Report of the special session of "Chesapeake Bay Ecological Forecasting: Moving Ecosystem Modeling from Research to Operation" of the 2010 Chesapeake Modeling Symposium

Moving ecosystem modeling from research to applications and operations has direct management relevance and will be integral to achieving the water quality and living resource goals of the 2010 Chesapeake Bay Executive Order. Yet despite decades of ecosystem modeling efforts of linking climate to water quality, plankton and fish, ecological models are rarely taken to the operational phase. In an effort to promote operational ecosystem modeling and ecological forecasting in Chesapeake Bay, a meeting was convened on this topic at the 2010 Chesapeake Modeling Symposium (May, 10-11). These presentations show that tremendous progress has been made over the last five years toward the development of operational ecological forecasting models, and that efforts in Chesapeake Bay are leading the way nationally. Ecological forecasts predict the impacts of chemical, biological, and physical changes on ecosystems, ecosystem components, and people. They have great potential to educate and inform not only ecosystem management, but also the outlook and opinion of the general public, for whom we manage coastal ecosystems. In the context of the Chesapeake Bay Executive Order, ecological forecasting can be used to identify favorable restoration sites, predict which sites and species will be viable under various climate scenarios, and predict the impact of a restoration project on water quality.

Ecological niche modeling, cytogenetics and phylogeography of the genera Geomalacus and Letourneuxia (Gastropoda, Pulmonata) from the Iberian-Moroccan region

First described more that 150 years ago, the systematics of the genera Geomalacus and Letourneuxia (Arionidae, Gastropoda, Pulmonata) is still challenging. The taxonomic classification of arionid species is based on extremely labile characters such as body size or color that depends both on diet and environment, as well as age. Moreover, there is little information on the genetic diversity and population structure of the Iberian slugs that could provide extra clues to disentangle their problematic classification. The present work uses different analytical tools such as habitat suitability (Ecological Niche Modeling - ENM), cytogenetic analysis and phylogeography to establish the geographical distribution and evolutionary history of these pulmonate slugs. The potential distribution of the four Geomalacus species was modeled using ENM, which allowed the identification of new locations for G. malagensis, including a first report in Portugal. Also, it was predicted a much wider distribution for G. malagensis and G. oliveirae than previously known. Classical cytogenetic analyses were assayed with reproductive and a novel use of somatic tissues (mouth and tentacles) returning the number of chromosomes for the four Geomalacus species and L. numidica (n = 31, 2n = 62) and the respective karyotypes. G. malagensis and L. numidica present similar chromosome morphologies and karyotypic formulae, being more similar to each other than the Geomalacus among themselves. We further reconstructed the phylogeny of the genera Geomalacus and Letourneuxia using partial sequences of the mitochondrial cytochrome oxidase subunit I (COI) and the nuclear ribosomal small subunit (18S rRNA), and applied an independent evolutionary rate method, the indicator vectors correlation, to evaluate the existence of cryptic diversity within species. The five nominal species of Geomalacus and Letourneuxia comprise 14 well-supported cryptic lineages. Letourneuxia numidica was retrieved as a sister group of G. malagensis. G. oliveirae is paraphyletic with respect to G. anguiformis. According to our dating estimates, the most recent common ancestor of Geomalacus dates back to the Middle Miocene (end of the Serravallian stage). The major lineage splitting events within Geomalacus occurred during the dry periods of the Zanclean stage (5.3-3.6 million years) and some lineages were confined to more humid mountain areas of the Iberian Peninsula, which lead to a highly geographically structured mitochondrial genetic diversity. The major findings of this are the following: (1) provides updated species distribution maps for the Iberian Geomalacus expanding the known geographic distribution of the concerned species, (2) unravels the cryptic diversity within the genera Geomalacus and Letourneuxia, (3) Geomalacus oliveirae is paraphyletic with G. anguiformis and (4) Letourneuxia numidica is sister group of G. malagensis.

Adding value to ecological risk assessment with population modeling

Current measures used to estimate the risks of toxic chemicals are not relevant to the goals of the environmental protection process, and thus ecological risk assessment (ERA) is not used as extensively as it should be as a basis for cost-effective management of environmental resources. Appropriate population models can provide a powerful basis for expressing ecological risks that better inform the environmental management process and thus that are more likely to be used by managers. Here we provide at least five reasons why population modeling should play an important role in bridging the gap between what we measure and what we want to protect. We then describe six actions needed for its implementation into management-relevant ERA.

