Predicting the trends of vertebrate species richness as a response to wind farms installation in mountain ecosystems of northwest Portugal

The main objectives of this work were to examine the performance of a holistic stochastic dynamic methodology (StDM) in predicting the trends of the vertebrate species richness (amphibians, reptiles, birds and mammals) in response to changes induced by the ongoing wind farm installation in mountain areas of northwest Portugal. The StDM is a sequential modelling process developed in order to estimate the ecological status of changed ecosystems that have been damaged by anthropogenic disturbances. The performance of two complementary temporal approaches was tested, taking into account either annual or seasonal influences. The data used in the dynamic model construction included true gradients of environmental changes and was sampled from 2004 to 2006. The dynamic model developed was preceded by a conventional multivariate statistical procedure performed to discriminate the significant relationships between the selected ecological components, such as the species richness of each vertebrate group and the structural changes in habitat conditions. The results show the capacity of the model in capturing the dynamics of the studied system by predicting consistent trends for the global vertebrate species richness under complex and variable environmental scenarios. The average annual approach is considered sufficient for the aims of the most Environmental Impact Assessments while the seasonal approach is recommended for more detailed studies, namely regarding specific population, guilds or community dynamics.     

A Stochastic Dynamic Methodology (StDM) to simulate the effects of fire on vegetation and bird communities in Pinus pinaster stands

Worldwide forests have been impacted by broad-scale anthropogenic pressures, such as fire and logging, leading to the disruption of the structure and functioning of these systems. The present paper examined the applicability of a holistic Stochastic Dynamic Methodology (StDM) in predicting the tendencies of passerine bird communities in maritime pine (Pinus pinaster) stands as a response to the changes induced by fire occurrence. The StDM is a sequential modelling process developed in order to predict the ecological status of changed ecosystems, from which management strategies can be designed. The case of the pine stands of central Portugal was used to test the StDM performance in the scope of the wildfire problems. The datasets used in the dynamic model construction included the main gradients of environmental and biological characteristics of the studied maritime pine stands over space and time. The ecological integrity of the pine stands can be partly assessed by the observation of the occurrence of passerine indicators. The dynamic model developed was preceded by a conventional multivariate statistical procedure performed to discriminate the significant relationships between conceptually isolated key-components of the studied ecosystems. The final model provided some basis to analyse the responses of selected passerine indicators to fire scenarios that characterize the region. In this context, the simulation results are encouraging since they seem to demonstrate the StDM reliability in capturing the passerine community dynamics by predicting the behavioural pattern of indicators roughly associated with their structural and functional composition and habitat main characteristics.     

湖泊-流域生态系统管理的内容与方法

在流域生态系统管理研究综述的基础上,对湖泊一流域生态系统管理的概念进行了界定,对水环境管理、综合流域管理与流域生态系统管理之间的差异进行了对比分析。确定了生态系统生态学、流域生态学、生态系统健康和流域方法为湖泊．流域生态系统管理的理论基础,生态系统方法和流域分析为其方法学基础。在上述分析的基础上,提出了湖泊．流域生态系统管理的6个主要步骤：研究范围界定、基础信息收集与基本生态学问题的分析和评价、管理目标设定、系统综合、生态系统综合评价、适应性管理;识别出湖泊-流域生态系统管理中的3个关键问题：①生态系统管理中的不确定性和障碍分析;②流域土地利用变化对湖泊水质和生态系统的影响;③流域生态子系统与社会子系统的关联。     

Investigating the causes of fish community change in the Dahan River (Taiwan) using an autecology matrix

Recent work in river restoration and water resources management has seen the need to change the focus of analysis from reach to watershed scales to better define causes of watershed impairment. However, comprehensive investigations at a watershed scale are hindered by difficulties in using reach data that was collected for analysis at small spatial and short temporal scales. This is especially true for ecological and biological data. The approach assembles assessment and monitoring data and uses an autecology matrix to identify the changes in environmental and ecological conditions that may be associated with community change over spatial and temporal scales appropriate for ecosystem analysis in watersheds. The analysis uses a weight-of-evidence approach based on the percent of the community associated with a matrix factor. We have used the autecology matrix to examine historical fish community data from the Dahan River, Taiwan. The results show that the method provides an improved understanding of historical influences on fish community structure and supports a process-based analysis of community change over watershed scales and historic time periods. Further the method helps identify habitat requirements for the fish communities at each sampling site, supporting management and ecological restoration objectives.     

