Langfristigkeit ökosystemarer Forschung

Time in environmental science continues to be almost neglected. In connection with rising ecological problems long-term research, as is necessary for the evaluation and prediction of environmental conditions, seems to be an exception. Complex ecosystems, e.g. terrestrial and marine systems, however, are characterized by long lasting natural time-scales which must be taken into account by analyzing them. In this context the role of time in ecosystems research becomes important. Therefore, a data base was conceptualized that could take up the main characteristics of ecosystematic analyses. Annual research reports of ecosystem research centers were revised for taking them up into the data base. In this way it is possible to evaluate the pool of data on special aspects, especially on the time parameters of the analyses. One result — besides a partial unequality of the time scales in experiment and reality — is that long-term research in terrestrial ecosystems is very rare. The reasons for this are various, but cannot divert from the need for a greater consideration of longterm observation of the ecosystems. Otherwise the ecosystematic research will not be able to meet their tasks of evaluation and prediction.     

The integration of land and marine spatial planning

Whilst spatial planning has evolved as a tool to manage the development and use of the terrestrial environment over decades, the development of spatial planning systems for the marine environment are in their infancy. This paper focuses on the integration of land and marine based spatial planning systems. This is informed by a brief overview of the regional development of the lands and seas of Europe which underlie spatial planning systems and by a discussion of respective spatial planning systems in terms of economic sectors, land use, and sea use. The integration of spatial planning systems is then considered, followed by evaluation of relationships between spatial planning, and the wider field of environmental management. This includes consideration of organisational and geographical scales, technical management including legal aspects, policy, strategic planning and time scales of decision-making. The paper identifies the significant factors which must be considered in the integration of marine and terrestrial planning systems over the coming decades.     

Using geospatial business intelligence paradigm to design a multidimensional conceptual model for efficient coastal erosion risk assessment

One of the main challenges in Coastal Erosion Risk Assessment (CERA) is integrating and analysis of conflicting data in various time periods and spatial scales through dissimilar environmental, social, and economic criteria. Currently, Geographical Information Systems (GIS) are widely used in risk assessment despite their drawbacks and limitations as transactional systems for multi-scales, multi-epochs, and multi-themes analysis. Hence, an analytical conceptual framework is proposed in this paper based on geospatial business intelligence paradigm to develop a Spatial Multidimensional Conceptual Model (SMCM) to assess coastal erosion risk. The model is designed based on Spatial On-Line Analytical Processing (SOLAP) platform, on the top of both analytical and transactional paradigms, to allow fast synthesis of cross-tabulated data and easy comparisons over space, scales, epochs, and themes. This objective is achieved through a comprehensive integration of multiple environmental, social, and economic criteria as well as their interactions at various scales. It also takes into account multiple elements at risk such as people, infrastructure, and built environment as different dimensions of analysis. Using this solution allows decision makers to benefit from on-demand, interactive, and comprehensive information in a way that is not possible using GIS alone. The developed model can easily be adapted for any other coastal region through the proposed framework to perform risk assessment. The advantages and drawbacks of the proposed framework are also discussed and new research perspectives are presented.     

Variation partitioning involving orthogonal spatial eigenfunction submodels

When partitioning the variation of univariate or multivariate ecological data with respect to several submodels of spatial eigenfunctions (e.g., Moran's eigenvector maps, MEM) acting as explanatory data, a problem occurs: although the submodels are constructed to be orthogonal to one another, the partitioning based on adjusted R2 statistics produces nonzero values in the intersections between spatial submodels. This phenomenon is described and two solutions are proposed. The first solution is to apportion the intersection fractions proportionally to the variation explained by each submodel. The second solution consists in creating a hierarchy among the spatial submodels, in accordance with hierarchy theory. These solutions lead to new partitioning equations that are described in the Appendix. R functions are provided to carry out partitioning with respect to environmental variables and spatial eigenfunction submodels. This development is important for the correct interpretation of spatial modeling results implying explanatory environmental data as well as submodels of spatial eigenfunctions involving two or more spatial scales.     

