Estimating species richness in hyper-diverse large tree communities

Exploring intrinsic distribution of phytoplankton relative abundance and biomass in combination with continental-scale field investigation and microcosm experiment

Phytoplankton are primary producers in aquatic ecosystems and their diversity directly affects the community stability and primary productivity. However, the commonly used diversity indices (such as Shannon and Pielou indices) were originally derived from other fields rather than ecology and did not have a direct biological explanatory function. There is still a need to incorporate biological explanatory functions into diversity evaluation methods and theories to bridge the gap between phytoplankton biodiversity and biological characteristics. This study aimed to explicate the intrinsic distribution patterns of phytoplankton relative abundance and biomass. Our study demonstrated an exponential distribution pattern of phytoplankton relative abundance and biomass ranking through field investigations of 367 phytoplankton samples in China and microcosm experiments, respectively. Microcosm experiments illustrated that the linear distribution of the specific growth rate ranking resulted in an exponential distribution of the relative phytoplankton biomass ranking due to exponential growth patterns. Through mathematical deduction, it was found that the three indices a, k and N in the exponential distribution could be considered as the critical relative abundance of extinction, competition coefficient and the environmental taxa capacity, respectively. We found that a was positively correlated with Shannon index and Pielou index, k was negatively correlated with Shannon index, Pielou index and Chao1 index. In addition, N and Chao1 index were almost exactly the same. Our study obtained these indices based on the distribution pattern of phytoplankton, enabling a comprehensive analysis of the phytoplankton community and providing novel insights for further evaluating the health of aquatic ecosystems.

The dominance-diversity dilemma in animal conservation biology

The alteration of environmental conditions has two major outcomes on the demographics of living organisms: population decline of the common species and extinction of the rarest ones. Halting the decline of abundant species as well as the erosion of biodiversity require solutions that may be mismatched, despite being rooted in similar causes. In this study, we demonstrate how rank abundance distribution (RAD) models are mathematical representations of a dominance-diversity dilemma. Across 4,375 animal communities from a range of taxonomic groups, we found that a reversed RAD model correctly predicts species richness, based solely on the relative dominance of the most abundant species in a community and the total number of individuals. Overall, predictions from this RAD model explained 69% of the variance in species richness, compared to 20% explained by simply regressing species richness on the relative dominance of the most abundant species. Using the reversed RAD model, we illustrate how species richness is co-limited by the total abundance of a community and the relative dominance of the most common species. Our results highlight an intrinsic trade-off between species richness and dominance that is present in the structure of RAD models and real-world animal community data. This dominance-diversity dilemma suggests that withdrawing individuals from abundant populations might contribute to the conservation of species richness. However, we posit that the positive effect of harvesting on biodiversity is often offset by exploitation practices with negative collateral consequences, such as habitat destruction or species bycatches.

Tree Species Biodiversity in Small Ukrainian Towns

Biodiversity is one of the important indicators of ecosystem sustainability, which is less studied in relation to urban areas, particularly Ukraine, and even more so in small towns. Taking the example of three small Ukrainian towns, the biodiversity of tree species is compared in the article. The studied small towns are located in two geographic zones, mixed forests (Polissya) and forest-steppe, and administratively, in Chernihiv and Kyiv regions. An inventory of trees in the residential area of Ukrainka, in the central streets of Novhorod-Siverskyi, as well as in the central part and all streets of Vyshhorod was conducted. Thus, the species richness of trees both between the studied towns and between its central part and the whole territory of the town was compared. The species structure of tree plantings in terms of richness, diversity, dominance, alignment, and similarity was assessed. The obtained results were subjected to cluster and correlation analysis. It was found that the greatest diversity is characteristic for the dendroflora of the whole territory of Vyshhorod, which is the fastest growing town, located closest to the capital. The dendrofloras of small towns were quite similar to each other. The values of similarity indices varied in a wide range: Jacquard’s index from 0.26 to 0.56 and Sørensen index from 0.41 to 0.66. The lowest degree of similarity was found with the dendroflora of the most distant Novgorod-Siverskyi town. Mainly the urban plantings require enrichment of the tree species composition, taking into account their resistance to anthropogenic impacts and climate change.

An Overview of Modern Applications of Negative Binomial Modelling in Ecology and Biodiversity

Negative binomial modelling is one of the most commonly used statistical tools for analysing count data in ecology and biodiversity research. This is not surprising given the prevalence of overdispersion (i.e., evidence that the variance is greater than the mean) in many biological and ecological studies. Indeed, overdispersion is often indicative of some form of biological aggregation process (e.g., when species or communities cluster in groups). If overdispersion is ignored, the precision of model parameters can be severely overestimated and can result in misleading statistical inference. In this article, we offer some insight as to why the negative binomial distribution is becoming, and arguably should become, the default starting distribution (as opposed to assuming Poisson counts) for analysing count data in ecology and biodiversity research. We begin with an overview of traditional uses of negative binomial modelling, before examining several modern applications and opportunities in modern ecology/biodiversity where negative binomial modelling is playing a critical role, from generalisations based on exploiting its Poisson-gamma mixture formulation in species distribution models and occurrence data analysis, to estimating animal abundance in negative binomial N-mixture models, and biodiversity measures via rank abundance distributions. Comparisons to other common models for handling overdispersion on real data are provided. We also address the important issue of software, and conclude with a discussion of future directions for analysing ecological and biological data with negative binomial models. In summary, we hope this overview will stimulate the use of negative binomial modelling as a starting point for the analysis of count data in ecology and biodiversity studies.

