Agriculture and development transform forest ecosystems to human‐modified landscapes. Decades of research in ecology have generated myriad concepts for the appropriate management of these landscapes. ...Yet, these concepts are often contradictory and apply at different spatial scales, making the design of biodiversity‐friendly landscapes challenging. Here, we combine concepts with empirical support to design optimal landscape scenarios for forest‐dwelling species. The supported concepts indicate that appropriately sized landscapes should contain ≥ 40% forest cover, although higher percentages are likely needed in the tropics. Forest cover should be configured with c. 10% in a very large forest patch, and the remaining 30% in many evenly dispersed smaller patches and semi‐natural treed elements (e.g. vegetation corridors). Importantly, the patches should be embedded in a high‐quality matrix. The proposed landscape scenarios represent an optimal compromise between delivery of goods and services to humans and preserving most forest wildlife, and can therefore guide forest preservation and restoration strategies.
We review key concepts on species responses to landscape disturbances to prioritize management strategies for conservation of forest wildlife. We design optimal landscape scenarios for preserving most forest wildlife and promoting the delivery of goods and services to humans. The proposed scenarios can therefore guide forest preservation and restoration strategies in human‐modified landscapes.
This paper examines the usage and measurement of "landscape connectivity" in 33 recent studies. Connectivity is defined as the degree to which a landscape facilitates or impedes movement of organisms ...among resource patches. However, connectivity is actually used in a variety of ways in the literature. This has led to confusion and lack of clarity related to (1) function vs structure, (2) patch isolation vs landscape connectivity and, (3) corridors vs connectivity. We suggest the term connectivity should be reserved for its original purpose. We highlight nine studies; these include modeling studies that actually measured connectivity in accordance with the definition, and empirical studies that measured key components of connectivity. We found that measurements of connectivity provide results that can be interpreted as recommending habitat fragmentation to enhance landscape connectivity. We discuss reasons for this misleading conclusion, and suggest a new way of quantifying connectivity, which avoids this problem. We also recommend a method for reducing sampling intensity in landscape-scale empirical studies of connectivity.
Human activities exert stress on and create disturbances to ecosystems, decreasing their diversity, resilience and ultimately the health of ecosystems and their vegetation. In environments with rapid ...changes in vegetation health (VH), progress is needed when it comes to monitoring these changes and underlying causes. There are different approaches to monitoring VH such as in situ species approaches and the remote‐sensing approach.
Here we provide an overview of in situ species approaches, that is, the biological, the phylogenetic, and the morphological species concept, as well as an overview of the remote‐sensing spectral trait/spectral trait variations concept to monitor the status of VH as well as processes of stress, disturbances, and resource limitations affecting VH. The approaches are compared with regard to their suitability for monitoring VH, and their advantages, disadvantages, potential, and requirements for being linked are discussed.
No single approach is sufficient to monitor the complexity and multidimensionality of VH over the short to long term and on local to global scales. Rather, every approach has its pros and cons, making it all the more necessary to link approaches. In this paper, we present a framework and list crucial requirements for coupling approaches and integrating additional monitoring elements to form a multisource vegetation health monitoring network (MUSO‐VH‐MN).
When it comes to linking the different approaches, data, information, models or platforms in a MUSO‐VH‐MN, big data with its complexity and syntactic and semantic heterogeneity and the lack of standardized approaches and VH protocols pose the greatest challenge. Therefore, Data Science with the elements of (a) digitalization, (b) semantification, (c) ontologization, (d) standardization, (e) Open Science, as well as (f) open and easy analyzing tools for assessing VH are important requirements for monitoring, linking, analyzing, and forecasting complex and multidimensional changes in VH.
•Data mining are the only way to extract knowledge from complex and large data.•Environmental research is an interdisciplinary research.•Therefore classical data mining approaches are sectoral and ...insufficient.•Linked open data is a new approach for extract knowledge in interdisciplinary data.