Die Bedeutung der Zeit

Reflecting on time and its nature has a long tradition in such fields as philosophy, economy and the social sciences. In environmental sciences, however, time has not been taken into sufficient consideration to date, although many ecologists are indeed aware of its importance. Because of the importance of time for mankind, nature and the surrounding world, an outline incorporating time is possible and would lead to new insights in environmental sciences. Here, our idea of a balanced or “healthy” ecosystem, or the consequences of our actions such as depletion of the ozon layer, the greenhouse effect or floods are taken into consideration. A reflection on time its a basic requirement for our understanding of environmental science, its tools and our environmental and political actions. A consideration of time in environmental sciences means, among other things, to think of and to act in accordance with time scales and the rhythms of man and nature.     

Wissenschaftstheoretische Grundlagen der Beobachtung von GVO-Umweltwirkungen

Goal, Scope and Background

The release of genetically modified organisms (GMOs) may result in ecological impacts to be monitored. This series presents the state-of-art concerning the environmental monitoring of ecological impacts of GMOs. Since this monitoring is conducted by several authorities, the methodical comparability and spatial validity of the monitoring data is crucial. Thus, the respective fundamentals of philosophy of science and of statistics as well as the methodical standardisation form the focus of this article.

Main Features

At first, the monitoring requirements will be described from the viewpoint of ecology. Consequently, the GMO monitoring needs to be integrated into existing programmes. Exposure and effects of GMOs have to be monitored across the hierarchy of ecological organisation. For monitoring to be efficient and sufficient, the design must be founded in terms of philosophy of science and statistics. Hence the fundamentals concerning the verification of hypotheses represent another feature of this article. The data used for the verification of hypotheses must comply with quality criteria. One of these criteria is the spatial and temporal extrapolation of the monitoring results, which is the third feature in this introductory article.

Conclusions

The evaluation of hypotheses on GMO impacts requires the linkage to ecosystems research and environmental monitoring. These both are, in terms of methodology, complementary and of significant technical and scientific value for GMO monitoring.

Recommendations

GMO-monitoring should closely be connected with ecosystems research and environmental monitoring. The levels of ecological organisation should be covered as well as the exposure and the effects of GMOs. The expected GMO exposure and effects should be considered by localising the monitoring sites.

Perspectives

Further articles of this series will deal with network designing, GMO pollen monitoring and extrapolation of site-specific measurements and modelling results.     

Origins and consequences of global and local stressors: incorporating climatic and non-climatic phenomena that buffer or accelerate ecological change

With research into the ecological effects of climatic change intensifying over the past decade, there has been an effort to increase the scale of experiments from a focus on individual organisms to incorporate the effects of the structure and functioning of entire ecosystems. As the scale of investigation becomes increasingly broad, however, the number of seemingly contradictory outcomes also increases. In reality, however, change or persistence of ecological patterns represents interplay of processes across diverse scales of space and time. At one extreme, non-climatic influences can dominate local and short-term processes that protect systems against change or accelerate change. Here, we draw on case studies that demonstrate such contrasting situations, presenting examples where local conditions can either ameliorate or exacerbate the predicted effects of climate change. By incorporating examples of stressors that originate and manifest at different spatial scales, we also attempt to refine some of the efforts surrounding research into the effects of climate change.     

Inferring ecological and behavioral drivers of African elephant movement using a linear filtering approach

Understanding the environmental factors influencing animal movements is fundamental to theoretical and applied research in the field of movement ecology. Studies relating fine-scale movement paths to spatiotemporally structured landscape data, such as vegetation productivity or human activity, are particularly lacking despite the obvious importance of such information to understanding drivers of animal movement. In part, this may be because few approaches provide the sophistication to characterize the complexity of movement behavior and relate it to diverse, varying environmental stimuli. We overcame this hurdle by applying, for the first time to an ecological question, a finite impulse-response signal-filtering approach to identify human and natural environmental drivers of movements of 13 free-ranging African elephants (Loxodonta africana) from distinct social groups collected over seven years. A minimum mean-square error (MMSE) estimation criterion allowed comparison of the predictive power of landscape and ecological model inputs. We showed that a filter combining vegetation dynamics, human and physical landscape features, and previous movement outperformed simpler filter structures, indicating the importance of both dynamic and static landscape features, as well as habit, on movement decisions taken by elephants. Elephant responses to vegetation productivity indices were not uniform in time or space, indicating that elephant foraging strategies are more complex than simply gravitation toward areas of high productivity. Predictions were most frequently inaccurate outside protected area boundaries near human settlements, suggesting that human activity disrupts typical elephant movement behavior. Successful management strategies at the human-elephant interface, therefore, are likely to be context specific and dynamic. Signal processing provides a promising approach for elucidating environmental factors that drive animal movements over large time and spatial scales.     

