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  • 11
    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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  • 12
    Language: English
    In: PLoS ONE, 01 January 2014, Vol.9(10), p.e109677
    Description: Relationships between host and microbial diversity have important ecological and applied implications. Theory predicts that these relationships will depend on the spatio-temporal scale of the analysis and the niche breadth of the organisms in question, but representative data on host-microbial...
    Keywords: Sciences (General)
    E-ISSN: 1932-6203
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  • 13
    In: Methods in Ecology and Evolution, February 2018, Vol.9(2), pp.235-244
    Description: The ability to provide reliable projections for the current and future distribution of land‐cover is fundamental if we wish to protect and manage our diminishing natural resources. Two inter‐related revolutions make map productions feasible at unprecedented resolutions—the availability of high‐resolution remotely sensed data and the development of machine‐learning algorithms. However, ground‐truthed data needed for training models is in most cases spatially and temporally clustered. Therefore, map production requires extrapolation of models from one place to another and the uncertainty cost of such extrapolation is rarely explored. In other words, the focus has mainly been on projections, and less on quantifying their reliability. Using the concept of “forecast horizon”, we suggest that the predictability of land‐cover classification models should be quantitatively explored as a continuum against distances measured along multiple dimensions—space, time, environmental and spectral. Focusing on ten agricultural sites from England and using models specifically designed to predict multivariate decay‐curves, we ask: how does a model's predictive performance decay with distance? More specifically, we explored if we could predict the proficiency (kappa statistics) of a model trained in one site when making predictions in another site based on the spatial, temporal, spectral and environmental distances between sites. We found that model proficiency decays with distance between sites in each dimension. More importantly, we found for the first time, that it is possible to predict the performance a model transferred to or from a novel site will have, based on its distances from known sites. The spectral distance variables were the most important when predicting model transferability. Exploring model transferability as a continuum may have multiple usages including predicting uncertainty values in space and time, prioritisation of strategies for ground‐truth data collection, and optimising model characteristics for defined tasks.
    Keywords: Community Similarity ; Earth‐Observation ; Forecast Horizon ; Habitat Mapping ; Predictive Ecology ; Random Forest ; Remote Sensing ; Signature Extension ; Species‐Distribution Models ; Uncertainty
    ISSN: 2041-210X
    E-ISSN: 2041-210X
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  • 14
    Language: English
    In: Biodiversity and Conservation, 2019, Vol.28(3), pp.769-786
    Description: To understand patterns of alpha, beta and gamma diversities in fragmented landscapes we need to explore the three scale components in relation to potential drivers in a scale-dependent manner. Often, the drivers themselves can be partitioned to alpha, beta and gamma diversities. Thus, one can hypothesize that the scale-components of species diversity and drivers’ diversity match, i.e., that species alpha diversity is mainly explained by drivers’ alpha diversity, beta by beta and gamma by gamma. Here, we explore this ‘scale-matching’ hypothesis for spiders in two fragmented agricultural landscapes. In each landscape, we sampled spiders and their potential prey in 12 patches. Then, we sub-sampled pseudo-landscapes in which we calculated spider alpha, beta and gamma diversities using multiplicative diversity-partitioning. Next, we used variance partitioning analysis to explore the relative contribution of eleven explanatory variables from five thematic groups (sampling intensity, area, connectivity, habitat diversity and prey diversity), while further partitioning the habitat and prey diversities to their corresponding alpha, beta and gamma diversities. We found considerable evidence for scale-matching, with spiders’ alpha and beta diversities explained mostly by the corresponding alpha and beta diversities (respectively) of prey and/or habitat. We further found a strong effect of connectivity on spider beta diversity, but not on alpha and gamma diversities. For spiders gamma diversity, a cross-scale effect was observed. Our results suggest that multiple drivers from multiple scales interact in structuring patterns of spider alpha, beta and gamma diversities in agro-ecosystems, yet the strongest effects are of those drivers that match in scale.
