The selection, testing and application of terrestrial insects as bioindicators

Although the uses and merits of terrestrial insects as indicators have been extensively discussed, there is a lack of clear definition, goal directedness and hypothesis testing in studies in the field. In an attempt to redress some of these issues and outline an approach for further studies, three categories of terrestrial insect indicators, corresponding to differences in their application, are proposed, i.e. environmental, ecological and biodiversity indicators. The procedures in terrestrial insect bioindicator studies should start with a clear definition of the study objectives and proposed use of the bioindicator, as well as with a consideration of the scale at which the study is to be carried out. Bioindication studies are conducted at a variety of spatial and temporal scales within the context of earth-system processes, but the objectives of the study will largely determine the scale at which it would be optimally conducted. There is a tendency for studies to be conducted below their space-time scaling functions, giving them apparent predictability. The selection of potential indicator taxa or groups is then based on a priori suitability criteria, the identification of predictive relationships between the indicator and environmental variables and, most importantly, the development and testing of hypotheses according to the correlative patterns found. Finally, recommendations for the use of the indicator in monitoring should be made. Although advocating rigorous, long-term protocols to identify indicators may presently be questionable in the face of the urgency with which conservation decisions have to be made, this approach is critical if bioindicators are to be used with any measurable degree of confidence.     

Spatial factors contribute to benthic diatom structure in streams across spatial scales: Considerations for biomonitoring

Diatoms are widely used in the biological monitoring of streams because they are strong responders to environmental change, but dispersal and spatial factors can play important and potentially confounding roles in the presence, absence, and abundance of species along with characterizing species–environment relationships. To examine how spatial factors affect diatom community structure and biomonitoring, multiple scales were sampled including the Western Allegheny Plateau (n = 58), Leading Creek watershed (n = 18), and the adjacent Shade River watershed (n = 21) in southeast Ohio. Partitioning of spatial, environmental, and spatially-structured environmental variation was conducted on diatom assemblages and on diatom metrics used in biomonitoring. At the regional scale, diatom assemblages and metrics had strong relationships with agricultural (e.g., significant correlations with nutrients, conductivity, and pasture/row crops in the watershed) and alkalinity gradients. Diatom assemblages and metrics in both watersheds were strongly associated with acid mine drainage (AMD) impacts, and when spatial factors were set as covariables in CCAs, relationships with AMD gradients became even stronger, indicating the need to consider how spatial factors could reduce the strength of diatom-environment relationships. Metrics calculated at all scales had very little variation explained exclusively by spatial factors, likely because multiple species are combined into a simplified metric that reduces the effects of species dispersal. Local environmental variables accounted for 57, 42, and 42% of the total variation explained (TVE), and spatial variables accounted for 28, 31, and 37% of the TVE in the regional, Leading Creek, and Shade River datasets, respectively. The amounts of variation in diatom assemblages explained solely by spatial factors at these scales were substantial and similar to what has been reported at continental, national, and large regional (Level I Omernik ecoregions) scales (approximately 1/3 of TVE). Although amounts of variation explained are similar across scales, processes underlying the spatial structure likely differ. In addition to describing ecological patterns, recognizing the potential influence of spatial factors could improve the identification and management of environmental problems at a range of scales, as well as aid in the development of new research questions and hypotheses aimed at exploring factors that could explain portions of the spatially explicit variation.     

Counting on β-Diversity to Safeguard the Resilience of Estuaries

Coastal ecosystems are often stressed by non-point source and cumulative effects that can lead to local-scale community homogenisation and a concomitant loss of large-scale ecological connectivity. Here we investigate the use of β-diversity as a measure of both community heterogeneity and ecological connectivity. To understand the consequences of different environmental scenarios on heterogeneity and connectivity, it is necessary to understand the scale at which different environmental factors affect β-diversity. We sampled macrofauna from intertidal sites in nine estuaries from New Zealand’s North Island that represented different degrees of stress derived from land-use. We used multiple regression models to identify relationships between β-diversity and local sediment variables, factors related to the estuarine and catchment hydrodynamics and morphology and land-based stressors. At local scales, we found higher β-diversity at sites with a relatively high total richness. At larger scales, β-diversity was positively related to γ-diversity, suggesting that a large regional species pool was linked with large-scale heterogeneity in these systems. Local environmental heterogeneity influenced β-diversity at both local and regional scales, although variables at the estuarine and catchment scales were both needed to explain large scale connectivity. The estuaries expected a priori to be the most stressed exhibited higher variance in community dissimilarity between sites and connectivity to the estuary species pool. This suggests that connectivity and heterogeneity metrics could be used to generate early warning signals of cumulative stress.     