Dynamic macroecology on ecological time-scales

Aim The discipline of macroecology is increasingly being regarded as an effective vehicle for the evaluation of recent population‐ to ecosystem‐level responses to widespread human and environmental influences. However, due to the prevalent use of time‐averaged and cumulative data in macroecological analyses, the majority of the patterns that emerge from research in this field can be regarded as static. Here we review the application of dynamic macroecological analyses to changes in relationships between macroecological variables on seasonal to decadal scales. We illustrate the strength of this perspective for documenting changing patterns and testing hypotheses related to these dynamics on ecological time‐scales. Location Studies were compiled and reviewed from terrestrial and aquatic ecosystems. Methods We review examples of temporal changes in macroecological patterns driven by recent anthropogenic influences and environmental change. Results The dynamic nature of macroecological patterns on ecological time‐scales has been revealed in recent years across a wide range of ecosystems, largely through the development, maintenance and analysis of biotic and environmental monitoring time series. The resultant analyses complement examinations of dynamics over evolutionary time and have similarly revealed that static portrayals can conceal important temporal dynamics that underlie the patterns of interest. As a consequence, static depictions, resting as they do on comparative analyses in which the validity of space‐for‐time substitutions is assumed, may be of limited use for testing hypotheses related to the mechanisms underlying the patterns revealed and, by extension, the development of reliable predictions of future states. Main conclusions Recent dynamic macroecological analyses have demonstrated the utility of combined spatial and temporal replication, and have contributed to hypothesis testing related to the mechanistic processes underlying changes in macroecological patterns on ecological time‐scales. We suggest four specific avenues of future research to further the development and application of temporal approaches on similar time‐scales within the field of macroecology.     

Modelling the performance of bird surveys in non-standard weather conditions: General applications with special reference to mountain ecosystems

Although considerable effort has been made to identify the appropriate climatic conditions for bird surveys, considered as standard conditions, in many occasions these conditions are not fulfilled. These are for instance the case of environmental impact assessments (EIA), where the field work is, recurrently, carried out in variable and non-standard weather conditions or in the scope of general ecological monitoring (GEM) programs, where different taxa (birds and other animal groups) are sampled simultaneously with distinct methodological requirements. The present work examined the applicability of a stochastic dynamic methodology (StDM) for predicting the richness and diversity of passerine surveys in mountain habitats characterized by variable and, predominantly, non-standard weather conditions. The relative variations of these metrics are the underlying database of our StDM model, providing some basis to analyse the accuracy of bird surveys. This model focuses on the interactions between conceptually isolated key-components, such as the passerine richness and diversity, and the influence of the prevailing climatic conditions.The proposed model was preceded by a conventional multivariate statistical procedure performed to discriminate the significant relationships between the selected metrics versus climatic variables. Since this statistical analysis is static, the dataset recorded from the field included true gradients of weather conditions (ranging from standard to extreme conditions). The results of the StDM simulations revealed significant variations in the performance of passerine surveys in response to several combinations of non-standard weather conditions, which enable us to calculate the appropriated correction factors for discrete climatic scenarios. This could be used, in the future, to improve the quality of passerine diversity and richness estimates, namely in the scope of EIA studies when the climatic conditions are inevitably adverse for rigorous passerine surveys.     

A new stochastic dynamic tool to improve the accuracy of mortality estimates for bats killed at wind farms

Although generally considered environmentally friendly, wind power has been associated with extensive mortality of birds and bats. In this perspective, there is a need for reliable estimates of fatalities at wind farms, where the heterogeneity of the basic information, used among environmental assessment studies, is unlikely to support an accurate universal estimation method. We tested the applicability of the Stochastic Dynamic Methodology (StDM) to estimate bat fatalities, based on multifactorial cause–effect relationships (by integrating multi-model inference statistical analysis and dynamic modelling) between mortality estimates, detected fatalities and the selected key-components of the reality, such as the real number of bat mortalities simulated, the rate of carcasses removal, the searcher efficiency, the monitoring periodicity and the number of turbines for different realistic scenarios associated with particular wind farm conditions. Although some existing mortality estimators are considered accurate, the choice of a given universal formula for all mortality assessments, based on deterministic parameters and assumptions, may originate unsuspected errors. Therefore, we propose a flexible dynamic modelling framework, the StDM estimator, where the obtained algorithms are adaptable to the universe of application intended. The StDM estimator takes into account random, non-constant and scenario dependent parameters, providing bias-corrected estimates. The StDM estimator was applied for the European wind farm context and validated in the most cases tested, through the confrontation with independent data. Overall, this approach is considered a valuable tool to improve the quality of mortality estimates at onshore wind facilities, within the local, environmental and methodological gradients (including the cases where no mortality is detected), namely in the scope of environmental impact assessments and general ecological monitoring programmes.     

