Forecasting extinction risk with nonstationary matrix models.

Matrix population growth models are standard tools for forecasting population change and for managing rare species, but they are less useful for predicting extinction risk in the face of changing environmental conditions. Deterministic models provide point estimates of lambda, the finite rate of increase, as well as measures of matrix sensitivity and elasticity. Stationary matrix models can be used to estimate extinction risk in a variable environment, but they assume that the matrix elements are randomly sampled from a stationary (i.e., non-changing) distribution. Here we outline a method for using nonstationary matrix models to construct realistic forecasts of population fluctuation in changing environments. Our method requires three pieces of data: (1) field estimates of transition matrix elements, (2) experimental data on the demographic responses of populations to altered environmental conditions, and (3) forecasting data on environmental drivers. These three pieces of data are combined to generate a series of sequential transition matrices that emulate a pattern of long-term change in environmental drivers. Realistic estimates of population persistence and extinction risk can be derived from stochastic permutations of such a model. We illustrate the steps of this analysis with data from two populations of Sarracenia purpurea growing in northern New England. Sarracenia purpurea is a perennial carnivorous plant that is potentially at risk of local extinction because of increased nitrogen deposition. Long-term monitoring records or models of environmental change can be used to generate time series of driver variables under different scenarios of changing environments. Both manipulative and natural experiments can be used to construct a linking function that describes how matrix parameters change as a function of the environmental driver. This synthetic modeling approach provides quantitative estimates of extinction probability that have an explicit mechanistic basis.     

Dissolution behavior of metals from particulate matter emissions

Exposure to air particulate matter (PM) is linked to numerous health effects. In order to improve the understanding of the role of its metallic components, their solubility was examined by using serial short-contact dissolutions totalling 1?h and additional sequential contact periods of 1, 4, and 8 days. The dissolution experiments were performed in solutions containing the main biological electrolytes. ICPMS determinations were used to quantify the dissolved metals. The total compositions were determined after closed vessel microwave digestion. Large variations in the rate and completeness of the dissolutions were observed. Fast and extensive dissolutions within the short-contact time (e.g., Zn, Cd) as well as slow dissolutions persisting during the last contact period (e.g., Ni, Cu, Sb, Pb) were found for smelting emissions. The multi-element determinations also made it possible to identify relationships between dissolution of different metals and define gradual composition changes of residual PM. When comparing with dissolutions performed in de-ionized water, similar major fractions were observed at short-contact time for minor components of smelting or combustion emissions (e.g., V, Ni, Cd), suggesting a preponderance of easily available forms at the surface of the relatively inert particle cores. The use of these time sequential methods may help in (1) modeling metal partitioning in biological media and (2) investigating the causes of adverse effects attributed to air PM.     

Evaluating predictive errors of a complex environmental model using a general linear model and least square means

A general linear model (GLM) was used to evaluate the deviation of predicted values from expected values for a complex environmental model. For this demonstration, we used the default level interface of the regional mercury cycling model (R-MCM) to simulate epilimnetic total mercury concentrations in Vermont and New Hampshire lakes based on data gathered through the EPAs Regional Environmental Monitoring and Assessment Program (REMAP). The response variable for the GLM was defined as R-MCMs predictive error: the difference between observed mercury concentrations and modeled mercury concentrations in each lake. Least square means of the response variable are used as an estimate of the magnitude and significance of bias, i.e., a statistically discernable trend in predictive errors for a given lake type, e.g., acidic, stratified, or oligotrophic. Using our approach, we determined lake types where significant over-prediction and under-prediction of epilimnetic total mercury concentration was occurring, i.e., regions in parameter space where the model demonstrated significant bias was distinguished from regions where no significant bias existed. This technique is most effective for finding regions of parameter space where bias is significant. Drawing conclusions concerning regions that show no significant bias can be misleading. The significant interaction terms in the GLM demonstrated that addressing this problem using univariate statistical techniques would lead to a loss of important information.     

