Effect of Land Cover and Ecosystem Mapping on Ecosystem‐Risk Assessment in the Little Karoo,South Africa

Extinction‐risk assessments aim to identify biological diversity features threatened with extinction. Although largely developed at the species level, these assessments have recently been applied at the ecosystem level. In South Africa, national legislation provides for the listing and protection of threatened ecosystems. We assessed how land‐cover mapping and the detail of ecosystem classification affected the results of risk assessments that were based on extent of habitat loss. We tested 3 ecosystem classifications and 4 land‐cover data sets of the Little Karoo region, South Africa. Degraded land (in particular, overgrazed areas) was successfully mapped in just one of the land‐cover data sets. From <3% to 25% of the Little Karoo was classified as threatened, depending on the land‐cover data set and ecosystem classification applied. The full suite of threatened ecosystems on a fine‐scale map was never completely represented within the spatial boundaries of a coarse‐scale map of threatened ecosystems. Our assessments highlight the importance of land‐degradation mapping for the listing of threatened ecosystems. On the basis of our results, we recommend that when budgets are constrained priority be given to generating more‐detailed land‐cover data sets rather than more‐detailed ecosystem classifications for the assessment of threatened ecosystems. El Efecto de la Cobertura Terrestre y el Mapeo de Ecosistemas en la Valoración de Riesgos en los Ecosistemas en Little Karoo, Sudáfrica     

Aquatic metabolism and ecosystem health assessment using dissolved O2 stable isotope diel curves.

Dissolved O2 concentration and delta18O-O2 diel curves can be combined to assess aquatic photosynthesis, respiration, and metabolic balance, and to disentangle some of the confounding factors associated with interpretation of traditional O2 concentration curves. A dynamic model is used to illustrate how six key environmental and biological parameters interact to affect diel O2 saturation and delta18O-O2 curves, thereby providing a fundamental framework for the use of delta18O-O2 in ecosystem productivity studies. delta18O-O2 provides information unavailable from concentration alone because delta18O-O2 and saturation curves are not symmetrical and can be used to constrain gas exchange and isotopic fractionation by eliminating many common assumptions. Changes in key parameters affect diel O2 saturation and delta18O-O2 curves as follows: (1) an increase in primary production and respiration rates increases the diel range of O2 saturation and delta18O-O2 and decreases the mean delta18O-O2 value; (2) a decrease in the primary production to respiration ratio (P:R) decreases the level of O2 saturation and increases the delta18O-O2 values; (3) an increase in the gas exchange rate decreases the diel range of O2 saturation and delta18O-O2 values and moves the mean O2 saturation and delta18O-O2 values toward atmospheric equilibrium; (4) a decrease in strength of the respiratory isotopic fractionation (alphaR closer to 1) has no effect on O2 saturation and decreases the delta18O-O2 values; (5) an increase in the delta18O of water has no effect on O2 saturation and increases the minimum (daytime) delta18O-O2 value; and (6) an increase in temperature reduces O2 solubility and thus increases the diel range of O2 saturation and delta18O-O2 values. Understanding the interplay between these key parameters makes it easier to decipher the controls on O2 and delta18O-O2, compare aquatic ecosystems, and make quantitative estimates of ecosystem metabolism. The photosynthesis to respiration to gas exchange ratio (P:R:G) is better than the P:R ratio at describing and assessing the vulnerability of aquatic ecosystems under various environmental stressors by providing better constrained estimates of ecosystem metabolism and gas exchange.     

A Global Indicator for Biological Invasion

Abstract:  "Trends in invasive alien species" is one of only two indicators of threat to biodiversity that form part of the Convention on Biological Diversity's (CBD) framework for monitoring progress toward its "2010 target" (i.e., the commitment to achieve by 2010 a significant reduction in the current rate of biodiversity loss). To date, however, there is no fully developed indicator for invasive alien species (IAS) that combines trends, derived from a standard set of methods, across species groups, ecosystems, and regions. Here we provide a rationale for the form and characteristics of an indicator of trends in IAS that will meet the 2010 framework goal and targets for this indicator. We suggest single and composite indicators that include problem-status and management-status measures that are designed to be flexible, readily disaggregated, and as far as possible draw on existing data. The single indicators at national and global scales are number of IAS and numbers of operational management plans for IAS. Global trends in IAS are measured as the progress of nations toward the targets of stabilizing IAS numbers and the implementation of IAS management plans. The proposed global indicator thus represents a minimum information set that most directly addresses the indicator objective and simultaneously aims to maximize national participation. This global indicator now requires testing to assess its accuracy, sensitivity, and tractability. Although it may not be possible to achieve the desired objective for a global indicator of biological invasion by 2010 as comprehensively as desired, it seems possible to obtain trend estimates for a component of the taxa, ecosystems, and regions involved. Importantly, current indicator development initiatives will also contribute to developing the mechanisms necessary for monitoring global trends in IAS beyond 2010.     