Two-zone model for stream and river ecosystems

A mechanistic two-zone model is developed to represent the food web dynamics of stream and river ecosystems by considering the benthic and nonbenthic (or water-column) zones as two separate, but interacting biotopes. Flow processes, solar radiation, and temperature are the dynamic external environmental drivers. State variables are defined to represent the hierarchical levels of detritus, limiting nutrient, vegetation, and invertebrates. The fish trophic level is included as a constant input parameter. Model parameters, constants, and boundary conditions are defined based on watershed as well as channel hydrology, stream geomorphology, and biological activities. Recent advances in ecological science and engineering are used in representing important biogeochemical processes. In particular, the turbulent diffusion, as well as sloughing or detachment, processes are defined based on these recent advancements. The two-zone model was evaluated for a gravel bed prealpine Swiss stream named River Necker with data for the study period of January 1992 through December 1994. The model was able to capture the general trends and magnitudes of the food web state variables. A comprehensive relative sensitivity analysis with five moment-based measures found that approximately 5% of the model parameters were important in predicting benthic vegetation. Results of sensitivity analysis guided the model calibration. Simulated benthic vegetation with the calibrated model, which was obtained by adjusting only four parameters, corresponded with observed data. Hydrology-dependent sloughing and detachment were dominant in determining the response of benthic vegetation and invertebrates. The proposed two-zone food web model is a potentially useful research tool for stream and river ecosystems.     

Temporal coherence of aboveground net primary productivity in mesic grasslands

Synchrony in ecological variables over wide geographic areas suggests that large‐scale environmental factors drive the structure and function of ecosystems and override more local‐scale environmental variation. Described also as coherence, this phenomenon has been documented broadly in the ecological literature and has recently received increasing attention as scientists attempt to quantify the impacts of global changes on organisms and their habitats. Using a mesic grassland site in North America, we assessed coherence in ecosystem function by quantifying similarity in aboveground net primary production (ANPP) dynamics in 48 permanent sampling locations (PSLs) over a 16‐yr period. Our primary objective was to characterize coherence across a broad geographic region (with similar ecosystem structure and function), and we hypothesized that precipitation and a similar fire frequency would strengthen coherence between PSLs. All 48 PSLs at our site (Konza Prairie Biological Station, Manhattan, KS, USA; KPBS) were exposed to a similar regional driver of ANPP (precipitation); however, local drivers (including differences in fire frequency and soil depth at different topographic positions) varied strongly among individual PSLs. For the purpose of this assessment, the watershed‐level experimental design of KPBS was considered a model, which represented different fire management strategies across the Great Plains Region. Our analyses revealed a site‐level (KPBS) coherence in ANPP dynamics of 0.53 for the period of 1984–1999. Annual fire enhanced coherence among PSLs to 0.76, whereas less frequent fire (fire exclusion or a 4‐yr fire return interval) failed to further increase coherence beyond that of the KPBS site level. Soil depth also strongly influenced coherence among PSLs with shallow soils at upland sites showing strong coherence across fire regimes and annually burned uplands closely linked to annual precipitation dynamics. The lack of coherence in ecosystem function in PSLs with deep soils and low fire frequencies suggests that conservation and management efforts will need to be more location specific in such areas where biotic interactions may be more important than regional abiotic drivers.     

