Stochastic Dynamic Methodology Research Articles

How good are the ecological assumptions and predictions made in the past? Insights from a dynamic modelling approach applied to changing landscapes

Worldwide landscape changes and the uncertainty about its impacts on species abundances, distributions and on ecosystems structure and functioning, have been increasing the value of modelling tools in a very obvious way. Thirteen years ago, the first holistic stochastic dynamic methodology (StDM) application was published (Ecological Indicators 3(4), 285–303 by Santos and Cabral (2004)) intended for predicting ecological indicators trends in face of realistic scenarios of land use/land cover changes. The application of this StDM framework provided some basis to simulate landscape changes and predict the subsequent response of pertinent ecological indicators. Nevertheless, the results reliability could only be evaluated with subsequent independent information checking. In this work, based on independent data obtained thirteen years after, we compare the simulated land use changes and predicted responses of the selected ecological indicators with the respective real trends. The comparisons made confirmed that the implemented scenario was realistic and the ecological indicators’ response mostly accurate. This allowed for demonstrating the proposed frameworks potential and its use in landscape planning and managing of agro-environmental measures. Our approach also provides a promising and intuitive baseline to support risk assessments for land use changes, derived from ecological models linked with ecological monitoring, crucial to guide decision makers and environmental managers.

A spatially-explicit dynamic modelling framework to assess habitat suitability for endangered species: The case of Red-billed Chough under land use change scenarios in Portugal

Habitat loss and degradation are frequently associated with negative effects on biodiversity in natural and semi-natural landscapes. Innovative spatio-temporal modelling tools may be valuable to support the design and optimization of conservation strategies for endangered species in a context of changes in land use and land cover (LULC). The Red-billed Chough (Pyrrhocorax pyrrhocorax) is a vulnerable corvid that exhibits high levels of population fragmentation and decline trends, due to its dependence on open habitats shaped by traditional agriculture and pastoralism. Here, we apply a spatially-explicit framework based on the stochastic dynamic methodology (StDM) principles to capture the trends of relevant population foraging ground attributes, assumed as proxies of the habitat suitability for two endangered Portuguese chough populations. StDM simulations were produced in order to capture the densities and distribution patterns of foraging flocking choughs in response to the complexity and dynamics of some realistic abandonment LULC scenarios expected to occur in the near future. Our modelling outputs reflect satisfactorily the seasonality of these patterns and suggest that the maintenance of traditional agriculture/pastoralism practices in at least 75% of the actual favourable areas is mandatory to ensure the foraging habitat suitability for choughs, evidencing the species high vulnerability in their last refuges in Portugal. This modelling approach represents a step forward in evaluating the consequences of LULC changes on endangered birds, which can be used in the future to guide effective conservation planning in the scope of habitat suitability management.

A modelling framework to predict bat activity patterns on wind farms: An outline of possible applications on mountain ridges of North Portugal.

Worldwide ecological impact assessments of wind farms have gathered relevant information on bat activity patterns. Since conventional bat study methods require intensive field work, the prediction of bat activity might prove useful by anticipating activity patterns and estimating attractiveness concomitant with the wind farm location. A novel framework was developed, based on the stochastic dynamic methodology (StDM) principles, to predict bat activity on mountain ridges with wind farms. We illustrate the framework application using regional data from North Portugal by merging information from several environmental monitoring programmes associated with diverse wind energy facilities that enable integrating the multifactorial influences of meteorological conditions, land cover and geographical variables on bat activity patterns. Output from this innovative methodology can anticipate episodes of exceptional bat activity, which, if correlated with collision probability, can be used to guide wind farm management strategy such as halting wind turbines during hazardous periods. If properly calibrated with regional gradients of environmental variables from mountain ridges with windfarms, the proposed methodology can be used as a complementary tool in environmental impact assessments and ecological monitoring, using predicted bat activity to assist decision making concerning the future location of wind farms and the implementation of effective mitigation measures.

Are Directors Really Irrelevant to Capital Structure Choice?

DeAngelo and Roll (2015) observe that leverage cross-sections change significantly over even short periods. This finding is largely incompatible with existing models operating on the assumption that firms choose leverage levels in isolation. In this paper we ask how executive networks might affect the decision making process. Using a sample of large US firms, we apply the stochastic actor-based dynamic social network methodology to show that firms sharing directors in common tend to become more similar in capital structure. Network influence effects are economically significant even on top of traditionally employed capital structure determinant variables, and also robust to director selection effects.

