Detecting pattern in biological stressor response relationships using model based cluster analysis

Environmental monitoring of aquatic systems is needed to estimate the quality of the systems, to evaluate standards and to study stressor–response relationships. Monitoring programs often focus on the collection of biological, chemical and physical measures of the system. An important concern is the effect of chemical and physical stressors on the biological community. Evaluation of relationships may be difficult as the extent of the relationship is not known. From a management perspective, interest is on what factors affect the biological community and where these factors have an influence. The focus of this paper is on the use of regression based cluster analysis as a tool for finding relationships between a single biological response and a suite of environmental stressors. The approach to cluster analysis uses a penalized regression classification likelihood and Markov Chain Model Composition Monte Carlo. This approach allows for simultaneous development of regression models and clustering of the regression models. The method is applied to the analysis of a data set describing stressors/response relationship in Ohio.     

Quantifying biological integrity by taxonomic completeness: its utility in regional and global assessments.

Water resources managers and conservation biologists need reliable, quantitative, and directly comparable methods for assessing the biological integrity of the world's aquatic ecosystems. Large-scale assessments are constrained by the lack of consistency in the indicators used to assess biological integrity and our current inability to translate between indicators. In theory, assessments based on estimates of taxonomic completeness, i.e., the proportion of expected taxa that were observed (observed/expected, O/E) are directly comparable to one another and should therefore allow regionally and globally consistent summaries of the biological integrity of freshwater ecosystems. However, we know little about the true comparability of O/E assessments derived from different data sets or how well O/E assessments perform relative to other indicators in use. I compared the performance (precision, bias, and sensitivity to stressors) of O/E assessments based on five different data sets with the performance of the indicators previously applied to these data (three multimetric indices, a biotic index, and a hybrid method used by the state of Maine). Analyses were based on data collected from U.S. stream ecosystems in North Carolina, the Mid-Atlantic Highlands, Maine, and Ohio. O/E assessments resulted in very similar estimates of mean regional conditions compared with most other indicators once these indicators' values were standardized relative to reference-site means. However, other indicators tended to be biased estimators of O/E, a consequence of differences in their response to natural environmental gradients and sensitivity to stressors. These results imply that, in some cases, it may be possible to compare assessments derived from different indicators by standardizing their values (a statistical approach to data harmonization). In situations where it is difficult to standardize or otherwise harmonize two or more indicators, O/E values can easily be derived from existing raw sample data. With some caveats, O/E should provide more directly comparable assessments of biological integrity across regions than is possible by harmonizing values of a mix of indicators.     

A nonparametric procedure for analyzing repeated measures of spatially correlated data

Many agricultural, biological, and environmental studies involve detecting temporal changes of a response variable, based on data observed at sampling sites in a spatial region and repeatedly over several time points. That is, data are repeated measures over time and are potentially correlated across space. The traditional repeated-measures analysis allows for time dependence but assumes that the observations at different sampling sites are mutually independent, which may not be suitable for field data that are correlated across space. In this paper, a nonparametric large-sample inference procedure is developed to assess the time effects while accounting for the spatial dependence using a block bootstrap. For illustration, the methodology is applied to describe the population changes of root-lesion nematodes over time in a production field in Wisconsin.     

An overview of systems analysis methods in delineating environmental quality indices

Environmental quality indices (EQIs) have been developed for a variety of purposes ranging from enforcement of environmental standards, to analysis of trends of environmental degradation or improvement, to scientific research. EQIs currently in use are not organized within an integrated framework and thus it has been difficult to analyze adequately complex, multidisciplinary, large-scale, global phenomena. In this paper we compare four different approaches to developing EQIs within a systems perspective. Our analysis suggests that: (1) non-linear regression models that represent an ecosystem's response to different impacts within a stress-response framework (method of response functions) are useful tools for analysis of environmental data; (2) non-equilibrium thermodynamics models based on the concept of exergy, which represents the free energy a system possesses in relation to its environment, provide a common basis for representing many aspects of ecosystem development and response to environmental impacts as a single measure; (3) diagram models based on the concept of emergy, which represents both environmental values and economic values with a single measure, provide a common basis for integrating economic development and environmental protection values into one index; and (4) complex systems simulation models based on general systems theory, which use the methodologies of systems analysis and simulation to identify, quantify, and interrelate EQIs within a dynamic systems context, provide explicit linkages between causes and effects (vertical integration) and identify cross-linkages among different environmental issues (horizontal integration).     

