Spatial modelling for population replacement of mosquito vectors at continental scale

Malaria is one of the deadliest vector-borne diseases in the world. Researchers are developing new genetic and conventional vector control strategies to attempt to limit its burden. Novel control strategies require detailed safety assessment to ensure responsible and successful deployments. Anopheles gambiae sensu stricto (s.s.) and Anopheles coluzzii, two closely related subspecies within the species complex Anopheles gambiae sensu lato (s.l.), are among the dominant malaria vectors in sub-Saharan Africa. These two subspecies readily hybridise and compete in the wild and are also known to have distinct niches, each with spatially and temporally varying carrying capacities driven by precipitation and land use factors.We model the spread and persistence of a population-modifying gene drive system in these subspecies across sub-Saharan Africa by simulating introductions of genetically modified mosquitoes across the African mainland and its offshore islands. We explore transmission of the gene drive between the two subspecies that arise from different hybridisation mechanisms, the effects of both local dispersal and potential wind-aided migration to the spread, and the development of resistance to the gene drive. Given the best current available knowledge on the subspecies’ life histories, we find that an introduced gene drive system with typical characteristics can plausibly spread from even distant offshore islands to the African mainland with the aid of wind-driven migration, with resistance beginning to take over within a decade. Our model accounts for regional to continental scale mechanisms, and demonstrates a range of realistic dynamics including the effect of prevailing wind on spread and spatio-temporally varying carrying capacities for subspecies. As a result, it is well-placed to answer future questions relating to mosquito gene drives as important life history parameters become better understood.     

A conceptual framework for selecting environmental indicator sets

In recent years, environmental indicators have become a vital component of environmental impact assessments and “state of the environment” reporting. This has increased the influence of environmental indicators on environmental management and policy making at all scales of decision making. However, the scientific basis of the selection process of the indicators used in environmental reporting can be significantly improved. In many studies no formal selection criteria are mentioned and when selection criteria are used they are typically applied to indicators individually. Often, no formal criteria are applied regarding an indicator's analytical utility within the total constellation of a selected set of indicators. As a result, the indicator selection process is subject to more or less arbitrary decisions, and reports dealing with a similar subject matter or similar geographical entities may use widely different indicators and consequently paint different pictures of the environment. In this paper, a conceptual framework for environmental indicator selection is proposed that puts the indicator set at the heart of the selection process and not the individual indicators. To achieve this objective, the framework applies the concept of the causal network that focuses on the inter-relation of indicators. The concept of causal networks can facilitate the identification of the most relevant indicators for a specific domain, problem and location, leading to an indicator set that is at once transparent, efficient and powerful in its ability to assess the state of the environment.     

A vegetation map of tropical continental Asia at scale 1:5 million

Abstract. A vegetation map at scale 1:5 million is presented. * * Attached on the inside of this issue's back cover.
It covers Bangladesh, Burma (Myanmar), India, Cambodia, Laos, Thailand, Vietnam and Sri Lanka and fills a conspicuous gap in the cartography of tropical vegetation, following the publication of vegetation maps of South America, Africa and Malaysia. For conformity, it is presented as one sheet at a scale of 1:5 million. It uses the basic map of the American Geographical Society (1942; bipolar oblique conformal projection) which forms the base for FAO's Soil map of the world. Basic information was obtained from many published maps, unpublished observations and satellite data. The limits of the main vegetation types have been updated with a complete set of Landsat MSS images (369 scenes) with a mosaic of Landsat TM data for 1991 and with recent forest maps from Asia. Nine main vegetation units, which are groups of forest formations, have been identified and mapped, including woodlands, thickets and wooded savannas. Agricultural land has been shown in a uniform pale green colour in order to clearly express the extent of human impacts on woody vegetation. In spite of the necessary oversimplification of the ground data, this map is probably the most explicit expression of the remaining forest stands and of the regression of natural vegetation in the region. It can be considered as a benchmark for future monitoring of tropical vegetation.     

A genetically encoded indicator for monitoring intracellular chloride

Inhibitory drives in the nervous system are provided by synapses operating mostly through Cl⁻ ion channels. We describe a novel CFP-YFP-based fluorescence Cl⁻ indicator, Cl-sensor, characterized by a high sensitivity, which can be successfully used as a tool for monitoring intracellular Cl⁻ in various biological preparations. Neirofiziologiya/Neurophysiology, Vol. 39, Nos. 4/5, pp. 380–381, July–October, 2007.     

