Costefficiency of biodiversity indicators for ...

doi: 10.1111/j.1365-2664.2010.01864.x

Journal of Applied Ecology

Cost-efficiency of biodiversity indicators for Mediterranean ecosystems and the effects of socio-economic factors Yael Mandelik1*, Uri Roll2 and Aliza Fleischer3 1

Department of Entomology, The Hebrew University of Jerusalem, PO Box 12, Rehovot 76100, Israel; Biomathematics Unit, Department of Zoology, Tel-Aviv University, Tel-Aviv 69978, Israel; and 3Department of Agricultural Economics and Management, The Hebrew University of Jerusalem, PO Box 12, Rehovot 76100, Israel 2

Summary 1. Biodiversity assessments usually rely on indicators as surrogates for direct measures. Although the ecological validity of indicators has been extensively studied, their economic feasibility and costeffectiveness have seldom been assessed. 2. Here we present a novel generic framework for analysing the cost-effectiveness of biodiversity indicators and the effect of budget allocations on the quality of biodiversity surveys. We sampled a suite of environmental and biological indicators in a Mediterranean ecosystem and calculated their cost-effectiveness using measures of species richness, rarity and composition. 3. Environmental indicators were the cheapest indicator for richness and rarity but not for composition patterns, and they conveyed low accuracy ( 0Æ001 for each).

Fig. 3. The effect of indicators on the probability of erroneous site prioritization. Shown are the best-performing single, pair and triplet indicators (having the lowest average mistakes among all single, pair and triplet indicators, respectively). Be-beetles, Plplants, Mo-moths, Null probability-baseline probability of making such mistakes, Average mistakes-the average mistakes of all 26 indicators and sets of indicators. (a) Species richness, (b) species composition.

Discussion This study provides the first cost-efficiency analyses of environmental and biological biodiversity indicators for a Mediterranean ecosystem. By presenting the use of a cost-efficiency frontier and analysing how it is affected by different socioeconomic factors, we provide a generic framework that can be instructive for other Mediterranean ecosystems, as well as other biomes. Though this study is prone to case-specific issues, such as the sampling effort and sampling techniques applied, our survey addressed main seasonal and spatial variation in the ecosystem, and we obtained comprehensive data sets (Mandelik et al. 2002; Mandelik 2005). An important finding was that a minimal representation of c. 70% of the variation in diversity patterns is feasible, even with limited funds (less than 10 000 USD), if a cost-efficient indicator is applied. Favourable conservation outcomes in other problems related to the economics of biodiversity conservation have been obtained upon incorporation of cost-benefit information (Naidoo & Adamowicz 2005; Naidoo & Ricketts 2006),

2010 The Authors. Journal compilation 2010 British Ecological Society, Journal of Applied Ecology

Cost-efficiency of biodiversity indicators 7 120 000 Equipment

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especially when costs and benefits were strongly positively correlated (Ferraro 2003). In our study, plants were the cheapest cost-efficient indicator for richness and composition patterns. However, the marginal costs of representing the additional c. 30% of diversity variation are high, requiring c. 9 times the initial budget. Thus, the accuracy needed is a main factor in determining the budget requirements of biodiversity surveys. We further found that the cost-efficiency of biodiversity indicators is, to a great extent, context-dependent, and affected by socioeconomic factors. Environmental variables are the cheapest indicator for local-scale diversity assessments in the studied ecosystem (Grantham et al. 2008), but have two major drawbacks: they reflect richness (and rarity) patterns, but not composition, and they provide only coarse diversity assessments (70% of the variation, as in most cases), as well as an indication of compositional patterns, plants are the cheapest indicator. Plants are a major component along the efficiency frontiers, and are also the most efficient single taxon for site prioritization. The cost-effectiveness of plants may stem from the lower costs of floral compared to faunal surveys (particularly of small-bodied, species-rich arthropod taxa), and the relatively high performance of plants as ecological indicators (Lawton 1983), including successful application of the higher taxa and similar approaches (Mandelik et al. 2007; Mazaris et al. 2010). Interestingly, ecological assessments of development projects (e.g. Environmental Impact Assessments, EIAs), are usually funded at or above 10 000 USD. However, an analysis of the ecological quality of EIAs in Israel has shown that most are of poor quality and fall short of this threshold (Mandelik, Dayan & Feitelson 2005).

Moths

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Fig. 4. Expected costs of the biodiversity survey in three Mediterranean-climate countries ⁄ regions differing in socio-economic factors and in survey cost structure: Morocco, Israel, and California (see details of cost-structure differences and estimations in the text).

