Disentangling scale dependencies in species environmental niches and distributions

Understanding species’ responses to environmental conditions, and how these ­species–environment associations shape spatial distributions, are longstanding goals in ecology and biogeography. However, an essential component of species–environment relationships – the spatial unit, or grain, at which they operate – remains unresolved. We identify three components of scale‐dependence in analyses of species–environment associations: 1) response grain, the grain at which species respond most strongly to their environment; 2) environment spatial structure, the pattern of spatial autocorrelation intrinsic to an environmental factor; and 3) analysis grain, the grain at which analyses are conducted and ecological inferences are made. We introduce a novel conceptual framework that defines these scale components in the context of analyzing species–environment relationships, and provide theoretical examples of their interactions for species with various ecological attributes. We then use a virtual species approach to investigate the impacts of each component on common methods of measuring and predicting species–environment relationships. We find that environment spatial structure has a substantial impact on the ability of even simple, univariate species distribution models (SDMs) to recover known species–­environment associations at coarse analysis grains. For simulated environments with ‘fine’ and ‘intermediate’ spatial structure, model explanatory power, and the frequency with which simple SDMs correctly estimated a virtual species’ response to the simulated environment, dramatically declined as analysis grain increased. Informed by these results, we use a scaling analysis to identify maximum analysis grains for individual environmental factors, and a scale optimization procedure to determine the grain of maximum predictive accuracy. Implementing these analysis grain thresholds and model performance standards in an example east African study system yields more accurate distribution predictions, compared to SDMs independently constructed at arbitrary analysis grains. Finally, we integrate our conceptual framework with virtual and empirical results to provide practical recommendations for researchers asking common questions about species–environment relationships.     

Importance of spatial autocorrelation in modeling bird distributions at a continental scale

Spatial autocorrelation in species' distributions has been recognized as inflating the probability of a type I error in hypotheses tests, causing biases in variable selection, and violating the assumption of independence of error terms in models such as correlation or regression. However, it remains unclear whether these problems occur at all spatial resolutions and extents, and under which conditions spatially explicit modeling techniques are superior. Our goal was to determine whether spatial models were superior at large extents and across many different species. In addition, we investigated the importance of purely spatial effects in distribution patterns relative to the variation that could be explained through environmental conditions. We studied distribution patterns of 108 bird species in the conterminous United States using ten years of data from the Breeding Bird Survey. We compared the performance of spatially explicit regression models with non-spatial regression models using Akaike's information criterion. In addition, we partitioned the variance in species distributions into an environmental, a pure spatial and a shared component. The spatially-explicit conditional autoregressive regression models strongly outperformed the ordinary least squares regression models. In addition, partialling out the spatial component underlying the species' distributions showed that an average of 17% of the explained variation could be attributed to purely spatial effects independent of the spatial autocorrelation induced by the underlying environmental variables. We concluded that location in the range and neighborhood play an important role in the distribution of species. Spatially explicit models are expected to yield better predictions especially for mobile species such as birds, even in coarse-grained models with a large extent.     

The data void in modeling current and future distributions of tropical species

Conserving biodiversity in the face of climate change requires a predictive ecology of species distributions. Nowhere is this need more acute than in the tropics, which harbor the majority of Earth's species and face rapid and large climate and land‐use changes. However, the study of species distributions and their responses to climate change in high diversity tropical regions is potentially crippled by a lack of basic data. We analyzed a database representing more than 800 000 unique geo‐referenced natural history collections to determine what fraction of tropical plant species has sufficient numbers of available collections for use in the habitat or niche models commonly used to predict species responses to climate change. We found that more than nine out of 10 species from the three principle tropical realms are so poorly collected (n < 20 records) that they are essentially invisible to modern modeling and conservation tools. In order to predict the impact of climate change on tropical species, efforts must be made to increase the amount of data available from tropical countries through a combination of collecting new specimens and digitizing existing records.     

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.     

Host plant availability potentially limits butterfly distributions under cold environmental conditions

Species ranges are shaped by both climatic factors and interactions with other species. The stress gradient hypothesis predicts that under physiologically stressful environmental conditions abiotic factors shape range edges while in less stressful environments negative biotic interactions are more important. Butterflies provide a suitable system to test this hypothesis since larvae of most species depend on biotic interactions with a specific set of host plants, which in turn can shape patterns of occurrence and distribution. Here we modelled the distribution of 92 butterfly and 136 host plant species with three different modelling algorithms, using distribution data from the Swiss biodiversity monitoring scheme at a 1 × 1 km spatial resolution. By comparing the ensemble prediction for each butterfly species and the corresponding host plant(s), we assessed potential constraints imposed by host plant availability on distribution of butterflies at their distributional limits along the main environmental gradient, which closely parallels an elevational gradient. Our results indicate that host limitation does not play a role at the lower limit. At the upper limit 50% of butterfly species have a higher elevational limit than their primary host plant, and 33% have upper elevational limits that exceed the limits of both primary and secondary hosts. We conclude that host plant limitation was not relevant to butterfly distributional limits in less stressful environments and that distributions are more likely limited by climate, land use or antagonistic biotic interactions. Obligatory dependency of butterflies on their host plants, however, seems to represent an important limiting factor for the distribution of some species towards the cold, upper end of the environmental gradient, suggesting that biotic factors can shape ranges in stressful environments. Thus, predictions by the stress gradient hypothesis were not always applicable.     