Response of a Fuzzy INdex of Ecosystem integrity (FINE) to water and sedimentary chemical data in two northern Adriatic lagoons

The European WFD (2000/60/EC) requires the assessment of the ecological quality status of water bodies, and gives great importance to the biological components of the ecosystem. A multimetric, fuzzy-based index for the evaluation of environmental quality (FINE: Fuzzy INdex of Ecosystem integrity) has been developed. The FINE index was calculated at 7 sites in the Sacca di Goro and the Valli di Comacchio (Adriatic Sea), where water and sedimentary chemical data were available for the years 2004 and 2005. A significant positive correlation was found between FINE values and dissolved oxygen, while significant negative correlations were observed between FINE values and transparency, nitrogen and phosphorous in the water column, and heavy metals and PCB in the sediments.     

Response of a Fuzzy INdex of Ecosystem integrity (FINE) to water and sedimentary chemical data in two northern Adriatic lagoons

The European WFD (2000/60/EC) requires the assessment of the ecological quality status of water bodies, and gives great importance to the biological components of the ecosystem. A multimetric, fuzzy-based index for the evaluation of environmental quality (FINE: Fuzzy INdex of Ecosystem integrity) has been developed. The FINE index was calculated at 7 sites in the Sacca di Goro and the Valli di Comacchio (Adriatic Sea), where water and sedimentary chemical data were available for the years 2004 and 2005. A significant positive correlation was found between FINE values and dissolved oxygen, while significant negative correlations were observed between FINE values and transparency, nitrogen and phosphorous in the water column, and heavy metals and PCB in the sediments.     

Sensitivity and Vulnerability in Marine Environments: an Approach to Identifying Vulnerable Marine Areas

Abstract:  Marine environments have suffered from a lack of quantitative methods for delineating areas that are sensitive or vulnerable to particular stresses, natural and anthropogenic. We define sensitivity as the degree to which marine features respond to stresses, which are deviations of environmental conditions beyond the expected range. Vulnerability can then be defined as the probability that a feature will be exposed to a stress to which it is sensitive. Using these definitions, we provide a quantitative methodology for identifying vulnerable marine areas based on valued ecological features, defined as biological or physical features, processes, or structures deemed by humans to have environmental, social, cultural, or economic significance. The vulnerability of the valued ecological features is a function of their sensitivity to particular stresses and their vulnerability to those stresses. We used the methodology to demonstrate how vulnerable marine areas for two groups of endangered whale species (inshore and offshore) could be identified with a predictive habitat model and acoustic stress surfaces. Acoustic stress surfaces were produced for ferry traffic, commercial shipping traffic, potential offshore oil production, and small-boat traffic. The vulnerabilities of the two whale groups to the four stressors considered in this example were relatively similar; however, inshore species were more sensitive to on-shelf, coastal activities such as offshore hydrocarbon production, ferry traffic, and small-boat traffic. Our approach demonstrates how valued features can be associated with stresses and the likelihood of encountering these stresses (vulnerability) in order to identify geographic areas for management and conservation purposes. The method can be applied to any combination of valued ecological features and stressors.     

Dispersal limitation and environmental heterogeneity shape scale-dependent diversity patterns in plant communities

Understanding the large-scale distribution of species diversity requires distinguishing two of the primary factors that cause compositional differences: dispersal limitation and environmental variation. In a community with a naturally discontinuous spatial structure, we asked (1) at what scale(s) nonrandom variation in species composition occurs and (2) at what scale(s) such variation is associated with spatial separation, indicative of dispersal limitation, and at what scale(s) variation is associated with environmental heterogeneity? We sampled 50 seeps (small wetlands) on five serpentine outcrops. Using a randomization model, we showed that additive beta diversity (a measure of community dissimilarity) was lower than random within seeps and higher than random among both seeps and outcrops. Using Mantel tests, we showed that plant community dissimilarity, in both the full seep assemblage as well as in a subset of seep endemics, at the two larger scales was associated with different forms of environmental heterogeneity and, at the largest scale, was also associated with geographic distance. We conclude that diversity in this system is shaped by multiple scales of heterogeneity and by dispersal limitation at the largest scale.     

Community response patterns: evaluating benthic invertebrate composition in metal-polluted streams.

Human activities are modifying the condition and character of ecosystems at a rapid rate. Because of these rapid changes, questions concerning how ecosystems and their assemblages respond to anthropogenic stressors have been of general interest. Accurate prediction of assemblage composition in ecosystems with anthropogenic degradation requires that we assess both how assemblages respond to stressors and the generality of the responses. We ask whether assemblage composition among stream sites becomes more similar after exposure to a common stressor. Using data from biological monitoring programs in the southern Rocky Mountain ecoregion of Colorado and in West Virginia, we compare benthic invertebrate similarity and assemblage composition among sites having different levels (background, low, medium, and high) of heavy-metal pollution. Invertebrate assemblages were most similar within the background metal category, and similarity was progressively lower in low, medium, and high metal categories. An analysis of the frequency of occurrence of genera within metal categories reveals taxonomic shifts that conform to expectations based on metal tolerance of benthic invertebrates. However, different metal-tolerant genera were found at different metal-impacted sites, suggesting that local abiotic and biotic processes may influence the identity of the metal-tolerant genera that become established in polluted sites. Low community similarity in the medium and high-metal categories suggests that accurate prediction of assemblage composition at impacted sites may be challenging.     

