Landscape Ecology

The main conservation risks for wild non-human primates (NHP) in Costa Rica, Mesoamerica, is deforestation and the allocation of lands for agriculture. This is because they result in a mosaic of forest patches that differ in size and ecological properties. NHP, being the vertebrates with the highest risk and rate of extinction, slowly adapt to this rapid environmental change, optimizing their metabolic costs to survive and reproduce. One way to balance these costs is to use forest patches depending on the benefits they provide, such as food, shelter, or social contact. To understand the possible environmental factors that predict the usage of a series of 8 connected forest patches by Ateles geoffroyi, Alouata paliatta, and Sapajus imitator we collected demographic, behavioral, climatological and other environmental data from 2018 until 2021. We used information-theoretic metrics to identify the factors that best explained the presence and behavior of the species of interest in the forest patches studied, and fit the data to a set of models built informed a priori. Using the best explanatory factors, we k-fold cross-validated 9 classifier algorithms to identify the best predictive models for the presence of the monkeys studied and their behavioral patterns given the data. Presence was highest in warmer, more humid days, especially when other groups were present in the same patch. Behavioral patterns were different in each patch; monkeys rested more often when other groups of the same species were present, and foraged more during warmer, more humid days, and smaller groups. Predictive models for the presence of the species studied, trained with the 3 best explanatory factors, reached an accuracy between 70-96%, with Gradient Boost Classifier performing the best. In contrast, behavioral patterns were more unpredictable, with the the algorithms tested only reaching between 43-51% accuracy, the AdaBoost Classifier being the best. Our findings suggest that the usage of the 8 forest patches monitored by the monkeys studied depends on patch characteristics, not related to size nor the presence of a reserve, by the presence of other NHP in the patch and the meteorological conditions. Further work on the ecological characteristics of these patches can clarify the mechanisms modulating behavioral patterns.

ContextSpecies distribution models are used to predict habitat suitability for a species, by quantifying the environmental characteristics that allow a species to occupy a geographical area. The abundance and range of pine marten (Martes martes) has declined substantially in Great Britain, with remaining populations restricted to Scotland. ObjectivesHere, we perform species distribution modelling using BIOMOD2 platform to determine habitat suitability, and inform the identification of potential reintroduction sites for pine marten in Great Britain. MethodsUsing a global range dataset of 4,189 occurrences and seven environmental variables, ensemble species distribution models were used to predict habitat suitability across Europe at 1 km resolution and Great Britain at 100 m resolution. ResultsAcross the extent of both Europe and Britain, results indicate high suitability in areas with woody vegetation cover in low topographic positions, and notably low in urban areas and extensive areas of arable land. In Britain, high habitat suitability is identified across substantial areas in the South East of England, parts of South West of England, East Yorkshire and Gloucestershire, with pockets of suitable habitat along the West Coast of Britain. The results indicate that elevation and land cover are important drivers of suitability. ConclusionHabitat suitability modelling at a high resolution of 100m proves effective for informing potential reintroduction sites for pine marten in Britain. We also demonstrate the importance of using occurrence data from pine martens global range to predict optimal habitat suitability.

Key Biodiversity Areas (KBAs) are a cornerstone of global biodiversity conservation, influencing international strategic plans and helping protect thousands of species. KBAs are identified through quantitative criteria, among which the most recent is Criterion E. KBA Criterion E uses Spatial Conservation Prioritization techniques to identify highly irreplaceable sites, representing a promising tool for effective expansion of the KBA network. However, it has rarely been tested or applied at large scales. Here, we carried out a continental application of KBA Criterion E in Europe, using Species Distribution Models (SDMs) for 5,529 species of insects and 972 tetrapods. We stress-tested the application of Criterion E by changing the following settings: irreplaceability threshold, metrics of irreplaceability, representation targets, spatial resolution, and cost of planning units. Under the standard Criterion E settings, we identified 23 potential KBAs for insects, mostly along northern European coasts, and 88 for tetrapods, mostly concentrated in Mediterranean islands and southern Europe. These sites slightly overlapped with existing KBAs, showing that Criterion E can capture biodiversity patterns overlooked by other criteria. Our results also showed that the identification of highly irreplaceable areas is very sensitive to analytical choices. The strict irreplaceability threshold currently required, associated with the definition of representation targets, limited the selection of important sites almost exclusively to those containing very narrow-range species, and when such species were absent, important sites were preferentially selected on coasts, where the cost of planning units (represented by land extent) was minimized. Our analysis showed both opportunities and challenges of Criterion E and its applications with SDMs. We propose potential adjustments to the definition and guidelines of Criterion E, to improve its applicability at large spatial scales and on different taxa. Improvements of KBA Criterion E will ensure that KBAs continue to substantially contribute to the global conservation of biodiversity.

