Using multiple metrics to estimate seasonal landscape connectivity for Blanding’s turtles (<Emphasis Type="Italic">Emydoidea blandingii</Emphasis>) in a fragmented landscape

Context

Landscapes and animal behavior can exhibit temporal variability and connectivity estimates should consider this phenomenon. In many species, timing of activities such as nesting, mate searching, and hibernation occurs during distinct periods in which movement events may differ, along with physical characteristics of the surrounding landscape.

Objectives

We estimate movement, landscape conductance, and patch importance for a turtle species across two seasonal activity periods (spring, late summer) in a fragmented agricultural region. Three connectivity approaches are compared to identify their advantages and disadvantages.

Methods

A least-cost distance model, circuit-based approach, and patch-based index were used to collectively describe the potential functional connectivity of Blanding’s turtle (Emydoidea blandingii) across a multi-temporal scale in an agricultural region of south western Ontario.

Results

Connectivity decreased further into the active season exhibited through lower conductance of the landscape and fewer pathways, while the importance of habitat nodes shifted due to temporal variability in the number and distribution of nodes. Models provided different yet complimentary information, with least-cost models overestimating discrete pathways yet providing a secondary measure of landscape barriers. The circuit-based model estimated corridors of least resistance providing an overall characterization of the landscape, while patch-based indices provided key information on the importance of individual habitat patches.

Conclusion

Findings highlight the importance of including a temporal aspect in connectivity modelling as results demonstrate a change in functional connectivity over time. We also recommend employing multiple connectivity metrics to capture variation in movement behavior.

     

A multi-species approach for assessing the impact of land-cover changes on landscape connectivity

Context

Land-cover changes (LCCs) could impact wildlife populations through gains or losses of natural habitats and changes in the landscape mosaic. To assess such impacts, we need to focus on landscape connectivity from a diachronic perspective.

Objectives

We propose a method for assessing the impact of LCCs on landscape connectivity through a multi-species approach based on graph theory. To do this, we combine two approaches devised to spatialize the variation of multi-species connectivity and to quantify the importance of types of LCCs for single-species connectivity by highlighting the possible contradictory effects.

Methods

We begin with a list of landscape species and create virtual species with similar ecological requirements. We model the ecological network of these virtual species at two dates and compute the variation of a local and global connectivity metric to assess the impacts of the LCCs on their dispersal capacities.

Results

The spatial variation of multi-species connectivity showed that local impacts range from ?6.4% to +3.2%. The assessment of the impacts of types of LCCs showed a variation in global connectivity ranging from ?45.1% for open-area reptiles to +170.2% for natural open-area birds with low-dispersion capacities.

Conclusions

This generic approach can be reproduced in a large variety of spatial contexts by adapting the selection of the initial species. The proposed method could inform and guide conservation actions and landscape management strategies so as to enhance or maintain connectivity for species at a landscape scale.

     

Choose your neighborhood wisely: implications of subsampling and autocorrelation structure in simultaneous autoregression models for landscape ecology

Context

Large datasets that exhibit residual spatial autocorrelation are common in landscape ecology, introducing issues with model inference. Computationally intensive statistical techniques such as simultaneous autoregression (SAR) are used to provide credible inference, yet landscape studies make choices about autocorrelation structure and data reduction techniques without adequate understanding of the consequences for model estimation and inference.

Objectives

Our goal is to understand the effects of misspecification of neighborhood size, subsampling, and data partitioning on SAR estimation and inference.

Methods

We use remotely sensed burn severity for a large wildfire in north-central Washington State as a case study. First we estimate SAR for remotely sensed burn severity data at multiple subsampling intensities, data partitions, and neighborhood distances. Second, we simulate landscape burn severity data with SAR errors and calculate type I error rates for SAR estimated at the simulation neighborhood distance, and at misspecified neighborhood distances.

Results

Subsampling and misspecification of the neighborhood result in spurious inference and modified coefficient estimates. Type I error rates are close to the specified α-level when the model is estimated at both the simulation neighborhood and the distance that minimizes AIC.

