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Berlin Brandenburg

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  • 1
    Language: Spanish
    In: Conservation Biology, February 2012, Vol.26(1), pp.150-159
    Description: :  Habitat loss reduces species diversity, but the effect of habitat fragmentation on number of species is less clear because fragmentation generally accompanies loss of habitat. We compared four methods that aim to decouple the effects of fragmentation from the effects of habitat loss. Two methods are based on species‐area relations, one on Fisher's alpha index of diversity, and one on plots of cumulative number of species detected against cumulative area sampled. We used these methods to analyze the species diversity of spiders in 2, 3.2 × 4 km agricultural landscapes in Southern Judea Lowlands, Israel. Spider diversity increased as fragmentation increased with all four methods, probably not because of the additive within‐patch processes, such as edge effect and heterogeneity. The positive relation between fragmentation and species diversity might reflect that most species can disperse through the fields during the wheat‐growing season. We suggest that if a given area was designated for the conservation of spiders in Southern Judea Lowlands, Israel, a set of several small patches may maximize species diversity over time.
    Keywords: Arthropods ; Landscape ; Sloss ; Species Diversity ; Species‐Area Relation ; Artrópodos ; Diversidad De Especies ; Paisaje ; Relación Especies‐Área ; Sloss
    ISSN: 0888-8892
    E-ISSN: 1523-1739
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  • 2
    In: PLoS ONE, 2016, Vol.11(12)
    Description: Understanding the main processes that affect community similarity have been the focus of much ecological research. However, the relative effects of environmental and spatial aspects in structuring ecological communities is still unresolved and is probably scale-dependent. Here, we examine the effect of habitat identity and spatial distance on fine-grained community similarity within a biogeographic transition zone. We compared four hypotheses: i) habitat identity alone, ii) spatial proximity alone, iii) non-interactive effects of both habitat identity and spatial proximity, and iv) interactive effect of habitat identity and spatial proximity. We explored these hypotheses for spiders in three fragmented landscapes located along the sharp climatic gradient of Southern Judea Lowlands (SJL), Israel. We sampled 14,854 spiders (from 199 species or morphospecies) in 644 samples, taken in 35 patches and stratified to nine different habitats. We calculated the Bray-Curtis similarity between all samples-pairs. We divided the pairwise values to four functional distance categories (same patch, different patches from the same landscape, adjacent landscapes and distant landscapes) and two habitat categories (same or different habitats) and compared them using non-parametric MANOVA. A significant interaction between habitat identity and spatial distance was found, such that the difference in mean similarity between same-habitat pairs and different-habitat pairs decreases with spatial distance. Additionally, community similarity decayed with spatial distance. Furthermore, at all distances, same-habitat pairs had higher similarity than different-habitats pairs. Our results support the fourth hypothesis of interactive effect of habitat identity and spatial proximity. We suggest that the environmental complexity of habitats or increased habitat specificity of species near the edge of their distribution range may explain this pattern. Thus, in transitions zones care should be taken when using habitats as surrogate of community composition in conservation planning since similar habitats in different locations are more likely to support different communities.
    Keywords: Research Article ; Ecology And Environmental Sciences ; Biology And Life Sciences ; Ecology And Environmental Sciences ; Biology And Life Sciences ; Ecology And Environmental Sciences ; Biology And Life Sciences ; Ecology And Environmental Sciences ; Biology And Life Sciences ; Ecology And Environmental Sciences ; Earth Sciences ; Biology And Life Sciences ; Ecology And Environmental Sciences ; Biology And Life Sciences ; Ecology And Environmental Sciences
    E-ISSN: 1932-6203
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  • 3
    Language: English
    In: Journal of Statistical Software, 01 September 2018, Vol.86(1), pp.1-20
    Description: The geographical area occupied by a species is a valuable measure for assessing its conservation status. Coarse-grained occupancy maps are available for many taxa, e.g., as atlases, but often at spatial resolutions too coarse for conservation use. However, mapping occupancy at fine spatial resolution across the entire extent of the species' distribution is often prohibitively expensive for the majority of species. Occupancy downscaling is a technique to estimate finer scale occupancy from coarse scale maps, by using the occupancyarea relationship (OAR) which reflects how the proportion of area occupied increases with spatial grain size. Models that describe the OAR are fitted to observed occupancies at the available coarse-grain sizes and then extrapolated to predict occupancy at the finer grain sizes required. The downscale package in the R programming environment provides users with easy-to-use functions for downscaling occupancy with ten published models. First, upgrain calculates occupancy for multiple grain sizes larger than the input data. Normal methods for aggregating raster data increase the extent of the focal area as grain size increases which is undesirable, so the function fixes the extent for all grain sizes, assigning unsampled cells as absences. Four suggested methods are provided to enable this and upgrain.threshold provides diagnostic plots that allow the user to explore the inherent trade-off between making assumptions about unsampled locations and discarding information from sampled locations. downscale fits nine possible models to the data generated from upgrain. hui.downscale fits the special case of the Hui model. predict and plot extrapolate the fitted models to predict and plot occupancy at finer grain sizes. Finally, ensemble.downscale simultaneously fits two or more of the downscaling models and calculates mean predicted occupancy across all selected models. Here we describe the package and apply the functions to atlas data of a hypothetical UK species.
