Paola Mairota

Forest disturbances under climate change

Forest disturbances are sensitive to climate. However, our understanding of disturbance dynamics in response to climatic changes remains incomplete, particularly regarding large-scale patterns, interaction effects and dampening feedbacks. Here we provide a global synthesis of climate change effects on important abiotic (fire, drought, wind, snow and ice) and biotic (insects and pathogens) disturbance agents. Warmer and drier conditions particularly facilitate fire, drought and insect disturbances, while warmer and wetter conditions increase disturbances from wind and pathogens. Widespread interactions between agents are likely to amplify disturbances, while indirect climate effects such as vegetation changes can dampen long-term disturbance sensitivities to climate. Future changes in disturbance are likely to be most pronounced in coniferous forests and the boreal biome. We conclude that both ecosystems and society should be prepared for an increasingly disturbed future of forests.

Translating land cover/land use classifications to habitat taxonomies for landscape monitoring: a Mediterranean assessment

Periodic monitoring of biodiversity changes at a landscape scale constitutes a key issue for conservation managers. Earth observation (EO) data offer a potential solution, through direct or indirect mapping of species or habitats. Most national and international programs rely on the use of land cover (LC) and/or land use (LU) classification systems. Yet, these are not as clearly relatable to biodiversity in comparison to habitat classifications, and provide less scope for monitoring. While a conversion from LC/LU classification to habitat classification can be of great utility, differences in definitions and criteria have so far limited the establishment of a unified approach for such translation between these two classification systems. Focusing on five Mediterranean NATURA 2000 sites, this paper considers the scope for three of the most commonly used global LC/LU taxonomies—CORINE Land Cover, the Food and Agricultural Organisation (FAO) land cover classification system (LCCS) and the International Geosphere-Biosphere Programme to be translated to habitat taxonomies. Through both quantitative and expert knowledge based qualitative analysis of selected taxonomies, FAO-LCCS turns out to be the best candidate to cope with the complexity of habitat description and provides a framework for EO and in situ data integration for habitat mapping, reducing uncertainties and class overlaps and bridging the gap between LC/LU and habitats domains for landscape monitoring—a major issue for conservation. This study also highlights the need to modify the FAO-LCCS hierarchical class description process to permit the addition of attributes based on class-specific expert knowledge to select multi-temporal (seasonal) EO data and improve classification. An application of LC/LU to habitat mapping is provided for a coastal Natura 2000 site with high classification accuracy as a result.

The Earth Observation Data for Habitat Monitoring (EODHaM) System

To support decisions relating to the use and conservation of protected areas and surrounds, the EU-funded BIOdiversity multi-SOurce monitoring System: from Space TO Species (BIO_SOS) project has developed the Earth Observation Data for HAbitat Monitoring (EODHaM) system for consistent mapping and monitoring of biodiversity. The EODHaM approach has adopted the Food and Agriculture Organization Land Cover Classification System (LCCS) taxonomy and translates mapped classes to General Habitat Categories (GHCs) from which Annex I habitats (EU Habitats Directive) can be defined. The EODHaM system uses a combination of pixel and object-based procedures. The 1st and 2nd stages use earth observation (EO) data alone with expert knowledge to generate classes according to the LCCS taxonomy (Levels 1 to 3 and beyond). The 3rd stage translates the final LCCS classes into GHCs from which Annex I habitat type maps are derived. An additional module quantifies changes in the LCCS classes and their components, indices derived from earth observation, object sizes and dimensions and the translated habitat maps (i.e., GHCs or Annex I). Examples are provided of the application of EODHaM system elements to protected sites and their surrounds in Italy, Wales (UK), the Netherlands, Greece, Portugal and India.

Potential of remote sensing to predict species invasions A modelling perspective

Understanding the causes and effects of species invasions is a priority in ecology and conservation biology. One of the crucial steps in evaluating the impact of invasive species is to map changes in their actual and potential distribution and relative abundance across a wide region over an appropriate time span. While direct and indirect remote sensing approaches have long been used to assess the invasion of plant species, the distribution of invasive animals is mainly based on indirect methods that rely on environmental proxies of conditions suitable for colonization by a particular species. The aim of this article is to review recent efforts in the predictive modelling of the spread of both plant and animal invasive species using remote sensing, and to stimulate debate on the potential use of remote sensing in biological invasion monitoring and forecasting. Specifically, the challenges and drawbacks of remote sensing techniques are discussed in relation to: i) developing species distribution models, and ii) studying life cycle changes and phenological variations. Finally, the paper addresses the open challenges and pitfalls of remote sensing for biological invasion studies including sensor characteristics, upscaling and downscaling in species distribution models, and uncertainty of results.