AimWhile many studies on ectotherm thermal tolerance consider temperature exposure, the frequency of temperature exposures is emerging as an important and generally overlooked driver of survival and fitness which may influence species ranges. We use a physiologically-informed species distribution model to evaluate the influence of temperature fluctuations on the historical distribution and intensity of defoliation of a Lepidopteran forest pest, and on predicted future defoliation. LocationEastern Canada. Time period2006-2016, projections to 2041-2070. Major taxa studiedChoristoneura fumiferana (Lepidoptera: Tortricidae, spruce budworm). MethodsWe combined publicly-available maps of spruce budworm-induced defoliation between 2006-2016 in the Canadian province of Quebec with climate, forest composition, and de novo temperature fluctuation predictors to train a species distribution model. Our model evaluated how predictor categories influence spruce budworm defoliation and compared these results to a model trained without temperature fluctuations. Additionally, we predicted future spruce budworm defoliation under 2041-2070 climate change conditions using the models trained with and without temperature fluctuation predictors to determine the impact of temperature fluctuations on future defoliation predictions. ResultsWe found that the inclusion of temperature fluctuation predictors improved model performance, and these predictors ranked highly in importance relative to predictors in other categories. The model trained with temperature fluctuation predictors also predicted vastly different defoliation distribution and severity across Quebec, Ontario, and Labrador than the model trained without them under future climate. Main conclusionsOur study reveals the previously overlooked importance of temperature fluctuations on landscape-scale spruce budworm defoliation and demonstrates the importance of including physiologically-informed predictors in species distribution models. It also provides a novel framework for including thermal variation in correlative species distribution models of ectotherms.

Since the onset of the COVID-19 pandemic, researchers mainly examined how socio-economic, demographic, and environmental factors are related to disparities in SARS-CoV-2 infection rates. However, we dont know the extent to which racial disparities in environmental exposure are related to racial disparities in SARS-CoV-2 infection rates. To address this critical issue, we gathered black vs. white infection records from 1416 counties in the contiguous United States. For these counties, we used 30m-spatial resolution land cover data and racial mappings to quantify the racial disparity between black and white peoples two types of environmental exposure, including exposures to various types of landscape settings and urban development intensities. We found that racial disparities in SARS-CoV-2 infection rates and racial disparities in exposure to various types of landscapes and urban development intensities were significant and showed similar patterns. Specifically, less racial disparity in exposure to forests outside park, pasture/hay, and urban areas with low and medium development intensities were significantly associated with lower racial disparities in SARS-CoV-2 infection rates. Distance was also critical. The positive association between racial disparities in environmental exposures and racial disparity in SARS-CoV-2 infection rates was strongest within a comfortable walking distance (approximately 400m). HighlightsO_LIRacial dot map and landcover map were used for population-weighted analysis. C_LIO_LIRacial disparity in environmental exposures and SARS-CoV-2 infection were linked. C_LIO_LIForests outside park are the most beneficial landscape settings. C_LIO_LIUrban areas with low development intensity are the most beneficial urban areas. C_LIO_LILandscape and urban exposures within the 400m buffer distances are most beneficial. C_LI

Landscape heterogeneity has an impact on wildlife behavior, their interactions, and their persistence. Urban landscapes are among the worlds most heterogeneous landscapes, yet current global landcover maps classify developed land in a single landcover type. This limits the spatial scale at which urban ecologists can approach research questions. OpenStreetMap (OSM), an open-source mapping platform, can be leveraged to enhance the representation of landscape heterogeneity in developed areas. For this, we extracted OSM features with attributes representing infrastructure, land use and green cover, integrating these into a continental landcover map through a globally applicable computational framework. We validated our OSM-enhanced landcover layer against existing remote sensing, aerial photography, and local governmental maps for 33 cities in North America. Our frameworks output provides an 89% accurate representation of landscape heterogeneity. We discuss caveats, potential improvements, and ecological applications. Our OSM-based landcover enhancement framework will facilitate the use of open-source landscape information for improved ecological modeling and urban planning.

