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

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Simple predictions of instream habitat model outputs for target fish populations

1. Bottom-up approaches based on individual behaviour can help to identify key variables influencing populations at larger scales. Instream habitat models have been developed to predict the consequences, for populations in stream reaches, of fish preferences for particular hydraulic conditions observed at the scale of individuals. Conventional instream habitat models (e.g. PHABSIM) predict habitat values for species or life stages in reaches, and their changes with discharge. Despite their worldwide use, they have been subject to continuing criticism and have been mainly limited to site-specific case studies.
2. We ran conventional instream habitat models in 58 French stream reaches dominated by brown trout. Using non-linear mixed effect models, we demonstrated that the outputs of instream habitat models (habitat values for three trout life stages and five other species) are predictable from average characteristics of reaches (discharge, depth, width and bed particle size).
3. Our models closely reflect variations in habitat values within-reaches (with discharge) and between-reaches. Within-reach changes are linked to the Reynolds number of reaches, while between-reach changes depend mainly on the Froude number at median daily discharge. These two dimensionless variables combine discharge, mean depth and mean width of reaches. Independent model validations showed robust model predictions that are consistent with studies of habitat values for brown trout made in larger streams from western North America.
4. Our results contribute to identifying the main hydraulic variables governing estimates of fish habitat values. They should facilitate habitat studies in multiple streams, at the basin or larger scales, while reducing their cost. They should enhance the biological validation of habitat model predictions, which remains critical.     

Poor Transferability of Species Distribution Models for a Pelagic Predator,the Grey Petrel,Indicates Contrasting Habitat Preferences across Ocean Basins

Species distribution models (SDMs) are increasingly applied in conservation management to predict suitable habitat for poorly known populations. High predictive performance of SDMs is evident in validations performed within the model calibration area (interpolation), but few studies have assessed SDM transferability to novel areas (extrapolation), particularly across large spatial scales or pelagic ecosystems. We performed rigorous SDM validation tests on distribution data from three populations of a long-ranging marine predator, the grey petrel Procellaria cinerea, to assess model transferability across the Southern Hemisphere (25-65°S). Oceanographic data were combined with tracks of grey petrels from two remote sub-Antarctic islands (Antipodes and Kerguelen) using boosted regression trees to generate three SDMs: one for each island population, and a combined model. The predictive performance of these models was assessed using withheld tracking data from within the model calibration areas (interpolation), and from a third population, Marion Island (extrapolation). Predictive performance was assessed using k-fold cross validation and point biserial correlation. The two population-specific SDMs included the same predictor variables and suggested birds responded to the same broad-scale oceanographic influences. However, all model validation tests, including of the combined model, determined strong interpolation but weak extrapolation capabilities. These results indicate that habitat use reflects both its availability and bird preferences, such that the realized distribution patterns differ for each population. The spatial predictions by the three SDMs were compared with tracking data and fishing effort to demonstrate the conservation pitfalls of extrapolating SDMs outside calibration regions. This exercise revealed that SDM predictions would have led to an underestimate of overlap with fishing effort and potentially misinformed bycatch mitigation efforts. Although SDMs can elucidate potential distribution patterns relative to large-scale climatic and oceanographic conditions, knowledge of local habitat availability and preferences is necessary to understand and successfully predict region-specific realized distribution patterns.     

Bio‐energetics underpins the spatial response of North Sea plaice (Pleuronectes platessa L.) and sole (Solea solea L.) to climate change

Climate change is currently one of the main driving forces behind changes in species distributions, and understanding the mechanisms that underpin macroecological patterns is necessary for a more predictive science. Warming sea water temperatures are expected to drive changes in ectothermic marine species ranges due to their thermal tolerance levels. Here, we develop a mechanistic tool to predict size‐ and season‐specific distributions based on the physiology of the species and the temperature and food conditions in the sea. The effects of climate conditions on physiological‐based habitat utilization was then examined for different size‐classes of two commercially important fish species in the North Sea, plaice, Pleuronectes platessa, and sole, Solea solea. The two species provide an attractive comparison as they differ in their physiology (e.g. preferred temperature range). Combining dynamic energy budget (DEB) models with the temperature and food conditions estimated by an ecosystem model (ERSEM), allowed spatial differences in potential growth (as a proxy for habitat quality) to be estimated for 2 years with contrasting temperature and food conditions. The resulting habitat quality maps were in broad agreement with observed ontogenetic and seasonal changes in distribution as well as with the recent changes in distribution which could be attributed to an increase in coastal temperatures. Our physiological‐based model provides a powerful tool to explore the effect of climate change on the spatio‐temporal fish dynamics, predict effects of local or broad‐scale environmental changes and provide a physiological basis for observed changes in species distributions.     