Ecological niche modeling and geographical distribution of pollinator and plants: A case study of Peponapis fervens (Smith, 1879) (Eucerini: Apidae) and Cucurbita species (Cucurbitaceae)

The bees of the Peponapes genus (Eucerini, Apidae) have a Neotropical distribution with the center of species diversity located in Mexico and are specialized in Cucurbita plants. which have many species of economic importance. such as squashes and pumpkins Peponapis fervens is the only species of the genus known from southern South America The Cucurbita species occurring in the same area as P fervens Include four domesticated species (C ficifolia, C maxima maxima, C moschata and C pepo) and one non-domesticated species (Cucurbita maxima andreana) It was suggested that C. in andreana was the original pollen source to P fervens, and this bee expanded its geographical range due to the domestication of Cucurbita The potential geographical areas of these species were determined and compared using ecological niche modeling that was performed with the computational system openModeller and GARP with best subsets algorithm The climatic variables obtained through modeling were compared using Cluster Analysis Results show that the potential areas of domesticated species practically spread all over South America The potential area of P fervens Includes the areas of C m andreana but reaches a larger area, where the domesticated species of Cucurbita also Occur The Cluster Analysis shows a high climatic similarity between P fervens and C. m. andreana Nevertheless. P fervens presents the ability to occupy areas with wider ranges of climatic variables and to exploit resources provided by domesticated species (C) 2009 Elsevier B V All rights reserved

Ecological niche modeling and principal component analysis of Krameria Loefl. (Krameriaceae)

Krameria plants are found in arid regions of the Americas and present a floral system that attracts oil-collecting bees. Niche modeling and multivariate tools were applied to examine ecological and geographical aspects of the 18 species of this genus, using occurrence data obtained from herbaria and literature. Niche modeling showed the potential areas of occurrence for each species and the analysis of climatic variables suggested that North American species occur mostly in deserted or xeric ecoregions with monthly precipitation below 140 mm and large temperature ranges. South American species are mainly found in deserted ecoregions and subtropical savannas where monthly precipitation often exceeds 150 mm and temperature ranges are smaller. Principal Component Analysis (PCA) performed with values of temperature and precipitation showed that the distribution limits of Krameria species are primarily associated with maximum and minimum temperatures. Modeling of Krameria species proved to be a useful tool for analyzing the influence of the ecological niche variables in the geographical distribution of species, providing new information to guide future investigations. (C) 2011 Elsevier Ltd. All rights reserved.

Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling

Analyses of ecological data should account for the uncertainty in the process(es) that generated the data. However, accounting for these uncertainties is a difficult task, since ecology is known for its complexity. Measurement and/or process errors are often the only sources of uncertainty modeled when addressing complex ecological problems, yet analyses should also account for uncertainty in sampling design, in model specification, in parameters governing the specified model, and in initial and boundary conditions. Only then can we be confident in the scientific inferences and forecasts made from an analysis. Probability and statistics provide a framework that accounts for multiple sources of uncertainty. Given the complexities of ecological studies, the hierarchical statistical model is an invaluable tool. This approach is not new in ecology, and there are many examples (both Bayesian and non-Bayesian) in the literature illustrating the benefits of this approach. In this article, we provide a baseline for concepts, notation, and methods, from which discussion on hierarchical statistical modeling in ecology can proceed. We have also planted some seeds for discussion and tried to show where the practical difficulties lie. Our thesis is that hierarchical statistical modeling is a powerful way of approaching ecological analysis in the presence of inevitable but quantifiable uncertainties, even if practical issues sometimes require pragmatic compromises.

Ecological-economic modeling for biodiversity management: Potential, pitfalls, and prospects

Ecologists and economists both use models to help develop strategies for biodiversity management. The practical use of disciplinary models, however, can be limited because ecological models tend not to address the socioeconomic dimension of biodiversity management, whereas economic models tend to neglect the ecological dimension. Given these shortcomings of disciplinary models, there is a necessity to combine ecological and economic knowledge into ecological-economic models. It is insufficient if scientists work separately in their own disciplines and combine their knowledge only when it comes to formulating management recommendations. Such an approach does not capture feedback loops between the ecological and the socioeconomic systems. Furthermore, each discipline poses the management problem in its own way and comes up with its own most appropriate solution. These disciplinary solutions, however are likely to be so different that a combined solution considering aspects of both disciplines cannot be found. Preconditions for a successful model-based integration of ecology and economics include (1) an in-depth knowledge of the two disciplines, (2) the adequate identification and framing of the problem to be investigated, and (3) a common understanding between economists and ecologists of modeling and scale. To further advance ecological-economic modeling the development of common benchmarks, quality controls, and refereeing standards for ecological-economic models is desirable.