Assessing sensitivities of marine areas to stressors based on biological traits

Analysis of the biological traits (e.g., feeding mode and size) that control how organisms interact with their environment has been used to identify environmental drivers of, or impacts on, species and to explain the importance of biodiversity loss. Biological trait analysis (BTA) could also be used within risk-assessment frameworks or in conservation planning if one understands the groups of traits that predict the sensitivity of habitats or communities to specific human activities. Deriving sensitivities from BTA should extend sensitivity predictions to a variety of habitats, especially those in which it would be difficult to conduct experiments (e.g., due to depth or risk to human life) and to scales beyond the norm of most experiments. We used data on epibenthos, collected via video along transects at 27 sites in a relatively pristine region of the seafloor, to determine scales of natural spatial variability of derived sensitivities and the degree to which predictions of sensitivity differed among 3 stressors (extraction of species, sedimentation, and suspended sediments) or were affected by underlying community compositions. We used 3 metrics (weighted abundance, abundance of highly sensitive species, and number of highly sensitive species) to derive sensitivity to these stressors and simulated the ability of these metrics to detect a range of stressor intensities. Regardless of spatial patterns of sensitivities across the sampled area, BTA distinguished differences in sensitivity to different stressors. The BTA also successfully separated differences in community composition from differences in sensitivity to stressors. Conversely, the 3 metrics differed widely in their ability to detect simulated impacts and likely reflect underlying ecological processes, suggesting that use of multiple metrics would be informative for spatial planning and allocating conservation priorities. Our results suggest BTA could be used as a first step in strategic prioritization of protected areas and as an underlying layer for spatial planning.     

Instream and offstream environmental conditions and stream biotic integrity: Importance of scale and site similarities for learning and prediction

Two different methods to predict biotic integrity were tested and compared in the present paper. The first one tries to predict the fish indices of biotic integrity (IBI) at the state or regional scale based on the most similar observations to a specific target site of interest using the simple to implement k-nearest neighbors (or kNN) method. Two different distance functions were considered to find the k-nearest neighbors: the Euclidean and the Mahalanobis. The second method was applied on the same datasets and consisted of a step-wise multiple regression. The two modeling approaches yielded similar results but kNN proved to be more time-efficient and very fast computationally for the given dataset sizes, which allowed applying a leave-one-out cross validation.In an attempt to reveal the importance of scale in the prediction of IBI, regression models were constructed at the state (or regional) scale and at the more refined cluster of sampling sites scale. Clusters of sites were extracted using Kohonen's self-organizing maps (SOM) followed by k-means clustering of the SOM neurons. Cluster-level regression models, constructed after site patterning, performed better in IBI prediction than global regression models constructed without any previous site patterning. The importance of identifying groups of sites with similar environmental characteristics affecting the IBI was revealed. The combined use of site patterning and regression modeling for IBI prediction also helped identifying important variables acting at the local scale which remain latent at the global scale.     

Trajectories of zooplankton recovery in the Little Rock Lake whole-lake acidification experiment.

Understanding the factors that affect biological recovery from environmental stressors such as acidification is an important challenge in ecology. Here we report on zooplankton community recovery following the experimental acidification of Little Rock Lake, Wisconsin, USA. One decade following cessation of acid additions to the northern basin of Little Rock Lake (LRL), recovery of the zooplankton community was complete. Approximately 40% of zooplankton species in the lake exhibited a recovery lag in which biological recovery to reference basin levels was delayed by 1-6 yr after pH recovered to the level at which the species originally responded. Delays in recovery such as those we observed in LRL may be attributable to "biological resistance" wherein establishment of viable populations of key acid-sensitive species following water quality improvements is prevented by other components of the community that thrived during acidification. Indeed, we observed that the recovery of species that thrived during acidification tended to precede recovery of species that declined during acidification. In addition, correspondence analysis indicated that the zooplankton community followed different pathways during acidification and recovery, suggesting that there is substantial hysteresis in zooplankton recovery from acidification. By providing an example of a relatively rapid recovery from short-term acidification, zooplankton community recovery from experimental acidification in LRL generally reinforces the positive outlook for recovery reported for other acidified lakes.     

