Habitat distribution modelling,under the present climatic scenario,of the threatened endemic Iberian Delphinium fissum subsp. sordidum (Ranunculaceae) and implications for its conservation

We modelled the potential habitat of a threatened species D. fissum subsp. sordidum, an endemic hemicryptophyte with a disjunct distribution in the Iberian Peninsula. Maxent was used to predict the subspecies habitat suitability by relating field sample-based distributional information with environmental and topographic variables. Our results suggest that the model performed well, predicting with high accuracy the current distribution of the species. The variables that most contributed to the model were Mean Temperature of Wettest Quarter (MTWtQ), Precipitation of Warmest Quarter (PWmQ), Temperature Annual Range (TAR) and Slope (Slo). These variables are biological significant for the taxon, as they have decisive influence in the critical stages of germination and fruiting. The current and potential distributional areas identified by the model fall mainly in regions with some degree of environmental protection, with some exceptions. A recovery plan for the species should be considered. Species Distribution Modelling cannot substitute long-term monitoring programmes, yet it is a useful tool for identifying appropriate areas of taxon occurrence, and thus allow for efficient use of the economic and human resources.     

Modeling distributions of disjunct populations of the Sierra Madre Sparrow

ABSTRACT Sierra Madre Sparrows (Xenospiza baileyi) are among the least known of all bird species in Mexico. Recent surveys have discovered previously unknown populations and the current known distribution of Sierra Madre Sparrows consists of two populations separated by >800 km. We used available distributional information to develop ecological niche models that (1) predict much of the distribution potential of the species, (2) establish that the broad disjunction separating the two populations has ecological correlates that appear to be important to the distributional of these sparrows, and (3) illustrate the extremely restricted ecological distribution of the species. We used two sets of climatic and topographic variables, with one including all 22 variables available and the second with only six variables that were positively related to quality of distributional models. Although indications of differences between the two sets of populations were found based on the full 22‐dimensional environmental dataset, such a highly dimensional analysis is vulnerable to over‐fitting; models based on the reduced dataset indicated that the two populations occur in areas with similar ecological conditions. Our models also suggest that southern population of Sierra Madre Sparrows covers most of their potential range in that region. The potential range of the northern population, however, extends beyond known points of occurrence. To clarify the distribution of Sierra Madre Sparrows and evaluate their status and conservation opportunities, detailed searches for additional populations in areas identified by the model are needed.     

Predicting reptile distributions at the mesoscale: relation to climate and topography

Aim To explore the respective power of climate and topography to predict the distribution of reptiles in Switzerland, hence at a mesoscale level. A more detailed knowledge of these relationships, in combination with maps of the potential distribution derived from the models, is a valuable contribution to the design of conservation strategies. Location All of Switzerland. Methods Generalized linear models are used to derive predictive habitat distribution models from eco‐geographical predictors in a geographical information system, using species data from a field survey conducted between 1980 and 1999. Results The maximum amount of deviance explained by climatic models is 65%, and 50% by topographical models. Low values were obtained with both sets of predictors for three species that are widely distributed in all parts of the country (Anguis fragilis, Coronella austriaca, and Natrix natrix), a result that suggests that including other important predictors, such as resources, should improve the models in further studies. With respect to topographical predictors, low values were also obtained for two species where we anticipated a strong response to aspect and slope, Podarcis muralis and Vipera aspis. Main conclusions Overall, both models and maps derived from climatic predictors more closely match the actual reptile distributions than those based on topography. These results suggest that the distributional limits of reptile species with a restricted range in Switzerland are largely set by climatic, predominantly temperature‐related, factors.     

