An Environmental Domain Classification of New Zealand and Its Use as a Tool for Biodiversity Management

Abstract:   Successful biodiversity management, including the selection and subsequent management of protected areas, depends in large measure on classifications showing land areas with similar ecosystem character. In contrast to widely used, qualitative land-classification techniques, we used a numerical classification of explicit spatial layers describing aspects of New Zealand's climate and landforms. We chose input variables for their strong functional links with major physiological processes of trees and high statistical correlations with geographic distributions of individual tree species as determined from previous studies. Higher-level divisions of the resulting classification were dominated by macroclimatic variation associated with change in both latitude and orographic protection provided by New Zealand's main mountain ranges, but variation in landform became more important at finer scales of classification. Classification units showed marked variation in the proportional extent of both indigenous vegetation cover and land set aside for conservation purposes. Indigenous ecosystem remnants of the highest priority for increased protection occurred in warm, lowland domains, particularly in drier environments, where both indigenous cover and protected areas are of minimal geographic extent. Such results underline the considerable potential of an environmental classification to provide a landscape context for systematic conservation management, particularly in environments where the natural ecosystem pattern has been severely modified by human activity.     

A Comparison of Direct and Environmental Domain Approaches to Planning Reservation of Forest Higher Plant Communities and Species in Tasmania

The planning of representative systems of nature conservation reserves can be based on a wide variety of criteria. The ready availability of data on the physical attributes of the environment, and the patchiness of biological data, have made reservation planning based on environmental classifications an attractive option for decision makers. We developed an environmental classification based on ecologically relevant variables and used it to plan a forest reserve system for Tasmania. We then used biological distributional data and the same targets and procedures to choose a forest reserve system. The analyses based on the environmental classification selected the same areas as equivalent analyses based on biological distributional data to a greater degree than could be expected by chance. Many rare species and communities missed selection by environmental classificatory units (environmental domains), however, and the proportions of ranges of selected taxa varied widely. Conversely, environmental domains were missed by a reservation strategy based only on biological data. These domains might reflect some gaps in the biological data. A reservation planning approach based on both biological data and domains may produce better results than either used in isolation.     

Algorithms and biplots for double constrained correspondence analysis

Correspondence analysis with linear external constraints on both the rows and the columns has been mentioned in the ecological literature, but lacks full mathematical treatment and easily available algorithms and software. This paper fills this gap by defining the method as maximizing the fourth-corner correlation between linear combinations, by providing novel algorithms, which demonstrate relationships with related methods, and by making a detailed study of possible biplots and associated approximations. The method is illustrated using ecological data on the abundances of species in sites and where the species are characterized by traits and sites by environmental variables. The trait data and environment data form the external constraints and the question is which traits and environmental variables are associated, how these associations drive species abundances and how they can be displayed in biplots. With microbiome data becoming widely available, these and related multivariate methods deserve more study as they might be routinely used in the future.     

Remote sensing: Using the spatial domain

Objects in the terrestrial environment interact differentially with electromagnetic radiation according to their essential physical, chemical and biological properties. This differential interaction is manifest as variability in scattered radiation according to wavelength, location, time, geometries of illumination and observation and polarization. If the population of scattered radiation could be measured, then estimation of these essential properties would be straightforward. The only problem would be linking such estimates to environmental variables of interest. This review paper is divided into three parts. Part 1 is an overview of the attempts that have been made to sample the five domains of scattered radiation (spectral, spatial, temporal, geometrical, polarization) and then to use the results of this sampling to estimate environmental variables of interest. Part one highlights three issues: first, that relationships between remotely sensed data and environmental variables of interest are indirect; second, our ability to estimate these environmental variables is dependent upon our ability to capture a sound representation of variability in scattered radiation and third, a considerable portion of the useful information in remotely sensed images resides in the spatial domain (within the relations between the pixels in the image). This final point is developed in Part 2 that explores ways in which the spatial domain is utilized to describe spatial variation in remotely sensed and ground data; to design optimum sampling schemes for image data and ground data and to increase the accuracy with which remotely sensed data can be used to estimate both discontinuous and continuous variables. Part 3 outlines two specific uses of information in the spatial domain; first, to select an optimum spatial resolution and second, to inform an image classification.     