Turning back the tide? Local-scale impacts of climate change may have positive effects by restoring natural riverine habitat and reducing invasive fish density

1. Global biodiversity is increasingly threatened by habitat loss, climate change, and biological invasion. However, predictions of impacts on native fauna are hampered by an inadequate knowledge of how these factors interact and how climate change will affect the distribution, abundance, and behaviour of both native and invasive species, not least as most predictions are based on the long-term effects of temperature alone.
2. Here, we present a case study illustrating how local-scale climate change impacts (increased temperature, reduced rainfall, shifts in peak rainfall) affected the hydrology of a channelised lowland European river (reduced flow, reduction in flood events, increased siltation, macrophyte growth), allowing native fish species to recolonise the bankside zone and reducing the density of invasive round goby Neogobius melanostomus by removing its preferred habitat.
3. While most studies predict long-term negative impacts on global fish populations, some suggest potential direct and indirect benefits at a local scale. We are of the opinion that, at a local scale, climate change impacts on fish will be more nuanced and complex than long-term predictions suggest, resulting in both positive and negative effects, with consolation prizes in the face of larger losses. While impacts on fish will differ between regions and/or continents, depending on the specific impacts of climate change, identification of positive effects will be essential in clarifying long-range forecasts and identifying management procedures for mitigating overall negative impacts.

     

Predicting the spatial distribution of the blue-spotted maskray Neotrygon kuhlii (Myliobatiformes, Dasyatidae) on the Australian North and Northwest Shelf comparing two different methods of habitat modeling

Knowledge about distribution and habitat requirements of species is important for analyzing their role in marine ecosystems or establishing sanctuaries. However, knowledge is scarce especially in many chondrichthyan species. In this study, the spatial distribution of the stingray Neotrygon kuhlii on the Australian North and Northwest Shelf was predicted model-based for the first time. Predictions based on two different types of habitat suitability models, logistic regression and maximum entropy modeling. Catch data of N. kuhlii from Australian trawl surveys combined with randomly selected pseudo-absences were used for modeling together with data sets of several environmental variables. Both modeling methods yielded plausible and validated habitat suitability models containing water depth and salinity as significant independent variables. The model-based predictions of the probability of occurrence of N. kuhlii were similar for both methods and thus emphasized the goodness of the models. Following the predictions, N. kuhlii has its highest probability of occurrence in about 60 m water depth and at a salinity of about 35 PSU. The results indicate that both modeling methods are powerful tools to predict spatial distribution and habitat quality for marine fish species. Therefore, they are suitable for detecting possible distribution in areas with only few field records.     

On the use of climate covariates in aquatic species distribution models: are we at risk of throwing out the baby with the bath water?

Species distribution models (SDMs) in river ecosystems can incorporate climate information by using air temperature and precipitation as surrogate measures of instream conditions or by using independent models of water temperature and hydrology to link climate to instream habitat. The latter approach is preferable but constrained by the logistical burden of developing water temperature and hydrology models. We therefore assessed whether regional scale, freshwater SDM predictions are fundamentally different when climate data versus instream temperature and hydrology are used as covariates. Maximum entropy (MaxEnt) SDMs were built for 15 freshwater fishes using one of two covariate sets: 1) air temperature and precipitation (climate variables) in combination with physical habitat variables; or 2) water temperature, hydrology (instream variables) and physical habitat. Three procedures were then used to compare results from climate vs instream models. First, equivalence tests assessed average pairwise differences (site‐specific comparisons throughout each species’ range) among climate and instream models. Second, ‘congruence’ tests determined how often the same stream segments were assigned high habitat suitability by climate and instream models. Third, Schoener's D and Warren's I niche overlap statistics quantified range‐wide similarity in predicted habitat suitability from climate vs instream models. Equivalence tests revealed small, pairwise differences in habitat suitability between climate and instream models (mean pairwise differences in MaxEnt raw scores for all species < 3 × 10^–4). Congruence tests showed a strong tendency for climate and instream models to predict high habitat suitability at the same stream segments (median congruence = 68%). D and I statistics reflected a high margin of overlap among climate and instream models (median D = 0.78, median I = 0.96). Overall, we found little support for the hypothesis that SDM predictions are fundamentally different when climate versus instream covariates are used to model fish species’ distributions at the scale of the Columbia Basin.     