Emerging patterns of genetic variation in the New Zealand endemic scallop Pecten novaezelandiae

Both historical and contemporary processes influence the genetic structure of species, but the relative roles of such processes are still difficult to access. Population genetic studies of species with recent evolutionary histories such as the New Zealand endemic scallop Pecten novaezelandiae (<1 Ma) permit testing of the effects of recent processes affecting gene flow and shaping genetic structure. In addition, studies encompassing the entire distributional range of species can provide insight into colonization processes. Analyses of genetic variation in P. novaezelandiae (952 individuals from 14 locations, genotyped at 10 microsatellite loci) revealed a weak but significant regional structure across the distributional range of the species, as well as latitudinal gradients of genetic diversity and differentiation: estimates of migration rates supported these patterns. Our results suggest that the observed genetic structure and latitudinal gradients reflect a stepping‐stone model of colonization (north to south) and emerging divergence of populations as a result of ongoing limitations to gene flow and insufficient time to reach migration–drift equilibrium. The low levels of interpopulation and interregional genetic differentiation detected over hundreds of kilometres reflect the recent evolutionary history of P. novaezelandiae and stand in contrast to patterns reported for other evolutionary older species at the same spatial scale. The outcomes of this study contribute to a better understanding of evolutionary processes influencing the genetic variation of species and provide vital information on the genetic structure of P. novaezelandiae.     

Influences of temporal independence of data on modelling species distributions

Modelling species distributions has been widely used to understand present and future potential distributions of species, and can provide adaptation and mitigation information as references for conservation and management under climate change. However, various methods of data splitting to develop and validate functions of the models do not get enough attention, which may mislead the interpretation of predicted results. We used the Taiwanese endemic birds to test the influences of temporal independence of datasets on model performance and prediction. Training and testing data were considered to be independent if they were collected during different survey periods (1993–2004 and 2009–2010). The results indicated no significant differences of six model performance measures (AUC, kappa, TSS, accuracy, sensitivity, and specificity) among the combinations of training and testing datasets. Both species- and grid cell-based assessments differed significantly between predictions by the annual and pooled training data. We also found an average of 85.8% similarity for species presences and absences in different survey periods. The remaining dissimilarity was mostly caused by species observed in the late survey period but not in the early one. The method of data splitting, yielding training and testing data, is critical for resulting model species distributions. Even if similar model performance exists, different methods can lead to different species distributional maps. More attention needs to be given to this issue, especially when amplifying these models to project species distributions in a changing world.     

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.     

The limits to biogeographical distributions: insights from the northward range extension of the marine snail, Kelletia kelletii (Forbes, 1852)

Aim The development of accurate models predicting species range shifts in response to climate change requires studies on the population biology of species whose distributional limits are in the process of shifting. We examine the population biology of an example system using the recent northward range expansion of the marine neogastropod Kelletia kelletii (Forbes, 1852). Location This is a marine coastal shelf neogastropod species whose range extends from Isla Asuncion (Baja California, Mexico) to Monterey (CA, USA). Research sites spanned the extent of the range. Methods We examine abundance distributions and size frequency distributions of K. kelletii for evidence of factors determining historic and contemporary distributional patterns. Population studies were supplemented by historic and contemporary hydrographic data, including seawater temperature data from California Cooperative Oceanic Fisheries Investigations (CalCOFI ) and National Data Buoy Center (NDBC), and seawater circulation data. Results The structure of recently established populations varied dramatically from that of historic populations. Markedly low densities and irregular size frequency distributions characterized recently established populations and suggested only occasionally successful recruitment. The point of transition between historic and recently established populations also corresponded to the location of a gradient in seawater temperature and the confluence of two major oceanic currents. The accumulated data suggest that temperature and/or barriers to dispersal could have set both contemporary patterns in population structure as well as the former northern range limit. Main conclusions Early life stages play a critical role in determining distributional patterns of K. kelletii. Dispersal barriers and temperature limitation are two plausible mechanisms that could determine both contemporary and historic distributional patterns. Future studies on this species should attempt to tease apart the relative importance of these factors in maintaining the populations at the northern edge of the range.     