ContextAs global urbanization intensifies, Urban Green Spaces (UGS) are pivotal for biodiversity conservation and climate change mitigation. However, comparative assessments of UGS spatial configuration and connectivity across diverse urban landscapes remain limited. ObjectivesThis study aims to assess the spatial arrangement and connectivity of UGS across 28 European capital cities. Additionally, we evaluate how Network Science metrics derived from Graph Theory can complement traditional landscape ecology metrics to provide a more comprehensive understanding of UGS at a large scale. MethodsWe developed a European Urban Vegetation Map using Earth observation data to classify UGS at 10m resolution across the selected capitals. We then analyzed UGS connectivity for each city utilizing 40 traditional landscape metrics and a Graph-Theory-based approach. ResultsWhile traditional landscape metrics effectively quantified fragmentation, they often remain strongly correlated with total vegetation abundance. In contrast, Network Science metrics provided specific insights into UGS functional connectivity, distinguishing the quality of ecological links beyond spatial proximity. This integration allowed us to cluster European capitals into three distinct typologies: unconnected compact cities, large metropolises with complex peri-urban dynamics, and high-connectivity cities with robust networks. These findings demonstrate that graph-based indices effectively complement traditional metrics, highlighting that relying solely on green space percentage is insufficient for assessing the ecological resilience of urban environments. ConclusionsThese results underscore the relevance of Earth observation-based UGS assessment and demonstrate that graph-based landscape connectivity analysis outperforms simple abundance metrics. Therefore, effective assessment requires integrating structural metrics with graph-based connectivity to support resilient urban biodiversity.

O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/674386v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@1226384org.highwire.dtl.DTLVardef@b3949eorg.highwire.dtl.DTLVardef@1e24ae0org.highwire.dtl.DTLVardef@1a1c773_HPS_FORMAT_FIGEXP M_FIG C_FIG Human impacts on ecosystems have accelerated globally, driving a 10% decline in terrestrial biodiversity and a 70% decline in wildlife populations over the past five decades. These losses are closely linked to habitat modification and fragmentation, highlighting the urgent need for management strategies grounded in a clear understanding of how wildlife use landscapes and navigate human-altered areas. Connectivity between populations is critical for species persistence and is shaped by the interplay between landscape features and species movement. Most connectivity studies pursue application-focused goals, such as designing corridors or assessing land-use effects, often targeting single species within specific landscapes. While these approaches provide depth, they limit the development of general principles that apply across species and regions. Graph theory offers a powerful framework to distill complex connectivity patterns into comparable metrics, creating opportunities to identify such generalities. In this study, we examined connectivity patterns for 11 Indian carnivore species distributed across heterogeneous landscapes. Using secondary data, we developed habitat networks from species distribution models that incorporated both habitat quality and matrix resistance. We then applied graph theory to generate networks based on connectivity between identified habitat nodes, enabling comparisons across species and landscapes. Our results show that while connectivity patterns differ markedly among species, broad trends emerge. Larger-bodied species like tigers, which in our study are often threatened species, can overcome the effects of fragmentation better than smaller bodied species, however their connectivity is dependent on the existence of high-quality patches. Fragmented and heterogenous landscapes were always associated with modular, less efficient networks irrespective of the species. Importantly, landscape characteristics had greater influence on network-level connectivity properties, while species traits more strongly determined node-level structural complexity of the network. By integrating network theory with multispecies analyses across diverse landscapes, our work moves beyond single-species case studies to identify general drivers of connectivity. In terms of conservation, our approach allows us to generate broad insights into drivers of population connectivity informing strategies for lesser-known species and guide more effective, landscape-scale management in an era of rapid environmental change.