Modeling freshwater fish distributions using multiscale landscape data: A case study of six narrow range endemics

Species distribution models (SDMs) have become integral tools in scientific research and conservation planning. Despite progress in the assessment of various statistical models for use in SDMs, little has been done in way of evaluating appropriate ecological models. In this paper, we evaluate the multiscale filter framework as a suitable theoretical model for predicting freshwater fish distributions in the upper Green River system (Ohio River drainage), USA. The spatial distributions of six fishes with contrasting biogeographies were modeled using boosted regression trees and multiscale landscape data. Species biogeography did not appear to affect predictive performance and all models performed well statistically with receiver operating characteristic area under the curve (AUC) ranging from 0.87 to 0.98. Predictive maps show accurate estimations of ranges for five of six species based on historical collections. The relative influence of each type of environmental feature and spatial scale varied markedly with between species. A hierarchical effect was detected for narrowly distributed species. These species were highly influenced by soil composition at larger spatial scales and land use/land cover (LULC) patterns at more proximal scales. Conversely, LULC pattern was the most influential feature for widely distributed at all spatial scales. Using multiscale data capable of capturing hierarchical landscape influences allowed production of accurate predictive models and provided further insight into factors controlling freshwater fish distributions.     

Toward an ecosystem approach to ICM: assessing ecological provinces at sea by remote sensing

The ecosystem approach requires that all elements of an ecosystem, and their mutual interactions, be taken into consideration in any management effort. The selection of suitable geographical units, where this approach can be taken, requires the assessment of ecological provinces, characterized by a coherent set of environmental traits. The marine side of coastal zones, where the interaction between atmosphere, land and sea is not bounded by evident geographic markers, represents a critical factor in this assessment. A coastal province can be defined by physical setting, but also by its bio-geo-chemical features, ideally on the basis of synoptic remote sensing data, collected at space/time scales not accessible by other means. Classifications based on indicators such as temperature, wind speed and chlorophyll-like pigments, demonstrate the identification of potential ecological provinces in the Mediterranean Sea. The results suggest remote sensing as the ideal tool to set up the basis for an ecosystem approach to the management of each province.     

Advancing the Integration of History and Ecology for Conservation

Abstract: The important role of humans in the development of current ecosystems was recognized decades ago; however, the integration of history and ecology in order to inform conservation has been difficult. We identified four issues that hinder historical ecological research and considered possible solutions. First, differences in concepts and methods between the fields of ecology and history are thought to be large. However, most differences stem from miscommunication between ecologists and historians and are less substantial than is usually assumed. Cooperation can be achieved by focusing on the features ecology and history have in common and through understanding and acceptance of differing points of view. Second, historical ecological research is often hampered by differences in spatial and temporal scales between ecology and history. We argue that historical ecological research can only be conducted at extents for which sources in both disciplines have comparable resolutions. Researchers must begin by clearly defining the relevant scales for the given purpose. Third, periods for which quantitative historical sources are not easily accessible (before AD 1800) have been neglected in historical ecological research. Because data from periods before 1800 are as relevant to the current state of ecosystems as more recent data, we suggest that historical ecologists actively seek out data from before 1800 and apply analytic methods commonly used in ecology to these data. Fourth, humans are not usually considered an intrinsic ecological factor in current ecological research. In our view, human societies should be acknowledged as integral parts of ecosystems and societal processes should be recognized as driving forces of ecosystem change.     