Native range climate is insufficient to predict anuran invasive potential

Niche conservatism explains biological invasions worldwide. However, a plethora of ecological processes may lead invasive species to occupy environments that are different from those found within native ranges. Here, we assess the potential invadable areas of  the world’s most pervasive invasive amphibians: the cane toad, Rhinella marina?+?R. horribilis, and the North American bullfrog, Lithobates catesbeianus. The uncontrolled spread of such voracious, large-bodied, and disease-tolerant anurans has been documented to impact native faunas worldwide. To disentangle their invasion-related niche dynamics, we compared the predictive ability and distributional forecasts of ecological niche models calibrated with information from native, invaded and pooled (native?+?invaded) ranges. We found that including occurrences from invaded ranges improved model accuracy for both studied species. Non-native occurrences also accounted for 54% and 61% increase in the total area of potential distribution of the cane toad and bullfrog, respectively. Besides, the latter species occupied locations with climatic conditions that are more extreme than those found within its native range. Our results indicate that the occupancy of environments different from those found in native ranges increases the overall potential distribution of the studied invasive anuran species. Therefore, climate information on native ranges alone is insufficient to explain and anticipate the distributional patterns of invasion of cane toads and bullfrogs, underestimating predictions of potential invadable distribution. Moreover, such an observed expansion of realized niches towards occupancy of climates not found within native ranges also has clear implications for invasion risk assessments based on climate modelling worldwide.

     

Gene-specific universal mammalian sequence-tagged sites: Application to the canine genome

We are developing a genetic map of the dog based partly upon markers contained within known genes. In order to facilitate the development of these markers, we have used polymerase chain reaction (PCR) primers designed to conserved regions of genes that have been sequenced in at least two species. We have refined the method for designing primers to maximize the number that produce successful amplifications across as many mammalian species as possible. We report the development of primer sets for 11 loci in detail:CFTR, COL10A1, CSFIR, CYP1A1, DCN1, FES, GHR, GLB1, PKLR, PVALB, andRB1. We also report an additional 75 primer sets in the appendices. The PCR products were sequenced to show that the primers amplify the expected canine genes. These primer sets thus define a class of gene-specific sequence-tagged sites (STSs). There are a number of uses for these STSs, including the rapid development of various linkage tools and the rapid testing of genomic and cDNA libraries for the presence of their corresponding genes. Six of the eleven gene targets reported in detail have been proposed to serve as “anchored reference loci” for the development of mammalian genetic maps [O'Brien, S. J.,et al., Nat. Genet. 3:103, 1993]. The primer sets should cover a significant portion of the canine genome for the development of a linkage map. In order to determine how useful these primer sets would be for the other genome projects, we tested the 11 primer sets on the DNA from species representing five mammalian orders. Eighty-four percent of the gene-species combinations amplified successfully. We have named these primer sets “universal mammalian sequence-tagged sites” because they should be useful for many mammalian genome projects.     

Evaluating the use of macroscale variables as proxies for local aquatic variables and to model stream fish distributions

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Areas of endemism of Mexican mammals: reanalysis applying the optimality criterion

In order to test Mexican areas of endemism of mammals identified by previous parsimony analyses of endemicity (PAEs), we applied the optimality criterion to three data matrices (based on point records, potential distributional models and the fill option in software NDM). We modelled the ecological niches of 429 terrestrial mammal species using the genetic algorithm for rule-set prediction (GARP) and models were projected as potential distributional areas. We overlapped the point occurrence data and the individual maps of potential distributions to a grid of 1° latitude–longitude. Three matrices of 247 grid cells (areas) and 429 species were built: (1) a binary matrix with '0' for absence and '1' for presence of at least one record of the species inside the grid-cell; (2) a three-state matrix similar to (1) but assigning the state '2' to the assumed presence in the model of potential distribution; and (3) a three-state matrix similar to (2), but applying the fill option of software NDM instead of using a model. The optimality criterion was performed in NDM version 2.7 and results were examined with VNDM version 2.7. The first and second matrices showed 13 areas of endemism and the third identified 16 areas of endemism. NDM provided a better resolution than PAE, allowing us to identify several new areas of endemism, previously undetected. Ecological niche models, projected as potential distributional areas, and the optimality criterion are very useful to identify areas of endemism, although they should be used with caution because they may overpredict potential distributional areas. PAE seems to underestimate the areas of endemism identified.  © 2009 The Linnean Society of London, Biological Journal of the Linnean Society , 2009, 98 , 468–478.     