Conclusions

By evaluating the effectiveness of pre-burn fuel reduction treatments on subsequent wildfire burn severity, we demonstrate that misspecification of the neighborhood distance and subsampling the data compromises inference and estimation. Using AIC to choose the neighborhood distance provides type I error rates near the stated α-level in simulated data.

     

Sensitivity of resource selection and connectivity models to landscape definition

Context

The definition of the geospatial landscape is the underlying basis for species-habitat models, yet sensitivity of habitat use inference, predicted probability surfaces, and connectivity models to landscape definition has received little attention.

Objectives

We evaluated the sensitivity of resource selection and connectivity models to four landscape definition choices including (1) the type of geospatial layers used, (2) layer source, (3) thematic resolution, and (4) spatial grain.

Methods

We used GPS telemetry data from pumas (Puma concolor) in southern California to create multi-scale path selection function models (PathSFs) across landscapes with 2500 unique landscape definitions. To create the landscape definitions, we identified seven geospatial layers that have been shown to influence puma habitat use. We then varied the number, sources, spatial grain, and thematic resolutions of these layers to create our suite of plausible landscape definitions. We assessed how PathSF model performance (based on AIC) was affected by landscape definition and examined variability among the predicted probability of movement surfaces, connectivity models, and road crossing locations.

Results

We found model performance was extremely sensitive to landscape definition and identified only seven top models out of our suite of definitions (<1%). Spatial grain and the number of geospatial layers selected for a landscape definition significantly affected model performance measures, with finer grains and greater numbers of layers increasing model performance.

Conclusions

Given the sensitivity of habitat use inference, predicted probability surfaces, and connectivity models to landscape definition, out results indicate the need for increased attention to landscape definition in future studies.

     

Spatial dynamics of coastal forest bird assemblages: the influence of landscape context,forest type,and structural connectivity

Context

Complex structural connectivity patterns can influence the distribution of animals in coastal landscapes, particularly those with relatively large home ranges, such as birds. To understand the nuanced nature of coastal forest avifauna, where there may be considerable overlap in assemblages of adjacent forest types, the concerted influence of regional landscape context and vegetative structural connectivity at multiple spatial scales warrants investigation.

Objectives

This study determined whether species compositions of coastal forest bird assemblages differ with regional landscape context or with forest type, and if this is influenced by structural connectivity patterns measured at multiple spatial scales.

Methods

Three replicate bird surveys were conducted in four coastal forest types at ten survey locations across two regional landscape contexts in northeast Australia. Structural connectivity patterns of 11 vegetation types were quantified at 3, 6, and 12 km spatial scales surrounding each survey location, and differences in bird species composition were evaluated using multivariate ordination analysis.

Results

Bird assemblages differed between regional landscape contexts and most coastal forest types, although Melaleuca woodland bird assemblages were similar to those of eucalypt woodlands and rainforests. Structural connectivity was primarily correlated with differences in bird species composition between regional landscape contexts, and correlation depended on vegetation type and spatial scale.

Conclusions

Spatial scale, landscape context, and structural connectivity have a combined influence on bird species composition. This suggests that effective management of coastal landscapes requires a holistic strategy that considers the size, shape, and configuration of all vegetative components at multiple spatial scales.

     

Climate change amplifies the interactions between wind and bark beetle disturbances in forest landscapes

Context

Growing evidence suggests that climate change could substantially alter forest disturbances. Interactions between individual disturbance agents are a major component of disturbance regimes, yet how interactions contribute to their climate sensitivity remains largely unknown.

Objectives

Here, our aim was to assess the climate sensitivity of disturbance interactions, focusing on wind and bark beetle disturbances.

Methods

We developed a process-based model of bark beetle disturbance, integrated into the dynamic forest landscape model iLand (already including a detailed model of wind disturbance). We evaluated the integrated model against observations from three wind events and a subsequent bark beetle outbreak, affecting 530.2 ha (3.8 %) of a mountain forest landscape in Austria between 2007 and 2014. Subsequently, we conducted a factorial experiment determining the effect of changes in climate variables on the area disturbed by wind and bark beetles separately and in combination.

