Integrating multiple landscape-scale drivers in the lichen epiphyte response: climatic setting, pollution regime and woodland spatial-temporal structure

Aim To quantify the role of multiple biodiversity drivers – pollution, woodland structure and climate – controlling lichen epiphyte composition and diversity. Location  Scotland, north‐west Europe. Methods Four compatible datasets were assembled: site‐scale species distribution data (response) and base‐line modelled data on climate, pollution loads and extent of old‐growth woodland (explanatory variables). First, partial‐canonical correspondence analysis was used: (1) to compare the importance of environmental variables to pure spatial effects and (2) to partition the importance of environmental variables in explaining species composition. Secondly, patterns of species richness were investigated using multiple least‐squares regression. Results Old‐growth woodland was the most important control of species richness. Pollution was the most important explanatory variable for species composition. The impact of pollution on composition (and to a lesser extent on richness) is explained: (1) By recovery of lichens with declining SO₂ pollution, although with epiphyte composition shifted by the recent effects of N‐pollution and (2) By the limited spatial extent of severe pollution, and generally low‐to‐moderate pollution loads across our study area, combined with the positive effect of old‐growth woodland extent in controlling species richness. The effect of climate and old‐growth woodland on species composition covaried, supporting an interaction between habitat quality and climatic setting, which may be important in understanding the epiphyte response to climate change. Conclusions Advances in conservation planning will likely require an integrated approach to understanding simultaneous effects of multiple drivers, providing opportunities for integrated management strategies. Our study provides a preliminary example of this approach by combining three key biodiversity drivers into a single framework for lichen epiphytes. Thus, reducing pollution loads may make old‐growth woodland that currently exists in a polluted landscape available for colonization, thereby extending the available habitat for epiphytes, and facilitating an effective species response to climate change.     

Uncertainties in the response of a forest landscape to global climatic change

Many studies have been conducted to quantify the possible ecosystem/landscape response to the anticipated global warming. However, there is a large amount of uncertainty in the future climate predictions used for these studies. Specifically, the climate predictions can be very different based on a variety of global climate models and alternative greenhouse emission scenarios. In this study, we coupled a forest landscape model, LANDIS-II, and a forest process model, PnET-II, to examine the uncertainty (that results from the uncertainty in the future climate predictions) in the forest-type composition prediction for a transitional forest landscape [the Boundary Water Canoe Area]. Using an improved global-sensitivity analysis technique [Fourier amplitude sensitivity test], we also quantified the amount of uncertainty in the forest-type composition prediction contributed by different climate variables including temperature, CO₂, precipitation and photosynthetic active radiation (PAR). The forest landscape response was simulated for the period 2000–2400 ad based on the differential responses of 13 tree species under an ensemble of 27 possible climate prediction profiles (monthly time series of climate variables). Our simulations indicate that the uncertainty in the forest-type composition becomes very high after 2200 ad , which is close to the time when the current forests are largely removed by windthrow disturbances and natural mortality. The most important source of uncertainty in the forest-type composition prediction is from the uncertainty in temperature predictions. The second most important source is PAR, the third is CO₂ and the least important is precipitation. Our results also show that if the optimum photosynthetic temperature rises due to CO₂ enrichment, the forest landscape response to climatic change measured by forest-type composition may be substantially reduced.     

Neophyte species richness at the landscape scale under urban sprawl and climate warming

Aim Land use and climate are two major components of global environmental change but our understanding of their simultaneous and interactive effects upon biodiversity is still limited. Here, we investigated the relationship between the species richness of neophytes, i.e. non‐native vascular plants introduced after 1500 AD, and environmental covariates to draw implications for future dynamics under land‐use and climate change. Location Switzerland, Central Europe. Methods The distribution of vascular plants was derived from a systematic national grid of 1 km² quadrates (n = 456; Swiss Biodiversity Monitoring programme) including 1761 species, 122 of which were neophytes. Generalized linear models (GLMs) were used to correlate neophyte species richness with environmental covariates. The impact of land‐use and climate change was thereafter evaluated by projections for the years 2020 and 2050 using scenarios of moderate and strong changes for climate warming (IPCC) and urban sprawl (NRP 54). Results Mean annual temperature and the amount of urban areas explained neophyte species richness best, with a high predictive power of the corresponding model (cross‐validated D² = 0.816). Climate warming had a stronger impact on the potential increase in the mean neophyte species richness (up to 191% increase by 2050) than ongoing urban sprawl (up to 10% increase) independently from variable interactions and model extrapolations to non‐analogue environments. Main conclusions In contrast to other vascular plants, the prediction of neophyte species richness at the landscape scale in Switzerland requires few variables only, and regions of highest species richness of the two groups do not coincide. The neophyte species richness is basically driven by climatic (temperature) conditions, and urban areas additionally modulate small‐scale differences upon this coarse‐scale pattern. According to the projections climate warming will contribute to the future increase in neophyte species richness much more than ongoing urbanization, but the gain in new neophyte species will be highest in urban regions.     