Relationships between survival and habitat suitability of semi‐aquatic mammals

Spatial distribution and habitat selection are integral to the study of animal ecology. Habitat selection may optimize the fitness of individuals. Hutchinsonian niche theory posits the fundamental niche of species would support the persistence or growth of populations. Although niche‐based species distribution models (SDMs) and habitat suitability models (HSMs) such as maximum entropy (Maxent) have demonstrated fair to excellent predictive power, few studies have linked the prediction of HSMs to demographic rates. We aimed to test the prediction of Hutchinsonian niche theory that habitat suitability (i.e., likelihood of occurrence) would be positively related to survival of American beaver (Castor canadensis), a North American semi‐aquatic, herbivorous, habitat generalist. We also tested the prediction of ideal free distribution that animal fitness, or its surrogate, is independent of habitat suitability at the equilibrium. We estimated beaver monthly survival probability using the Barker model and radio telemetry data collected in northern Alabama, United States from January 2011 to April 2012. A habitat suitability map was generated with Maxent for the entire study site using landscape variables derived from the 2011 National Land Cover Database (30‐m resolution). We found an inverse relationship between habitat suitability index and beaver survival, contradicting the predictions of niche theory and ideal free distribution. Furthermore, four landscape variables selected by American beaver did not predict survival. The beaver population on our study site has been established for 20 or more years and, subsequently, may be approaching or have reached the carrying capacity. Maxent‐predicted increases in habitat use and subsequent intraspecific competition may have reduced beaver survival. Habitat suitability‐fitness relationships may be complex and, in part, contingent upon local animal abundance. Future studies of mechanistic SDMs incorporating local abundance and demographic rates are needed.     

Relationships among map resolution, fish assemblages, and habitat variables in a coral reef ecosystem

Benthic maps are broad-scale characterizations that lack the detailed environmental attributes which have been the focus of most prior empirical studies linking reef fish and habitat. We used multivariate analyses to quantify correlations among fish assemblages, local habitat variables, and reef types depicted in benthic maps. Benthic maps of a study system in the U.S. Virgin Islands with high (100 m² minimum mapping unit) and low (4,048 m² minimum mapping unit) spatial resolution, respectively, were evaluated. Benthic maps depicted six reef types and two shelf positions (lagoon vs. shelf). Fish assemblages and local environmental variables were quantified at random sites throughout the landscape by diver surveys. Multivariate ordination based on either fish assemblages or environmental data did not result in well-separated groups of sites. Mapped reef types were not associated with distinct values of either local environmental variables or fish assemblages. Reef types exhibited substantial overlap in ordination plots based on benthic characteristics with groupings based on fish assemblages showing even greater overlap. Ordination patterns involving reef type were largely the same for both low- and high-resolution maps. In contrast, sites showed clear groups for lagoon and shelf in ordinations based on both environmental variables and fish assemblage composition, respectively. These results suggest that knowledge of the overall fish assemblage or fine-scale environmental characteristics could not be used to predict reef type depicted in benthic maps or vice versa. In contrast, reef zone could be used to predict fish assemblage or fine-scale environmental variables and vice versa.     