Results

iLand was well able to reproduce observations with regard to area, temporal sequence, and spatial pattern of disturbance. The observed disturbance dynamics was strongly driven by interactions, with 64.3 % of the area disturbed attributed to interaction effects. A +4 °C warming increased the disturbed area by +264.7 % and the area-weighted mean patch size by +1794.3 %. Interactions were found to have a ten times higher sensitivity to temperature changes than main effects, considerably amplifying the climate sensitivity of the disturbance regime.

Conclusions

Disturbance interactions are a key component of the forest disturbance regime. Neglecting interaction effects can lead to a substantial underestimation of the climate change sensitivity of disturbance regimes.

     

Integrating a generic focal species,metapopulation capacity,and connectivity to identify opportunities to link fragmented habitat

Context

A challenge devising revegetation strategies in fragmented landscapes is conserving for the widest spectrum of biodiversity. Habitat network reconstruction should improve landscape capacity to maintain species populations. However, the location of revegetation often fails to account for species occurrence and dispersal processes operating across spatial scales.

Objectives

Our objective was to integrate metapopulation theory with estimates of landscape capacity and dispersal pathways to highlight connectivity gaps. Maintenance of populations could thereby be facilitated through reconnecting habitat networks across regional and broader scales, with assumed benefit for the dispersal needs of less sensitive species.

Methods

Predicted occupancy and metapopulation capacity were calculated for a generic focal species derived from fragmentation-sensitive woodland birds, mammals and reptiles. A metapopulation connectivity analysis predicted regional dispersal links to identify likely routes through which individuals may move to contribute to the viability of the population. We used the revegetation programmes of the Brigalow–Nandewar Biolinks project, eastern New South Wales, Australia, to demonstrate our approach.

Results

Landscape capacity of the current landscape varied across the region. Low-value links between populations provided greatest opportunities for revegetation and improved landscape capacity. Where regional connectivity did not indicate a pathway between populations, broader scale connectivity provided guidance for revegetation.

Conclusions

The metapopulation-based model, coupled with a habitat dispersal network analysis, provided a platform to inform revegetation locations and better support biodiversity. Our approach has application for directing on-ground action to support viable populations, assess the impact of revegetation schemes or monitor the progress of staged implementations.

     

Employing landscape metrics in an open population model to estimate demographic parameters of a grassland bird

Context

The importance of landscape context is increasingly recognized when studying relationships between populations. Recent advances in open population modeling allow the employment of landscape metrics to estimate demographic parameters underlying population variation through time and space.

Objectives

Our primary objectives were to (1) describe the influence of landscape metrics on demographic parameters in the grasshopper sparrow (Ammodramus savannarum) and (2) quantify the contributions of these demographic parameters in influencing variation in territory counts through time. We anticipated results would allow us to make recommendations for prioritizing site conservation for this grassland-obligate species of regional conservation concern.

Methods

We employed territory counts spanning 13 years from Massachusetts, USA in open population models to estimate the effects of landscape metrics, territory density, and site quality on three demographic parameters.

Results

The best model estimated highest initial numbers of territories in larger, more distant sites. Overall growth rates <1 were estimated during 1993–2005, while growth rates >1 were estimated in larger sites with a higher habitat quality index and low to medium relative density. Highest rates of annual immigration were estimated for larger sites. Growth rate explained the greatest proportion of variation in territory counts through time.

Conclusions

Open population models allowed us to identify the effects of landscape context on multiple grasshopper sparrow demographic parameters. We encourage further application of these and related models to grassland birds. Beyond maintaining grasslands in the region, we recommend the conservation of large, distant, and previously occupied sites to benefit regional populations.

     

Conservation planning with spatially explicit models: a case for horseshoe bats in complex mountain landscapes

Context

Context Bats are considered as an ecological indicator of habitat quality due to their sensitivity to human-induced ecosystem changes. Hence, we will focus the study on two indicator species of bats as a proxy to evaluate structure and composition of the landscape to analyze anthropic pressures driving changes in patterns.

Objectives

This study develops a spatially-explicit model to highlight key habitat nodes and corridors which are integral for maintaining functional landscape connectivity for bat movement. We focus on a complex mountain landscape and two bat species: greater (Rhinolophus ferrumequinum) and lesser (Rhinolophus hipposideros) horseshoe bats which are known to be sensitive to landscape composition and configuration.