The influence of watershed land use cover on stream fish diversity and size-at-age of a generalist fish

Changes in land use have manifold effects on stream ecosystems. Consequently, the degradation of watersheds can cause extreme responses if the resilience of the stream is exceeded, triggering changes in fish communities and a reorganization of the ecosystem. Fish community surveys are frequently used to evaluate the impact of anthropogenic pressures on freshwater streams. Dynamic indices such as individual growth are also interesting because they integrate the effects of environmental conditions through time, providing an assessment in the long term. In this study we have investigated the ecological implications of watershed land use cover on fish diversity and growth of the generalist species Umbra limi (central mudminnow) in six streams in Southern Ontario (Canada). In detail, the growth of U. limi has been explored using a Dynamic Energy Budget (DEB) model, which pursues a mechanistic explanation of the bioenergetics of an individual under different environmental conditions. Given the mechanistic approach, the outcomes of the DEB model can provide a solid foundation for extrapolating the conclusions of this study to a broader spatial scale. The results of this study reveal that the proportion of modified land use of the watershed (agricultural and urban land) can reach a tipping point beyond which the functioning of the stream abruptly changes. Consequently, land use cover may be used as a precautionary indicator for watershed management. The results also demonstrate that U. limi could be used as a sentinel species to identify potential impacts on fish diversity and size-at-age as a cost-effective indicator for stream monitoring programs.     

Temporal and spatial constraints on community assembly during microbial colonization of wood in seawater

Wood falls on the ocean floor form chemosynthetic ecosystems that remain poorly studied compared with features such as hydrothermal vents or whale falls. In particular, the microbes forming the base of this unique ecosystem are not well characterized and the ecology of communities is not known. Here we use wood as a model to study microorganisms that establish and maintain a chemosynthetic ecosystem. We conducted both aquaria and in situ deep-sea experiments to test how different environmental constraints structure the assembly of bacterial, archaeal and fungal communities. We also measured changes in wood lipid concentrations and monitored sulfide production as a way to detect potential microbial activity. We show that wood falls are dynamic ecosystems with high spatial and temporal community turnover, and that the patterns of microbial colonization change depending on the scale of observation. The most illustrative example was the difference observed between pine and oak wood community dynamics. In pine, communities changed spatially, with strong differences in community composition between wood microhabitats, whereas in oak, communities changed more significantly with time of incubation. Changes in community assembly were reflected by changes in phylogenetic diversity that could be interpreted as shifts between assemblies ruled by species sorting to assemblies structured by competitive exclusion. These ecological interactions followed the dynamics of the potential microbial metabolisms accompanying wood degradation in the sea. Our work showed that wood is a good model for creating and manipulating chemosynthetic ecosystems in the laboratory, and attracting not only typical chemosynthetic microbes but also emblematic macrofaunal species.     

Forecasting Physicochemical Variables by a Classification Tree Method. Application to the Berre Lagoon (South France)

The dynamics of the "Etang de Berre", a brackish lagoon situated close to the French Mediterranean sea coast, is strongly disturbed by freshwater inputs coming from an hydroelectric power station. The system dynamics has been described as a sequence of daily typical states from a set of physicochemical variables such as temperature, salinity and dissolved oxygen rates collected over three years by an automatic sampling station. Each daily pattern summarizes the evolution, hour by hour of the physicochemical variables. This article presents results of forecasts of the states of the system subjected to the simultaneous effects of meteorological conditions and freshwater releases. We recall the main step of the classification tree method used to build up the predictive model (Classification and Regression Trees, Breiman et al., 1984) and we propose a transfer procedure in order to test the stability of the model. Results obtained on the Etang de Berre data set allow us to describe and predict the effects of the environmental variables on the system dynamics with a margin of error. The transfer procedure applied after the tree building process gives a maximum gain in prediction accuracy of about 15%.     

Measuring complexity to infer changes in the dynamics of ecological systems under stress

Despite advances in our mechanistic understanding of ecological processes, the inherent complexity of real-world ecosystems still limits our ability in predicting ecological dynamics especially in the face of on-going environmental stress. Developing a model is frequently challenged by structure uncertainty, unknown parameters, and limited data for exploring out-of-sample predictions. One way to address this challenge is to look for patterns in the data themselves in order to infer the underlying processes of an ecological system rather than to build system-specific models. For example, it has been recently suggested that statistical changes in ecological dynamics can be used to infer changes in the stability of ecosystems as they approach tipping points. For computer scientists such inference is similar to the notion of a Turing machine: a computational device that could execute a program (the process) to produce the observed data (the pattern). Here, we make use of such basic computational ideas introduced by Alan Turing to recognize changing patterns in ecological dynamics in ecosystems under stress. To do this, we use the concept of Kolmogorov algorithmic complexity that is a measure of randomness. In particular, we estimate an approximation to Kolmogorov complexity based on the Block Decomposition Method (BDM). We apply BDM to identify changes in complexity in simulated time-series and spatial datasets from ecosystems that experience different types of ecological transitions. We find that in all cases, K_BDM complexity decreased before all ecological transitions both in time-series and spatial datasets. These trends indicate that loss of stability in the ecological models we explored is characterized by loss of complexity and the emergence of a regular and computable underlying structure. Our results suggest that Kolmogorov complexity may serve as tool for revealing changes in the dynamics of ecosystems close to ecological transitions.     

