Green Infrastructure Design Based on Spatial Conservation Prioritization and Modeling of Biodiversity Features and Ecosystem Services

Environmental Management, Journal Year: 2015, Volume and Issue: 57(2), P. 251 - 256

Published: Sept. 22, 2015

There is high-level political support for the use of green infrastructure (GI) across Europe, to maintain viable populations and provide ecosystem services (ES). Even though GI inherently a spatial concept, modern tools planning have not been recognized, such as in recent European Environment Agency (EEA) report. We outline toolbox methods useful design that explicitly accounts biodiversity ES. Data on species occurrence, habitats, environmental variables are increasingly available via open-access internet platforms. Such data can be synthesized by statistical distribution modeling, producing maps features. These, together with ES, form basis design. argue conservation prioritization (SCP) effective design, overall SCP goal cost-effective allocation efforts. Corridors currently promoted EEA means implementing but they typically target needs only subset regional pool. would help ensure provides balanced solution requirements many features (e.g., species, habitat types) ES simultaneously manner. necessary make into an operational concept combating loss promoting

Language: Английский

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Hierarchical Species Distribution Models

Current Landscape Ecology Reports, Journal Year: 2016, Volume and Issue: 1(2), P. 87 - 97

Published: June 1, 2016

Language: Английский

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Testing methods in species distribution modelling using virtual species: what have we learnt and what are we missing?

Ecography, Journal Year: 2019, Volume and Issue: 42(12), P. 2021 - 2036

Published: June 9, 2019

Species distribution models (SDMs) have become one of the major predictive tools in ecology. However, multiple methodological choices are required during modelling process, some which may a large impact on forecasting results. In this context, virtual species, i.e. use simulations involving fictitious species for we perfect knowledge its occurrence–environment relationships and other relevant characteristics, increasingly popular to test SDMs. This approach provides simple ecologist framework under model properties, as well effects different choices, allows teasing out targeted factors with great certainty. simplification is therefore very useful setting up standards best practice principles. As result, numerous studies been published over last decade. The topics covered include differences performance between statistical models, sample size, choice threshold values, methods generate pseudo‐absences presence‐only data, among many others. These already made contribution practices Recent software developments greatly facilitated simulation at least three packages that effect. procedure has not homogeneous, introduces subtleties interpretation results, across packages. Here 1) review main contributions SDM literature; 2) compare approaches packages; 3) propose set recommendations future context

Language: Английский

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Is more data always better? A simulation study of benefits and limitations of integrated distribution models

Ecography, Journal Year: 2020, Volume and Issue: 43(10), P. 1413 - 1422

Published: July 14, 2020

Species distribution models are popular and widely applied ecological tools. Recent increases in data availability have led to opportunities challenges for species modelling. Each source has different qualities, determined by how it was collected. As several sources can inform on a single species, ecologists often analysed just one of the sources, but this loses information, as some discarded. Integrated (IDMs) were developed enable inclusion multiple datasets model, whilst accounting collection protocols. This is advantageous because allows efficient use all available, improve estimation account biases collection. What not yet known when integrating does bring advantages. Here, first time, we explore potential limits IDMs using simulation study spatially biased, opportunistic, presence‐only dataset with structured, presence–absence dataset. We four scenarios based real problems; small sample sizes, low levels detection probability, correlations between covariates lack knowledge drivers bias For each scenario ask; do see improvements parameter or accuracy spatial pattern prediction IDM versus modelling either alone? found integration alone unable correct data. Including covariate explain adding flexible term improved performance beyond models, including producing most accurate robust estimates. Increasing size having no correlated also estimation. These results demonstrate under which conditions integrated provide benefits over sources.

Language: Английский

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Species-Habitat Associations: Spatial data, predictive models, and ecological insights

Published: Dec. 1, 2020

Ecologists develop species-habitat association (SHA) models to understand where species occur, why they are there and else might be. This knowledge can be used designate protected areas, estimate anthropogenic impacts on living organisms assess risks from invasive or disease spill-over wildlife humans. Here, we describe the state of art in SHA models, looking beyond apparent correlations between positions their local environment. We highlight importance ecological mechanisms, synthesize diverse modelling frameworks motivate development new analytical methods. Above all, aim synthetic, bringing together several apparently disconnected pieces theory, taxonomy, spatiotemporal scales, mathematical statistical technique our field. The first edition this ebook reviews ecology associations, mechanistic interpretation existing empirical shared foundations that help us draw scientific insights field data. It will interest graduate students professionals for an introduction literature SHAs, practitioners seeking analyse data animal movements distributions quantitative ecologists contribute methods addressing limitations current incarnations models.

Language: Английский

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