Sensitivity of Systematic Reserve Selection to Decisions about Scale, Biological Data, and Targets: Case Study from Southern British Columbia

Abstract:  The identification of conservation areas based on systematic reserve-selection algorithms requires decisions related to both spatial and ecological scale. These decisions may affect the distribution and number of sites considered priorities for conservation within a region. We explored the sensitivity of systematic reserve selection by altering values of three essential variables. We used a 1:20,000–scale terrestrial ecosystem map and habitat suitability data for 29 threatened vertebrate species in the Okanagan region of British Columbia, Canada. To these data we applied a reserve-selection algorithm to select conservation sites while altering selection unit size and shape, features of biodiversity (i.e., vertebrate species), and area conservation targets for each biodiversity feature. The spatial similarity, or percentage overlap, of selected sets of conservation sites identified (1) with different selection units was ≤40%, (2) with different biodiversity features was 59%, and (3) with different conservation targets was ≥94%. Because any selected set of sites is only one of many possible sets, we also compared the conservation value (irreplaceability) of all sites in the region for each variation of the data. The correlations of irreplaceability were weak for different selection units (0.23 ≤ r ≤ 0.67), strong for different biodiversity features ( r = 0.84), and mixed for different conservation targets ( r = 0.16; 0.16; 1.00). Because of the low congruence of selected sites and weak correlations of irreplaceability for different selection units, recommendations from studies that have been applied at only one spatial scale must be considered cautiously.     

Sustainability assessment of entire forest value chains: Integrating stakeholder perspectives and indicators in decision support tools

The optimization of value chains is an important process to promote sustainable development, since value chains are closely linked to the satisfaction of human needs and combine different driving forces for environmental change. This article presents a methodological approach for the participatory development of value-chain wide sustainability indicator sets and their integration into a decision support tool in the specific case study of the chain “construction and refurbishment with wood”. There are numerous indicator sets for sustainable development of forests and sustainable forestry available at different levels, ranging from local, regional and national to global scale assessments. Some efforts were also made to integrate later production stages of forest value chains (such as wood processing) in the assessment scope (e.g. for chain-of-custody certification). However, no indicator set has so far been available covering environmental, social and economic aspects for the entire value chain of building with timber. This gap was closed through applied sustainability research in the project “Holzwende 2020: Sustainable future markets for wood in the building sector”.     

Accounting for geophysical information in geostatistical characterization of unexploded ordnance (UXO) sites

Efficient and reliable unexploded ordnance (UXO) site characterization is needed for decisions regarding future land use. There are several types of data available at UXO sites and geophysical signal maps are one of the most valuable sources of information. Incorporation of such information into site characterization requires a flexible and reliable methodology. Geostatistics allows one to account for exhaustive secondary information (i.e.,, known at every location within the field) in many different ways. Kriging and logistic regression were combined to map the probability of occurrence of at least one geophysical anomaly of interest, such as UXO, from a limited number of indicator data. Logistic regression is used to derive the trend from a geophysical signal map, and kriged residuals are added to the trend to estimate the probabilities of the presence of UXO at unsampled locations (simple kriging with varying local means or SKlm). Each location is identified for further remedial action if the estimated probability is greater than a given threshold. The technique is illustrated using a hypothetical UXO site generated by a UXO simulator, and a corresponding geophysical signal map. Indicator data are collected along two transects located within the site. Classification performances are then assessed by computing proportions of correct classification, false positive, false negative, and Kappa statistics. Two common approaches, one of which does not take any secondary information into account (ordinary indicator kriging) and a variant of common cokriging (collocated cokriging), were used for comparison purposes. Results indicate that accounting for exhaustive secondary information improves the overall characterization of UXO sites if an appropriate methodology, SKlm in this case, is used.     

