Estimating species richness in hyper‐diverse large tree communities

Steege, Hans ter; Sabatier, Daniel; Oliveira, Sylvia Mota de; Magnusson, William E.; Molino, Jean‐François; Gomes, Vitor Hugo Freitas; Pos, Edwin; Salomão, Rafael de Paiva

doi:10.1002/ecy.1813

Cited by 18 publications

(24 citation statements)

References 41 publications

(103 reference statements)

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“…Our results show that most of the species are rare with few individuals and only a few species are very abundant in both sampling areas which is consistent with the previously reported pattern that most species at a trophic level are rare and relatively few species are abundant (Preston 1948;Brown 1984). Species abundance distribution models (McGill et al 2007) show best fits for logseries distribution which is an indicator of many rare species in a community (McGill et al 2007;Slik et al 2015;Steege et al 2017). …”

Section: Discussionsupporting

confidence: 91%

“…Some of the measures frequently used to overcome the issues related to underestimation of species richness at alpha level include accumulation curves, fitting parametric distributions of relative abundance, and techniques based on the distribution of individuals among species or of species among samples (Colwell & Coddington 1994). Some of these measures such as species accumulation curves and abundance distribution models have recently been used in estimating the tropical tree species richness at regional and global scale (Slik et al 2015;Steege et al 2017). Selection of appropriate sampling area and strategies are, thus, important in estimation of tree species richness especially in diverse tropical forest communities.…”

Section: Introductionmentioning

confidence: 99%

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Assessment of Tree Alpha Diversity in Chapramari Wildlife Sanctuary, Eastern Himalaya

Rana¹,

Kumar²,

Rawat³

2017

Pleione

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Assessment of tree species diversity in species rich tropical forests, especially in Eastern Himalaya, requires careful planning and adequate sampling. We assessed alpha diversity of tree species in Chapramari Wildlife Sanctuary, located at the foot-hills of Eastern Himalaya employing two scales of sampling i.e., multiple small quadrats of 0.1 ha in more open forest and a large grid of 5 ha in comparatively dense forest. Species rarefaction curves show accumulation of species up to 10 ha sampling area indicating that large sample size is required to estimate alpha diversity.Comparison of species richness, diversity, density and basal area indicate that open forest show higher species richness and diversity but density and basal area is higher in dense forest. Species abundance distribution demonstrates few dominants in the forest community with best fit models in log-series distribution denoting many rare species. Thus studies in species rich forest communities need consideration of sufficient sampling area for evaluation of alpha diversity. Rarity of species, especially in the present times of climate change needs to be properly evaluated in ecological studies to assess the conservation status of Himalayan forest communities.

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Section: Discussionsupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Assessment of Tree Alpha Diversity in Chapramari Wildlife Sanctuary, Eastern Himalaya

Rana¹,

Kumar²,

Rawat³

2017

Pleione

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“…; ter Steege et al . ) which applied Fisher's logseries to estimate species richness did not provide 95% confidence interval for the estimated richness. One possibility for this is because the variance formula provided in Fisher's original paper (Fisher et al .…”

Section: Introductionmentioning

confidence: 95%

“…; ter Steege et al . ) showed that no popular nonparametric estimators could predict a reasonable number of tropical tree species, as all of them predicted richness values that were too small and largely deviated from ecologists’ estimation. Other similar works (Chiarucci et al .…”

Section: Introductionmentioning

confidence: 99%

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Rarefaction and extrapolation of species richness using an area‐based Fisher's logseries

Chen

Shen

2017

Ecology and Evolution

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Fisher's logseries is widely used to characterize species abundance pattern, and some previous studies used it to predict species richness. However, this model, derived from the negative binomial model, degenerates at the zero‐abundance point (i.e., its probability mass fully concentrates at zero abundance, leading to an odd situation that no species can occur in the studied sample). Moreover, it is not directly related to the sampling area size. In this sense, the original Fisher's alpha (correspondingly, species richness) is incomparable among ecological communities with varying area sizes. To overcome these limitations, we developed a novel area‐based logseries model that can account for the compounding effect of the sampling area. The new model can be used to conduct area‐based rarefaction and extrapolation of species richness, with the advantage of accurately predicting species richness in a large region that has an area size being hundreds or thousands of times larger than that of a locally observed sample, provided that data follow the proposed model. The power of our proposed model has been validated by extensive numerical simulations and empirically tested through tree species richness extrapolation and interpolation in Brazilian Atlantic forests. Our parametric model is data parsimonious as it is still applicable when only the information on species number, community size, or the numbers of singleton and doubleton species in the local sample is available. Notably, in comparison with the original Fisher's method, our area‐based model can provide asymptotically unbiased variance estimation (therefore correct 95% confidence interval) for species richness. In conclusion, the proposed area‐based Fisher's logseries model can be of broad applications with clear and proper statistical background. Particularly, it is very suitable for being applied to hyperdiverse ecological assemblages in which nonparametric richness estimators were found to greatly underestimate species richness.

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Estimating and interpreting migration of Amazonian forests using spatially implicit and semi‐explicit neutral models

Pos

Andino

Sabatier

et al. 2017

Ecology and Evolution

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With many sophisticated methods available for estimating migration, ecologists face the difficult decision of choosing for their specific line of work. Here we test and compare several methods, performing sanity and robustness tests, applying to large‐scale data and discussing the results and interpretation. Five methods were selected to compare for their ability to estimate migration from spatially implicit and semi‐explicit simulations based on three large‐scale field datasets from South America (Guyana, Suriname, French Guiana and Ecuador). Space was incorporated semi‐explicitly by a discrete probability mass function for local recruitment, migration from adjacent plots or from a metacommunity. Most methods were able to accurately estimate migration from spatially implicit simulations. For spatially semi‐explicit simulations, estimation was shown to be the additive effect of migration from adjacent plots and the metacommunity. It was only accurate when migration from the metacommunity outweighed that of adjacent plots, discrimination, however, proved to be impossible. We show that migration should be considered more an approximation of the resemblance between communities and the summed regional species pool. Application of migration estimates to simulate field datasets did show reasonably good fits and indicated consistent differences between sets in comparison with earlier studies. We conclude that estimates of migration using these methods are more an approximation of the homogenization among local communities over time rather than a direct measurement of migration and hence have a direct relationship with beta diversity. As betadiversity is the result of many (non)‐neutral processes, we have to admit that migration as estimated in a spatial explicit world encompasses not only direct migration but is an ecological aggregate of these processes. The parameter m of neutral models then appears more as an emerging property revealed by neutral theory instead of being an effective mechanistic parameter and spatially implicit models should be rejected as an approximation of forest dynamics.

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Estimating species richness in hyper‐diverse large tree communities

Cited by 18 publications

References 41 publications

Assessment of Tree Alpha Diversity in Chapramari Wildlife Sanctuary, Eastern Himalaya

Assessment of Tree Alpha Diversity in Chapramari Wildlife Sanctuary, Eastern Himalaya

Rarefaction and extrapolation of species richness using an area‐based Fisher's logseries

Estimating and interpreting migration of Amazonian forests using spatially implicit and semi‐explicit neutral models

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