Combining Best Professional Judgement and Quantile Regression Splines to Improve Characterisation of Macrofaunal Responses to Enrichment

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Combining Best Professional Judgement and Quantile Regression Splines to Improve Characterisation of Macrofaunal Responses to Enrichment G Model ECOIND-817; No. of Pages 13 ARTICLE IN PRESS Ecological Indicators xxx (2011) xxx–xxx Contents lists available at ScienceDirect Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind Combining best professional judgement and quantile regression splines to improve characterisation of macrofaunal responses to enrichment Nigel B. Keeley a,b,∗, Catriona K. Macleod b,c, Barrie M. Forrest a a Cawthron Institute, Nelson 7010, New Zealand b University of Tasmania, Sandy Bay, Tasmania, Australia c Tasmanian Aquaculture and Fisheries Institute, Taroona, Tasmania, Australia article info abstract Article history: Many benthic quality indices rely on categorising impacts by assigning species to ecological-groups (EGs) Received 21 December 2010 that reflect their tolerance to pollution. This is usually based on best professional judgement (BPJ) by Received in revised form 10 March 2011 experts with access to relevant ecological and taxonomic information. However, international applica- Accepted 11 March 2011 bility of such indices is restricted in areas where the species taxonomy, biology and response to pollution are poorly understood. In this study we describe an approach that enables objective allocation of EGs in Keywords: situations where species information is limited. This approach utilised BPJ to categorise the environmen- Salmon farm tal condition of benthic habitats around fish farms in New Zealand in relation to defined enrichment stages Enrichment gradient Biotic index (ESs). Quantile regression was then used to model distributions of select taxa. The experts assigned ES AMBI scores from 1 to 7, for stations that ranged from relatively natural to excessively enriched (i.e. near-azoic), Eco-group respectively, with judgements based on a suite of quantitative and qualitative indicators of enrichment, Benthic impact assessment but without reference to detailed species information. The individual BPJ estimates were highly corre- lated, with minimal bias, indicating good agreement among the experts. Forty key indicator taxa were identified and quantile regression models based on ES (derived as a continuous explanatory variable) were fitted for 34. Abundances of the same taxa were also modelled in response to a more traditional enrich- ment indicator (organic content, %OM) for comparison with the BPJ technique. The regression approach characterised enrichment responses and objectively identified both the upper and lower tolerance limits of a range of taxa according to their ES and %OM. The models discriminated a number of key indicator taxa, including several that were responsive to low-level changes in ES, but not necessarily %OM. There was reasonable agreement (59%) between EGs derived using the regression approach and those defined using the AMBI database (one of the most commonly applied benthic quality indices). Moreover, the regression method allowed the classification of 10 additional taxa for which our ecological understand- ing was limited. A key outcome of this study was the acknowledgement that EG characterisations for species need to be regionally validated, no matter how well defined they might appear to be. The com- bined BPJ/regression analysis approach described provides a valid means of both assigning and validating EG classifications, which will be particularly useful in situations where the taxa are poorly defined, and will enable existing biotic indices to be more broadly applied and interpreted. © 2011 Elsevier Ltd. All rights reserved. 1. Introduction widely between locations and applications (Carroll et al., 2003; Kalantzi and Karakassis, 2006), often relying on subjective expert Physical and chemical changes to sediments beneath finfish opinion, also referred to as best professional judgement (BPJ, farms can result in profound ecological effects (e.g. Brooks et al., Weisberg et al., 2008). Having a validated suite of standard metrics, 2002; Buschmann et al., 2006; Kalantzi and Karakassis, 2006). cross-referenced with BPJ that can reliably define environmental Accordingly, in many countries environmental monitoring and quality would greatly improve our ability to compare both environ- assessment is undertaken to evaluate benthic conditions against mental effects, and management and regulatory responses across environmental quality criteria. However, these quality criteria vary broad geographic regions. Many ecological indices have been developed with a view to better informing BPJ; with several tested specifically for ∗ aquaculture-related benthic effects (e.g.: AZTI’s Marine Biotic Index Corresponding author at: Cawthron Institute, Coastal & Freshwater, 