How does forest landscape structure explain tree species richness in a Mediterranean context?

Although the strong relationship between vegetation and climatic factors is widely accepted, other landscape composition and configuration characteristics could be significantly related with vegetation diversity patterns at different scales. Variation partitioning was conducted in order to analyse to what degree forest landscape structure, compared to other spatial and environmental factors, explained forest tree species richness in 278 UTM 10 × 10 km cells in the Mediterranean region of Catalonia (NE Spain). Tree species richness variation was decomposed through linear regression into three groups of explanatory variables: forest landscape (composition and configuration), environmental (topography and climate) and spatial variables. Additionally, the forest landscape characteristics which significantly contributed to explain richness variation were identified through a multiple regression model. About 60% of tree species richness variation was explained by the whole set of variables, while their joint effects explained nearly 28%. Forest landscape variables were those with a greater pure explanatory power for tree species richness (about 15% of total variation), much larger than the pure effect of environmental or spatial variables (about 2% each). Forest canopy cover, forest area and land cover diversity were the most significant composition variables in the regression model. Landscape configuration metrics had a minor effect on forest tree species richness, with the exception of some shape complexity indices, as indicators of land use intensity and edge effects. Our results highlight the importance of considering the forest landscape structure in order to understand the distribution of vegetation diversity in strongly human-modified regions like the Mediterranean.     

Climate and land-use interactively shape butterfly diversity in tropical rainforest and savanna ecosystems of southwestern China

Human-induced habitat conversion and degradation, along with accelerating climatic change, have resulted in considerable global biodiversity loss. Nevertheless, how local ecological assemblages respond to the interplay between climate and land-use change remains poorly understood. Here, we examined the effects of climate and land-use interactions on butterfly diversity in different ecosystems of southwestern China. Specifically, we investigated variation in the alpha and beta diversities of butterflies in different landscapes along human-modified and climate gradients. We found that increasing land-use intensity not only caused a dramatic decrease in butterfly alpha diversity but also significantly simplified butterfly species composition in tropical rainforest and savanna ecosystems. These findings suggest that habitat modification by agricultural activities increases the importance of deterministic processes and leads to biotic homogenization. The land-use intensity model best explained species richness variation in the tropical rainforest, whereas the climate and land-use intensity interaction model best explained species richness variation in the savanna. These results indicate that climate modulates the effects of land-use intensity on butterfly alpha diversity in the savanna ecosystem. We also found that the response of species composition to climate varied between sites: specifically, species composition was strongly correlated with climatic distance in the tropical rainforest but not in the savanna. Taken together, our long-term butterfly monitoring data reveal that interactions between human-modified habitat change and climate change have shaped butterfly diversity in tropical rainforest and savanna. These findings also have important implications for biodiversity conservation under the current era of rapid human-induced habitat loss and climate change.     

Selecting from correlated climate variables: a major source of uncertainty for predicting species distributions under climate change

Correlative species distribution models are frequently used to predict species’ range shifts under climate change. However, climate variables often show high collinearity and most statistical approaches require the selection of one among strongly correlated variables. When causal relationships between species presence and climate parameters are unknown, variable selection is often arbitrary, or based on predictive performance under current conditions. While this should only marginally affect current range predictions, future distributions may vary considerably when climate parameters do not change in concert. We investigated this source of uncertainty using four highly correlated climate variables together with a constant set of landscape variables in order to predict current (2010) and future (2050) distributions of four mountain bird species in central Europe. Simulating different parameterization decisions, we generated a) four models including each of the climate variables singly, b) a model taking advantage of all variables simultaneously and c) an un‐weighted average of the predictions of a). We compared model accuracy under current conditions, predicted distributions under four scenarios of climate change, and – for one species – evaluated back‐projections using historical occurrence data. Although current and future variable‐correlations remained constant, and the models’ accuracy under contemporary conditions did not differ, future range predictions varied considerably in all climate change scenarios. Averaged models and models containing all climate variables simultaneously produced intermediate predictions; the latter, however, performed best in back‐projections. This pattern, consistent across different modelling methods, indicates a benefit from including multiple climate predictors in ambiguous situations. Variable selection proved to be an important source of uncertainty for future range predictions, difficult to control using contemporary information. Small, but diverging changes of climate variables, masked by constant overall correlation patterns, can cause substantial differences between future range predictions which need to be accounted for, particularly when outcomes are intended for conservation decisions.     