Short‐term effects of a severe drought on avian diversity and abundance in a Pampas Agroecosystem

Agroecosystems are naturally variable at different scales showing strong environmental variations through time and in space. Therefore, temporal dynamics should be taken into account to understand the species‐habitat relationship and provide information for biodiversity management. Droughts are climatic events that introduce variations in environmental conditions by reducing food resources and are increasing in severity and frequency due to global climate change. In 2008, a severe drought occurred in the argentine Pampas, which allowed us to test its short‐term effect on avian diversity patterns according to different land uses. Also, we could test how variations in net primary productivity affected bird populations and explore the usefulness of this ecological process as an indicator of ecosystem service supply applicable to different ecological contexts. We surveyed bird diversity in two consecutive years (2007–2008) in the Rolling Pampas of Argentina, and we assessed environmental attributes at two spatial scales. We explored the relationship between bird diversity and an ecosystem services provision index (ESPI) that uses a proxy of net primary productivity and its intra‐annual variation to test its adequacy in the changing conditions, we conducted our surveys. Results showed that drought affected negatively both species richness and abundance as a consequence of net primary productivity reductions. There was not a clear association of diversity changes with land use, and it is probable that the effect of drought is a complex combination of productivity, land use and spatial scale. ESPI proved robust in front of the environmental changes, and its predictive capacity was better at larger scales. These results are promising for the assessment of ecosystem services provision in a context of global climate changes. Abstract in Spanish is available with online material.     

Ancient woodland indicators signal the climate change risk for dispersal-limited species

The climate change risk to biodiversity operates alongside a range of anthropogenic pressures. These include habitat loss and fragmentation, which may prevent species from migrating between isolated habitat patches in order to track their suitable climate space. Predictive modelling has advanced in scope and complexity to integrate: (i) projected shifts in climate suitability, with (ii) spatial patterns of landscape habitat quality and rates of dispersal. This improved ecological realism is suited to data-rich model species, though its broader generalisation comes with accumulated uncertainties, e.g. incomplete knowledge of species response to variable habitat quality, parameterisation of dispersal kernels etc. This study adopts ancient woodland indicator species (lichen epiphytes) as a guild that couples relative simplicity with biological rigour. Subjectively-assigned indicator species were statistically tested against a binary habitat map of woodlands of known continuity (>250 yr), and bioclimatic models were used to demonstrate trends in their increased/decreased environmental suitability under conditions of ‘no dispersal’. Given the expectation of rapid climate change on ecological time-scales, no dispersal for ancient woodland indicators becomes a plausible assumption. The risk to ancient woodland indicators is spatially structured (greater in a relative continental compared to an oceanic climatic zone), though regional differences are weakened by significant variation (within regions) in woodland extent. As a corollary, ancient woodland indicators that are sensitive to projected climate change scenarios may be excellent targets for monitoring climate change impacts for biodiversity at a site-scale, including the outcome of strategic habitat management (climate change adaptation) designed to offset risk for dispersal-limited species.     

Developing a systematic simulation-based approach for selecting indicators in strategic cumulative effects assessments with multiple environmental valued components