Multiple sources of predictive uncertainty in modeled estimates of net ecosystem CO2 exchange

Net ecosystem CO₂ exchange (NEE) is typically measured directly by eddy covariance towers or is estimated by ecosystem process models, yet comparisons between the data obtained by these two methods can show poor correspondence. There are three potential explanations for this discrepancy. First, estimates of NEE as measured by the eddy-covariance technique are laden with uncertainty and can potentially provide a poor baseline for models to be tested against. Second, there could be fundamental problems in model structure that prevent an accurate simulation of NEE. Third, ecosystem process models are dependent on ecophysiological parameter sets derived from field measurements in which a single parameter for a given species can vary considerably. The latter problem suggests that with such broad variation among multiple inputs, any ecosystem modeling scheme must account for the possibility that many combinations of apparently feasible parameter values might not allow the model to emulate the observed NEE dynamics of a terrestrial ecosystem, as well as the possibility that there may be many parameter sets within a particular model structure that can successfully reproduce the observed data. We examined the extent to which these three issues influence estimates of NEE in a widely used ecosystem process model, Biome-BGC, by adapting the generalized likelihood uncertainty estimation (GLUE) methodology. This procedure involved 400,000 model runs, each with randomly generated parameter values from a uniform distribution based on published parameter ranges, resulting in estimates of NEE that were compared to daily NEE data from young and mature Ponderosa pine stands at Metolius, Oregon. Of the 400,000 simulations run with different parameter sets for each age class (800,000 total), over 99% of the simulations underestimated the magnitude of net ecosystem CO₂ exchange, with only 4.07% and 0.045% of all simulations providing satisfactory simulations of the field data for the young and mature stands, even when uncertainties in eddy-covariance measurements are accounted for. Results indicate fundamental shortcomings in the ability of this model to produce realistic carbon flux data over the course of forest development, and we suspect that much of the mismatch derives from an inability to realistically model ecosystem respiration. However, difficulties in estimating historic climate data are also a cause for model-data mismatch, particularly in a highly ecotonal region such as central Oregon. This latter difficulty may be less prevalent in other ecosystems, but it nonetheless highlights a challenge in trying to develop a dynamic representation of the terrestrial biosphere.     

Spatial patterns of forest characteristics in the western United States derived from inventories.

In the western United States, forest ecosystems are subject to a variety of forcing mechanisms that drive dynamics, including climate change, land-use/land-cover change, atmospheric pollution, and disturbance. To understand the impacts of these stressors, it is crucial to develop assessments of forest properties to establish baselines, determine the extent of changes, and provide information to ecosystem modeling activities. Here we report on spatial patterns of characteristics of forest ecosystems in the western United States, including area, stand age, forest type, and carbon stocks, and comparisons of these patterns with those from satellite imagery and simulation models. The USDA Forest Service collected ground-based measurements of tree and plot information in recent decades as part of nationwide forest inventories. Using these measurements together with a methodology for estimating carbon stocks for each tree measured, we mapped county-level patterns across the western United States. Because forest ecosystem properties are often significantly different between hardwood and softwood species, we describe patterns of each. The stand age distribution peaked at 60-100 years across the region, with hardwoods typically younger than softwoods. Forest carbon density was highest along the coast region of northern California, Oregon, and Washington and lowest in the arid regions of the Southwest and along the edge of the Great Plains. These results quantify the spatial variability of forest characteristics important for understanding large-scale ecosystem processes and their controlling mechanisms. To illustrate other uses of the inventory-derived forest characteristics, we compared them against examples of independently derived estimates. Forest cover compared well with satellite-derived values when only productive stands were included in the inventory estimates. Forest types derived from satellite observations were similar to our inventory results, though the inventory database suggested more heterogeneity. Carbon stocks from the Century model were in good agreement with inventory results except in the Pacific Northwest and part of the Sierra Nevada, where it appears that harvesting and fire in the 20th century (processes not included in the model runs) reduced measured stand ages and carbon stocks compared to simulations.     