Mountain summer farms in Røldal,western Norway –; vegetation classification and patterns in species turnover and richness

This paper discusses vegetation types and diversity patterns in relation to environment and land-use at summer farms, a characteristic cultural landscape in the Norwegian mountains. Floristic data (189 taxa) were collected in 130 4-m² sample plots within 12 summer farms in Røldal, western Norway. The study was designed to sample as fully as possible the range of floristic, environmental, and land-use conditions. Vegetation types delimited by two-way indicator species analysis were consistent with results from earlier phytosociological studies. Detrended correspondence analysis and canonical correspondence analysis show that rather than being distinct vegetation types, the major floristic variation is structured along a spatial gradient from summer farm to the surrounding heathland vegetation. Species richness (alpha diversity) was modelled against environmental variables by generalized linear modelling and compositional turnover (beta diversity) by canonical correspondence analysis. Most environmental factors made significant contributions, but the spatial distance-to-farm gradient was the best predictor of both species richness and turnover. While summer farms reduce mean species richness at the plot scale, the compositional heterogeneity of the upland landscapes is increased, thereby creating ‘ecological room’ for additional vegetation types and species. Within an overall similarity across scales, soil variables (pH, base saturation, LOI, phosphate and nitrogen) differed considerably in their explanatory power for richness and turnover. A difference between ‘productivity limiting’ factors and ‘environmental sieves’ is proposed as an explanation. Species turnover with altitude is relatively low in grasslands as compared to heaths.     

Plant community composition and species richness in the High Arctic tundra: From the present to the future

Arctic plant communities are altered by climate changes. The magnitude of these alterations depends on whether species distributions are determined by macroclimatic conditions, by factors related to local topography, or by biotic interactions. Our current understanding of the relative importance of these conditions is limited due to the scarcity of studies, especially in the High Arctic. We investigated variations in vascular plant community composition and species richness based on 288 plots distributed on three sites along a coast‐inland gradient in Northeast Greenland using a stratified random design. We used an information theoretic approach to determine whether variations in species richness were best explained by macroclimate, by factors related to local topography (including soil water) or by plant‐plant interactions. Latent variable models were used to explain patterns in plant community composition. Species richness was mainly determined by variations in soil water content, which explained 35% of the variation, and to a minor degree by other variables related to topography. Species richness was not directly related to macroclimate. Latent variable models showed that 23.0% of the variation in community composition was explained by variables related to topography, while distance to the inland ice explained an additional 6.4 %. This indicates that some species are associated with environmental conditions found in only some parts of the coast–inland gradient. Inclusion of macroclimatic variation increased the model's explanatory power by 4.2%. Our results suggest that the main impact of climate changes in the High Arctic will be mediated by their influence on local soil water conditions. Increasing temperatures are likely to cause higher evaporation rates and alter the distribution of late‐melting snow patches. This will have little impact on landscape‐scale diversity if plants are able to redistribute locally to remain in areas with sufficient soil water.     

Ice age legacies in the geographical distribution of tree species richness in Europe

