Assessing the role of EO in biodiversity monitoring: options for integrating in-situ observations with EO within the context of the EBONE concept

The European Biodiversity Observation Network (EBONE) is a European contribution on terrestrial monitoring to GEO BON, the Group on Earth Observations Biodiversity Observation Network. EBONE’s aims are to develop a system of biodiversity observation at regional, national and European levels by assessing existing approaches in terms of their validity and applicability starting in Europe, then expanding to regions in Africa. The objective of EBONE is to deliver: 1. A sound scientific basis for the production of statistical estimates of stock and change of key indicators; 2. The development of a system for estimating past changes and forecasting and testing policy options and management strategies for threatened ecosystems and species; 3. A proposal for a cost-effective biodiversity monitoring system. There is a consensus that Earth Observation (EO) has a role to play in monitoring biodiversity. With its capacity to observe detailed spatial patterns and variability across large areas at regular intervals, our instinct suggests that EO could deliver the type of spatial and temporal coverage that is beyond reach with in-situ efforts. Furthermore, when considering the emerging networks of in-situ observations, the prospect of enhancing the quality of the information whilst reducing cost through integration is compelling. This report gives a realistic assessment of the role of EO in biodiversity monitoring and the options for integrating in-situ observations with EO within the context of the EBONE concept (cfr. EBONE-ID1.4). The assessment is mainly based on a set of targeted pilot studies. Building on this assessment, the report then presents a series of recommendations on the best options for using EO in an effective, consistent and sustainable biodiversity monitoring scheme. The issues that we faced were many: 1. Integration can be interpreted in different ways. One possible interpretation is: the combined use of independent data sets to deliver a different but improved data set; another is: the use of one data set to complement another dataset. 2. The targeted improvement will vary with stakeholder group: some will seek for more efficiency, others for more reliable estimates (accuracy and/or precision); others for more detail in space and/or time or more of everything. 3. Integration requires a link between the datasets (EO and in-situ). The strength of the link between reflected electromagnetic radiation and the habitats and their biodiversity observed in-situ is function of many variables, for example: the spatial scale of the observations; timing of the observations; the adopted nomenclature for classification; the complexity of the landscape in terms of composition, spatial structure and the physical environment; the habitat and land cover types under consideration. 4. The type of the EO data available varies (function of e.g. budget, size and location of region, cloudiness, national and/or international investment in airborne campaigns or space technology) which determines its capability to deliver the required output. EO and in-situ could be combined in different ways, depending on the type of integration we wanted to achieve and the targeted improvement. We aimed for an improvement in accuracy (i.e. the reduction in error of our indicator estimate calculated for an environmental zone). Furthermore, EO would also provide the spatial patterns for correlated in-situ data. EBONE in its initial development, focused on three main indicators covering: (i) the extent and change of habitats of European interest in the context of a general habitat assessment; (ii) abundance and distribution of selected species (birds, butterflies and plants); and (iii) fragmentation of natural and semi-natural areas. For habitat extent, we decided that it did not matter how in-situ was integrated with EO as long as we could demonstrate that acceptable accuracies could be achieved and the precision could consistently be improved. The nomenclature used to map habitats in-situ was the General Habitat Classification. We considered the following options where the EO and in-situ play different roles: using in-situ samples to re-calibrate a habitat map independently derived from EO; improving the accuracy of in-situ sampled habitat statistics, by post-stratification with correlated EO data; and using in-situ samples to train the classification of EO data into habitat types where the EO data delivers full coverage or a larger number of samples. For some of the above cases we also considered the impact that the sampling strategy employed to deliver the samples would have on the accuracy and precision achieved. Restricted access to European wide species data prevented work on the indicator ‘abundance and distribution of species’. With respect to the indicator ‘fragmentation’, we investigated ways of delivering EO derived measures of habitat patterns that are meaningful to sampled in-situ observations

CityScapeLab Berlin: A Research Platform for Untangling Urbanization Effects on Biodiversity

Urban biodiversity conservation requires an understanding of how urbanization modulates biodiversity patterns and the associated ecosystem services. While important advances have been made in the conceptual development of urban biodiversity research over the last decades, challenges remain in understanding the interactions between different groups of taxa and the spatiotemporal complexity of urbanization processes. The CityScapeLab Berlin is a novel experimental research platform that allows the testing of theories on how urbanization affects biodiversity patterns and biotic interactions in general and the responses of species of conservation interest in particular. We chose dry grassland patches as the backbone of the research platform because dry grasslands are common in many urban regions, extend over a wide urbanization gradient, and usually harbor diverse and self-assembled communities. Focusing on a standardized type of model ecosystem allowed the urbanization effects on biodiversity to be unraveled from effects that would otherwise be masked by habitat- and land-use effects. The CityScapeLab combines different types of spatiotemporal data on (i) various groups of taxa from different trophic levels, (ii) environmental parameters on different spatial scales, and (iii) on land-use history. This allows for the unraveling of the effects of current and historical urban conditions on urban biodiversity patterns and the related ecological functions.BMBF, 01LC1501, BIBS-Verbund: Bridging in Biodiversity Science (BIBS