Living within dynamic social-ecological freshwater systems: System parameters and the role of ecological engineering

The objective of ecological engineering is to design sustainable ecosystems that integrate human communities and their natural environment for the benefit of both. In this paper, we illustrate how social-ecological modeling can be used as a tool to clarify this objective at a landscape scale for freshwater systems. Coupled social-ecological systems (SESs) are open, dynamic systems subject to both ecological and socioeconomic perturbations. Here we demonstrate the interactive effects of social and technological uncertainties on SES dynamics over time. Additionally, we integrate research on ecosystem stability, social-ecological modeling, and ecological engineering to offer guidance for research at the human-environment interface. Based on a case study of Lake Erie's Sandusky watershed, we use an integrated human-biophysical model to investigate the influence of two parameters on SES dynamics: (1) regional societal preferences that impact watershed management and (2) technological innovation that alters agricultural nutrient efficiency. Our results illustrate ways in which SES dynamics and optimum management strategies depend on societal preferences within the region, indicating a key area of uncertainty for future investigation. As guidance for SES restoration, our model results also illustrate the conditions under which technological change that increases nutrient efficiency on farms can and cannot create a win-win, or increase both human welfare and SES resistance to eutrophication simultaneously. Using these results, we elucidate the value of ecological engineering and offer guidance for assessments of ecological engineering projects using social-ecological modeling.     

Contagious Disturbance, Ecological Memory, and the Emergence of Landscape Pattern

Landscapes are strongly shaped by the degree of interaction between pattern and process. This paper examines how ecological memory, the degree to which an ecological process is shaped by its past modifications of a landscape, influences landscape dynamics. I use a simulation model to examine how ecological memory shapes the landscape dynamics produced by the interaction of vegetative regrowth and fire. The model illustrated that increased ecological memory increased the strength and spatial extent of landscape pattern. The extent of these changes depended upon the relative rates of vegetative recovery and fire initiation. When ecological memory is strong, landscape pattern is persistent; pattern tends to be maintained rather than destroyed by fire. The generality of the simulation model suggests that these results may also apply to disturbance processes other than fire. The existence of ecological memory in ecosystems may allow processes to produce ecological pattern that can entrain other ecosystem variables. The methods presented in this paper to analyze pattern in model ecosystems could be used to detect such pattern in actual ecosystems. Received 14 November 2000; accepted 21 September 2001.     

Integration of research and management in optimizing multiple uses of reservoirs: the experience in South America and Brazilian case studies

The construction of large reservoirs in South America and particularly in Brazil has intensified within the last 50 years. Built up primarily for hydroelectricity production, these artificial ecosystems now serve purposes such as: water storage for public use; fisheries and aquaculture; recreation; tourism, and irrigation. These artificial ecosystems were also built up with the purpose of enhancing the regional development. These activities produce multiple impacts among which are eutrophication, a serious problems with various ecological, economic, and social consequences. Basic studies on reservoirs have identified their main ecological characteristics and described some of their fundamental mechanisms of functioning. This information cover: factors involved in reservoir complexity, such as spatial scale, vertical and horizontal heterogeneity, temporal variation at several time scales and, in some cases, watershed/reservoir relationships, and the impact of non-point and point sources of nutrients. Numerous studies have been dedicated to describing aquatic biota, its relationship with environmental factors, and the impacts of degradation/pollution/ eutrophication on biodiversity. Research priorities have been established mainly by limnologists in response to needs identified by environmental impact assessment. Due to the pressure of multiple uses, population growth, and economic factors, aquatic scientists now face a number of questions being posed by managers and engineers. These questions relate to a wide range of practical problems, the solutions to which depend on accumulated data related to the structural complexities and function mechanisms previously referred to. These problems can be divided into three main groups: (i) eutrophication processes and their characteristics, and water quality control; (ii) impact of fisheries, aquaculture, and exotic species introduction on the biota and the water quality; (iii) sustainable development of reservoirs and optimization management of their multiple uses. Predictive capabilities required in the face of these problems should be based on modeling development and intensive use of data-bases resulting from long-term studies on reservoirs. The predictions thus made possible would involve economic factors and the costs of recovery of eutrophic reservoirs. The experience of reservoir research and management in South America and in Brazil is, probably part of a world wide tendency on reservoir research and development with the aim to optimize multiple uses. Based on the cooperation of limnologists and engineers, ecosystem management models must eventually be applied at the watershed / reservoir level, where demands on the part of managers and decision makers will certainly promote a predictive, integrated, and adaptive approach,based on fundamental research.     

