Predicting species distributions in poorly-studied landscapes

Conservationists are increasingly relying on distribution models to predict where species are likely to occur, especially in poorly-surveyed but biodiverse areas. Modeling is challenging in these cases because locality data necessary for model formation are often scarce and spatially imprecise. To identify methods best suited to modeling in these conditions, we compared the success of three algorithms (Maxent, Mahalanobis Typicalities and Random Forests) at predicting distributions of eight bird and eight mammal species endemic to the eastern slopes of the central Andes. We selected study species to have a range of locality sample sizes representative of the data available for endemic species of this region and also that vary in their distribution characteristics. We found that for species that are known from moderate numbers (N = 38–94) of localities, the three methods performed similarly for species with restricted distributions but Maxent and Random Forests yielded better results for species with wider distributions. For species with small numbers of sample localities (N = 5–21), Maxent produced the most consistently successful results, followed by Random Forests and then Mahalanobis Typicalities. Because evaluation statistics for models derived from few localities can be suspect due to the poor spatial representation of the evaluation data, we corroborated these results with review by scientists familiar with the species in the field. Overall, Maxent appears to be the most capable method for modeling distributions of Andean bird and mammal species because of the consistency of results in varying conditions, although the other methods have strengths in certain situations. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Predicting the species composition of Nardus stricta communities by logistic regression modelling

Question: Predictive models in plant ecology usually deal with single species or community types. Little effort has so far been made to predict the species composition of a community explicitly. The modelling approach presented here provides a conceptual framework on how to achieve this by combining habitat models for a large number of species to an additive community model. Our approach is exemplified by Nardus stricta communities (acidophilous, low‐productive grassland). Location: Large areas of Germany, 0–2040 m a.s.l. Methods: Logistic regression is applied for individual species models which are subsequently combined for an explicit prediction of species composition. Several parameters reflecting soil, management and climatic conditions serve as predictor variables. For validation, bootstrap and jackknife resampling procedures are used as well as ordination techniques (DCA, CCA). Results: We calculated significant models for 138 individual species. The predictions of species composition and species richness yield good agreements with the observed data. DCA and CCA results show that the community model preserves the main patterns in floristic space. Conclusions: Our approach of predicting species composition is an effective tool that can be applied in nature conservation, e.g. to assess the effects of different site conditions and alternative management scenarios on species composition and richness.     

Scenario-based assessment of future land use change on butterfly species distributions

Species distribution models (SDMs) are increasingly used to predict environmentally induced range shifts of habitats of plant and animal species. Consequently SDMs are valuable tools for scientifically based conservation decisions. The aims of this paper are (1) to identify important drivers of butterfly species persistence or extinction, and (2) to analyse the responses of endangered butterfly species of dry grasslands and wetlands to likely future landscape changes in Switzerland. Future land use was represented by four scenarios describing: (1) ongoing land use changes as observed at the end of the last century; (2) a liberalisation of the agricultural markets; (3) a slightly lowered agricultural production; and (4) a strongly lowered agricultural production. Two model approaches have been applied. The first (logistic regression with principal components) explains what environmental variables have significant impact on species presence (and absence). The second (predictive SDM) is used to project species distribution under current and likely future land uses. The results of the explanatory analyses reveal that four principal components related to urbanisation, abandonment of open land and intensive agricultural practices as well as two climate parameters are primary drivers of species occurrence (decline). The scenario analyses show that lowered agricultural production is likely to favour dry grassland species due to an increase of non-intensively used land, open canopy forests, and overgrown areas. In the liberalisation scenario dry grassland species show a decrease in abundance due to a strong increase of forested patches. Wetland butterfly species would decrease under all four scenarios as their habitats become overgrown.     