    Keywords: Agroecosystems ; Araneae ; Community composition ; Effective diversity ; Fragmentation ; Meta-community ; Scale
    ISSN: 0960-3115
    E-ISSN: 1572-9710
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  • 15
    Language: English
    In: Biological Conservation, September 2017, Vol.213, pp.252-255
    Description: In 2013, the Group on Earth Observations Biodiversity Observation Network (GEO BON) developed the framework of Essential Biodiversity Variables (EBVs), inspired by the Essential Climate Variables (ECVs). The EBV framework was developed to distill the complexity of biodiversity into a manageable list of priorities and to bring a more coordinated approach to observing biodiversity on a global scale. However, efforts to address the scientific challenges associated with this task have been hindered by diverse interpretations of the definition of an EBV. Here, the authors define an EBV as a critical biological variable that characterizes an aspect of biodiversity, functioning as the interface between raw data and indicators. This relationship is clarified through a multi-faceted stock market analogy, drawing from relevant examples of biodiversity indicators that use EBVs, such as the Living Planet Index and the UK Spring Index. Through this analogy, the authors seek to make the EBV concept accessible to a wider audience, especially to non-specialists and those in the policy sector, and to more clearly define the roles of EBVs and their relationship with biodiversity indicators. From this we expect to support advancement towards globally coordinated measurements of biodiversity.
    Keywords: Biodiversity ; Indicator ; Priority Measurement ; Biodiversity Observation Network ; Living Planet Index ; UK Spring Index ; Agriculture ; Biology ; Ecology
    ISSN: 0006-3207
    E-ISSN: 18732917
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  • 16
    Language: English
    In: ISPRS Journal of Photogrammetry and Remote Sensing, February 2018, Vol.136, pp.1-12
    Description: The increasing need for high quality Habitat/Land-Cover (H/LC) maps has triggered considerable research into novel machine-learning based classification models. In many cases, H/LC classes follow pre‐defined hierarchical classification schemes (e.g., CORINE), in which fine H/LC categories are thematically nested within more general categories. However, none of the existing machine-learning algorithms account for this pre-defined hierarchical structure. Here we introduce a novel Random Forest (RF) based application of hierarchical classification, which fits a separate local classification model in every branching point of the thematic tree, and then integrates all the different local models to a single global prediction. We applied the hierarchal RF approach in a NATURA 2000 site in Italy, using two land-cover (CORINE, FAO-LCCS) and one habitat classification scheme (EUNIS) that differ from one another in the shape of the class hierarchy. For all 3 classification schemes, both the hierarchical model and a flat model alternative provided accurate predictions, with kappa values mostly above 0.9 (despite using only 2.2–3.2% of the study area as training cells). The flat approach slightly outperformed the hierarchical models when the hierarchy was relatively simple, while the hierarchical model worked better under more complex thematic hierarchies. Most misclassifications came from habitat pairs that are thematically distant yet spectrally similar. In 2 out of 3 classification schemes, the additional constraints of the hierarchical model resulted with fewer such serious misclassifications relative to the flat model. The hierarchical model also provided valuable information on variable importance which can shed light into “black-box” based machine learning algorithms like RF. We suggest various ways by which hierarchical classification models can increase the accuracy and interpretability of H/LC classification maps.
