BIO_SOS is a response to the Call for proposals FP7-SPACE-2010-1, addressing topic ‘Stimulating the development of GMES services in specific areas’ with application to (B) Biodiversity. It is a 3-year project: starting date 1 December 2012. Whilst the establishment of Natura 2000 sites in Europe provided protection for a diverse range of important habitats, human activities have resulted in changes in the surrounding landscape that are influencing the condition of contained habitats and their associated biodiversity. Recognising the ability of remote sensing data, particularly those provided by high (HR) and very high resolution (VHR) sensors, the BIO_SOS project aims to develop a cost-effective pre-operational ecological modelling system suitable for timely multiannual monitoring of Natura 2000 sites, including their surrounding areas, with this referred to as EO Data for Habitat Monitoring (EOD HaM). As input, EODHaM is using satellite-derived and in situ data to drive models of species distributions and dynamics at both the habitat and landscape levels. The proposed system is characterised by a twostage knowledge-based (i.e. deductive learning) classification scheme for land cover (LC) and habitat mapping and includes components for change monitoring. A third stage is devoted to automated biodiversity indicator extraction and analysis of change under a range of scenarios. Ontologies and semantic networks are used to formally represent The EODHaM system, which is expected to provide improved operational core service products, is being developed for sites in the Mediterranean countries of Italy, Portugal and Greece and western Europe (the Netherlands and Wales) and further evaluated for tropical environments in Brazil and India, where the availability of advanced monitoring systems is critical for conservation of biodiversity. The modelling framework developed is expected to provide a deeper understanding of the impacts of human-induced pressures (e.g. agricultural expansion, mining and road construction) on biodiversity conservation, which will lead to the development of new downstream services. Key output products include LC and LCC maps based on the Food and Agricultural Organisation (FAO) land cover classification scheme (LCCS) with these translated to general habitat categories (Bunce et al., 2008), Annex I and EUNIS habitat categories. The products generated by the BIO_SOS project will be available to allow the impacts of past, current and present policies on Natura 2000 sites and their surroundings to be better evaluated.

This research aimed at evaluating the effectiveness of the process of Natura 2000 implementation at the national and regional levels in avoiding the loss and fragmentation of dry grasslands in the SCI/SPA IT9120007 “Murgia Alta” (Apulia region, Italy). Based on a comparison between Corine Land Cover maps for years 1990, 1999, 2006, historic habitat fragmentation was analysed for the whole site extent by means of identification and quantification of fragmentation geometries and size variation intervals of the remaining focal habitat patches. Based on a 2006-2007 high-resolution land-use map, the current level of habitat fragmentation was assessed in 8 transects by a quantitative analysis of the landscape pattern, and by integrating indexes representing both the global and the local approach to landscape pattern analysis. The results indicate that heavy habitat loss and fragmentation occurred during the 1990-1999 period, mostly due to conversion to arable land. This period approximately corresponds to a delay in transposing the UE “Habitats” Directive at the national and regional levels. Negligible changes can be detected, at this scale, for the 1999-2006 period, after Natura 2000 site selection and boundary definition had been approved. Natura 2000 boundary definition in the study area can be considered as appropriate, as a very small surface of the focal habitat is found outside of Natura 2000. An analysis at a finer scale and the joint use of different indices highlight the spatial variability of the current fragmentation and provides indications of the pressure nearby the focal habitat. Besides quantifying the recent change and the current status of dry grasslands in the study area, this analysis shows that the integration of complementary information derived from different approaches and the availability of maps at different spatial scales are necessary to monitoring habitat fragmentation, both within and outside Natura 2000, an essential element for assessing the effectiveness of conservation policies.

