Probabilistic patterns of inundation and biogeomorphic changes due to sea-level rise along the northeastern U.S. Atlantic coast

Context

Coastal landscapes evolve in response to sea-level rise (SLR) through a variety of geologic processes and ecological feedbacks. When the SLR rate surpasses the rate at which these processes build elevation and drive lateral migration, inundation is likely.

Objectives

To examine the role of land cover diversity and composition in landscape response to SLR across the northeastern United States.

Methods

Using an existing probabilistic framework, we quantify the probability of inundation, a measure of vulnerability, under different SLR scenarios on the coastal landscape. Resistant areas—wherein a dynamic response is anticipated—are defined as unlikely (p < 0.33) to inundate. Results are assessed regionally for different land cover types and at 26 sites representing varying levels of land cover diversity.

Results

Modeling results suggest that by the 2050s, 44% of low-lying, habitable land in the region is unlikely to inundate, further declining to 36% by the 2080s. In addition to a decrease in SLR resistance with time, these results show an increasing uncertainty that the coastal landscape will continue to evolve in response to SLR as it has in the past. We also find that resistance to SLR is correlated with land cover composition, wherein sites containing land cover types adaptable to SLR impacts show greater potential to undergo biogeomorphic state shifts rather than inundating with time.

Conclusions

Our findings support other studies that have highlighted the importance of ecological composition and diversity in stabilizing the physical landscape and suggest that flexible planning strategies, such as adaptive management, are particularly well suited for SLR preparation in diverse coastal settings.

     

Grassland-to-crop conversion in agricultural landscapes has lasting impact on the trait diversity of bees

Context

Global pollinator decline has motivated much research to understand the underlying mechanisms. Among the multiple pressures threatening pollinators, habitat loss has been suggested as a key-contributing factor. While habitat destruction is often associated with immediate negative impacts, pollinators can also exhibit delayed responses over time.

Objectives

We used a trait-based approach to investigate how past and current land use at both local and landscape levels impact plant and wild bee communities in grasslands through a functional lens.

Methods

We measured flower and bee morphological traits that mediate plant–bee trophic linkage in 66 grasslands. Using an extensive database of 20 years of land-use records, we tested the legacy effects of the landscape-level conversion of grassland to crop on flower and bee trait diversity.

Results

Land-use history was a strong driver of flower and bee trait diversity in grasslands. Particularly, bee trait diversity was lower in landscapes where much of the land was converted from grassland to crop long ago. Bee trait diversity was also strongly driven by plant trait diversity computed with flower traits. However, this relationship was not observed in landscapes with a long history of grassland-to-crop conversion. The effects of land-use history on bee communities were as strong as those of current land use, such as grassland or mass-flowering crop cover in the landscape.

Conclusions

Habitat loss that occurred long ago in agricultural landscapes alters the relationship between plants and bees over time. The retention of permanent grassland sanctuaries within intensive agricultural landscapes can offset bee decline.

     

Additive and synergistic effects of land cover,land use and climate on insect biodiversity

Context

We address the issue of adapting landscapes for improved insect biodiversity conservation in a changing climate by assessing the importance of additive (main) and synergistic (interaction) effects of land cover and land use with climate.

Objectives

We test the hypotheses that ant richness (species and genus), abundance and diversity would vary according to land cover and land use intensity but that these effects would vary according to climate.

Methods

We used a 1000 m elevation gradient in eastern Australia (as a proxy for a climate gradient) and sampled ant biodiversity along this gradient from sites with variable land cover and land use.

Results

Main effects revealed: higher ant richness (species and genus) and diversity with greater native woody plant canopy cover; and lower species richness with higher cultivation and grazing intensity, bare ground and exotic plant groundcover. Interaction effects revealed: both the positive effects of native plant canopy cover on ant species richness and abundance, and the negative effects of exotic plant groundcover on species richness were greatest at sites with warmer and drier climates.