    Keywords: Area of Occupancy ; Atlas Data ; Conservation ; Iucn Red List ; Occupancy-Area Relationship ; Mathematics
    E-ISSN: 1548-7660
    Source: Directory of Open Access Journals (DOAJ)
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  • 4
    In: Methods in Ecology and Evolution, November 2018, Vol.9(11), pp.2273-2284
    Description: The area of occupancy (AOO) is a widely used index in conservation assessments, notably in criteria B2 of the International Union for Conservation of Nature (IUCN) red‐list. However, IUCN guidelines require assessing AOO at finer resolution than is generally available. For this reason, extrapolation techniques have been proposed to predict finer AOO from coarser resolution data. Here, we apply 10 published downscaling models to the distributions of a large number of plant and bird species' in contrasting landscapes. We further compare the output of two ensemble models, one relying on all 10 downscaling models and one a subset of five models that can be fit rapidly and robustly, with minimal oversight required. We further compare the accuracy of downscaled predictions with respect to species prevalence. Across all landscapes and taxa, the models predicted AOO consistently. Some, such as the power law and Hui models, were nonlinear with respect to species prevalence. Some models consistently over or under predicted, such as the Nachman and Poisson models. Furthermore, some models proved to give more variable predictions than other models, e.g. Nachman and power law. For these reasons, none of these models are suitable for downscaling if used individually. The Thomas model was also rejected, because it is too computationally intensive, even though its predictions are relatively unbiased. The most effective model, when used by itself, was the improved binomial model. However, the two ensemble models were able to provide accurate predictions of AOO with low variability compared to using any one single model. There was no significant loss in performance using the simpler ensemble model, and therefore this solution is the least computationally intensive and requires least user oversight. Our results show that downscaling models could be potential tools to reliably estimate AOO for conservation assessments. Under circumstances where there is no a priori reason to prefer one model over another then an ensemble of these models may be the best solution for batch analysis of IUCN status under criteria B2. Moreover, we foresee the use of downscaling for the production of other biodiversity indicators, such as for invasive species monitoring. La zone d'occupation (AOO) est un indice largement utilisé dans les évaluations de conservation, notamment pour le critère B2 de la liste rouge de l'Union internationale pour la conservation de la nature (UICN). Cependant, les directives de l'UICN exigent une évaluation de l'AOO à une résolution plus fine que celle qui est généralement disponible. Pour cette raison, des techniques d'extrapolation ont été proposées pour prédire les AOO plus finement à partir de données de résolution plus grossières. Ici, nous appliquons 10 modèles publiés de réduction d'échelle à la distribution d'un grand nombre d'espèces de plantes et d'oiseaux dans des paysages contrastés. Nous comparons ensuite les résultats de deux modèles d'ensemble, l'un reposant sur les 10 modèles de réduction d'échelle et l'autre sur un sous‐ensemble de 5 modèles pouvant s'adapter rapidement et de manière robuste, et nécessitant un minimum d'inspection. Nous comparons ensuite la précision des prévisions à échelle réduite en ce qui concerne l'incidence des espèces. Pour tous les paysages et les taxons, les modèles ont prédit l'AOO de manière cohérente. Certains, tels que la loi de puissance et les modèles Hui, étaient non linéaires en ce qui concerne la prévalence des espèces. Certains modèles ont constamment sur‐ ou sous‐prédit, tels que les modèles Nachman et Poisson. En outre, quelques modèles ont donné des prédictions plus variables que d'autres, par exemple Nachman et la loi de puissance. Pour ces raisons, aucun de ces modèles ne convient à la réduction d'échelle s'il est utilisé individuellement. Le modèle de Thomas a également été rejeté, car il est trop intensif en calcul, même si ses prédictions sont relativement non biaisées. Le modèle le plus efficace, lorsqu'il était utilisé seul, fut le modèle binomial amélioré. Cependant, les deux modèles d'ensemble ont été en mesure de fournir des prédictions précises de l'AOO avec une faible variabilité par rapport à l'utilisation d'un seul modèle unique. Il n'y a pas eu de perte significative de performance en utilisant le modèle d'ensemble plus simple. Par conséquent, cette solution est la moins gourmande en calculs et nécessite moins de supervision de la part de l'utilisateur. Nos résultats montrent que les modèles de réduction d'échelle pourraient être des outils potentiels pour estimer de manière fiable les AOO pour les évaluations de conservation. Dans des circonstances ù il n'y a aucune raison a priori de préférer un modèle à un autre, un ensemble de ces modèles peut être la meilleure solution pour l'analyse par lots du statut UICN selon le critère B2. En outre, nous prévoyons l'utilisation de