Density derived estimates of standing crop and net primary production in the giant kelp <Emphasis Type="Italic">Macrocystis pyrifera</Emphasis>

Assemblages of macroalgae are believe to be among the most productive ecosystems in the world, yet difficulties in obtaining direct estimates of biomass and primary production have led to few macroalgal data sets from which the consequences of long-term change can be assessed. We evaluated the validity of using two easily measured population variables (frond density and plant density) to estimate the more difficult to measure variables of standing crop and net primary production (NPP) in the giant kelp Macrocystis pyrifera off southern California. Standing crop was much more strongly correlated to frond density than to plant density. Frond density data collected in summer were particularly useful for estimating annual NPP, explaining nearly 80% of the variation in the NPP from year to year. Data on frond densities also provided a relatively good estimate of seasonal NPP for the season that the data were collected. In contrast, estimates of seasonal and annual NPP derived from plant density data were less reliable. These results indicate that data on frond density collected at the proper time of year can make assessments of NPP by giant kelp more tractable. They also suggest that other easily measured variables that are strongly correlated with standing crop, such as surface canopy area, might serve as similarly useful proxies of NPP.     

Biological benthic tools as indicators of coastal marine ecosystems health

Coastal marine ecosystems are increasingly subjected to environmental stress and degradation due to pollution. Several research programmes have addressed this problem and produced relevant data sets for specific areas, often including consistent sets of environmental and biological variables. The value of existing information gathered from these types of data can be largely increased by combining them into a common data set to determine globally applicable relationships. To perform this exercise, the Intergovernmental Oceanographic Commission (IOC) of UNESCO has recently formed the Ad hoc Study Group on Benthic Indicators (http://www.ioc.unesco.org/benthicindicators) with the aim of developing robust indicators of benthic health. In this paper, initial products and ongoing activities of this international initiative are described and discussed. An expansion of initial IOC/UNESCO research on benthic fauna-organic carbon relationships is also presented. As part of this follow-up research, the relationship between total organic carbon concentrations of sediment and abundance, biomass and species diversity of benthic macrofauna was evaluated using data sets from 2 different regions of the world comprising 3 different coastal marine environments. The ability of identifying threshold levels in selected variables that could serve as indicators of related adverse environmental conditions leading to stress in the benthos is envisaged within the frame of a larger joint analysis, carried out by the IOC/UNESCO Study Group on Benthic Indicators, of merged data sets from several coastal regions worldwide.     

Biological benthic tools as indicators of coastal marine ecosystems health

Coastal marine ecosystems are increasingly subjected to environmental stress and degradation due to pollution. Several research programmes have addressed this problem and produced relevant data sets for specific areas, often including consistent sets of environmental and biological variables. The value of existing information gathered from these types of data can be largely increased by combining them into a common data set to determine globally applicable relationships. To perform this exercise, the Intergovernmental Oceanographic Commission (IOC) of UNESCO has recently formed the Ad hoc Study Group on Benthic Indicators (http://www.ioc.unesco.org/benthicindicators) with the aim of developing robust indicators of benthic health. In this paper, initial products and ongoing activities of this international initiative are described and discussed. An expansion of initial IOC/UNESCO research on benthic fauna-organic carbon relationships is also presented. As part of this follow-up research, the relationship between total organic carbon concentrations of sediment and abundance, biomass and species diversity of benthic macrofauna was evaluated using data sets from 2 different regions of the world comprising 3 different coastal marine environments. The ability of identifying threshold levels in selected variables that could serve as indicators of related adverse environmental conditions leading to stress in the benthos is envisaged within the frame of a larger joint analysis, carried out by the IOC/UNESCO Study Group on Benthic Indicators, of merged data sets from several coastal regions worldwide.     

Estimating the normal background rate of species extinction

A key measure of humanity's global impact is by how much it has increased species extinction rates. Familiar statements are that these are 100–1000 times pre‐human or background extinction levels. Estimating recent rates is straightforward, but establishing a background rate for comparison is not. Previous researchers chose an approximate benchmark of 1 extinction per million species per year (E/MSY). We explored disparate lines of evidence that suggest a substantially lower estimate. Fossil data yield direct estimates of extinction rates, but they are temporally coarse, mostly limited to marine hard‐bodied taxa, and generally involve genera not species. Based on these data, typical background loss is 0.01 genera per million genera per year. Molecular phylogenies are available for more taxa and ecosystems, but it is debated whether they can be used to estimate separately speciation and extinction rates. We selected data to address known concerns and used them to determine median extinction estimates from statistical distributions of probable values for terrestrial plants and animals. We then created simulations to explore effects of violating model assumptions. Finally, we compiled estimates of diversification—the difference between speciation and extinction rates for different taxa. Median estimates of extinction rates ranged from 0.023 to 0.135 E/MSY. Simulation results suggested over‐ and under‐estimation of extinction from individual phylogenies partially canceled each other out when large sets of phylogenies were analyzed. There was no evidence for recent and widespread pre‐human overall declines in diversity. This implies that average extinction rates are less than average diversification rates. Median diversification rates were 0.05–0.2 new species per million species per year. On the basis of these results, we concluded that typical rates of background extinction may be closer to 0.1 E/MSY. Thus, current extinction rates are 1,000 times higher than natural background rates of extinction and future rates are likely to be 10,000 times higher. Estimación de la Tasa Normal de Extinción de Especies     