Conclusions

Impacts of climate change on insect biodiversity may be mitigated to some degree through landscape adaptation by increasing woody native vegetation cover and by reducing land use intensity, the cover of exotic vegetation and of bare ground. Evidence of synergistic effects suggests that landscape adaptation may be most effective in areas which are currently warmer and drier, or are projected to become so as a result of climate change.

     

The importance of land-use legacies for modeling present-day species distributions

Context

Land-use legacies play an important role in shaping contemporary species distributions. However, land-use legacies are rarely considered in species distribution models (SDMs) that aim to model present-day species distributions across the landscape, even though they can lead to a species absence in suitable areas. SDMs that do not account for land-use legacies will likely result in biased predictions of species distributions.

Objective

We examine the importance of land-use legacies for modeling present-day distributions of tree species at a regional scale, assessing how the addition of land-use legacy variables improves predictive power of SDMs.

Methods

We generated land-use legacy variables using raster layers of reconstructed historical agricultural land use and 3310 inventory plots. SDMs were developed for six forest tree species based on climatic, edaphic, and topographic variables, and with (SDM_LU) and without (SDM_Base) land-use legacy variables. We compared the predictive power between SDM_LU and SDM_Base models and then quantified the local importance of land-use legacy variables relative to other abiotic variables.

Results

Our results show that the importance of land-use legacy variables for present-day species distributions and the improvement on the predictive power of SDMs is species-specific. The inclusion of land-use legacy variables improved SDMs primarily by lowering errors of commission and increasing the overall accuracy of prediction.

Conclusion

The influence of land-use legacies on SDMs suggests that, for some tree species, incorporating land-use legacies can accurately identify suitable areas that are not occupied by the species due to land-use legacies, and advance our understanding of their present-day distributions.

     

Information theory as a consistent framework for quantification and classification of landscape patterns

Context

Quantitative grouping of similar landscape patterns is an important part of landscape ecology due to the relationship between a pattern and an underlying ecological process. One of the priorities in landscape ecology is a development of the theoretically consistent framework for quantifying, ordering and classifying landscape patterns.

Objective

To demonstrate that the information theory as applied to a bivariate random variable provides a consistent framework for quantifying, ordering, and classifying landscape patterns.

Methods

After presenting information theory in the context of landscapes, information-theoretical metrics were calculated for an exemplar set of landscapes embodying all feasible configurations of land cover patterns. Sequences and 2D parametrization of patterns in this set were performed to demonstrate the feasibility of information theory for the analysis of landscape patterns.

Results

Universal classification of landscape into pattern configuration types was achieved by transforming landscapes into a 2D space of weakly correlated information-theoretical metrics. An ordering of landscapes by any single metric cannot produce a sequence of continuously changing patterns. In real-life patterns, diversity induces complexity—increasingly diverse patterns are increasingly complex.

Conclusions

Information theory provides a consistent, theory-based framework for the analysis of landscape patterns. Information-theoretical parametrization of landscapes offers a method for their classification.

     

Integrating landscape ecology into landscape practice in Central African Rainforests

Context

We describe how large landscape-scale conservation initiatives involving local communities, NGOs and resource managers have engaged with landscape scientists with the goal of achieving landscape sustainability. We focus on two landscapes where local people, practitioners and landscape ecologists have co-produced knowledge to design conservation interventions.

Objective

We seek to understand how landscape ecology can engage with practical landscape management to contribute to managing landscapes sustainably.

Methods

We focus on two large tropical landscapes: the Sangha Tri-National landscape (Cameroon, Republic of Congo and the Central African Republic) and the Batéké-Léfini Landscape (Gabon and Republic of Congo). We evaluate (1) a participatory method used in the Sangha Tri-National landscape that embeds interdisciplinary researchers and practitioners within a landscape to apply transdisciplinary learning to landscape conservation and (2) a participatory landscape zoning method where interdisciplinary teams of conservation practitioners analyse local land and resource use in the Batéké-Léfini landscape.