Network-based exploration and visualisation of ecological data

Networks – structured graphs consisting of sets of nodes connected by edges – provide a rich framework for data visualisation and exploratory analyses. Although rarely used for the visualisation of ecological data, networks are well suited to this purpose, including data that one might not normally think of as a network. We present a simple method for transforming a data matrix into network format, and show how this can be used as the basis for interactive exploratory analyses of ecological data.The method is demonstrated using a database of marine zooplankton samples acquired in the Southern Ocean. The network analyses revealed zooplankton community structures that are in good agreement with previously published results. Variations in community structure were observed to be related to the temporal and spatial pattern of sampling, as well as to physical environmental factors such as sea ice cover. The analyses also revealed a number of errors in the data, including taxon identification errors and instrument failures.The method allows the analyst to generate networks from different combinations of variables in the data set, and to examine the effects of varying parameters such as the scales of spatial, temporal, and taxonomic aggregation. This flexibility allows the analyst to rapidly gain a number of perspectives on the data and provides a powerful mechanism for exploration.     

Paleobiology, community ecology, and scales of ecological pattern

The fossil record provides a wealth of data on the role of regional processes and historical events in shaping biological communities over a variety of time scales. The Quaternary record with its evidence of repeated climatic change shows that both terrestrial and marine species shifted independently rather than as cohesive assemblages over scales of thousands of years. Larger scale patterns also show a strong individualistic component to taxon dynamics; assemblage stability, when it occurs, is difficult to separate from shared responses to low rates of environmental change. Nevertheless, the fossil record does suggest that some biotic interactions influence large-scale ecological and evolutionary patterns, albeit in more diffuse and protracted fashions than those generally studied by community ecologists. These include: (1) the resistance by incumbents to the establishment of new or invading taxa, with episodes of explosive diversification often appearing contingent on the removal of incumbents at extinction events; (2) steady states of within-habitat and global diversity at longer time scales (10(7)-l0(8) yr), despite enormous turnover of taxa; and (3) morphological and biogeographic responses to increased intensities of predation and substratum disturbance over similarly long time scales. The behavior of species and communities over the array of temporal and spatial scales in the fossil record takes on additional significance for framing conservation strategies, and for understanding recovery of species, lineages, and communities from environmental changes.     

Environmental change and declining resource availability for small-mammal communities in the Great Basin

Changes in climate and land use can impact natural systems across all levels of ecological organization. Most documented and anticipated effects consider species' properties, including phenologies, geographic distributions, and abundances. Responses of higher-level aggregate community or ecosystem properties have not been considered as they are assumed to be relatively stable due to compensatory dynamics and diversity-stability relationships. However, this assumption may not be as fundamental as previously thought. Here we assess stability in the aggregate properties of total abundance, biomass, and energy consumption for small-mammal communities in the Great Basin, using paired historical and modern survey data spanning nearly a century of environmental change. Results show marked declines in each aggregate property independent of spatial scale, elevation, or habitat type, and a reallocation of available biomass and energy favoring diet and habitat generalists. Because aggregate properties directly reflect resource availability, our findings indicate a regionwide decline in resources of 50% over the past century, which may signal a resource crisis. This work illustrates the power of using aggregate properties as indicators of ecological conditions and environmental change at broad spatial and temporal scales.     

Appreciating Ecological Complexity: Habitat Contours as a Conceptual Landscape Model

Abstract:  Organisms respond to their surroundings at multiple spatial scales, and different organisms respond differently to the same environment. Existing landscape models, such as the "fragmentation model" (or patch-matrix-corridor model) and the "variegation model," can be limited in their ability to explain complex patterns for different species and across multiple scales. An alternative approach is to conceptualize landscapes as overlaid species-specific habitat contour maps. Key characteristics of this approach are that different species may respond differently to the same environmental conditions and at different spatial scales. Although similar approaches are being used in ecological modeling, there is much room for habitat contours as a useful conceptual tool. By providing an alternative view of landscapes, a contour model may stimulate more field investigations stratified on the basis of ecological variables other than human-defined patches and patch boundaries. A conceptual model of habitat contours may also help to communicate ecological complexity to land managers. Finally, by incorporating additional ecological complexity, a conceptual model based on habitat contours may help to bridge the perceived gap between pattern and process in landscape ecology. Habitat contours do not preclude the use of existing landscape models and should be seen as a complementary approach most suited to heterogeneous human-modified landscapes.     

