The regional framing of climate change: towards a place-based perspective on regional climate change perception in north Frisia

Numerous studies have begun to tackle the social and cultural dimensions of perceiving and framing climate change. Scholars from geography and environmental psychology in particular have started to highlight the importance of so-called place-based approaches to studying regional and local framings of climate change. This paper stands in this tradition. It reports on findings derived from a nationwide survey of perceptions of and reactions to extreme weather events and interviews conducted with inhabitants of three islands in the coastal region of North Frisia (Germany). Coastal dwellers understand climate change through the lens of local and regional experiences of meteorological phenomena, seasonal changes, knowledge of the sea, and changes in local flora and fauna. Our detailed ecolinguistic analysis revealed six prevailing conceptual metaphors: Climate change is an enemy, preventing climate change is fight/war, climate change is punishment for human sins, climate change is overheating/heat, climate change is hot air/hoax and climate change is eco-dictatorship. These metaphors were used to make sense of climate change at the regional level and provide insights into place-based social and cultural conceptualisations of climate change. An understanding of these meanings should feed into developing more grounded climate change adaptation and mitigation strategies in coastal regions.     

Evaluating and managing wildlife impacts of climate change under uncertainty

Wildlife managers face the daunting task of managing wildlife in light of uncertainty about the nature and extent of future climate change and variability and its potential adverse impacts on wildlife. A conceptual framework is developed for managing wildlife under such uncertainty. The framework uses fuzzy logic to test hypotheses about the extent of the wildlife impacts of past climate change and variability, and fuzzy multiple attribute evaluation to determine best compensatory management actions for adaptively managing the potential adverse impacts of future climate change and variability on wildlife. A compensatory management action is one that can offset some of the potential adverse impacts of climate change and variability on wildlife. Implementation of the proposed framework requires wildlife managers to: (1) select climate impact states, hypotheses about climate impact states, possible management actions for alleviating adverse wildlife impacts of climate change and variability, and future climate change scenarios; (2) choose biological attributes or indicators of species integrity; (3) adjust those attributes for changes in non-climatic variables; (4) define linguistic variables and associated triangular fuzzy numbers for rating both the acceptability of biological conditions under alternative management actions and the relative importance of biological attributes; (5) select minimum or maximum acceptable levels of the attributes and reliability levels for chance constraints on the biological attributes; and (6) define fuzzy sets on the extent of species integrity and biological conditions and select a fuzzy relation between species integrity and biological conditions. A constructed example is used to illustrate a hypothetical application of the framework by a wildlife management team. An overall best compensatory management action across all climate change scenarios is determined using the minimax regret criterion, which is appropriate when the management team cannot assign or is unwilling to assign probabilities to the future climate change scenarios. Application of the framework can be simplified and expedited by incorporating it in a web-based, interactive, decision support tool.     

Evaluating long-term hydrological impacts of regional urbanisation in Hanyang,China, using a GIS model and remote sensing

China has experienced a rapid urbanisation, especially since the 1980s; however, the environmental impacts of this process are not fully investigated. Hanyang (Hubei Province, south China) was selected as a typical case to investigate runoff and non-point source (NPS) pollution impacts of urbanisation. A water quality simulation model (L-THIA) was applied to determine the long-term implications of different degrees of regional urbanisation impacts on NPS pollutants. Land-use patterns in 1987, 1998 and 2003 were analysed to evaluate the temporal variation of urbanisation, and the precipitation dataset from 1975 to 2003 was used to estimate the mean annual runoff and NPS pollutants. The contributions of different land-use categories to average annual runoff and NPS pollutant production were assessed by the means of a regression model. Results show that urban/impervious lands increased by 30.4% between 1987 and 2003, with the most increase occurring in 1998–2003. Industrial and forestlands have the most and least impact, respectively, on mean annual runoff and NPS pollutants. A combination of L-THIA and the regression model was found to be useful as a decision support tool for regional and urban planning from the perspectives of water quality control.     

气候变化对生物多样性的影响:脆弱性和适应

气候变化对生物多样性影响及其适应直接关系着未来生物多样性的保护.气候变化对生物多样性影响、生物多样性在气候变化影响下的脆弱性、生物多样性适应气候变化方面进行了总结分析,对存在的问题进行了讨论,对今后研究提出了一些建议.过去的气候变化已使物种物候、分布和丰富度等改变,使一些物种灭绝、部分有害生物危害强度和频率增加,使一些生物入侵范围扩大、生态系统结构与功能改变等.未来的气候变化仍将使物种物候和行为、分布和丰富度等改变,使一些物种灭绝、使有害生物爆发频率和强度增加,并将可能使生态系统结构与功能发生改变等.生物多样性适应气候变化包括了自然适应和人为适应两个方面,自然适应体现在物种适应性进化、迁移、生态系统稳定性和弹性等,人为适应体现在种质基因保存、物种异地保护、自然保护区规划设计、生态系统适应性管理、生态恢复和气候灾害防御等.目前,生物多样性对气候变化影响的脆弱性、生物多样性自然适应和人为适应气候变化方面的研究都还不系统深入,需要加强生物多样性自然适应和人为适应气候变化方面的研究.     