Aim This study uses a high‐resolution simulation of the Last Glacial Maximum (LGM) climate to assess: (1) whether LGM climate still affects the geographical species richness patterns in the European tree flora and (2) the relative importance of modern and LGM climate as controls of tree species richness in Europe. Location The parts of Europe that were unglaciated during the LGM. Methods Atlas data on the distributions of 55 tree species were linked with data on modern and LGM climate and climatic heterogeneity in a geographical information system with a 60‐km grid. Four measures of species richness were computed: total richness, and richness of the 18 most restricted species, 19 species of medium incidence (intermediate species) and 18 most widespread species. We used ordinary least‐squares regression and spatial autoregressive modelling to test and estimate the richness–climate relationships. Results LGM climate constituted the best single set of explanatory variables for richness of restricted species, while modern climate and climatic heterogeneity was best for total and widespread species richness and richness of intermediate species, respectively. The autoregressive model with all climatic predictors was supported for all richness measures using an information‐theoretic approach, albeit only weakly so for total species richness. Among the strongest relationships were increases in total and intermediate richness with climatic heterogeneity and in restricted richness with LGM growing‐degree‐days. Partial regression showed that climatic heterogeneity accounted for the largest unique variation fraction for intermediate richness, while LGM climate was particularly important for restricted richness. Main conclusions LGM climate appears to still affect geographical patterns of tree species richness in Europe, albeit the relative importance of modern and LGM climate depends on range size. Notably, LGM climate is a strong richness control for species with a restricted range, which appear to still be associated with their glacial refugia.     

Patterns of woody plant species richness in the Iberian Peninsula: environmental range and spatial scale

Aim Climate‐based models often explain most of the variation in species richness along broad‐scale geographical gradients. We aim to: (1) test predictions of woody plant species richness on a regional spatial extent deduced from macro‐scale models based on water–energy dynamics; (2) test if the length of the climate gradients will determine whether the relationship with woody species richness is monotonic or unimodal; and (3) evaluate the explanatory power of a previously proposed ‘water–energy’ model and regional models at two grain sizes. Location The Iberian Peninsula. Methods We estimated woody plant species richness on grid maps with c. 2500 and 22,500 km² cell size, using geocoded data for the individual species. Generalized additive models were used to explore the relationships between richness and climatic, topographical and substrate variables. Ordinary least squares regression was used to compare regional and more general water–energy models in relation to grain size. Variation partitioning by partial regression was applied to find how much of the variation in richness was related to spatial variables, explanatory variables and the overlap between these two. Results Water–energy dynamics generate important underlying gradients that determine the woody species richness even over a short spatial extent. The relationships between richness and the energy variables were linear to curvilinear, whereas those with precipitation were nonlinear and non‐monotonic. Only a small fraction of the spatially structured variation in woody species richness cannot be accounted for by the fitted variables related to climate, substrate and topography. The regional models accounted for higher variation in species richness than the water–energy models, although the water–energy model including topography performed well at the larger grain size. Elevation range was the most important predictor at all scales, probably because it corrects for ‘climatic error’ due to the unrealistic assumption that mean climate values are evenly distributed in the large grid cells. Minimum monthly potential evapotranspiration was the best climatic predictor at the larger grain size, but actual evapotranspiration was best at the smaller grain size. Energy variables were more important than precipitation individually. Precipitation was not a significant variable at the larger grain size when examined on its own, but was highly significant when an interaction term between itself and substrate was included in the model. Main conclusions The significance of range in elevation is probably because it corresponds to several aspects that may influence species diversity, such as climatic variability within grid cells, enhanced surface area, and location for refugia. The relative explanatory power of energy and water variables was high, and was influenced by the length of the climate gradient, substrate and grain size of the analysis. Energy appeared to have more influence than precipitation, but water availability is also determined by energy, substrate and topographic relief.     

Land-cover data improve bioclimatic models for anurans and turtles at a regional scale

Aim We investigated whether accounting for land cover could improve bioclimatic models for eight species of anurans and three species of turtles at a regional scale. We then tested whether accounting for spatial autocorrelation could significantly improve bioclimatic models after statistically controlling for the effects of land cover. Location Nova Scotia, eastern Canada. Methods Species distribution data were taken from a recent (1999–2003) herpetofaunal atlas. Generalized linear models were used to relate the presence or absence of each species to climate and land‐cover variables at a 10‐km resolution. We then accounted for spatial autocorrelation using an autocovariate or third‐order trend surface of the geographical coordinates of each grid square. Finally, variance partitioning was used to explore the independent and joint contributions of climate, land cover and spatial autocorrelation. Results The inclusion of land cover significantly increased the explanatory power of bioclimatic models for 10 of the 11 species. Furthermore, including land cover significantly increased predictive performance for eight of the 11 species. Accounting for spatial autocorrelation improved model fit for rare species but generally did not improve prediction success. Variance partitioning demonstrated that this lack of improvement was a result of the high correlation between climate and trend‐surface variables. Main conclusions The results of this study suggest that accounting for the effects of land cover can significantly improve the explanatory and predictive power of bioclimatic models for anurans and turtles at a regional scale. We argue that the integration of climate and land‐cover data is likely to produce more accurate spatial predictions of contemporary herpetofaunal diversity. However, the use of land‐cover simulations in climate‐induced range‐shift projections introduces additional uncertainty into the predictions of bioclimatic models. Further research is therefore needed to determine whether accounting for the effects of land cover in range‐shift projections is merited.     