Methods

Species distribution models are used to delineate high-quality foraging habitat for each species using opportunistic ultrasonic bat data. We then performed connectivity analysis combining (modelled) suitable foraging habitat and (known) roost sites. We use graph-theory and the deviation in the probability of connectivity to quantify resilience of the landscape connectivity to perturbations.

Results

Both species were confined to lowlands (<1000 m elevation) and avoided areas with high road densities. Greater horseshoe bats were more generalist than lesser horseshoe bats which tended to be associated with broadleaved and mixed forests.

Conclusions

The spatially-explicit models obtained were proven crucial for prioritizing foraging habitats, roost sites and key corridors for conservation. Hence, our results are being used by key stakeholders to help integrate conservation measures into forest management and conservation planning at the regional level. The approach used can be integrated into conservation initiatives elsewhere.

     

Cumulative patterns of logging and fire (1940–2009): consequences on the structure of the eastern Canadian boreal forest

Context

Although logging has affected circumboreal forest dynamics for nearly a century, very few studies have reconstructed its influence on landscape structure at the subcontinental scale.

Objectives

This study aims to document spatiotemporal patterns of logging and fire since the introduction of logging in the early twentieth-century, and to evaluate the effects of these disturbances on landscape structure.

Methods

We used historical (1940–2009) logging and fire maps to document disturbance patterns across a 195,000-km² boreal forest landscape of eastern Canada. We produced multitemporal (1970s–2010s) mosaics providing land cover status using Landsat imagery.

Results

Logging significantly increased the rate of disturbance (+74 %) in the study area. The area affected by logging increased linearly with time resulting in a significant rejuvenation of the landscape along the harvesting pattern (south–north progression). From 1940 to 2009, fire was the dominant disturbance and showed a more random spatial distribution than logging. The recent increase of fire influence and the expansion of the proportion of area classified as unproductive terrestrial land suggest that regeneration failures occurred.

Conclusions

This study reveals how logging has modified the disturbances dynamics, following the progression of the logging frontier. Future management practices should aim for a dispersed spatial distribution of harvests to generate landscape structures that are closer to natural conditions, in line with ecosystem-based management. The challenges of defining sustainable practices will remain complex with the predicted increase in fire frequency, since this factor, in combination with logging, can alter both the structure and potentially the resilience of boreal forest.

     

Interactions of landscape disturbances and climate change dictate ecological pattern and process: spatial modeling of wildfire,insect, and disease dynamics under future climates

Context

Interactions among disturbances, climate, and vegetation influence landscape patterns and ecosystem processes. Climate changes, exotic invasions, beetle outbreaks, altered fire regimes, and human activities may interact to produce landscapes that appear and function beyond historical analogs.

Objectives

We used the mechanistic ecosystem-fire process model FireBGCv2 to model interactions of wildland fire, mountain pine beetle (Dendroctonus ponderosae), and white pine blister rust (Cronartium ribicola) under current and future climates, across three diverse study areas.

Methods

We assessed changes in tree basal area as a measure of landscape response over a 300-year simulation period for the Crown of the Continent in north-central Montana, East Fork of the Bitterroot River in western Montana, and Yellowstone Central Plateau in western Wyoming, USA.

Results

Interacting disturbances reduced overall basal area via increased tree mortality of host species. Wildfire decreased basal area more than beetles or rust, and disturbance interactions modeled under future climate significantly altered landscape basal area as compared with no-disturbance and current climate scenarios. Responses varied among landscapes depending on species composition, sensitivity to fire, and pathogen and beetle suitability and susceptibility.

Conclusions

Understanding disturbance interactions is critical for managing landscapes because forest responses to wildfires, pathogens, and beetle attacks may offset or exacerbate climate influences, with consequences for wildlife, carbon, and biodiversity.

     

Exploring dispersal barriers using landscape genetic resistance modelling in scarlet macaws of the Peruvian Amazon

Context

Dispersal is essential for species persistence and landscape genetic studies are valuable tools for identifying potential barriers to dispersal. Macaws have been studied for decades in their natural habitat, but we still have no knowledge of how natural landscape features influence their dispersal.

