Predicting forest grouse distribution taking account of spatial autocorrelation

Spatially explicit, multi-scale models for predictions of species potential distribution can be useful tools for integrating biodiversity considerations in planning and strategic environmental assessment. In such models, the occurrences of focal species are related to habitat and landscape variables, which in urbanising areas should also include effects of urban disturbances. Moreover, the accuracy of the spatial predictive models may be affected by spatial autocorrelation, which means that a part of the variance is explained by neighbouring values. The aim of this study was to explore the effects of habitat and disturbance patterns on the distribution of two forest grouse species, Tetrao urogallus and Bonasa bonasia, and to detect and model the effects of spatial autocorrelation. The distribution of the two species could be explained in terms of reduction of a main predator, habitat quality, quantity and connectivity, including urban disturbances. The residuals of the initial regressions showed positive spatial autocorrelation that could be quantified by using a spatial probit model. The application of the spatial probit model revealed strongly significant spatial dependencies for both species. Furthermore, the model fit could be increased for T. urogallus by applying this model. The results implied that both species distributions might be affected by both reactions to the underlying land-use pattern, but also by interaction with neighbours. The use of the spatial probit model is a way to incorporate spatial interactions that otherwise cannot be captured by the independent variables.     

Modelling the presence of anchovy Engraulis encrasicolus in the Aegean Sea during early summer, based on satellite environmental data

Acoustic and satellite environmental data as well as bathymetry data were used to model the presence of anchovy, Engraulis encrasicolus during early summer in the northern Aegean Sea (Eastern Mediterranean). Generalized Additive Models (GAMs) were used for modelling and subsequently applied in a predictive mode to identify those areas in the Greek Seas and the entire Mediterranean basin that could support species’ presence. Model results were evaluated with the estimation of Receiver Operating Characteristic (ROC)-plots as well as qualitatively, based on (a) acoustic data from concurrent studies in certain areas of the northern Aegean Sea that were not included in the estimation of the GAM model and (b) historical acoustic data from the central Aegean and Ionian Seas. Mapping the estimated environmental conditions in the Mediterranean basin indicated areas that generally agree with the known distribution grounds of anchovy, such as the straits of Sicily and coastal waters of Tunisia, areas in the Tyrrhenian Sea, the Adriatic Sea, the Gulf of Lions and the Catalan Sea. Guest editor: V. D. Valavanis Essential Fish Habitat Mapping in the Mediterranean     

Calibrating Ellenberg indicator values for moisture,acidity, nutrient availability and salinity in the Netherlands

A general calibration of Ellenberg indicator values for moisture, acidity, nutrient availability and salinity was carried out on a large database of relevées and environmental variables from a variety of ecosystems in the Netherlands.Satisfying relationships with Ellenberg indicator values for moisture, acidity and salinity were found for mean groundwater level in spring time, soil pH and chloride concentration in groundwater. For mean groundwater level in spring and chloride concentration in groundwater subdivision of the database led to clearer relationships with indicator values. For the Ellenberg indicator value for nutrient availability satisfying calibration results were only achieved with data on standing crops and N stock in standing crop. The relationship with soil chemical variables was less clear.Although the correlation between indicator and measured values is obvious, the variation around the regression lines is considerable. However, because of the size and composition of the database, it is unlikely that our calibration results can be much improved by adding more (Dutch) data.The calibration results will be applied in the multi-stress model SMART-MOVE, developed to predict changes in species composition due to acidification, eutrophication and the effects of lowering groundwater.     

Modelling of rare plant species richness by landscape variables in an agriculture area in Finland

A multivariate linear regression model is proposed for predicting and mapping rare vascular plant species richness in Finnish agricultural landscapes according to landscape variables. The data used in developing the model were derived from a floristic inventory from 105 0.5 km × 0.5 km grid squares. Using a stepwise multiple regression technique, four landscape variables were found to explain 71.8% of the variability in the number of rare plant species. The results suggest that the local `hotspots' of rare plants (squares with 5 rare taxa) are mainly found in heterogeneous river valleys, where extensive semi-natural grasslands and herb-rich forests occur on the steep slopes. According to other similar studies, intermediate human disturbance increases the number of rare species in agricultural landscapes. It appears that empirical models based on landscape variables derived from digital maps can provide relatively accurate surrogates for extensive field surveys and fine-scale observations on the distributions of rare taxa in agricultural landscapes. Potential reasons for the performance of the model and the ecology and habitats of the species concerned are discussed.     