Factors determining the distribution of the spur-thighed tortoise Testudo graeca in south-east spain: a hierarchical approach

This work represents the first attempt to model the habitat-species relationships of a species of terrestrial tortoise on a large scale. We applied hierarchical variance partition methodology to Generalised Lineal Models (GLMs), with the presence of the tortoise in 1 km² cells as the response variable. We posited the existence of a hierarchical scheme of factors (including climate, relief and lithology, and land-use) that determine the distribution of Testugo graeca in southeastern Spain. We also identified the environmental variables within each factor with the greatest explanatory power and decoupled local vs landscape effects. Climate, followed by relief and lithology, and then land-use, turned out to be the most important factor shaping the distribution of T. graeca in south-east Spain as well as determining the presence of the species within its range. Univariate models showed that the main climate constraints were related to rainfall and extreme minimum temperatures, two factors which could be related to constraints imposed by the length of the annual activity period and productivity. Finally, multi-scale decomposition suggested that neighbouring habitat and local dynamics may also be important in the distribution of the species at the landscape scale.     

Unpacking the mechanisms captured by a correlative species distribution model to improve predictions of climate refugia

Climate refugia are regions that animals can retreat to, persist in and potentially then expand from under changing environmental conditions. Most forecasts of climate change refugia for species are based on correlative species distribution models (SDMs) using long‐term climate averages, projected to future climate scenarios. Limitations of such methods include the need to extrapolate into novel environments and uncertainty regarding the extent to which proximate variables included in the model capture processes driving distribution limits (and thus can be assumed to provide reliable predictions under new conditions). These limitations are well documented; however, their impact on the quality of climate refugia predictions is difficult to quantify. Here, we develop a detailed bioenergetics model for the koala. It indicates that range limits are driven by heat‐induced water stress, with the timing of rainfall and heat waves limiting the koala in the warmer parts of its range. We compare refugia predictions from the bioenergetics model with predictions from a suite of competing correlative SDMs under a range of future climate scenarios. SDMs were fitted using combinations of long‐term climate and weather extremes variables, to test how well each set of predictions captures the knowledge embedded in the bioenergetics model. Correlative models produced broadly similar predictions to the bioenergetics model across much of the species' current range – with SDMs that included weather extremes showing highest congruence. However, predictions in some regions diverged significantly when projecting to future climates due to the breakdown in correlation between climate variables. We provide unique insight into the mechanisms driving koala distribution and illustrate the importance of subtle relationships between the timing of weather events, particularly rain relative to hot‐spells, in driving species–climate relationships and distributions. By unpacking the mechanisms captured by correlative SDMs, we can increase our certainty in forecasts of climate change impacts on species.     

Bird species richness is associated with phylogenetic relatedness,plant species richness,and altitudinal range in Inner Mongolia

Bird species richness is mediated by local, regional, and historical factors, for example, competition, environmental heterogeneity, contemporary, and historical climate. Here, we related bird species richness with phylogenetic relatedness of bird assemblages, plant species richness, topography, contemporary climate, and glacial‐interglacial climate change to investigate the relative importance of these factors. This study was conducted in Inner Mongolia, an arid and semiarid region with diverse vegetation types and strong species richness gradients. The following associated variables were included as follows: phylogenetic relatedness of bird assemblages (Net Relatedness Index, NRI), plant species richness, altitudinal range, contemporary climate (mean annual temperature and precipitation, MAT and MAP), and contemporary‐Last Glacial Maximum (LGM) change in climate (change in MAT and change in MAP). Ordinary least squares linear, simultaneous autoregressive linear, and Random Forest models were used to assess the associations between these variables and bird species richness across this region. We found that bird species richness was correlated negatively with NRI and positively with plant species richness and altitudinal range, with no significant correlations with contemporary climate and glacial–interglacial climate change. The six best combinations of variables ranked by Random Forest models consistently included NRI, plant species richness, and contemporary‐LGM change in MAT. Our results suggest important roles of local ecological factors in shaping the distribution of bird species richness across this semiarid region. Our findings highlight the potential importance of these local ecological factors, for example, environmental heterogeneity, habitat filtering, and biotic interactions, in biodiversity maintenance.     