Long-term monitoring data meet freshwater species distribution models: Lessons from an LTER-site

Long-term monitoring datasets provide a solid framework for ecological research. Such a dataset from the German long-term ecological research (LTER) site Rhine-Main-Observatory was used to set up a species distribution model (SDM) for the Kinzig catchment. The extensive knowledge on the monitoring data provided by the LTER-site framework allowed to calibrate a robust model for 175 taxa of stream macroinvertebrates and to project their distributions on the Kinzig River stream network using bioclimatic, topographical, hydrological, land use and geological predictors. On average, model performance was good, with a TSS of 0.83 (±0.09 SD) and a ROC of 0.95 (±0.03 SD). The model delivered valuable insights on three sources of bias that plague SDMs in general: (a) level of taxonomic identification of the modeled organisms, (b) the spatial arrangement of sampling sites, and (c) the sampling intensity at each sampling site. Taxonomic resolution did not affect SDM performance. The distribution of high predicted probabilities of occurrence in the stream network coincided with those segments in the stream network most densely and frequently sampled, indicating both a spatial and temporal sampling bias. Species richness curves confirmed the temporal sampling bias. Next to spatial bias, sampling frequency also plays an important role in data collection, affecting further analysis and modeling procedures. Results indicate an underrepresentation of low order streams, an important aspect that should be addressed by both monitoring schemes and modeling approaches.     

Analytical theory of species abundance distributions of a random community model

We review the history and recent progress of the analytical theories of a random community models. In particular, we focus on a global stability analysis of replicator equations with random interactions and species abundance distributions based on statistical mechanics.     

Small-scale environmental heterogeneity and the analysis of species distributions along gradients

Abstract. The observed distribution of a species along an environmental gradient is strongly affected by environmental variability within a quadrat. Because a quadrat does not represent a point along an environmental gradient, but rather a range of conditions, it is likely to contain species not typically associated with the mean conditions in the quadrat. Systematic relationships exist between a species' true distribution, the observed distribution as a function of mean quadrat environment, and the frequency distribution of the environment within that quadrat. The observed species habitat breadth increases and the observed maximum abundance decreases as within-quadrat environmental heterogeneity increases. If species distributions or beta diversities are to be compared among species or coenoclines, they should be correctedforintra-quadratheterogeneity.Wederive simple corrections for environmental heterogeneity. The distributions of hardwood forest understory species along a soil acidity gradient in the North Carolina piedmont are presented as an example.     

Song recognition by temporal cues in a group of closely related bushcricket species (genus Tettigonia)

Female phonotaxis of Tettigonia viridissima and T. caudata was investigated on a walking compensator to determine the temporal parameters of the male song used for song recognition, and to compare them with the previously described pulse rate filtering of T. cantans. The T. cantans song is continuous with a ≈30-Hz pulse rate. The T. caudata song has a higher pulse rate (≈40 Hz) and duty cycle than T. cantans and a distinct verse structure. The T. viridissima song is continuous with a double-pulse pattern. While the pulse rate is essential for song recognition in T. cantans, neither pulse rate not verse structure were essential for song recognition in T. caudata: females responded to signals above a minimum duty cycle. T. viridissima females did not require the double-pulse structure, but a single long pulse, equivalent to the duration of the double pulses and interval between them, was effective. Song attractiveness was limited by a minimum duration of the merged double pulse, and by minimum and maximum duration of the interval between them. Pulse rate recognition had little if any importance in either of the species investigated. Thus, the three congeners use different mechanisms for temporal song recognition. Accepted: 18 June 1998     

Global patterns of fern species diversity: An evaluation of fern data in GBIF

Despite that several studies have shown that data derived from species lists generated from distribution occurrence records in the Global Biodiversity Information Facility (GBIF) are not appropriate for those ecological and biogeographic studies that require high sampling completeness, because species lists derived from GBIF are generally very incomplete, Suissa et al. (2021) generated fern species lists based on data with GBIF for 100 km × 100 km grid cells across the world, and used the data to determine fern diversity hotspots and species richness–climate relationships. We conduct an evaluation on the completeness of fern species lists derived from GBIF at the grid–cell scale and at a larger spatial scale, and determine whether fern data derived from GBIF are appropriate for studies on the relations of species composition and richness with climatic variables. We show that species sampling completeness of GBIF is low (<40%) for most of the grid cells examined, and such low sampling completeness can substantially bias the investigation of geographic and ecological patterns of species diversity and the identification of diversity hotspots. We conclude that fern species lists derived from GBIF are generally very incomplete across a wide range of spatial scales, and are not appropriate for studies that require data derived from species lists in high completeness. We present a map showing global patterns of fern species diversity based on complete or nearly complete regional fern species lists.     