遥感与GIS支持下的盘锦湿地水禽栖息地适宜性评价

盘锦湿地位于辽河三角洲的核心地带,是重要的水禽栖息地。受自然和人类活动的影响,该地区水禽栖息的生境面积不断缩小,致使湿地生态系统受到严重破坏,生物种群数量急剧下降。在此背景下,以GIS/RS为技术手段,根据盘锦湿地环境特点,采用层次分析法,从干扰条件、水源状况、遮蔽物以及食物丰富度4个方面对盘锦湿地水禽栖息地适宜性进行空间分析,再按照不同指标因子的等级划分,得到适宜性最好、适宜性良好、适宜性一般以及适宜性最差4个等级的盘锦湿地水禽栖息地适宜性评价图。结果表明:(1)盘锦湿地水禽栖息地适宜性最好、适宜性良好、适宜性一般、适宜性差的面积分别为803.79,913.47,905.29,1023.65hm2,分别占总面积的22.04%,25.05%,24.83%,28.07%;(2)前三类总面积为2622.55 hm2,占71.93%,说明盘锦地区水禽栖息的适宜地区较多,主要分布于流域、河漫滩等人为干扰较少的芦苇湿地以及食物和水源比较充足的大面积耕地中;(3)基于遥感分析和GIS空间分析技术,能有效、简便地对湿地生态环境处于快速动态变化中的水禽栖息地适宜性进行快速、客观准确的评价分析,其结果能为盘锦湿地水禽种群及其生存环境的规划、保护、管理和决策提供数据支持和科学依据。     

LIVES: a new habitat modelling technique for predicting the distribution of species’ occurrences using presence-only data based on limiting factor theory

Predictive modelling techniques using presence-only data have attracted increasing attention because they can provide information on species distributions and their potential habitat for conservation and ecosystem management. However, the existing predictive modelling techniques have several limitations. Here, we propose a novel predictive modelling technique, Limiting Variable and Environmental Suitability (LIVES), for predicting the distributions and potential habitats of species using presence-only data. It is based on limiting factor theory, which postulates that the occurrence of a species is only determined by the factor that most limits its distribution. LIVES predicts the suitability of a candidate grid cell for a species in terms of limiting environmental factor. It also predicts the most limiting factor or the potential limiting factor at the grid cell. The environmental factors can be climatic, geological, biological and any other relevant environmental factors, whether quantitative or qualitative. The predicted habitats consist of the current distribution of the species and the potentially suitable areas for the species where there is currently no record of occurrence. We also compare several properties of LIVES and other predictive modelling techniques. On the basis of 1,000 simulations, the average predictions of LIVES are more accurate than the two other commonly used modelling techniques (BIOCLIM and DOMAIN) for presence-only data.     

Vegetation change in semiarid communities

In arid regions, the effects of grazing or sparing management on natural communities of long-lived plants generally take decades to become evident. Event-driven dynamic behavior, unpredictable and low rainfall and complicated interactions between species make it difficult to assess probabilities and time scales of vegetation change.To gain a better understanding of the main processes and mechanisms involved in vegetation change, we have developed a spatially explicit individual based model that simulates changes in plant communities over long time spans. The model, based on life-history attributes of the five dominant component plant species of a typical Karoo shrub community, follows the fate of each individual plant within the community, the sum of which is community dynamics. The model explores the differential effects of a realistic range of rainfall pattern on the abilities of these species to compete, survive, grow and reproduce.The specific aim of the model is to identify key processes of vegetation change and to calculate probabilities and timespans for transitions between different vegetation states. Such knowledge is needed for species conservation and sustained animal production.We show that the time-scale for changes of the dynamic state of the system are long compared with human lifespans. Employing the full range of possible rainfall scenarios showed that short-term community dynamics (years to decades) and species composition depend strongly on the short-term (years) sequence of rainfall events. In all simulation experiments the final vegetation state varied by more than 37% after a 60 year simulation period. Simulating resting of an overgrazed part of the shrub community indicated that little improvement in rangland condition was likely during a period of 60 years. Even such active management, as (simulated) clearing of unpalatable shrubs, resulted in only a 66% probability that degraded shrubland would be in good condition after 60 years resting. Simulated overgrazing of a rangeland in good initial condition only became obvious 40 or 50 years after the initiation of heavy grazing, and after 70 years the mean vegetation state eventually reached that of an overgrazed rangeland.     