Prediction of response of zooplankton biomass to climatic and oceanic changes

This paper examines the long-term variation in zooplankton biomass in response to climatic and oceanic changes, using a neural network as a nonlinear multivariate analysis method. Zooplankton data collected from 1951 to 1990 off the shore of northeastern Japan were analyzed. We considered patterns of the Kuroshio and the Oyashio, sea surface temperature, and meteorological parameters as environmental factors that affect zooplankton biomass. Back propagation neural networks were trained to generate mapping functions between environmental variables and zooplankton biomass. The performance of the network models was tested by varying the numbers of input and hidden units. Changes in zooplankton biomass could be predicted from environmental conditions. The neural network yielded predictions with smaller errors than those of predictions determined by linear multiple regression. The sensitivity analysis of networks was used to extract predictive knowledge. The air pressure, sea surface temperature, and some indices of atmospheric circulation were the primary factors for predictions. The patterns of the Kuroshio and the Oyashio demonstrated different effects among sea areas.     

GIS-based niche modeling for mapping species' habitat

Ecological "niche modeling" using presence-only locality data and large-scale environmental variables provides a powerful tool for identifying and mapping suitable habitat for species over large spatial extents. We describe a niche modeling approach that identifies a minimum (rather than an optimum) set of basic habitat requirements for a species, based on the assumption that constant environmental relationships in a species' distribution (i.e., variables that maintain a consistent value where the species occurs) are most likely to be associated with limiting factors. Environmental variables that take on a wide range of values where a species occurs are less informative because they do not limit a species' distribution, at least over the range of variation sampled. This approach is operationalized by partitioning Mahalanobis D2 (standardized difference between values of a set of environmental variables for any point and mean values for those same variables calculated from all points at which a species was detected) into independent components. The smallest of these components represents the linear combination of variables with minimum variance; increasingly larger components represent larger variances and are increasingly less limiting. We illustrate this approach using the California Gnatcatcher (Polioptila californica Brewster) and provide SAS code to implement it.     

A heuristic approach to predicting water beetle diversity in temporary and fluctuating waters

An understanding of the causal mechanisms and processes that shape macroinvertebrate communities at a local scale has important implications for the management and conservation of freshwater biodiversity. Here we compare the performance of linear and non-linear statistics to explore diversity-environment relationships using data from 76 temporary and fluctuating ponds in two regions of southern England. We focus on aquatic beetle assemblages, which have been shown to be excellent surrogates of wider freshwater macroinvertebrate diversity. Ponds in the region contained a rich coleopteran fauna, totaling 68 species, which provided an excellent model system with which to compare the performance of two non-linear procedures (artificial neural networks—ANNs and generalised additive models—GAMs) and one more traditional linear approach (Multiple linear regression—MLR) to modelling diversity-environment relationships. Of all approaches employed, the best fit was obtained using an ANN model with only four input variables (conductivity, turbidity, magnesium concentration and depth). This model accounted for 82% of the observed variability in Shannon diversity index across ponds. In contrast, the best GAM and MLR models only explained 50% and 14% of this variation, respectively. Contribution profile analysis of conductivity, turbidity, magnesium concentration and depth, obtained from the best fit ANN through a hierarchical cluster analysis, allowed the identification of direct and proxy effects in relation to the environmental variables measured in this study. In each case, distinct clusters of ponds were identified in contribution profile analysis, suggesting that ponds across the two regions fall into a number of discrete groups, whose beetle faunas respond in subtly yet significantly different ways to key environmental variables. Aquatic coleopteran diversity in ponds in the two regions appears to be driven at a local scale by changes in relatively few physicochemical gradients, which are related to diversity in a clearly non-linear manner.     

Evaluation of Biogeographic Classification Schemes for Conservation Planning: Application to New Zealand's Coastal Marine Environment

Abstract:  For many regions worldwide, multiple and often contrasting biogeographic classifications exist that are derived from a variety of taxa and techniques. This presents a challenge for managers who must choose appropriate large-scale spatial frameworks for systematic conservation planning. We demonstrate how systematically collected community data can be used to evaluate existing biogeographic classifications, identify the most appropriate metric for biogeographic patterns seen in other taxonomic groups, and develop an independent biogeographic classification scheme for systematic conservation planning. We evaluated 6 existing biogeographic classifications for New Zealand's nearshore marine environment with community-similarity metrics derived from abundance and presence–absence data for macroalgae (107 species) and mobile macroinvertebrates (44 species). The concordance between community metrics and the previous classifications was high, as indicated by a high multivariate classification success (CS) (74.3–98.3%). Subsequently, we carried out an independent classification analysis on each community metric to identify biogeographic units within a hierarchical spatial framework. The classification derived from macroalgal presence–absence data achieved the highest CS and could be used as a mesoscale classification scheme in which 11 regional groupings (i.e., bioregions) (CS = 73.8–84.8%) are nested within northern and southern biogeographic provinces (CS = 90.3–98.7%). These techniques can be used in systematic conservation planning to inform the design of representative and comprehensive networks of marine protected areas through evaluation of the current coverage of marine reserves in each bioregion. Currently, 0.22% of the territorial sea around mainland New Zealand is protected in no-take marine protected areas in which 0–1.5% of each bioregion represented.     