Spatial Scaling of Environmental Variables Improves Species-Habitat Models of Fishes in a Small,Sand-Bed Lowland River

Habitat suitability and the distinct mobility of species depict fundamental keys for explaining and understanding the distribution of river fishes. In recent years, comprehensive data on river hydromorphology has been mapped at spatial scales down to 100 m, potentially serving high resolution species-habitat models, e.g., for fish. However, the relative importance of specific hydromorphological and in-stream habitat variables and their spatial scales of influence is poorly understood. Applying boosted regression trees, we developed species-habitat models for 13 fish species in a sand-bed lowland river based on river morphological and in-stream habitat data. First, we calculated mean values for the predictor variables in five distance classes (from the sampling site up to 4000 m up- and downstream) to identify the spatial scale that best predicts the presence of fish species. Second, we compared the suitability of measured variables and assessment scores related to natural reference conditions. Third, we identified variables which best explained the presence of fish species. The mean model quality (AUC = 0.78, area under the receiver operating characteristic curve) significantly increased when information on the habitat conditions up- and downstream of a sampling site (maximum AUC at 2500 m distance class, +0.049) and topological variables (e.g., stream order) were included (AUC = +0.014). Both measured and assessed variables were similarly well suited to predict species’ presence. Stream order variables and measured cross section features (e.g., width, depth, velocity) were best-suited predictors. In addition, measured channel-bed characteristics (e.g., substrate types) and assessed longitudinal channel features (e.g., naturalness of river planform) were also good predictors. These findings demonstrate (i) the applicability of high resolution river morphological and instream-habitat data (measured and assessed variables) to predict fish presence, (ii) the importance of considering habitat at spatial scales larger than the sampling site, and (iii) that the importance of (river morphological) habitat characteristics differs depending on the spatial scale.     

Disregarding the edaphic dimension in species distribution models leads to the omission of crucial spatial information under climate change: the case of Quercus pubescens in France

Species distribution modelling is an easy, persuasive and useful tool for anticipating species distribution shifts under global change. Numerous studies have used only climate variables to predict future potential species range shifts and have omitted environmental factors important for determining species distribution. Here, we assessed the importance of the edaphic dimension in the niche‐space definition of Quercus pubescens and in future spatial projections under global change over the metropolitan French forest territory. We fitted two species distribution models (SDM) based on presence/absence data (111 013 plots), one calibrated from climate variables only (mean temperature of January and climatic water balance of July) and the other one from both climate and edaphic (soil pH inferred from plants) variables. Future predictions were conducted under two climate scenarios (PCM B2 and HadCM3 A2) and based on 100 simulations using a cellular automaton that accounted for seed dispersal distance, landscape barriers preventing migration and unsuitable land cover. Adding the edaphic dimension to the climate‐only SDM substantially improved the niche‐space definition of Q. pubescens, highlighting an increase in species tolerance in confronting climate constraints as the soil pH increased. Future predictions over the 21st century showed that disregarding the edaphic dimension in SDM led to an overestimation of the potential distribution area, an underestimation of the spatial fragmentation of this area, and prevented the identification of local refugia, leading to an underestimation of the northward shift capacity of Q. pubescens and its persistence in its current distribution area. Spatial discrepancies between climate‐only and climate‐plus‐edaphic models are strengthened when seed dispersal and forest fragmentation are accounted for in predicting a future species distribution area. These discrepancies highlight some imprecision in spatial predictions of potential distribution area of species under climate change scenarios and possibly wrong conclusions for conservation and management perspectives when climate‐only models are used.     

Current and future distribution of a parasite with complex life cycle under global change scenarios: Echinococcus multilocularis in Europe

Global change is expected to have complex effects on the distribution and transmission patterns of zoonotic parasites. Modelling habitat suitability for parasites with complex life cycles is essential to further our understanding of how disease systems respond to environmental changes, and to make spatial predictions of their future distributions. However, the limited availability of high quality occurrence data with high spatial resolution often constrains these investigations. Using 449 reliable occurrence records for Echinococcus multilocularis from across Europe published over the last 35 years, we modelled habitat suitability for this parasite, the aetiological agent of alveolar echinococcosis, in order to describe its environmental niche, predict its current and future distribution under three global change scenarios, and quantify the probability of occurrence for each European country. Using a machine learning approach, we developed large-scale (25 × 25 km) species distribution models based on seven sets of predictors, each set representing a distinct biological hypothesis supported by current knowledge of the autecology of the parasite. The best-supported hypothesis included climatic, orographic and land-use/land-cover variables such as the temperature of the coldest quarter, forest cover, urban cover and the precipitation seasonality. Future projections suggested the appearance of highly suitable areas for E. multilocularis towards northern latitudes and in the whole Alpine region under all scenarios, while decreases in habitat suitability were predicted for central Europe. Our spatially explicit predictions of habitat suitability shed light on the complex responses of parasites to ongoing global changes.     