Objectives

We tested for correlations between landscape resistance models and the current population genetic structure of macaws in continuous rainforest to explore natural barriers to their dispersal.

Methods

We studied scarlet macaws (Ara macao) over a 13,000 km² area of continuous primary Amazon rainforest in south-eastern Peru. Using remote sensing imagery from the Carnegie Airborne Observatory, we constructed landscape resistance surfaces in CIRCUITSCAPE based on elevation, canopy height and above-ground carbon distribution. We then used individual- and population-level genetic analyses to examine which landscape features influenced gene flow (genetic distance between individuals and populations).

Results

Across the lowland rainforest we found limited population genetic differentiation. However, a population from an intermountain valley of the Andes (Candamo) showed detectable genetic differentiation from two other populations (Tambopata) located 20–60 km away (F _ST = 0.008, P = 0.001–0.003). Landscape resistance models revealed that genetic distance between individuals was significantly positively related to elevation.

Conclusions

Our landscape resistance analysis suggests that mountain ridges between Candamo and Tambopata may limit gene flow in scarlet macaws. These results serve as baseline data for continued landscape studies of parrots, and will be useful for understanding the impacts of anthropogenic dispersal barriers in the future.

     

Consequences of a large-scale fragmentation experiment for Neotropical bats: disentangling the relative importance of local and landscape-scale effects

Context

Habitat loss, fragmentation and degradation are widespread drivers of biodiversity decline. Understanding how habitat quality interacts with landscape context, and how they jointly affect species in human-modified landscapes, is of great importance for informing conservation and management.

Objectives

We used a whole-ecosystem manipulation experiment in the Brazilian Amazon to investigate the relative roles of local and landscape attributes in affecting bat assemblages at an interior-edge-matrix disturbance gradient.

Methods

We surveyed bats in 39 sites, comprising continuous forest (CF), fragments, forest edges and intervening secondary regrowth. For each site, we assessed vegetation structure (local-scale variable) and, for five focal scales, quantified habitat amount and four landscape configuration metrics.

Results

Smaller fragments, edges and regrowth sites had fewer species and higher levels of dominance than CF. Regardless of the landscape scale analysed, species richness and evenness were mostly related to the amount of forest cover. Vegetation structure and configurational metrics were important predictors of abundance, whereby the magnitude and direction of response to configurational metrics were scale-dependent. Responses were ensemble-specific with local-scale vegetation structure being more important for frugivorous than for gleaning animalivorous bats.

Conclusions

Our study indicates that scale-sensitive measures of landscape structure are needed for a more comprehensive understanding of the effects of fragmentation on tropical biota. Although forest fragments and regrowth habitats can be of conservation significance for tropical bats our results further emphasize that primary forest is of irreplaceable value, underlining that their conservation can only be achieved by the preservation of large expanses of pristine habitat.

     

The past and future of modeling forest dynamics: from growth and yield curves to forest landscape models

Context

Quantitative models of forest dynamics have followed a progression toward methods with increased detail, complexity, and spatial extent.

Objectives

We highlight milestones in the development of forest dynamics models and identify future research and application opportunities.

Methods

We reviewed milestones in the evolution of forest dynamics models from the 1930s to the present with emphasis on forest growth and yield models and forest landscape models We combined past trends with emerging issues to identify future needs.

Results

Historically, capacity to model forest dynamics at tree, stand, and landscape scales was constrained by available data for model calibration and validation; computing capacity; model applicability to real-world problems; and ability to integrate biological, social, and economic drivers of change. As computing and data resources improved, a new class of spatially explicit forest landscape models emerged.

Conclusions

We are at a point of great opportunity in development and application of forest dynamics models. Past limitations in computing capacity and in data suitable for model calibration or evaluation are becoming less restrictive. Forest landscape models, in particular, are ready to transition to a central role supporting forest management, planning, and policy decisions.

Recommendations

Transitioning forest landscape models to a central role in applied decision making will require greater attention to evaluating performance; building application support staffs; expanding the included drivers of change, and incorporating metrics for social and economic inputs and outputs.