Ecological niche modelling of Cantharellus species in Benin,and revision of their conservation status

Of the eight Cantharellus species known from Benin, seven have been encountered under similar macroecological conditions. The present work attempts to generate a more complete distribution of these seven species. Forty-eight occurrences of the target species and four explanatory variables including three bioclimatic variables and a land cover variable were used to build an ensemble model from five modelling approaches under the Biomod2 package of R software. Results showed a distribution restricted to the Bassila and Atacora mountain range phytogeographic districts with excellent statistical performance (TSS = 0.98, AUC = 0.99). This distribution is governed mainly by high soil moisture and high potential evapotranspiration, thus defining only gallery forests as the most suitable habitat for chanterelles in Sudano-guinean and Soudanese ecozones of Benin. Based on IUCN criterion B1 and sub-criteria B1a and B1c(i), these seven species were categorized under the Endangered (EN) threat category according to our results.     

Livestock-driven land use change to model species distributions: Egyptian vulture as a case study

Species distribution models (SDMs) are increasingly used to predict species ranges and their shifts under future scenarios of global environmental change (GEC). SDMs are thus incorporating key drivers of GEC (e.g. climate, land use) to improve predictions of species’ habitat suitability (i.e. as an indicator of species occurrence). Yet, most SDMs incorporating land use only consider dominant land cover types, largely ignoring other key aspects of land use such as land management intensity and livestock. We developed SDMs including main land use components (i.e. land cover, livestock and its management intensity) to assess their relative importance in shaping habitat suitability for the Egyptian vulture, an endangered raptor linked to livestock presence. We modelled current and future (2020 and 2050) habitat suitability for this vulture using an organism-centred approach. This allowed us to account for basic species’ habitat needs (i.e. nesting cliff) while gaining insight into our variables of interest (i.e. livestock and land cover). Once nest-site requirements were fulfilled, land use variables (i.e. openland and sheep and goat density) were the main factors determining species’ habitat suitability. Current suitable area could decrease by up to 6.81% by 2050 under scenarios with rapid economic growth but no focus on environmental conservation and rural development. Local solutions to environmental sustainability and rural development could double current habitat suitability by 2050. Land use is expected to play a key role in determining Egyptian vulture's distribution through land cover change but also through changes in livestock management (i.e. species and stocking density). Change in stocking densities (sheep and goats/km²) becomes thus an indicator of habitat suitability for this vulture in our study area. Abandonment of agro-pastoral practises (i.e. below ∼15–20 sheep and goats/km²) will negatively influence the species distribution. Nonetheless, livestock densities above these values will not further increase habitat suitability. Given the widespread impacts of livestock on ecosystems, the role of livestock and its management intensity in SDMs for other (non-livestock-related) species should be further explored.     

Key issues for the development and application of the species sensitivity distribution (SSD) model for ecological risk assessment