Forecasting Climate Change Effects on Salamander Distribution in the Highlands of Central Mexico1

A generalized decline of amphibian populations is occurring worldwide. The causes for such a decline are not completely understood; however, climate change has been identified as a possible cause for amphibian extinction, among others. Ecological niche modeling has proven to be a useful tool to predict potential distribution of species in the context of climatic changes. In this paper, we used the Genetic Algorithm for Rule‐set Prediction (GARP) to model the potential distributions of two species of plethodontid salamanders: Pseudoeurycea cephalica and P. leprosa. We projected their potential distributions under climatic scenarios expected in 50 yr based on a conservative scenario of global climate change and assuming a moderate dispersal ability for both species. Our analyses suggest that climate change effects may pose an additional long‐term risk to both species of plethodontid salamanders, with a more dramatic scenario in the case of P. leprosa. By the year 2050, this species may lose almost 75 percent of its distributional area, and this projection is even worse when deforestation (in the way it is occurring at present) is considered within the predicted model. Our results concur with those obtained for species with limited dispersal capability because they do not track changing climates, but rather face a loss of distributional area. The survival of these species is not secure, even though their potential distributional area falls within a considerable number of natural protected areas.     

Connecting species’ geographical distributions to environmental variables: range maps versus observed points of occurrence

Connecting the geographical occurrence of a species with underlying environmental variables is fundamental for many analyses of life history evolution and for modeling species distributions for both basic and practical ends. However, raw distributional information comes principally in two forms: points of occurrence (specific geographical coordinates where a species has been observed), and expert-prepared range maps. Each form has potential short-comings: range maps tend to overestimate the true occurrence of a species, whereas occurrence points (because of their frequent non-random spatial distribution) tend to underestimate it. Whereas previous comparisons of the two forms have focused on how they may differ when estimating species richness, less attention has been paid to the extent to which the two forms actually differ in their representation of a species’ environmental associations. We assess such differences using the globally distributed avian order Galliformes (294 species). For each species we overlaid range maps obtained from IUCN and point-of-occurrence data obtained from GBIF on global maps of four climate variables and elevation. Over all species, the median difference in distribution centroids was 234 km, and median values of all five environmental variables were highly correlated, although there were a few species outliers for each variable. We also acquired species’ elevational distribution mid-points (mid-point between minimum and maximum elevational extent) from the literature; median elevations from point occurrences and ranges were consistently lower (median −420 m) than mid-points. We concluded that in most cases occurrence points were likely to produce better estimates of underlying environmental variables than range maps, although differences were often slight. We also concluded that elevational range mid-points were biased high, and that elevation distributions based on either points or range maps provided better estimates.     

Physiology in ecological niche modeling: using zebra mussel's upper thermal tolerance to refine model predictions through Bayesian analysis

Climate change and human-mediated dispersal are increasingly influencing species’ geographic distributions. Ecological niche models (ENMs) are widely used in forecasting species’ distributions, but are weak in extrapolation to novel environments because they rely on available distributional data and do not incorporate mechanistic information, such as species’ physiological response to abiotic conditions. To improve accuracy of ENMs, we incorporated physiological knowledge through Bayesian analysis. In a case study of the zebra mussel Dreissena polymorpha, we used native and global occurrences to obtain native and global models representing narrower and broader understanding of zebra mussel’ response to temperature. We also obtained thermal limit and survival information for zebra mussel from peer-reviewed literature and used the two types of information separately and jointly to calibrate native models. We showed that, compared to global models, native models predicted lower relative probability of presence along zebra mussel's upper thermal limit, suggesting the shortcoming of native models in predicting zebra mussel's response to warm temperature. We also found that native models showed improved prediction of relative probability of presence when thermal limit was used alone, and best approximated global models when both thermal limit and survival data were used. Our result suggests that integration of physiological knowledge enhances extrapolation of ENM in novel environments. Our modeling framework can be generalized for other species or other physiological limits and may incorporate evolutionary information (e.g. evolved thermal tolerance), thus has the potential to improve predictions of species’ invasive potential and distributional response to climate change.     

