Spatial impacts of climate factors on regional agricultural and forestry biomass resources in north-eastern province of China

The dynamics of agricultural and forestry biomass are highly sensitive to climate change, particularly in high latitude regions. Heilongjiang Province was selected as research area in North-east China. We explored the trend of regional climate warming and distribution feature of biomass resources, and then analyzed on the spatial relationship between climate factors and biomass resources. Net primary productivity (NPP) is one of the key indicators of vegetation productivity, and was simulated as base data to calculate the distribution of agricultural and forestry biomass. The results show that temperatures rose by up to 0.37°C/10a from 1961 to 2013. Spatially, the variation of agricultural biomass per unit area changed from -1.93 to 5.85 t·km^–2·a^–1 during 2000–2013. More than 85% of farmland areas showed a positive relationship between agricultural biomass and precipitation. The results suggest that precipitation exerts an overwhelming climate influence on agricultural biomass. The mean density of forestry biomass varied from 10 to 30 t·km^–2. Temperature had a significant negative effect on forestry biomass in Lesser Khingan and northern Changbai Mountain, because increased temperature leads to decreased Rubisco activity and increased respiration in these areas. Precipitation had a significant positive relationship with forestry biomass in south-western Changbai Mountain, because this area had a warmer climate and stress from insufficient precipitation may induce xylem cavitation. Understanding the effects of climate factors on regional biomass resources is of great significance in improving environmental management and promoting sustainable development of further biomass resource use.

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Assessment of future climate change impacts on water-heat-salt migration in unsaturated frozen soil using CoupModel

• A model coupling water-heat-salt of unsaturated frozen soil was established. • Future temperature, precipitation, and evaporation increase in freeze–thaw period. • Soil water, heat, and salt transport are closely coupled during freeze–thaw period. • Freeze–thaw cycles and future climate change can exacerbate salinization. The transport mechanisms of water, heat, and salt in unsaturated frozen soil, as well as its response to future climate change are in urgent need of study. In this study, western Jilin Province in north-eastern China was studied to produce a model of coupled water-heat-salt in unsaturated frozen soil using CoupModel. The water, heat, and salt dynamics of unsaturated frozen soil under three representative concentration pathway (RCP) scenarios were simulated to analyze the effects of future climate change on unsaturated frozen soil. The results show that water, heat, and salt migration are tightly coupled, and the soil salt concentration in the surface layer (10 cm) exhibits explosive growth after freezing and thawing. The future (2020–2099) meteorological factors in the study area were predicted using the Statistical Downscaling Model (SDSM). For RCP2.6, RCP4.5, and RCP8.5 scenarios, future temperatures during the freeze–thaw period increased by 2.68°C, 3.18°C, and 4.28°C, respectively; precipitation increased by 30.28 mm, 28.41 mm, and 32.17 mm, respectively; and evaporation increased by 93.57 mm, 106.95 mm, and 130.57 mm, respectively. Climate change will shorten the freeze–thaw period, advance the soil melting time from April to March, and enhance water and salt transport. Compared to the baseline period (1961–2005), future soil salt concentrations at 10 cm increased by 1547.54 mg/L, 1762.86 mg/L, and 1713.66 mg/L under RCP2.6, RCP4.5, and RCP8.5, respectively. The explosive salt accumulation is more obvious. Effective measures should be taken to prevent the salinization of unsaturated frozen soils and address climate change.     

Characterization of storm wave asymmetries with functional data analysis

Functional data analysis (FDA) is a set of tools developed to perform statistical analysis on data having a functional form. In our case we consider the one-dimensional wave surface profiles registered during a North-Sea storm as functional data. The data is split into 20 min intervals within which an individual wave is defined as the profile between two consecutive downcrossings. After registration of these individual waves to the interval [0, 1], the mean wave profile for the entire 20 min interval is obtained along with the first two derivatives of this mean profile. We analyze the shape of these mean waves and their derivatives and show how they change as a function of the significant wave height, which is a measure of the severity of the sea for the corresponding time interval. We also look at the evolution of the energy, as represented by the phase diagram, as a function of significant wave height. The results show the asymmetry in vertical and horizontal scales for real data. Comparison with a Gaussian wave simulation model calculated from the actual wave spectra shows important differences in symmetry and shape of the average wave and seem to indicate that the greatest difference in the distribution of energy during the wave cycle lies in the second and fourth quarters of the wave period. FDA can be applied to derive information on the individual and average wave profiles and also provide an understanding of the variation in energy throughout the wave phase.     

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.     

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.     

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.     

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.     