Characterization of biological responses under different environmental conditions: A hierarchical modeling approach

A natural river system is organized as a nested hierarchy of interconnected habitats with specific environmental conditions to which the biological community has adapted. Due to this hierarchical structure, identifying the role of different stressors on the biological community is a formidable task. Efforts trying to link stressors to biological integrity have always been bound to the geographic scale of the selected study area, leading to scale-specific results. In this research, an attempt is made to lift this limitation and develop a hierarchical, scale-sensitive methodology that can identify the significant environmental stressors to the biological community at different scales. Sites with similar background environmental conditions are clustered using self-organizing maps (SOM). This is used to identify stressors which affect the biological community throughout the area of study - called environmental gradients or large-scale stressors. Subsequently, these clusters of similar observations (sampling sites) are progressively sub-divided using environmental variables with a significant but localized effect on the biological community - called small-scale stressors. A parent group of sites is split only when the resulting sub-groups have significantly different biological responses. At the end of this recursive sites decomposition procedure, the original set of observations is organized as a tree of environmentally homogeneous groups of observations characterized by unique biological responses to multiple stressors with different geographic extents. The developed hierarchical analysis methodology has been validated using a large-size dataset of environmental observations from the State of Ohio. Our results show that habitat degradation and increased nutrient loading are the large-scale stressors with a widespread impact in Ohio. Other stressors, such as heavy metals, pH or nitrate concentrations have significant albeit localized effects on biological integrity.     

Assessing water quality in the Ceyhan River basin (Turkey) with the use of aquatic macrophytes

As a major biotic component of many lotic ecosystems, macrophytes consist a major component of running waters are often used as indicators within the European Water Framework Directive (WFD) to establish ecological quality. In this study, we investigated macrophyte community structure (e.g. composition, abundance and diversity) in Ceyhan River Basin located in the Southeastern Anatolian Region in Turkey. Data was collected during 2014–2015 from river sites located throughout the basin to evaluate the relationship between aquatic vegetation and river physico-chemical factors. The ecological status of the river basin was also calculated based on Macrophyte Biological Index for River (IBMR). In total, 33 macrophyta taxa were observed. According to their biological classification (life form), filamentous algae (FA), free floating (FF), floating leaved (FL) and submersed (S) macrophytes reached their maximum abundance value in summer, while emergent (E) macrophytes were at their maximum abundance in both summer and autumn. The ecological status of the Ceyhan River basin ranged from moderate to bad. The values found are reasonably comparable to IBMR scores recorded in rivers of other Mediterranean countries. IBMR index may be suitable to some extent to establish a basis for ecological quality assessment in Turkish River systems.     

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.     

The role of local and landscape level measures of greenness in modelling boreal plant species richness

Measurements of primary productivity and its heterogeneity based on satellite images can provide useful estimates of species richness and distribution patterns. However, species richness at a given site may depend not only on local habitat quality and productivity but also on the characteristics of the surrounding landscape. In this study we investigated whether the predictions of species richness of plant families in northern boreal landscape in Finland can be improved by incorporating greenness information from the surrounding landscape, as derived from remotely sensed data (mean, maximum, standard deviation and range values of NDVI derived from Landsat ETM), into local greenness models. Using plant species richness data of 28 plant families from 440 grid cells of 25 ha in size, generalized additive models (GAMs) were fitted into three different sets of explanatory variables: (1) local greenness only, (2) landscape greenness only, and (3) combined local and landscape greenness. The derived richness–greenness relationships were mainly unimodal or positively increasing but varied between different plant families, and depended also on whether greenness was measured as mean or maximum greenness. Incorporation of landscape level greenness variables improved significantly both the explanatory power and cross-validation statistics of the models including only local greenness variables. Landscape greenness information derived from remote sensing data integrated with local information has thus the potentiality to improve predictive assessments of species richness over extensive and inaccessible areas, especially in high-latitude landscapes. Overall, the significant relationship between plants and surrounding landscape quality detected here suggests that landscape factors should be considered in preserving species richness of boreal environments, as well as in conservation planning for biodiversity in other environments.     