    Keywords: Classification ; Machine-Learning ; Hierarchical Models ; Random Forest ; Natura 2000 ; Habitat/Land-Cover ; Engineering ; Geography
    ISSN: 0924-2716
    E-ISSN: 1872-8235
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  • 17
    In: Journal of Applied Ecology, October 2016, Vol.53(5), pp.1341-1350
    Description: Political commitment and policy instruments to halt biodiversity loss require robust data and a diverse indicator set to monitor and report on biodiversity trends. Gaps in data availability and narrow‐based indicator sets are significant information barriers to fulfilling these needs. In this paper, the reporting requirements of seven global or European biodiversity policy instruments were reviewed using the list of Essential Biodiversity Variables (EBVs) as an analytical framework. The reporting requirements for the most comprehensive policy instrument, the United Nation's Strategic Plan for Biodiversity 2011–2020, were compared with the indicator set actually used for its reporting, to identify current information gaps. To explore the extent to which identified gaps could be bridged, the potential contribution of data mobilization, modelling and further processing of existing data was assessed. The information gaps identified demonstrate that decision‐makers are currently constrained by the lack of data and indicators on changes in the EBV classes Genetic Composition and, to a lesser extent, Species Populations for which data is most often available. Furthermore, the results show that even when there is a requirement for specific information for reporting, the indicators used may not be able to provide all the information, for example current Convention of Biological Diversity indicators provide relatively little information on changes in the Ecosystem Function and Ecosystem Structure classes. This gap could be partly closed by using existing indicators as proxies, whereas additional indicators may be computed based on available data (e.g. for EBVs in the Ecosystem Structure class). However, for the EBV class Genetic Composition, no immediate improvement based on proxies or existing data seems possible. Synthesis and applications. Using Essential Biodiversity Variables (EBVs) as a tool, theory‐driven comparisons could be made between the biodiversity information gaps in reporting and indicator sets. Analytical properties, such as an identification of which data and indicator(s) are relevant per EBV, will need to be addressed before EBVs can actually become operational and facilitate the integration of data flows for monitoring and reporting. In the meantime, a first analysis shows that existing indicators and available data offer considerable potential for bridging the identified information gaps. Using Essential Biodiversity Variables (EBVs) as a tool, theory‐driven comparisons could be made between the biodiversity information gaps in reporting and indicator sets. Analytical properties, such as an identification of which data and indicator(s) are relevant per , will need to be addressed before s can actually become operational and facilitate the integration of data flows for monitoring and reporting. In the meantime, a first analysis shows that existing indicators and available data offer considerable potential for bridging the identified information gaps.
    Keywords: Biodiversity Data ; Biodiversity Indicator Partnership ; Convention On Biological Diversity ; Data Mobilization ; Data Sources ; Indicators ; Instrument ; Monitoring ; Policy ; Reporting
    ISSN: 0021-8901
    E-ISSN: 1365-2664
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  • 18
    In: Methods in Ecology and Evolution, September 2017, Vol.8(9), pp.1092-1102
    Description: Species distribution models (SDM) are widely used to predict occupancy patterns at fine resolution over wide extents. However, SDMs generally ignore the effect of biotic interactions and tend to overpredict the number of species that can coexist at a given location and time (hereafter, the alpha‐capacity). We developed an extension of SDMs that integrates species‐level and community‐level modelling to account for the above drivers. The alpha‐adjusted SDM takes the Probabilities of Occurrence (PoO) for all species of a community and the site's alpha‐capacity and adjusts the PoO, such that: (i) their sum will equal the alpha‐capacity as predicted by probability theory; and (ii) the adjusted PoO are dependent upon the relative suitability of each species for that site. The new method was tested using community data comprising 87 freshwater invertebrate species in an LTER watershed in Germany. We explored the ability of the method to predict alpha and beta‐diversity patterns. We further focused on the effect on model performance at the species‐level of the error associated with modelling alpha‐capacity, of differences in gamma diversity (the size of the community) and of the type of community (random or guild‐based). The models that predicted alpha‐capacity contained considerable error, and thus adjusting the PoO according to the modelled alpha‐capacity resulted with decreased performance at the species level. However, when using the observed alpha‐capacity to mimic a good alpha‐capacity model, the alpha‐adjusted SDMs usually resulted in increased performance. We further found that the alpha‐adjusted SDM was better than the original SDM at predicting beta‐diversity patterns, especially when using similarity indices that are sensitive to double absences. Using the alpha‐adjusted SDM approach may increase the predictive performance at the species and community levels if alpha‐capacity can be assessed or modelled with sufficient accuracy, especially in relatively small communities of closely interacting species. With better models to predict alpha‐capacity being developed, alpha‐adjusted SDM has considerable potential to provide more realistic predictions of species‐distribution patterns.
    Keywords: Alpha‐Capacity ; Beta Diversity ; Coexistence ; Competition ; Freshwater Environment ; Gamma Diversity ; Macroecological Models ; Random‐Forest ; Spatial Ecology ; Species Distribution Models
    ISSN: 2041-210X
    E-ISSN: 2041-210X
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  • 19
    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...
    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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  • 20
    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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