The species sensitivity distribution (SSD) model is one of the most commonly used methods for ecological risk assessment based on the potentially affected fraction (PAF) of and the combined PAF (msPAF) as quantitative indicators. There are usually four steps for the development of SSD models and their applications: (1) obtain the toxicity data of the pollutants; (2) fit the SSD curves; (3) calculate the potentially affected fractions (PAFs) of the individual pollutants for the ecological risk assessment of an individual pollutant; and (4) calculate the accumulated multi-substance potentially affected fractions (msPAFs) for the joint ecological risk assessment of multiple pollutants. Among the above mentioned four steps, the first two steps are paramount. In the present study, the following six key issues are discussed: (1) how to select the appropriate species, (2) how to preprocess the toxicity data collected from the ecotoxicity database, (3) how to transform the acute toxicity data into chronic data, (4) how to best fit the toxicity data, (5) how to calculate the msPAF of multiple pollutants, and (6) how to determine the uncertainty of the SSD model”. In response to these questions, several principles were proposed to select appropriate species; three data processing methods, including the geometric mean, weight assigning and using all raw data without processing, were compared to determine the appropriate method for the DDT (dichloro diphenyl trichloroethane) toxicity data preprocessing. The method of acute to chronic ratio (ACR) and binary correlation analysis were contrasted using the zinc toxicity data for the transformation of the acute toxicity data into chronic data. The Burr III, Loglogistic and Lognormal models were compared to determine the best fit model using the DDT toxicity data for invertebrates. The concentration addition or response addition were discussed to calculate msPAF according to the toxic model of action (TMoA). The uncertainties of the SSD models for five heavy metals and for eight polycyclic aromatic hydrocarbons (PAHs) were performed. The comparison of the coefficients of variation (CVs) for the toxicity data and exposure levels in Lake Chaohu for eight polycyclic aromatic hydrocarbons (PAHs) were also presented to demonstrate the uncertainties of the ecological risks assessed by the SSD model based on 5000 Monte Carlo simulations.     

The effect of habitat stability on benthic invertebrate communities: the utility of species abundance distributions

Spatial and temporal patterns in the species abundance distribution of benthic invertebrate communities of 11 freshwater habitats (10 streams and a wind-swept lake shore) were examined with respect to habitat stability. Abundance patterns varied markedly between seasons at most sites. However, mean abundance distributions at 4 of the 5 unstable sites and the 2 most stable sites were dominated by one or two taxa with a large number of rare species, whereas sites of intermediate stability had more equitable distributions. Both the log series and log normal distributions were statistically indistinguishable, at the 5% level, from all the observed mean abundance patterns. In contrast, graphical comparisons of the observed and fitted distributions suggested the log series may be the better fit at most of the unstable sites and the two most stable sites, whereas the more equitable distribution at sites of intermediate stability suggested the log normal distribution was the better fit. If conditions at a site favoured one or two species, either through severe physical conditions, or through competitive superiority in the absence of disturbance then the log series distribution may result. However, if no species in the community was strongly advantaged over others, a log normal distribution should result. Given the discriminating power of the appropriate statistical test it may not, however, be possible to pick up these differences without graphical comparisons as well.     

Predicting species distributions from checklist data using site‐occupancy models

Aim (1) To increase awareness of the challenges induced by imperfect detection, which is a fundamental issue in species distribution modelling; (2) to emphasize the value of replicate observations for species distribution modelling; and (3) to show how ‘cheap’ checklist data in faunal/floral databases may be used for the rigorous modelling of distributions by site‐occupancy models. Location Switzerland. Methods We used checklist data collected by volunteers during 1999 and 2000 to analyse the distribution of the blue hawker, Aeshna cyanea (Odonata, Aeshnidae), a common dragonfly in Switzerland. We used data from repeated visits to 1‐ha pixels to derive ‘detection histories’ and apply site‐occupancy models to estimate the ‘true’ species distribution, i.e. corrected for imperfect detection. We modelled blue hawker distribution as a function of elevation and year and its detection probability of elevation, year and season. Results The best model contained cubic polynomial elevation effects for distribution and quadratic effects of elevation and season for detectability. We compared the site‐occupancy model with a conventional distribution model based on a generalized linear model, which assumes perfect detectability (p = 1). The conventional distribution map looked very different from the distribution map obtained using site‐occupancy models that accounted for the imperfect detection. The conventional model underestimated the species distribution by 60%, and the slope parameters of the occurrence–elevation relationship were also underestimated when assuming p = 1. Elevation was not only an important predictor of blue hawker occurrence, but also of the detection probability, with a bell‐shaped relationship. Furthermore, detectability increased over the season. The average detection probability was estimated at only 0.19 per survey. Main conclusions Conventional species distribution models do not model species distributions per se but rather the apparent distribution, i.e. an unknown proportion of species distributions. That unknown proportion is equivalent to detectability. Imperfect detection in conventional species distribution models yields underestimates of the extent of distributions and covariate effects that are biased towards zero. In addition, patterns in detectability will erroneously be ascribed to species distributions. In contrast, site‐occupancy models applied to replicated detection/non‐detection data offer a powerful framework for making inferences about species distributions corrected for imperfect detection. The use of ‘cheap’ checklist data greatly enhances the scope of applications of this useful class of models.     