Integrated model projections of climate change impacts on a North American lake

Climate change is likely to impact terrestrial and aquatic ecosystems via numerous physical and biological mechanisms. This study outlines a framework for projecting potential impacts of climate change on lakes using linked environmental models. Impacts of climate drivers on catchment hydrology and thermal balance in Onondaga Lake (New York State) are simulated using mechanistic models HSPF and UFILS4. Outputs from these models are fed into a lake ecosystem model, developed in AQUATOX. Watershed simulations project increases in the magnitude of peak flows and consequent increases in catchment nutrient export as the magnitude of extreme precipitation events increases. This occurs concurrently with a decrease in annual stream discharge as a result of increased evapotranspiration. Simulated lake water temperatures increase by as much as 5 °C during the 2040-2069 time period, accompanied by a prolonging of the duration of summer stratification. Projected changes include shifts in the timing of nutrient cycling between lake sediments and water column. Plankton taxa projected to thrive under climate change include green algae and Bosmina longirostris. Responses for species at higher trophic levels are mixed. Benthic macroinvertebrates may either prosper (zebra mussels) or decline (chironomids), while fish (e.g., gizzard shad) exhibit high seasonal variability without any clear trend.     

Null model analysis of species associations using abundance data

The influence of negative species interactions has dominated much of the literature on community assembly rules. Patterns of negative covariation among species are typically documented through null model analyses of binary presence/absence matrices in which rows designate species, columns designate sites, and the matrix entries indicate the presence (1) or absence (0) of a particular species in a particular site. However, the outcome of species interactions ultimately depends on population-level processes. Therefore, patterns of species segregation and aggregation might be more clearly expressed in abundance matrices, in which the matrix entries indicate the abundance or density of a species in a particular site. We conducted a series of benchmark tests to evaluate the performance of 14 candidate null model algorithms and six covariation metrics that can be used with abundance matrices. We first created a series of random test matrices by sampling a metacommunity from a lognormal species abundance distribution. We also created a series of structured matrices by altering the random matrices to incorporate patterns of pairwise species segregation and aggregation. We next screened each algorithm-index combination with the random and structured matrices to determine which tests had low Type I error rates and good power for detecting segregated and aggregated species distributions. In our benchmark tests, the best-performing null model does not constrain species richness, but assigns individuals to matrix cells proportional to the observed row and column marginal distributions until, for each row and column, total abundances are reached. Using this null model algorithm with a set of four covariance metrics, we tested for patterns of species segregation and aggregation in a collection of 149 empirical abundance matrices and 36 interaction matrices collated from published papers and posted data sets. More than 80% of the matrices were significantly segregated, which reinforces a previous meta-analysis of presence/absence matrices. However, using two of the metrics we detected a significant pattern of aggregation for plants and for the interaction matrices (which include plant-pollinator data sets). These results suggest that abundance matrices, analyzed with an appropriate null model, may be a powerful tool for quantifying patterns of species segregation and aggregation.     

Modeling the combined effect of nutrients and pyrene on the plankton population: Validation using mesocosm experiment data and scenario analysis

A new integrated model that includes a hydrodynamic model coupled with a contaminant fate and effect sub-model and an ecological sub-model is presented and validated using data from mesocosm experiments. The experiments were carried out in the Isefjord (Denmark) and include the combined effects of nutrients and pyrene addition on the lower trophic levels of bacteria, zooplankton and phytoplankton. The model was able to correctly represent the main dynamics observed in the mesocosms during the 11 days of the experiment and thereby confirmed that it is possible to represent short-term changes in the system with a simplified food-web model on a small spatial and temporal scale. Finally, the validated model was used to carry out a scenario analysis to investigate the effects of a contaminant pulse at different pyrene concentrations and different release timings. Results showed that the ecosystem's vulnerability to a pyrene pulse depends on the initial condition of the system. Stronger biomass reduction was observed when the pulse was released during the zooplankton bloom. Conversely, when the pulse was added at low biomass and before the bloom, the system showed a tendency to behave non-linearly.     

