A Regional Retrospective Assessment of the Potential Stressors Causing the Decline of the Cherry Point Pacific Herring Run

Landis, Wayne G.; Duncan, P. Brent; Hayes, Emily; Markiewicz, April J.; Thomas, Jill F.

doi:10.1080/10807030490438201

Cited by 38 publications

(22 citation statements)

References 14 publications

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“…Traditional ecological assessment demands the expert scoring or a hierarchical analysis process to set the weights (Walker et al 2001;Landis et al 2004;Wiegers et al 1998), whereas, the involvement of humans can be saved in the artificial neural network model. In fact, the learning process of the network is the process of concentrative confirmation of the data structure.…”

Section: Structure Of Artificial Neural Network Model Mlpmentioning

confidence: 99%

“…For one chemical stressor, the ratio value model and exposure-response model are adopted (Suter 1993;USEPA 1998b, Barnthouse 1986, but for the multiple stressors, multiple receptors, large-scale of complex regional landscape, the methods are not suitable because many uncertainties will appear during the extending from one stressor small-scale to multiple stressors large-scale. In the regional ecological risk assessment, Relative Risk Assessment (RRA) based on weight-of-evidence (WOE) is used widely (Hart Landis et al 2004;Moraes et al 2002;Obery and Landis 2002;Walker et al 2001;Wiegers et al 1998;Landis and Matthews 1997;Serveiss et al 2004;Menzie et al 1996;Cormier et al 2000). WOE approaches reported in the literature vary broadly from subjective and qualitative to quantitative and the categories of which include qualitative combination, expert ranking, consensus ranking, semi-quantitative ranking, and sediment quality triad (Burton et al 2002).…”

Section: Introductionmentioning

confidence: 99%

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Ecological Risk Assessment of Regions Along the Roadside of the Qinghai-Tibet Highway and Railway Based on an Artificial Neural Network

Hui

Jinsong

Cao

et al. 2007

Human and Ecological Risk Assessment: An International Journal

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A concept model of regional risk was constructed for the characteristics of ecosystems alongside the Qinghai-Tibet highway and railway based on the MLP (Multilayer perceptron) model. Seven indices such as snow hazard, drought hazard, and landslide were selected in order to evaluate the integrated ecological risk of the ecosystems along the study area. Results show that the Qaidam montane desert zone had the greatest average risk value (4.26), followed by the Golog-Nagqu high-cold scrub meadow zone (2.80) and the East Qinghai and Qilian montane steppe zone (2.73) among the ecosystems within the six natural zones within the study region. As far as land cover types are concerned, the top three ecological risk values appear in the needle-leaved forest (4.31), desert (4.12), and land without vegetation (3.62), which are higher than those in the other seven types in the study site. Although the risk values are influenced by natural factors and human activities, they are more strongly controlled by natural factors. According to the ecological risk characteristics, the ecosystems within the study area are subdivided into four subregions, including the Qaidam basin region (high risk), the Xidatan to Damxung region (moderate risk), and the Eastern Qinghai-Qilian (slight risk) and Southern Xizang (Tibet) region (slighter risk).

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Section: Structure Of Artificial Neural Network Model Mlpmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ecological Risk Assessment of Regions Along the Roadside of the Qinghai-Tibet Highway and Railway Based on an Artificial Neural Network

Hui

Jinsong

Cao

et al. 2007

Human and Ecological Risk Assessment: An International Journal

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“…Aluminum is not a major toxicant in the alkaline waters of the Estuary (pH range 7.2 to 8.0) (USGS 2011). Aluminum toxicity occurs in acidified waters in parts of Puget Sound (Washington, USA) (Landis et al 2004), the southern regions of the Canadian Shield, and the northeastern United States, including the upper tributaries of Chesapeake Bay (Maryland and Virginia) where granitic soils have little buffering capacity (Hall et al 1993). In acidified waters below pH 6.0, aluminum occurs as monomeric species (Al 3+ , AlOH 2+ , Al(OH) 2 + , Al (OH) 3°, and Al(OH) 4 − ) that deposit on gill surfaces and smother the animal.…”

