Estimation of regional richness in marine benthic communities: quantifying the error

Abstract

Species richness is the most widely used measure of biodiversity. It is considered crucial for testing numerous ecological theories. While local species richness is easily determined by sampling, the quantification of regional richness relies on more or less complete species inventories, expert estimation, or mathematical extrapolation from a number of replicated local samplings. However the accuracy of such extrapolations is rarely known. In this study, we compare the common estimators MM (Michaelis-Menten), Chao1, Chao2, ACE (Abundance-based Coverage Estimator), and the first and second order Jackknifes against the asymptote of the species accumulation curve, which we use as an estimate of true regional richness. Subsequently, we quantified the role of sample size, i.e., number of replicates, for precision, accuracy, and bias of the estimation. These replicates were sub-sets of three large data sets of benthic assemblages from the NE Atlantic: (i) soft-bottom sediment communities in the Western Baltic (n = 70); (ii) hard-bottom communities from emergent rock on the Island of Helgoland, North Sea (n = 52), and (iii) hardbottom assemblages grown on artificial substrata in Madeira Island, Portugal (n = 56). For all community types, Jack2 showed a better performance in terms of bias and accuracy while MM exhibited the highest precision. However, in virtually all cases and across all sampling efforts, the estimators underestimated the regional species richness, regardless of habitat type, or selected estimator. Generally, the amount of underestimation decreased with sampling effort. A logarithmic function was applied to quantify the bias caused by low replication using the best estimator, Jack2. The bias was more obvious in the soft-bottom environment, followed by the natural hard-bottom and the artificial hard-bottom habitats, respectively. If a weaker estimator in terms of performance is chosen for this quantification, more replicates are required to obtain a reliable estimation of regional richness.

Local–regional richness relationship in fouling assemblages – Effects of succession

“…Moreover, Jack2 was found to have prime performance among five other estimators in a study performed at almost identical circumstances as we use here (Canning-Clode et al 2008). Finally, we used the double-log regression to distinguish between 'type I' and 'type II' curves.…”

“…Total species and functional groups accumulation curves for the Mediterranean region were constructed based on only 35 replicates because 10 panels were lost during the second sampling. Using almost identical conditions as we use here (substratum, panel size and depth), Canning-Clode et al (2008) quantified the error by Jack2 in estimating regional richness. At this level of sampling effort (n ¼ 35), Jack2 underestimated true richness by 6.8% only (Canning-Clode et al 2008).…”

“…Using almost identical conditions as we use here (substratum, panel size and depth), Canning-Clode et al (2008) quantified the error by Jack2 in estimating regional richness. At this level of sampling effort (n ¼ 35), Jack2 underestimated true richness by 6.8% only (Canning-Clode et al 2008). Therefore, we do not expect severe implications in the local-regional richness analysis.…”

“…Subsequently, the estimation error was quantified as a function of sampling effort using the estimator with overall prime performance -Jack2. With 45 samples (the level of sampling effort used in this report to estimate regional richness), Jack2 performed best underestimating true richness by 3.5% only (Canning-Clode et al 2008).…”

Padrões ecológicos locais e multidecadais da ictiofauna do estuário Cananéia-Iguape

“…Maximização da riqueza fundamenta explicações das relações entre PRE e comunidades locais (Whittaker, 1960;Hugueny & Paugy, 1995;Oberdorff, 1998;Cornell, 1999;Hewitt et al, 2010), bem como argumentos de conservação da biodiversidade (May, 1988;Anderson et al, 2011;Passos et al, 2012), descrição de padrões biogeográficos (Gotelli & Colwell, 2001;Miloslavich, 2011) e calibração de taxas de extinções locais e regionais (Pimm et al, 1995;Jackson & Sax, in press). Porém, inventários completos de espécies são extremamente difíceis de obter (Canning-Clode et al, 2008), sobretudo em áreas tropicais megadiversas, devido à complexa estruturação espaço-temporal das assembleias impor uma série de dificuldades metodológicas amostrais Longino et al 2002;Magurran, 2004;Watkins et al, 2005). Em resposta a essa demanda, diversas técnicas de estimação foram desenvolvidas para extrapolar a riqueza de um 'universo conhecido' para um 'desconhecido', i.e., extrapolar de um número de razoável de amostras ao número real de espécies de uma determinada área .…”