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  • 1
    Online Resource
    Online Resource
    Wiley ; 2017
    In:  Journal of Biogeography Vol. 44, No. 4 ( 2017-04), p. 937-949
    In: Journal of Biogeography, Wiley, Vol. 44, No. 4 ( 2017-04), p. 937-949
    Abstract: Although species‐occupancy distributions (SODs) and species‐area relationships (SARs) arise from the two marginal sums of the same presence/absence matrices, the two biodiversity patterns are usually explored independently. Here, we aim to unify the two patterns for isolate‐based data by constraining the SAR to conserve information from the SOD. Location Widespread. Methods Focusing on the power‐model SAR, we first developed a constrained form that conserved the total number of occupancies from the SOD. Next, we developed an additive‐constrained SAR that conserves the entire shape of the SOD within the power‐model SAR function, using a single parameter (the slope of the endemics‐area relationship). We then relate this additive‐constrained SAR to multiple‐sites similarity measures, based on a probabilistic view of Sørensen similarity. We extend the constrained and additive‐constrained SAR framework to 23 published SAR functions. We compare the fit of the original and constrained forms of 12 SAR functions using 154 published data sets, covering various spatial scales, taxa and systems. Main conclusions In all 23 SAR functions, the constrained form had one parameter less than the original form. In all 154 data sets the model with the highest weight based on the corrected Akaike's information criteria (wAICc) had a constrained form. The constrained form received higher wAICc than the original form in 98.79% of valid pairwise cases, approaching the wAICc expected under identical log‐likelihood. Our work suggests, both theoretically and empirically, that all SAR functions may have one unnecessary parameter, which can be excluded from the function without reduction in goodness‐of‐fit. The more parsimonious constrained forms are also easier to interpret as they reflect the probability of a randomly chosen occupancy to be found in an isolate. The additive‐constrained SARs accounts for two complimentary turn‐over components of occupancies: turnover between species and turnover between sites.
    Type of Medium: Online Resource
    ISSN: 0305-0270 , 1365-2699
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2017
    detail.hit.zdb_id: 2020428-0
    detail.hit.zdb_id: 188963-1
    SSG: 12
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  • 2
    Online Resource
    Online Resource
    Wiley ; 2019
    In:  Diversity and Distributions Vol. 25, No. 12 ( 2019-12), p. 1832-1845
    In: Diversity and Distributions, Wiley, Vol. 25, No. 12 ( 2019-12), p. 1832-1845
    Abstract: The Area of Occupancy (AOO) of a species is often utilized to assess extinction risk for determining IUCN Red List status. However, the recommended raw‐counts method of summing occupied grid cells likely reflects only sampling effort, as the majority of species have not been sampled across their entire range at the fine grains required by IUCN. More accurate measurements can be generated at coarser grains (so‐called atlas data) as false absences are reduced. If we fit the occupancy‐area relationship to these data, we can extrapolate the relationship down to estimate occupancy at finer grains. Numerous models have been proposed to carry out such occupancy downscaling, but have only been tested on a limited range of species. Methods We test the ability of downscaling models to recover fine grain AOO against the raw‐counts method for 28,900 virtual species with a wide range of prevalence and aggregation characteristics, subsampled to reflect common spatial biases in sampling effort. We address several questions for ensuring accurate downscaling: How to generate accurate atlas data? How far can we accurately extrapolate the occupancy‐area relationship given perfect data? Can occupancy downscaling overcome false absences at fine grain sizes? And how does sampling bias and coverage affect accuracy? Results Downscaling was more accurate than the raw‐counts method in all scenarios except where sampling coverage was very high and/or the sampling bias was positively related to the species distribution. However, if atlas data contained many false absences, then even downscaling under‐estimated actual occupancy. Main conclusions Occupancy downscaling has the potential to be a useful tool for estimating AOO for IUCN Red List assessments, especially when sampling coverage is low and the currently recommended method is ineffective. However, its application should be tailored to the species’ characteristics, as well as the sampling coverage and bias of the species’ records.
