Anna F. Cord

Modelling the species distribution of flat-headed cats (Prionailurus planiceps), an endangered South-East Asian small felid.

Background The flat-headed cat (Prionailurus planiceps) is one of the worlds least known, highly threatened felids with a distribution restricted to tropical lowland rainforests in Peninsular Thailand/Malaysia, Borneo and Sumatra. Throughout its geographic range large-scale anthropogenic transformation processes, including the pollution of fresh-water river systems and landscape fragmentation, raise concerns regarding its conservation status. Despite an increasing number of camera-trapping field surveys for carnivores in South-East Asia during the past two decades, few of these studies recorded the flat-headed cat. Methodology/Principal Findings In this study, we designed a predictive species distribution model using the Maximum Entropy (MaxEnt) algorithm to reassess the potential current distribution and conservation status of the flat-headed cat. Eighty-eight independent species occurrence records were gathered from field surveys, literature records, and museum collections. These current and historical records were analysed in relation to bioclimatic variables (WorldClim), altitude (SRTM) and minimum distance to larger water resources (Digital Chart of the World). Distance to water was identified as the key predictor for the occurrence of flat-headed cats (>50% explanation). In addition, we used different land cover maps (GLC2000, GlobCover and SarVision LLC for Borneo), information on protected areas and regional human population density data to extract suitable habitats from the potential distribution predicted by the MaxEnt model. Between 54% and 68% of suitable habitat has already been converted to unsuitable land cover types (e.g. croplands, plantations), and only between 10% and 20% of suitable land cover is categorised as fully protected according to the IUCN criteria. The remaining habitats are highly fragmented and only a few larger forest patches remain. Conclusion/Significance Based on our findings, we recommend that future conservation efforts for the flat-headed cat should focus on the identified remaining key localities and be implemented through a continuous dialogue between local stakeholders, conservationists and scientists to ensure its long-term survival. The flat-headed cat can serve as a flagship species for the protection of several other endangered species associated with the threatened tropical lowland forests and surface fresh-water sources in this region.

glUV: a global UV-B radiation data set for macroecological studies

Summary Macroecology has prospered in recent years due in part to the wide array of climatic data, such as those provided by the WorldClim and CliMond data sets, which has become available for research. However, important environmental variables have still been missing, including spatial data sets on UV-B radiation, an increasingly recognized driver of ecological processes. We developed a set of global UV-B surfaces (glUV) suitable to match common spatial scales in macroecology. Our data set is based on remotely sensed records from NASAs Ozone Monitoring Instrument (Aura-OMI). Following a similar approach as for the WorldClim and CliMond data sets, we processed daily UV-B measurements acquired over a period of eight years into monthly mean UV-B data and six ecologically meaningful UV-B variables with a 15-arc minute resolution. These bioclimatic variables represent Annual Mean UV-B, UV-B Seasonality, Mean UV-B of Highest Month, Mean UV-B of Lowest Month, Sum of Monthly Mean UV-B during Highest Quarter and Sum of Monthly Mean UV-B during Lowest Quarter. We correlated our data sets with selected variables of existing bioclimatic surfaces for land and with Terra–MODIS Sea Surface Temperature for ocean regions to test for relations to known gradients and patterns. UV-B surfaces showed a distinct seasonal variance at a global scale, while the intensity of UV-B radiation decreased towards higher latitudes and was modified by topographic and climatic heterogeneity. UV-B surfaces were correlated with global mean temperature and annual mean radiation data, but exhibited variable spatial associations across the globe. UV-B surfaces were otherwise widely independent of existing bioclimatic surfaces. Our data set provides new climatological information relevant for macroecological analyses. As UV-B is a known driver of numerous biological patterns and processes, our data set offers the potential to generate a better understanding of these dynamics in macroecology, biogeography, global change research and beyond. The glUV data set containing monthly mean UV-B data and six derived UV-B surfaces is freely available for download at: http://www.ufz.de/gluv.

Realigning the land-sharing/land-sparing debate to match conservation needs: considering diversity scales and land- use history

The “land sharing versus land sparing” concept provides a framework for comparing potential land use patterns in terms of trade-offs between biodiversity conservation and agricultural yields at a landscape scale. Here, we raise two additional aspects to be considered in the sparing/sharing debate, supported by a review of available literature. First, beta and gamma (instead of alpha) diversity measures capture landscape scale variance in biodiversity in response to land use changes and should be considered for the long-term management of agricultural landscapes. Moreover, beta and gamma diversity may better account for comparisons of biodiversity between spared and shared land use options. Second, land use history has a pronounced influence on the complexity and variance in agricultural habitat niches at a landscape scale, which in turn may determine the relevance of sparing or sharing land use options. Appropriate and comparable biodiversity metrics and the recognition of landscape history are two vital preconditions in aligning biological conservation goals with maximized yields within the sparing/sharing framework.