Indicator selection is a critical step in conducting effective strategic cumulative effects assessments. Selecting an appropriate set of indicators to represent multiple and sometimes disparate values is particularly challenging because the interpretation of impacts depends on indicator roles and relationships among indicators. However, systematic approaches for selection of indicators for strategic cumulative effects assessments (CEA) are unclear. For a 909,000 ha case study area involving 214 watersheds in coastal British Columbia, we defined a suite of twenty indicators linked to six Valued Components (VCs) that could be forecasted for forest, riparian and species at risk as three key values consistent with present land-use planning policies in British Columbia, Canada. We used spatio-temporal process-based models to project and integrate the stressor–response relationships between forest harvesting and run-of-river power resource management activities and the suite of selected indicators. For a likely development scenario, we assessed the correlative structure among projected indicator responses and, using a PCA-based analysis of outcomes, identified both patterns of potential redundancies and ecological processes linking indicators and dominant processes influencing VCs. Our results suggest that strategic CEAs will benefit if indicator selections are not made independently for each VC. Identifying the type of indicator, i.e., pressure or condition, and scale of its representation was important in determining if assessed impacts for individual indicators could be appropriately integrated to quantify overall impacts in the landscape. Consideration of indicator–indicator relationships both within and among VCs clarifies the influences of spatial scale, potential redundancies among indicators, and the role of underlying ecological processes in interpreting and aggregating indicator responses. Our case study demonstrates that relative scales of ecological processes, disturbances and management actions can limit how cohesive the interpretations of impacts may be across VCs in strategic CEA. Analysis of correlative structures among the twenty indicators suggested criteria-based statistical redundancies occurred between only two indicator pairs, however PCA suggested three ecological processes (road disturbance, Spotted Owl habitat state, retention and recruitment of old forest) were operating to relate behaviors of multiple indicators. Careful consideration of the interacting roles of ecological processes as they relate to values is required when determining appropriate indicators and designing how best to aggregate indicator results into an effective strategic CEA. A three step systematic and generalizable approach to forecasting present and future states of indicators will improve efficiencies and effectiveness of strategic CEA.     

Latitudinal patterns of forest ecosystem stability across spatial scales as affected by biodiversity and environmental heterogeneity

Our planet is facing a variety of serious threats from climate change that are unfolding unevenly across the globe. Uncovering the spatial patterns of ecosystem stability is important for predicting the responses of ecological processes and biodiversity patterns to climate change. However, the understanding of the latitudinal pattern of ecosystem stability across scales and of the underlying ecological drivers is still very limited. Accordingly, this study examines the latitudinal patterns of ecosystem stability at the local and regional spatial scale using a natural assembly of forest metacommunities that are distributed over a large temperate forest region, considering a range of potential environmental drivers. We found that the stability of regional communities (regional stability) and asynchronous dynamics among local communities (spatial asynchrony) both decreased with increasing latitude, whereas the stability of local communities (local stability) did not. We tested a series of hypotheses that potentially drive the spatial patterns of ecosystem stability, and found that although the ecological drivers of biodiversity, climatic history, resource conditions, climatic stability, and environmental heterogeneity varied with latitude, latitudinal patterns of ecosystem stability at multiple scales were affected by biodiversity and environmental heterogeneity. In particular, α diversity is positively associated with local stability, while β diversity is positively associated with spatial asynchrony, although both relationships are weak. Our study provides the first evidence that latitudinal patterns of the temporal stability of naturally assembled forest metacommunities across scales are driven by biodiversity and environmental heterogeneity. Our findings suggest that the preservation of plant biodiversity within and between forest communities and the maintenance of heterogeneous landscapes can be crucial to buffer forest ecosystems at higher latitudes from the faster and more intense negative impacts of climate change in the future.     

The relative importance of spatial and environmental processes in distribution of benthic chironomid larvae within a large and shallow lake

Although chironomids are popular model organisms in ecological research and indicators of bioassessment, the relative role of dispersal and environmental filtering in their community assembly is still poorly known, especially at fine spatial scales. In this study, we applied a metacommunity framework and used various statistical tools to examine the relative role of spatial and local environmental factors in distribution of benthic chironomid taxa and their assemblages in large and shallow Lake Balaton, Hungary. Contrary to present predictions on the metacommunity organisation of aquatic insects with winged terrestrial adults, we found that dispersal limitation can considerably affect distribution of chironomids even at lake scale. However, we also revealed the predominant influence of environmental filtering, and strong taxa–environment relationships were observed especially along sediment type, sediment organic matter content and macrophyte coverage gradients. We account that identified reference conditions and assemblages along with specified optima and tolerances of the abundant taxa can contribute to our understanding of chironomid ecology and be utilised in shallow lake bioassessment. Further, we propose that predictive models of species–environment relationships should better take into account pure spatial structuring of local communities and species-specific variability of spatial processes and environmental control even at small spatial scales.     