    Type of Medium: Online Resource
    ISSN: 1366-9516 , 1472-4642
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2019
    detail.hit.zdb_id: 2020139-4
    detail.hit.zdb_id: 1443181-6
    SSG: 12
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  • 3
    Online Resource
    Online Resource
    Wiley ; 2012
    In:  Conservation Biology Vol. 26, No. 1 ( 2012-02), p. 150-159
    In: Conservation Biology, Wiley, Vol. 26, No. 1 ( 2012-02), p. 150-159
    Type of Medium: Online Resource
    ISSN: 0888-8892
    Language: English
    Publisher: Wiley
    Publication Date: 2012
    detail.hit.zdb_id: 2020041-9
    SSG: 12
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  • 4
    Online Resource
    Online Resource
    Wiley ; 2020
    In:  Conservation Biology Vol. 34, No. 1 ( 2020-02), p. 148-157
    In: Conservation Biology, Wiley, Vol. 34, No. 1 ( 2020-02), p. 148-157
    Abstract: Article impact statement : Conservation offset schemes may yield greater additionality through avoided deforestation and restoration if implemented in smaller areas.
    Type of Medium: Online Resource
    ISSN: 0888-8892 , 1523-1739
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2020
    detail.hit.zdb_id: 2020041-9
    SSG: 12
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  • 5
    Online Resource
    Online Resource
    Wiley ; 2018
    In:  Methods in Ecology and Evolution Vol. 9, No. 11 ( 2018-11), p. 2273-2284
    In: Methods in Ecology and Evolution, Wiley, Vol. 9, No. 11 ( 2018-11), p. 2273-2284
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 2041-210X , 2041-210X
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2018
    detail.hit.zdb_id: 2528492-7
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  • 6
    Online Resource
    Online Resource
    Wiley ; 2018
    In:  Methods in Ecology and Evolution Vol. 9, No. 2 ( 2018-02), p. 235-244
    In: Methods in Ecology and Evolution, Wiley, Vol. 9, No. 2 ( 2018-02), p. 235-244
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 2041-210X , 2041-210X
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2018
    detail.hit.zdb_id: 2528492-7
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  • 7
    In: Journal of Applied Ecology, Wiley, Vol. 53, No. 5 ( 2016-10), p. 1341-1350
    Abstract: 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 EBV 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.
    Type of Medium: Online Resource
    ISSN: 0021-8901 , 1365-2664
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2016
    detail.hit.zdb_id: 2020408-5
    detail.hit.zdb_id: 410405-5
    SSG: 12
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  • 8
    In: Global Change Biology, Wiley, Vol. 28, No. 12 ( 2022-06), p. 3754-3777
    Abstract: Biodiversity conservation faces a methodological conundrum: Biodiversity measurement often relies on species, most of which are rare at various scales, especially prone to extinction under global change, but also the most challenging to sample and model. Predicting the distribution change of rare species using conventional species distribution models is challenging because rare species are hardly captured by most survey systems. When enough data are available, predictions are usually spatially biased towards locations where the species is most likely to occur, violating the assumptions of many modelling frameworks. Workflows to predict and eventually map rare species distributions imply important trade‐offs between data quantity, quality, representativeness and model complexity that need to be considered prior to survey and analysis. Our opinion is that study designs need to carefully integrate the different steps, from species sampling to modelling, in accordance with the different types of rarity and available data in order to improve our capacity for sound assessment and prediction of rare species distribution. In this article, we summarize and comment on how different categories of species rarity lead to different types of occurrence and distribution data depending on choices made during the survey process, namely the spatial distribution of samples (where to sample) and the sampling protocol in each selected location (how to sample). We then clarify which species distribution models are suitable depending on the different types of distribution data (how to model). Among others, for most rarity forms, we highlight the insights from systematic species‐targeted sampling coupled with hierarchical models that allow correcting for overdispersion and spatial and sampling sources of bias. Our article provides scientists and practitioners with a much‐needed guide through the ever‐increasing diversity of methodological developments to improve the prediction of rare species distribution depending on rarity type and available data.