Climate more important than soils for predicting forest biomass at the continental scale

Above-ground biomass in forests is critical to the global carbon cycle as it stores and sequesters carbon from the atmosphere. Climate change will disrupt the carbon cycle hence understanding how climate and other abiotic variables determine forest biomass at broad spatial scales is important for validating and constraining Earth System models and predicting the impacts of climate change on forest carbon stores. We examined the importance of climate and soil variables to explaining above-ground biomass distribution across the Australian continent using publicly available biomass data from 3130 mature forest sites, in 6 broad ecoregions, encompassing tropical, subtropical and temperate biomes. We used the Random Forest algorithm to test the explanatory power of 14 abiotic variables (8 climate, 6 soil) and to identify the best-performing models based on climate-only, soil-only and climate plus soil. The best performing models explained ~50% of the variation (climate-only: R² = 0.47 ± 0.04, and climate plus soils: R² = 0.49 ± 0.04). Mean temperature of the driest quarter was the most important climate variable, and bulk density was the most important soil variable. Climate variables were consistently more important than soil variables in combined models, and model predictive performance was not substantively improved by the inclusion of soil variables. This result was also achieved when the analysis was repeated at the ecoregion scale. Predicted forest above-ground biomass ranged from 18 to 1066 Mg ha⁻¹, often under-predicting measured above-ground biomass, which ranged from 7 to 1500 Mg ha⁻¹. This suggested that other non-climate, non-edaphic variables impose a substantial influence on forest above-ground biomass, particularly in the high biomass range. We conclude that climate is a strong predictor of above-ground biomass at broad spatial scales and across large environmental gradients, yet to predict forest above-ground biomass distribution under future climates, other non-climatic factors must also be identified.     

Meta-analysis of diazotrophic signatures across terrestrial ecosystems at the continental scale

Biological nitrogen fixation performed by diazotrophs forms a cornerstone of Earth's terrestrial ecosystem productivity. However, the composition, diversity and distribution of soil diazotrophs are poorly understood across different soil ecosystems. Furthermore, the biological potential of the key diazotroph species in relation to key environmental parameters is unknown. To address this, we used meta-analysis approach to merge together 39 independent diazotroph amplicon sequencing (nifH gene) datasets consisting of 1988 independent soil samples. We then employed multiple statistical analyses and machine-learning approaches to compare diazotroph community differences and indicator species between terrestrial ecosystems on a global scale. The distribution, composition and structure of diazotroph communities varied across seven different terrestrial ecosystems, with community composition exhibiting an especially clear effect. The Cyanobacteria were the most abundant taxa in crust ecosystems (accounting for ~45% of diazotrophs), while other terrestrial ecosystems were dominated by Proteobacteria, including Alpha-, Beta- and Gamma-Proteobacteria (accounting for ~70% of diazotrophs). Farmland ecosystems harboured the highest and crust ecosystems the lowest alpha and phylogenetic diversities. Azospirillum zeae, Skermanella aerolata and four Bradyrhizobium species were identified as key indicator species of potential diazotroph activity. Overall, diazotroph abundances and distribution were affected by multiple environmental parameters, including soil pH, nitrogen, organic carbon, C:N ratio and annual mean precipitation and temperature. Together, our findings suggest that based on the relative abundance and diversity of nifH marker gene, diazotrophs have adapted to a range of environmental niches globally.     

Importance of spatial autocorrelation in modeling bird distributions at a continental scale

Spatial autocorrelation in species' distributions has been recognized as inflating the probability of a type I error in hypotheses tests, causing biases in variable selection, and violating the assumption of independence of error terms in models such as correlation or regression. However, it remains unclear whether these problems occur at all spatial resolutions and extents, and under which conditions spatially explicit modeling techniques are superior. Our goal was to determine whether spatial models were superior at large extents and across many different species. In addition, we investigated the importance of purely spatial effects in distribution patterns relative to the variation that could be explained through environmental conditions. We studied distribution patterns of 108 bird species in the conterminous United States using ten years of data from the Breeding Bird Survey. We compared the performance of spatially explicit regression models with non-spatial regression models using Akaike's information criterion. In addition, we partitioned the variance in species distributions into an environmental, a pure spatial and a shared component. The spatially-explicit conditional autoregressive regression models strongly outperformed the ordinary least squares regression models. In addition, partialling out the spatial component underlying the species' distributions showed that an average of 17% of the explained variation could be attributed to purely spatial effects independent of the spatial autocorrelation induced by the underlying environmental variables. We concluded that location in the range and neighborhood play an important role in the distribution of species. Spatially explicit models are expected to yield better predictions especially for mobile species such as birds, even in coarse-grained models with a large extent.     