Differences in spatial predictions among species distribution modeling methods vary with species traits and environmental predictors

Prediction maps produced by species distribution models (SDMs) influence decision‐making in resource management or designation of land in conservation planning. Many studies have compared the prediction accuracy of different SDM modeling methods, but few have quantified the similarity among prediction maps. There has also been little systematic exploration of how the relative importance of different predictor variables varies among model types and affects map similarity. Our objective was to expand the evaluation of SDM performance for 45 plant species in southern California to better understand how map predictions vary among model types, and to explain what factors may affect spatial correspondence, including the selection and relative importance of different environmental variables. Four types of models were tested. Correlation among maps was highest between generalized linear models (GLMs) and generalized additive models (GAMs) and lowest between classification trees and GAMs or GLMs. Correlation between Random Forests (RFs) and GAMs was the same as between RFs and classification trees. Spatial correspondence among maps was influenced the most by model prediction accuracy (AUC) and species prevalence; map correspondence was highest when accuracy was high and prevalence was intermediate (average prevalence for all species was 0.124). Species functional type and the selection of climate variables also influenced map correspondence. For most (but not all) species, climate variables were more important than terrain or soil in predicting their distributions. Environmental variable selection varied according to modeling method, but the largest differences were between RFs and GLMs or GAMs. Although prediction accuracy was equal for GLMs, GAMs, and RFs, the differences in spatial predictions suggest that it may be important to evaluate the results of more than one model to estimate the range of spatial uncertainty before making planning decisions based on map outputs. This may be particularly important if models have low accuracy or if species prevalence is not intermediate.     

Effects of restricting environmental range of data to project current and future species distributions

We examine the consequences of restricting the range of environmental conditions over which niche-based models are developed to project potential future distributions of three selected European tree species to assess first, the importance of removing absences beyond species known distributions ("naughty noughts") and second the importance of capturing the full environmental range of species. We found that restricting the environmental range of data strongly influenced the estimation of response curves, especially towards upper and lower ends of environmental ranges. This induces changes in the probability values towards upper and lower environmental boundaries, leading to more conservative scenarios in terms of changes in distribution projections.
Using restricted data analogous to not capturing the fun species' environmental range, reduces strongly the combinations of environmental conditions under which the models are calibrated, and reduces the applicability of the models for predictive purposes. This may generate unpredictable effects on the tails of the species response curves, yielding spurious projections into the future provided that probability of occurrence is not set to zero outside the environmental limits of the species. Indeed, as the restricted data does not capture the whole of the response curve, projections of future species distributions based of ecological niche modelling may be only valid if niche models are able to approach the complete response curve of environmental predictors.     

Variation among species in proteomic sulphur content is related to environmental conditions

The elemental composition of proteins influences the quantities of different elements required by organisms. Here, we considered variation in the sulphur content of whole proteomes among 19 Archaea, 122 Eubacteria and 10 eukaryotes whose genomes have been fully sequenced. We found that different species vary greatly in the sulphur content of their proteins, and that average sulphur content of proteomes and genome base composition are related. Forces contributing to variation in proteomic sulphur content appear to operate quite uniformly across the proteins of different species. In particular, the sulphur content of orthologous proteins was frequently correlated with mean proteomic sulphur contents. Among prokaryotes, proteomic sulphur content tended to be greater in anaerobes, relative to non-anaerobes. Thermophiles tended to have lower proteomic sulphur content than non-thermophiles, consistent with the thermolability of cysteine and methionine residues. This work suggests that persistent environmental growth conditions can influence the evolution of elemental composition of whole proteomes in a manner that may have important implications for the amount of sulphur used by living organisms to build proteins. It extends previous studies that demonstrated links between transient changes in environmental conditions and the elemental composition of subsets of proteins expressed under these conditions.     

Intraspecific trait variation in three common grass species reveals finescale species adjustment to local environmental conditions

Aims Studies along environmental gradients have shown that intraspecific trait variation (ITV) may contribute considerably to community-level trait variation. However, we lack knowledge about how the extent of ITV varies on a local scale and whether a varying extent of ITV is related to differences in local environmental site and plant community characteristics.     