Using spatial simulations of habitat modification for adaptive management of protected areas: Mediterranean grassland modification by woody plant encroachment

SUMMARY Spatial simulation may be used to model the potential effects of current biodiversity approaches on future habitat modification under differing climate change scenarios. To illustrate the approach, spatial simulation models, including landscape-level forest dynamics,weredevelopedforasemi-naturalgrassland of conservation concern in a southern Italian protected area, which was exposed to woody vegetation encroachment. A forest landscape dynamics simulator (LANDIS-II) under conditions of climate change, current fire and alternative management regimes was used to develop scenario maps. Landscape pattern metrics provided data on fragmentation and habitat quality degradation, and quantified the spatial spread of different tree species within grassland habitats. The models indicated that approximately one-third of the grassland area would be impacted by loss, fragmentation and degradation in the next 150 years. Differing forest management regimes appear to influence the type of encroaching species and the density of encroaching vegetation. Habitat modifications are likely to affect species distribution and interactions, as well as local ecosystem functioning, leading to changes in estimated conservation value. A site-scale conservation strategy based on feasible integrated fire and forest management options is proposed, consideringthedebateontheeffectivenessofprotected areas for the conservation of ecosystem services in a changing climate. This needs to be tested through further modelling and scenario analysis, which would benefit from the enhancement of current modelling capabilities of LANDIS-II and from combination with remotesensingtechnologies,toprovideearlysignalsof environmentalshiftsbothwithinandoutsideprotected areas.

Very high resolution Earth Observation features for testing the direct and indirect effects of landscape structure on local habitat quality

Abstract Modelling the empirical relationships between habitat quality and species distribution patterns is the first step to understanding human impacts on biodiversity. It is important to build on this understanding to develop a broader conceptual appreciation of the influence of surrounding landscape structure on local habitat quality, across multiple spatial scales. Traditional models which report that ‘habitat amount’ in the landscape is sufficient to explain patterns of biodiversity, irrespective of habitat configuration or spatial variation in habitat quality at edges, implicitly treat each unit of habitat as interchangeable and ignore the high degree of interdependence between spatial components of land-use change. Here, we test the contrasting hypothesis, that local habitat units are not interchangeable in their habitat attributes, but are instead dependent on variation in surrounding habitat structure at both patch- and landscape levels. As the statistical approaches needed to implement such hierarchical causal models are observation-intensive, we utilise very high resolution (VHR) Earth Observation (EO) images to rapidly generate fine-grained measures of habitat patch internal heterogeneities over large spatial extents. We use linear mixed-effects models to test whether these remotely-sensed proxies for habitat quality were influenced by surrounding patch or landscape structure. The results demonstrate the significant influence of surrounding patch and landscape context on local habitat quality. They further indicate that such an influence can be direct, when a landscape variable alone influences the habitat structure variable, and/or indirect when the landscape and patch attributes have a conjoined effect on the response variable. We conclude that a substantial degree of interaction among spatial configuration effects is likely to be the norm in determining the ecological consequences of habitat fragmentation, thus corroborating the notion of the spatial context dependence of habitat quality.

Very high resolution Earth observation features for monitoring plant and animal community structure across multiple spatial scales in protected areas

Abstract Monitoring the status and future trends in biodiversity can be prohibitively expensive using ground-based surveys. Consequently, significant effort is being invested in the use of satellite remote sensing to represent aspects of the proximate mechanisms (e.g., resource availability) that can be related to biodiversity surrogates (BS) such as species community descriptors. We explored the potential of very high resolution (VHR) satellite Earth observation (EO) features as proxies for habitat structural attributes that influence spatial variation in habitat quality and biodiversity change. In a semi-natural grassland mosaic of conservation concern in southern Italy, we employed a hierarchical nested sampling strategy to collect field and VHR-EO data across three spatial extent levels (landscape, patch and plot). Species incidence and abundance data were collected at the plot level for plant, insect and bird functional groups. Spectral and textural VHR-EO image features were derived from a Worldview-2 image. Three window sizes (grains) were tested for analysis and computation of textural features, guided by the perception limits of different organisms. The modelled relationships between VHR-EO features and BS responses differed across scales, suggesting that landscape, patch and plot levels are respectively most appropriate when dealing with birds, plants and insects. This research demonstrates the potential of VHR-EO for biodiversity mapping and habitat modelling, and highlights the importance of identifying the appropriate scale of analysis for specific taxonomic groups of interest. Further, textural features are important in the modelling of functional group-specific indices which represent BS in high conservation value habitat types, and provide a more direct link to species interaction networks and ecosystem functioning, than provided by traditional taxonomic diversity indices.