Environmental variables

The costs of representing richness, rarity, and composition are similar at the high end of the cost-efficiency curve (representing over c. 90% of the variation). At lower values, richness (and rarity) is more costly to represent than composition. Measures of species composition better reflect cross-taxon congruency in both diversity patterns and responses to disturbances than measures of species richness (Barlow et al. 2007) and are thus given preference in conservation applications (Margules & Pressey 2000; Margules & Sarkar 2007). Our analysis shows that the representation of composition patterns is also more cost-effective than richness. As expected, the more taxa sampled, the higher the ecological performance achieved. However, the cost-efficiency of a taxonomically extensive sampling strategy is low, as evidenced by the relatively flat shape of the efficiency frontiers and the high marginal costs of improving the ecological performance: beyond the initial level of representation of c. 70–75% of the variation in diversity patterns, a 1% increase in the representation of richness and composition patterns costs an average 2475 USD and 3755 USD, respectively (Fig. 2). Highly similar diminishing returns on investment in surveys have been found in other studies (Grantham et al. 2008). Therefore, the choice of any of the indicator(s) appearing on the cost-efficiency frontier should ultimately be based on the accuracy needed in mapping biodiversity, and on the urgency of conservation action (Grantham et al. 2009). Taxonomic identification of species-rich invertebrate taxa is expensive, and makes up a large part of the total cost of surveying these groups, as has been found in tropical ecosystems (Lawton et al. 1998; Gardner et al. 2008). Hence, the availability of taxonomic expertise is a critical factor in determining the cost-effectiveness of surveying most invertebrates, including those regarded as good biodiversity indicators such as some beetle and spider groups (McGeoch 1998; Hilty & Merenlender 2000; Pearce & Venier 2006). For the faunal taxa that require laborious identification, the higher the number of species, the higher the sampling costs, in contrast to prior assumptions (Rohr, Mahan & Kim 2007). Beetles, despite their good indicative ability in Mediterranean ecosystems (Mandelik


8 Y. Mandelik, U. Roll & A. Fleischer et al. 2007; Zamora, Verdu´ & Galante 2007), appear only in the second half of the efficiency frontiers. Furthermore, reducing field expenses by using sets of indicators sampled by the same technique, such as beetles and spiders sampled with pitfall traps, does not improve cost-efficiency (but see Gardner et al. 2008). In countries where labour is costly, species-rich taxa that require high expertise and time for their identification may not appear on the cost-efficiency frontier, despite having good ecological performance. In those countries, cost-effectiveness analysis might lead to the application of an indicator(s) with lower indicative abilities if its sampling is less labour-intensive. The ‘taxonomic impediment’ – the severe shortage of taxonomic expertise in most parts of the world (Giangrande 2003), might further decrease the cost-efficiency of indicators needing expert identification, and may enhance the application of gauges that are ecologically less favourable. Furthermore, over-reliance on cost-efficiency analyses may limit the search for, and development of, new indicators that are currently less cost-efficient due to poor taxonomic knowledge and lack of sampling methodologies (Pawar 2003). A need for taxonomic out-sourcing due to lack of inhouse knowledge will affect mostly developing economies, as this may consume a large part of their total survey budget, as illustrated in our analysis for Morocco. Though travel and lodging costs might be higher than the cheapest rate we accounted for, they constituted c. 10% of all survey expenses, and thus have a limited impact on the survey’s cost structure. Naturally, some of our cost estimations might not always be fully realized; nonetheless, our analysis illustrates two contrasting extremes along a gradient of interconnected socio-economic factors characteristic of the Mediterranean biome. Our analysis further showed that the cost structure of biodiversity surveys greatly affects the total costs of surveying different indicators, and consequently the optimal selection of indicator(s). Hence, the accuracy of conservation decision-making is to a great extent context-dependent and will ultimately be dictated not only by overall funding allocation but also by socio-economic factors, mainly per capita GDP and availability of in-house taxonomic knowledge. The development and application of DNA barcode technology may affect our results and conclusions. These technologies are likely to reduce the cost of identifying species-rich taxa (Hebert et al. 2003; Kress et al. 2005; Hajibabaei et al. 2007) and the need for taxonomic out-sourcing, placing additional taxa on the cost-efficiency frontier. The application of the higher taxa and similar approaches may similarly reduce cost of taxonomic identification and affect the cost-efficiency frontier (Mazaris et al. 2010). Our generic framework may facilitate reallocation of survey funds to expand and ⁄ or better focus the spatial, temporal, and taxonomic scope of biodiversity surveys and to include gauges for functions and processes that are essential for long-term management of ecosystems (Kremen 2005). The data produced using cost-efficient indicators would ultimately improve the link between monitoring programmes and procedures of

risk analysis, site prioritization and adaptive management (Cleary 2006). To achieve this goal, however, cost-efficiency analyses of indicators in other ecosystems, on different spatial scales and with different taxa are needed, so that general guidelines for the optimal choice of indicators can be formulated. Data acquisition is only the first step in effective conservation. In light of ever-limited conservation budgets and intense development pressures, data acquisition is competing with subsequent conservation actions for time and money (Grantham et al. 2008). The trade-offs between the cost and time required to get more data vs. applying it in subsequent conservation actions and the urgency of doing so (rate of habitat conversion and fragmentation; Grantham et al. 2009) should ultimately dictate the allocation of time and money spent on the different stages of the conservation process. The strong diminishing-return pattern in acquiring additional data on biodiversity found here and in other studies (Bode et al. 2008b; Grantham et al. 2008) points to the need to move away from the traditional approach of trying to get as much data as possible to a more critical and holistic evaluation of the marginal value of additional data to the conservation process as a whole.

Acknowledgements We thank J. Hortal, S. Meiri, M. Coll and three anonymous reviewers for their most thoughtful and valuable comments and E. Ungar for statistical advice. U.R. is supported by the Adams Fellowship Programme of the Israel Academy of Sciences and Humanities. This study was funded by the Hebrew University of Jerusalem Ring Center for Interdisciplinary Environmental Research.

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Supporting Information

Table S2. Cost estimations in California and Morocco for conducting the same survey that was conducted in Israel. Table S3. Results of regression tests for the different indicators and sets of indicators. As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials may be re-organized for online delivery, but are not copy-edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.

Additional Supporting Information may be found in the online version of this article. Table S1. Detailed costs of labour, equipment and supplies, and travel and lodging for the different biological and environmental indicators surveyed in Israel.