Modeling and spatial prediction of pre-settlement patterns of forest distribution using witness tree data

At the time of European settlement, land surveys were conducted progressively westward throughout the United States. Outside of the original 13 colonies, surveys generally followed the Public Land Survey system in which trees, called witness trees, were regularly recorded at 1 mi by 1 mi grid intersections. This unintentional sampling provides insight into the composition and structure of pre-European settlement forests, which is used as baseline data to assess forest change following settlement. In this paper, a model for the Public Land Surveys of east central Alabama is developed. Assuming that the locations of trees of each species are realized from independent Poisson processes whose respective log intensities are linear functions of environmental covariates (i.e., elevation, landform, and physiographic province), the species observed at the survey grid intersections are independently sampled from a generalized logistic regression model. If all 68 species found in the survey were included, the model would be highly over-parameterized, so only the distribution of the most common taxon, pines, will be considered at this time. To assess the impact of environmental factors not included in the model, a hidden Gaussian random field shall be added as a random effect. A Markov Chain Monte Carlo algorithm is developed for Bayesian inference on model parameters, and for Bayes posterior prediction of the spatial distribution of pines in east central Alabama. Received: June 2004 / Revised: November 2004     

Modeling the annual thermal regime of lake Ohrid,Yugoslavia, using daily weather data

The time-dependent characteristics of the five weather variables which control the annual thermal response of Lake Ohrid are analyzed in detail. These are daily values for solar radiation, air temperature, humidity, wind speed and cloud cover. A simple numerical model of the lake's thermal response, forced by thermally driven density mixing, is developed and tested using observed thermal profiles for verification. The numerical model successfully reproduces the major features of the lake's thermal regime over a 6 y period from 1972 to 1977, the average root mean square value for the simulated profiles being 1.2°C with extremes of 2.2 and 0.3°C and a standard deviation of 0.4°C.     

Modeling freshwater fish distributions using multiscale landscape data: A case study of six narrow range endemics

Species distribution models (SDMs) have become integral tools in scientific research and conservation planning. Despite progress in the assessment of various statistical models for use in SDMs, little has been done in way of evaluating appropriate ecological models. In this paper, we evaluate the multiscale filter framework as a suitable theoretical model for predicting freshwater fish distributions in the upper Green River system (Ohio River drainage), USA. The spatial distributions of six fishes with contrasting biogeographies were modeled using boosted regression trees and multiscale landscape data. Species biogeography did not appear to affect predictive performance and all models performed well statistically with receiver operating characteristic area under the curve (AUC) ranging from 0.87 to 0.98. Predictive maps show accurate estimations of ranges for five of six species based on historical collections. The relative influence of each type of environmental feature and spatial scale varied markedly with between species. A hierarchical effect was detected for narrowly distributed species. These species were highly influenced by soil composition at larger spatial scales and land use/land cover (LULC) patterns at more proximal scales. Conversely, LULC pattern was the most influential feature for widely distributed at all spatial scales. Using multiscale data capable of capturing hierarchical landscape influences allowed production of accurate predictive models and provided further insight into factors controlling freshwater fish distributions.     

Uncertainty analysis of climate change mitigation options in the forestry sector using a generic carbon budget model

Industrialized countries agreed on a reduction of greenhouse gas emissions under the Kyoto Protocol. Many countries elected forest management activities and the resulting net balance of carbon emissions and removals of non-CO₂ greenhouse gases by forest management in their climate change mitigation measures. In this paper a generic dynamic forestry model (FORMICA) is presented. It has an empirical basis. Several modules trace C pools relevant for the Kyoto Protocol and beyond: biomass, litter, deadwood and soil, and harvested wood products. The model also accounts for the substitution of fossil fuels by wood products and bioenergy.     

Linking species- and ecosystem-level impacts of climate change in lakes with a complex and a minimal model

To study the interaction between species- and ecosystem-level impacts of climate change, we focus on the question of how climate-induced shifts in key species affect the positive feedback loops that lock shallow lakes either in a transparent, macrophyte-dominated state or, alternatively, in a turbid, phytoplankton-dominated state. We hypothesize that climate warming will weaken the resilience of the macrophyte-dominated clear state. For the turbid state, we hypothesize that climate warming and climate-induced eutrophication will increase the dominance of cyanobacteria. Climate change will also affect shallow lakes through a changing hydrology and through climate change-induced eutrophication. We study these phenomena using two models, the full ecosystem model PCLake and a minimal dynamic model of lake phosphorus dynamics. Quantitative predictions with the complex model show that changes in nutrient loading, hydraulic loading and climate warming can all lead to shifts in ecosystem state. The minimal model helped in interpreting the non-linear behaviour of the complex model. The main output parameters of interest for water quality managers are the critical nutrient loading at which the system will switch from clear to turbid and the much lower critical nutrient loading – due to hysteresis – at which the system switches back. Another important output parameter is the chlorophyll-a level in the turbid state. For each of these three output parameters we performed a sensitivity analysis to further understand the dynamics of the complex model PCLake. This analysis showed that our model results are most sensitive to changes in temperature-dependence of cyanobacteria, planktivorous fish and zooplankton. We argue that by combining models at various levels of complexity and looking at multiple aspects of climate changes simultaneously we can develop an integrated view of the potential impact of climate change on freshwater ecosystems.     