There is an increasing need of effective monitoring systems for habitat quality assessment. Methodsbased on remote sensing (RS) features, such as vegetation indices, have been proposed as promisingapproaches, 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 2000site.We collected in-field data on five habitat quality indicators in vegetation plots from woodland habitatsof 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 severalquality indicators.Three indicators were mainly explained by predictors related to landscape and neighbourhood struc-ture. Overall, competing models based on the products of the automated knowledge-driven system hadthe best performance to explain quality indicators, compared to models based on manually classifiedland cover data.The system outputs in terms of both land cover classes and spectral/landscape indices were consideredin the study, which highlights the advantages of combining EO data with RS techniques and improvedmodelling based on sound ecological hypotheses. Our findings strongly suggest that some features ofhabitat quality, such as structure and habitat composition, can be effectively monitored from EO datacombined with in-field campaigns as part of an integrative monitoring framework for habitat statusassessment.

Graph theory derived models and measures are increasingly being used to quantify landscape connectivity in order to contribute to conservation biology and management. This is particularly relevant in the case of real landscapes in which local actions may have crucial consequences for maintaining biodiversity on large scale. A number of graphs were compared sharing an identical node weight definition and whose link weights representing functional patch-connectivity, were derived from conceptually different approaches. Habitat suitability was taken into account. Calculated patch-connectivity was compared between all the graphs and these differences, evaluated by a set of indices describing network properties at the element structure level, were investigated.

I boschi e le risorse forestali in genere hanno avuto nella storia del nostro pianeta e della civiltà umana un ruolo fondamentale. L’atmosfera come la conosciamo oggi, respirabile ed adatta alla vita degli animali, è stata raggiunta solo nel Carbonifero grazie alla grande diffusione delle piante terrestri che avvenne in quel periodo. Le piante produssero grandi quantità di ossigeno come sottoprodotto della funzione clorofilliana necessaria al loro sostentamento. Questa produzione di ossigeno, unitamente al seppellimento di grandi quantità di sostanza organica contenente carbonio prima presenti nell’aria come anidride carbonica, permise a partire da circa 350 milioni di anni fa, di arrivare al 21% di ossigeno libero che oggi caratterizza l’atmosfera. Le foreste sono state, inoltre, un centro importante per l’evoluzione delle civiltà umane fornendo materiali legnosi e non legnosi necessari al riscaldamento, alla costruzione di utensili, attrezzature ed abitazioni, al sostentamento, all’alimentazione e alla cura del corpo. Al tempo stesso i boschi sono stati luoghi dove si sono sviluppati miti, leggende e i primi riti religiosi che hanno caratterizzato le nostre civiltà nel corso dei secoli. Nella cultura corrente è acquisito che gli alberi e le risorse forestali possiedano un’ampia ed articolata multifunzionalità che comprende la fornitura di materiali e di servizi, ma che abbraccia anche la sfera del benessere. Nell’ambito di questa multifunzionalità un ruolo importante che si sta evidenziando nel corso degli ultimi due decenni è quello relativo alla possibilità di contrastare i processi di cambiamento climatico determinati dal rilascio di grandi quantità di gas serra, prevalentemente anidride carbonica, e di limitare i processi di riscaldamento dell’atmosfera (global change). Si tratta quindi di svolgere una funzione essenziale alla nostra stessa sopravvivenza. La crescente preoccupazione della comunità scientifica per i fenomeni legati al cambiamento climatico e al riscaldamento dell’atmosfera, potenziati dalle attività umane legate soprattutto all’uso dei combustibili fossili e alle trasformazioni di uso delle coperture del suolo, ha determinato la necessità di una presa di coscienza anche da parte dell’opinione pubblica generale rispetto alla possibilità di assumere comportamenti individuali congrui. Tale possibilità passa attraverso il potenziamento dei livelli di informazione attinenti ai fattori di regolazione degli scambi atmosferici in grado di influenzare gli elementi del clima globale, in particolare temperatura dell’aria e il regime delle precipitazioni. Scopo di questo volume è quello di contribuire a questa informazione per gli aspetti relativi agli ecosistemi forestali, componenti dei paesaggi e della biosfera cui la scienza riconosce un ruolo chiave nella regolazione del bilancio del carbonio, così nel mantenimento della biodiversità e della identità culturale dei popoli della terra. Per contribuire, in definitiva, a formare nell’opinione pubblica, la capacità critica per discriminare tra atteggiamenti di consumo etico e sostenibile come alternativa a quelli “predatori”. Tra gli ecosistemi forestali, i boschi in particolare, sono in grado di conservare anche per periodi di tempo relativamente lunghi il carbonio assimilato, soprattutto nelle strutture somatiche delle loro componenti caratterizzanti (gli alberi) e nel suolo forestale. Inoltre l’uso dei prodotti legnosi consente di mantenere “bloccate” considerevoli quantità di carbonio per tutto il ciclo di vita delle opere e dei manufatti. Gli ecosistemi forestali, quindi, nel ciclo geochimico ed in quello biogeochimico del carbonio, svolgono contemporaneamente le funzioni di pool di scambio e di pool di riserva. La specie umana è sempre stata in qualche modo dipendente dagli ecosistemi forestali, sia attraverso l’impiego dei beni materiali prodotti dal b

