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  • 1
    Online Resource
    Online Resource
    Berlin : Humboldt-Universität zu Berlin
    UID:
    edochu_18452_21401
    Format: 1 Online-Ressource (7 Seiten)
    Content: Effective conservation capable of mitigating global biodiversity declines require thorough knowledge on species distributions and their drivers. A species ecological niche determines its geographic distribution, and species distribution models (SDMs) can be used to predict them. For various reasons, e.g., the lack of spatial data on relevant environmental factors, SDMs fail to characterize important ecological relationships. We argue that SDMs do not yet include relevant environmental information, which can be measured with remote sensing (RS). RS may benefit SDMs because it provides information on e.g., ecosystem function, health and structure, complete spatial assessment, and reasonable temporal repeat for the processes that determine geographical distributions. However, RS data is still seldom included in such studies with the exception of climate data. Here we provide a guide for researchers aiming to improve their SDM studies, describing how they might include RS data in their specific study. We propose how to improve models of species ecological niches, by including measures of habitat quality (e.g., productivity), nutritional values, and seasonal or life-cycle events. To date, several studies have shown that using ecologically-relevant environmental predictors derived from RS improve model performance and transferability, and better approximate a species ecological niche. These data, however, are not a panacea for SDMs, as there are cases in which RS predictors are not appropriate, too costly, or exhibit low predictive power. The integration of multiple environmental predictors derived from RS in SDMs can thus improve our knowledge on processes driving biodiversity change and improve our capacity for biodiversity conservation.
    Content: Peer Reviewed
    In: Frontiers in Ecology and Evolution, Lausanne : Frontiers Media S.A., 7,2019
    Language: English
    URL: Volltext  (kostenfrei)
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  • 2
    UID:
    edochu_18452_22899
    Format: 1 Online-Ressource (17 Seiten)
    Content: Species distribution models (SDMs) constitute the most common class of models across ecology, evolution and conservation. The advent of ready‐to‐use software packages and increasing availability of digital geoinformation have considerably assisted the application of SDMs in the past decade, greatly enabling their broader use for informing conservation and management, and for quantifying impacts from global change. However, models must be fit for purpose, with all important aspects of their development and applications properly considered. Despite the widespread use of SDMs, standardisation and documentation of modelling protocols remain limited, which makes it hard to assess whether development steps are appropriate for end use. To address these issues, we propose a standard protocol for reporting SDMs, with an emphasis on describing how a study's objective is achieved through a series of modeling decisions. We call this the ODMAP (Overview, Data, Model, Assessment and Prediction) protocol, as its components reflect the main steps involved in building SDMs and other empirically‐based biodiversity models. The ODMAP protocol serves two main purposes. First, it provides a checklist for authors, detailing key steps for model building and analyses, and thus represents a quick guide and generic workflow for modern SDMs. Second, it introduces a structured format for documenting and communicating the models, ensuring transparency and reproducibility, facilitating peer review and expert evaluation of model quality, as well as meta‐analyses. We detail all elements of ODMAP, and explain how it can be used for different model objectives and applications, and how it complements efforts to store associated metadata and define modelling standards. We illustrate its utility by revisiting nine previously published case studies, and provide an interactive web‐based application to facilitate its use. We plan to advance ODMAP by encouraging its further refinement and adoption by the scientific community.
    Content: Peer Reviewed
    Note: This article was supported by the German Research Foundation (DFG) and the Open Access Publication Fund of Humboldt-Universität zu Berlin.
    In: Oxford [u.a.] : Wiley-Blackwell, 43,9, Seiten 1261-1277
    Language: English
    URL: Volltext  (kostenfrei)
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  • 3
    UID:
    edochu_18452_26017
    Format: 1 Online-Ressource (13 Seiten)
    Content: The global increase in demand for wood products, calls for a more sustainable management of forests to optimize both the production of wood and the conservation of forest biodiversity. In this paper, we evaluate the status and future trends of forest birds in Central European forests, assuming different forest management scenarios that to a varying degree respond to the demand for wood production. To this end, we use niche models (Boosted Regression Trees and Generalized Linear Models) to model the responses of 15 forest bird species to predictors related to forest stand (e.g., stand volume of specific tree species) and landscape structure (e.g., percentage cover), and to climate (bioclimatic variables). We then define five distinct forest management scenarios, ranging from set-aside to productivity-driven scenarios, project them 100 years into the future, and apply our niche models into these scenarios to assess the birds’ responses to different forest management alternatives. Our models show that the species’ responses to management vary reflecting differences in their ecological niches, and consequently, no single management practice can benefit all species if applied across the whole landscape. Thus, we conclude that in order to promote the overall forest bird species richness in the study region, it is necessary to manage the forests in a multi-functional way, e.g., by spatially optimizing the management practices in the landscape.
    Content: Peer Reviewed
    In: Lausanne : Frontiers Media, 5
    Language: English
    URL: Volltext  (kostenfrei)
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