Context

Despite decades of research, there is an intense debate about the consistency of the hump-shaped pattern describing the relationship between diversity and disturbance as predicted by the intermediate disturbance hypothesis (IDH). Previous meta-analyses have not explicitly considered interactive effects of disturbance frequency and intensity of disturbance on plant species diversity in terrestrial landscapes.

Objective

We conducted meta-analyses to test the applicability of IDH by simultaneously examining the relationship between species richness, disturbance frequency (quantified as time since last disturbance as originally proposed) and intensity of disturbance in forest landscapes.

Methods

The effects of disturbance frequency, intensity, and their interaction on species richness was evaluated using a mixed-effects model.

Results

We found that species richness peaks at intermediate frequency after both high and intermediate disturbance intensities, but the richness-frequency relationship differed between intensity classes.

Conclusions

Our study highlights the need to measure multiple disturbance components that could help reconcile conflicting empirical results on the effect of disturbance on plant species diversity.

Context

Methods for detecting contemporary, fine-scale population genetic structure in continuous populations are scarce. Yet such methods are vital for ecological and conservation studies, particularly under a changing landscape.

Objectives

Here we present a novel, spatially explicit method that we call landscape relatedness (LandRel). With this method, we aim to detect contemporary, fine-scale population structure that is sensitive to spatial and temporal changes in the landscape.

Methods

We interpolate spatially determined relatedness values based on SNP genotypes across the landscape. Interpolations are calculated using the Bayesian inference approach integrated nested Laplace approximation. We empirically tested this method on a continuous population of brown bears (Ursus arctos) spanning two counties in Sweden.

Results

Two areas were identified as differentiated from the remaining population. Further analysis suggests that inbreeding has occurred in at least one of these areas.

Conclusions

LandRel enabled us to identify previously unknown fine-scale structuring in the population. These results will help direct future research efforts, conservation action and aid in the management of the Scandinavian brown bear population. LandRel thus offers an approach for detecting subtle population structure with a focus on contemporary, fine-scale analysis of continuous populations.

Context

Objective identification of locations on transportation networks, where animal-vehicle collisions (AVC) occur more frequently than expected (hotspots), is an important step for the effective application of mitigation measures.

Objectives

We introduce the KDE+ software which is a programmed version of the KDE+ method for effective identification of traffic accident hotspots. The software can be used in order to analyze animal-vehicle collision data.

Methods

The KDE+ method is based on principles of Kernel Density Estimation (KDE). The symbol ‘+’ indicates that the method allows for the objective selection of significant clusters and for the ranking of the hotspots. It is also simultaneously applicable to an unlimited number of road segments.

Results

We applied the KDE+ method to the entire Czech road network. The hotspots were ranked according to their significance. The resulting hotspots represent a short overall road length which should require a more detailed assessment in the field. The 100 most important clusters of AVC represent, for example, only 19.7 km of the entire road network (37,469 km).

Conclusions

We present an objective method for hotspots identification which can be used for AVC data. This method is unique because it determines the significance level of hotspots in an objective way. The prioritization of hotspots allows a transportation manager to effectively allocate resources to a feasible number of identified hotspots. We describe the software, data preparation and present the KDE+ application to AVC data.

Context

In response to predominantly local and private approaches to landscape change, landscape ecologists should critically assess the multiscalar influences on landscape design.

Objectives

This study develops a governance framework for Nassauer and Opdam’s “Design-in-Science” model. Its objective is to create an approach for examining hierarchical constraints on landscape design in order to investigate linkages among urban greening initiatives, patterns of landscape change, and the broader societal values driving those changes. It aims to provide an integrative and actionable approach for landscape sustainability science.

Methods

This framework is examined through an ethnographic study of public policy processes surrounding the urban tree initiatives in Boston, MA; Philadelphia, PA; and Baltimore, MD.

Results

These initiatives demonstrate the impact of political and economic decentralization on urban landscape patterns. Their collaborative governance approach incorporates diverse resources to implement programming at a fine-scale. The predominant tree giveaway program fragments the urban and regional forest.

Conclusion

Spatial and temporal fragmentation undermines the long-term security of urban greening programs, and it suggests reconsideration of the role of state regimes in driving broad scale spatial planning.