Do the passerine traits’ dynamic patterns indicate the ecological status of agro-forestry ecosystems? A modelling approach for “Montado” management assessments

ContextThe “Montado”, a human shaped oak agro-forestry dominated landscape of South Western Europe has been experiencing series of changes following the implementation of different agricultural policies. Such changes are responsible for shifting the structure and composition of “Montado” landscape and hence the biodiversity of the system, either by agriculture abandonment or intensification. Traditional oak agro-forestry management practices, which focused on improving the ecological status of a single and common land use (oak agro-forestry), is taken as a conservation paradigm against the perceived changes in the system. AimsWe aimed to identify the effect of oak agro-forestry management on “Montado” biodiversity as indicator for ecological status of “Montado” landscape. MethodsA recently developed spatially explicit Stochastic Dynamic Methodology (StDM) was applied to model the spatial and temporal patterns of the land use/land cover changes and predict responses in biodiversity patterns, with a focus on passerine functional traits (grassland, woodland and generalist species richness), considering scenarios with and without oak agro-forestry management. ResultsModel outputs showed that oak agro-forestry management favored the expansion of oak agro-forestry at the expense of other land uses, mainly oak forest and agricultural areas. On the other hand, passerine richness exhibits a gradual decline facing the intensification of oak agro-forestry management practices, with higher declining rate observed for grassland passerine species. ConclusionThe oak agro-forestry management does not seem to improve the ecological status of “Montado” landscape, and neither does its abandonment. Hence the conservation paradigm should focus on improving the multi-functionality of the system than merely focusing on a single and common land use

Impacts of land use and infrastructural changes on threatened Little Bustard Tetrax tetrax breeding populations: quantitative assessments using a recently developed spatially explicit dynamic modelling framework

SummaryWith the combination of worldwide landscape changes and the uncertainty about the impact on species abundance and distribution, the value of spatio-temporal modelling tools is increasingly obvious. The Little Bustard Tetrax tetrax breeds on low-intensity arable cultivation and pastoral land and is currently threatened by diverse landscape modifications. The aim of this research was to predict Little Bustard population trends in the face of realistic scenarios of land use and infrastructure changes, applying a recently developed spatially explicit framework, based on the stochastic dynamic methodology (StDM). The application of this approach provided some basis to analyse the responses of breeding populations’ spatial distribution and abundance to the scenarios implemented. Since some of these scenarios represent local and/or regional risks to the viability of Little Bustard breeding populations, the results obtained demonstrate the potential of the proposed framework for landscape planning in the scope of the conservation of this threatened species. This approach also provides a promising baseline to support ecological risk assessments for other species, derived from ecological models with increased predictive power and intuitiveness to decision makers and environmental managers.

A stochastic dynamic model to assess land use change scenarios on the ecological status of fluvial water bodies under the Water Framework Directive

This method development paper outlines an integrative stochastic dynamic methodology (StDM) framework to anticipate land use (LU) change effects on the ecological status of monitored and non-monitored lotic surface waters under the Water Framework Directive (WFD). Tested in the Alto Minho River Basin District in North West Portugal, the model is an innovative step towards developing a decision-making and planning tool to assess the influence impacts such as LU change and climate change on these complex systems. Comprising a series of sequential steps, a Generalized Linear Model based, competing model Multi Model Inference (MMI) approach was used for parameter estimation to identify principal land use types (distal factors) driving change in biological and physicochemical support elements (proximal factors) in monitored water bodies. The framework integrated MMI constants and coefficients of selected LU categories in the StDM simulations and spatial projections to simulate the ecological status of monitored and non-monitored lotic waterbodies in the test area under 2 scenarios of (1) LU intensification and (2) LU extensification. A total of 100 simulations were run for a 50year period for each scenario. Spatially dynamic projections of WFD metrics were obtained, taking into account the occurrence of stochastic wildfire events which typically occur in the study region and are exacerbated by LU change. A marked projected decline to “Moderate” ecological status for most waterbodies was detected under intensification but little change under extensification; only a few waterbodies fell to “moderate” status. The latter scenario describes the actual regional socio-economic situation of agricultural abandonment due to rural poverty, partly explaining the projected lack of change in ecological status. Based on the WFD “one out all out” criterion, projected downward shifts in ecological status were due to physicochemical support elements, namely increased phosphorus levels. Little or no change in status was driven by Intercalibrated Biological Quality Elements, indicating innate resilience and raising questions concerning uncertainty, the effect of pressures other than land use and metric redundancy and the WFD classification process.

A Minimal Invasive Method to Forecast the Effects of Anthropogenic Disturbance on Tropical Cave Beetle Communities.