Mapping Key Indicators of Forest Restoration in the Amazon Using a Low-Cost Drone and Artificial Intelligence

Monitoring the vegetation structure and species composition of forest restoration (FR) in the Brazilian Amazon is critical to ensuring its long-term benefits. Since remotely piloted aircrafts (RPAs) associated with deep learning (DL) are becoming powerful tools for vegetation monitoring, this study aims to use DL to automatically map individual crowns of Vismia (low resilience recovery indicator), Cecropia (fast recovery indicator), and trees in general (this study refers to individual crowns of all trees regardless of species as All Trees). Since All Trees can be accurately mapped, this study also aims to propose a tree crown heterogeneity index (TCHI), which estimates species diversity based on: the heterogeneity attributes/parameters of the RPA image inside the All Trees results; and the Shannon index measured by traditional fieldwork. Regarding the DL methods, this work evaluated the accuracy of the detection of individual objects, the quality of the delineation outlines and the area distribution. Except for Vismia delineation (IoU = 0.2), DL results presented accurate values in general, as F1 and IoU were always greater than 0.7 and 0.55, respectively, while Cecropia presented the most accurate results: F1 = 0.85 and IoU = 0.77. Since All Trees results were accurate, the TCHI was obtained through regression analysis between the canopy height model (CHM) heterogeneity attributes and the field plot data. Although TCHI presented robust parameters, such as p-value < 0.05, its results are considered preliminary because more data are needed to include different FR situations. Thus, the results of this work show that low-cost RPA has great potential for monitoring FR quality in the Amazon, because Vismia, Cecropia, and All Trees can be automatically mapped. Moreover, the TCHI preliminary results showed high potential in estimating species diversity. Future studies must assess domain adaptation methods for the DL results and different FR situations to improve the TCHI range of action.

Biased-corrected richness estimates for the Amazonian tree flora

Amazonian forests are extraordinarily diverse, but the estimated species richness is very much debated. Here, we apply an ensemble of parametric estimators and a novel technique that includes conspecific spatial aggregation to an extended database of forest plots with up-to-date taxonomy. We show that the species abundance distribution of Amazonia is best approximated by a logseries with aggregated individuals, where aggregation increases with rarity. By averaging several methods to estimate total richness, we confirm that over 15,000 tree species are expected to occur in Amazonia. We also show that using ten times the number of plots would result in an increase to just ~50% of those 15,000 estimated species. To get a more complete sample of all tree species, rigorous field campaigns may be needed but the number of trees in Amazonia will remain an estimate for years to come.

Scaling issues of neutral theory reveal violations of ecological equivalence for dominant Amazonian tree species

Neutral models are often used as null models, testing the relative importance of niche versus neutral processes in shaping diversity. Most versions, however, focus only on regional scale predictions and neglect local level contributions. Recently, a new formulation of spatial neutral theory was published showing an incompatibility between regional and local scale fits where especially the number of rare species was dramatically under-predicted. Using a forward in time semi-spatially explicit neutral model and a unique large-scale Amazonian tree inventory data set, we show that neutral theory not only underestimates the number of rare species but also fails in predicting the excessive dominance of species on both regional and local levels. We show that although there are clear relationships between species composition, spatial and environmental distances, there is also a clear differentiation between species able to attain dominance with and without restriction to specific habitats. We conclude therefore that the apparent dominance of these species is real, and that their excessive abundance can be attributed to fitness differences in different ways, a clear violation of the ecological equivalence assumption of neutral theory.

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

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.

Upscaling species richness and abundances in tropical forests

The quantification of tropical tree biodiversity worldwide remains an open and challenging problem. More than two-fifths of the number of worldwide trees can be found either in tropical or in subtropical forests, but only ≈0.000067% of species identities are known. We introduce an analytical framework that provides robust and accurate estimates of species richness and abundances in biodiversity-rich ecosystems, as confirmed by tests performed on both in silico-generated and real forests. Our analysis shows that the approach outperforms other methods. In particular, we find that upscaling methods based on the log-series species distribution systematically overestimate the number of species and abundances of the rare species. We finally apply our new framework on 15 empirical tropical forest plots and quantify the minimum percentage cover that should be sampled to achieve a given average confidence interval in the upscaled estimate of biodiversity. Our theoretical framework confirms that the forests studied are comprised of a large number of rare or hyper-rare species. This is a signature of critical-like behavior of species-rich ecosystems and can provide a buffer against extinction.

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

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.