Ecological application of wavelet analysis in the scaling of spatial distribution patterns of Ceratoides ewersmanniana

Ecological experiments are usually conducted on small scales, but the ecological and environmental issues are usually on large scales. Hence, there is a clear need of scaling. Namely, when we deal with patterns and processes on larger scales, a special connection needs to be established on the small scales that we are familiar with. Here we presented a wavelet analysis method that could build relationships between spatial distribution patterns on different scales. With this method, we also studied how spatial heterogeneity and distribution patterns changed with the scale. We investigated the distribution and the habitat of C. ewersmanniana in two plots (200 m × 200 m; the distance between these two plots is 15 km) at Mosuowan desert. The results demonstrated that spatial heterogeneity and distribution patterns were incorporated into larger scales when the wavelet scale varied from one (5 m) to four (20 m). However, if the wavelet scale was above five (25 m), the spatial distribution patterns varied placidly, the oscillation frequency of landforms stabilized at 110 m, and the dynamic quantity period of C. ewersmanniana stabilized at 115–125 m. We also identified signal mutation points with wavelet analysis and verified the heterogeneity degree of local space with position variance. We found that position variance decomposed the distribution patterns on large scales into small sampling plots, and the position with the largest variance also had the strongest heterogeneity. In a word, the wavelet analysis method could scale-up spatial distribution patterns and habitat heterogeneity. With this method and other methods derived from this one, such as wavelet scale, wavelet variance, position variance and extremely direct-viewing graphs, wavelet analysis could be widely applied in solving the scaling problem in ecological and environmental studies.     

Land use and climate influences on waterbirds in the Prairie Potholes

Aim We examined the influences of regional climate and land‐use variables on mallard (Anas platyrhynchos), blue‐winged teal (Anas discors), ruddy duck (Oxyura jamaicensis) and pied‐billed grebe (Podilymbus podiceps) abundances to inform conservation planning in the Prairie Pothole Region of the United States. Location The US portion of Bird Conservation Region 11 (US‐BCR11, the Prairie Potholes), which encompasses six states within the United States: Montana, North Dakota, South Dakota, Nebraska, Minnesota and Iowa. Methods We used data from the North American Breeding Bird Survey (NABBS), the National Land Cover Data Set, and the National Climatic Data Center to model the effects of environmental variables on waterbird abundance. We evaluated land‐use covariates at three logarithmically related spatial scales (1000, 10,000 and 100,000 ha), and constructed hierarchical spatial count models a priori using information from published habitat associations. Model fitting was performed using a hierarchical modelling approach within a Bayesian framework. Results Models with the same variables expressed at different scales were often in the best model subset, indicating that the influence of spatial scale was small. Both land‐use and climate variables contributed strongly to predicting waterbird abundance in US‐BCR11. The strongest positive influences on waterbird abundance were the percentage of wetland area across all three spatial scales, herbaceous vegetation and precipitation variables. Other variables that we included in our models did not appear to influence waterbirds in this study. Main conclusions Understanding the relationships of waterbird abundance to climate and land use may allow us to make predictions of future distribution and abundance as environmental factors change. Additionally, results from this study can suggest locations where conservation and management efforts should be focused.     

Climate and landscape influence on indicators of lake carbon cycling through spatial patterns in dissolved organic carbon

Freshwater ecosystems are strongly influenced by both climate and the surrounding landscape, yet the specific pathways connecting climatic and landscape drivers to the functioning of lake ecosystems are poorly understood. Here, we hypothesize that the links that exist between spatial patterns in climate and landscape properties and the spatial variation in lake carbon (C) cycling at regional scales are at least partly mediated by the movement of terrestrial dissolved organic carbon (DOC) in the aquatic component of the landscape. We assembled a set of indicators of lake C cycling (bacterial respiration and production, chlorophyll a, production to respiration ratio, and partial pressure of CO₂), DOC concentration and composition, and landscape and climate characteristics for 239 temperate and boreal lakes spanning large environmental and geographic gradients across seven regions. There were various degrees of spatial structure in climate and landscape features that were coherent with the regionally structured patterns observed in lake DOC and indicators of C cycling. These different regions aligned well, albeit nonlinearly along a mean annual temperature gradient; whereas there was a considerable statistical effect of climate and landscape properties on lake C cycling, the direct effect was small and the overall effect was almost entirely overlapping with that of DOC concentration and composition. Our results suggest that key climatic and landscape signals are conveyed to lakes in part via the movement of terrestrial DOC to lakes and that DOC acts both as a driver of lake C cycling and as a proxy for other external signals.     