Urban liveability is closely related to the landscape features that support human societies. The promotion of liveability influences the well-being of both human and non-human inhabitants of the city, as the two share the same urban habitat. In this paper, we propose a classification of urban landscapes tailored to facilitate the acquisition of interdisciplinary knowledge on the multi-inhabitant liveability of urban landscapes. Our classification represents the layered urban dimensions through four different subclassifications: local and landscape Urban Form, Anthropic Imprint, and Biophysical Conditions. We developed a flexible and scalable methodology that enables us to expand to other cities, by using only opensource geospatial and remote sensing data. We modelled our four subclassifications at 10-metres through a set of automatic pipelines, running Principal Component Analysis as dimensionality reduction and KMeans unsupervised classification. In this paper we explore the application of our methodology in the framework of ECOLOPES, a project that investigates the human and non-human liveability in cities through the computational design of green building envelopes. We apply the methodology to three distinct European cities, i.e. Vienna, Munich, and Genoa. We produced a set of 12 raster maps from 64 variables, with a total of 32 classes and 1264 possible combinations. We analysed inter and intra-urban class frequencies by highlighting the primary spatial signatures over each sub-classification, and between the overall Functional Urban Areas and the core areas. In this paper we discuss how this approach could foster multidisciplinary studies on urban liveability that hold into account not just humans, but all living inhabitants in cities. From this application, we envision a second step where our methodology will be applied to the full extent of European cities.

AimBiogeographic regionalization has fascinated biogeographers and ecologists for centuries and is endued with new vitality by evolutionary perspectives. However, progress is scant for most insect groups due to shortfalls in distribution and phylogenetic information, namely Wallacean and Darwinian shortfalls respectively. Here, we used the western Palearctic ants as the case to tackle these shortfalls and test their biogeographic structure through novel distribution data and phylogenetic approaches. LocationWestern Palearctic realm. TaxonAnts (Formicidae). MethodsFirstly, we developed a refined database integrating the occurrences of 747 ant species across 207 regions of the western Palearctic realm, based on newly expert-validated records derived from the existing global ant biodiversity informatics. Using range estimates for these species derived from polygons and species distribution modelling, we produced species assemblages in 50 x 50 km grid cells. We calculated taxonomic and phylogenetic turnover of ant assemblages, performing hierarchical clustering analysis using the Simpson dissimilarity index to delineate biogeographic structure. ResultsAt both the regional list- and grid assemblage-levels, the Mediterranean has higher turnover and more biogeographic regions than northern Europe, both taxonomically and phylogenetically. Delineations based on grid assemblages detected more detailed biogeographic transitions, while those based on regional lists showed stronger insularity in biogeographic structure. The phylogenetic regionalization suggested closer but varied affinities between assemblages in comparison to the taxonomic approach. Main conclusionsHere, we integrated expert-validated regional lists, species distribution modelling, and a recent phylogeny to tackle Wallacean and Darwinian shortfalls for an important insect group by developing a next-generation map of biogeographic regionalization for the western Palearctic ants. The results of this study suggest strong constraints from geographic barriers and potential effects of climatic history on ant distributions and evolutionary history, and also provide baseline spatial information for future investigations of regional insect distributions.

ContextPromoting heterogeneous agricultural landscapes could help to reduce the negative impacts of habitat conversion on biota. However, the benefits of landscape heterogeneity can vary among spatial scales and taxa. ObjectivesTo design biodiversity-friendly landscapes, we use nationwide bird survey data and land use maps to examine the effects of compositional heterogeneity, configurational heterogeneity, and habitat amount at different scales on the species richness of different bird groups. MethodsWe examine the effects of configurational heterogeneity (measured using edge density), compositional heterogeneity (Shannons diversity index of habitat types), and habitat amount (proportion of forest and farmland cover) at both transect (local) and landscape (0.5, 1, or 2 km) scales on the species richness of all breeding birds, forest birds, farmland birds, and introduced birds. ResultsTotal species richness had a hump-shaped relationship with local forest cover, and with farmland cover at landscape scale. Richness of both forest birds and richness of farmland birds increased with Shannons diversity index of habitat types at both local and landscape scales, but only increased with the amount of their preferred habitat at the local scale. Richness of introduced birds was greater in landscapes with higher edge density, suggesting those species are associated with human-dominated landscapes. ConclusionsHigh compositional heterogeneity with low configurational heterogeneity at the landscape scale may help maintain native bird richness while minimising the spread of introduced species in Taiwan. These results can help guide land use planning to achieving biodiversity goals in a country with intensive land-use competition.