Pond-Breeding Amphibian Species Richness and Habitat Selection in a Beaver-Modified Landscape

ABSTRACT Beaver (Castor canadensis) activity creates wetland habitats with varying hydroperiods important in maintaining habitat diversity for pond-breeding amphibians with significantly different breeding habitat requirements. We documented pond-breeding amphibian assemblages in 71 freshwater wetlands in Acadia National Park, Maine, USA. Using 15 variables describing local pond conditions and wetland landscape characteristics, we developed a priori models to predict sites with high amphibian species richness and used model selection with Akaike's Information Criterion to judge the strength of evidence supporting each model. We developed single-species models to predict wood frog (Rana sylvatica), bullfrog (R. catesbeiana), and pickerel frog (R. palustris) breeding site selection. Sites with high species richness were best predicted by 1) connectivity of wetlands in the landscape through stream corridors and 2) wetland modification by beaver. Wood frog breeding habitat was best predicted by temporary hydroperiod, lack of fish, and absence of current beaver activity. Wood frog breeding was present in abandoned beaver wetlands nearly as often as in nonbeaver wetlands. Bullfrog breeding was limited to active beaver wetlands with fish and permanent water. Pickerel frog breeding sites were best predicted by connectivity through stream corridors within the landscape. As beavers have recolonized areas of their former range in North America, they have increased the number and diversity of available breeding sites in the landscape for pond-breeding amphibians. The resulting mosaic of active and abandoned beaver wetlands both supports rich amphibian assemblages and provides suitable breeding habitat for species with differing habitat requirements. Land managers should consider the potential benefits of minimal management of beavers in promoting and conserving amphibian and wetland diversity at a landscape scale.     

From topography to hydrology—The modifiable area unit problem impacts freshwater species distribution models

Species distribution models (SDMs) are statistical tools to identify potentially suitable habitats for species. For SDMs in river ecosystems, species occurrences and predictor data are often aggregated across subcatchments that serve as modeling units. The level of aggregation (i.e., model resolution) influences the statistical relationships between species occurrences and environmental predictors—a phenomenon known as the modifiable area unit problem (MAUP), making model outputs directly contingent on the model resolution. Here, we test how model performance, predictor importance, and the spatial congruence of species predictions depend on the model resolution (i.e., average subcatchment size) of SDMs. We modeled the potential habitat suitability of 50 native fish species in the upper Danube catchment at 10 different model resolutions. Model resolutions were derived using a 90‐m digital‐elevation model by using the GRASS‐GIS module r.watershed. Here, we decreased the average subcatchment size gradually from 632 to 2 km². We then ran ensemble SDMs based on five algorithms using topographical, climatic, hydrological, and land‐use predictors for each species and resolution. Model evaluation scores were consistently high, as sensitivity and True Skill Statistic values ranged from 86.1–93.2 and 0.61–0.73, respectively. The most contributing predictor changed from topography at coarse, to hydrology at fine resolutions. Climate predictors played an intermediate role for all resolutions, while land use was of little importance. Regarding the predicted habitat suitability, we identified a spatial filtering from coarse to intermediate resolutions. The predicted habitat suitability within a coarse resolution was not ported to all smaller, nested subcatchments, but only to a fraction that held the suitable environmental conditions. Across finer resolutions, the mapped predictions were spatially congruent without such filter effect. We show that freshwater SDM predictions can have consistently high evaluation scores while mapped predictions differ significantly and are highly contingent on the underlying subcatchment size. We encourage building freshwater SDMs across multiple catchment sizes, to assess model variability and uncertainties in model outcomes emerging from the MAUP.     