A hyperspectral indicator system for rangeland degradation on the Tibetan Plateau: A case study towards spaceborne monitoring

The Tibetan Plateau suffers from progressive degradation caused by over-grazing due to improper livestock management, global climate change and herbivory from small mammals. Therefore, a robust indicator system for rangeland degradation has to be developed and tested. This paper investigates local patterns of degradation at two sites (Lake Namco and Mt. Kailash) in Xizang province (China) that are covered by vegetation types typical of a large portion of the plateau. The suitability of a two-indicator system is analysed using hyperspectral field measurements, and its transferability to spaceborne data is tested. The indicators are (1) land-cover fractions derived from linear spectral unmixing and (2) chlorophyll content as a proxy for nutrient and water availability calculated using hyperspectral vegetation indices and partial least squares regression. Because cattle remain near settlements overnight in the local semi-nomadic pastoral system, it can be expected that grazing intensity is highest near the settlement and declines with increasing distance. Therefore, we tested the effect of distance on both indicators using a Spearman correlation analysis. The predicted chlorophyll content and land cover fractions of the indicator system were in good agreement with field observations (correlation coefficients between 0.70 and 0.98). High correlations between distance from settlements and land-cover fractions at both study sites demonstrated that the land-cover fraction is a reliable indicator for degradation. A positive correlation between distance from settlements and photosynthetically active vegetation (PV) revealed over-grazing patterns at the first site. Furthermore, the chlorophyll indicator was proven suitable because chlorophyll concentration declined with increasing distance from settlements. This underlines the over-grazing pattern because cattle excrement was the only external source of nutrients in the ecosystem and it was positively correlated with grazing intensity. However, at the second site, we found a significant positive effect of distance on the amount of photosynthetically non-active vegetation; no effect of distance on PV and chlorophyll content was found. Therefore, no evidence of pasture degradation was detected at the second site. Regarding the potential use of satellite data for degradation monitoring, we found that (1) the land-cover indicator derived from multispectral data was more robust than using noise-filtered hyperspectral information and (2) the chlorophyll amount indicator was estimated from simulated EnMAP data with low error rates. Because the proposed two-indicator system can be derived from multi- and hyperspectral satellite data and combines site conditions and local plant cover, it provides a time-saving and robust method to measure pasture degradation across large areas, assuming that respective satellite data are available.     

Regional and local drivers of macroinvertebrate assemblages in boreal springs

Aim To identify the most important environmental drivers of benthic macroinvertebrate assemblages in boreal springs at different spatial scales, and to assess how well benthic assemblages correspond to terrestrially derived ecoregions. Location Finland. Methods Benthic invertebrates were sampled from 153 springs across four boreal ecoregions of Finland, and these data were used to analyse patterns in assemblage variation in relation to environmental factors. Species data were classified using hierarchical divisive clustering (twinspan ) and ordinated using non‐metric multidimensional scaling. The prediction success of the species and environmental data into a priori (ecoregions) and a posteriori (twinspan ) groups was compared using discriminant function analysis. Indicator species analysis was used to identify indicator taxa for both a priori and a posteriori assemblage types. Results The main patterns in assemblage clusters were related to large‐scale geographical variation in temperature. A secondary gradient in species data reflected variation in local habitat structure, particularly abundance of minerogenic spring brooks. Water chemistry variables were only weakly related to assemblage variation. Several indicator species representing southern faunistic elements in boreal springs were identified. Discriminant function analysis showed poorer success in classifying sites into ecoregions based on environmental than on species data. Similarly, when classifying springs into the twinspan groups, classification based on species data vastly outperformed that based on environmental data. Main conclusions A latitudinal zonation pattern of spring assemblages driven by regional thermal conditions is documented, closely paralleling corresponding latitudinal patterns in both terrestrial and freshwater assemblages in Fennoscandia. The importance of local‐scale environmental variables increased with decreasing spatial extent. Ecoregions provide an initial stratification scheme for the bioassessment of benthic macroinvertebrates of North European springs. Our results imply that climate warming, landscape disturbance and degradation of spring habitat pose serious threats to spring biodiversity in northern Europe, especially to its already threatened southern faunistic elements.     