Results

We find that landscape ecology’s tradition of understanding the historical context of resource use can inform landscape conservation practice and natural resource mapping. We also find that the Sangha Group provides an example for landscape ecology on how to integrate local people and their knowledge to better understand and influence landscape processes.

Conclusions

Place-based engagement as well as the uptake of co-produced knowledge by policy makers are key in enabling sustainable landscapes. Success occurs when researchers, local communities and resource managers engage directly with landscape processes.

     

Linear downscaling from MODIS to landsat: connecting landscape composition with ecosystem functions

Context

The open and free access to Landsat and MODIS products have greatly promoted scientific investigations on spatiotemporal change in land mosaics and ecosystem functions at landscape to regional scales. Unfortunately, there is a major mismatch in spatial resolution between MODIS products at coarser resolution (≥?250 m) and landscape structure based on classified Landsat scenes at finer resolution (30 m).

Objectives

Based on practical needs for downscaling popular MODIS products at 500 m resolution to match classified land cover at Landsat 30 m resolution, we proposed an innovative modelling approach so that landscape structure and ecosystem functions can be directly studied for their interconnections. As a proof-of-concept of our downscaling approach, we selected the watershed of the Kalamazoo River in southwestern Michigan, USA as the testbed.

Methods

MODIS products for three fundamental variables of ecosystem function are downscaled to ensure the approach can be extrapolated to multiple functional measurements. They are blue-sky albedo (0–1), evapotranspiration (ET, mm), and gross primary production (GPP, Mg C ha^?1 year^?1). An object-oriented classification of Landsat images in 2011 was processed to generate a land cover map for landscape structure. The downscaling model was tested for the five Level IV ecoregions within the watershed.

Results

We achieved satisfactory downscaling models for albedo, ET, and GPP for all five ecoregions. The adjusted R² was?>?0.995 for albedo, 0.915–0.997 for ET, and 0.902–0.962 for GPP. The estimated albedo, ET, and GPP values appear different in the region. The estimated albedo was the lowest for water (0.076–0.107) and the highest for cropland (0.166–0.172). Estimated ET was the highest for the built-up cover type (525.6–687.1 mm) and the lowest for forest (209.7–459.7 mm). The estimated GPP was the highest for the build-up cover type (8.65–9.85 Mg C ha^?1 year^?1) and the lowest for forest.

Conclusions

Estimated values for albedo, ET, and GPP appear reasonable for their ranges in the Kalamazoo River region and are consistent with values reported in the literature. Despite these promising results, the downscaling approach relies on strong assumptions and can carry substantial uncertainty. It is only valid at a spatial scale where similar climate, soil, and landforms exist (i.e., values in isolated patches of the same cover type are similar). Plausibly, the uncertainties associated with each estimation, as well as the model residuals, can be explored for other pattern-process relationships within the landscape.

     

Synergistic effects of climate and land cover: grassland birds are more vulnerable to climate change

Context

Climate change is not occurring over a homogeneous landscape and the quantity and quality of available land cover will likely affect the way species respond to climate change. The influence of land cover on species’ responses to climate change, however, is likely to differ depending on habitat type and composition.

Objectives

Our goal was to investigate responses of forest and grassland breeding birds to over 20 years of climate change across varying gradients of forest and grassland habitat. Specifically, we investigated whether (i) increasing amounts of available land cover modify responses of forest and grassland-dependent birds to changing climate and (ii) the effect of increasing land cover amount differs for forest and grassland birds.

Methods

We used Bayesian spatially-varying intercept models to evaluate species- and community-level responses of 30 forest and 10 grassland birds to climate change across varying amounts of their associated land cover types.

Results

Responses of forest birds to climate change were weak and constant across a gradient of forest cover. Conversely, grassland birds responded strongly to changing climatic conditions. Specifically, increasing temperatures led to higher probabilities of localized extinctions for grassland birds, and this effect was intensified in regions with low amounts of grassland cover.