Linking ecosystem services and human‐values theory

Understanding why people make the decisions they do remains a fundamental challenge facing conservation science. Ecosystem service (ES) (a benefit people derive from an ecosystem) approaches to conservation reflect efforts to anticipate people's preferences and influence their environmental behavior. Yet, the design of ES approaches seldom includes psychological theories of human behavior. We sought to alleviate this omission by applying a psychological theory of human values to a cross‐cultural ES assessment. We used interviews and focus groups with fish workers from 28 coral reef fishing communities in 4 countries to qualitatively identify the motivations (i.e., human values) underlying preferences for ES; quantitatively evaluate resource user ES priorities; and identify common patterns among ES motivations and ES priorities (i.e., trade‐offs and synergies). Three key findings are evident that align with human values theory. First, motivations underlying preferences for individual ESs reflected multiple human values within the same value domain (e.g., self‐enhancement). Second, when averaged at community or country scales, the order of ES priorities was consistent. However, the order belied significant variation that existed among individuals. Third, in line with human values theory, ESs related to one another in a consistent pattern; certain service pairs reflected trade‐off relationships (e.g., supporting and provisioning), whereas other service pairs reflected synergistic relationships (e.g., supporting and regulating). Together, these findings help improve understanding of when and why convergence and trade‐offs in people's preferences for ESs occur, and this knowledge can inform the development of suitable conservation actions.     

The Influence of Model Structure on Conclusions about the Viability and Harvesting of Serengeti Wildebeest

We investigate how the viability and harvestability predicted by population models are affected by details of model construction. Based on this analysis we discuss some of the pitfalls associated with the use of classical statistical techniques for resolving the uncertainties associated with modeling population dynamics. The management of the Serengeti wildebeest (Connochaetes taurinus) is used as a case study. We fitted a collection of age-structured and unstructured models to a common set of available data and compared model predictions in terms of wildebeest viability and harvest. Models that depicted demographic processes in strikingly different ways fitted the data equally well. However, upon further analysis it became clear that models that fit the data equally well could nonetheless have very different management implications. In general, model structure had a much larger effect on viability analysis (e.g., time to collapse) than on optimal harvest analysis (e.g., harvest rate that maximizes harvest). Some modeling decisions, such as including age-dependent fertility rates, did not affect management predictions, but others had a strong effect (e.g., choice of model structure). Because several suitable models of comparable complexity fitted the data equally well, traditional model selection methods based on the parsimony principle were not practical for judging the value of alternative models. Our results stress the need to implement analytical frameworks for population management that explicitly consider the uncertainty about the behavior of natural systems.     

Aktuelle Entwicklungen bei Umweltindikatorensystemen

The current monitoring of the status of the environment and natural resources is a substantial basis for the implementation of a sustainable development as a model of the environmental policy in the terms of Agenda 21. For the 90s, in particular by the mandate of Rio 1992, both the international institutions (international level), e.g. the OECD, the United Nation Commission of Sustainable Development and the European Environmental Agency, and the environmental protection authorities of the different countries (national level) e.g. Germany, and of the federal states of Germany (regional level) are developing environmental indicator systems. In the context of the Local Agenda 21, indicators for the ecological, economic, social and institutional categories are set up on a communal level. In the environmental indicator systems different issues, e.g. climate change, ozone layer depletion, water quality and water resources, waste, toxic contamination, biodiversity, landscape protection, soil resources, forest resources, etc., are described. Concepts with driving forces, pressure, state, impact and/or response indicators are applied. The German Council of Environmental Advisors (SRU) requires that the selection and aggregation of indicators must be transparent and comprehensibly documented and that the chosen indicators must be oriented at political targets. The following article gives an overview of relevant actual environmental indicator systems, their concepts and structuring, and points to the further research and developmental requirements within this area.     