Space-for-time substitutions in climate change ecology and evolution

In an epoch of rapid environmental change, understanding and predicting how biodiversity will respond to a changing climate is an urgent challenge. Since we seldom have sufficient long-term biological data to use the past to anticipate the future, spatial climate–biotic relationships are often used as a proxy for predicting biotic responses to climate change over time. These ‘space-for-time substitutions’ (SFTS) have become near ubiquitous in global change biology, but with different subfields largely developing methods in isolation. We review how climate-focussed SFTS are used in four subfields of ecology and evolution, each focussed on a different type of biotic variable – population phenotypes, population genotypes, species' distributions, and ecological communities. We then examine the similarities and differences between subfields in terms of methods, limitations and opportunities. While SFTS are used for a wide range of applications, two main approaches are applied across the four subfields: spatial in situ gradient methods and transplant experiments. We find that SFTS methods share common limitations relating to (i) the causality of identified spatial climate–biotic relationships and (ii) the transferability of these relationships, i.e. whether climate–biotic relationships observed over space are equivalent to those occurring over time. Moreover, despite widespread application of SFTS in climate change research, key assumptions remain largely untested. We highlight opportunities to enhance the robustness of SFTS by addressing key assumptions and limitations, with a particular emphasis on where approaches could be shared between the four subfields.     

Differences in epiphyte biomass and community composition along landscape and within-crown spatial scales

Vascular epiphytes contribute to the structural, compositional, and functional complexity of tropical montane cloud forests because of their high biomass, diversity, and ability to intercept and retain water and nutrients from atmospheric sources. However, human-caused climate change and forest-to-pasture conversion are rapidly altering tropical montane cloud forests. Epiphyte communities may be particularly vulnerable to these changes because of their dependence on direct atmospheric inputs and host trees for survival. In Monteverde, Costa Rica, we measured vascular epiphyte biomass, community composition, and richness at two spatial scales: (1) along an elevation gradient spanning premontane forests to montane cloud forests and (2) within trees along branches from inner to outer crown positions. We also compared epiphyte biomass and distribution at these scales between two different land-cover types, comparing trees in closed canopy forest to isolated trees in pastures. An ordination of epiphyte communities at the level of trees grouped forested sites above versus below the cloud base, and separated forest versus pasture trees. Species richness increased with increasing elevation and decreased from inner to outer branch positions. Although richness did not differ between land-cover types, there were significant differences in community composition. The variability in epiphyte community organization between the two spatial scales and between land-cover types underscores the potential complexity of epiphyte responses to climate and land-cover changes.     

The spatial heterogeneity between Japanese encephalitis incidence distribution and environmental variables in Nepal

Background

To identify potential environmental drivers of Japanese Encephalitis virus (JE) transmission in Nepal, we conducted an ecological study to determine the spatial association between 2005 Nepal JE incidence, and climate, agricultural, and land-cover variables at district level.

Methods

District-level data on JE cases were examined using Local Indicators of Spatial Association (LISA) analysis to identify spatial clusters from 2004 to 2008 and 2005 data was used to fit a spatial lag regression model with climate, agriculture and land-cover variables.

Results

Prior to 2006, there was a single large cluster of JE cases located in the Far-West and Mid-West terai regions of Nepal. After 2005, the distribution of JE cases in Nepal shifted with clusters found in the central hill areas. JE incidence during the 2005 epidemic had a stronger association with May mean monthly temperature and April mean monthly total precipitation compared to mean annual temperature and precipitation. A parsimonious spatial lag regression model revealed, 1) a significant negative relationship between JE incidence and April precipitation, 2) a significant positive relationship between JE incidence and percentage of irrigated land 3) a non-significant negative relationship between JE incidence and percentage of grassland cover, and 4) a unimodal non-significant relationship between JE Incidence and pig-to-human ratio.