Conclusions

Within the context of northeastern forests and grasslands, we conclude that forests serve as a possible buffer to the impacts of climate change on birds. Conversely, species occupying open, fragmented grassland areas might be particularly at risk of a changing climate due to the diminished buffering capacity of these ecosystems.

     

Influence of high-resolution data on the assessment of forest fragmentation

Context

Remote sensing has been a foundation of landscape ecology. The spatial resolution (pixel size) of remotely sensed land cover products has improved since the introduction of landscape ecology in the United States. Because patterns depend on spatial resolution, emerging improvements in the spatial resolution of land cover may lead to new insights about the scaling of landscape patterns.

Objective

We compared forest fragmentation measures derived from very high resolution (1 m²) data with the same measures derived from the commonly used (30 m?×??30 m; 900 m²) Landsat-based data.

Methods

We applied area-density scaling to binary (forest; non-forest) maps for both sources to derive source-specific estimates of dominant (density ≥?60%), interior (≥?90%), and intact (100%) forest.

Results

Switching from low- to high-resolution data produced statistical and geographic shifts in forest spatial patterns. Forest and non-forest features that were “invisible” at low resolution but identifiable at high resolution resulted in higher estimates of dominant and interior forest but lower estimates of intact forest from the high-resolution source. Overall, the high-resolution data detected more forest that was more contagiously distributed even at larger spatial scales.

Conclusion

We anticipate that improvements in the spatial resolution of remotely sensed land cover products will advance landscape ecology through re-interpretations of patterns and scaling, by fostering new landscape pattern measurements, and by testing new spatial pattern-ecological process hypotheses.

     

Scenarios of long-term farm structural change for application in climate change impact assessment

Towards 2050, climate change is one of the possible drivers that will change the farming landscape, but market, policy and technological development may be at least equally important. In the last decade, many studies assessed impacts of climate change and specific adaptation strategies. However, adaptation to climate change must be considered in the context of other driving forces that will cause farms of the future to look differently from today’s farms. In this paper we use a historical analysis of the influence of different drivers on farm structure, complemented with literature and stakeholder consultations, to assess future structural change of farms in a region under different plausible futures. As climate change is one of the drivers considered, this study thus puts climate change impact and adaptation into the context of other drivers. The province of Flevoland in the north of The Netherlands was used as case study, with arable farming as the main activity. To account for the heterogeneity of farms and to indicate possible directions of farm structural change, a farm typology was developed. Trends in past developments in farm types were analyzed with data from the Dutch agricultural census. The historical analysis allowed to detect the relative importance of driving forces that contributed to farm structural changes. Simultaneously, scenario assumptions about changes in these driving forces elaborated at global and European levels, were downscaled for Flevoland, to regional and farm type level in order to project impacts of drivers on farm structural change towards 2050. Input from stakeholders was also used to detail the downscaled scenarios and to derive historical and future relationships between drivers and farm structural change. These downscaled scenarios and future driver-farm structural change relationships were used to derive quantitative estimations of farm structural change at regional and farm type level in Flevoland. In addition, stakeholder input was used to also derive images of future farms in Flevoland. The estimated farm structural changes differed substantially between the two scenarios. Our estimations of farm structural change provide a proper context for assessing impacts of and adaptation to climate change in 2050 at crop and farm level.     

The transiogram as a graphic metric for characterizing the spatial patterns of landscapes

Context

Landscape metrics play an important role in measurement, analysis, and interpretation of spatial patterns of landscapes. There are a variety of different landscape metrics widely used in landscape ecology. However, existing landscape metrics are mostly non-graphic and single-value indices, which may not be sufficient to describe the complex spatial correlation and interclass relationships of various landscapes. As a transition probability diagram over the lag distance, the transiogram, which emerged in recent years, essentially provides a new graphic metric for measuring and visualizing the auto and cross correlations of landscape categories.

Objectives

To explore the capability of the transiogram for measuring spatial patterns of categorical landscape maps and compare it with existing landscape metrics.