A simulation model to evaluate the impacts of invasive earthworms on soil carbon dynamics

Recent studies have reported that earthworm invasions alter native communities and impact nutrient cycling in terrestrial ecosystems. We developed a simulation model to evaluate the potential impacts of earthworm invasions on carbon dynamics, taking into consideration earthworm feeding strategies and priming effects on the microorganisms through their casting activities. Responses of carbon stocks (forest litter, soil organic matter, microbial biomass and earthworm populations) and carbon fluxes (litter decomposition, earthworm consumption, and microbial respiration) were used to evaluate an earthworm invasion of a forest ecosystem. Data from a northern temperate forest (Arnot Forest, New York) were adapted for model calibration and evaluation. Simulation results suggest that the impact and outcome of earthworm invasions are affected by pre-invasion resource availability (litter and soil organic matter), invasive earthworm assemblages (particularly feeding strategy), and invasion history (associated with earthworm population dynamics). The abovementioned factors may also determine invasion progress of earthworm species. The accuracy of the model could be improved by the addition of environmental modules (e.g., soil water regimes), precise parameters accounting for individual species attributes under different environmental conditions (e.g. utilization ability of different types of food resources), as well as earthworm population dynamics (size and structure) and interactions with predators and other invasive/indigenous species during the invasion progress. Such an earthworm invasion model could provide valuable evaluation of the complicated responses of carbon dynamics to earthworm invasions in a range of forest ecosystems, particularly under global change scenarios.     

The changing sociocultural context of wildlife conservation

We introduced a multilevel model of value shift to describe the changing social context of wildlife conservation. Our model depicts how cultural-level processes driven by modernization (e.g., increased wealth, education, and urbanization) affect changes in individual-level cognition that prompt a shift from domination to mutualism wildlife values. Domination values promote beliefs that wildlife should be used primarily to benefit humans, whereas mutualism values adopt a view that wildlife are part of one's social network and worthy of care and compassion. Such shifts create emergent effects (e.g., new interest groups) and challenges to wildlife management organizations (e.g., increased conflict) and dramatically alter the sociopolitical context of conservation decisions. Although this model is likely applicable to many modernized countries, we tested it with data from a 2017–2018 nationwide survey (mail and email panel) of 43,949 residents in the United States. We conducted hierarchical linear modeling and correlational analysis to examine relationships. Modernization variables had strong state-level effects on domination and mutualism. Higher levels of education, income, and urbanization were associated with higher percentages of mutualists and lower percentages of traditionalists, who have strong domination values. Values affected attitudes toward wildlife management challenges; for example, states with higher proportions of mutualists were less supportive of lethal control of wolves (Canis lupus) and had lower percentages of active hunters, who represent the traditional clientele of state wildlife agencies in the United States. We contend that agencies will need to embrace new strategies to engage and represent a growing segment of the public with mutualism values. Our model merits testing for application in other countries.     

Research review trends of food analysis in Latvia: major and trace element content

The current paper involves overview of several studies concerning quantitative major and trace element analysis of different food samples, such as products of plant origin, e.g., locally grown vegetables (carrots, onions, potatoes) and products of animal origin derived with or without processing (cottage cheese, eggs, honey). Food samples were collected over the territory of Latvia in the time period 2009–2012. Sample pre-treatment was chosen according to the product specifics but mostly wet mineralization with concentrated nitric acid was applied. Analysis of major elements (e.g., Ca, Fe, K, Mg, Na) and trace elements (e.g., As, Cd, Co, Cr, Cu, Ni, Pb, Se, Zn) was performed by appropriate quantitative analytical technique: atomic absorption spectrometry, inductively coupled plasma mass spectrometry or total reflection X-ray fluorescence spectrometry. Not only the influence of environmental factors (e.g., geographical or seasonal impact) was detected in element dissemination in food, also botanical origin (if applicable) and applied agricultural praxis, product processing and storage specifics were found to be important. Possible food contamination by potentially toxic elements can be associated mostly with the consequences of anthropogenic activities. The studies revealed the significance of food research in the context of environmental science.     