Section: Metals and The Metalloid Seleniummentioning

confidence: 99%

Life Histories, Salinity Zones, and Sublethal Contributions of Contaminants to Pelagic Fish Declines Illustrated with a Case Study of San Francisco Estuary, California, USA

Brooks

Fleishman

Brown

et al. 2011

Estuaries and Coasts

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Human effects on estuaries are often associated with major decreases in abundance of aquatic species. However, remediation priorities are difficult to identify when declines result from multiple stressors with interacting sublethal effects. The San Francisco Estuary offers a useful case study of the potential role of contaminants in declines of organisms because the waters of its delta chronically violate legal water quality standards; however, direct effects of contaminants on fish species are rarely observed. Lack of direct lethality in the field has prevented consensus that contaminants may be one of the major drivers of coincident but unexplained declines of fishes with differing life histories and habitats (anadromous, brackish, and freshwater). Estuaries and Coasts (2012) 35:603-621 DOI 10.1007/s12237-011-9459-6 indicates that examining the effects of contaminants and other stressors on specific life stages in different seasons and salinity zones of the estuary is critical to identifying how several interacting stressors could contribute to a general syndrome of declines. Moreover, warming water temperatures of the magnitude projected by climate models increase metabolic rates of ectotherms, and can hasten elimination of some contaminants. However, for other pollutants, concurrent increases in respiratory rate or food intake result in higher doses per unit time without changes in the contaminant concentrations in the water. Food limitation and energetic costs of osmoregulating under altered salinities further limit the amount of energy available to fish; this energy must be redirected from growth and reproduction toward pollutant avoidance, enzymatic detoxification, or elimination. Because all of these processes require energy, bioenergetics methods are promising for evaluating effects of sublethal contaminants in the presence of other stressors, and for informing remediation. Predictive models that evaluate the direct and indirect effects of contaminants will be possible when data become available on energetic costs of exposure to contaminants given simultaneous exposure to non-contaminant stressors.

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“…A new tool for large-scale risk assessment is required. The Causal Analysis method (EPA 2000;Norton et al 2002), the hierarchical patch dynamic paradigm (HPDP) (Landis 2003;Landis et al 2004) and the procedure for ecological tiered assessment of risks (PETAR) (Moraes et al 2002;Moraes & Molander 2004) are the main approaches for regional ecological risk assessment at present.…”

Section: Discussionmentioning

confidence: 99%

Study on the procedure and approach for ecological risk regionalisation

Yang¹,

Yongli²,

Weintng³

et al. 2007

New Zealand Journal of Agricultural Research

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Ecological risk regionalisation is a process for determining the distribution of eco-risk. The basis of ecological risk regionalisation is regional ecological risk assessment. Existing procedures for ecological risk assessment often aim at natural ecosystems and are used mainly for retrospective evaluation of risks. This paper presents a tiered ecological risk assessment procedure (TERAP) for complex ecosystems. The approach is not only for current evaluation, but also for retrospective and future evaluations. Based on the TERAP, a regional ecological risk assessment model was constructed. The TERAP has three tiers: the first is demarcation of the area affected by a particular stressor; the second is a semi-quantitative assessment of the risk index A07112; which is gained from the vulnerability index and integrated into the hazard index; the third is quantitative loss evaluation under the risk index. The main factors in the ecological risk assessment model are ecosystem attributes and socio-economy status. According to the main factors in the model, the change in the ecosystem into the future is simulated and the socioeconomic mathematical model is constructed. In this paper, taking Chongming Island as an example, the approach taken for simulation of the future ecosystem is analysis with the CLUE-S model. The end purpose of current and future ecological risk regionalisation is to direct region programming and risk management.

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A Regional Retrospective Assessment of the Potential Stressors Causing the Decline of the Cherry Point Pacific Herring Run

Cited by 38 publications

References 14 publications

Ecological Risk Assessment of Regions Along the Roadside of the Qinghai-Tibet Highway and Railway Based on an Artificial Neural Network

Ecological Risk Assessment of Regions Along the Roadside of the Qinghai-Tibet Highway and Railway Based on an Artificial Neural Network

Life Histories, Salinity Zones, and Sublethal Contributions of Contaminants to Pelagic Fish Declines Illustrated with a Case Study of San Francisco Estuary, California, USA

Study on the procedure and approach for ecological risk regionalisation

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