Four challenges to an effective national nature assessment

Comprehensive biodiversity assessments play an essential role in strengthening global and national conservation strategies. The recently announced first U.S. National Nature Assessment (NNA) provides an unparalleled opportunity to comprehensively review status and trends of biodiversity at all levels. This broad context can help in the coordination of actions to conserve individual species and ecosystems. The scientific assessments that informed the Kunming–Montreal Global Biodiversity Framework adopted at the 2022 Convention on Biological Diversity (CBD) conference of parties provide models for synthesizing information on trends at multiple levels of biodiversity, including decline in abundance and distribution of species, loss of populations and genetic diversity, and degradation and loss of ecosystems and their services. The assessments then relate these trends to data on drivers of biodiversity loss and pathways to their mitigation. The U.S. NNA can augment such global analyses and avoid the pitfalls encountered by previous U.S. efforts by ensuring policy-relevant design, data accessibility, and inclusivity in process and product and by incorporating spatial data relevant to national and subnational audiences. Although the United States is not formally a CBD party, an effective NNA should take full advantage of the global context by including indicators adopted at the 2022 meeting and incorporating an independent review mechanism that supports periodic stocktaking and ratcheting up of ambition in response to identified shortfalls in stemming biodiversity loss. The challenges to design of an effective U.S. assessment are relevant globally as nations develop assessments and reporting to support the new global biodiversity framework's targets. By considering and incorporating the diverse ways in which society values and benefits from nature, such assessments can help bridge the gap between research and conservation practice and communicate the extent of the biodiversity crisis to the public, fostering broad-based support for transformative change in humanity's relationship to the natural world.     

Forecasting the Spread of Invasive Rainbow Smelt in the Laurentian Great Lakes Region of North America

Abstract:  Rainbow smelt ( Osmerus mordax ) have invaded many North American lakes, often resulting in the extirpation of native fish populations. Yet, their invasion is incipient and provides the rationale for identifying ecosystems likely to be invaded and where management and prevention efforts should be focused. To predict smelt presence and absence, we constructed a classification-tree model based on habitat data from 354 lakes in the native range for smelt in southern Maine. Maximum lake depth, lake area, and Secchi depth (surrogate measure of lake productivity) were the most important predictors. We then used our model to identify lakes vulnerable to invasion in three regions outside the smelt's native range: northern Maine (52 of 244 lakes in the non-native range), Ontario (4447 of 8110), and Wisconsin (553 of 5164). We further identified a subset of lakes with a strong potential for impact (potential–impact lakes) based on the presence of fish species that are affected by rainbow smelt. Ninety-four percent of vulnerable lakes in the non-native range in Maine are also potential–impact lakes, as are 94% and 58% of Ontario and Wisconsin's vulnerable lakes, respectively. Our modeling approach can be applied to other invaders and regions to identify invasion-prone ecosystems, thus aiding in the management of invasive species and the efficient allocation of invasive species mitigation and prevention resources.     

Derivation of the Extrinsic Values of Biological Diversity from Its Intrinsic Value and of Both from the First Principles of Evolution

Conservation ethics have been based on 2 philosophical value systems: extrinsic value (defined broadly to include all values that derive from something external to the thing valued) and intrinsic value. Valuing biological diversity on the basis of an extrinsic value system is problematic because measurement is often difficult; extrinsic value changes as spatial or temporal scales change; extrinsic value differs on the basis of external factors; some species have trivial or negative extrinsic values; and extrinsic value varies across human cultures and societies and with such factors as socioeconomic conditions, individual experiences, and educational backgrounds. Valuing biological diversity on the basis of an intrinsic value system also poses challenges because intrinsic value can be seen as a disguised form of human extrinsic value; intrinsic value is initially ambiguous as to which objects or characteristics of biological diversity are to being valued; all aspects of biological diversity (e.g., species and ecosystems) are transitory; species and ecosystems are not static concrete entities; and intrinsic value of one species is often in conflict with the intrinsic value of other species. Extrinsic and intrinsic value systems share a common origin, such that extrinsic values are always derived from intrinsic value and life mutely expresses both intrinsic and extrinsic values—these are derived from and are products of biological evolution. Probing the values that underlie conservation helps the community clearly articulate its aims. Derivación de los Valores Extrínsecos de la Biodiversidad a Partir de sus Valores Intrínsecos y de Ambos a Partir de los Primeros Principios de la Evolución     

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.