An optimization approach for sustainable release of e-flows for lake restoration and preservation: Model development and a case study of Baiyangdian Lake, China

In this paper, we describe the development of a model for the sustainable release of e-flows from the regional water resource infrastructure (e.g., reservoirs, rivers with available water) for lake restoration and preservation, and use the model in a case study of Baiyangdian Lake, China. First, we define the sustainable environmental flows (e-flows), with an emphasis on the ecological importance of temporal variation in factors such as water level (depth). By analyzing historical data on the suitable range of water levels in the lake, we evaluated fluctuations using canonical correspondence analysis and frequency distribution analysis. The temporal variations required by the ecosystem of the lake were also assessed. Based on this approach, we developed an optimization model for sustainable release of e-flows. We used the adaptive genetic algorithm approach to solve the model and determine the required release of e-flows. Scenario analysis then provided a range of potential management strategies for the e-flows. The optimal results are helpful to the lake managers to establish sustainable e-flow release schemes for the lake restoration and preservation.     

Towards a framework for assessment and management of cumulative human impacts on marine food webs

Effective ecosystem‐based management requires understanding ecosystem responses to multiple human threats, rather than focusing on single threats. To understand ecosystem responses to anthropogenic threats holistically, it is necessary to know how threats affect different components within ecosystems and ultimately alter ecosystem functioning. We used a case study of a Mediterranean seagrass (Posidonia oceanica) food web and expert knowledge elicitation in an application of the initial steps of a framework for assessment of cumulative human impacts on food webs. We produced a conceptual seagrass food web model, determined the main trophic relationships, identified the main threats to the food web components, and assessed the components’ vulnerability to those threats. Some threats had high (e.g., coastal infrastructure) or low impacts (e.g., agricultural runoff) on all food web components, whereas others (e.g., introduced carnivores) had very different impacts on each component. Partitioning the ecosystem into its components enabled us to identify threats previously overlooked and to reevaluate the importance of threats commonly perceived as major. By incorporating this understanding of system vulnerability with data on changes in the state of each threat (e.g., decreasing domestic pollution and increasing fishing) into a food web model, managers may be better able to estimate and predict cumulative human impacts on ecosystems and to prioritize conservation actions.     

A review of the health impacts of barium from natural and anthropogenic exposure

There is an increasing public awareness of the relatively new and expanded industrial barium uses which are potential sources of human exposure (e.g., a shale gas development that causes an increased awareness of environmental exposures to barium). However, absorption of barium in exposed humans and a full spectrum of its health effects, especially among chronically exposed to moderate and low doses of barium populations, remain unclear. We suggest a systematic literature review (from 1875 to 2014) on environmental distribution of barium, its bioaccumulation, and potential and proven health impacts (in animal models and humans) to provide the information that can be used for optimization of future experimental and epidemiological studies and developing of mitigative and preventive strategies to minimize negative health effects in exposed populations. The potential health effects of barium exposure are largely based on animal studies, while epidemiological data for humans, specifically for chronic low-level exposures, are sparse. The reported health effects include cardiovascular and kidney diseases, metabolic, neurological, and mental disorders. Age, race, dietary patterns, behavioral risks (e.g., smoking), use of medications (those that interfere with absorbed barium in human organism), and specific physiological status (e.g., pregnancy) can modify barium effects on human health. Identifying, evaluating, and predicting the health effects of chronic low-level and moderate-level barium exposures in humans is challenging: Future research is needed to develop an understanding of barium bioaccumulation in order to mitigate its potential health impacts in various exposured populations. Further, while occupationally exposed at-risk populations exist, it is also important to identify potentially vulnerable subgroups among non-occupationally exposed populations (e.g., elderly, pregnant women, children) who are at higher risk of barium exposure from drinking water and food.     