Land use changes represent one of the most important components of global environmental change and have a strong influence on carbon cycling. As a consequence of changes in economy during the last century, areas of marginal agriculture have been abandoned leading to secondary successions. The encroachment of woody plants into grasslands, pastures and croplands is generally thought to increase the carbon stored in these ecosystems even though there are evidences for a decrease in soil carbon stocks after land use change. In this paper, we investigate the effects of woody plant invasion on soil carbon and nitrogen stocks along a precipitation gradient (200–2,500 mm) using original data from paired experiment in Italian Alps and Sicily and data from literature (Guo and Gifford Glob Change Biol 8(4):345–360, 2002). We found a clear negative relationship (−0.05% C mm−1) between changes in soil organic carbon and precipitation explaining 70% of the variation in soil C stocks after recolonization: dry sites gain carbon (up to +67%) while wet sites lose carbon (up to −45%). In our data set, there seem to be two threshold values for soil carbon accumulation: the first one is 900 mm of mean annual rainfall, which separates the negative from the positive ratio values; the second one is 750 mm, which divides the positive values in two groups of sites. Most interestingly, this threshold of 750 mm corresponds exactly to a bioclimatic threshold: sites with <750 mm mean annual rainfall is classified as thermo-mediterranean sites, while the ones >750 mm are classified as mesomediterranean sites. This suggests that apart from rainfall also temperature values have an important influence on soil carbon accumulation after abandonment. Moreover, our results confirmed that the correlation between rainfall and trend in soil organic carbon may be related to nitrogen dynamics: carbon losses may occur only if there is a substantial decrease in soil nitrogen stock which occurs in wetter sites probably because of the higher leaching.

Archaeological remains generated by human activities in prehistorical and historical times result in anthropogenic spatial components of modern landscapes. The study of these features in geographical areas of interest can be advantageous to assist in both cultural heritage protection policies and the efficient management of the archaeological risk impacts inherent in infrastructural planning. As such, predictive distribution models are becoming an investigation method for archaeological landscapes. Moreover, the use of remote sensing data in this type of model, both in archaeological and ecological contexts, is gaining momentum. Based on presence-only data, two inductive predictive modelling approaches, i.e., a geographical information system (GIS)-based multiparametric spatial analysis (MPSA) and maximum entropy model (MaxEnt), were tested, integrated and evaluated for archaeological applications at different scales over the case study area of the Tavoliere Plain (Southern Italy). Presence data included sites identified via both remote sensing and archaeological survey techniques. Environmental variables included topographic, geomorphological and remote sensing derived attributes. Two contrasting repeatable criteria (i.e., correlation coefficients plus MaxEnt statistics and spatial principal component analysis (PCA) were used to select non-redundant environmental variables and Akaike's information criterion (AIC) to select the most parsimonious model configurations. A threshold analysis of the presence data was performed on the best model to define the minimum size of the presence data set with respect to the extent of the study area as well as the relative amounts of remote sensing derived response data to reach a stable performance of the model. The results show a higher performance of the MaxEnt models with respect to the GIS_MSPA models regardless of scale, indicating that the two approaches cannot be considered alternatives. The most parsimonious configurations of these models indicate that, depending on their scale, both spatial PCA and MaxEnt statistics can be used to select non-redundant input variables. The threshold analysis suggests that for the illustrated case study, a density of 0.2 presence sites/km2 and 45% of remotely sensed sites are necessary to improve and stabilise the performance of the model. Critical issues and opportunities for advances in the performance of the MaxEnt approach in an operational archaeological context are highlighted. These results suggest stakeholders should deal with predictive modelling prior to the undertaking of both land development and scientific field surveys and offer advice for informed decisions at this regard.