Plant species richness is influenced by complex interactions between biotic and abiotic factors that operate on different spatial scales. Since spatial scales vary continuously in nature, it is expected that multiple factors simultaneously affect species richness and composition at an intermediate spatial scale (i.e., the mesoscale landscape level). Previous studies have shown that local topography and elevation are important factors for shaping mesoscale landscape-level plant species richness; however, the relative importance of these factors has rarely been examined. Here, we used spatially explicit woody plant survey data to investigate the relative importance of topography, elevation, and disturbance at the mesoscale landscape level. We found that topography and elevation are important drivers of plant species richness and composition at the mesoscale landscape level and affect different components (i.e., the number of species and species composition, respectively). Our study also found that closely-related species coexisted along the elevational gradient, suggesting that niche partitioning among closely-related species is a fundamentally important feature of mesoscale species richness pattern. Furthermore, we found that specialization in a habitat of closely-related species can be established even within a limited environmental gradient. This suggests that biotic interactions among closely-related species may be an important factor driving habitat specialization, and biotic interactions may play an important role in shaping landscape-scale biodiversity patterns.

Among vector-borne diseases, tick-borne diseases (TBD) are a major concern for human health. Mapping the distribution of important tick species is thus a major challenge for efficient prevention. Due to its specific ecological requirements, Ixodes ricinus, the main tick species in Europe responsible for TBD transmission, lives mostly in woodlands but also at the interface between woodlands and pastures or crops and along hedgerows. At the landscape scale, extensive variations in tick densities are observed but remain poorly understood. In that aim, we built a statistical model to identify the landscape variables influencing the abundance of questing I. ricinus nymphs, using GLMM approaches and MCMC estimates. This model was fitted on a data set based on a field sampling of ticks conducted during 3 years in 2 different agricultural landscapes in northwest and southwest France, for a total of 5390 sampling units. Among 12 variables investigated, 4 were finally kept in the model: woodland perimeter, woodland distance, road distance and building perimeter. Then, we developed a R package that simulates the abundance of questing nymphs within a given agricultural landscape, taking into account the influence of the different habitats as determined by the above statistical model. The maps obtained as an output from this simulator will be a useful tool for visualizing TBD risk, notably for stake-holders involved in landscape management and public health decisions. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=88 SRC="FIGDIR/small/663759v1_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@f950aborg.highwire.dtl.DTLVardef@1f0f27org.highwire.dtl.DTLVardef@11bde05org.highwire.dtl.DTLVardef@8d2202_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsIxodes ricinus abondance is influenced by landscape characteristics Tick sampling was carried out in heterogeneous agricultural landscapes Informative variables related to habitats were identified by statistical analysis Woodlands, roads and buildings influence tick densities The resulting model was used to build a simulator of tick at-risk zones

Habitat fragmentation is often highlighted as a driver of tick-borne disease hazard and spillover risk via reduction in biodiversity. However, habitat fragmentation can have divergent impacts on host, vector, and pathogen dynamics depending on the distribution of fragment sizes and the levels of connectivity to surrounding habitat, particularly when habitat fragments are embedded in an urban matrix. We examine how extreme habitat fragmentation influences host community composition in an urban landscape and determine its cascading impacts on Ixodes scapularis vector abundance and infection prevalence with human pathogenic Borrelia burgdorferi, Babesia microti, and Anaplasma phagocytophilum. We utilize camera-trapping and live mammal-trapping methods to quantify the availability of vertebrate hosts to questing larval ticks and relate relative host activity to the resulting density of nymphs and nymphal infection prevalence; the combination of these metrics determines the tick-borne disease hazard (i.e. the density of infected questing nymphs - DIN). We found that increased habitat connectivity in urban areas shifted the composition of the host community from human-adapted to forest-dependent species, species which inhabit forested habitats for all or a portion of their lifecycles. The resulting increased encounter probability between ticks and forest-dependent species increased the density of nymphs and nymphal infection prevalence with host-limited pathogens, A. microti and A. phagocytophilum, amplifying local tick-borne disease hazard. Host encounter probability of all species examined did not increase B. burgdorferi nymphal infection prevalence, likely due to this pathogens wider host range; whereas increased deer encounter probability decreased the nymphal infection prevalence of B. burgdorferi. These findings emphasize the importance of host identity, rather than host diversity, in shaping the heterogenous distribution of tick-borne pathogen risk in highly fragmented urban forest patches and suggest a non-linear association between disease risk and host biodiversity.