Indicator Taxa, Rapid Biodiversity Assessment, and Nestedness in an Endangered Ecosystem

Abstract: To prioritize areas for conservation, biologists and managers need information on species diversity in threatened habitats. The resources available for such inventories remain severely limited, increasing the need to develop speedier ways to estimate the status of target habitats. We present a study of the use of such techniques in the highly fragmented oak savannas of southern Ontario, including selection of indicator taxa, use of rapid biodiversity assessment based on morphospecies, and analysis of community structure. We found that butterflies and skippers can be used to predict richness among Hymenoptera in the study sites, which is consistent with the hypothesis that these easily surveyed Lepidoptera are good candidates for indicator status. Richness values for hymenoptera morphospecies in these savanna remnants were strongly correlated with species richness scores as estimated by systematists, although nonspecialists tended to "split" species into more than one morphospecies. Finally, both the Hymenoptera and Lepidoptera communities in these oak savannas exhibited a high degree of nestedness, suggesting that local extinctions, mostly undocumented, are important determinants of the richness patterns across these widely separated savanna study sites. We found no evidence of significant spatial autocorrelation, probably because of the wide separation of study sites.     

A Successful Predictive Model of Species Richness Based on Indicator Species

Abstract:  Because complete species inventories are expensive and time-consuming, scientists and land managers seek techniques to alleviate logistic constraints on measuring species richness, especially over large spatial scales. We developed a method to identify indicators of species richness that is applicable to any taxonomic group or ecosystem. In an initial case study, we found that a model based on the occurrence of five indicator species explained 88% of the deviance of species richness of 56 butterflies in a mountain range in western North America. We validated model predictions and spatial transferability of the model using independent, newly collected data from another, nearby mountain range. Predicted and observed values of butterfly species richness were highly correlated with 93% of the observed values falling within the 95% credible intervals of the predictions. We used a Bayesian approach to update the initial model with both the model-building and model-validation data sets. In the updated model, the effectiveness of three of the five indicator species was similar, whereas the effectiveness of two species was reduced. The latter species had more erratic distributions in the validation data set than in the original model-building data set. This objective method for identifying indicators of species richness could substantially enhance our ability to conduct large-scale ecological assessments of any group of animals or plants in any geographic region and to make effective conservation decisions.     

Probability distributions of arsenic in soil from brownfield sites in Beijing (China): statistical characterization of the background populations and implications for site assessment studies

A probabilistic analysis was performed on soil arsenic concentration data from 4 brownfield sites at Beijing (Chaoyang and Haidian Districts), involved in environmental assessment studies. The available data sets were processed to provide a statistical characterization of the background populations and differentiate “anomalous data” from the natural range of variation of arsenic concentrations in soil. The site-specific background distributions and the existing wide-scale background values defined for the Beijing area were compared, discussing related implications for the definition of metal contamination soil screening levels (SSLs) in site assessment studies. The statistical analysis of As data sets discriminated site-specific background populations, encompassing 88% to 94% of the sample data, from outliers values, associated with either subsoil natural enrichments or possible anthropogenic releases. Upper Baseline Concentration (UBC) limits (+ 2σ level), including most of the site-specific metal background variability, were derived based on the statistical characterization of the background populations. Sites in the Chaoyang South District area had UBC values in the range 10.4–12.6 mg·kg^-1. These ranges provide meaningful SSL values to be adopted for As in local site assessment studies. Using the wide-scale background value for the Beijing area would have erroneously classified most of the areas in the subject sites as potentially contaminated.     

A new method for spatially moving correlation analysis in geomedicine

A method of correlation analysis within a spatially moving window was applied to two sets of epidemiological/geochemical data in Norway, (1) mortality/disability rates of multiple sclerosis (MS) versus atmospheric fallout of Mg and concentrations of Se in overbank sediment, and (2) incidence rates of malignant melanoma of the skin (MM) versus concentrations of Ca and K in overbank sediment. It appears that n = 17 observation sites within the moving window is a practical compromise between noise in the data at small values of n and a spatial resolution good enough to detect trends in the distribution patterns of the correlation coefficient. For MS versus Mg, MS versus Se and MM versus Ca the correlation coefficients are generally negative and depict systematic distribution patterns with anomalous clusters of sites with good correlation. For MS versus Se the correlation coefficients also form an additional cluster of positive coefficients. Tests with permutated data show that more than 70% of the negative correlation coefficients for MS versus Mg and for MM versus Ca are both significantly different from zero at p<0.05, while less than 15% of those for MS versus Se are significant at the same level. For MM versus K the correlation coefficients are randomly distributed and not significantly different from zero. The described correlations may be effects of confounders and do not per se indicate any causal relationships. However, further research based on these results may well lead to the identification of possible aetiological factors.     