Fourth-corner generation of plant functional response groups

Plant functional response groups (PFGs) are now widely established as a tool to investigate plant—environment relationships. Different statistical methods to form PFGs are used in the literature. One way is to derive emergent groups by classifying species based on correlation of biological attributes and subjecting these groups to tests of response to environmental variables. Another way is to search for associations of occurrence data, environmental variables and trait data simultaneously. The fourth-corner method is one way to assess the relationships between single traits and habitat factors. We extended this statistical method to a generally applicable procedure for the generation of plant functional response groups by developing new randomization procedures for presence/absence data of plant communities. Previous PFG groupings used either predefined groups or emergent groups i.e. classifications based on correlations of biological attributes (Lavorel et al Trends Ecol Evol 12:474–478, 1997), of the global species pool and assessed their functional response. However, since not all PFGs might form emergent groups or may be known by experts, we used a permutation procedure to optimise functional grouping. We tested the method using an artificial test data set of virtual plants occurring in different disturbance treatments. Direct trait-treatment relationships as well as more complex associations are incorporated in the test data. Trait combinations responding to environmental variables could be clearly distinguished from non-responding combinations. The results are compared with the method suggested by Pillar (J Veg Sci 10:631–640) for the identification of plant functional groups. After exploring the statistical properties using an artificial data set, the method is applied to experimental data of a greenhouse experiment on the assemblage of plant communities. Four plant functional response groups are formed with regard to differences in soil fertility on the basis of the traits canopy height and spacer length.     

Making Consistent IUCN Classifications under Uncertainty

Abstract: The World Conservation Union (IUCN) defined a set of categories for conservation status supported by decision rules based on thresholds of parameters such as distributional range, population size, population history, and risk of extinction. These rules have received international acceptance and have become one of the most important decision tools in conservation biology because of their wide applicability, objectivity, and simplicity of use. The input data for these rules are often estimated with considerable uncertainty due to measurement error, natural variation, and vagueness in definitions of parameters used in the rules. Currently, no specific guidelines exist for dealing with uncertainty. Interpretation of uncertain data by different assessors may lead to inconsistent classifications because attitudes toward uncertainty and risk may have an important influence on the classification of threatened species. We propose a method of dealing with uncertainty that can be applied to the current IUCN criteria without altering the rules, thresholds, or intent of these criteria. Our method propagates the uncertainty in the input parameters and assigns the evaluated species either to a single category (as the current criteria do) or to a range of plausible categories, depending on the nature and extent of uncertainties.     

RIVPACS models for predicting the expected macroinvertebrate fauna and assessing the ecological quality of rivers

The European Union Water Framework Directive recognises the need for and value of biological monitoring. This paper reviews the modelling approach known as River Invertebrate Prediction and Classification System (RIVPACS for assessing the ecological quality of river sites using macroinvertebrate sampling. The RIVPACS philosophy is to develop statistical relationships between the fauna and the environmental characteristics of a large set of high quality reference sites which can be used to predict the macroinvertebrate fauna to be expected at any site in the absence of pollution or other environmental stress. The observed fauna at new test sites can then be compared with their site-specific expected fauna to derive indices of ecological quality. All methodological decisions in any such model development have implications for the reliability, precision and robustness of any resulting indices for assessing the ecological quality and ecological grade (‘status’) of individual river stretches. The choice of reference sites and environmental predictor variables, the site classification and discrimination methods, the estimation of the expected fauna, and indices for comparing the agreement, or lack of it, between the observed and expected fauna, are all discussed. The indices are assessed on the reference sites and on a separate test set of 340 sites, which are subject to a wide range of types and degrees of impairment.     

Invasion essentials: does secondary chemistry plasticity contribute to the invasiveness of Thymus vulgaris L.?