Developing landscape habitat models for rare amphibians with small geographic ranges: a case study of Siskiyou Mountains salamanders in the western USA

To advance the development of conservation planning for rare species with small geographic ranges, we determined habitat associations of Siskiyou Mountains salamanders (Plethodon stormi) and developed habitat suitability models at fine (10 ha), medium (40 ha), and broad (202 ha) spatial scales using available Geographic Information Systems data and logistic regression analysis with an information theoretic approach. Across spatial scales, there was very little support for models with structural habitat features, such as tree canopy cover and conifer diameter. Model-averaged 95% confidence intervals for regression coefficients and associated odds ratios indicated that the occurrence of Siskiyou Mountains salamanders was positively associated with rocky soils and Pacific madrone (Abutus menziesii) and negatively associated with elevation and white fir (Abies concolor); these associations were consistent across 3 spatial scales. The occurrence of this species also was positively associated with hardwood density at the medium spatial scale. Odds ratios projected that a 10% decrease in white fir abundance would increase the odds of salamander occurrence 3.02–4.47 times, depending on spatial scale. We selected the model with rocky soils, white fir, and Oregon white oak (Quercus garryana) as the best model across 3 spatial scales and created habitat suitability maps for Siskiyou Mountains salamanders by projecting habitat suitability scores across the landscape. Our habitat suitability models and maps are applicable to selection of priority conservation areas for Siskiyou Mountains salamanders, and our approach can be easily adapted to conservation of other rare species in any geographical location.     

Factors affecting spatial and temporal distribution of Ostracoda assemblages in different macrophyte habitats of a shallow lake (Lake Fehér,Hungary)

Spatial and temporal distribution, abundance and habitat preferences of ostracod assemblages were studied in various habitats of Lake Fehér in Hungary. Of the 20 species Fabaeformiscandona hyalina and Cypridopsis hartwigi are new to Hungary. Significant spatial differences were recorded in the composition and abundance of the Ostracoda assemblages in the different habitats. The most diverse and abundant assemblages were found in the reed-belt, however, the mean abundance of the species differed in the different macrophyte communities of the reed-belt. The species richness and abundance were considerably lower in disturbed sites than in the other habitats. Most of the 20 species showed distinct habitat preferences and ecological tolerances. Especially the presence and coverage of the vegetation and the dissolved oxygen content influenced the distribution and abundance of the species.     

Using functional–structural plant models to study, understand and integrate plant development and ecophysiology

Functional–structural plant models (FSPMs) explore and integrate relationships between a plant’s structure and processes that underlie its growth and development. In recent years, the range of topics being addressed by scientists interested in functional–structural plant modelling has expanded greatly. FSPM techniques are now being used to dynamically simulate growth and development occurring at the microscopic scale involving cell division in plant meristems to the macroscopic scales of whole plants and plant communities. The plant types studied also cover a broad spectrum from algae to trees. FSPM is highly interdisciplinary and involves scientists with backgrounds in plant physiology, plant anatomy, plant morphology, mathematics, computer science, cellular biology, ecology and agronomy. This special issue of Annals of Botany features selected papers that provide examples of comprehensive functional–structural models, models of key processes such as partitioning of resources, software for modelling plants and plant environments, data acquisition and processing techniques and applications of functional–structural plant models for agronomic purposes.     

Functional–structural plant models: a growing paradigm for plant studies

A number of research groups in various areas of plant biology as well as computer science and applied mathematics have addressed modelling the spatiotemporal dynamics of growth and development of plants. This has resulted in development of functional–structural plant models (FSPMs). In FSPMs, the plant structure is always explicitly represented in terms of a network of elementary units. In this respect, FSPMs are different from more abstract models in which a simplified representation of the plant structure is frequently used (e.g. spatial density of leaves, total biomass, etc.). This key feature makes it possible to build modular models and creates avenues for efficient exchange of model components and experimental data. They are being used to deal with the complex 3-D structure of plants and to simulate growth and development occurring at spatial scales from cells to forest areas, and temporal scales from seconds to decades and many plant generations. The plant types studied also cover a broad spectrum, from algae to trees. This special issue of Annals of Botany features selected papers on FSPM topics such as models of morphological development, models of physical and biological processes, integrated models predicting dynamics of plants and plant communities, modelling platforms, methods for acquiring the 3-D structures of plants using automated measurements, and practical applications for agronomic purposes.