Assessing landscape values: a proposal for a multidimensional conceptual model

To evaluate a specific form of resource management, for example, the maintenance of valuable cultural landscapes, the contributions of many different viewpoints must be considered. In this paper, a model for assessing and integrating the different aspects contributing to landscape evaluation is proposed. Some results of its use in landscape assessment in the Madrid region (central Spain) are discussed.The model takes into account five assessment dimensions which are considered to be independent—the ecological, productive, economic, social and cultural evaluative systems and it recognises and assumes conflicts and trade-offs between these components. The inclusion of the production system as an independent assessment dimension is original and is justified because it is the link that connects the ecological and economic systems. Since this dimension acts most directly on the ecosystem, the production system includes the attribute of ecological or strong (in the sense of [For the Common Good, Beacon Press, Boston, 1994]) sustainability.The evaluation method places special emphasis on defining a conceptual framework, and from this defines scenarios in comparison with which particular development models (landscape planning, resources management) can be evaluated. Seven scenarios, coming from a hypothetical, traditional sustainable scenario of resources management, are defined. One of these, the sound sustainable scenario, is defended as the only one viable in the long term, particularly in developing countries.This method was employed to evaluate the agricultural and natural landscape of the Madrid region municipalities. The multivariate approach adopted was based on the selection of indicator sets for each evaluative dimension. The structure of relationships among indicators was then analysed separately for each dimension and values assigned to the municipalities according to their position on the main axes of the multivariate analyses. Each municipality was assigned to one of the seven development scenarios by means of discriminant analysis. The approach’s greatest assets are its flexibility in the selection of the indicators and the efficacy in its monitoring and comparison of the different analysed cases once a rigorous conceptual framework was established.The paper discusses the conditions for the sustainability of the human activities and provides a method for evaluating and comparing scenarios of resources management.     

Quantifying the Indicator Power of an Indicator Species

Abstract:  Biodiversity indicator species are needed for classifying biotopes and sites for conservation, and a number of methods have been developed for determining indicator species for this purpose. Nevertheless, in addition to site classification, there is sometimes a need to define an indicator species that indicates the occurrence of another species. For example, when a species of interest (target species) is difficult to detect or identify, a reliable indicator species can function as a tool that saves time and money. We derived a method that provides a quantitative measure of the indicator power (IP) of an indicator species for the target species or any species assemblage. We calculated the measure of IP from a presence–absence matrix that covered several sites. The method provided a list of indicator species, the presence of which reliably indicated the presence of another species (e.g., a threatened or rare species in a given area). The IP of the species was highest when the number of shared occurrences between the indicator species and the target species was high and, simultaneously, when the indicator species and the target species occurred separately in only a few cases. The IP was also positively influenced by the number of sites with no occurrences of either the indicator or the target species. Our method can also be used to quantify different types of species occurrence indications. We refer to these types as presence–presence, presence–absence, absence–presence, and absence–absence indications. To clarify the use of the method, we examined the situation with red-listed polypores in White-backed Woodpecker (Dendrocopos leucotos) habitats in Fennoscandia and found some suitable indicator species. Our method provides a new, objective way to evaluate the IP of an indicator species.     

