Academia.eduAcademia.edu

Figure 1 – uploaded by Daniel Dauer

See full PDFdownloadDownload figure
Evaluation of target and reference conditions setting methods, regarding different issues. Note that scores are high: 3, moderate: 2, and low: 1, except in the case of data needs, which are opposite (the lowest data need the highest score). BPJ: best professional judgment (note that it can be used both in setting reference conditions and targets); WFD: Water Framework Directive; HD: Habitats Directive; CWA: Clean Water Act; OSPAR: Oslo-Paris Convention. Based partially upon the discussions within the OSPAR/Marine Strategy Framework Directive Workshop on approaches to determining Good Environmental Status for biodiversity, held in Utrecht (Netherlands), 23-24 November 2010. based upon the data used for their creation may be inappropriate for predicting changes in Drivers-Pressures-States. In dynamic and highly variable marine environments our modelling capabilities are insufficient for defining reference conditions (Hering et al., 2010). This difficulty is overemphasized when natural oscillation occurs. Hence, this method scored the second value (Table 1). To achieve model predictions of high robustness, a large data set that is spa- tially and temporally intensive is required—in many eco-regions such data sets do not exist. The applicability of model predictions between eco-regions is of unknown validity. Most importantly, the complexities of this approach tend to make transparency and com- prehensibility for stakeholders and policy makers low. tion of the biological attributes representative of the aquatic biota expected to be found in the ecoregion or type, through quantitative models, i.e. using habitat suitability models, multiple regression models, etc. (CIS, 2003a). However, there are numerous issues in developing and applying such models: (i) selection of a con- ceptual ecological paradigm identifying the minimal number of dependent and independent variables of the model; (ii) identifi- cation and incorporation of linear and non-linear relationships; (iii) generalized models that are based primarily upon concep- tual relationships may produce predictions of little applicability to different ecoregions or at smaller spatial scales necessary for restoration decisions; and (iv) models that have robust predictions

Table 1 Evaluation of target and reference conditions setting methods, regarding different issues. Note that scores are high: 3, moderate: 2, and low: 1, except in the case of data needs, which are opposite (the lowest data need the highest score). BPJ: best professional judgment (note that it can be used both in setting reference conditions and targets); WFD: Water Framework Directive; HD: Habitats Directive; CWA: Clean Water Act; OSPAR: Oslo-Paris Convention. Based partially upon the discussions within the OSPAR/Marine Strategy Framework Directive Workshop on approaches to determining Good Environmental Status for biodiversity, held in Utrecht (Netherlands), 23-24 November 2010. based upon the data used for their creation may be inappropriate for predicting changes in Drivers-Pressures-States. In dynamic and highly variable marine environments our modelling capabilities are insufficient for defining reference conditions (Hering et al., 2010). This difficulty is overemphasized when natural oscillation occurs. Hence, this method scored the second value (Table 1). To achieve model predictions of high robustness, a large data set that is spa- tially and temporally intensive is required—in many eco-regions such data sets do not exist. The applicability of model predictions between eco-regions is of unknown validity. Most importantly, the complexities of this approach tend to make transparency and com- prehensibility for stakeholders and policy makers low. tion of the biological attributes representative of the aquatic biota expected to be found in the ecoregion or type, through quantitative models, i.e. using habitat suitability models, multiple regression models, etc. (CIS, 2003a). However, there are numerous issues in developing and applying such models: (i) selection of a con- ceptual ecological paradigm identifying the minimal number of dependent and independent variables of the model; (ii) identifi- cation and incorporation of linear and non-linear relationships; (iii) generalized models that are based primarily upon concep- tual relationships may produce predictions of little applicability to different ecoregions or at smaller spatial scales necessary for restoration decisions; and (iv) models that have robust predictions

Related Figures (2)

Fig. 1. The DPSIR approach showing relationships between Drivers-Pressure-State of change-Impact-Responses variables, in assessing environmental quality status in marine waters. Environmental status can be considered as a gradation from pristine conditions (high status in absence of human pressures) to an irrecoverable status (bad status, ir a maximum human pressure). Assessment systems need to set reference conditions or baseline targets along the pressure (and subsequent state) gradient to assist in statu: assessment and for monitoring progress against time and actions. In this step the development and validation of impact assessment methods is needed. Adapted from Borja and Dauer (2008) and Cochrane et al. (2010). BPJ—best professional judgment.
Fig. 1. The DPSIR approach showing relationships between Drivers-Pressure-State of change-Impact-Responses variables, in assessing environmental quality status in marine waters. Environmental status can be considered as a gradation from pristine conditions (high status in absence of human pressures) to an irrecoverable status (bad status, ir a maximum human pressure). Assessment systems need to set reference conditions or baseline targets along the pressure (and subsequent state) gradient to assist in statu: assessment and for monitoring progress against time and actions. In this step the development and validation of impact assessment methods is needed. Adapted from Borja and Dauer (2008) and Cochrane et al. (2010). BPJ—best professional judgment.
Use of M-AMBI in assessing benthic quality status, in different locations and water types, under different human pressures. The response of the method in assessing the status is included (significant: when the response to the pressure is as expected; not significant: when there is not response; unclear: sometimes works and in other case: not), together with the method used in setting reference conditions. NS: not specified; U: undisturbed; H: historical data; M: modelling; BPJ: best professional judgment MM: minimum (AMBI) and maximum (richness, diversity) values provided by AMBI software (http://ambi.azti.es) within the dataset used in the analysis. SP: Spain, IT: Italy FR: France, US: United States, PT: Portugal, D: Germany, AL: Albania, GR: Greece, CY: Cyprus, RO: Romania; BU: Bulgaria. into account differences in estuary and lagoon types or the salinity gradient, which produces different reference conditions, depend- ing on the habitats present (Muxika et al., 2007; Borja et al., 2009b). Hence, adequate reference conditions for the natural gradient asso- ciated to transitional waters are needed for a correct ecosystem quality assessment, as stated by different authors (e.g. Muxika et al., 2007; Teixeira et al., 2008). environmental an biological parameters. Most areas have multi- ple pressures and so the change against reference conditions has to accommodate cumulative, synergistic and antagonistic effects (Crain et al., 2008).
Use of M-AMBI in assessing benthic quality status, in different locations and water types, under different human pressures. The response of the method in assessing the status is included (significant: when the response to the pressure is as expected; not significant: when there is not response; unclear: sometimes works and in other case: not), together with the method used in setting reference conditions. NS: not specified; U: undisturbed; H: historical data; M: modelling; BPJ: best professional judgment MM: minimum (AMBI) and maximum (richness, diversity) values provided by AMBI software (http://ambi.azti.es) within the dataset used in the analysis. SP: Spain, IT: Italy FR: France, US: United States, PT: Portugal, D: Germany, AL: Albania, GR: Greece, CY: Cyprus, RO: Romania; BU: Bulgaria. into account differences in estuary and lagoon types or the salinity gradient, which produces different reference conditions, depend- ing on the habitats present (Muxika et al., 2007; Borja et al., 2009b). Hence, adequate reference conditions for the natural gradient asso- ciated to transitional waters are needed for a correct ecosystem quality assessment, as stated by different authors (e.g. Muxika et al., 2007; Teixeira et al., 2008). environmental an biological parameters. Most areas have multi- ple pressures and so the change against reference conditions has to accommodate cumulative, synergistic and antagonistic effects (Crain et al., 2008).
Related topics:
Environmental ScienceEcological Indicators
580 California St., Suite 400
San Francisco, CA, 94104
© 2025 Academia. All rights reserved