D6.4 provides a quantitative assessment of historical changes of focal habitat and a quantitative analysis of landscape structure at the regional level in IT3 (PART 1) and anticipates the methodological outlines of the undertaken comparative habitat and landscape modelling exercise across BIO_SOS sites (PART2). These are required to support validation of the different stages outputs, of the EODHaM system.

To identify appropriate indicators for BIO_SOS we have used previous expert assessments of the SEBI “Streamlining European 2010 Biodiversity Indicators”, as well as the ongoing work for the CBD2020 assessments. BIO_SOS will focus on three main headline indicators covering: (i) habitats of European interest; (ii) abundance and distribution of selected plant species; and (iii) fragmentation and functional connectivity of Natural and semi-Natural areas. BIO_SOS will add indicators for pressure that can be detected through land cover changes at very fine resolution

Coppice silviculture has a long tradition in Italy. Societal demands have led to the development of forest management techniques for integrating wood production with other kinds of forest uses and regulations have been issued to limit forest degradation. In Italy, 35% of the national forest cover is currently managed under coppice silvicultural systems that provide 66% of the annual wood production. Fuel-wood demand is increasing and a large amount of fuelwood is currently imported in Italy. Modern coppice practices differ from those adopted in the past and may have a reduced impact on ecosystem characteristics and processes. Nevertheless, coppice silviculture has a bad reputation mostly on grounds that are beyond economic, technical and ecological rationales. Neither cessation of use nor a generalized conversion from coppice to high forest are likely to respond simultaneously to the many demands deriving from complex and articulated political and economic perspectives operating at global, European, national, regional and forest stand-level scales. Different approaches of modern silviculture to coppice successfully tested in Italy for more than a decade are illustrated. We propose to combine different options at the stand and sub-stand level, including either development without human interference or conversion to high forest, and to apply these approaches within the framework of novel forest management plans and regionally consistent administrative procedures. This bottom-up approach represents a potential solution to the socio-economic and environmental challenges affecting coppicing as a silvicultural system.

Crop damages by wildlife is a frequent form of human-wildlife conflict. Identifying areas where the risk of crop damages is highest is pivotal to set up preventive measures and reduce conflict. Species distribution models are routinely used to predict species distribution in response of environmental changes. The aim of this paper was assessing whether species distribution models can allow to identify the areas most at risk of crop damages, helping to set up management strategies aimed at the mitigation of human-wildlife conflicts. We obtained data on wild boar Sus scrofa damages to crops in the Alta Murgia National Park, Southern Italy, and related them to landscape features, to identify areas where the risk of wild boar damages is highest. We used MaxEnt to build species distribution models. We identified the spatial scale at which landscape mostly affects the distribution damages, and optimized the regularization parameter of models, through an information-theoretic approach based on AIC. Wild boar damages quickly increased in the period 2007-2011; cereals and legumes were the crops more affected. Large areas of the park have a high risk of wild boar damages. The risk of damages was related to low cover of urban areas or olive grows, intermediate values of forest cover, and high values of shrubland cover within a 2-km radius. Temporally independent validation data demonstrated that models can successfully predict damages in the future. Species distribution models can accurately identify the areas most at risk of wildlife damages, as models calibrated on data collected during only a subset of years correctly predicted damages in the subsequent year.

