred levels of gene flow between pairs of localities spread over much of the 4000 km range of B. elegans exhibited a weaker relationship with geographical separation than that expected for a linear array of populations in which all genetic exchange takes place between adjacent populations. In this paper, I examined the pattern of gene flow (inferred from the frequencies of eight polymorphic allozyme loci) in B. elegans at a smaller (1 to 50 km) spatial scale to determine (1) whether gene flow at this spatial scale conformed to the expectations of the stepping-stone model, and (2) whether continuing long-distance gene flow or historical changes in gene flow were responsible for the weak relationship between gene flow and distance observed previously at the rangewide spatial scale. Between May and August 1992, I collected 75 adults from each of 18 localities along the coast of Sonoma County, California, USA. These populations of B. elegans were significantly subdivided both among localities separated by 1 to 50 km (F _LT =0.053, Se=0.0075) and among patches separated by 4 to 8 m (F PL=0.026, SE=0.0023). The observed slope and correlation (r ²=0.54) between inferred levels of gene flow and the geographic distance at the 1 to 50 km spatial scale conformed to equilibrium expectations (obtained by simulation) for a linear stepping-stone model, although those from the rangewide spatial scale did not. This implies that the mechanisms conferring patterns of inferred genetic differentiation between localities in B. elegans differ fundamentally with spatial scale. At a scale of 1 to 50 km, continuing gene flow and drift have equilibrated and the process of isolation-bydistance may facilitate local adaptive change. At a broader spatial scale, historical changes in gene flow, perhaps affected by late Pleistocene climatic fluctuations, disrupt the equilibration of gene flow and genetic drift, so that genetic differentiation may not increase continuously with separation between populations.