Harmonizing Biodiversity Conservation and Productivity in the Context of Increasing Demands on Landscapes

Abstract Biodiversity conservation and agricultural production are often seen as mutually exclusive objectives. Strategies for reconciling them are intensely debated. We argue that harmonization between biodiversity conservation and crop production can be improved by increasing our understanding of the underlying relationships between them. We provide a general conceptual framework that links biodiversity and agricultural production through the separate relationships between land use and biodiversity and between land use and production. Hypothesized relationships are derived by synthesizing existing empirical and theoretical ecological knowledge. The framework suggests nonlinear relationships caused by the multifaceted impacts of land use (composition, configuration, and intensity). We propose solutions for overcoming the apparently dichotomous aims of maximizing either biodiversity conservation or agricultural production and suggest new hypotheses that emerge from our proposed framework.

Priorities to Advance Monitoring of Ecosystem Services Using Earth Observation

Managing ecosystem services in the context of global sustainability policies requires reliable monitoring mechanisms. While satellite Earth observation offers great promise to support this need, significant challenges remain in quantifying connections between ecosystem functions, ecosystem services, and human well-being benefits. Here, we provide a framework showing how Earth observation together with socioeconomic information and model-based analysis can support assessments of ecosystem service supply, demand, and benefit, and illustrate this for three services. We argue that the full potential of Earth observation is not yet realized in ecosystem service studies. To provide guidance for priority setting and to spur research in this area, we propose five priorities to advance the capabilities of Earth observation-based monitoring of ecosystem services.

Coupling Satellite Data with Species Distribution and Connectivity Models as a Tool for Environmental Management and Planning in Matrix-Sensitive Species.

Abstract Climate change and anthropogenic habitat fragmentation are considered major threats for global biodiversity. As a direct consequence, connectivity is increasingly disrupted in many species, which might have serious consequences that could ultimately lead to the extinction of populations. Although a large number of reserves and conservation sites are designated and protected by law, potential habitats acting as inter-population connectivity corridors are, however, mostly ignored in the common practice of environmental planning. In most cases, this is mainly caused by a lack of quantitative measures of functional connectivity available for the planning process. In this study, we highlight the use of fine-scale potential connectivity models (PCMs) derived from multispectral satellite data for the quantification of spatially explicit habitat corridors for matrix-sensitive species of conservation concern. This framework couples a species distribution model with a connectivity model in a two-step framework, where suitability maps from step 1 are transformed into maps of landscape resistance in step 2 filtered by fragmentation thresholds. We illustrate the approach using the sand lizard (Lacerta agilis L.) in the metropolitan area of Cologne, Germany, as a case study. Our model proved to be well suited to identify connected as well as completely isolated populations within the study area. Furthermore, due to its fine resolution, the PCM was also able to detect small linear structures known to be important for sand lizards’ inter-population connectivity such as railroad embankments. We discuss the applicability and possible implementation of PCMs to overcome shortcomings in the common practice of environmental impact assessments.

Integration of satellite remote sensing data in ecosystem modelling at local scales: Practices and trends

Spatiotemporal ecological modelling of terrestrial ecosystems relies on climatological and biophysical Earth observations. Due to their increasing availability, global coverage, frequent acquisition and high spatial resolution, satellite remote sensing (SRS) products are frequently integrated to in situ data in the development of ecosystem models (EMs) quantifying the interaction among the vegetation component and the hydrological, energy and nutrient cycles. This review highlights the main advances achieved in the last decade in combining SRS data with EMs, with particular attention to the challenges modellers face for applications at local scales (e.g. small watersheds). We critically review the literature on progress made towards integration of SRS data into terrestrial EMs: (1) as input to define model drivers; (2) as reference to validate model results; and (3) as a tool to sequentially update the state variables, and to quantify and reduce model uncertainty. The number of applications provided in the literature shows that EMs may profit greatly from the inclusion of spatial parameters and forcings provided by vegetation and climatic-related SRS products. Limiting factors for the application of such models to local scales are: (1) mismatch between the resolution of SRS products and model grid; (2) unavailability of specific products in free and public online repositories; (3) temporal gaps in SRS data; and (4) quantification of model and measurement uncertainties. This review provides examples of possible solutions adopted in recent literature, with particular reference to the spatiotemporal scales of analysis and data accuracy. We propose that analysis methods such as stochastic downscaling techniques and multi-sensor/multi-platform fusion approaches are necessary to improve the quality of SRS data for local applications. Moreover, we suggest coupling models with data assimilation techniques to improve their forecast abilities. This review encourages the use of SRS data in EMs for local applications, and underlines the necessity for a closer collaboration among EM developers and remote sensing scientists. With more upcoming satellite missions, especially the Sentinel platforms, concerted efforts to further integrate SRS into modelling are in great demand and these types of applications will certainly proliferate.