Different kinds of approach to landscape configuration analysis were applied to a benchmark landuse/land cover map. A pre-evaluation landscape pattern analysis (LPA), a morphological spatial pattern analysis (MSPA) a landscape mosaic analysis and a landscape variation analysis, were carried out. These analyses provide a site and scale specific composite set of indices which can be used as a change biodiversity indicator set with reference to the CBD/SEBI focal areas: status and trends of the components of biological diversity, Ecosystem integrity, and ecosystem goods and services

The state of the art on the methodologies for assessing fragmentation and connectivity have been delineated and the scope of the BIO_SOS research on these issues brought into context

D4.3 anticipates the description of the protocol for accuracy assessment of the thematic maps generated from the EODHaM system (PART 1), provides examples for the instantiation of such a protocol for two training sites (PART 2), specializes the protocol to infield collection of data relating to General Habitat Categories (GHCs) as well as flora, fauna and soil data (PART 3). These are required to support validation of the different stages outputs, of the EODHaM system.

D6.1 finds the relation between vegetation types derived from Land Cover maps and habitat types related to the Habitat Directive and other classification systems widely used in Europe for habitat mapping. Three Land Cover class sets are compared and related to the habitats of interest to select the class set providing an unequivocal class description closest to habitat description and consequently the most useful for the successive provision of Habitat maps from Land Cover maps.

The Deliverable analyses four case studies and builds different approaches of Ecological Niche Models. In all the cases habitat typology is the main variable that emerges as most significant in explaining the model of ecological niche. The results have both theoretical importance (for instance, the use of GHCs improves the prediction of the model for the distribution of some species in Alta Murgia Parco Nazionale) and practical relevance for stakeholders (for instance, the models enable generation of a risk map predicting areas of potential vulnerability to damage by wild boars in Alta Murgia). Presence-absence species distribution data on Alta Murgia were provided by the management Authority of the National Park (i.e., Ente Parco). BIO_SOS project would like to thank Ente Parco Nazionale dell’ Alta Murgia

Protected areas are experiencing increased levels of human pressure. To enable appropriate conserva-tion action, it is critical to map and monitor changes in the type and extent of land cover/use and habitatclasses, which can be related to human pressures over time. Satellite Earth observation (EO) data andtechniques offer the opportunity to detect such changes. Yet association with field information and expertinterpretation by ecologists is required to interpret, qualify and link these changes to human pressure.There is thus an urgent need to harmonize the technical background of experts in the field of EO dataanalysis with the terminology of ecologists, protected area management authorities and policy makers inorder to provide meaningful, context-specific value-added EO products. This paper builds on the DPSIRframework, providing a terminology to relate the concepts of state, pressures, and drivers with the appli-cation of EO analysis. The type of pressure can be inferred through the detection of changes in state (i.e.changes in land cover and/or habitat type and/or condition). Four broad categories of changes in stateare identified, i.e. land cover/habitat conversion, land cover/habitat modification, habitat fragmentationand changes in landscape connectivity, and changes in plant community structure. These categories ofchange in state can be mapped through EO analyses, with the goal of using expert judgement to relatechanges in state to causal direct anthropogenic pressures. Drawing on expert knowledge, a set of pro-tected areas located in diverse socio-ecological contexts and subject to a variety of pressures are analysedto (a) link the four categories of changes in state of land cover/habitats to the drivers (anthropogenic pres-sure), as relevant to specific target land cover and habitat classes; (b) identify (for pressure mapping) themost appropriate spatial and temporal EO data sources as well as interpretations from ecologists andfield data useful in connection with EO data analysis. We provide detailed examples for two protectedareas, demonstrating the use of EO data for detection of land cover/habitat change, coupled with expertinterpretation to relate such change to specific anthropogenic pressures. We conclude with a discussionof the limitations and feasibility of using EO data and techniques to identify anthropogenic pressures,suggesting additional research efforts required in this direction.