    Type of Medium: Online Resource
    ISSN: 1354-1013 , 1365-2486
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2022
    detail.hit.zdb_id: 2020313-5
    SSG: 12
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  • 9
    In: Ecology, Wiley, Vol. 100, No. 12 ( 2019-12)
    Abstract: Habitat destruction is the single greatest anthropogenic threat to biodiversity. Decades of research on this issue have led to the accumulation of hundreds of data sets comparing species assemblages in larger, intact, habitats to smaller, more fragmented, habitats. Despite this, little synthesis or consensus has been achieved, primarily because of non‐standardized sampling methodology and analyses of notoriously scale‐dependent response variables (i.e., species richness). To be able to compare and contrast the results of habitat fragmentation on species’ assemblages, it is necessary to have the underlying data on species abundances and sampling intensity, so that standardization can be achieved. To accomplish this, we systematically searched the literature for studies where abundances of species in assemblages (of any taxa) were sampled from many habitat patches that varied in size. From these, we extracted data from several studies, and contacted authors of studies where appropriate data were collected but not published, giving us 117 studies that compared species assemblages among habitat fragments that varied in area. Less than one‐half (41) of studies came from tropical forests of Central and South America, but there were many studies from temperate forests and grasslands from all continents except Antarctica. Fifty‐four of the studies were on invertebrates (mostly insects), but there were several studies on plants (15), birds (16), mammals (19), and reptiles and amphibians (13). We also collected qualitative information on the length of time since fragmentation. With data on total and relative abundances (and identities) of species, sampling effort, and affiliated meta‐data about the study sites, these data can be used to more definitively test hypotheses about the role of habitat fragmentation in altering patterns of biodiversity. There are no copyright restrictions. Please cite this data paper and the associated Dryad data set if the data are used in publications.
    Type of Medium: Online Resource
    ISSN: 0012-9658 , 1939-9170
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2019
    detail.hit.zdb_id: 1797-8
    detail.hit.zdb_id: 2010140-5
    SSG: 12
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  • 10
    In: Methods in Ecology and Evolution, Wiley, Vol. 9, No. 8 ( 2018-08), p. 1787-1798
    Abstract: Biodiversity includes multiscalar and multitemporal structures and processes, with different levels of functional organization, from genetic to ecosystemic levels. One of the mostly used methods to infer biodiversity is based on taxonomic approaches and community ecology theories. However, gathering extensive data in the field is difficult due to logistic problems, especially when aiming at modelling biodiversity changes in space and time, which assumes statistically sound sampling schemes. In this context, airborne or satellite remote sensing allows information to be gathered over wide areas in a reasonable time. Most of the biodiversity maps obtained from remote sensing have been based on the inference of species richness by regression analysis. On the contrary, estimating compositional turnover (β‐diversity) might add crucial information related to relative abundance of different species instead of just richness. Presently, few studies have addressed the measurement of species compositional turnover from space. Extending on previous work, in this manuscript, we propose novel techniques to measure β‐diversity from airborne or satellite remote sensing, mainly based on: (1) multivariate statistical analysis, (2) the spectral species concept, (3) self‐organizing feature maps, (4) multidimensional distance matrices, and the (5) Rao's Q diversity. Each of these measures addresses one or several issues related to turnover measurement. This manuscript is the first methodological example encompassing (and enhancing) most of the available methods for estimating β‐diversity from remotely sensed imagery and potentially relating them to species diversity in the field.
    Type of Medium: Online Resource
    ISSN: 2041-210X , 2041-210X
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2018
    detail.hit.zdb_id: 2528492-7
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