Incorporating Human Beliefs and Behaviors into Wildlife Ecology

Like much of the global biosphere, wildlife species have experienced rapid declines during the Anthropocene. Wildlife ecologists have responded to these crises by developing a range of technologies, techniques, and large datasets, which together have revolutionized the field, provided novel insights into the movements and behaviors of animals, and identified new risks and impacts to wildlife in a human-dominated world. While these advances have been vitally important, wildlife ecology has been slower to recognize and incorporate humans themselves into its new research domains. The chapters of this dissertation explore methods for better incorporating human behaviors, beliefs, actions, and infrastructure into the theories and approaches in wildlife ecology that have flourished in the last two decades. The research presented here demonstrates the importance of linking human beliefs and behaviors to wildlife ecology both by presenting novel findings and by showing the opportunities missed when narrow approaches are applied to complex socio-ecological problems.In Chapter 1, I provide a general introduction on the theories underlying this research, contextualize the research questions in light of the loss and recovery of large predators, and describe the research site where I collected much of the data for this dissertation. In Chapter 2, I apply the methods of movement ecology to some of the first fine-scale telemetry data collected on rifle hunters. I draw conclusions about their individual, site-level, and regional-level hunting behaviors and discuss the broad implications of these findings for hunting management. In Chapter 3, I examine livestock-predator conflict using approaches from both ecology and the social sciences. I describe a form of selection bias that is likely widespread but unreported due to the omission of social data from ecological models of conflict, and I offer guidelines for combining and translating ecological and social research on conflict. In Chapter 4, I explore the ecological impacts of one of the most globally widespread human constructions, the fence. I show for the first time the potential extent of fencing at large scales and discuss the wide variety of ecological effects of fences for both humans and ecosystems. I further highlight biases and gaps in fence research that have thus far limited a complete understanding of the environmental effects of these features. In Chapter 5, I conclude by making recommendations regarding how research might better incorporate human perceptions, decisions, and actions into ecology

Reductions in connectivity and habitat quality drive local extinctions in a plant diversity hotspot

It is well documented that habitat loss is a major cause of biodiversity decline. However, the roles of the different aspects of habitat loss in local extinctions are less understood. Anthropogenic destruction of an area of habitat causes immediate local extinction but subsequently three additional gradual drivers influence the likelihood of delayed extinction: decreased habitat patch size, lower connectivity and habitat deterioration. We investigated the role of these drivers in local extinctions of 82 declining species in a UK biodiversity hotspot. We combined a unique set of ≈7000 vegetation surveys and habitat maps from the 1930s with contemporary species’ occurrences. We extrapolated from these surveys to the whole 2500-km2 study area using habitat suitability surfaces. The strengths of drivers in explaining local extinctions over this 70 year period were determined by contrasting connectivity, patch size and habitat quality loss for locations at which a species went extinct and those with persisting occurrences. Species’ occurrences declined on average by 60%, with half of local extinctions attributable to immediate habitat loss and half to the gradual processes causing delayed extinctions. On average, locations where a species persisted had a 73% higher contemporary connectivity than those suffering extinctions, but showed no differences in historical connectivity. Furthermore, locations with extinctions experienced a 37% greater decline in suitability associated with changes in habitat type. The strength of the drivers and the proportion of extinctions depended on the species’ habitat specialism, but were affected only minimally by life-history characteristics. In conclusion, we identified a hierarchy of drivers influencing local extinction: with connectivity loss being the strongest, suitability change being moderately important, but changes in habitat patch size having only weak effects. We suggest conservation efforts could be most effective by strengthening connectivity along with reducing habitat deterioration, which would benefit a wide range of species

Using Remote Sensing and Biogeographic Modeling to Understand the Oak Savannas of the Sheyenne National Grassland, North Dakota, USA

Oak savannas are valuable and complex ecosystems that provide multiple ecosystem goods and services, including grazing for livestock, watershed regulation, and recreation. These ecosystems of the woodland-prairie ecoregion of the Midwestern United States are, however, in danger of disappearing. The Sheyenne National Grassland, North Dakota, a protected Prairie grassland-savanna, is a representative of such rare habitats, where oak savanna is found at the landscape scale. In this research, I map the distribution patterns of oak savanna in the Sheyenne using a combination of remote sensing and geospatial datasets, including landscape topography, soils, and fire disturbance. Further, I interpret the performance of a suite of advanced Species Distribution Modeling approaches including Maximum Entropy, Random Forest, Generalized Boosted Model, and Classification Tree to analyze the primary environmental and management factors influencing oak distributions at landscape scales. Woody canopy cover was estimated with high classification accuracy (80-95%) for two study areas of the Sheyenne National Grassland. Among the four species distribution modeling approaches tested, the Random Forest (RF) approach provided the best predictive model. RF model parameters indicate that oak trees favor gently sloping locations, on well-drained upland and sandy soils, with north-facing aspect. While no direct data on water relationships were possible in this research, the importance of the topographic and soil variables in the SDM presumably reflect oak preference for locations and soils that are not prone to water saturation, with milder summer temperatures (i.e. northern aspects), providing conditions suitable for seedling establishment and growth. This research increases our understanding of the biogeography of Midwestern tall-grass oak savannas and provides a decision-support tool for oak savanna management