Comparing biological classifications of freshwater phytoplankton: a case study from South China

The use of ecological classification systems is becoming more and more widely used when studying phytoplankton. Grouping phytoplankton species into ecologically coherent groups allow to reduce redundancy and in this way, to handle a minor number of biological variables when investigating the ecological status of aquatic ecosystems. Three ecological classifications are mostly used when freshwater phytoplankton is studied: functional groups or coda, morpho-functional groups (MFGs) and morphology-based functional groups (MBFGs). In this study, these three ecological classifications were comparatively used along with two taxonomic classifications based on species and genera to analyse phytoplankton response to environmental variability in three sub-tropical Chinese reservoirs. Canonical correspondence analysis was performed to compare the five mentioned biological classifications. When ecological classifications were used, the percentage of variance explained in the biological groups?Cenvironmental variables was higher than that explained by the taxonomic classifications. Coda and MFGs showed a very high degree of overlapping, but since coda are associated to very detailed environmental templates, this method was more helpful in explaining phytoplankton variability in relation to environmental factors.     

Development of a stochastic dynamic model for ecological indicators’ prediction in changed Mediterranean agroecosystems of north-eastern Portugal

A holistic stochastic dynamic model was developed by focusing on the interactions between conceptually isolated key-components, such as local passerine guilds and changes in habitat conditions, in Mediterranean agroecosystems of the “Terra Quente Transmontana region” (north-eastern Portugal). The ecological integrity of the typical patchwork of this region, with respect to land use, can be partly assessed by the observation of the occurrence of passerine guilds. These important indicators and state variables are the underlying database of our model. This model aimed the prediction of the ecological changes which can be expected when olive orchards are being intensified. The model proposed was preceded by a conventional multivariate statistical procedure (stepwise multiple regression analysis) performed to discriminate the significant relationships between guild richness and environmental variables. Since this statistical analysis is static, the dataset recorded from the field included true gradients of habitat changes. The model parameters were estimated from the results of the stochastic treatment and from regional data regarding tendencies within the use of land. A period of 50 years was considered. The final model provided some basis to analyse the responses of passerine guilds to the environmental scenarios that will characterize the new agroecosystems of the region. The model simulations were incorporated into a Geographical Information System (GIS) approach. The results of the simulation revealed a structural drift within the different guild richness in response to the expected gradient of habitat changes. The possible local extinction of several species within the less well-represented guilds, such as the steppe passerine species, may be associated with a predictable reduction in ecological integrity of the typical agroecosystems. Therefore, a new structure of the passerine communities indicates that future agroecosystems will diverge from the initial or actual ecological state.     

Exploring macroinvertebrate species distributions at regional and local scales across a sandy beach geographic continuum

Exposed sandy beaches are highly dynamic ecosystems where macroinvertebrate species cope with extremely variable environmental conditions. The majority of the beach ecology studies present exposed beaches as physically dominated ecosystems where abiotic factors largely determine the structure and distribution of macrobenthic communities. However, beach species patterns at different scales can be modified by the interaction between different environmental variables, including biotic interactions. In this study, we examined the role of different environmental variables for describing the regional and local scale distributions of common macrobenthic species across 39 beaches along the North coast of Spain. The analyses were carried out using boosted regression trees, a relatively new technique from the field of machine learning. Our study showed that the macroinvertebrate community on exposed beaches is not structured by a single physical factor, but instead by a complex set of drivers including the biotic compound. Thus, at a regional scale the macrobenthic community, in terms of number of species and abundance, was mainly explained by surrogates of food availability, such as chlorophyll a. The results also revealed that the local scale is a feasible way to construct general predictive species-environmental models, since relationships derived from different beaches showed similar responses for most of the species. However, additional information on aspects of beach species distribution can be obtained with large scale models. This study showed that species-environmental models should be validated against changes in spatial extent, and also illustrates the utility of BRTs as a powerful analysis tool for ecology data insight.