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  • 1
    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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  • 2
    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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