Modeling carcass removal time for avian mortality assessment in wind farms using survival analysis

In monitoring studies at wind farms, the estimation of bird and bat mortality caused by collision must take into account carcass removal by scavengers or decomposition. In this paper we propose the use of survival analysis techniques to model the time of carcass removal. The proposed method is applied to data collected in ten Portuguese wind farms. We present and compare results obtained from semiparametric and parametric models assuming four main competing lifetime distributions (exponential, Weibull, log-logistic and log-normal). Both homogeneous parametric models and accelerated failure time models were used. The fitted models enabled the estimation of the carcass persistence rates and the calculation of a scavenging correction factor for avian mortality estimation. Additionally, we discuss the impact that the distributional assumption can have on parameter estimation. The proposed methodology integrates the survival probability estimation problem with the analysis of covariate effects. Estimation is based on the most suitable model while simultaneously accounting for censored observations, diminishing scavenging rate estimation bias. Additionally, the method establishes a standardized statistical procedure for the analysis of carcass removal time in subsequent studies.     

Modeling the impacts of calcite precipitation on the epilimnion of an ultraoligotrophic, hard-water lake

A mass-balance model of calcite precipitation was developed to investigate the interactions of the varied processes governing the generation and fate of calcite in lakes. The model was used in conjunction with data to assess the evolution and impact of calcite precipitation for calcareous, ultraoligotrophic Torch Lake, Michigan (USA). This lake is an ideal setting for implementation of a baseline modeling study of calcite precipitation where the physical drivers could be evaluated without being dominated, as in many systems, by biological processes. The model provides a representation of calcite precipitation with particulate surface area changing over time, and demonstrates that it is possible for the change in water clarity to be explained by calcite precipitation employing standard optical models. Using the mass balance model to quantify the roles of the various chemical, biological and physical processes interacting in the lake's epilimnion, it was shown that the seasonal temperature rise and air-water CO₂ exchange drive calcite precipitation much more than primary production for this ultraoligotrophic system.     

A measure of sustainability of Brazilian agribusiness using directional distance functions and data envelopment analysis

The social and environmental impacts caused by the modernization of Brazilian agribusiness have evoked a growing interest in the search of balance between social-economics development and the adequate use of natural resources, driving the country to sustainable development. Therefore, the main aim of this article is to estimate a set of indicators of relative efficiency in the Brazilian agricultural sector, which satisfy the concept of Pareto optimality, potentiates simultaneously both economic, ecological dimensions and social functions. To reach this purpose, the method of directional distance functions and data envelopment analysis was applied. These indicators confirm, in different ways, the hypothesis that it is possible to perform consistent productive strategies with the maximization of social welfare, despite the apparent antagonism among these three dimensions. In addition, it is shown that efficient Brazilian states tend to combine the three dimensions in different ways. Hence, it can be concluded that several equilibrium taken sustainable can be achieved through different actions on poverty and environmental impact reduction without necessarily generating productive inefficiencies. This result can be considered of prominent importance for sustainable development in Brazil and can also serve as a reference in the definition of goals of the plan ‘Brazil without Misery’ and international commitments to reduce Greenhouse Gas – GHG – in Brazil, especially for the 17 inefficient Brazilian states.     

Nonparametric analysis of directional data based on data depth

Data depth is a statistical method whose primary aim is to order data of a reference space according to centrality. This is particularly appealing for directional data because no standard ordering is available, not even in the circular case. Moreover, the depth approach deals in a natural way with the peculiar aspects of directional data, i.e., the lack of a zero-direction and the wrap-around effect. The paper reviews the data depth program concentrating on typical applications to directional distributions and data. Two geometrical depth functions, simplicial depth and Tukey’s depth, are considered. Several depth-based summaries, including angular medians, depth regions and dispersion parameters are illustrated, the latter with some novel results. Examples cover symmetric and asymmetric distributions as well as real-data applications.     