Using abiotic variables to predict importance of sites for species representation

In systematic conservation planning, species distribution data for all sites in a planning area are used to prioritize each site in terms of the site's importance toward meeting the goal of species representation. But comprehensive species data are not available in most planning areas and would be expensive to acquire. As a shortcut, ecologists use surrogates, such as occurrences of birds or another well‐surveyed taxon, or land types defined from remotely sensed data, in the hope that sites that represent the surrogates also represent biodiversity. Unfortunately, surrogates have not performed reliably. We propose a new type of surrogate, predicted importance, that can be developed from species data for a q% subset of sites. With species data from this subset of sites, importance can be modeled as a function of abiotic variables available at no charge for all terrestrial areas on Earth. Predicted importance can then be used as a surrogate to prioritize all sites. We tested this surrogate with 8 sets of species data. For each data set, we used a q% subset of sites to model importance as a function of abiotic variables, used the resulting function to predict importance for all sites, and evaluated the number of species in the sites with highest predicted importance. Sites with the highest predicted importance represented species efficiently for all data sets when q = 25% and for 7 of 8 data sets when q = 20%. Predicted importance requires less survey effort than direct selection for species representation and meets representation goals well compared with other surrogates currently in use. This less expensive surrogate may be useful in those areas of the world that need it most, namely tropical regions with the highest biodiversity, greatest biodiversity loss, most severe lack of inventory data, and poorly developed protected area networks.     

Multicriteria prioritization and partial order in environmental sciences

The complexity of the present data-centric world finds its expression in the increasing number of multi-indicator systems. This has led to the development of multicriteria ranking systems based on partial orders. Order theory is a main pillar of structural mathematics. Partial orders help to reveal why an object of interest holds a certain ranking position and how much it is subject to change if a composite indicator is upgraded. Order theory helps to derive linear or weak orders without indicator weighting schemes. Hence, rankings obtained from decision support systems (DSS) which depend on many parameters beyond the data matrix can be checked and discrepancies can lead to examine the parameters of the DSS. Order theory helps discover association and implication structures derived from formal concept lattices. Association and implication networks among the attributes of the data matrix allow more insights into multi-indicator systems and lead to new hypotheses and motivate further research. Some new and innovative concepts, like separated subsets, antagonistic indicators, ranking stability fields are rendered. Separated subsets are the typical outcome of a partial order analysis; their identification leads to antagonistic indicators, which are responsible for the separatedness of object’s subsets. Numerical aggregation can be performed step-by-step and the question which values of a weight lead to an order inversion is of high interest. The concept of stability fields is one possible answer, discussed in this paper. After an outline of partial order theory some more specific theoretical results are shown, then we discuss the role of composite indicators in the light of partial order and give some examples of interesting applications of partial order. Finally examples are selected from real life case studies of watersheds, environmental performance evaluations, child well being, geographic and administrative regions and more.     

Nest site limitation and facultative polygyny in the ant Leptothorax longispinosus

Summary The ant L. longispinosus displays geographic variation in its pattern of facultative polygyny (Fig. 2). In nature, nest density and frequency of multiple queening are positively associated over three sites. A putative causal relation between availability of vacant nest sites and polygyny was examined in New York, where a plot was seeded with additional nest sites and monitored for 24 months. Both queen number and worker number per nest on the experimental plot were reduced relative to controls (Fig. 4, Fig. 5), indicating that scarcity of available nest sites influences the pattern of polygyny in this species. The observed demographic changes resulted from fractionation of existing colonies; adding nest sites induced polydomy. Although numbers of adult ants changed with addition of nest sites, the numbers of immatures were no different after 2 years (Table 1), suggesting that the population was undergoing growth to expand into the additional sites. These results are the first direct experimental evidence linking polygyny to an ecological parameter for any ant species.     

Seasonal variations in different physico-chemical parameters of the effluents of Century Pulp and Paper Mill, Lal Kuan, Uttarakhand

The present study was undertaken with the objective to study the characteristics of the effluent of Century Pulp and Paper Mill, Lalkuan (Uttarakhand) in different seasons. The variations in the physicochemical characteristics were observed and monitored up to 12 months at three different sites. Mean values of temperature, pH, chlorides and total phenols of the effluent were found below, whereas colour, BOD5, COD and lignin concentrations were above the minimum national standards (MINAS).     