Can we predict habitat quality from space? A multi-indicator assessment based on an automated knowledge-driven system

Abstract There is an increasing need of effective monitoring systems for habitat quality assessment. Methods based on remote sensing (RS) features, such as vegetation indices, have been proposed as promising approaches, complementing methods based on categorical data to support decision making. Here, we evaluate the ability of Earth observation (EO) data, based on a new automated, knowledge-driven system, to predict several indicators for oak woodland habitat quality in a Portuguese Natura 2000 site. We collected in-field data on five habitat quality indicators in vegetation plots from woodland habitats of a landscape undergoing agricultural abandonment. Forty-three predictors were calculated, and a multi-model inference framework was applied to evaluate the predictive strength of each data set for the several quality indicators. Three indicators were mainly explained by predictors related to landscape and neighbourhood structure. Overall, competing models based on the products of the automated knowledge-driven system had the best performance to explain quality indicators, compared to models based on manually classified land cover data. The system outputs in terms of both land cover classes and spectral/landscape indices were considered in the study, which highlights the advantages of combining EO data with RS techniques and improved modelling based on sound ecological hypotheses. Our findings strongly suggest that some features of habitat quality, such as structure and habitat composition, can be effectively monitored from EO data combined with in-field campaigns as part of an integrative monitoring framework for habitat status assessment.

Challenges and opportunities in harnessing satellite remote-sensing for biodiversity monitoring

Abstract The ability of remote-sensing technologies to rapidly deliver data on habitat quantity (e.g., amount, configuration) and quality (e.g., structure, distribution of individual plant species, habitat types and/or communities, persistence) across a range of spatial resolutions and temporal frequencies is increasingly sought-after in conservation management. However, several problematic issues (e.g., imagery correction and registration, image interpretation, habitat type and quality definitions, assessment and monitoring procedures, uncertainties inherent in mapping, expert knowledge integration, scale selection, analysis of the interrelationships between habitat quality and landscape structure) challenge the effective and reliable use of such data and techniques. We discuss these issues, as a contribution to the development of a common language, framework and suite of research approaches among ecologists, remote-sensing experts and stakeholders (conservation managers) on the ground, and highlight recent theoretical and applied advances that provide opportunities for meeting these challenges. Reconciling differing stakeholder perspectives and needs will boost the timely provisioning of reliable information on the current and changing distribution of biodiversity to enable effective conservation management.

Earth observation for habitat mapping and biodiversity monitoring

Biodiversity – the variety of life forms and our “natural capital nd life-insurance” (European Commission, 2011) – is on decline Isbell, 2010; Trochet and Schmeller, 2013), with consequences on cosystem function and stability, and ultimately human well-being Naeem et al., 2009). Since 1992, the International Convention on iological Diversity, short CBD, has bundled the United Nations’ oint effort to halt or at least lower the accelerated loss of biodiersity, but indeed it remains one of the key global challenges that equires a concerted, effective use of latest technology. As by the nd of 2010 (the “International Year of Biodiversity”) the global ociety became aware that the ambitious goal of “halting biodiverity” has not been reached, the importance of both observation and echnology development became even more important. Safeguarding the integrity of species and ecosystems is a lobal challenge with continental, regional, and ultimately local mplications – with biodiversity being a glocalized phenomenon. eographically this manifests in a hierarchy of scales, from biomes, ver (systems of) ecosystems down to communities, populations nd species. The spatial variability of critical parameters at each ierarchical level can be used as an indication of current state nd conditions, distribution, and temporal dynamic of biodiverity. Observing and monitoring aspects of biodiversity, at any level nd scale, can thus be approximated by analysing the composition, ariability and changes of tangible entities (i.e. habitats) and their patial patterns (Bock et al., 2005). Remote sensing technology has he capacity to provide spatially explicit information relevant to the ulti-scale perspective required by ecologists (to investigate the elationships between pattern and processes) and land managers to design and implement conservation actions). This information hus complements data obtained through standardized, in situ sureys related to very local aspects of biodiversity, by representing ntegrated higher-level characteristics such as those of ecological eighbourhoods (Addicot et al., 1987), defined by the upper (extent, bject/scene size) and lower (grain, spatial resolution) limits of ata information content and perception (Wiens, 1989) and cited iterature). The matching of various resolution levels of satellite sensor amilies with the organizational levels of biological systems and rganism perception is one aspect – the correspondence with patial and temporal domains of environmental policies another. atellite Earth observation (EO) has started to become a ubiquitous