Bovine tuberculosis (bTB), a zoonotic disease caused by Mycobacterium bovis, continues to challenge eradication efforts in Ireland and the UK, partly due to the role of the European badger (Meles meles) as a wildlife reservoir. Traditional management strategies often rely on sett (burrow) locations to infer badger distribution, which implicitly assumes a correlation with abundance. This study uses data from Irelands national badger culling and vaccination programme (2019-2025) to decouple badger and sett distributions using spatial point process modelling via log-Gaussian Cox processes. By separately modelling the environmental drivers of main sett and badger distributions, and validating outputs for ecological realism with independent badger body weight data, we demonstrate that sett and badger densities are governed by distinct ecological processes. Sett densities are driven by landscape features such as elevation, slope, and proximity to forest edges, while badger densities are more influenced by recent culling history and pasture availability. Our results reveal a spatial mismatch between high-density sett areas and high-density badger areas, highlighting the need for refined metrics in wildlife-based bTB management. These findings underscore the importance of integrating independently derived wildlife distribution models into disease control policies for more sustainable and effective bTB management.

Swidden agriculture is a widespread anthropogenic disturbance regime in tropical forests. Swidden research often posits that aggregate levels of forest disturbance correlate with increased land degradation, however a landscape configuration approach may distinguish when swidden degrades landscapes and when it diversifies them. Here we analyze how the configuration of swidden mosaics relates to vegetation diversity. Using satellite imagery from 18 swidden societies across the African, Southeast Asian, and American tropics, we quantify patch geometry using landscape metrics and estimate vegetation diversity from spectral variation and develop a nonlinear hierarchical Bayesian model that links the structure of swidden mosaics with vegetation diversity. Our analyses reveal three dominant gradients of swidden mosaic patterns: (1) aggregation versus interspersion of land-cover types; (2) spatial dispersion versus synchronization of disturbed patches; and (3) alternative modes of landscape connectivity. Across sites, vegetation diversity exhibits a consistent nonlinear response, peaking at intermediate levels of disturbance intensity. These results demonstrate that swidden agriculture does not produce a singular, degradative outcome. Instead, its effects on vegetation diversity depend on how disturbance is spatially configured. By shifting attention from area-based measures of deforestation to landscape configuration, this study reframes swidden as a spatial process with the potential for diversity-enhancing outcomes.

The landscape connectivity of cities is increasingly recognized as crucial for biodiversity conservation and ecosystem services. Yet, modelling ecological connectivity in cities remains challenging because landscape resistance is often based on expert judgment rather than empirical evidence, leading to varying modelling results and limited use for planning. We developed and tested a data-driven framework for empirically parametrizing resistance and movement-distance parameters in functional connectivity models from movement-proxy data - information on the presence/absence of animal movement from direct observation or camera traps. At each step, we ensured that the connectivity model reflected the behavioural and spatial properties of the observations. We applied the framework for the common blackbird (Turdus merula) in Munich, Germany. We used observations of flying blackbirds as movement-proxy data in a logistic regression framework, testing alternative combinations of resistance and movement distances. Model selection identified the parameter sets best supported by the data. The resulting parameters were validated using repeated out-of-sample validation and compared against an expert-based connectivity model. Connectivity derived from empirically estimated parameters increased the probability of observing flying blackbirds. Across repeated validations, the empirical model achieved a mean AUC of 0.76 and R2 of 0.17. It performed moderately better than the expert-based model. Depending on their height, buildings exhibited varying resistance to flying blackbirds. Results indicate that expert assessments may oversimplify urban barriers. The approach provides a transparent, reproducible framework for using movement-proxy data to derive maps of landscape resistance. It offers a step toward more data-driven urban connectivity modelling.