Integrating Biological and Social Values When Prioritizing Places for Biodiversity Conservation

The consideration of information on social values in conjunction with biological data is critical for achieving both socially acceptable and scientifically defensible conservation planning outcomes. However, the influence of social values on spatial conservation priorities has received limited attention and is poorly understood. We present an approach that incorporates quantitative data on social values for conservation and social preferences for development into spatial conservation planning. We undertook a public participation GIS survey to spatially represent social values and development preferences and used species distribution models for 7 threatened fauna species to represent biological values. These spatially explicit data were simultaneously included in the conservation planning software Zonation to examine how conservation priorities changed with the inclusion of social data. Integrating spatially explicit information about social values and development preferences with biological data produced prioritizations that differed spatially from the solution based on only biological data. However, the integrated solutions protected a similar proportion of the species’ distributions, indicating that Zonation effectively combined the biological and social data to produce socially feasible conservation solutions of approximately equivalent biological value. We were able to identify areas of the landscape where synergies and conflicts between different value sets are likely to occur. Identification of these synergies and conflicts will allow decision makers to target communication strategies to specific areas and ensure effective community engagement and positive conservation outcomes. Integración de Valores Biológicos y Sociales al Priorizar Sitios para la Conservación de la Biodiversidad     

Using community‐level metrics to monitor the effects of marine protected areas on biodiversity

Marine protected areas (MPAs) are used to protect species, communities, and their associated habitats, among other goals. Measuring MPA efficacy can be challenging, however, particularly when considering responses at the community level. We gathered 36 abundance and 14 biomass data sets on fish assemblages and used meta‐analysis to evaluate the ability of 22 distinct community diversity metrics to detect differences in community structure between MPAs and nearby control sites. We also considered the effects of 6 covariates—MPA size and age, MPA size and age interaction, latitude, total species richness, and level of protection—on each metric. Some common metrics, such as species richness and Shannon diversity, did not differ consistently between MPA and control sites, whereas other metrics, such as total abundance and biomass, were consistently different across studies. Metric responses derived from the biomass data sets were more consistent than those based on the abundance data sets, suggesting that community‐level biomass differs more predictably than abundance between MPA and control sites. Covariate analyses indicated that level of protection, latitude, MPA size, and the interaction between MPA size and age affect metric performance. These results highlight a handful of metrics, several of which are little known, that could be used to meet the increasing demand for community‐level indicators of MPA effectiveness.     

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).     

Darstellung maskierter Nutzungseffekte auf naturraumspezifische Artengemeinschaften grasiger Feldraine mithilfe von Restvarianzmustern