Feedbacks between conservation and social-ecological systems

Robust ways to meet objectives of environmental conservation and social and economic development remain elusive. This struggle may in part be related to insufficient understanding of the feedbacks between conservation initiatives and social-ecological systems, specifically, the ways in which conservation initiatives result in social changes that have secondary effects on the environments targeted by conservation. To explore this idea, we sampled peer-reviewed articles addressing the social and environmental dimensions of conservation and coded each paper according to its research focus and characterization of these feedbacks. The majority of articles in our sample focused either on the effect of conservation initiatives on people (e.g., relocation, employment) or the effect of people on the environment (e.g., fragmentation, conservation efficacy of traditional management systems). Few studies in our sample empirically addressed both the social dynamics resulting from conservation initiatives and subsequent environmental effects. In many cases, one was measured and the other was discussed anecdotally. Among the studies that describe feedbacks between social and environmental variables, there was more evidence of positive (amplifying) feedbacks between social and environmental outcomes (i.e., undesirable social outcomes yielded undesirable environmental effects and desirable social outcomes yielded desirable environmental effects). The major themes within the sampled literature include conflict between humans and wild animals, social movements, adaptive comanagement, loss of traditional management systems, traditional ecological knowledge, human displacement and risks to livelihoods, and conservation and development. The narratives associated with each theme can serve as hypotheses for facilitating further discussion about conservation issues and for catalyzing future studies of the feedbacks between conservation and social-ecological systems.     

An integrated modeling framework for performing environmental assessments: Application to ecosystem services in the Albemarle-Pamlico basins (NC and VA, USA)

The U.S. Environmental Protection Agency uses environmental models to inform rulemaking and policy decisions at multiple spatial and temporal scales. As decision-making has moved towards integrated thinking and assessment (e.g. media, site, region, services), the increasing complexity and interdisciplinary nature of modern environmental problems has necessitated a new generation of integrated modeling technologies. Environmental modelers are now faced with the challenge of determining how data from manifold sources, types of process-based and empirical models, and hardware/software computing infrastructure can be reliably integrated and applied to protect human health and the environment.In this study, we demonstrate an Integrated Modeling Framework that allows us to predict the state of freshwater ecosystem services within and across the Albemarle-Pamlico Watershed, North Carolina and Virginia (USA). The Framework consists of three facilitating technologies: Data for Environmental Modeling automates the collection and standardization of input data; the Framework for Risk Assessment of Multimedia Environmental Systems manages the flow of information between linked models; and the Supercomputer for Model Uncertainty and Sensitivity Evaluation is a hardware and software parallel-computing interface with pre/post-processing analysis tools, including parameter estimation, uncertainty and sensitivity analysis. In this application, five environmental models are linked within the Framework to provide multimedia simulation capabilities: the Soil Water Assessment Tool predicts watershed runoff; the Watershed Mercury Model simulates mercury runoff and loading to streams; the Water quality Analysis and Simulation Program predicts water quality within the stream channel; the Habitat Suitability Index model predicts physicochemical habitat quality for individual fish species; and the Bioaccumulation and Aquatic System Simulator predicts fish growth and production, as well as exposure and bioaccumulation of toxic substances (e.g., mercury).Using this Framework, we present a baseline assessment of two freshwater ecosystem services-water quality and fisheries resources-in headwater streams throughout the Albemarle-Pamlico. A stratified random sample of 50 headwater streams is used to draw inferences about the target population of headwater streams across the region. Input data is developed for a twenty-year baseline simulation in each sampled stream using current land use and climate conditions. Monte Carlo sampling (n = 100 iterations per stream) is also used to demonstrate some of the Framework's experimental design and data analysis features. To evaluate model performance and accuracy, we compare initial (i.e., uncalibrated) model predictions (water temperature, dissolved oxygen, fish density, and methylmercury concentration within fish tissue) against empirical field data. Finally, we ‘roll-up’ the results from individual streams, to assess freshwater ecosystem services at the regional scale.     

Combining data and simulated data for space–time fields: application to ozone

This paper presents a theory for modeling random environmental spatial-temporal fields that allows simulated data (numerical-physical model output) to be combined with measurements made at fixed monitoring sites. That theory involves Bayesian hierarchical models that provide temporal forecasts and spatial predictions along with appropriate credibility intervals. A by-product is a method for re-calibrating the simulated data to bring it into line with the measurements for certain applications. While the approach covers a broad domain of potential applications, this paper addresses a field of particular importance, ground level ozone concentrations over the eastern and central USA. A univariate model is developed and illustrated with hourly ozone fields. A multivariate alternative is also provided and illustrated with daily concentration fields. The forecasts and predictions they provide are compared with those from other approaches.     