Modeling scale up of anthropogenic impacts from individual pollinator behavior to pollination systems

Understanding how anthropogenic disturbances affect plant–pollinator systems has important implications for the conservation of biodiversity and ecosystem functioning. Previous laboratory studies show that pesticides and pathogens, which have been implicated in the rapid global decline of pollinators over recent years, can impair behavioral processes needed for pollinators to adaptively exploit floral resources and effectively transfer pollen among plants. However, the potential for these sublethal stressor effects on pollinator–plant interactions at the individual level to scale up into changes to the dynamics of wild plant and pollinator populations at the system level remains unclear. We developed an empirically parameterized agent-based model of a bumblebee pollination system called SimBee to test for effects of stressor-induced decreases in the memory capacity and information processing speed of individual foragers on bee abundance (scenario 1), plant diversity (scenario 2), and bee–plant system stability (scenario 3) over 20 virtual seasons. Modeling of a simple pollination network of a bumblebee and four co-flowering bee-pollinated plant species indicated that bee decline and plant species extinction events could occur when only 25% of the forager population showed cognitive impairment. Higher percentages of impairment caused 50% bee loss in just five virtual seasons and system-wide extinction events in less than 20 virtual seasons under some conditions. Plant species extinctions occurred regardless of bee population size, indicating that stressor-induced changes to pollinator behavior alone could drive species loss from plant communities. These findings indicate that sublethal stressor effects on pollinator behavioral mechanisms, although seemingly insignificant at the level of individuals, have the cumulative potential in principle to degrade plant–pollinator species interactions at the system level. Our work highlights the importance of an agent-based modeling approach for the identification and mitigation of anthropogenic impacts on plant–pollinator systems.     

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.     

Understand the local and regional contributions on air pollution from the view of human health impacts

• PM_2.5-related deaths were estimated to be 227 thousand in BTH & surrounding regions. • Local emissions contribute more to PM_2.5-related deaths than PM_2.5 concentration. • Local controls are underestimated if only considering its impacts on concentrations. • Rural residents suffer larger impacts of regional transport than urban residents. • Reducing regional transport benefits in mitigating environmental inequality. The source-receptor matrix of PM_2.5 concentration from local and regional sources in the Beijing-Tianjin-Hebei (BTH) and surrounding provinces has been created in previous studies. However, because the spatial distribution of concentration does not necessarily match with that of the population, such concentration-based source-receptor matrix may not fully reflect the importance of pollutant control effectiveness in reducing the PM_2.5-related health impacts. To demonstrate that, we study the source-receptor matrix of the PM_2.5-related deaths instead, with inclusion of the spatial correlations between the concentrations and the population. The advanced source apportionment numerical model combined with the integrated exposure–response functions is used for BTH and surrounding regions in 2017. We observed that the relative contribution to PM_2.5-related deaths of local emissions was 0.75% to 20.77% larger than that of PM_2.5 concentrations. Such results address the importance of local emissions control for reducing health impacts of PM_2.5 particularly for local residents. Contribution of regional transport to PM_2.5-related deaths in rural area was 22% larger than that in urban area due to the spatial pattern of regional transport which was more related to the rural population. This resulted in an environmental inequality in the sense that people staying in rural area with access to less educational resources are subjected to higher impacts from regional transport as compared with their more resourceful and knowledgeable urban compatriots. An unexpected benefit from the multi-regional joint controls is suggested for its effectiveness in reducing the regional transport of PM_2.5 pollution thus mitigating the associated environmental inequality.     

A regional forest ecosystem carbon budget model: impacts of forest age structure and landuse history

This study investigated the impacts of landuse history and forest age structure on regional carbon fluxes for the forests in the Pacific Northwest of the United States based on a two-stage modeling strategy. In the first stage, an individual-based forest ecosystem carbon flux model (IntCarb) at stand scale is developed. IntCarb combines components from the ZELIG and CENTURY models to simulate forest development and heterotrophic respiration, respectively. Stand scale carbon fluxes simulated by IntCarb strongly depend on stand age. A forest stand can be a carbon sink for up to 200 years old with a peak at 30–40 years old. Old-growth stands are carbon neutral to the atmosphere in the long term. For any particular year, an old-growth stand can be either a carbon sink or source. The interannual variation of Net Ecosystem Productivity (NEP) for an old-growth stand is primarily determined by heterotrophic respiration. Due to the high spatial variability of stand ages, forest age structure needs to be taken into account to improve estimation of carbon budgets of forest ecosystems over large areas. In the stand stage, a regional carbon budget model (RegCarb) is developed to estimate regional carbon fluxes over large areas based on forest age structure, adjusting for the nonrespiratory carbon losses (timber harvesting). Our initial estimate with RegCarb for the Pacific Northwest of the United States indicates that this region was a tremendous carbon source to the atmosphere from 1890 to 1990 due to extensive logging of old-growth forest. Projection for the role of forests in this region in global carbon cycle in the future strongly depend on the amount of timber to be harvested, i.e. how the age structure of forests in this region is to be altered.     

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.