Inferring species richness using multispecies occupancy modeling: Estimation performance and interpretation

Multispecies occupancy models can estimate species richness from spatially replicated multispecies detection/non‐detection survey data, while accounting for imperfect detection. A model extension using data augmentation allows inferring the total number of species in the community, including those completely missed by sampling (i.e., not detected in any survey, at any site). Here we investigate the robustness of these estimates. We review key model assumptions and test performance via simulations, under a range of scenarios of species characteristics and sampling regimes, exploring sensitivity to the Bayesian priors used for model fitting. We run tests when assumptions are perfectly met and when violated. We apply the model to a real dataset and contrast estimates obtained with and without predictors, and for different subsets of data. We find that, even with model assumptions perfectly met, estimation of the total number of species can be poor in scenarios where many species are missed (>15%–20%) and that commonly used priors can accentuate overestimation. Our tests show that estimation can often be robust to violations of assumptions about the statistical distributions describing variation of occupancy and detectability among species, but lower‐tail deviations can result in large biases. We obtain substantially different estimates from alternative analyses of our real dataset, with results suggesting that missing relevant predictors in the model can result in richness underestimation. In summary, estimates of total richness are sensitive to model structure and often uncertain. Appropriate selection of priors, testing of assumptions, and model refinement are all important to enhance estimator performance. Yet, these do not guarantee accurate estimation, particularly when many species remain undetected. While statistical models can provide useful insights, expectations about accuracy in this challenging prediction task should be realistic. Where knowledge about species numbers is considered truly critical for management or policy, survey effort should ideally be such that the chances of missing species altogether are low.     

Insights gained from a web-based atlas of halocyprid ostracods of the Southern Ocean

Planktonic ostracods are an important, but poorly studied component of open ocean plankton communities, which inhabit all depths and play a significant role in detrital cycles. A web-based atlas (http://ocean.iopan.gda.pl/ostracoda) of the distribution of Southern Ocean planktonic ostracods has been developed compiling all extractable published data together with a considerable amount of unpublished data from samples collected during Discovery investigations (1929–1952). The northern boundary of the Southern Ocean was taken pragmatically as 52°S. The website includes information that includes distributional maps, taxonomic drawings (mostly original), size data and systematic notes on 47 species. All the data are freely downloadable as PDF files and are thus available to anyone, anywhere, with access to the web.Published data are subject to a number of errors generated by faulty identifications and changes in the taxonomy. Most, but not all, published data could be included in drawing up the maps. Not all publications have included detailed positional data and from those that included distributional maps, it was not always possible to relate the plotted distributions to the published station listings. A lack of archived data and specimens for some of the records meant dubious records could not be validated. Data are now generally archived by national oceanographic data centres, but unless supported by voucher specimens further confusion may arise for those current species which are found to include cryptic species after classical morphological studies or molecular studies.One species (Boroecia antipoda) had an apparently anomalous distribution; specimens archived in the Copenhagen Museum were reexamined and the anomalies were shown to result from the fact that some of the specimens belong to a novel species. Generally, the limits to the distributional ranges of the species showed little coherence with major oceanographic features, such as the Antarctic convergence and hence, biogeographical provinces; possible reasons are discussed. Despite these possible inherent errors, the website not only provides a resource for species identification, but is also proving to be a powerful tool for generation of hypotheses and highlighting taxonomic problems.     

Species distribution models for Peruvian plantcutter improve with consideration of biotic interactions

Biotic interactions have been controversial in distributional ecology, mainly in regards to whether they have effects over broad extents, with the negative view known as the Eltonian noise hypothesis (ENH). In this study, we evaluated the ENH for Phytotoma raimondii, a restricted‐range Peruvian endemic bird species: we developed models based on 1) only abiotic conditions, 2) only host plant distributions, and 3) both abiotic conditions and host plant distributions; models were evaluated with partial receiver operating characteristic test and Akaike information criteria metrics. We rejected the ENH for this case: biotic interactions improved the model. The frequency with which exceptions to the ENH are detected has important implications for distributional ecology and methods for estimating distributions of species.     