Does local habitat fragmentation affect large‐scale distributions? The case of a specialist grassland bird

Aim

Although the negative effects of habitat fragmentation have been widely documented at the landscape scale, much less is known about its impacts on species distributions at the biogeographical scale. We hypothesize that fragmentation influences the large‐scale distribution of area‐ and edge‐sensitive species by limiting their occurrence in regions with fragmented habitats , despite otherwise favourable environmental conditions. We test this hypothesis by assessing the interplay of climate and landscape factors influencing the distribution of the calandra lark, a grassland specialist that is highly sensitive to habitat fragmentation.

Location

Iberia Peninsula, Europe.

Methods

Ecological niche modelling was used to investigate the relative influence of climate/topography, landscape fragmentation and spatial structure on calandra lark distribution. Modelling assumed explicitly a hierarchically structured effect among explanatory variables, with climate/topography operating at broader spatial scales than landscape variables. An eigenvector‐based spatial filtering approach was used to cancel bias introduced by spatial autocorrelation. The information theoretic approach was used in model selection, and variation partitioning was used to isolate the unique and shared effects of sets of explanatory variables.

Results

Climate and topography were the most influential variables shaping the distribution of calandra lark, but incorporating landscape metrics contributed significantly to model improvement. The probability of calandra lark occurrence increased with total habitat area and declined with the number of patches and edge density. Variation partitioning showed a strong overlap between variation explained by climate/topography and landscape variables. After accounting for spatial structure in species distribution, the explanatory power of environmental variables remained largely unchanged.

Main conclusions

We have shown here that landscape fragmentation can influence species distributions at the biogeographical scale. Incorporating fragmentation metrics into large‐scale ecological niche models may contribute for a better understanding of mechanism driving species distributions and for improving predictive modelling of range shifts associated with land use and climate changes.

     

Diversity of forest plant species at the community and landscape scales in Switzerland

Abstract

Conservation strategies increasingly refer to indicators derived from large biological data. However, such data are often unique with respect to scale and species groups considered. To compare richness patterns emerging from different inventories, we analysed forest species richness at both the landscape and the community scales in Switzerland. Numbers of forest species were displayed using nationwide distributional species data and referring to three different definitions of forest species. The best regression models on a level of four predictor variables ranged between adj. R ² = 0.50 and 0.66 and revealed environmental heterogeneity/energy, substrate (rocky outcrops) and precipitation as best explanatory variables of forest species richness at the landscape scale. A systematic sample of community data (n = 729; 30 m², 200 m², 500 m²) was examined with respect to nationwide community diversity and plot species richness. More than 50% of all plots were assigned to beech forests (Eu-Fagion, Cephalanthero-Fagion, Luzulo-Fagion and Abieti-Fagion), 14% to Norway spruce forests (Vaccinio-Piceion) and 13% to silver fir forests (Piceo-Abietion). Explanatory variables were derived from averaged indicator values per plot, and from biophysical and disturbance factors. The best models for plot species richness using four predictor variables ranged between adj. R ² = 0.31 and 0.34. Light (averaged L-indicator, tree canopy) and substrate (averaged R-indicator and pH) had the highest explanatory power at all community scales. By contrast, the influence of disturbance variables was very small, as only a small portion of plots were affected by this factor. The effects of disturbances caused by extreme events or by management would reduce the tree canopies and lead to an increase in plant species richness at the community scale. Nevertheless, such community scale processes will not change the species richness at the landscape scale. Instead, the variety of different results derived from different biological data confirms the diversity of aspects to consider. Therefore, conservation strategies should refer to value systems.     

