Relevance of Spatial and Temporal Scales to Ecological Risk Assessment

Johnson, Alan R.; Turner, Sandra J.

doi:10.1002/9780470593028.ch4

Cited by 4 publications

(5 citation statements)

References 34 publications

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“…Although recognized since long (Allen and Starr, 1982), hierarchical organization has been generally undervalued as a mean of understanding the connections between local processes and large-scale distribution patterns of transfer and risk assessment at various time and space scales (Giraudoux et al 2013). This is consistent with the view of Johnson and Turner (Johnson and Turner 2010) who considered that, as natural phenomena and anthropogenic changes have characteristic spatial scales, it is critically important to incorporate explicitly scale issues into the design and implementation of ecological risk assessments. A conceptual way to address such issues lies in the "hierarchical patch dynamics paradigm" which conceives ecological systems as nested hierarchical systems (Landis 2003) and we believe that this framework is relevant to conceptualizing exposure assessment of ARs in a spatially explicit context.…”

Section: Introductionsupporting

confidence: 53%

“…and the bank vole in biocide context or the common voles in PPP, could be trapped within 100 m. Over 100 m, exposed rodents are rare but can be found up to 750 m from treatments. It is essential to keep in mind that most of the data presented here were obtained on local scale and that the patterns described cannot be extrapolated to larger scales without caution, as pointed out by Johnson and Turner (2010). Indeed, when trappings are carried out at the close proximity of the bait stations, the proportion of exposed individuals in both target and non-target species could be overestimated because, generally, the populations that do not live close to treated areas are not included in the sampling plan even though they are likely less or not exposed.…”

Section: B Spatial Patterns Of Small Mammal Exposure To Armentioning

confidence: 99%

“…Locally, habitat quality may influence home range and diet availability and thus, spatial activity pattern and bait consumption of a species. Therefore, as previously discussed for many other questions in ecology, both the understanding and prediction of AR fate and effects in terrestrial ecosystems rely on our ability to study patterns and processes on the appropriate scales and develop models that bridge across scales (Levin 1992;Johnson and Turner 2010;Chave 2013).…”

Section: Introductionmentioning

confidence: 99%

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Spatial Dimensions of the Risks of Rodenticide Use to Non-target Small Mammals and Applications in Spatially Explicit Risk Modeling

Cœurdassier

Fritsch

Jacquot

et al. 2017

Emerging Topics in Ecotoxicology

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Both target and non-target small mammals are exposed to rodenticides (AR). A better understanding of the drivers controlling this exposure is critical for the conservation of threatened small mammal species but also because they may represent important pathways of poisoning for birds of prey and carnivore mammals. Here, we consider the spatial components involved in the process of small mammal exposure to ARs with the aim to address how these can be used in spatially explicit risk assessment. We present how various drivers operate on multiple spatial scales. On continental and/or regional scales, both biogeographical distribution of small mammals and other species of conservation value and international/national regulations of AR applications (indoor vs outdoor…) could be used to identify some countries or states where exposure is more likely. For application at the local scale (i.e. few km²), we reviewed published studies that analysed the spatial pattern of small mammal exposure to ARs according to species and distance to treatments. We evidence that most of the small mammals exposed to AR are found in the immediate vicinity of treatment areas, i.e., within 100 m. Over 100 m, exposed rodents are rare but can be found until 750 m distance from treatment areas. Species traits related to spatial dimension such as habitat preferences, home range size and mobility also influence exposure. Exposure is variable, in terms of proportion of contaminated individuals and levels of residues, for species showing small home-range size and a limited spatial mobility. The level of exposure depends on whether the main habitat of the given species is similar or not to the one of the target rodent. For instance, exposure of the common vole, a grassland species, is low when ARs are used indoor while it can be highly exposed when bromadiolone is applied outdoor to control the water vole, a sympatric species. For small mammals exhibiting a relatively large home-range size and a high spatial mobility such as the wood mouse and the bank vole, the exposure is commonly reported within a lower range than target species. Although this has not been studied in details, we also address how landscape and/or habitat features may modulate exposure, suggesting that landscape management may help to mitigate the risk of ARs to small mammals. Finally, we discuss both the advantages and disadvantages of statistical, analytical or simulation models to assess potential or actual exposure of NTSM to AR in a spatially explicit way.We conclude that in order to analyse global patterns in usage and exposure risks, large scale statistical modelling should be used while for detailed site specific assessments, simulation models may be more appropriate.