Background and aim Grassy field margins have a high relevance in agricultural landscapes regarding the preservation of typical arthropod communities, their biodiversity and the ecological system functions linked with it. The structure of terrestrial communities is affected by anthropogenic impairment, which can lead to the replacement of sensitive by more tolerant species. The negative influence of pesticides on fauna and flora as well as the associated functional aspects (ecological system functions) and the reduction of biodiversity are undisputed since longer for agrarian systems and can also be assumed for grassy field margins due to spray-drift. The case study presented here examined the effect of influences related to utilization on the plant and arthropod communities of grassy field margins. Reference sites, on which due to missing farming in the direct surrounding countryside no utilization influences on the communities were present, were compared to non-target sites, on which these influences could not be excluded. Sites in three German macrochores were examined: in the Jülicher Börde, at the northeastern edge of the Leipziger low lands and in the area Mainfranken near Würzburg, all of them intensely used agricultural landscapes. Beside the vegetation, the epigeic arthropod communities of carabid beetles, spiders, springtails, hymenopterans, hover flies and ladybirds as well as abiotic parameters were included in the analysis. The aim of the study was the development of a statistical exclusion procedure which is capable to quantify the amount of variation in field community data which can be attributed to isolated factors. Special attention was paid to non-observable utilization impacts like undocumented pesticide application. The extraction of patterns of residual variance allowed for the uncovering of masked effects on a scale below the obvious abundance pattern. Materials and methods The variability in the species composition was visualized with the help of non-metric multi-dimensional scaling (NMDS). Indicator species analysis revealed those species which could be recognized as statistically significant indicators for local conditions. The relationship between the species composition and the environmental factors was statistically modeled by canonical correspondence analysis. By variance partitioning it was possible to extract the variance portion which could be bound to a set of covariables. For the remaining residual variance it could not be excluded that this resulted from a pesticide influence. Results The analyses based on the ecological distance (Bray-Curtis) showed that the communities in references sites and non-target sites could be clearly distinguished in all three landscapes. Based on the portions of variance which could be explained statistically by a utilization related influence, two different directions of reaction to the utilization related variables could be stated. On the one hand there were sensitive species, showing reduced abundances in the non-target sites, on the other hand there were species increasing in abundance in the non-target sites, recruiting from the group of strong competitors. Discussion By the use of residual variances a clear influence of utilization related parameters on the community of soil arthropods and vegetation could be shown. The observed abundances shifts between reference sites and non-target sites in the raw data were attributed to a multi-dimensional factor complex which could be split up by the use of partial ordination methods, quantifying the relationships to the utilization related parameters. It was demonstrated that special utilization correlated patterns could be found in the raw data after eliminating the influence of the covariables. Conclusions Utilization related influences in the non-target sites evened out the characteristic communities for the three landscapes towards a comprehensive, ubiquitous species composition. The hypothesis of decreasing abundance of sensitive species and the augmentation of tolerant species due to a potential pesticide influence could be underpinned and quantified. Sensitive species which showed a significant reduction in abundance between reference and non-target sites showed a much higher sensitivity to the influences and thus were assigned a higher indicator potential than tolerant species. From the group of the sensitive species Pardosa palustris and Poecilus cupreus could be isolated as two promising macrochore-specific indicator species for the masked effects. Recommendations and perspectives A macrochore-specific assessment of utilization effects was requested for future studies due to the different sensitivities of the species in the three landscapes. The elaboration and specifying of reference conditions for terrestrial agrarian systems is an important task for the future. The presented approach deduces a macrochore-specific, complex effect pattern of anthropogenic impairment on terrestrial species communities. It can be used to extract masked effects and by this facilitates a more sustainable use of e.g. plant protection products. Furthermore it provides an opportunity to validate evaluation systems for the effects of utilization impacts on terrestrial vegetation and arthropods. Additionally the species sensitivities with respect to the macrochores allow a regionalized assessment of ecotoxicological effects and the integration into spatially explicit effect assessment models.     

Applications of artificial neural networks for patterning and predicting aquatic insect species richness in running waters

Two artificial neural networks (ANNs), unsupervised and supervised learning algorithms, were applied to suggest practical approaches for the analysis of ecological data. Four major aquatic insect orders (Ephemeroptera, Plecoptera, Trichoptera, and Coleoptera, i.e. EPTC), and four environmental variables (elevation, stream order, distance from the source, and water temperature) were used to implement the models. The data were collected and measured at 155 sampling sites on streams of the Adour–Garonne drainage basin (South-western France). The modelling procedure was carried out following two steps. First, a self-organizing map (SOM), an unsupervised ANN, was applied to classify sampling sites using EPTC richness. Second, a backpropagation algorithm (BP), a supervised ANN, was applied to predict EPTC richness using a set of four environmental variables. The trained SOM classified sampling sites according to a gradient of EPTC richness, and the groups obtained corresponded to geographic regions of the drainage basin and characteristics of their environmental variables. The SOM showed its convenience to analyze relationships among sampling sites, biological attributes, and environmental variables. After accounting for the relationships in data sets, the BP used to predict the EPTC richness with a set of four environmental variables showed a high accuracy (r=0.91 and r=0.61 for training and test data sets respectively). The prediction of EPTC richness is thus a valuable tool to assess disturbances in given areas: by knowing what the EPTC richness should be, we can determine the degree to which disturbances have altered it. The results suggested that methodologies successively using two different neural networks are helpful to understand ecological data through ordination first, and then to predict target variables.