Comparison of watershed disturbance predictive models for stream benthic macroinvertebrates for three distinct ecoregions in western US

The successful use of macroinvertebrates as indicators of stream condition in bioassessments has led to heightened interest throughout the scientific community in the prediction of stream condition. For example, predictive models are increasingly being developed that use measures of watershed disturbance, including urban and agricultural land-use, as explanatory variables to predict various metrics of biological condition such as richness, tolerance, percent predators, index of biotic integrity, functional species traits, or even ordination axes scores. Our primary intent was to determine if effective models could be developed using watershed characteristics of disturbance to predict macroinvertebrate metrics among disparate and widely separated ecoregions. We aggregated macroinvertebrate data from universities and state and federal agencies in order to assemble stream data sets of high enough density appropriate for modeling in three distinct ecoregions in Oregon and California. Extensive review and quality assurance of macroinvertebrate sampling protocols, laboratory subsample counts and taxonomic resolution was completed to assure data comparability. We used widely available digital coverages of land-use and land-cover data summarized at the watershed and riparian scale as explanatory variables to predict macroinvertebrate metrics commonly used by state resource managers to assess stream condition. The “best” multiple linear regression models from each region required only two or three explanatory variables to model macroinvertebrate metrics and explained 41–74% of the variation. In each region the best model contained some measure of urban and/or agricultural land-use, yet often the model was improved by including a natural explanatory variable such as mean annual precipitation or mean watershed slope. Two macroinvertebrate metrics were common among all three regions, the metric that summarizes the richness of tolerant macroinvertebrates (RICHTOL) and some form of EPT (Ephemeroptera, Plecoptera, and Trichoptera) richness. Best models were developed for the same two invertebrate metrics even though the geographic regions reflect distinct differences in precipitation, geology, elevation, slope, population density, and land-use. With further development, models like these can be used to elicit better causal linkages to stream biological attributes or condition and can be used by researchers or managers to predict biological indicators of stream condition at unsampled sites.     

The unusual suspect: Land use is a key predictor of biodiversity patterns in the Iberian Peninsula

Although land use change is a key driver of biodiversity change, related variables such as habitat area and habitat heterogeneity are seldom considered in modeling approaches at larger extents. To address this knowledge gap we tested the contribution of land use related variables to models describing richness patterns of amphibians, reptiles and passerines in the Iberian Peninsula. We analyzed the relationship between species richness and habitat heterogeneity at two spatial resolutions (i.e., 10 km × 10 km and 50 km × 50 km). Using both ordinary least square and simultaneous autoregressive models, we assessed the relative importance of land use variables, climate variables and topographic variables. We also compare the species–area relationship with a multi-habitat model, the countryside species–area relationship, to assess the role of the area of different types of habitats on species diversity across scales. The association between habitat heterogeneity and species richness varied with the taxa and spatial resolution. A positive relationship was detected for all taxa at a grain size of 10 km × 10 km, but only passerines responded at a grain size of 50 km × 50 km. Species richness patterns were well described by abiotic predictors, but habitat predictors also explained a considerable portion of the variation. Moreover, species richness patterns were better described by a multi-habitat species-area model, incorporating land use variables, than by the classic power model, which only includes area as the single explanatory variable. Our results suggest that the role of land use in shaping species richness patterns goes beyond the local scale and persists at larger spatial scales. These findings call for the need of integrating land use variables in models designed to assess species richness response to large scale environmental changes.     

Species–area models to assess biodiversity change in multi-habitat landscapes: The importance of species habitat affinity

Species–area relationships (SARs) are a common tool to assess the impacts of habitat loss on species diversity. Species–area models that include habitat effects may better describe biodiversity patterns; also the shape of the SAR may be best described by other models than the classical power model. We compared the fit of 24 SAR models, i.e. eight basic models using three approaches: (i) single-habitat models, (ii) multi-habitat models which account for the effect of the habitat composition on total species diversity (= choros models) and (iii) multi-habitat models which also account for the differential use of habitats by different species groups (= countryside models). We use plant diversity data from a multi-habitat landscape in NW Portugal. Countryside models had the best fit both when predicting species–area patterns of species groups and of total species richness. Overall, choros models had a better fit than single-habitat models. We also tested the application of multi-habitat models to land-use change scenarios. Estimates of species richness using the choros model only depended on the number of habitats in the landscape. In contrast, for the countryside model, estimates of species richness varied continuously with the relative proportion of the different habitat types in the landscape, and projections suggest that land-use change impacts may be moderated by a species’ ability to use multiple habitats in the landscape. We argue that the countryside SAR is a better model to assess the impacts of land-use changes than the single-habitat SAR or the choros model, as species often face habitat change instead of real habitat loss, and species response to change is contingent on their differential use of habitats in the landscape.