Can biotic indicators distinguish between natural and anthropogenic environmental stress in estuaries?

Abstract

Please cite this article as: Tweedley, J.R., Warwick, R.M., Potter, I.C., Can biotic indicators distinguish between natural and anthropogenic environmental stress in estuaries?, Journal of Sea Research (2015), doi: 10.1016/j.seares. 2015.04.001 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. significantly between estuarine and coastal environments, i.e. they were unaffected by natural differences between these two environments, but both were significantly correlated with overall heavy metal concentrations. Furthermore, Δ + was correlated significantly with the Cu, Zn, Cd, Hg and Pb concentrations and Λ + was correlated significantly with the Cr, Ni, Cu, Cd and Hg concentrations. Thus, one or both of these two taxonomic distinctness indices are significantly correlated with the concentrations for each of these seven heavy metals. These taxonomic distinctness indices are therefore considered appropriate indicators of anthropogenic disturbance in estuaries, as they allow a regional reference condition to be set from which significant departures can then be determined.

Are dominant plant species more susceptible to leaf‐mining insects? A case study at Saihanwula Nature Reserve, China

“…Compared with abundance data, presence–absence data have several advantages: (1) Presence–absence data can increase efficiency in ecological and conservation research because they are easier to collect than abundance data and are much less costly in terms of time, price, and human resources, especially at large spatial or temporal scales (Badenhausser, Amouroux, & Bretagnolle, 2007; Casner, Forister, Ram, & Shapiro, 2014; Fukuda, Mouton, & De Baets, 2012; Furnas, 2013; Gu & Swihart, 2004; Gutiérrez, Harcourt, Díez, Gutiérrez Illán, & Wilson, 2013; Joseph, Field, Wilcox, & Possingham, 2006; MacKenzie & Nichols, 2004; Ribas & Padial, 2015). (2) In many cases, when differences among groups are large, presence–absence data can provide adequate indicators to describe ecological patterns, which are often in agreement with those obtained from abundance data (Carneiro, Bini, & Rodrigues, 2010; Landeiro et al., 2012; Melo, 2005; Ribas & Padial, 2015; Tweedley, Warwick, & Potter, 2015). (3) Presence–absence data can remove much of the noise induced by sampling biases or errors, whereas large sampling errors can lead to unreliable abundance data (Hirst & Jackson, 2007; Jackson & Harvey, 1997).…”

The use of epilithic diatoms grown on artificial substrata to indicate water quality changes in the lower reaches of the St Lucia Estuary, South Africa

“…Similar regulatory drivers apply to microphytobenthos (MPB) populations, with sediment type, organic content and particle size providing additional factors (Aktan et al, 2014). However, although various methods centred on community structural variables (species richness, biomass, diversity and evenness indices) are accepted for detecting anthropogenic-induced change, in naturally stressed ecosystems such as estuaries, distinguishing between natural and anthropogenic factors can often be difficult (Elliot and Quintino, 2007;Tweedley et al, 2015). For this reason, a multimetric approach incorporating both water chemistry variables and structural variables, is generally considered more accurate (Lemley et al, 2015) for extrapolating the anthropogenic signal from the background noise (Elliot and Quintino, 2007).…”

Influence of physico-chemical and biotic factors on the distribution of a penaeid in a temperate estuary

“…These transformed and averaged data were subjected to coherent species analysis (Somerfield and Clarke, 2013;Tweedley et al, 2015) to determine whether the spatial and temporal pattern of change in the abundance of M. dalli was statistically indistinguishable to any other species. Species occurring in less than 10 of the 1,040 (< 1%) and 832 (< 1.25%) of the total number of samples from the nearshore and offshore waters, respectively, were excluded from this analysis as they add only random noise to the species similarities (Clarke and Warwick, 2001;Veale et al, 2014).…”