A Bayesian-weighted approach to predicting the number of newly discovered rare species.

In natural ecological communities, most species are rare and thus susceptible to extinction. Consequently, the prediction and identification of rare species are of enormous value for conservation purposes. How many newly found species will be rare in the next field survey? We took a Bayesian viewpoint and used observed species abundance information in an ecological sample to develop an accurate way to estimate the number of new rare species (e.g., singletons, doubletons, and tripletons) in an additional unknown sample. A similar method has been developed for incidence-based data sets. Five seminumerical tests (3 abundance cases and 2 incidence cases) showed that our proposed Bayesian-weight estimator accurately predicted the number of new rare species with low relative bias and low relative root mean squared error and, accordingly, high accuracy. Finally, we applied the proposed estimator to 6 conservation-directed empirical data sets (3 abundance cases and 3 incidence cases) and found the prediction of new rare species was quite accurate; the 95% CI covered the true observed value very well in most cases. Our estimator performed similarly to or better than an unweighted estimator derived from Chao et al. and performed consistently better than the naïve unweighted estimator. We recommend our Bayesian-weight estimator for conservation applications, although the unweighted estimator of Chao et al. may be better under some circumstances. We provide an R package RSE (rare species estimation) at https://github.com/ecomol/RSE for implementation of the estimators.

Un Método con Ponderación Bayesiana para Predecir el Número de Especies Raras Recientemente Descubiertas Resumen En las comunidades ecológicas naturales, la mayoría de las especies son raras y por lo tanto susceptibles a la extinción. Como consecuencia, la predicción e identificación de las especies raras son de enorme valor para los propósitos de la conservación. ¿Cuántas especies recientemente descubiertas serán clasificadas como raras en el siguiente censo de campo? Tomamos un punto de vista bayesiano y utilizamos información de la abundancia observada de especies en una muestra ecológica para desarrollas una manera certera para estimar el número de nuevas especies raras (p. ej.: singleton, doubleton, y tripleton) en una muestra adicional desconocida. Un método similar se ha desarrollado para conjuntos de datos basados en la incidencia. Cinco pruebas semi-numéricas (tres casos de abundancia y dos casos de incidencia) mostraron que nuestra propuesta de estimador con ponderación bayesiana predijo con certeza el número de nuevas especies raras con un bajo sesgo relativo y un bajo error de la raíz cuadrada media relativa y, de manera acorde, una alta certeza. Finalmente, aplicamos el estimador propuesto a seis conjuntos de datos empíricos dirigidos hacia la conservación (tres casos de abundancia y tres casos de incidencia) y encontramos que la predicción de nuevas especies raras fue certera; el 95% del CI cubrió muy bien al verdadero valor observado en la mayoría de los casos. Nuestro estimador funcionó de manera similar o incluso mejor que un estimador sin ponderación derivado de Chao et al. (2015) y funcionó constantemente mejor que el simple estimador sin ponderación. Recomendamos nuestro estimador con ponderación bayesiana para ser aplicado en la conservación, aunque el estimador sin ponderación de Chao et al. (2015) puede ser mejor bajo ciertas circunstancias. Proporcionamos un paquete R para RSE (estimación de especies raras) en https://github.com/ecomol/RSE para la implementación de los estimadores.

Estimating parasite host range.

Estimating the number of host species that a parasite can infect (i.e. host range) provides key insights into the evolution of host specialism and is a central concept in disease ecology. Host range is rarely estimated in real systems, however, because variation in species relative abundance and the detection of rare species makes it challenging to confidently estimate host range. We applied a non-parametric richness indicator to estimate host range in simulated and empirical data, allowing us to assess the influence of sampling heterogeneity and data completeness. After validating our method on simulated data, we estimated parasite host range for a sparsely sampled global parasite occurrence database (Global Mammal Parasite Database) and a repeatedly sampled set of parasites of small mammals from New Mexico (Sevilleta Long Term Ecological Research Program). Estimation accuracy varied strongly with parasite taxonomy, number of parasite occurrence records, and the shape of host species-abundance distribution (i.e. the dominance and rareness of species in the host community). Our findings suggest that between 20% and 40% of parasite host ranges are currently unknown, highlighting a major gap in our understanding of parasite specificity, host-parasite network structure, and parasite burdens.