Increasing our understanding of spatial patterns of plant secondary chemistry variation may help us understand the mechanisms that underlie plant invasions involving species which exhibit chemical phenotypic variation. We analysed qualitative and quantitative variation in the essential oil terpenes produced by Thymus vulgaris L. (thyme) plants inside and at the edge of thyme-invaded plant communities on north- and south-facing slopes at ten sites across Central Otago, New Zealand. We tested the hypotheses that (1) there are foliar terpene composition quantitative and qualitative differences between the inside and edge of thyme-invaded communities and that these differences vary between north- and south-facing slopes and (2) that environmental variables affect the quantity and quality of thyme essential oil composition. Terpene concentrations were determined using ethanol extraction and GC FID/MS. Environmental data were collected from inside, at the edge and outside of thyme stands. We used multivariate statistical analysis to test differences in foliar terpene composition and linear modelling to investigate the effects of environmental variables on terpene composition. We found no evidence of a link between aspect- and invasion edge-related patterns of variation of thyme’s essential oil chemistry. Site-related differences for aspect and position in thyme stands indicated that thyme’s terpene production may be modulated by environmental factors such as soil moisture, clay, carbon, silt, sand, colloid, N and NH₄ ⁺. The role of thyme’s novel chemistry in the processes that underlie the spatial patterns of thyme’s successful invasion is modulated by environmental factors.     

色谱及联用技术在药物污染检测中的应用

医药品是一类新型环境污染物,由于其具有长期输入、环境中降解/去除缓慢、生物效应显著等特性被界定为"准持久性"污染物,受到社会各界的广泛关注.为更好了解该类污染物在环境中的迁移转化规律和生物效应,分析检测是必不可少的环节,本文就近年来我国运用色谱及联用技术检测药物污染的方法进行综述.     

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.     

Unraveling the complexities of variation in female mate preference for vertical bars in the swordtail, Xiphophorus cortezi

Investigations into the nature of mate choice suggest that variation in female mate preferences is often context dependent, varying in response to genetic and environmental influences on female condition as well as to external environmental stimuli. Determining whether variation in female mate preference is adaptive requires understanding the variables involved that produce this variation and how they interact. Comparative, multivariate studies of wild-caught adult females can be used as initial assessments of variation in female mate preferences, providing valuable insights into the parameters that influence female preferences under natural conditions. We examined variation in female preferences for the pigment pattern vertical bars across five populations of the swordtail, Xiphophorus cortezi. The populations we examined are genetically differentiated and varied in the frequency of males and females with bars. We also considered a variable indicative of within-individual variation (size, as influenced by age) and a variable that varies across individuals (genotype for vertical bars: barred or barless). Using Akaike information criterion, all candidate models explaining variation in strength of preference included female bar state, female size, and population. We suggest that a combination of genetic (bar state) and environmental (female size) conditions influenced how they responded to experience with both male phenotypes in X. cortezi. Future studies should examine the possibility that barred and barless females respond differently over an environmental gradient.     

Using discriminant analysis to determine the breaking criterion for an ISW propagating over a ridge

This study aims to develop methods that are capable of deciding the breaking criterion for an internal solitary wave (ISW) propagating over a submarine ridge. Laboratory experiments were conducted in a wave tank to measure ISWs propagating over a submarine ridge. The results suggest that the ISW-ridge interaction can be grouped according to three degrees of magnitude based on the blockage parameter ζ and the degree of blocking B. For classification reasons, we first present an alternative decision model for evaluating the interaction of ISWs with an underwater ridge in a two-layer system. This approach is based on a multivariate statistical method and discriminant analysis. Information obtained from the eigenvalues is used to combine different ratio measures which are defined according to every single input and output. The discriminant model effectively classifies units into distinct predefined groups. An experimental simulation is conducted to demonstrate the practical implementation of the ISW-ridge interaction. The results of the method applied in this example are statistically significant, demonstrating the effectiveness of the ISW-ridge interaction classification method.     