To identify appropriate bio-indicators species for BIO_SOS we have used existing datasets (as indicated in D4.1) and we have checked a recent scientific literature in order to provide a scientific consistent selection of bio-indicator species. Our choice is based on (i) the possibility to compare niche models with and without GHCs; (ii) the usefulness for stakeholders; (iii) the possibility to carry out comparison among different sites; and (iv) the actual availability of the distributional data.

Eleven test sites in Europe (mainly in the Mediterranean) were selected for testing and ground verification of the methods to be developed in BIO_SOS. All sites are important areas for the conservation of biodiversity. Their landscape structure is analysed as well as the pressures and threats of each site, recognizing habitat loss and fragmentation to be of seminal importance. The analysis concludes with the identification of key biodiversity indicators that need to be monitored, and the definition of the spatial and temporal resolution of this monitoring.

To support decisions relating to the use and conservation of protected areas and surrounds, the EU-fundedBIOdiversity multi-SOurce monitoring System: from Space TO Species (BIO SOS) project has developedthe Earth Observation Data for HAbitat Monitoring (EODHaM) system for consistent mapping and mon-itoring of biodiversity. The EODHaM approach has adopted the Food and Agriculture Organization LandCover Classification System (LCCS) taxonomy and translates mapped classes to General Habitat Cate-gories (GHCs) from which Annex I habitats (EU Habitats Directive) can be defined. The EODHaM systemuses 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 to3 and beyond). The 3rd stage translates the final LCCS classes into GHCs from which Annex I habitat typemaps 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 protectedsites and their surrounds in Italy, Wales (UK), the Netherlands, Greece, Portugal and India.

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 forlandscape monitoring—a major issue for conservation. This study also highlights the need to modify the FAOLCCS 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.

Monitoring habitat change in protected areas is important for nature conservation. Changes in habitat extent as well as landscape and habitat configuration are often caused by human pressure within protected areas and at their boundaries, further impacting biodiversity and species distributions. Thus the availability of quantitative spatial information on landscape mosaic arrangements is critical for biodiversity-related monitoring. While measuring habitat extent is a relatively straightforward task, approaches for measuring habitat fragmentation are debated, and the selection of a relevant set of indices is contingent to both the landscape and the question being asked. This research aims to develop approaches to select a set of site-specific and scale-specific methods to assess human impact on landscape and habitat spatial patterns. Landscape pattern analysis, morphological pattern analysis, landscape mosaic analysis and discontinuities detection were employed to evaluate a Natura 2000 site in Southern Italy, Murgia Alta, partly designated and managed as a National Park. Quantitative landscape pattern analysis indicates that integrated information from multiple indices can provide a more complete understanding of landscape spatial pattern, especially as related to locations experiencing disturbance and pressure. A landscape- and observation-scale specific set of indices identified for this landscape generated insights on the relation between landscape heterogeneity and fragmentation. This facilitated the ranking of sample landscapes according to a fragmentation gradient in relation to matrix quality. Morphological spatial pattern analysis provided pixel based structural characterisation of the landscape. Conducted across a range of edge widths, this analysis enabled identification of baseline site-scale edge value. Landscape mosaic analysis characterised the structure of the landscape by means of landscape diversity profiling. All three approaches provided information on baseline spatial pattern and fragmentation which can be very useful for future change detection and monitoring. Discontinuities detection indicated “critical points” of transitions in management where threats to biodiversity and ecosystems integrity may be likely. Thus, the findings of the site- and scale-specific landscape quantitative revealed significant potential to capture information on major landscape structural features, i.e. provide a baseline picture that identifies problematic areas of increased fragmentation, indicating the need for priority in monitoring. Therefore, the proposed approach may assist in providing early warning signals for immediate response to pressures increasing habitat fragmentation. These would enable local authorities to develop a baseline for change detection that can then be used to collect consistent data for monitoring, change assessment, and ultimately more effective management.