The rapid development in information and computer technology has facilitated an extreme increase in the collection and storage of digital data. However, the associated rapid increase in digital data volumes does not automatically correlate with new insights and advances in our understanding of those data. The relatively new technique of data mining offers a promising way to extract knowledge and patterns from large, multidimensional and complex data sets. This paper therefore aims to provide a comprehensive overview of existing data mining techniques and related tools and to illustrate the potential of data mining for different research areas by means of example applications. Despite a number of conventional data mining techniques and methods, these classical approaches are restricted to isolated or “silo” data sets and therefore remain primarily stand alone and specialized in nature. Highly complex and mostly interdisciplinary questions in environmental research cannot be answered sufficiently using isolated or area-based data mining approaches. To this end, the linked open data (LOD) approach will be presented as a new possibility in support of complex and inter-disciplinary data mining analysis. The merit of LOD will be explained using examples from medicine and environmental research. The advantages of LOD data mining will be weighed against classical data mining techniques. LOD offers unique and new possibilities for interdisciplinary data analysis, modeling and projection for multidimensional, complex landscapes and may facilitate new insights and answers to complex environmental questions. Our paper aims to encourage those research scientists which do not have extensive programming and data mining knowledge to take advantage of existing data mining tools, to embrace classical data mining and LOD approaches in support of gaining more insight and recognizing patterns in highly complex data sets.
The ecological interpretation of landscape patterns is one of the major objectives in landscape ecology. Both landscape patterns and ecological processes need to be quantified before statistical ...relationships between these variables can be examined. Landscape indices provide quantitative information about landscape pattern. Response variables or process rates quantify the outcome of ecological processes (e.g., dispersal success for landscape connectivity or Morisita's index for the spatial distribution of individuals). While the principal potential of this approach has been demonstrated in several studies, the robustness of the statistical relationships against variations in landscape structure or against variations of the ecological process itself has never been explicitly investigated. This paper investigates the consistency of correlations between a set of landscape indices (calculated with Fragstats) and three response variables from a simulated dispersal process across heterogeneous landscapes (cell immigration, dispersal success and search time) against variation in three experimental treatments (control variables): habitat amount, habitat fragmentation and dispersal behavior. I found strong correlations between some landscape indices and all three response variables. However, 68% of the statistical relationships were highly inconsistent and sometimes ambiguous for different landscape structures and for differences in dispersal behavior. Correlations between one landscape index and one response variable could range from highly positive to highly negative when derived from different spatial patterns. I furthermore compared correlation coefficients obtained from artificially generated (neutral) landscape models with those obtained from Landsat TM images. Both landscape representations produced equally strong and weak statistical relationships between landscape indices and response variables. This result supports the use of neutral landscape models in theoretical analyses of pattern-process relationships.PUBLICATION ABSTRACT
We conducted a factorial simulation experiment to analyze the relative importance of movement pattern, boundary-crossing probability, and mortality in habitat and matrix on population density, and ...its dependency on habitat fragmentation, as well as inter-patch distance. We also examined how the initial response of a species to a fragmentation event may affect our observations of population density in post-fragmentation experiments. We found that the boundary-crossing probability from habitat to matrix, which partly determines the emigration rate, is the most important determinant for population density within habitat patches. The probability of crossing a boundary from matrix to habitat had a weaker, but positive, effect on population density. Movement behavior in habitat had a stronger effect on population density than movement behavior in matrix. Habitat fragmentation and inter-patch distance may have a positive or negative effect on population density. The direction of both effects depends on two factors. First, when the boundary-crossing probability from habitat to matrix is high, population density may decline with increasing habitat fragmentation. Conversely, for species with a high matrix-to-habitat boundary-crossing probability, population density may increase with increasing habitat fragmentation. Second, the initial distribution of individuals across the landscape: we found that habitat fragmentation and inter-patch distance were positively correlated with population density when individuals were distributed across matrix and habitat at the beginning of our simulation experiments. The direction of these relationships changed to negative when individuals were initially distributed across habitat only. Our findings imply that the speed of the initial response of organisms to habitat fragmentation events may determine the direction of observed relationships between habitat fragmentation and population density. The time scale of post-fragmentation studies must, therefore, be adjusted to match the pace of post-fragmentation movement responses.
•Patch matrix model and gradient model are important for landscape quantification.•Process effect patterns, therefore we called “process–pattern interaction (PPI)”.•Process characteristics are basis ...for selection discrete and continuous indicators.•Process characteristics are basis for type of quantitative approach in landscapes.•We suggest a model combination depending on the processes in landscapes.
For quantifying and modelling of landscape patterns, the patch matrix model (PMM) and the gradient model (GM) are fundamental concepts of landscape ecology. While the PMM model has been the backbone for our advances in landscape ecology, it may also hamper truly universal insights into process–pattern relationships.
The PMM describes landscape structures as a mosaic of discretely delineated homogenous areas. This requires simplifications and assumptions which may even result in errors which propagate through subsequent analyses and may reduce our ability to understand effects of landscape structure on ecological processes. Alternative approaches to represent landscape structure should therefore be evaluated. The GM represents continuous surface characteristics without arbitrary vegetation or land-use classification and therefore does not require delineation of discrete areas with sharp boundaries. The GM therefore lends itself to be a more realistic representation of a particular surface characteristic. In the paper PMM and GM are compared regarding their prospects and limitations. Suggestions are made regarding the potential use and implementation of both approaches for process–pattern analysis.