la réduction d'échelle pour la production d'autres indicateurs de la biodiversité, tels que le suivi des espèces invasives. Het verspreidingsgebied (area of occupancy, AOO) is een veelgebruikte index bij vaststellingen voor natuurbehoud, onder meer in criterium B2 van de rode lijst van de International Union for Conservation of Nature (IUCN). IUCN richtlijnen vereisen echter dat het AOO vastgesteld wordt op een fijnere resolutie dan algemeen beschikbaar is. Daarom werden er extrapolatietechnieken voorgesteld om fijner het AOO te voorspellen uit data met grovere resolutie. Hier passen we 10 gepubliceerde schaalverkleiningsmodellen toe op de distributies van een groot aantal plant‐ en vogelsoorten in contrasterende landschappen. Verder vergelijken we nog de output van twee ensemble modellen, één afhankelijk van alle 10 schaalverkleiningsmodellen en één van een subset van 5 modellen die op snelle en robuuste wijze gefit kunnen worden met zo min mogelijk toezicht vereist. Verder vergelijken we de nauwkeurigheid van in schaal verkleinde voorspellingen ten aanzien van het algemeen voorkomen van soorten. Over alle modellen en taxa heen voorspelden de modellen het AOO op consistente wijze. Sommigen, zoals de power law en Hui modellen, waren niet‐lineair ten aanzien van het voorkomen van soorten. Sommige modellen waren consistent in over‐ of onderschattingen, zoals de Nachtmann en Poisson modellen. Verder bleken sommige modellen variabelere voorspellingen te geven dan anderen, e.g. Nachtmann en power law. Omwille van deze redenen zijn geen van deze modellen geschikt voor schaalverkleining als ze individueel gebruikt worden. Het Thomas model werd ook afgewezen omdat het computationeel te intensief is, ook al zijn de voorspellingen relatief onbevooroordeeld. Het op zich meest effectieve model was het verbeterde binomiale model. De twee ensemble modellen waren echter in staat om nauwkeurige voorspellingen van het AOO te voorzien, met lage variabiliteit vergeleken met het gebruik van één enkel model. Er was geen significant verlies aan performantie bij gebruik van het simpelere ensemble model en dus is deze oplossing de minst computationeel intensieve met het minste toezicht door de gebruiker vereist. Onze resultaten tonen dat schaalverkleiningsmodellen potentiële hulpmiddelen kunnen zijn om betrouwbare schattingen te maken van het AOO in het kader van natuurbehoud. In omstandigheden waar er geen a priori reden is om één model boven een ander te verkiezen zou een ensemble van deze modellen de beste oplossing kunnen zijn voor batch analyse van IUCN status onder criterium B2. Bovendien verwachten we gebruik bij schaalverkleining voor de productie van andere biodiversiteitsindicatoren, zoals monitoren van invasieve soorten.
    Keywords: Area Of Occupancy ; Atlas ; Conservation Assessment ; Red Listing ; Scale
    ISSN: 2041-210X
    E-ISSN: 2041-210X
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  • 5
    Language: English
    In: Conservation biology : the journal of the Society for Conservation Biology, 04 June 2019
    Description: Offset schemes help to avoid or revert habitat loss through the protection of existing habitat (avoided deforestation) and/or the restoration of degraded areas (natural regrowth), respectively. The spatial scale of an offset scheme may influence which of these two outcomes is favoured and is an important aspect of the scheme's design. However, how spatial scale influences the trade-off between the preservation of existing habitat and restoration of degraded areas is poorly understood. Here, we used the largest forest offset scheme in the world, which is part if the Brazilian Forest Code, to explore how implementation at different spatial scales may affect the outcome in terms of the area of avoided deforestation and/or regrowth. Allowing offsets over large spatial scales led to a greater area of avoided deforestation and only a small area allocated to regrowth, whilst restricting offsets to small spatial scales led to the opposite pattern. The greatest total area (avoided deforestation and regrowth combined) was directed to conservation when implementing the scheme at small scales, especially in locations that are already highly deforested. To maximize conservation gains from avoided deforestation and regrowth, the design of the Brazilian forest offset scheme should have a "think local" focus by restricting the spatial scale in which offsets occur. A "think local" strategy could help to ensure that conservation benefits stay localized and promote the recovery of degraded areas in the most threatened forest landscapes. This article is protected by copyright. All rights reserved.
    Keywords: Amazon ; Avoided Deforestation ; Conservation ; Offsets ; Private Lands ; Restoration ; Spatial Scale
    ISSN: 08888892
    E-ISSN: 1523-1739
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  • 6
    In: Diversity and Distributions, December 2019, Vol.25(12), pp.1832-1845
    Keywords: Aoo ; Area Of Occupancy ; Conservation ; Iucn Red List ; Oar ; Occupancy Downscaling ; Occupancy‐Area Relationship ; Red Listing
    ISSN: 1366-9516
    E-ISSN: 1472-4642
    Source: Wiley Open Access (John Wiley & Sons, Inc.)
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