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, were developed for a semi-natural grassland 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, considering the debate on the effectiveness of protected 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 remote sensing technologies, to provide early signals of environmental shifts both within and outside protected areas.

Changes in habitat extent as well as landscape and habitat structure are often caused by human pressure within protected areas and at their boundaries, with consequences for biodiversity and species distributions. Thus quantitative spatial information on landscape mosaic arrangements is essential, for monitoring for nature conservation, as also specified by frameworks such as the Convention on Biological Diversity (CBD), and the European Union’s Habitat Directive. While measuring habitat extent is a relatively straightforward task, approaches for measuring habitat fragmentation are debated. This research aims to delineate a framework that enables the integration of different approaches to select a set of site- and scale-specific indices and synthetic descriptors and develop a comprehensive quantitative assessment of variations in human impact on the landscape, through assessment of habitat spatial patterns, which can be used as a baseline for monitoring. This framework is based on the use of established methodologies and free software, and can thus be widely applied across sites. For each landscape and observation scale, the framework permits the identification of the most relevant indices, and appropriate parameters for their computation. We illustrate the use of this framework through a case study in a protected area in Italy, to indicate that integrated information from multiple approaches can provide a more complete understanding of landscape and habitat spatial pattern, especially related to locations experiencing disturbance and pressure. First, identification of a parsimonious set of traditional LPIs for a specific landscape and spatial scale provides insights on the relation between landscape heterogeneity and habitat fragmentation. These can be used for both change assessment and ranking of different sections of the study area according to a fragmentation gradient in relation to matrix quality. Second, morphological spatial pattern analysis (MSPA), provides a pixel based structural characterisation of the landscape. Third, compositional characterisation of the landscape at the pixel level is provided by landscape mosaic analysis. These three approaches provide quantitative assessments through indices which can be used singly or in combination to derive three synthetic descriptors for a comprehensive quantitative baseline representation of landscape structure that can be used for monitoring: the first descriptor, landscape diversity profiling, based on the output of landscape mosaic analysis, at the landscape level, complements the evaluation which at the pixel level can be obtained by more complex modelling; the second descriptor, obtained combining of the outputs of MSPA and the landscape mosaic analysis, informs on the local structural pattern gradient across the landscape space; the third descriptor, derived from the integration of selected LPIs and those derived from MSPA into a discontinuities detection procedure, allows for the identification of “critical points” of transitions in management where threats to biodiversity and ecosystems integrity may be likely. The framework developed has significant potential to capture information on major landscape structural features, identify problematic areas of increased fragmentation that can be used to prioritise research, monitoring and intervention, and provide early warning signals for immediate response to pressures increasing habitat fragmentation, with the goal of facilitating more effective management.

tModelling the empirical relationships between habitat quality and species distribution patterns is the firststep to understanding human impacts on biodiversity. It is important to build on this understanding todevelop a broader conceptual appreciation of the influence of surrounding landscape structure on localhabitat 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 configurationor spatial variation in habitat quality at edges, implicitly treat each unit of habitat as interchange-able 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 habitatattributes, but are instead dependent on variation in surrounding habitat structure at both patch- andlandscape levels. As the statistical approaches needed to implement such hierarchical causal models areobservation-intensive, we utilise very high resolution (VHR) Earth Observation (EO) images to rapidlygenerate fine-grained measures of habitat patch internal heterogeneities over large spatial extents. Weuse linear mixed-effects models to test whether these remotely-sensed proxies for habitat quality wereinfluenced by surrounding patch or landscape structure. The results demonstrate the significant influenceof surrounding patch and landscape context on local habitat quality. They further indicate that such aninfluence can be direct, when a landscape variable alone influences the habitat structure variable, and/orindirect when the landscape and patch attributes have a conjoined effect on the response variable. Weconclude that a substantial degree of interaction among spatial configuration effects is likely to be thenorm in determining the ecological consequences of habitat fragmentation, thus corroborating the notion of the spatial context dependence of habitat quality