Plant competition varies with community composition in an edaphically complex landscape

There is currently no consensus on how physical and biological factors affect competitive intensity. Tests of whether competitive intensity varies along axes of environmental change have commonly been conducted in systems with a single strong environmental gradient, such as productivity, a soil resource, or an environmental stress. Frequently, these same axes are associated with changes in species composition, yet few studies have asked whether shifts in the identity of competitors affect competitive intensity. We ask whether resources (nutrients, water), stressors (heavy metals, Ca:Mg ratio), productivity (aboveground biomass), or species identity (an ordination axis of plant community composition) were the best predictors of the intensity of competition in a heterogeneous grassland landscape that included multiple independent environmental gradients. The reproductive fitness of six annual plant species was measured in the presence and absence of competitors and used to calculate relative interaction intensity (RII). We found that RII was best predicted by community composition. Nutrient availability was also important, and a post hoc test showed that competitive intensity was best explained by the combined effects of community composition and nutrient availability. We argue that community composition may be the most effective metric for predicting competitive intensity in many ecosystems because it includes both the competitive effects of the local community and information about covarying environmental characteristics.     

Applying Biodiversity Gap Analysis in a Regional Nature Reserve Design for the Edge of Appalachia, Ohio (U.S.A.)

A biodiversity gap analysis is a method, now usually employing geographic information systems, for identifying deficiencies in existing biodiversity protection. Key principles of gap analysis were applied to a region of southcentral Ohio (U.S.A.) known as The Edge of Appalachia as part of a detailed, large-scale (1:24,000) nature reserve design project. By combining Landsat thematic mapper imagery with ancillary data (bedrock geology, elevation, slope, aspect, and stream proximity), a rule-based model was developed to differentiate and map the natural plant communities present in the 378-km² study area. The model was then used to generate a map depicting the most likely presettlement plant community distributions for the area. These two maps were compared against the 5273 ha owned and managed by state and local conservation organizations. For the current natural plant community distributions, regional land-protection efforts represented each plant community proportionally; however, comparison with the presettlement vegetation clearly identified serious historical losses of several plant community types. Our results suggest that future land acquisitions should emphasize those plant community types that were once more widespread in the region prior to European settlement, a time when natural processes were less compromised by human activity. Current and historical plant-community mapping results were combined and evaluated using the ownership parcel as the fundamental mapping unit. From parcel-based desirability maps a conservation plan was developed that addressed community deficiencies using a representation target of25% for each community type, as derived from the modeled presettlement landscape.     

Paleoecology and the Coarse-Filter Approach to Maintaining Biological Diversity

Abstract: The difficulties of saving millions of species from extinction often cause conservationists to focus on a higher level of biological organization, the community. They do so for two reasons: (1) communities are considered important biological entities in their own right; and (2) conserving representative samples of communities is seen as an efficient way to maintain high levels of species diversity. This approach will work if the chosen communities contain almost all species. Because it potentially saves most but not all species, community conservation is a "coarse-filter" approach to the maintenance of biological diversity, and contrasts with the "fine-filter" approach of saving individual species. Paleoecological information on the distribution of plant taxa in North America, however, indicates that most modern plant communities are less than 8,000 years old and therefore are not highly organized units reflecting long-term co-evolution among species. Rather, they are only transitory assemblages or co-occurrences among plant taxa that have changed in abundance, distribution, and association in response to the large climate changes of the past 20,000 years. During periods when climate changes are large, communities are too ephemeral to be considered important biological entities in their own right. Large climatic changes are also likely to occur during the next century because of increased concentrations of CO₂, and we therefore propose that the coarse-filter approach to selecting nature reserves should be more strongly influenced by the distribution of physical environments than by the distribution of modern communities. Ideally, nature reserves should also encompass a broad enough range of environments to allow organisms to adjust their local distribution in response to long-term environmental change and should be connected by regional corridors that would allow species to change their geographic distributions.     

Insight of chemical environmental risk and its management from the vinyl chloride accident

The combustion of vinyl chloride (VC) after the train derailment accident in Ohio, USA in February, 2023 has caused widespread concern around the world. This paper tried to analyze several issues concerning the accident, including the appropriateness of the VC combustion in the emergency response in this accident, the meanings of so-called “controlled combustion”, the potential environmental risks caused by VC and combustion by-products, and follow-up work. In our view, this accident had surely caused environmental and health risks to some extent. Hence, a comprehensive environmental risk assessment is necessary, and then the site with risk should be comprehensively remediated, hazardous waste should be harmlessly treated as soon as possible. Finally, this accident suggests that further efforts should be taken to bridge the gap between chemical safety management and their environmental risk management.