Effects of UV-B radiation on leaf hair traits of invasive plants—Combining historical herbarium records with novel remote sensing data

Ultraviolet-B (UV-B) radiation is a key but under-researched environmental factor that initiates diverse responses in plants, potentially affecting their distribution. To date, only a few macroecological studies have examined adaptations of plant species to different levels of UV-B. Here, we combined herbarium specimens of Hieracium pilosella L. and Echium vulgare L. with a novel UV-B dataset to examine differences in leaf hair traits between the plants’ native and alien ranges. We analysed scans of 336 herbarium specimens using standardized measurements of leaf area, hair density (both species) and hair length (H. pilosella only). While accounting for other bioclimatic variables (i.e. temperature, precipitation) and effects of herbivory, we examined whether UV-B exposure explains the variability and geographical distribution of these traits in the native (Northern Hemisphere) vs. the alien (Southern Hemisphere) range. UV-B explained the largest proportion of the variability and geographical distribution of hair length in H. pilosella (relative influence 67.1%), and hair density in E. vulgare (66.2%). Corresponding with higher UV-B, foliar hairs were 25% longer for H. pilosella and 25% denser for E. vulgare in records from the Southern as compared to those from the Northern Hemisphere. However, focusing on each hemisphere separately or controlling for its effect in a regression analysis, we found no apparent influence of UV-B radiation on hair traits. Thus, our findings did not confirm previous experimental studies which suggested that foliar hairs may respond to higher UV-B intensities, presumably offering protection against detrimental levels of radiation. We cannot rule out UV-B radiation as a possible driver because UV-B radiation was the only considered variable that differed substantially between the hemispheres, while bioclimatic conditions (e.g. temperature, precipitation) and other considered variables (herbivory damage, collection date) were at similar levels. However, given that either non-significant or inconclusive relationships were detected within hemispheres, alternative explanations of the differences in foliar hairs are more likely, including the effects of environment, genotypes or herbivory.

Modelling patterns of pollinator species richness and diversity using satellite image texture

Assessing species richness and diversity on the basis of standardised field sampling effort represents a cost- and time-consuming method. Satellite remote sensing (RS) can help overcome these limitations because it facilitates the collection of larger amounts of spatial data using cost-effective techniques. RS information is hence increasingly analysed to model biodiversity across space and time. Here, we focus on image texture measures as a proxy for spatial habitat heterogeneity, which has been recognized as an important determinant of species distributions and diversity. Using bee monitoring data of four years (2010–2013) from six 4 × 4 km field sites across Central Germany and a multimodel inference approach we test the ability of texture features derived from Landsat-TM imagery to model local pollinator biodiversity. Textures were shown to reflect patterns of bee diversity and species richness to some extent, with the first-order entropy texture and terrain roughness being the most relevant indicators. However, the texture measurements accounted for only 3–5% of up to 60% of the variability that was explained by our final models, although the results are largely consistent across different species groups (bumble bees, solitary bees). While our findings provide indications in support of the applicability of satellite imagery textures for modeling patterns of bee biodiversity, they are inconsistent with the high predictive power of texture metrics reported in previous studies for avian biodiversity. We assume that our texture data captured mainly heterogeneity resulting from landscape configuration, which might be functionally less important for wild bees than compositional diversity of plant communities. Our study also highlights the substantial variability among taxa in the applicability of texture metrics for modelling biodiversity.

A review of multi-criteria optimization techniques for agricultural land use allocation

Abstract Optimal land use allocation with the intention of ecosystem services provision and biodiversity conservation is one of the key challenges in agricultural management. Optimization techniques have been especially prevalent for solving land use problems; however, there is no guideline supporting the selection of an appropriate method. To enhance the applicability of optimization techniques for real-world case studies, this study provides an overview of optimization methods used for targeting land use decisions in agricultural areas. We explore their relative abilities for the integration of stakeholders and the identification of ecosystem service trade-offs since these are especially pertinent to land use planners. Finally, we provide recommendations for the use of the different optimization methods. For example, scalarization methods (e.g., reference point methods, tabu search) are particularly useful for a priori or interactive stakeholder integration; whereas Pareto-based approaches (e.g., evolutionary algorithms) are appropriate for trade-off analyses and a posteriori stakeholder involvement.