Evaluating the performance and management of artificial reefs using artificial reef multimetric index (ARMI)

Highlights

• Multimetric indices were developed for assessing fish assemblages at artificial reef.

• Metrics were selected based on ecological and economics aspects of fish assemblages.

• The newly indices discriminates both artificial reef and control area.

• These indices have potential to be used in the management of artificial reefs.

Abstract

Artificial reefs (ARs) have become an important tool to increase the ichthyofauna associated with artisanal and commercial fishing. Integrated evaluations of ARs are necessary to comprehend the condition and benefits of these structures. Multimetric indices (ARMIs) were developed to evaluate fish assemblages associated with artificial structures and to assess the efficiency of the AR compared to control area (CA). The AR global ranking index (ARMI_r) and AR local effect index (ARMI_e) include 16 metrics that represent four broad levels of the fish community: structure of fish assemblages, trophic structure, vulnerability and economic importance. Individual metrics were evaluated using a long-term dataset of fish assemblages from the AR and CA in northern Rio de Janeiro, Brazil. Bottom gillnets were used to sample the fish two times per year for 21 years after creating the reef. The ARMI_r general score for the AR was 51 out of a possible 100, with scores varying from 38 to 54. The ARMI_r general score for the CA was 39 out of 100, with scores varying from 35 to 47. Average ARMI_e values were 12% higher for the AR in relation to the CA. Spatial-temporal comparisons of the ARMI_r using generalized additive models revealed significant differences between AR and CA. Evaluations of the ARMI_e scores revealed that the assemblage structure and economic importance level significantly increased over time in the AR. The proposed indices combine structural and functional levels of fish, providing a robust and sensitive method to evaluate the ecological condition of ARs and adjacent areas. The data contribute to discussions related to ARs management and provides a baseline for defining fish assemblage metrics in assessment of localities with artificial structures.

Introduction

Artificial reefs (ARs) have become an important tool to increase associated ichthyofauna abundance, biomass and diversity, improving artisanal fishing catches and increase of underwater tourism (Macusi et al., 2017; Becker et al., 2018; Lima et al., 2019a). The inclusion of artificial structures increases the complexity of aquatic environments and may lead to changes in fish assemblages and trophic structure, in addition to potentially attracting vulnerable and commercially important species (Dafforn et al., 2015).

Recently, the study of ARs has shifted to a more integrated multidisciplinary approach (Becker et al., 2018; Lima et al., 2019a) with the goal of understanding the communities that colonize these structures and the associated socioeconomic impacts (Schaffer and Lawley, 2012; Hooper et al., 2015; Macusi et al., 2017). Various studies have integrated ecological and socioeconomic information to create protocols and guidelines for management policies of this reef type (Brochier et al., 2015; Techera and Chandler, 2015; Guan et al., 2016; Tolentino-Zondervan et al., 2018).

Initiatives like the National Artificial Reef Plan of USA (NOAA Fisheries, 2019), Australia's Oceans Policy (Australian Government, 2019), and the EU Water Framework Directive (Fabi et al., 2011) have emphasized comprehensive management of aquatic environments, including guidelines about the strategic use of low-impact, integrative ARs (Murray, 1994; Kim, 2001; Kheawwongjan and Kim, 2012). As such guidelines strongly depend on the ecological and socioeconomic concepts associated with the use of artificial structures, the tools available to manage ARs are still subjective, complex and often difficult to assimilate by decision makers.

To reduce ambiguity, multimetric indices are a viable alternative, combining diffuse information into a single numeric value. These indices should be relatively simple to derive, widely applicable, repeatable, consistent in terms of management and easy for the general public to understand (Borja and Dauer, 2008; Stoddard et al., 2008). Various multimetric indices, for example, the Estuarine Fish Community Index (Harrison and Whitfield, 2004), Transitional Fish Classification Index (Coates et al., 2007), AZTI's Fish Index (Uriarte and Borja, 2009), Estuarine Multimetric Fish Index (Delpech et al., 2010), Zeeschelde Estuarine Biotic Index (Breine et al., 2010) and Estuarine Fish Assessment Index (Cabral et al., 2012) have been used to evaluate the quality of ecosystems and to choose priority areas for conservation. However, some limitations when developing AR indices should be considered, such as the challenge of portraying the high complexity of ecological systems and the spatio-temporal variability of the environments where ARs are installed (Becker et al., 2018). Other challenges involve selecting metrics that can effectively represent the functioning of ARs and standardizing them to allow the comparison of scores from ecosystems and using different methodologies (Borja and Dauer, 2008; Stoddard et al., 2008).

Although the use of fish as an environmental quality indicator is not recent, the development of multimetric indices with these organisms is relatively new (Harrison and Whitfield, 2004; Coates et al., 2007; Delpech et al., 2010; Cabral et al., 2012). Among the advantages of using fish as a bioindicator the most notable are the possibility of non-destructive sampling, the relatively simple taxonomy of fish, the existence of extensive databases about the biology and ecology of fish species (Froese and Pauly, 2020), occupation by fish of all trophic levels (Pauly and Watson, 2005), their usefulness as indicators of long-term stress and their high value in public opinion.

The use of fish assemblages has been fundamental in the identification of long-term seasonal patterns governed by different environmental factors ((Scarcella et al., 2015; Becker et al., 2018; Lima et al., 2020). However, some disadvantages of using ichthyofauna as an indicator need to be considered, such as high mobility that can influence more local interpretations, high tolerance to habitat changes and sampling method (Whitfield and Elliott, 2002; Uriarte and Borja, 2009). Despite the importance of fish assemblages in the evaluation of aquatic environments, and although there are some recent publications that discuss the management of ARs (Techera and Chandler, 2015; Brochier et al., 2015; Guan et al., 2016; Tolentino-Zondervan et al., 2018), there is no objective assessment protocol that uses fish or any other biological or economic indicators to measure the efficiency of this reef type.

This study proposes two Artificial Reef Multimetric Indices – ARMIs using different metrics that represent broad levels of fish assemblages. The AR global ranking index (ARMI_r) was developed to evaluate the condition of fish assemblages in areas with artificial reefs. The AR local effect index (ARMI_e) was developed to measure the local effect of the AR compared with nearby reference areas. We hypothesized that areas with artificial structures have higher ARMI_r and ARMI_e scores than areas that lack these structures (control sites). Further, by using long-term data about fish assemblages in a multimetric format, the effectiveness of the levels that comprise the ARMIs can be examined in detail and generate values that support new guidelines for managing artificial habitats. This study could also support the capacity of AR to increase and restore reef fish abundance and biodiversity.