Responses of phytoplankton and periphytic diatoms to environmental factors in a tropical riverine reservoir北大核心CSCD

Riverine reservoirs have a short water retention time, which is ecologically more similar to that of rivers. Generally, phytoplankton-based approaches are used for lakes and periphytic diatom-based approach for rivers. To understand the differences in the responses of phytoplankton and periphytic diatoms to environmental variables for riverine reservoirs, we collected periphytic diatom samples on artificial substrata as well as phytoplankton samples from a tropical reservoir with a resident time less than 10 days. Our results showed that 131 phytoplankton species and 138 periphytic diatoms were detected; the variation of phytoplankton community was mainly reflected by the dominant species with a strong response to the environmental variables at a time scale, whereas the variation of periphytic diatom community was noted in both the species composition and the dominant species, with a strong response at spatial-temporal scales. The multivariate regression analysis and redundancy analysis showed that environmental factors have higher explanations for the variance of the periphytic diatom community (R2 = 0.27). Temperature was the key explanatory variable for phytoplankton, planktonic diatoms and periphytic diatoms (P < 0.01). However, dissolved oxygen and nitrate were also detected as significant explanatory factors associated with periphytic diatom community (P < 0.01). Thus, the periphytic diatoms were concluded to be more sensitive to environmental change and were associated with more environmental variables than phytoplankton. Periphytic diatoms appear to provide more ecological information than phytoplankton for riverine reservoirs. © 2018 Science Press. All rights reserved.     

Variation partitioning of species data matrices: estimation and comparison of fractions

Establishing relationships between species distributions and environmental characteristics is a major goal in the search for forces driving species distributions. Canonical ordinations such as redundancy analysis and canonical correspondence analysis are invaluable tools for modeling communities through environmental predictors. They provide the means for conducting direct explanatory analysis in which the association among species can be studied according to their common and unique relationships with the environmental variables and other sets of predictors of interest, such as spatial variables. Variation partitioning can then be used to test and determine the likelihood of these sets of predictors in explaining patterns in community structure. Although variation partitioning in canonical analysis is routinely used in ecological analysis, no effort has been reported in the literature to consider appropriate estimators so that comparisons between fractions or, eventually, between different canonical models are meaningful. In this paper, we show that variation partitioning as currently applied in canonical analysis is biased. We present appropriate unbiased estimators. In addition, we outline a statistical test to compare fractions in canonical analysis. The question addressed by the test is whether two fractions of variation are significantly different from each other. Such assessment provides an important step toward attaining an understanding of the factors patterning community structure. The test is shown to have correct Type I. error rates and good power for both redundancy analysis and canonical correspondence analysis.     

Ecological network analyses and their use for establishing reference domain in functional assessment of an estuary

We developed a framework to use ecological network analysis for functional assessment of large aquatic ecosystems in the context of ecosystem-based management. We established a reference domain for the Neuse River Estuary, North Carolina, USA, from changes over time. Four reference network models of the trophic structure of the estuary during early and late summers of 1997 and 1998 were constructed and analyzed. The estuary has experienced various symptoms of eutrophication during the past 20 years, including summer-time hypoxia and fish kills. The networks were used to quantify indices of nominal trophic dynamics and their variation. The ratio of biomass of nekton to that of macrobenthos, derived from network construction, was used to index severity of eutrophication and to promote accessibility of ecological network analysis to environmental management. The ratio increased from early to late summer, and network metrics demonstrated a variety of responses in association with that change. Some variables from network analysis, especially related to consumers, reflected some but not all of this change. Others reflected the most severe increase in the ratio in late summer 1997 when hypoxia was most extensive. We evaluated uncertainty and the modulating effects of hierarchy by comparing variation of input biomasses with integrative response variables. Relative variation in input variables was generally greater than that of the integrative response variables as predicted by hierarchy theory. Ecological network analysis has previously served as support for ecosystem-based management of large aquatic systems with some success. However, its use can be enhanced by making it more accessible to environmental managers and policy makers. Ways to do this include promoting simple metrics from network construction and explicitly associating network analysis to concepts familiar to the management community, such as functional assessment and reference.     

Implications of Phylogenetic Studies for Conservation of Genetic Diversity in Shiitake Mushrooms

We consider the impact of recent molecular phylogenetic analyses on conservation issues for shiitake mushrooms. Bused on mating compatibility, shiitake has been considered a single species, Lentinula edodes sensu lato . Phylogenetic analyses of ribosomal RNA internal transcribed spacers (Hibbett et al. 1995), however, suggest that shiitake is composed of four distinct lineages. Thus, under a phylogenetic species concept, four species of shiitake should be recognized. A classification of shiitake based on a phylogenetic species concept would convey information about evolutionary relationships and distribution of genetic variation, whereas a classification based on a biological species concept would lack such information. This is a clear case in which new insights into phylogenetic relationships can be used to target areas for conservation action and formulate international agricultural policies. At the heart of our argument is the belief that the basic units of both taxonomy and conservation should be unique evolutionary lineages of organisms identified through phylogenetic studies.