Designing landscapes to accommodate both humans and nature poses huge challenges, but is increasingly recognised as an essential component of conservation and land management. The land-sparing land-sharing framework has been proposed as a tool to address this challenge. However, it has been largely criticised for its simplicity. We provide a new conceptual framework amenable to the application of structured decision-making that moves beyond the dichotomy of land-sparing or land-sharing. Using this new framework, we present a general system model that can be used to make land management decisions for the conservation of species, ecosystem services and production land at different spatial scales. The model can be parameterised for specific systems using information about: the current state of the landscape, the rates of change between landscape states, and the cost and effectiveness of taking actions. To demonstrate the utility of the model we apply it to three different landscape types. Across our three case studies, we show that investment into one of three management actions (varying degrees of management and restoration) can move the system towards more biodiversity or more managed land depending on the objectives of the land manager. We show that the dynamic and flexible nature of the landscape is important to take into account rather than a static snapshot in time. Rather than focusing on establishing the perfect landscape with a set proportion dedicated to production and to biodiversity conservation, we argue that a more useful approach is to establish incremental movements towards a landscape that meets the goals of multiple objectives. Our framework can be used to illustrate to decision makers the costs and trade-offs of different actions and help them determine land management policy.

There is increasing evidence that the traits of organisms can differ in urban environments, but defining what makes an environment urban is difficult. There are many variables associated with increasing human impacts that might be associated with urbanization, and this makes it challenging to identify both how traits vary across diverse urbanized landscapes and the variables that might drive that variation. To better define the mosaic of urban environmental heterogeneity and identify similar types of environments that are comparable across its complexity, here we develop a multivariate landscape classification framework and apply it to classification of land area in the state of California, USA. We used a hierarchical cluster analysis to group 7,829 government census tracts into Environmental Zones based on a set of 19 independent environmental characteristics, including climate, land cover, pollution, and socio-economic variables. This analysis identified nine major Environmental Zones, which were differentiated based upon complex combinations of variables that did not align with conventional urban vs. natural dichotomies or gradients. Environmental Zones also occurred as mosaics of many zones within cities and differed in their relative abundance between cities, reflecting complex urban landscapes unique to each area. We then asked if these Environmental Zones were better able to explain trait variation than conventional urban vs. non-urban classification using a case study of the invasive, annual plant Centaurea melitensis, commonly found throughout much of California. Seeds from seventeen populations of C. melitensis were collected from six Environmental Zones, including two heavily urbanized, and four more natural/agricultural. Seeds were grown in greenhouse conditions, and eight vegetative traits were measured. No trait differed significantly between urban and non-urban sites, but four traits differed according to Environmental Types (length of longest leaf, SLA, root diameter, and the number of flowerheads). Traits differed between the heavily urbanized zones, as well as among the relatively more natural zones. Our results reveal that more complex multivariate classifications of the urban mosaic can identify similar, comparable environments across complex landscapes and better explain trait variation in organisms navigating urbanized environments.

The negative effect of fragmentation is one of the main concerns in the study of biodiversity loss in landscape ecology. The use of the matrix has been considered an important factor because it can change the relationship of a population with the configuration of the landscape. A systematic way to assess the effect of matrix quality in fragmented landscapes could lead to a better understanding of how matrices can be used to suppress the negative effect of fragmentation. We built a computational individual-based model capable of simulating bi-dimensional landscapes with three types of land cover (habitat, suitable matrix and hostile matrix) and individuals that inhabit those landscapes. We explored in which situations changes in the proportion of the suitable matrix in the landscape and the degree of usability of this suitable matrix can mitigate the negative effect of fragmentation per se. We observed that (i) an increase in the matrix quality (increases in the suitable matrix proportion and/or usability) can suppress the fragmentation effect in 47% of the simulated scenarios; (ii) the less usable the matrix is the more of it is needed to suppress the fragmentation effect; (iii) there is a level of usability below which increasing the suitable matrix proportion does cause the fragmentation effect to cease. These results point toward a landscape management that considers the similarity of the matrix to the native habitat under management. We suggest that an index to measure the usability of elements of the matrix could be an important tool to further the use of computational models in landscape management.