The ecological and anthropogenic process itself and its characteristics under investigation is decisive for: (i) the selection of discrete and/or continuous indicators, (ii) the type of the quantitative pattern analysis approach to be used (PMM/GM) and (iii) the data and the scale required in the analysis. Process characteristics and their effects on pattern characteristics in space and time are decisive for the applicability of the PMM or of the GM approach. A low hemeroby (high naturalness and low human pressure on landscapes) allows for high internal-heterogeneity in space and over time within patterns. Such landscapes can be captured with the GM approach. A high hemeroby reduces heterogeneity in space and time within patterns. For such landscapes we recommend the PMM model.
•Multi-landscape study of effects of farmland pattern on biodiversity in crop fields.•Seven taxa studied – birds, plants, butterflies, bees, syrphids, carabids and spiders.•Diversity and abundance of ...all taxa decline with increasing mean field size.•Field size effect is stronger than effect of area under cultivation.
Simple rules for landscape management seem elusive because different species and species groups are associated with different land cover types; a change in landscape structure that increases diversity of one group may reduce diversity of another. On the other hand, if simple landscape–biodiversity relationships do exist despite this complexity, they would have great practical benefit to conservation management. With these considerations in mind, we tested for consistent relationships between landscape heterogeneity and biodiversity in farmland (the cropped areas in agricultural landscapes), with a view to developing simple rules for landscape management that could increase biodiversity within farmland. Our measures of farmland heterogeneity were crop diversity and mean crop field size, where increases in crop diversity and/or decreases in mean field size represent increasing landscape heterogeneity. We sampled the abundance, and alpha, gamma and beta diversity of birds, plants, butterflies, syrphids, bees, carabids and spiders, in crop fields within each of 93 1km×1km agricultural landscapes. The landscapes were selected to represent three gradients in landscape composition and heterogeneity: proportion of the landscape in crop, mean crop field size and Shannon crop type diversity of the farmland. We found that mean crop field size had the strongest overall effect on biodiversity measures in crop fields, and this effect was consistently negative. Based on our results we suggest that, if biodiversity conservation in crop fields is a priority, policies and guidelines aimed at reducing crop field sizes should be considered.
Is habitat fragmentation bad for biodiversity? Fahrig, Lenore; Arroyo-Rodríguez, Víctor; Bennett, Joseph R. ...
Biological conservation,
February 2019, 2019-02-00, 2019-02, Letnik:
230
Journal Article
Recenzirano
Odprti dostop
In a review of landscape-scale empirical studies, Fahrig (2017a) found that ecological responses to habitat fragmentation per se (fragmentation independent of habitat amount) were usually ...non-significant (>70% of responses) and that 76% of significant relationships were positive, with species abundance, occurrence, richness, and other response variables increasing with habitat fragmentation per se. Fahrig concluded that to date there is no empirical evidence supporting the widespread assumption that a group of small habitat patches generally has lower ecological value than large patches of the same total area. Fletcher et al. (2018) dispute this conclusion, arguing that the literature to date indicates generally negative ecological effects of habitat fragmentation per se. They base their argument largely on extrapolation from patch-scale patterns and mechanisms (effects of patch size and isolation, and edge effects) to landscape-scale effects of habitat fragmentation. We argue that such extrapolation is unreliable because: (1) it ignores other mechanisms, especially those acting at landscape scales (e.g., increased habitat diversity, spreading of risk, landscape complementation) that can counteract effects of the documented patch-scale mechanisms; and (2) extrapolation of a small-scale mechanism to a large-scale pattern is not evidence of that pattern but, rather a prediction that must be tested at the larger scale. Such tests were the subject of Fahrig's review. We find no support for Fletcher et al.'s claim that biases in Fahrig's review would alter its conclusions. We encourage further landscape-scale empirical studies of effects of habitat fragmentation per se, and research aimed at uncovering the mechanisms that underlie positive fragmentation effects.
•Habitat fragmentation per se is a landscape-scale phenomenon.•Cross-scale extrapolation from edge effects to fragmentation effects is unreliable.•Most responses to habitat fragmentation per se are non-significant.•Most significant responses to habitat fragmentation per se are positive.•Sets of small habitat patches with a large total area have high conservation value.
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