Development and Application of a Predictive Model of Freshwater Fish Assemblage Composition to Evaluate River Health in Eastern Australia

Multivariate predictive models are widely used tools for assessment of aquatic ecosystem health and models have been successfully developed for the prediction and assessment of aquatic macroinvertebrates, diatoms, local stream habitat features and fish. We evaluated the ability of a modelling method based on the River InVertebrate Prediction and Classification System (RIVPACS) to accurately predict freshwater fish assemblage composition and assess aquatic ecosystem health in rivers and streams of south-eastern Queensland, Australia. The predictive model was developed, validated and tested in a region of comparatively high environmental variability due to the unpredictable nature of rainfall and river discharge. The model was concluded to provide sufficiently accurate and precise predictions of species composition and was sensitive enough to distinguish test sites impacted by several common types of human disturbance (particularly impacts associated with catchment land use and associated local riparian, in-stream habitat and water quality degradation). The total number of fish species available for prediction was low in comparison to similar applications of multivariate predictive models based on other indicator groups, yet the accuracy and precision of our model was comparable to outcomes from such studies. In addition, our model developed for sites sampled on one occasion and in one season only (winter), was able to accurately predict fish assemblage composition at sites sampled during other seasons and years, provided that they were not subject to unusually extreme environmental conditions (e.g. extended periods of low flow that restricted fish movement or resulted in habitat desiccation and local fish extinctions).     

黄山陈村水库上游河源溪流的鱼类群落及其纵向梯度格局

确定鱼类群落的分布格局及其对人类活动的响应,是合理保护、恢复和管理鱼类多样性的基础。基于2011年5月和10月自黄山陈村水库上游3条河源溪流共39个样点的调查数据,比较研究了溪流间鱼类群落及其纵向梯度格局的异同,着重探讨了人类活动对溪流鱼类群落纵向梯度格局的影响。研究结果显示,同人为干扰较轻的舒溪相比,人为干扰严重的浦溪和麻溪中水宽、底质和植被覆盖率等局域栖息地条件显著变化,这造成了后者的鱼类多样性显著下降及物种组成的显著变化,主要表现为敏感性的地方物种(如宽鳍鱲、光唇鱼、原缨口鳅等)数量减少、耐受性的广布物种(如泥鳅、麦穗鱼、高体鰟鲏等)数量增多。舒溪的鱼类物种数及其组成均与海拔显著相关,但这种"海拔-鱼类群落"关系在麻溪和浦溪中削弱甚至消失。底质、植被覆盖率对舒溪鱼类群落具有重要影响,但对浦溪和麻溪鱼类群落却无显著影响。研究结果表明,在子流域空间尺度上,诸如城镇化发展、土地利用、河道治理等人类活动可通过对局域栖息地条件的影响,导致溪流鱼类多样性下降及其物种组成的变化,破坏鱼类群落的纵向梯度格局,并改变栖息地与鱼类群落之间的联系。     

Evaluating models to assess the distribution of Buxus balearica in southern Spain

Question: Which is the best model to predict the habitat distribution of Buxus balearica Lam. in southern Spain? Location: Málaga and Granada, Spain, across an area of 38 180 km². Methods: Prediction models based on 17 environmental variables were tested. Six methods were compared: multivariate adaptive regression spline (MARS), maximum entropy approach to modelling species' distributions (Maxent), two generic algorithms based on environmental metrics dissimilarity (BIOCLIM and DOMAIN), Genetic Algorithm for Rule‐set Prediction (GARP), and supervised learning methods based on generalized linear classifiers (support vector machines, SVMs). To test the predictive power of the models we used the Kappa index. Results: Maxent most accurately predicted the habitat distribution of B. balearica, followed by MARS models. The other models tested yielded lower accuracy values. A comparison of the predictive power of the models revealed that climate variables made the highest contributions among the environmental variables studied. The variables that made the lowest contributions were the insolation models. To examine the sensitivity of the models to a reduction in the number of variables, a test showed that accuracy of over 0.90 was maintained by applying just three climatic variables (spring rainfall, mean temperature of the warmest month, and mean temperature of the coldest month). Maps derived from the algorithms of all models tested coincided well with the known distribution of the species. Conclusions: Model habitat prediction is a preliminary step towards highlighting areas of high habitat suitability of B. balearica. These data support the results of previous research, which show that MaxEnt is the best technique for modelling species distributions with small sample sizes.     