Biosensors for environmental monitoring

Increasing environmental legislation which controls the release and the levels of certain chemicals in the environment has created a need for reliable monitoring of these substances in air, soil and especially water. Conventional analytical techniques, although highly precise, suffer from the disadvantages of high cost, the need for trained personnel and the fact that they are mostly laboratory bound. Biosensors because of their specificity, fast response times, low cost, portability, ease of use and a continuous real time signal, can present distinct advantages in certain cases. Their biological base makes them ideal for toxicological measurements which are suited for health and safety applications. Over the last 3-4 years there has been an increase in the number of publications concerning biosensors for environmental monitoring, especially in the field of pesticide measurements.This paper reviews some of the more important developments over the past 3-4 years.     

Host and geographic structure of endophytic and endolichenic fungi at a continental scale

? Premise of the study: Endophytic and endolichenic fungi occur in healthy tissues of plants and lichens, respectively, playing potentially important roles in the ecology and evolution of their hosts. However, previous sampling has not comprehensively evaluated the biotic, biogeographic, and abiotic factors that structure their communities. ? Methods: Using molecular data we examined the diversity, composition, and distributions of 4154 endophytic and endolichenic Ascomycota cultured from replicate surveys of ca. 20 plant and lichen species in each of five North American sites (Madrean coniferous forest, Arizona; montane semideciduous forest, North Carolina; scrub forest, Florida; Beringian tundra and forest, western Alaska; subalpine tundra, eastern central Alaska). ? Key results: Endolichenic fungi were more abundant and diverse per host species than endophytes, but communities of endophytes were more diverse overall, reflecting high diversity in mosses and lycophytes. Endophytes of vascular plants were largely distinct from fungal communities that inhabit mosses and lichens. Fungi from closely related hosts from different regions were similar in higher taxonomy, but differed at shallow taxonomic levels. These differences reflected climate factors more strongly than geographic distance alone. ? Conclusions: Our study provides a first evaluation of endophytic and endolichenic fungal associations with their hosts at a continental scale. Both plants and lichens harbor abundant and diverse fungal communities whose incidence, diversity, and composition reflect the interplay of climatic patterns, geographic separation, host type, and host lineage. Although culture-free methods will inform future work, our study sets the stage for empirical assessments of ecological specificity, metabolic capability, and comparative genomics.     

Modelling the distribution and compositional variation of plant communities at the continental scale

Aim

We investigate whether (1) environmental predictors allow to delineate the distribution of discrete community types at the continental scale and (2) how data completeness influences model generalization in relation to the compositional variation of the modelled entities.

Location

Europe.

Methods

We used comprehensive datasets of two community types of conservation concern in Europe: acidophilous beech forests and base‐rich fens. We computed community distribution models (CDMs) calibrated with environmental predictors to predict the occurrence of both community types, evaluating geographical transferability, interpolation and extrapolation under different scenarios of sampling bias. We used generalized dissimilarity modelling (GDM) to assess the role of geographical and environmental drivers in compositional variation within the predicted distributions.

Results

For the two community types, CDMs computed for the whole study area provided good performance when evaluated by random cross‐validation and external validation. Geographical transferability provided lower but relatively good performance, while model extrapolation performed poorly when compared with interpolation. Generalized dissimilarity modelling showed a predominant effect of geographical distance on compositional variation, complemented with the environmental predictors that also influenced habitat suitability.

Main conclusions

Correlative approaches typically used for modelling the distribution of individual species are also useful for delineating the potential area of occupancy of community types at the continental scale, when using consistent definitions of the modelled entity and high data completeness. The combination of CDMs with GDM further improves the understanding of diversity patterns of plant communities, providing spatially explicit information for mapping vegetation diversity and related habitat types at large scales.

     

Continuous monitoring of predator control operations at landscape scale

Summary   Exotic predators are considered pests to wildlife and agriculture, requiring predator-control programs. Effective monitoring of predator-control operations is essential to justify their considerable cost, but often impossible in practice. The difficulties are especially severe if the target species is small and wide-ranging, and the area to be protected is inaccessible and/or extensive. A convenient model predator of this type, the feral Ferret ( Mustela furo ), is subject to control on farmland in New Zealand. We monitored the distribution of Ferrets over 2400 ha in the central North Island, before and throughout a standard control operation by professional trappers. We used 24 units of a new automated monitoring device, the Scentinel, set in a grid at 1 per 100 ha. Over 11 weeks (11 February to 29 April 2005, 1718 trap nights), we recorded 1559 visits by small mammals, including 198 by Ferrets. By the end of the 4th week, Ferrets had been detected at 17 of 24 sites. Removal of Ferrets from the study area by contractors began during the 5th week, and was reflected in significant declines in the number of Ferret visits recorded per day ( P  = 0.008) and the number of sites visited ( P  = 0.021). Analysis of our extensive repeat-survey data by site-occupancy methods confirmed these trends in greater detail, while also allowing for variation in detectability.     