Spatiotemporally explicit demographic modelling supports a joint effect of historical barriers to dispersal and contemporary landscape composition on structuring genomic variation in a red‐listed grasshopper

Inferring the processes underlying spatial patterns of genomic variation is fundamental to understand how organisms interact with landscape heterogeneity and to identify the factors determining species distributional shifts. Here, we use genomic data (restriction site‐associated DNA sequencing) to test biologically informed models representing historical and contemporary demographic scenarios of population connectivity for the Iberian cross‐backed grasshopper Dociostaurus hispanicus, a species with a narrow distribution that currently forms highly fragmented populations. All models incorporated biological aspects of the focal taxon that could hypothetically impact its geographical patterns of genomic variation, including (a) spatial configuration of impassable barriers to dispersal defined by topographic landscapes not occupied by the species; (b) distributional shifts resulting from the interaction between the species bioclimatic envelope and Pleistocene glacial cycles; and (c) contemporary distribution of suitable habitats after extensive land clearing for agriculture. Spatiotemporally explicit simulations under different scenarios considering these aspects and statistical evaluation of competing models within an Approximate Bayesian Computation framework supported spatial configuration of topographic barriers to dispersal and human‐driven habitat fragmentation as the main factors explaining the geographical distribution of genomic variation in the species, with no apparent impact of hypothetical distributional shifts linked to Pleistocene climatic oscillations. Collectively, this study supports that both historical (i.e., topographic barriers) and contemporary (i.e., anthropogenic habitat fragmentation) aspects of landscape composition have shaped major axes of genomic variation in the studied species and emphasizes the potential of model‐based approaches to gain insights into the temporal scale at which different processes impact the demography of natural populations.     

The effect of the extent of the study region on GIS models of species geographic distributions and estimates of niche evolution: preliminary tests with montane rodents (genus Nephelomys) in Venezuela

Aim Various techniques model a species’ niche and potential distribution by comparing the environmental conditions of occurrence localities with those of the overall study region (via a background or pseudoabsence sample). Here, we examine how changes in the extent of the study region (ignored or under‐appreciated in most studies) affect models of two rodents, Nephelomys caracolus and Nephelomys meridensis. Location North‐central South America. Methods We used Maxent to model the species' potential distributions via two methods of defining the study region. In Method 1 (typical of most studies to date), we calibrated the model in a large study region that included the ranges of both species. In Method 2, we calibrated the model using a smaller study region surrounding the localities of the focal species, and then applied it to the larger region. Because the study region of Method 1 is likely to include areas of suitable conditions that are unoccupied because of dispersal limitations and/or biotic interactions, this approach is prone to overfitting to conditions found near the occupied localities. In contrast, Method 2 should avoid such problems but may require further assumptions (‘clamping’ in Maxent ) to make predictions for areas with environmental conditions beyond those found in the smaller study region. For each method, we calculated several measures of geographic interpredictivity between predictions for the species (cross‐species AUC, cross‐species omission rate, and proportional geographic overlap). Results Compared with Method 1, Method 2 revealed a larger predicted area for each species, less concentrated around known localities (especially for N. caracolus). It also led to higher cross‐species AUC values, lower cross‐species omission rates and higher proportions of geographic overlap. Clamping was minimal and occurred primarily in regions unlikely to be suitable. Main conclusions Method 2 led to more realistic predictions and higher estimates of niche conservatism. Conclusions reached by many studies depend on the selection of an appropriate study region. Although detailed information regarding dispersal limitations and/or biotic interactions will typically be difficult to obtain, consideration of coarse distributional patterns, topography and vegetational zones often should permit delimitation of a much more reasonable study region than the extremely large ones currently in common use.