     

Identifying environmental drivers of spatial genetic structure of the European pine marten (<Emphasis Type="Italic">Martes martes</Emphasis>)

Context

In a global context of erosion of biodiversity, the current environmental policy in Europe is oriented towards the creation and the preservation of ecological networks for wildlife. However, most of the management guidelines arose from a structural landscape diagnostic without truly taking into consideration species’ needs.

Objectives

We tested whether and how landscape elements influence the functional connectivity of landscapes for a forest specialist species, the European pine marten (Martes martes), in Northeastern France.

Methods

We collected pine marten scats and tissues from 13 evenly distributed study sites across the whole study area in order to test several types of barriers such as highways, waterways, and open agricultural fields. We crossed the results of several methods: spatial autocorrelation analysis, causal modelling framework, and clustering methods.

Results

The study indicates significant genetic differentiation among the sampling sites. A signal of isolation by distance was detected but disappeared after partialling out landscape or barrier resistance. The only model that was fully supported by causal modelling was the one identifying waterways as the main driver of genetic differentiation. Moreover, clustering analyses indicated the presence of genetic clusters, suggesting that pine marten spatial genetic pattern could be explained by the presence of waterways but also by their reluctance to cross open fields.

Conclusions

The current ecological network could thus be improved by increasing permeability of waterways, in particular navigation canals, and by maintaining and restoring forested corridors in agricultural plains.

     

Multiple-scale prediction of forest loss risk across Borneo

Context

The forests of Borneo have among the highest biodiversity and also the highest forest loss rates on the planet.

Objectives

Our objectives were to: (1) compare multiple modelling approaches, (2) evaluate the utility of landscape composition and configuration as predictors, (3) assess the influence of the ratio of forest loss and persistence points in the training sample, (4) identify the multiple-scale drivers of recent forest loss and (5) predict future forest loss risk across Borneo.

Methods

We compared random forest machine learning and logistic regression in a multi-scale approach to model forest loss risk between 2000 and 2010 as a function of topographical variables and landscape structure, and applied the highest performing model to predict the spatial pattern of forest loss risk between 2010 and 2020. We utilized a naïve model as a null comparison and used the total operating characteristic AUC to assess model performance.

Results

Our analysis produced five main results. We found that: (1) random forest consistently outperformed logistic regression and the naïve model; (2) including landscape structure variables substantially improved predictions; (3) a ratio of occurrence to non-occurrence points in the training dataset that does not match the actual ratio in the landscape biases the predictions of both random forest and logistic regression; (4) forest loss risk differed between the three nations that comprise Borneo, with patterns in Kalimantan highly related to distance from the edge of the previous frontier of forest loss, while Malaysian Borneo showed a more diffuse pattern related to the structure of the landscape; (5) we predicted continuing very high rates of forest loss in the 2010–2020 period, and produced maps of the expected risk of forest loss across the full extent of Borneo.

Conclusions

These results confirm that multiple-scale modelling using landscape metrics as predictors in a random forest modelling framework is a powerful approach to landscape change modelling. There is immense immanent risk to Borneo’s forests, with clear spatial patterns of risk related to topography and landscape structure that differ between the three nations that comprise Borneo.

     

Using landscape graphs to delineate ecologically functional areas

Context

Landscape graphs are widely used to model connectivity and to support decision-making in conservation planning. Compartmentalization methods applied to such graphs aim to define clusters of highly interconnected patches. Recent studies show that compartmentalization based on modularity is suitable, but it applies to non-weighted graphs whereas most landscape graphs involve weighted nodes and links.

Objectives

We propose to adapt modularity computation to weighted landscape graphs and to validate the relevance of the resulting compartments using demographic or genetic data about the patches.

Methods

A weighted adjacency matrix was designed to express potential fluxes, associating patch capacities and inter-patch distances. Eight weighting scenarios were compared. The statistical evaluation of each compartmentalization was based on Wilks’ Lambda. These methods were performed on a grassland network where patches are documented by annual densities of water voles in the Jura massif (France).

Results

The scenarios in which patch capacity is assigned a small weight led to the more relevant results, giving high modularity values and low Wilks’ Lambda values. When considering a fixed number of compartments, we found a significant negative correlation between these two criteria. Comparison showed that compartments are ecologically more valid than graph components.

