Science Enabled by Specimen Data
Moreno, I., J. M. W. Gippet, L. Fumagalli, and P. J. Stephenson. 2022. Factors affecting the availability of data on East African wildlife: the monitoring needs of conservationists are not being met. Biodiversity and Conservation. https://doi.org/10.1007/s10531-022-02497-4
Understanding the status and abundance of species is essential for effective conservation decision-making. However, the availability of species data varies across space, taxonomic groups and data types. A case study was therefore conducted in a high biodiversity region—East Africa—to evaluate data biases, the factors influencing data availability, and the consequences for conservation. In each of the eleven target countries, priority animal species were identified as threatened species that are protected by national governments, international conventions or conservation NGOs. We assessed data gaps and biases in the IUCN Red List of Threatened Species, the Global Biodiversity Information Facility and the Living Planet Index. A survey of practitioners and decision makers was conducted to confirm and assess consequences of these biases on biodiversity conservation efforts. Our results showed data on species occurrence and population trends were available for a significantly higher proportion of vertebrates than invertebrates. We observed a geographical bias, with higher tourism income countries having more priority species and more species with data than lower tourism income countries. Conservationists surveyed felt that, of the 40 types of data investigated, those data that are most important to conservation projects are the most difficult to access. The main challenges to data accessibility are excessive expense, technological challenges, and a lack of resources to process and analyse data. With this information, practitioners and decision makers can prioritise how and where to fill gaps to improve data availability and use, and ensure biodiversity monitoring is improved and conservation impacts enhanced.
Rahman, D. A., Y. Santosa, I. Purnamasari, and A. A. Condro. 2022. Drivers of Three Most Charismatic Mammalian Species Distribution across a Multiple-Use Tropical Forest Landscape of Sumatra, Indonesia. Animals 12: 2722. https://doi.org/10.3390/ani12192722
Tropical Rainforest Heritage sites of Sumatra are some of the most irreplaceable landscapes in the world for biodiversity conservation. These landscapes harbor many endangered Asiatic mammals all suffering multifaceted threats due to anthropogenic activities. Three charismatic mammals in Sumatra: Elephas maximus sumatranus, Pongo abelii, and Panthera tigris sumatrae are protected and listed as Critically Endangered (CR) within the IUCN Red List. Nevertheless, their current geographic distribution remains unclear, and the impact of environmental factors on these species are mostly unknown. This study predicts the potential range of those species on the island of Sumatra using anthropogenic, biophysical, topographic, and climatic parameters based on the ensemble machine learning algorithms. We also investigated the effects of habitat loss from current land use, ecosystem availability, and importance of Indonesian protected areas. Our predictive model had relatively excellent performance (Sørensen: 0.81–0.94) and can enhance knowledge on the current species distributions. The most critical environmental predictors for the distribution of the three species are conservation status and temperature seasonality. This study revealed that more than half of the species distributions occurred in non-protected areas, with proportional coverage being 83%, 72%, and 54% for E.m. sumatranus, P. abelii, and P.t. sumatrae, respectively. Our study further provides reliable information on places where conservation efforts must be prioritized, both inside and outside of the protected area networks, to safeguard the ongoing survival of these Indonesian large charismatic mammals.
Arana, C., V. Pulido, A. Arana, A. Carlos, and L. Salinas. 2022. Distribución geográfica y abundancia poblacional de Plegadis ridgwayi, el ibis de la Puna (Threskiornithidae) con énfasis en las poblaciones del Perú. Revista Peruana de Biología 29: e22533. https://doi.org/10.15381/rpb.v29i3.22533
El ibis de la puna Plegadis ridgwayi, es una especie de Threskiornithidae que habita humedales andinos y realiza migraciones altitudinales hacia la costa. Datos propios, de GBIF, información bibliográfica y del Censo Neotropical de Aves Acuáticas (1992 a 2015) muestran que el ibis de la puna Plegadis ridgwayi se distribuye en Ecuador, Perú, Bolivia, Argentina y Chile, con las mayores densidades poblacionales en Perú y Bolivia en siete y tres localidades respectivamente, que acumulan más del 1% de la población biogeográfica. Se encuentran de 0 a 5000 m de altitud, con las mayores densidades entre 3000 a 4500 m y 0 a 500 m. La mayor incidencia de registros ocurre al sur y centro del Perú, así como costa del centro y norte del Perú. La ampliación de la distribución hacia el norte y costa peruana puede deberse a la disponibilidad ambiental y al deterioro de su hábitat andino. En cuatro humedales costeros del centro del Perú se registraron hasta 818 ibis en 2006, la gran mayoría en Pantanos de Villa y Paraíso. El número de migrantes costeros parece relacionado a la intensidad de sequías en la sierra del Perú central. La abundancia de ibis en el lago altoandino de Junín muestra una disminución histórica, con énfasis después de la sequía de 2004-2005. La expansión distribucional requiere investigar la posible hibridación con las otras especies del género antes alopátridas.