Methods

Sixteen commonly-used landscape metrics and transiograms (including auto- and cross-transiograms) were estimated and compared for land cover/use classes in four areas with different landscapes.

Results

Results show that (1) these transiograms can provide visual information about the proportions, aggregation levels, interclass adjacencies, and intra-class/interclass correlation ranges of landscape classes; (2) sills and auto-correlation ranges of transiograms are correlated with the values of some landscape metrics; and (3) the peak height ratios of idealized transiograms can effectively represent the juxtaposition strength of neighboring class pairs.

Conclusions

The transiogram can be an effective graphic metric for characterizing the auto-correlation of single classes (through auto-transiograms) and the complex interclass relationships, such as interdependency and juxtaposition, between different landscape classes (through cross-transiograms).

     

Predicting land cover change and avian community responses in rapidly urbanizing environments

We used an integrated modeling approach to simulate future land cover and predict the effects of future urban development and land cover on avian diversity in the Central Puget Sound region of Washington State, USA. We parameterized and applied a land cover change model (LCCM) that used output from a microsimulation model of urban development, UrbanSim, and biophysical site and landscape characteristics to simulate land cover 28 years into the future. We used 1991, 1995, and 1999 Landsat TM-derived land cover data and three different spatial partitions of our study area to develop six different estimations of the LCCM. We validated model simulations with 2002 land cover. We combined UrbanSim land use outputs and LCCM simulations to predict changes in avian species richness. Results indicate that landscape composition and configuration were important in explaining land cover change as well as avian species response to landscape change. Over the next 28 years, urban land cover was predicted to increase at the expense of agriculture and deciduous and mixed lowland forests. Land cover changes were predicted to reduce the total number of avian species, with losses primarily in native forest specialists and gains in common synanthropic species such as the American Crow (Corvus brachyrhynchos). The integrated modeling framework we present has potential applications in urban and natural resource planning and management and in assessing of the effects of policies on land development, land cover, and avian biodiversity.     

Nonparametric machine learning for mapping forest cover and exploring influential factors

Context

The contribution of forest ecosystem services to human well-being varies over space following the dynamics in forest cover. Use of machine learning models is increasing in projecting forest cover changes and investigating the drivers, yet references are still lacking for selecting machine learning models for spatial projection of forest cover patterns.

Objectives

We assessed the ability of nonparametric machine learning techniques to project the spatial distribution of forest cover and identify its drivers using a case study of Tasmania, Australia.

Methods

We developed, evaluated, and compared the performance of four nonparametric machine learning models: support vector regression (SVR), artificial neural networks (ANN), random forest (RF), and gradient boosted regression trees (GBRT).

Results

The results demonstrated that RF far outperformed the other three models in both fitting and projection accuracy, and required less computional costs. GBRT outperformed SVR and ANN in projection accuracy. However, RF exhibited serious overfitting due to the full growth of its decision trees. The influence rankings of explanatory variables on spatial patterns of forest cover were different under the four models. Land tenure type and rainfall were identified among the top four most influential variables by all four models. The ranking produced by the RF model was significantly different with topographic factors associated with land clearing and production costs (elevation and distance to timber facilities) being the two most influential variables.

Conclusions

We encourage practitioners to consider nonparametric machine learning methods, especially RF, when facing problems of complex environmental data modelling.

     

Forest proximity rather than local forest cover affects bee diversity and coffee pollination services

Context

As agricultural demands for land continues to expand, strategies are urgently needed to balance agricultural production with biodiversity conservation and ecosystem service provision in agricultural landscapes.

Objectives

We used a factorial landscape design to assess the relative contributions of forest proximity and local forest cover to bee diversity and the provision of coffee pollination services.

Methods

We quantified bee diversity and fruit set in 24 sun-grown coffee fields in Southeast Region of Brazil that were selected following a factorial sampling design to test the independent effects of local forest cover (in a radius of 400 m) and proximity to forest fragments. To assess the impact of landscape simplification, we also evaluated local coffee cover.