Bat assemblage of Guaribas Biological Reserve, an Atlantic forest conservation unit in North-eastern Brazil: Results of a two-stage long-term survey / Assembleia de morcegos da Reserva Biológica de Guaribas, uma unidade de conservação da Mata Atlântica no Nordeste do Brasil: resultados de um estudo de longa duração em duas etapas

Inventories are the basis of every work with biodiversity, with increased importance due to the current environmental crisis. Bats are one of the most diverse groups of mammals, with high ecologic versatility and are good bioindicators to monitor environmental impacts. We performed a two-stage survey at an Atlantic Forest reserve in the State of Paraíba, the first stage registering 187 individuals of 24 species and the second stage, 1073 individuals of 11 species; the second stage's richness being a subset of the first as pointed by the Mann-Whitney test. The second stage was more efficient in accumulating individuals, while the first accumulated species more efficiently. The diversity estimation (Chao 1) pointed that the survey was efficient in registering 93.75% of the species richness predicted for the area, and that diversity estimators are more reliable to evaluate sampling efficiency than methods based in number of captures. The inventory survey registered over 42% of the species richness registered for the State of Paraíba, as well as included a new register, Natalus stramineus, pointing that the bat richness for the state is yet to be sufficiently studied.

Inventários são a base de qualquer trabalho com biodiversidade, com sua importância exacerbada dada a crise ambiental atual. Morcegos são um dos grupos de mamíferos mais diversos, com alta versatilidade ecológica, e se apresentam como bons bioindicadores para monitorar impactos ambientais. O presente trabalho é um inventário de morcegos em longo prazo, dividido em duas etapas, numa reserva biológica de Floresta Atlântica no Estado da Paraíba, Brasil. A primeira etapa capturou 187 indivíduos de 24 espécies, o segundo registrou 1073 indivíduos de 11 espécies, sendo a diversidade deste um subgrupo da diversidade do primeiro, como apontado pelo teste de Mann-Whitney. O segundo estágio foi mais eficaz em acumular indivíduos, enquanto o primeiro acumulou espécies mais eficientemente. O estimador de diversidade Chao 1 apontou que o inventário foi eficiente em registrar 93,75% da riqueza de espécies prevista para a área, e que estimadores de diversidade são mais confiáveis para avaliar suficiência amostral que métodos baseados em número mínimo de capturas. O inventário registrou cerca de 42% da riqueza de espécies conhecida para o Estado da Paraíba, assim como incluiu um novo registro, Natalus stramineus, ressaltando que a riqueza de espécies de morcegos no estado está por ser suficientemente estudada.

An estimate of the number of tropical tree species.

The high species richness of tropical forests has long been recognized, yet there remains substantial uncertainty regarding the actual number of tropical tree species. Using a pantropical tree inventory database from closed canopy forests, consisting of 657,630 trees belonging to 11,371 species, we use a fitted value of Fisher's alpha and an approximate pantropical stem total to estimate the minimum number of tropical forest tree species to fall between ∼ 40,000 and ∼ 53,000, i.e., at the high end of previous estimates. Contrary to common assumption, the Indo-Pacific region was found to be as species-rich as the Neotropics, with both regions having a minimum of ∼ 19,000-25,000 tree species. Continental Africa is relatively depauperate with a minimum of ∼ 4,500-6,000 tree species. Very few species are shared among the African, American, and the Indo-Pacific regions. We provide a methodological framework for estimating species richness in trees that may help refine species richness estimates of tree-dependent taxa.

RICHEST--a web server for richness estimation in biological data.

UNLABELLED: Richness is defined as the number of distinct species or classes in a sample or population. Although richness estimation is an important practice, it requires mathematical and computational methods that are challenging to understand and implement. We have developed a web server, RICHness ESTimator (RICHEST), which implements three non-parametric statistical methods for richness estimation. Its user-friendly web interface allows users to analyze and compare their data conveniently over the web. AVAILABILITY: A web server hosting RICHEST is accessible at http://richest.cgb.indiana.edu/cgi-bin/index.cgi and the software is freely available for local installations.