Many tropical landscapes are changing rapidly, with uncertain outcomes for biodiversity, landscape function, and the corresponding landscape services. Therefore, monitoring and adaptively managing the drivers and consequences of landscape change while sustaining the production of essential resources have become research and policy priorities. In this perspective, we have applied a recent framework, the stochastic dynamic methodology (StDM), with the purpose of understanding the effects of natural and anthropogenic disturbances on caves' integrity using cave beetle communities (Coleoptera) as ecological indicators. The proposed method was preceeded by a generalized linear model for discriminating significant relationships between the selected indicators, the structural changes in the caves, and the epigean habitats associated. The obtained results showed different ecological trends in response to the environmental changes. Overall, the simulation results seem to demonstrate the StDM reliability in determining the effects of habitat dynamics, that is, the expansion of agricultural activities, in areas near the caves in the structure of cave beetle communities. The applied method, based on universal information-theoretic principles, can be easily implemented and interpreted by environmental managers and decision makers, enabling anticipating impacts and supporting the development of measures aimed at minimizing the identified problems.

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.

How do hydrological and climatic conditions influence the diversity and behavioural trends of water birds in small Mediterranean reservoirs? A community-level modelling approach

Wetlands are considered fundamental for the conservation of many species of birds. They are among the most threatened habitats on Earth, due to area loss through drainage and other land use changes, which lead to large-scale redistributions of birds and population declines. Although anthropogenic activities in wetlands tend to negatively affect water birds, by changing their natural habitat conditions, many species may benefit from the creation of artificial reservoirs, using these areas to feed, breed and rest. Reservoirs and some types of flooded agricultural fields are preferred by many species using Mediterranean extents for wintering and as stopover migration spots. The present paper examined the applicability of a Stochastic Dynamic Methodology (StDM) to predict the dynamics of the daylight water bird assemblages on artificial small reservoirs facing different climatic and hydrological conditions. The final model provided some basis to analyze the responses at community level (total abundance, species richness and behavioural categories) under very complex and variable environmental scenarios. The obtained results show how expected changes in the climatic and hydrological patterns will alter the bird community daylight dynamics, namely their composition and behavioural interactions.

Converting simple vegetation surveys in functional dynamics

This study assessed an integrated method for estimating the structural and functional trends of Mediterranean vegetation based on simple field characterizations. Despite considerable effort made by scientists to estimate vegetation dynamics, most methods are too complex to be applied and quantification achieved by non-specialists. We propose an alternative approach that converts simple measures such as generic vegetation cover into life forms or life traits. We used the Stochastic Dynamic Methodology, a dynamic approach based on a sequential process, to calculate the functional response to succession and anthropogenic disturbances using Raunkiaer's life form spectra. Using derived scenarios, the model was able to successfully translate functional and structural trends from simple field descriptions. The simulations demonstrate that this methodology can be used in environmental studies, particularly when specialised field botanists are not available.

A predictive modelling tool for assessing climate, land use and hydrological change on reservoir physicochemical and biological properties

Reservoirs are fundamental for water and energy supply but vulnerable to impacts including climate change. This paper outlines the steps in the development of a model to predict how climate, land use and hydrological change could affect the physiochemical and ecological quality of reservoirs in Portugal's Douro region. Climatic data will be downscaled for subsequent finer spatial scale models to develop scenarios and outputs. Field observations and satellite imagery analysis will create dynamic maps providing data on change in land use and vegetation cover, while Artificial Neural Networks will determine how climate, land use and vegetation cover change may influence catchment hydrology. Data from field surveys of biological indicators, greenhouse gas emissions plus additional research will be applied in the Stochastic Dynamic Methodology, a sequential modelling process based on statistical parameter estimation, developed to predict and model physiochemical and ecological changes in reservoirs. This interdisciplinary approach will provide vital modelling tools for end users essential for water resource management in Portugal and to comply with the EU Water Framework Directive.

Predicting trends of invasive plants richness using local socio-economic data: An application in North Portugal

This study assesses the potential of an integrated methodology for predicting local trends in invasive exotic plant species (invasive richness) using indirect, regional information on human disturbance. The distribution of invasive plants was assessed in North Portugal using herbarium collections and local environmental, geophysical and socio-economic characteristics. Invasive richness response to anthropogenic disturbance was predicted using a dynamic model based on a sequential modeling process (stochastic dynamic methodology—StDM). Derived scenarios showed that invasive richness trends were clearly associated with ongoing socio-economic change. Simulations including scenarios of growing urbanization showed an increase in invasive richness while simulations in municipalities with decreasing populations showed stable or decreasing levels of invasive richness. The model simulations demonstrate the interest and feasibility of using this methodology in disturbance ecology.