Conclusions

The method proposed is suitable for designing ecologically functional areas from weighted landscape graphs. Maximum modularity values can serve as a guide for setting the parameters of the adjacency matrix.

     

The plant functional traits that explain species occurrence across fragmented grasslands differ according to patch management,isolation, and wetness

Context

Landscape fragmentation significantly affects species distributions by decreasing the number and connectivity of suitable patches. While researchers have hypothesized that species functional traits could help in predicting species distribution in a landscape, predictions should depend on the type of patches available and on the ability of species to disperse and grow there.

Objectives

To explore whether different traits can explain the frequency of grassland species (number of occupied patches) and/or their occupancy (ratio of occupied to suitable patches) across a variety of patch types within a fragmented landscape.

Methods

We sampled species distributions over 1300 grassland patches in a fragmented landscape of 385 km² in the Czech Republic. Relationships between functional traits and species frequency and occupancy were tested across all patches in the landscape, as well as within patches that shared similar management, wetness, and isolation.

Results

Although some traits predicting species frequency also predicted occupancy, others were markedly different, with competition- and dispersal-related traits becoming more important for occupancy. Which traits were important differed for frequency and occupancy and also differed depending on patch management, wetness, and isolation.

Conclusions

Plant traits can provide insight into plant distribution in fragmented landscapes and can reveal specific abiotic, biotic, and dispersal processes affecting species occurrence in a patch type. However, the importance of individual traits depends on the type of suitable patches available within the landscape.

     

Processes and driving forces in changing cultural landscapes across Europe

Context

Cultural landscapes evolve over time. However, the rate and direction of change might not be in line with societal needs and more information on the forces driving these changes are therefore needed.

Objectives

Filling the gap between single case studies and meta-analyses, we present a comparative study of landscape changes and their driving forces based in six regions across Europe conducted using a consistent method.

Methods

A LULC analysis based on historical and contemporary maps from the nineteenth and twentieth century was combined with oral history interviews to learn more about perceived landscape changes, and remembered driving forces. Land cover and landscape changes were analysed regarding change, conversions and processes. For all case study areas, narratives on mapped land cover change, perceived landscape changes and driving forces were compiled.

Results

Despite a very high diversity in extent, direction and rates of change, a few dominant processes and widespread factors driving the changes could be identified in the six case study areas, i.e. access and infrastructure, political shifts, labor market, technological innovations, and for the more recent period climate change.

Conclusions

Grasping peoples’ perception supplements the analyses of mapped land use and land cover changes and allows to address perceived landscape changes. The list of driving forces determined to be most relevant shows clear limits in predictability: Whereas changes triggered by infrastructural developments might be comparatively easy to model, political developments cannot be foreseen but might, nevertheless, leave major marks in the landscape.

     

Wood fibre value simulation model: a new tool to assist measuring changes in forest landscapes by evaluating forest inventory

Context

A challenging issue in landscape ecology is the evaluation of changes in a forest landscape following a disturbance. This evaluation usually entails examining changes in the forest inventory, which represents the best information available for a given forest region.

Objectives

Our aim was to extend existing methods used to evaluate forest inventory to include additional variables, such as value-based forest product options, wood fibre attributes, and ecosystem services. Inclusion of such variables in forest inventory evaluations would allow research results to be presented from an economic perspective, which is often required for policy development and forest management decision-making.

Methods

We developed a value-based framework to evaluate forest inventory and implemented it in the wood fibre value simulation model. We then used a local data set from Manitoba, Canada, to show how the model can be applied to the mapping of new inventory layers to facilitate the evaluation of landscape changes.

Results

Five new inventory layers are mapped including bioenergy and heating value that can be directly used for evaluating landscape changes, and wood density, fibre length, and pulp yield, which can be combined with total wood volume to derive new variables or indices to express changes in landscape conditions.

Conclusions

Our model can contribute to the assessment of landscape changes by indicating the values a forest can have when it is used for different conservation or utilization purposes. The model can also support improved decision-making with respect to the management of forest resources.