Estimating macrozoobenthic species richness along an environmental gradient: Sample size matters

Dekker, R.

doi:10.1016/j.ecss.2012.06.013

Cited by 8 publications

(4 citation statements)

References 29 publications

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“…In fact, the results showed values of Lloyd's I p to be very closely correlated with those of Morisita's I m for all metrics at all spatial scales (Pearson's r > 0.8; p << 0.0001) and only Morisita's I m is reported in this study. Emergent assemblage attributes and indices dependent on relationships between means and variances (though not Morisita's I m ) may themselves be sensitive to total area and/or numbers of individuals sampled, as well as to features such as the occurrence of zero values (Hurlbert 1990, Bez 2000, Whittaker et al 2001, Gotelli & Colwell 2001, Beukema & Dekker 2012. The locality selected was one that did not show significant spatial variation in assemblage abundance across the 13 stations (1-way ANOVA F 12, 52 = 1.4, p > 0.2), and the same fixed size and number of core samples (and therefore of total area sampled) was used to represent all spatial scales, except of necessity for the smallest of the 5 (that of ca.…”

Section: Discussionmentioning

confidence: 99%

Spatial stability of macrobenthic seagrass biodiversity

Barnes¹

2013

Mar. Ecol. Prog. Ser.

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The magnitude and patchiness of macrobenthic biodiversity were compared across spatial scales spanning 5 orders of areal magnitude (ca. 2 m 2 to ca. 1.5 ha) in an intertidal seagrass bed in the warm-temperate Knysna estuarine bay, South Africa. The 75 component species and their populations were highly variably distributed across the site, abundances of the individual dominant animals being significantly patchy and composition of the macrofaunal assemblage being significantly non-uniform. Nevertheless, emergent assemblage attributes exhibited spatial constancy: values of assemblage metrics (faunal abundance, species richness, species density and species diversity) did not differ across the site or across spatial scales, and neither did the patterns of dispersion of species diversity, species richness or observed species density. Distribution of the 2 latter through space was even significantly uniform. Ecological arguments developed for temporal constancy of biodiversity seem broadly applicable to the spatial dimension at Knysna, suggesting that although apparently paradoxical, it may be the varying assemblage composition that permits this spatial stability. There is no evidence, however, that the Knysna seagrass macrobenthos is a competitively-structured assemblage with rigid niche-partitioning, a condition suggested to be necessary for temporal constancy; rather, it is more likely to be open, non-equilibrium and below carrying capacity.

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Section: Discussionmentioning

confidence: 99%

Spatial stability of macrobenthic seagrass biodiversity

Barnes¹

2013

Mar. Ecol. Prog. Ser.

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“…The estimation accuracy of fishery population characteristics is very sensitive to the sampling effort (Angermeier and Smogor 1995;Schoeman et al 2008). High sampling effort can result in the clustering of survey stations in the study area, which may be economically wasteful and have negative biological impacts on fragile habitats and species (Wang et al 2020), while inadequate sampling effort may produce estimates with insufficient accuracy or precision to meet conservation goals (Beukema and Dekker 2012). Many studies have considered the balance between precision and costs of the survey, and biological conservation, with an attempt to achieve appropriate sampling effort (Angermeier and Smogor 1995;Peterson and Rabeni 1995;Kennard et al 2006).…”

Section: Discussion Optimal Sampling Effort Based On Systematic Samplingmentioning

confidence: 99%

“…Furthermore, the distribution of fishery resources in the estuary waters shows a corresponding distribution pattern (Young et al 2018). Therefore, a cross-sectional survey based on an environmental gradient is used in the survey and assessment of fishery resources in estuaries and adjacent waters (Schoeman et al 2008;Beukema and Dekker 2012). Undeniably, a lot more systematic explorations are still required to investigate cross-sectional sampling design based on the distribution characteristics of key environmental factor gradients and related research on the design of the fishery resources survey sampling in the YRE.…”

Section: Selection Of the Quantity Of Trawl Survey Stations For Fishe...mentioning

confidence: 99%

Optimization of Sampling Effort for Different Fishery Groups in the Yangtze River Estuary, China

Zhao

Yang

2022

Mar Coast Fish

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The Yangtze River Estuary (YRE) plays an important role in the conservation of fishery resources in the Yangtze River basin, and the Chinese government has established a special fishing ban management area at the YRE. To assess effects of the fishing ban, survey design and appropriate effort need to be determined and implemented. This study used long-term trawl survey data of the YRE (2004YRE ( -2017 to explore the effects of survey effort (8-30 continuous survey stations selected by systematic and stratified sampling) on precision and accuracy of estimates of abundance of different fishery groups in the YRE. Estimated precision for a given number of survey stations varied by season and fishery group. Precision of systematic sampling fluctuated with the number of stations. Precision of stratified sampling with a proportional allocation scheme gradually increased with the number of survey stations. We suggest including 15-20 survey stations for all fishery groups sampled using the examined sampling methods.This study provides a research reference for selecting the survey effort (number of survey stations) for a fishery-independent survey in the YRE under the backgroud of conservation.

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“…However, respective studies on environmental constraints on richness estimation are largely missing. For instance, Beukema and Dekker (2012) found that species accumulation curves changed in shape along environmental gradients from near-shore to off-shore intertidal areas due to shifts in the relative abundances of rare and common species, allowing reliable richness estimates only for large aggregated samples.…”

Section: Introductionmentioning

confidence: 99%

Factors influencing the precision of species richness estimation in Japanese vascular plants

Ulrich

Kusumoto

Fattorini

et al. 2020

Diversity and Distributions

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Aim: Estimating species richness from a series of samples is an important and widely debated issue in ecology and biodiversity conservation. Numerous tests of respective richness estimators gave insights into the precision, the limitations and the pitfalls of richness forecasting. However, few benchmark tests used almost complete empiric census data obtained at those spatial scales where richness estimation is most useful for conservation management.Location: Japan. Methods:We use an extraordinary dataset on the spatial distribution of Japanese plants containing complete information on the occurrence of each Japanese plant species at the 10 × 10 km 2 grid cell level. We link the estimates of four estimators representing different theoretical approaches, Chao2, rarefaction, species-area relationships (SAR) and species abundance distributions (SAD), to environmental data using a fully nested sampling design.Results: Chao2 and rarefaction behaved very similar in all tests and significantly underestimated true richness below 40% sampling fraction. SAR and SAD were less precise than Chao2 and rarefaction at higher sampling fraction but also less affected by low sample size. In general, SAD provided robust estimates over the whole range of sampling fraction and 67.4% of estimates ranged within the 10% error level. Higher species spatial turnover increased and high evenness in occurrence decreased the precision of the SAD estimator. Precision of the four estimators was largely unaffected by environmental variability but increased with increasing latitude. Main conclusions:Our results strongly indicate that the pattern of Japanese plant species spatial distribution is sufficiently scale invariant for richness estimators to provide precise forecasting results at the country level. The simplest process to generate such a spatial distribution is ecological drift. K E Y W O R D Sbiodiversity, Japanese vegetation, species abundance distribution, species richness estimation, species-area relationship

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Estimating macrozoobenthic species richness along an environmental gradient: Sample size matters

Cited by 8 publications

References 29 publications

Spatial stability of macrobenthic seagrass biodiversity

Spatial stability of macrobenthic seagrass biodiversity

Optimization of Sampling Effort for Different Fishery Groups in the Yangtze River Estuary, China

Factors influencing the precision of species richness estimation in Japanese vascular plants

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