Estimating the influence of overhead transmission power lines and landscape context on the density of little bustard Tetrax tetrax breeding populations

Collision with conductors and earth cables is a known impact generated by transmission power lines, however there is virtually no information on how these infrastructures might affect bird distribution in a landscape context. With this work we specifically hypothesise that transmission power lines may affect the occurrence of a threatened bird, the little bustard ( Tetrax tetrax). To test this hypothesis we used a Stochastic Dynamic Methodology (StDM), analysing the effects of power lines in a landscape perspective and simulating population trends as a response to power line installation and habitat changes induced by agricultural shifts in southern Portugal. The data used in the dynamic model construction included relevant gradients of environmental conditions and was sampled during the breeding seasons of 2003–2006. Transmission power lines were significantly avoided by the little bustard and the developed StDM model showed that the distance to these utility structures is the most important factor determining breeding densities in sites with suitable habitat for the species, which possibly leads to displacement of populations and habitat fragmentation. The model simulations also provided the base to analyse the cumulative effects caused by the habitat degradation that can ultimately lead to the extinction of local populations. Within priority conservation sites, the dismantling of existing transmission lines should be considered whenever possible, in order to ensure adequate breeding habitat. The model is considered useful as an auxiliary tool to be used in environmental impact assessments, management and conservation studies.

A multi-scale approach to modelling spatial and dynamic ecological patterns for reservoir's water quality management

With growing levels of urbanization and agriculture throughout the world, it is increasingly important that both research and management efforts take into account the effects of this widespread landscape alteration and its consequences for natural systems. Freshwater ecosystems, namely reservoirs, are particularly sensitive to land use changes. In this context, modelling can be very useful, for decision support, as an investigative tool to forecast the outcome of various scenarios, to guide current management in order to meet future targets and to develop integrated frameworks for management accordingly to the Water Framework Directive (WFD). The present paper examined the applicability of a holistic Stochastic-Dynamic Methodology (StDM), coupled with a Cellular Automata (CA) model, in capturing how expected changes at land use level will alter the ecological status of lentic ecosystems, namely at physicochemical and biological levels. The methodology was applied to Portuguese reservoirs located in the Douro's basin and illustrated with a series of stochastic-dynamic and spatial outputs taking into account expected scenarios regarding land use changes. Overall, the simulation results are encouraging since they seem to demonstrate the tool reliability in capturing the stochastic environmental dynamics of the selected metrics facing spatial explicit scenarios. The ultimate goal was to couple monitoring assessment and the described modelling techniques to ease management and decision making regarding the practical implementation of the WFD, both at the scale of the reservoir body and at the scale of the respective river watershed dynamics.

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

Abstract 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.

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

Abstract 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) for reservoir's water quality management: Validation of a multi-scale approach in a south European basin (Douro, Portugal)

Abstract Worldwide aquatic ecosystems have been impacted by broad-scale environmental pressures such as agriculture, point and non-point-source pollution and land-use changes overlapping in space and time, 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 phytoplankton communities and physicochemical conditions in reservoirs as a response to the changes in the respective watershed soil use. The case of the Douro's basin (Portugal) was used to test the StDM performance in this multi-scale approach. 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 data used in the dynamic model construction included true gradients of environmental changes and was sampled from 1995 to 2004. The dynamic model developed was preceded by a conventional multivariate statistical procedure performed to discriminate the significant relationships between the selected ecological components. The model validation was based on independent data, for all the state variables considered. Overall, the simulation results are encouraging since they seem to demonstrate the StDM reliability in capturing the dynamics of the studied reservoirs. The StDM model simulations were validated for the most part of the twenty-two components selected as ecological indicators, with a performance of 50% for the physicochemical variables, 75% for the phytoplankton variables, and 100% for the Carlson trophic state indices (TSI). This approach provides a useful starting point, as a contribution for the practical implementation of the European Water Framework Directive, allowing the development of a true integrated assessment tool for water quality management, both at the scale of the reservoir body and at the scale of the respective river watershed dynamics.

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

Abstract 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.

Testing the Stochastic Dynamic Methodology (StDM) as a management tool in a shallow temperate estuary of south Europe (Mondego, Portugal)

A long-term monitoring program has been carried out since the early 1990s in the Mondego estuary, on Portugal's west coast, which is presently under heavy human pressure. In this shallow warm-temperate estuary, a significant macroalgal proliferation has been observed, which is a clear sign of nutrient enrichment. As a result of competition with algae, the extension of the seagrass meadows (mainly Zostera noltii) has been reduced. The present paper examined the applicability of a holistic Stochastic Dynamic Methodology (StDM) in predicting the tendencies of trophic key-components (macrophytes, macroalgae, benthic macroinvertebrate and wading birds) as a response to the changes in estuarine environmental conditions. 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 data used in the dynamic model construction included true gradients of environmental changes and was sampled from January 1993 to September 1995 and from December 1998 to December 2005. The dynamic model developed was preceded by a conventional multivariate statistical procedure performed to discriminate the significant relationships between the selected ecological components. The model validation was based on independent data collected from January 1996 to January 1997 and from February 1999 to April 2000 for all the state variables considered. Overall, the simulation results are encouraging since they seem to demonstrate the StDM reliability in capturing the trophic dynamics of the studied estuary, by predicting the behavioural pattern for the most part of the components selected, with a focus on the Zostera noltii meadows recovery after the implementation of important management measures.