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Section: Introductionsupporting

confidence: 53%

Section: B Spatial Patterns Of Small Mammal Exposure To Armentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatial Dimensions of the Risks of Rodenticide Use to Non-target Small Mammals and Applications in Spatially Explicit Risk Modeling

Cœurdassier

Fritsch

Jacquot

et al. 2017

Emerging Topics in Ecotoxicology

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“…Ecological systems are complex and heterogeneous; the ecological processes that produce ES occur across markedly different scales of space and time having different rates and controlling feedbacks (Kapustka ; Gamfeldt et al ; Johnson and Turner ). Ecological production functions must simplify this complexity enough to make modeling both tractable for modelers (from the standpoints of data availability and computational efficiency) and understandable to decision makers, without ignoring elements of complexity that alter ES delivery (Bradford et al ).…”

Section: Desired Attributes Of Ecological Production Functionsmentioning

confidence: 99%

Using ecological production functions to link ecological processes to ecosystem services

Bruins

Canfield

Duke

et al. 2016

Integr Envir Assess & Manag

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Ecological production functions (EPFs) link ecosystems, stressors, and management actions to ecosystem services (ES) production. Although EPFs are acknowledged as being essential to improve environmental management, their use in ecological risk assessment has received relatively little attention. Ecological production functions may be defined as usable expressions (i.e., models) of the processes by which ecosystems produce ES, often including external influences on those processes. We identify key attributes of EPFs and discuss both actual and idealized examples of their use to inform decision making. Whenever possible, EPFs should estimate final, rather than intermediate, ES. Although various types of EPFs have been developed, we suggest that EPFs are more useful for decision making if they quantify ES outcomes, respond to ecosystem condition, respond to stressor levels or management scenarios, reflect ecological complexity, rely on data with broad coverage, have performed well previously, are practical to use, and are open and transparent. In an example using pesticides, we illustrate how EPFs with these attributes could enable the inclusion of ES in ecological risk assessment. The biggest challenges to ES inclusion are limited data sets that are easily adapted for use in modeling EPFs and generally poor understanding of linkages among ecological components and the processes that ultimately deliver the ES. We conclude by advocating for the incorporation into EPFs of added ecological complexity and greater ability to represent the trade-offs among ES. Integr Environ Assess Manag 2017;13:52-61. © 2016 SETAC.

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“…More importantly, forest landscape restoration (FLR) is the long‐term process of regaining ecological functionality and enhancing human well‐being across deforested or degraded forest landscapes (Rietbergen‐McCracken, Sarre, & Maginnis, ). In practice, due to the heterogeneity of forest landscape units and the different interests of various stakeholders, massive restoration in same ways is actually not only unnecessary but also unfeasible to implement at large scale (Johnson & Turner, ; Orsi & Geneletti, ). Consequently, based on the outcomes of systematic ERA, decision‐makers can easily target different levels of risk areas and then plan the following restoration activities accordingly to mitigate the FLD risks for these areas.…”

Section: Introductionmentioning

confidence: 99%

Risk assessment of forest landscape degradation using Bayesian network modeling in the Miyun Reservoir catchment (China) with emphasis on the Beijing–Tianjin sandstorm source control program

Zhang

Zhang³

et al. 2018

Land Degrad Dev

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As the local strategic surface water source for Beijing City, the Miyun Reservoir catchment is suffering from the challenges of forest landscape degradation (FLD) under various natural and anthropogenic disturbances. This study developed a cause-effect Bayesian network model, consisting of three tiers of nodes, site indicators, external disturbance, and the outcome of FLD, to perform a regional ecological risk assessment to better understand the FLD risks (1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013) in the catchment. The effects of the Beijing-Tianjin sandstorm source control (BTSSC) Phase I program, a typical forest landscape restoration program, on the FLD were further examined. Overall, the higher level of either current occurrence or risk probability for FLD suggests the urgency of the following restoration efforts. The dominance of soil erosion for FLD indicates that soil erosion control should be a priority in the following Phase II program. Meanwhile, the uneven spatial distribution of high-risk areas suggests that the following efforts should be focused within the upstream Hebei Province. In addition, based on the comparison of site indicators between different risk areas, the study concluded that community livelihood activities and land degradation due to climate change have been the other two dominant driving forces contributing to FLD. Furthermore, the study found that the forest landscape has not been restored into the best state by the BTSSC program due to the multiple and complex challenges. Thus, the technical concept of nature-based solutions, including close-to-nature forest management technology and a comprehensive community-involvement governance model, was proposed for the following Phase II program. KEYWORDSBeijing-Tianjin sandstorm source control program, ecological risk assessment, forest landscape restoration, Miyun Reservoir, nature-based solutions

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Relevance of Spatial and Temporal Scales to Ecological Risk Assessment

Cited by 4 publications

References 34 publications

Spatial Dimensions of the Risks of Rodenticide Use to Non-target Small Mammals and Applications in Spatially Explicit Risk Modeling

Spatial Dimensions of the Risks of Rodenticide Use to Non-target Small Mammals and Applications in Spatially Explicit Risk Modeling

Using ecological production functions to link ecological processes to ecosystem services

Risk assessment of forest landscape degradation using Bayesian network modeling in the Miyun Reservoir catchment (China) with emphasis on the Beijing–Tianjin sandstorm source control program

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