Biosensors for environmental monitoring.

In this article we will outline several biosensor applications which may fill existing technology gaps in the area of environmental monitoring. The requirements for these environmental biosensors, as well as difficulties in commercialization, are also addressed.     

Plant functional traits and environmental filters at a regional scale

Abstract. Links between plant traits and the environment, i.e. sets of plant attributes consistently associated with certain environmental conditions, are the consequence of the filtering effect of climatic, disturbance and biotic conditions. These filters determine which components of a species pool are assembled into local communities. We aimed at testing for consistent association between plant traits and climatic conditions along a steep regional gradient, divided into 13 climatically homogeneous sectors, in central-western Argentina. We analyzed 19 vegetative and regeneration traits of the 100 most abundant species along the gradient. For each trait, we tested for homogeneity of frequencies of different categories between sectors and the regional species pool, using the χ² statistic. We rejected H₀ in 71% of the pair-wise comparisons, which strongly suggests a ‘filtering effect’ of climatic factors on key plant functions. Vegetative traits were filtered more often than regeneration traits. Specific leaf area, life span, ramification, canopy height, leaf weight ratio, carbon investment into support tissue and pollination mode were the traits showing differences in the largest number of pair-wise comparisons. This is probably the first attempt to detect, on a quantitative, statistically conservative basis, consistent linkages between climatic factors and numerous plant traits, over a broad spectrum of environmental conditions and plant growth forms. We discuss the advantages and limitations of this approach in predicting vegetation structure and functioning under present environmental conditions, and those expected for the next century as a consequence of global change.     

Implementing a composite indicator approach for prioritizing activated sludge-based wastewater treatment plants at large spatial scale

Successful implementation of the European Urban Wastewater Treatment Directive requires a deepened and multidisciplinary knowledge of the wastewater systems. The development of ready-to-use tools for decision makers is, in this sense, a challenge. This paper proposes a methodology to efficiently prioritize wastewater treatment plants (WWTPs) on the basis of the relative environmental, social and public health impacts, taking into account the territorial context issues. The proposed methodology has led to the implementation of a composite indicator. The several choices made for its development, from the definition of framework to the final outcomes, have been evaluated in depth and are supported through methodological and statistical techniques. The potential use of the composite indicator with the inherent advantages and limitations are discussed in order to provide a ready-to-use tool for final users for WWTPs prioritization. Moreover, a concise methodology for composite index implementation is described.     

国家尺度自然保护地生态系统联网监测指标体系构建与应用研究

自然保护地是维护国家生态安全, 提升生物多样性保护成效的重要载体, 对保护地生态系统进行实时、高频、多尺度的监测是认知其动态变化的有效手段, 也是实现自然保护地生态系统健康管理的基石。由于目前我国没有形成自然保护地生态系统监测网络, 缺少统一的联网监测指标体系, 导致多数自然保护地生态系统组成家底不清、动态不明, 应对生物多样性保护新问题的能力不足, 并且在国家尺度上的自然保护地生态系统健康状况及保护成效评估缺乏联网监测数据支撑。因此, 亟需构建国家尺度的自然保护地生态系统组成和动态监测网络, 以及一套科学、系统、规范的自然保护地生态系统联网监测指标体系。该文针对自然保护地生物多样性和生态系统监测的目标和内容, 参考国内外现有的生态系统监测网络的指标体系, 确定了自然保护地生态系统联网监测指标体系建立和选取的基本原则, 建立了一套适用于国家尺度的自然保护地生态系统联网监测指标体系, 并在6个国家级自然保护区进行示范。构建的指标体系针对构成生态系统的6类关键要素(生境要素、生物要素、气象要素、土壤要素、大气和水环境要素、景观要素)制定了30个监测指标, 有效应用于森林、草地、荒漠、湿地等生态系统类型的自然保护地, 能够实现对不同类型自然保护地生态系统组分和结构的现状和演变特征进行长期、动态化监测, 并可为自然保护地保护成效评估和健康管理提供规范化、标准化的基础数据。