Windows of change: temporal scale of analysis is decisive to detect ecosystem responses to climate change

Long-term ecological research has become a cornerstone of the scientific endeavour to better understand ecosystem responses to environmental change. This paper provides a perspective on how such research could be advanced. It emphasizes that a profound understanding of the mechanisms underlying these responses requires that records of ecologic processes be not only sufficiently long, but also collected at an appropriate temporal resolution. We base our argument on an overview of studies of climate impacts in limnic and marine ecosystems, suggesting that lakes and oceans respond to (short-term) weather conditions during critical time windows in the year. The observed response patterns are often time-lagged or driven by the crossing of thresholds in weather-related variables (such as water temperature and thermal stratification intensity). It becomes clear from the previous studies that average annual, seasonal or monthly climate data often fall short of characterizing the thermal dynamics that most organisms respond to. To illustrate such literature-based evidence using a concrete example, we compare 2?years of water temperature data from Müggelsee (Berlin, Germany) at multiple temporal scales (from hours to years). This comparison underlines the pitfalls of analysing data at resolutions not high enough to detect critical differences in environmental forcing. Current science initiatives that aim at improving the temporal resolution of long-term observatory data in aquatic systems will help to identify adequate timescales of analysis necessary for the understanding of ecosystem responses to climate change.     

Applying science to pressing conservation needs for penguins

More than half of the world's 18 penguin species are declining. We, the Steering Committee of the International Union for Conservation of Nature Species Survival Commission Penguin Specialist Group, determined that the penguin species in most critical need of conservation action are African penguin (Spheniscus demersus), Galápagos penguin (Spheniscus mendiculus), and Yellow-eyed penguin (Megadyptes antipodes). Due to small or rapidly declining populations, these species require immediate scientific collaboration and policy intervention. We also used a pairwise-ranking approach to prioritize research and conservation needs for all penguins. Among the 12 cross-taxa research areas we identified, we ranked quantifying population trends, estimating demographic rates, forecasting environmental patterns of change, and improving the knowledge of fisheries interactions as the highest priorities. The highest ranked conservation needs were to enhance marine spatial planning, improve stakeholder engagement, and develop disaster-management and species-specific action plans. We concurred that, to improve the translation of science into effective conservation for penguins, the scientific community and funding bodies must recognize the importance of and support long-term research; research on and conservation of penguins must expand its focus to include the nonbreeding season and juvenile stage; marine reserves must be designed at ecologically appropriate spatial and temporal scales; and communication between scientists and decision makers must be improved with the help of individual scientists and interdisciplinary working groups.     

Digital data sources and methods for conservation culturomics

Ongoing loss of biological diversity is primarily the result of unsustainable human behavior. Thus, the long-term success of biodiversity conservation depends on a thorough understanding of human–nature interactions. Such interactions are ubiquitous but vary greatly in time and space and are difficult to monitor efficiently at large spatial scales. However, the Information Age also provides new opportunities to better understand human–nature interactions because many aspects of daily life are recorded in a variety of digital formats. The emerging field of conservation culturomics aims to take advantage of digital data sources and methods to study human–nature interactions and thus to provide new tools for studying conservation at relevant temporal and spatial scales. Nevertheless, technical challenges associated with the identification, access, and analysis of relevant data hamper the wider adoption of culturomics methods. To help overcome these barriers, we propose a conservation culturomics research framework that addresses data acquisition, analysis, and inherent biases. The main sources of culturomic data include web pages, social media, and other digital platforms from which metrics of content and engagement can be obtained. Obtaining raw data from these platforms is usually desirable but requires careful consideration of how to access, store, and prepare the data for analysis. Methods for data analysis include network approaches to explore connections between topics, time-series analysis for temporal data, and spatial modeling to highlight spatial patterns. Outstanding challenges associated with culturomics research include issues of interdisciplinarity, ethics, data biases, and validation. The practical guidance we offer will help conservation researchers and practitioners identify and obtain the necessary data and carry out appropriate analyses for their specific questions, thus facilitating the wider adoption of culturomics approaches for conservation applications.