Context

Regime shifts are well known for driving penetrating ecological change, yet we do not recognise the consequences of these shifts much beyond species diversity and productivity. Sound represents a multidimensional space that carries decision-making information needed for some dispersing species to locate resources and evaluate their quantity and quality.

Objectives

Here we assessed the effect of regime shifts on marine soundscapes, which we propose has the potential function of strengthening the positive or negative feedbacks that mediate ecosystem shifts.

Methods

We tested whether biologically relevant cues are altered by regime shifts in kelp forests and seagrass systems and how specific such shifted soundscapes are to the type of driver; i.e. local pollution (eutrophication) vs. global change (ocean acidification).

Results

Here, we not only provide the first evidence for regime-shifted soundscapes, but also reveal that the modified cues of shifted ecosystems are similar regardless of spatial scale and type of environmental driver. Importantly, biological sounds can act as functional cues for orientation by dispersing larvae, and observed shifts in soundscape loudness may alter this function.

Conclusions

These results open the question as to whether shifted soundscapes provide a functional role in mediating the positive or negative feedbacks that govern the arrival of species associated with driving change or stasis in ecosystem state.

Context

Land-use/land-cover (LU/LC) dynamics is one of the main drivers of global environmental change. In the last years, aerial and satellite imagery have been increasingly used to monitor the spatial extent of changes in LU/LC, deriving relevant biophysical parameters (i.e. primary productivity, climate and habitat structure) that have clear implications in determining spatial and temporal patterns of biodiversity, landscape composition and ecosystem services.

Objectives

An innovative hierarchical modelling framework was developed in order to address the influence of nested attributes of LU/LC on community-based ecological indicators.

Methods

Founded in the principles of the spatially explicit stochastic dynamic methodology (StDM), the proposed methodological advances are supported by the added value of integrating bottom-up interactions between multi-scaled drivers.

Results

The dynamics of biophysical multi-attributes of fine-scale subsystem properties are incorporated to inform dynamic patterns at upper hierarchical levels. Since the most relevant trends associated with LU/LC changes are explicitly modelled within the StDM framework, the ecological indicators’ response can be predicted under different social-economic scenarios and site-specific management actions. A demonstrative application is described to illustrate the framework methodological steps, supporting the theoretic principles previously presented.

Conclusions

We outline the proposed multi-model framework as a promising tool to integrate relevant biophysical information to support ecosystem management and decision-making.

Context

Wild bee populations are currently under threat, which has led to recent efforts to increase pollinator habitat in North America. Simultaneously, U.S. federal energy policies are beginning to encourage perennial bioenergy cropping (PBC) systems, which have the potential to support native bees.

Objectives

Our objective was to explore the potentially interactive effects of crop composition, total PBC area, and PBC patches in different landscape configurations.

Methods

Using a spatially-explicit modeling approach, the Lonsdorf model, we simulated the impacts of three perennial bioenergy crops (PBC: willow, switchgrass, and prairie), three scenarios with different total PBC area (11.7, 23.5 and 28.8% of agricultural land converted to PBC) and two types of landscape configurations (PBC in clustered landscape patterns that represent realistic future configurations or in dispersed neutral landscape models) on a nest abundance index in an Illinois landscape.

Results

Our modeling results suggest that crop composition and PBC area are particularly important for bee nest abundance, whereas landscape configuration is associated with bee nest abundance at the local scale but less so at the regional scale.

Conclusions

Strategies to enhance wild bee habitat should therefore emphasize the crop composition and amount of PBC.

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.

Context

The habitat amount hypothesis has rarely been tested on plant communities. It remains unclear how habitat amount affect species richness in habitat fragments compared to island effects such as isolation and patch size.

Objectives

How do patch size and spatial distribution compared to habitat amount predict plant species richness and grassland specialist plant species in small grassland remnants? How does sampling area affect the prediction of spatial variables on species richness?

Methods

We recorded plant species density and richness on 131 midfield islets (small remnants of semi-natural grassland) situated in 27 landscapes in Sweden. Further, we tested how habitat amount, compared to focal patch size and distance to nearest neighbor predicted species density and richness of plants and of grassland specialists.