Predictor species: Improving assessments of rare species occurrence by modeling environmental co‐responses

Designing an effective conservation strategy requires understanding where rare species are located. Because rare species can be difficult to find, ecologists often identify other species called conservation surrogates that can help inform the distribution of rare species. Species distribution models typically rely on environmental data when predicting the occurrence of species, neglecting the effect of species' co‐occurrences and biotic interactions. Here, we present a new approach that uses Bayesian networks to improve predictions by modeling environmental co‐responses among species. For species from a European peat bog community, our approach consistently performs better than single‐species models and better than conventional multi‐species approaches that include the presence of nontarget species as additional independent variables in regression models. Our approach performs particularly well with rare species and when calibration data are limited. Furthermore, we identify a group of “predictor species” that are relatively common, insensitive to the presence of other species, and can be used to improve occurrence predictions of rare species. Predictor species are distinct from other categories of conservation surrogates such as umbrella or indicator species, which motivates focused data collection of predictor species to enhance conservation practices.     

Changes in plant species richness in a calcareous grassland following changes in environmental conditions

This study presents results of a three and a half year experiment in a calcareous grassland on the Baltic island of Öland, south-eastern Sweden. Environmental conditions, viz. nutrient regime, water availability, light conditions and grazing pressure were changed in a randomised experimental design with ten different combinations of these factors, including a control. Weather conditions during the study period fluctuated considerably. Only the combination of reduced light and increased soil nutrient availability decreased species richness significantly, presumably by inducing asymmetric competition for light by competitively strong species. Physical space also became less available for weaker competitors. In cases where only nutrients or only water were supplied in sufficient amounts, competitive exclusion was inhibited by either low nutrient or low water availability. Under conditions which combined high levels of nutrient and water supply with regular disturbance (i.e. simulated grazing) high species richness was maintained. Although there was a significant negative correlation between species richness and amount of above-ground biomass, the number of species was not significantly reduced in the treatment which combined fertilisation and irrigation. One reason for this may be that the heterogeneous light regime in this treatment still allowed species persistence and coexistence.     

Relationship between latex yield of Hevea brasiliensis and antecedent environmental parameters

A study on the relationship between latex yield and antecedent environmental data was undertaken for five clones (RRII203, RRII118, RRIM600, RRII105 and GT1) of Hevea brasiliensis (rubber) in Agartala, northeast India, a region in which rubber is not traditionally cultivated. The explained variance for the regression equations based on parameters determined on the day of tapping and up to 3 days prior to it, varied from 72% to 37% during the NWT period and 94-83% during the WT period. Soil moisture storage, 1 and 3 days prior to tapping, was found to be the primary parameter affecting yield for the NWT and WT periods, respectively. It was observed that the clone RRII105, with a comparatively lower yield to that of RRIM600, was more susceptible to daily WD conditions during the non-winter season. RRIM600 and RRII105 being high-yielding clones were also found to be fairly dependent on the AT of the day prior to tapping. The mean lag period correlation of this parameter with yield was also found to be higher during the WT period than during the NWT period. As a whole, the mean lag period based on prior measurements of environmental variables showed optimum correlation with yield at 15-20 days prior to the day of tapping. The study also confirms that varied responses of yield with environmental factors in this non-traditional region of rubber cultivation depend on clonal character.     

Dependency of seed dormancy types on embryo traits and environmental conditions in Ribes species

The hypothesis that seed dormancy may be dependent on environmental conditions and seed morphological traits was tested for six Ribes species, across an altitudinal gradient of 1300 m and a longitudinal separation of 120°. Embryo measurements and seed germination experiments were conducted for R. alpinum L., R. hudsonianum Richardson var. petiolare (Douglas) Jancz., R. nevadaense Kellogg, R. roezlii Regel var. cruentum (Greene) Rehder and R. speciosum Pursh, and data taken from the literature for R. multiflorum Kit. ex Schult. ssp. sandalioticum Arrigoni. Germination was compared with seed viability to reveal proportional seed dormancy, which was then correlated to seed/embryo morphological traits and these traits related to the seed provenance environment. The embryos of all the investigated species are linear underdeveloped and all had a morphological component of seed dormancy (MD). Seeds of R. roezlii, R. hudsonianum and R. nevadaense required a temperature and/or hormone pre‐treatment in order to germinate, highlighting morphophysiological seed dormancy (MPD). Seed dormancy was found to be strongly negatively correlated with embryo length, but not with embryo to seed (E:S) ratio or seed mass. Initial embryo length was positively related to mean annual temperature. Seed dormancy in the investigated Ribes species could be quantified and predicted by the interaction of embryo traits and environmental conditions. This approach may be helpful in assessing and predicting seed dormancy in the Ribes genus and in other genera and families with underdeveloped embryos.     