98 Halifax Street East, Nelson 7010, New Zealand. Tel.: +64 3 5393257; fax: +64 3 5469464. (AMBI), Borja et al., 2000; Multivariate-AMBI, Muxika et al., 2007; E-mail address: [email protected] (N.B. Keeley). BENTIX, Simboura and Zenetos, 2002; Infaunal Trophic Index, 1470-160X/$ – see front matter © 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.ecolind.2011.03.022 Please cite this article in press as: Keeley, N.B., et al., Combining best professional judgement and quantile regression splines to improve characterisation of macrofaunal responses to enrichment. Ecol. Indicat. (2011), doi:10.1016/j.ecolind.2011.03.022 G Model ECOIND-817; No. of Pages 13 ARTICLE IN PRESS 2 N.B. Keeley et al. / Ecological Indicators xxx (2011) xxx–xxx Word, 1978). Of these, the AMBI was recently proposed as a primary indicator of biological health beneath finfish farms internationally (DSRSA, 2010). The AMBI (and related indices, i.e. M-AMBI; BENTIX; MEDOCC, Pinedo and Jordana, 2008) classifies benthic communities according to five ecological groups (EGs), based on their sensitiv- ity to organic enrichment as defined by expert consensus (Borja, 2004). Expert consensus, although critical, is a subjective step in the process, which can be time-consuming and requires an in- depth knowledge of responses of individual taxa to enrichment (or other forms of disturbance). Furthermore, incorrect assignment of species to Eco-groups (EGs) may result in misclassification of impacts (Borja, 2004; Borja and Muxika, 2005; Simboura, 2003), and without site-specific validation, even closely related indices can imply a different quality status for the same site Aguado- Gimenez et al. (2007). In a preliminary appraisal of the AMBI with data from aquaculture operations in New Zealand, we found only 29% of the 200 taxa identified were specifically listed in the AMBI database (AMBI v4.0, February 2010); the recommended mini- mum requirement is 80% for robust application (Borja and Muxika, 2005). This highlights a major problem associated with the cur- rently available suite of indices, which is how to deal with fauna that have a high degree of endemism and/or which are poorly described. In many areas of the world the marine benthic fauna is still largely undescribed and as a result new locations will almost inevitably yield species whose response to enrichment is poorly Fig. 1. Map showing the position of the four salmon farms that comprised the study understood. sites within the Marlborough Sounds, New Zealand. In addition, macrofaunal responses to enrichment are generally complex, resulting from multiple biogeochemical and ecological interactions, and patterns are rarely adequately explained by a 2. Methods single continuous environmental variable (Borja et al., 2009). Con- sequently, current statistical modelling approaches cannot readily 2.1. Study sites, sampling and data selection incorporate the full suite of indicators used by experts to assess environmental quality in the BPJ process. Often relevant vari- The data for this study were obtained as part of a regular com- ables may be either deterministically qualitative or have responses pliance monitoring program for four Pacific salmon (Oncorhynchus where the outcomes cannot be interpreted independently of other tshawytscha) farms in the Marlborough Sounds, New Zealand variables. For example, although the mat-forming bacteria Beggia- (Fig. 1). Sampling at each farm was undertaken annually in early toa spp. (Beggiatoa) can be a clear indicator of enrichment (Crawford summer (October–November) from 2001 to 2009. Although flow et al., 2001; Hargrave et al., 2008; Macleod et al., 2004), absence regimes varied slightly between farms, background environmental of Beggiatoa may reflect either a lack of enrichment, or conditions and operational conditions were comparable (Table 1). The anal- where enrichment is so severe as to limit this species (i.e. bottom- yses presented here were based on a subset of the full dataset, water is anoxic or Beggiatoa is disturbed by out-gassing). BPJ offsets which deliberately encompassed a wide cross-section of annual these contradictions by taking into account all available informa- feed inputs and associated levels of impact, and data that were tion and interpreting indicators in the context of other measures consistent in sample size and distribution (i.e. no missing values of impact (e.g. Muxika et al., 2007; Teixeira et al., 2010; Weisberg for explanatory or derived biological variables; Table 1). The final et al., 2008). dataset included all four farms (1–4 sampling occasions per farm), However, the challenge of quantifying the responses of individ- spanned nine years and resulted in 74 sampling stations consisting ual taxa to the specified enrichment gradient remains. The basic of 24 observations beneath cages, 38 along enrichment gradients premise behind this involves identifying the conditions
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