Compositional analysis of topsoil metals and its associations with cancer mortality using spatial misaligned data

The presence of toxic metals in soil per se, and in soil impacted by mining, industry, agriculture and urbanisation in particular, is a major concern for both human health and ecotoxicology. The dual aim of this study was: to ascertain whether topsoil composition could influence the spatial distribution of mortality due to different types of cancer and to identify possible errors committed by epidemiological studies which analyse soil composition data as a closed number system. We conducted an ecological cancer mortality study, covering 861,440 cancer deaths (27 cancer sites) in 7917 Spanish mainland towns, from 1999 to 2008. Topsoil levels of Al, As, Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn were determined by ICP-MS at 13,317 sampling points. We transformed the topsoil data in two ways, i.e. log transformation and centred logratio transformation. Principal factor analysis was performed to obtain independent latent factors for the transformed variables. To estimate the effect on mortality of topsoil factor loadings, we fitted Besag, York and Mollié models embedded in geostatistical-spatial models. This model included soil sample locations and town centroids (non-aligned data), fitted using the integrated nested Laplace approximation (INLA) as a tool for Bayesian inference and stochastic partial differential equations (SPDE). All results were adjusted for socio-demographic variables. The results indicated that soil composition could have an influence on the spatial distribution and mortality patterns of cancer. The analysis adjusted for socio-demographic variables showed excess male mortality due to digestive system tumours in areas with soils containing higher Cd, Pb, Zn, Mn and Cu concentrations, bladder cancer in areas with soils containing higher Cd concentrations, and brain cancer in areas with soils containing As. In both sexes, cancer of oesophagus was associated with soils containing a higher lead content, while lung cancer was associated with soils containing a higher copper content. Stress should be laid on the importance of taking into account the compositional nature of the data in this type of analysis.     

Estimating population impacts via dynamic occupancy analysis of Before-After Control-Impact studies

Estimating environmental impacts on populations is one of the main goals of wildlife monitoring programs, which are often conducted in conjunction with management actions or following natural disturbances. In this study we investigate the statistical power of dynamic occupancy models to detect changes in local survival and colonization from detection-nondetection data, while accounting for imperfect detection probability, in a Before-After Control-Impact (BACI) framework. We simulated impacts on local survival and/or detection probabilities, and asked questions related to: (1) costs and benefits of different analysis models, (2) confounding changes in detection with changes in local survival, (3) sampling design trade-offs, and (4) species with low vs. high rates of turnover. Estimating seasonal effects on local survival and colonization, as opposed to estimating Before-After effects, had little effect on the power to detect changes in local survival. Estimating a parameter that accounted for pretreatment differences in local survival between Control and Impact sites decreased power by 50%, but it was critical to include when such differences existed. When the experimental treatment had a negative impact on species detectability but analysis assumed constant detection, the Type I error rates were dramatically inflated (0.20 0.33). In general, there was low power (< 0.5) to detect a 50% decrease in local survival for all combinations of sites (N = 50 vs. 100), seasons sampled (8 vs. 12), and visits per site per season (4 vs. 6). Unbalanced designs performed worse than balanced designs, with the exception of the case of treatments being implemented in different seasons at different sites. Adding more control sites improved the ability to detect changes in local survival. Surveying more seasons after impact resulted in modest power gains, but at least three seasons before impact were required to successfully implement BACI occupancy studies. Turnover rates had a low impact on power. Occupancy studies conducted in a BACI design offer the opportunity to detect environmental impacts on wildlife populations without the costs of intensive studies. However, given the low power to detect small changes (20%) in local survival, these studies should be used when researchers are confident that major treatment impacts will occur or very large sample sizes are obtainable.     

Uncertainty analysis on aquatic environmental impacts of urban land use change

Uncertainties hamper the implementation of strategic environmental assessment (SEA). In order to quantitatively characterize the uncertainties of environmental impacts, this paper develops an integrated methodology through uncertainty analysis on land use change, which combines the scenario analysis approach, stochastic simulation technique, and statistics. Dalian city in China was taken as a case study in the present work. The results predict that the Fuzhou River poses the highest environmental pollution risk with a probability of 89.63% for COD in 2020. Furthermore, the Biliu River, Fuzhou River, Zhuang River, and Dasha River have 100% probabilities for NH₃-N. NH₃-N is a more critical pollutant than COD for all rivers. For COD, industry is the critical pollution source for all rivers except the Zhuang River. For NH₃-N, agriculture is the critical pollution source for the Biliu River, Yingna River, and Dasha River, sewage for the Fuzhou River and Zhuang River, and industry for the Dengsha River. This methodology can provide useful information, such as environmental risk, environmental pressure, and extremely environmental impact, especially under considerations of uncertainties. It can also help to ascertain the significance of each pollution source and its priority for control in urban planning.