Results

Bee richness and abundance were higher in the proximity of forest fragments, but only bee abundance decreased when the coffee cover dominated the surrounding landscapes. Coffee fruit set was 16% higher overall with bee visitations compared with bee exclusion and increased to 20% when coffee bushes were near forest fragments, and the coffee cover was low. Surprisingly, local forest cover did not affect the bee community or coffee fruit set.

Conclusion

Our results provide clear evidence that the proximity of coffee crops to forest fragments can affect the abundance and richness of bees visiting the coffee flowers and thereby facilitate the provision of pollination services. The positive association between forest proximity and fruit set reinforces the importance of natural vegetation in enhancing bee diversity and, therefore, in the provision of pollination services. The negative effect of coffee cover on fruit set at the local scale suggests that the service demand can surpass the capacity of pollinators to provide it. These effects were independent of the local forest cover, although all studied landscapes had more than 20% remaining forest cover (within a 2 km radius), which is considered the extinction threshold for Atlantic Forest species. Interspersion of forest fragments and coffee plantations in regions with more than 20% of forest cover left could thus be a useful landscape management target for facilitating pollinator flows to coffee crops and thus for increasing coffee yields.

     

A combined grazing and fire management may reverse woody shrub encroachment in desert grasslands

Context

Fire and controlled grazing have been widely adopted as management interventions to counteract woody shrub proliferation in many arid and semiarid grassland systems. The actual intensity of grazing and fire, along with the timing of the interventions, however, are difficult to determine in practice.

Objectives

This study aims to establish model simulations to access the long-term landscape changes under different land management scenarios.

Methods

We developed a cellular automata model to evaluate landscape dynamics in response to scenarios of grazing, fire, time of intervention, and initial coverage of grasses and shrubs.

Results

With current grazing intensity and fire suppression, the landscape may shift to a shrub-dominated landscape in 100–150 years. An appropriate combination of grazing and fire management could help maintain over 50% of grass cover and reduce the shrub cover to less than 2%, keeping the landscape highly reversible. Even using 1% grazing intensity and periodic fire once a year, the management tools should be implemented in 60 years, otherwise, they may lose effectiveness and the vegetation transition to grasslands would become impossible.

Conclusions

This study highlighted that the reintroduction of fire not only directly removes shrubs but also reallocates soil water and resources among different microsites, which may accelerate grass recovery and suppress shrub regrowth, potentially reversing the shrub invasion process. The combined grazing and fire management plans should be carried out before a threshold time depending on the chosen management tools.

     

Consequences of Landscape Heterogeneity on Grassland Diversity and Productivity in the Espinal Agroforestry System of Central Chile

The current land use system in the anthropogenic savannas (Espinales) of the Mediterranean climate region of Chile, has resulted in considerable heterogeneity at the landscape level which is associated with different covers of the legume tree, Acacia caven. The effects of landscape heterogeneity on the diversity and productivity of herbaceous plant communities were studied in 29 plots of 1000 m², with a wide range of woody cover. A detrended correspondence analysis of the species × plots matrix explained 73% of the total variation and revealed the existence of two trends of variation in floristic composition: one associated with physiographic position (hillsides and flatlands) and the other related to the number of years since the last cutting, or coppicing, of A. caven. Despite the great majority of the original herbaceous species having disappeared as a result of the prevailing land use system, some native species have been able to survive especially on hillside areas with low grazing intensity. Woody cover was a good indicator of spatial heterogeneity and land use history. It was also correlated with stocking rate, above-ground biomass of herbaceous vegetation, and soil fertility (organic matter, nitrogen and phosphorus concentration), both on hillsides and flatlands. The relationship between woody cover and herbaceous plant species richness was significant and unimodal in flat land areas, and linear, and marginally significant, on hillsides. The consequences of land use changes on the conservation of the ecological and productive values of grasslands are analyzed.     