Results

A total of 381 plant species were recorded (including 85 grassland specialist species). A combination of patch size and isolation was better in predicting both density and richness of species compared to habitat amount. Almost 45% of species richness and 23% of specialist species were explained by island biogeography parameters compared to 19 and 11% by the amount of habitat. A scaled sampling method increased the explanation level of island biogeography parameters and habitat amount.

Conclusions

Habitat amount as a concept is not as good as island biogeography to predict species richness in small habitats. Priority in landscape planning should be on larger patches rather than several small, even if they are close together. We recommend a sampling area scaled to patch size in small habitats.

Context

The variation in spatial distribution between ecosystem services can be high. Hence, there is a need to spatially identify important sites for conservation planning. The term ‘ecosystem service hotspot’ has often been used for this purpose, but definitions of this term are ambiguous.

Objectives

We review and classify methods to spatially delineate hotspots. We test how spatial configuration of hotspots for a set of ecosystem services differs depending on the applied method. We compare the outcomes to a heuristic site prioritisation approach (Marxan).

Methods

The four tested hotspot methods are top richest cells, spatial clustering, intensity, and richness. In a conservation scenario we set a target of conserving 10 % of the quantity of five regulating and cultural services for the forest area of Telemark county, Norway.

Results

Spatial configuration of selected areas as retrieved by the four hotspots and Marxan differed considerably. Pairwise comparisons were at the lower end of the scale of the Kappa statistic (0.11–0.27). The outcomes also differed considerably in mean target achievement, cost-effectiveness in terms of land-area needed per unit target achievement and compactness in terms of edge-to-area ratio.

Conclusions

An ecosystem service hotspot can refer to either areas containing high values of one service or areas with multiple services. Differences in spatial configuration among hotspot methods can lead to uncertainties for decision-making. This also has consequences for analysing the spatial co-occurrence of hotspots of multiple services and of services and biodiversity.

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.

Context

Digital elevation models (DEMs) are widely used in landscape ecology to link topographic features with biotic and abiotic factors. However, to date, high-resolution, affordable, and easy to process elevation data are not available for many regions.

Objectives

Here we propose a field-based method for efficiently and inexpensively collecting or analysing already existing slope data. We compare the field approach to two commonly used remote sensing techniques to test the similarly of the DEMs using different methods.

Methods

To provide an ecological example of the method, we selected four 1-ha forest plots and compared the DEM generated by using our field method with those derived from: (i) coarse (~ 30 m pixel) data from the Shuttle Radar Topography Mission and (ii) high-resolution (~ 1 m) data from Light Detection and Ranging devices (LiDAR).

Results

Field- and LiDAR-based DEMs showed strong concordance in two of the four sites. The sites where field-based and LiDAR DEMs substantially differed, suffered from relatively few LiDAR sampling points. Diagnostic tests suggested that the field–LiDAR discrepancy was due to dense over-storey vegetation, which reduced LiDAR’s accuracy due to a failure to penetrate the forest canopy adequately in some areas.

Conclusions

Our method has the advantage of being quick and cheap to collect yet able to produce small-scale (plot-scale) DEMs of high quality. By using the R-code we have provided, ecologists will be able to use slope data (collected using any means) to generate a DEM without the need of specific skills in spatial sciences.

Context

The ability to detect ecological networks in landscapes is of utmost importance for managing biodiversity and planning corridors.

Objectives

The objective of this study was to evaluate the information provided by a synthetic aperture radar (SAR) image for landscape connectivity modeling compared to aerial photographs (APs).

Methods

We present a novel method that integrates habitat suitability derived from remote sensing imagery into a connectivity model to explain species abundance. More precisely, we compared how two resistance maps constructed using landscape and/or local metrics derived from AP or SAR imagery yield different connectivity values (based on graph theory), considering hedgerow networks and forest carabid beetle species as a model.

Results

We found that resistance maps using landscape and local metrics derived from SAR imagery improve landscape connectivity measures. The SAR model is the most informative, explaining 58% of the variance in forest carabid beetle abundance. This model calculates resistance values associated with homogeneous patches within hedgerows according to their suitability (canopy cover density and landscape grain) for the model species.