Relationships of bird community structure and species distributions to two environmental gradients in the northern boreal forest

Few studies have explicitly examined avian community structure in the North American northern boreal forest Herein we report upon the results of bird surveys in mature stands of boreal forest in the Great Clay Belt Ontario. Canada We related trends in avian community structure and individual bird species abundance to two environmental gradients described in a landbase classification scheme called the Forest Ecosystem Classification a moisture and a nutrient-richness gradient Variation in environmental characteristics is limited in the Ontario Clay Belt and this was reflected in short environmental gradient lengths However major trends in avian community structure were strongly associated with the nutrient-richness gradient axis summarized by this scheme Analyses of avian community composition indicated a continuum from moist coniferous habitats to drier aspen-dominated mixed woodlands with several bird species occurring in varying abundances across the width of both gradients 48 of the 58 species examined showed statistically significant associations with at least one of the two multivariate gradient axes A smaller proportion of short-distance migrant species were associated with these gradients than were species in either the neotropical migrant or resident categories We used a multivariate variable (habitat breadth) to compare degree of habitat specialization across different migratory groups A large proportion of neotropical migrants showed a high degree of habitat specialization m mature Clay Belt forests, and neotropical species with small habitat breadths were more commonly associated with habitats dominated by broad-leaved deciduous tree species than either short-distance or permanent resident species We discuss our findings in relation to the post-glacial history of the Clay Belt region     

Using ecological niche modeling to predict the distributions of two endangered amphibian species in aquatic breeding sites

Waterfowl can cause substantial reductions in plant standing crop, which may have ecological and economic consequences. However, what determines the magnitude of these reductions is not well understood. Using data from published studies, we derived the relationship between waterfowl density and reduction in plant standing crop. When waterfowl density was estimated as individuals ha⁻¹ no significant relationship with reduction in plant standing crop was detected. However, when waterfowl density was estimated as kg ha⁻¹ a significant, positive, linear relationship with reduction in plant standing crop was found. Whilst many previous studies have considered waterfowl species as homologous, despite large differences in body mass, our results suggest that species body mass is a key determinant of waterfowl impact on plant standing crop. To examine relative impacts of waterfowl groups based on species body mass, a measure of plant biomass reduction (R _s) per bird per hectare was calculated for each group. Comparison of R _s values indicated some differences in impact between different waterfowl groups, with swans having a greater per capita impact than smaller-bodied waterfowl groups. We present evidence that this difference is linked to disparities in individual body size and associated differences in intake rates, diet composition and energy requirements. Future research priorities are proposed, particularly the need for experiments that quantify the importance of factors that determine the magnitude of waterfowl impacts on plant standing crop.     

Ensemble forecasting of species distributions

Concern over implications of climate change for biodiversity has led to the use of bioclimatic models to forecast the range shifts of species under future climate-change scenarios. Recent studies have demonstrated that projections by alternative models can be so variable as to compromise their usefulness for guiding policy decisions. Here, we advocate the use of multiple models within an ensemble forecasting framework and describe alternative approaches to the analysis of bioclimatic ensembles, including bounding box, consensus and probabilistic techniques. We argue that, although improved accuracy can be delivered through the traditional tasks of trying to build better models with improved data, more robust forecasts can also be achieved if ensemble forecasts are produced and analysed appropriately.     

Phytoplankton communities and antecedent conditions: high resolution sampling in Esthwaite Water

1. The succession of a phytoplankton community was investigated through an intensive period of sampling and related to physical, chemical and biological conditions sampled at an equal, or higher, temporal resolution. 2. Phytoplankton samples were taken on a weekly basis from June to September 2004 and analysed for diversity, species composition, and contribution of different functional groups to total biomass. Physical and chemical data were collected on the sampling days, and physical environmental factors were also logged continuously throughout the period by automatic measuring stations. This continuous logging allowed community structure to be compared with physical data averaged over periods from a day to a week before each sampling date. 3. The Schmidt stability of the lake, a measure of the strength of stratification calculated from thermal data, showed a negative correlation with phytoplankton species diversity. This is consistent with the hypothesis that mixing was preventing exclusion by species that would otherwise dominate in stratified conditions. 4. At a functional level, stress tolerant (S‐type) species dominated during the stratified summer conditions, with small, colonising species (C types) and ruderal, disturbance tolerant species (R types) contributing little to the overall biomass. Of the stress tolerant species, the faster growing (S_C) phytoplankters were significantly favoured by more stable, stratified conditions and higher solar radiation. Increased abundance of this group resulted in decreased species diversity. Correlations were generally strongest when using the 6‐ to 7‐day averaged physical data, stressing the importance of continuous measurements of these drivers in phytoplankton studies.