Using species‐environmental amplitudes to predict pH values from vegetation

Question: Species optima or indicator values are frequently used to predict environmental variables from species composition. The present study focuses on the question whether predictions can be improved by using species environmental amplitudes instead of single values representing species optima. Location: Semi‐natural, deciduous hardwood forests of northwestern Germany. Methods: Based on a data set of 558 relevés, species responses (presence/absence) to pH were modelled with Huisman‐Olff‐Fresco (HOF) regression models. Species amplitudes were derived from response curves using three different methods. To predict the pH from vegetation, a maximum amplitude overlap method was applied. For comparison, predictions resulting from several established methods, i. e. maximum likelihood/present and absent species, maximum likelihood/present species only, mean weighted averages and mean Ellenberg indicator values were calculated. The predictive success (squared Pearson's r and root mean square error of prediction) was evaluated using an independent data set of 151 relevés. Results: Predictions based upon amplitudes defined by maximum Cohen's x probability threshold yield the best results of all amplitude definitions (R²= 0.75, RMSEP = 0.52). Provided there is an even distribution of the environmental variable, amplitudes defined by predicted probability exceeding prevalence are also suitable (R²= 0.76, RMSEP = 0.55). The prediction success is comparable to maximum likelihood (present species only) and – after rescaling – to mean weighted averages. Predicted values show a good linearity to observed pH values as opposed to a curvilinear relationship of mean Ellenberg indicator values. Transformation or rescaling of the predicted values is not required. Conclusions: Species amplitudes given by a minimum and maximum boundary for each species can be used to efficiently predict environmental variables from species composition. The predictive success is superior to mean Ellenberg indicator values and comparable to mean indicator values based on species weighted averages.     

Using a new framework of two-phase generalized additive models to incorporate prey abundance in spatial distribution models of juvenile slender lizardfish in Haizhou Bay,China

The predictive skill of species distribution models depends on the quality and quantity of input information. In addition to the physical environmental variables, prey availability is also one of the main drivers regulating spatial distribution of marine species. However, prey distribution data have rarely been considered in habitat models due to the lack of information on non-commercial prey species. This may lead to an incomplete view of species distributions and biased model predictions. In this study, we developed a new framework of two-phase generalized additive models (GAMs) based on the Tweedie distribution to incorporate the predicted prey abundance as covariates in habitat models, and applied this framework to juvenile slender lizardfish Saurida elongata in Haizhou Bay, China. This study demonstrated that the predictive skill of habitat models could be greatly improved through incorporating prey abundance as explanatory variables. The importance of prey distribution data in the habitat model confirms the essentiality of including prey data while modelling species distribution. Spatial overlap and GAM analysis demonstrated that not all dominant prey can be selected as potential explanatory variables and only those prey species showing high spatiotemporal occurrences with predators should be incorporated. The framework derived in this study could be extended to other marine organisms to improve the predictive skill of habitat models and enhance our understanding of the ecological mechanisms underlying the distribution of marine species.     

Predicting local fish species richness in the garonne river basin

The aim of this work was to predict local fish species richness in the Garonne river basin using three environmental variables (distance from the source, elevation and catchment area J. Commonly, patterns of fish species richness have been investigated using simple or multi-linear statistical models. Here, we used backpropagation of artificial neural networks (ANNs) to develop stochastic models of local fish diversity. Two independent data collections were used, the first one to build and test the model; the second one to validate the model. Correlation coefficients between observed values and predicted values both in the testing and the validation procedures were highly significant (r = 0.904, P< 0.001 and r = 0.822, P< 0.001, respectively J. The ANN model obtained using only three environmental variables succeeded in explaining ca 70 % of the total variation in local fish species richness. Through these findings, ANNs can be seen as a powerful predictive tool compared to traditional modelling approaches.