Conclusion

JE cases clustered in the terai prior to 2006 where it seemed to shift to the Kathmandu region in subsequent years. The spatial pattern of JE cases during the 2005 epidemic in Nepal was significantly associated with low precipitation and the percentage of irrigated land. Despite the availability of an effective vaccine, it is still important to understand environmental drivers of JEV transmission since the enzootic cycle of JEV transmission is not likely to be totally interrupted. Understanding the spatial dynamics of JE risk factors may be useful in providing important information to the Nepal immunization program.     

Effects of residual hydrocarbons on the reed community after 10 years of oil extraction and the effectiveness of different biological indicators for the long-term risk assessments

The selection of certain indicators is critical to undertake ecological risk assessments of long-term oil pollution and other environmental changes. The indicators should be easily and routinely monitored, be sensitive to pollution, respond to pollution in a predictable manner, and match the spatial and temporal scales of investigations. To compare the effectiveness of indicators for the long-term risk assessments, this study investigated the multiple ecological effects of chronic oil pollution on the plant community dominated by reed (Phragmites australis). The physiology, growth and reproduction of reed, together with the composition and productivity of the reed community, were measured around oil wells that have operated for approximately 10 years in the Yellow River Delta, eastern China. The predictive power of each indicator was evaluated using the coefficients of determination (R²) of linear regression models established for each indicator and soil Total Petroleum Hydrocarbons (TPH) concentration. The sensitivities of indicators were evaluated by comparing slopes of new established regression lines using standardized data. The top three indicators in terms of predictive power were leaf length, width and number, followed by the Shannon–Wiener index, Pielou evenness index and Simpson's diversity index. Community aboveground biomass, foliar projective coverage and species richness showed predictive power lower than those of the three diversity indexes, but higher than those of leaf net photosynthetic rate, reed height, aboveground biomass and vertical projective coverage of reed plants. Leaf transpiration, chlorophyll concentration and reed stem density showed no significant linear response to elevated soil TPH concentration. In terms of sensitivity, the top three biological indicators were Pielou evenness index, Simpson's diversity index and Shannon–Wiener index, followed by community vertical projective coverage, community aboveground biomass, and species richness. Leaf number, length and width were moderately sensitive, followed by reed coverage, aboveground biomass and height. The sensitivity of net photosynthetic rate was the lowest. The predictive power and sensitivities of indicators were compared in terms of their spatial and temporal scales. In conclusion, scale can be used to facilitate the selection of indicators, and the combination of different indicators may yield improved understanding of the various effects of elevated soil TPH concentration at the different biological levels.     

A global synthesis of climate vulnerability assessments on marine fisheries: Methods,scales, and knowledge co-production

Undertaking climate vulnerability assessments (CVAs) on marine fisheries is instrumental to the identification of regions, species, and stakeholders at risk of impacts from climate change, and the development of effective and targeted responses for fisheries adaptation. In this global literature review, we addressed three important questions to characterize fisheries CVAs: (i) what are the available approaches to develop CVAs in various social–ecological contexts, (ii) are different geographic scales and regions adequately represented, and (iii) how do diverse knowledge systems contribute to current understanding of vulnerability? As part of these general research efforts, we identified and characterized an inventory of frameworks and indicators that encompass a wide range of foci on ecological and socioeconomic dimensions of climate vulnerability on fisheries. Our analysis highlighted a large gap between countries with top research inputs and the most urgent adaptation needs. More research and resources are needed in low-income tropical countries to ensure existing inequities are not exacerbated. We also identified an uneven research focus across spatial scales and cautioned a possible scale mismatch between assessment and management needs. Drawing on this information, we catalog (1) a suite of research directions that could improve the utility and applicability of CVAs, particularly the examination of barriers and enabling conditions that influence the uptake of CVA results into management responses at multiple levels, (2) the lessons that have been learned from applications in data-limited regions, particularly the use of proxy indicators and knowledge co-production to overcome the problem of data deficiency, and (3) opportunities for wider applications, for example diversifying the use of vulnerability indicators in broader monitoring and management schemes. This information is used to provide a set of recommendations that could advance meaningful CVA practices for fisheries management and promote effective translation of climate vulnerability into adaptation actions.