Going with the flow: using species-discharge relationships to forecast losses in fish biodiversity

In response to the scarcity of tools to make quantitative forecasts of the loss of aquatic species from anthropogenic effects, we present a statistical model that relates fish species richness to river discharge. Fish richness increases logarithmically with discharge, an index of habitat space, similar to a species-area curve in terrestrial systems. We apply the species-discharge model as a forecasting tool to build scenarios of changes in riverine fish richness from climate change, water consumption, and other anthropogenic drivers that reduce river discharge. Using hypothetical reductions in discharges (of magnitudes that have been observed in other rivers), we predict that reductions of 20-90% in discharge would result in losses of 2-38% of the fish species in two biogeographical regions in the United States (Lower Ohio-Upper Mississippi and Southeastern). Additional data on the occurrence of specific species relative to specific discharge regimes suggests that fishes found exclusively in high discharge environments (e.g., Shovelnose sturgeon) would be most vulnerable to reductions in discharge. Lag times in species extinctions after discharge reduction provide a window of opportunity for conservation efforts. Applications of the species-discharge model can help prioritize such management efforts among species and rivers.     

Biogeochemical characterization of surface waters in the Aljustrel mining area (South Portugal)

Aljustrel mining area (South Portugal) is a part of the Iberian Pyrite Belt and encloses six sulfide mineral masses. This mine is classified of high environmental risk due to the large tailings’ volume and acid mine drainage (AMD)-affected waters generated by sulfides’ oxidation. The use of biological indicators (e.g., diatoms) revealed to be an important tool to address the degree of AMD contamination in waters. Multivariate analysis has been used as a relevant approach for the characterization of AMD processes. Cluster analysis was used to integrate the significant amount and diversity of variables (physicochemical and biological), discriminating the different types of waters, characterized by the high complexity occurring in this region. The distinction of two main marked phenomena was achieved: (1) the circumneutral-Na-Cl water type (sites DA, PF, BX, BF, RO, CB), expressing the geological contributions of the Cenozoic sediments of Sado river basin, with high diatom diversity (predominating brackish diatoms as Entomoneis alata); and (2) the acid–metal-sulfated water type (sites BM, JU, RJ, AA, MR, BE, PC, AF), reflecting both the AMD contamination and the dissolution of minerals (e.g., silicates) from the hosting rocks, potentiated by the extremely low pH. This last group of sites showed lower diatom diversity but with typical diatoms from acid- and metal-contaminated waters (e.g., Pinnularia aljustrelica). In addition to these two water types, this hierarchical classification method also allowed to distinguish individual cases in subclusters, for example, treated dams (DC, DD), with alkaline substances (lime/limestone), that changed the physicochemical dynamics of the contaminated waters.

     

Ecological Correlates of Extinction Proneness in Tropical Butterflies

Abstract:  Widespread and rapid losses of natural habitats and biodiversity have made the identification of extinction-prone species a major challenge in conservation biology. We assessed the relative importance of biologically relevant species traits (e.g., body size, ecological specialization) obtained from published records to determine the extinction probability of butterflies in a highly disturbed tropical landscape (i.e., Singapore). We also developed a taxon-specific model to estimate the extinction proneness of butterflies in Southeast Asia. Logistic regression analyses showed that adult habitat specialization, larval host plant specificity, geographical distribution, sexual dichromatism, and congenor density were significant and independent determinants of butterfly extinctions in Singapore. Among these traits, specificity of larval host plant and adult habitat specialization were the best correlates of extinction risks. We used this phenomenological extinction-regression model to estimate the relative extinction proneness of 416 butterfly species in Southeast Asia. Our results illustrate the utility of available taxon-specific data for a localized area in estimating the extinction proneness of closely related species on a regional scale. When intensive field studies are not forthcoming, especially in regions suffering from rapid biodiversity losses (e.g., Southeast Asia), similar approaches could be used to estimate extinction threats for other taxonomic groups.