A spatial analysis of land cover patterns and its implications for urban avifauna persistence under climate change

Context

Although biodiversity in cities is essential to ensure the healthy functioning of ecosystems and biosecurity over time, biodiversity loss resulting from human interventions in land cover patterns is widespread in urban landscapes. In the Southern Hemisphere, climate change is likely to accelerate the process of landscape upheavals, and consequently biodiversity loss.

Objectives & Methods

The aim of this research is to test the potentials of landscape pattern composition and configuration in safeguarding indigenous avifauna against the local impacts of climate change in urban landscapes, with reference to New Zealand. To build up a platform for landscape pattern interpretation, the literature was reviewed and semi-structured interviews with six subject-matter experts were conducted to provide information about the most important avifauna in the study area, key information on their ecological traits and niches, possible impacts of climate change on their primary habitats, and spatial requirements for ongoing species survival as the climate continues to change. A spatial analysis of land cover patterns was undertaken in Wellington, New Zealand using GIS and FRAGSTATS.

Results

Although there are still opportunities for biodiversity conservation in the study area, the current land cover patterns are unlikely to safeguard the selected species against climate change impacts.

Conclusions

Eight implications for avifauna persistence under climate change are discussed for the first time in relation to a New Zealand context. These implications can give rise to a higher level of informed decision-making on a wide range of practices for biodiversity conservation related to uncertainties associated with climate change.

     

Exploring subtle land use and land cover changes: a framework for future landscape studies

Land cover and land use changes can have a wide variety of ecological effects, including significant impacts on soils and water quality. In rural areas, even subtle changes in farming practices can affect landscape features and functions, and consequently the environment. Fine-scale analyses have to be performed to better understand the land cover change processes. At the same time, models of land cover change have to be developed in order to anticipate where changes are more likely to occur next. Such predictive information is essential to propose and implement sustainable and efficient environmental policies. Future landscape studies can provide a framework to forecast how land use and land cover changes is likely to react differently to subtle changes. This paper proposes a four step framework to forecast landscape futures at fine scales by coupling scenarios and landscape modelling approaches. This methodology has been tested on two contrasting agricultural landscapes located in the United States and France, to identify possible landscape changes based on forecasting and backcasting agriculture intensification scenarios. Both examples demonstrate that relatively subtle land cover and land use changes can have a large impact on future landscapes. Results highlight how such subtle changes have to be considered in term of quantity, location, and frequency of land use and land cover to appropriately assess environmental impacts on water pollution (France) and soil erosion (US). The results highlight opportunities for improvements in landscape modelling.     

Towards sustainable forest management in the European Union through polycentric forest governance and an integrated landscape approach

Context

Achieving sustainable development as an inclusive societal process, and securing sustainability and resilience of human societies as well as the natural environment are wicked problems. Realising sustainable forest management (SFM) policy in local landscapes is one example.

Objectives

Using the European Union as a case study for the implementation of SFM policy across multiple governance levels in different contexts, we discuss the benefits of adopting an integrated landscape approach with place and space, partnership and sustainability as three pillars.

Methods

We map the institutional frameworks for implementing SFM policy within all EU member states. Next, we analyse whether or not there is EU-level forest governance, and how power is distributed among EU, member state and operational levels.

Results

Mechanisms to steer a centralized forest governance approach towards SFM in the EU are marginal. Instead, there is a polycentric forest governance with 90 national and sub-national governments, which create and implement own and EU-wide SFM-related policies. Additionally, both among and within regional governance units there is a large variation in governance arrangements linked to land ownership at the operational level.

Conclusions

To effectively translate EU-wide SFM and SFM-related policies into action in local landscapes, it is crucial to acknowledge that there are different land ownership structures, landscape histories and alternative value chains based on multiple ecosystem services. Therefore regionally adapted landscape approaches engaging multiple stakeholders and actors through evidence-based landscape governance and stewardship towards sustainable forest landscape management are needed. Model Forest, Long-Term Socio-Ecological Research platform and Biosphere Reserve are three of many examples.