Conclusions

Our approach combines two important methods in landscape ecology: the construction of resistance maps and the use of buffers around sampling points to determine the importance of landscape factors. This study was carried out through an interdisciplinary approach involving remote sensing scientists and landscape ecologists. This study is a step forward in developing landscape metrics from satellites to monitor biodiversity.

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.

Context

Barrier effects of Large-scale Transportation Infrastructures (LTIs) are among the main factors contributing to the fragmentation of habitats. The reduction of dispersal across LTIs can drive small, local populations to extinction. To understand how LTIs modify dispersal, efficient and workable evaluation methods are required.

Objectives

We developed a method based on Mark-Release-Recapture surveys to estimate barrier effects of LTIs that could be easily applied in various landscape contexts and on any mobile species.

Methods

Our method uses dispersal kernels of animal movements to calculate an expected probability of crossing any particular linear feature. This probability is then compared to observed crossing events to estimate the barrier effect. We used simulations to test the reliability of our method and applied this framework on the butterfly Maniola jurtina in a landscape fragmented by a motorway and a railway.

Results

Simulations showed that our method was able to detect efficiently even weak barrier effects given that enough data are available. When sample size was reduced, our method was able to detect barrier effects only when the infrastructure width was small in comparison to the average movement capacity of organisms. In our case study, both infrastructures acted as significant barriers.

Conclusions

The power of our method is to use MRR data which are more representative of population processes than telemetry monitoring and are not limited by time-lag involved in genetic studies. This framework is of particular interest for conservation studies in order to assess how individual movements are modified by linear infrastructures.

Context

Linear transportation infrastructures traverse and separate wildlife populations, potentially leading to their short- and long-term decline at local and regional scales. To attenuate such effects, we need wildlife crossings suitable for a wide range of species.

Objectives

We propose a method for identifying the best locations for wildlife crossings along linear infrastructures so as to improve the connectivity of species with varying degrees of mobility and living in different habitats. We evaluate highway impacts on mammal species.

Methods

The study area is the Grésivaudan Valley, France. We used allometric relationships to create eight virtual species and model their connectivity networks, developing a nested method defining populations by daily travel distances and connecting them by dispersal. We tested the gain in connectivity for each species produced by 100 and 600 crossing locations respectively in crossable, i.e. with crossing infrastructures, and uncrossable highway scenarios. We identified the crossings that optimize the connectivity of the maximum number of species combining the results in multivariate analyses.

Results

Highly mobile species needing a large habitat area were the most sensitive to highways. The importance of locomotive performance in structuring the graphs decreased with highway impermeability. Depending on the species, the best locations improved connectivity by 0–10 and 2–75 % respectively in the crossable and uncrossable scenarios. Compromise locations were found for seven of the eight species in both scenarios.

Conclusions

This method could guide planners in identifying crossing locations to increase the connectivity of different species at regional scales over the long term.

Context

Ecological networks are often designed based on the degree of suitability and permeability of land cover classes, as obtained by estimating the statistical relationships between occurrence data and classes coverage using habitat suitability models (HSMs). Considering only the classes coverage, but not their spatial arrangement, frequently prevents HSMs from correctly identifying nodes and connectivity elements.

Objectives

We propose a new approach in the design of ecological networks starting from the relationship between occurrence data and both land cover classes coverage and spatial arrangement, as calculated for different simulated species perceptions of the landscape (SSPLs, corresponding to different combinations of classes alternatively assuming the role of nodes, connectivity elements, or matrix).

Methods

The approach consists of comparing the ability to explain the observed species occurrence of both the nodes coverage and the connectivity degree provided by both nodes and connectivity elements, calculated for each SSPL. The better performing SSPL will provide information about the land cover classes that should be considered in designing an ecological network for the species, as well as their role in the network.

Results

When applied to the Hazel Dormouse in an agricultural landscape in northern Italy, the method proved effective and allowed us to identify woodlands and hedgerows as nodes, and poplar cultivations, biomasses and reforestations as connectivity elements.

Conclusions

The proposed method can be adopted to identify nodes and connectivity elements for virtually every species sensitive to fragmentation, and has important practical implications when integrated in landscape management plans developed to guarantee ecological connectivity.