A tool for simulating and communicating uncertainty when modelling species distributions under future climates

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The role of biotic interactions in shaping distributions and realised assemblages of species: implications for species distribution modelling

Predicting which species will occur together in the future, and where, remains one of the greatest challenges in ecology, and requires a sound understanding of how the abiotic and biotic environments interact with dispersal processes and history across scales. Biotic interactions and their dynamics influence species' relationships to climate, and this also has important implications for predicting future distributions of species. It is already well accepted that biotic interactions shape species' spatial distributions at local spatial extents, but the role of these interactions beyond local extents (e.g. 10 km² to global extents) are usually dismissed as unimportant. In this review we consolidate evidence for how biotic interactions shape species distributions beyond local extents and review methods for integrating biotic interactions into species distribution modelling tools. Drawing upon evidence from contemporary and palaeoecological studies of individual species ranges, functional groups, and species richness patterns, we show that biotic interactions have clearly left their mark on species distributions and realised assemblages of species across all spatial extents. We demonstrate this with examples from within and across trophic groups. A range of species distribution modelling tools is available to quantify species environmental relationships and predict species occurrence, such as: (i) integrating pairwise dependencies, (ii) using integrative predictors, and (iii) hybridising species distribution models (SDMs) with dynamic models. These methods have typically only been applied to interacting pairs of species at a single time, require a priori ecological knowledge about which species interact, and due to data paucity must assume that biotic interactions are constant in space and time. To better inform the future development of these models across spatial scales, we call for accelerated collection of spatially and temporally explicit species data. Ideally, these data should be sampled to reflect variation in the underlying environment across large spatial extents, and at fine spatial resolution. Simplified ecosystems where there are relatively few interacting species and sometimes a wealth of existing ecosystem monitoring data (e.g. arctic, alpine or island habitats) offer settings where the development of modelling tools that account for biotic interactions may be less difficult than elsewhere.     

Effects of site and species characteristics on nested patterns of species composition in sedge meadows

Biotas of both geographical islands and habitat islands are often nested subsets of the biotas of successively more species-rich islands within the same system. The life history characteristics of a species may determine how that species contributes to the general pattern of species nestedness. Here, I investigate the floras of 56 sedge meadow wetlands in northern Illinois (USA) in order to characterize the degree of nestedness in these communities, determine which individual plant species contribute to the nested pattern, and investigate species characteristics that might be related to nonrandom patterns of distribution in individual plant species. The entire assemblage of species at all sedge meadows was significantly nested. Species richness and area were significantly correlated, and the nested pattern was closely related to site area, suggesting that species drop out of the assemblage in a predictable order as site area decreases. Some individual species exhibited nonrandom distributions across the sites, occurring more often in large, species-rich sites. Large sites were more likely than smaller sites to contain conservative species, i.e., those typical of pristine natural habitat, whereas nonconservative species were distributed more randomly among sites. Nested patterns of distribution of conservative species with respect to site area may result from their high probability of extinction on small sites or from a tendency for required habitats to co-occur on the same large sites. This revised version was published online in June 2006 with corrections to the Cover Date.