Sánchez, C. A., H. Li, K. L. Phelps, C. Zambrana-Torrelio, L.-F. Wang, P. Zhou, Z.-L. Shi, et al. 2022. A strategy to assess spillover risk of bat SARS-related coronaviruses in Southeast Asia. Nature Communications 13. https://doi.org/10.1038/s41467-022-31860-w
Emerging diseases caused by coronaviruses of likely bat origin (e.g., SARS, MERS, SADS, COVID-19) have disrupted global health and economies for two decades. Evidence suggests that some bat SARS-related coronaviruses (SARSr-CoVs) could infect people directly, and that their spillover is more frequent than previously recognized. Each zoonotic spillover of a novel virus represents an opportunity for evolutionary adaptation and further spread; therefore, quantifying the extent of this spillover may help target prevention programs. We derive current range distributions for known bat SARSr-CoV hosts and quantify their overlap with human populations. We then use probabilistic risk assessment and data on human-bat contact, human viral seroprevalence, and antibody duration to estimate that a median of 66,280 people (95% CI: 65,351–67,131) are infected with SARSr-CoVs annually in Southeast Asia. These data on the geography and scale of spillover can be used to target surveillance and prevention programs for potential future bat-CoV emergence. Coronaviruses may spill over from bats to humans. This study uses epidemiological data, species distribution models, and probabilistic risk assessment to map overlap among people and SARSr-CoV bat hosts and estimate how many people are infected with bat-origin SARSr-CoVs in Southeast Asia annually.
Barends, J. M., and B. Maritz. 2022. Dietary Specialization and Habitat Shifts in a Clade of Afro-Asian Colubrid Snakes (Colubridae: Colubrinae). Ichthyology & Herpetology 110. https://doi.org/10.1643/h2021058
Speciation through niche divergence often occurs as lineages of organisms colonize and adapt to new environments with novel ecological opportunities that facilitate the evolution of ecologically different phenotypes. In snakes, adaptive diversification may be driven by the evolution of traits relating to changes in their diets. Accordingly, habitatmediated differences in prey available to ancestral snakes as they colonized and occupied novel dynamic landscapes are likely to have been a strong selective agent behind the divergence and radiation of snakes across the globe. Using an ancestral reconstruction approach that considers the multivariate nature of ecological phenotypes while accounting for sampling variation between taxa, we explored how diet and macro-habitat use coevolved across a phylogeny of 67 species of Afro-Asian colubrine snakes. Our results show that the most recent common ancestor of this clade was likely a dietary generalist that occupied tropical forests in Asia. Deviations from this generalist diet to a variety of specialist diets each dominated by the utilization of single prey types repeatedly occurred as ancestral colubrines shifted from tropical forests to savanna and grassland habitats across Africa. We additionally found that dietary specialist species were on average smaller in maximum length than dietary generalists, congruent with established predator-size, preydiversity dynamics in snakes. We speculate that adaptive divergence in ancestral colubrines arose as a result of a selective regime that favored diets comprised of terrestrial prey, and that partitioning of different prey types led to the various forms of dietary specialization evident in these lineages today. Our findings provide new insights into the ecological correlates associated with the evolution of diet in snakes, thereby furthering our understanding of the driving forces behind patterns of snake diversification.
Pang, S. E. H., Y. Zeng, J. D. T. Alban, and E. L. Webb. 2022. Occurrence–habitat mismatching and niche truncation when modelling distributions affected by anthropogenic range contractions B. Leroy [ed.],. Diversity and Distributions 28: 1327–1343. https://doi.org/10.1111/ddi.13544
Aims Human-induced pressures such as deforestation cause anthropogenic range contractions (ARCs). Such contractions present dynamic distributions that may engender data misrepresentations within species distribution models. The temporal bias of occurrence data—where occurrences represent distributions before (past bias) or after (recent bias) ARCs—underpins these data misrepresentations. Occurrence–habitat mismatching results when occurrences sampled before contractions are modelled with contemporary anthropogenic variables; niche truncation results when occurrences sampled after contractions are modelled without anthropogenic variables. Our understanding of their independent and interactive effects on model performance remains incomplete but is vital for developing good modelling protocols. Through a virtual ecologist approach, we demonstrate how these data misrepresentations manifest and investigate their effects on model performance. Location Virtual Southeast Asia. Methods Using 100 virtual species, we simulated ARCs with 100-year land-use data and generated temporally biased (past and recent) occurrence datasets. We modelled datasets with and without a contemporary land-use variable (conventional modelling protocols) and with a temporally dynamic land-use variable. We evaluated each model's ability to predict historical and contemporary distributions. Results Greater ARC resulted in greater occurrence–habitat mismatching for datasets with past bias and greater niche truncation for datasets with recent bias. Occurrence–habitat mismatching prevented models with the contemporary land-use variable from predicting anthropogenic-related absences, causing overpredictions of contemporary distributions. Although niche truncation caused underpredictions of historical distributions (environmentally suitable habitats), incorporating the contemporary land-use variable resolved these underpredictions, even when mismatching occurred. Models with the temporally dynamic land-use variable consistently outperformed models without. Main conclusions We showed how these data misrepresentations can degrade model performance, undermining their use for empirical research and conservation science. Given the ubiquity of ARCs, these data misrepresentations are likely inherent to most datasets. Therefore, we present a three-step strategy for handling data misrepresentations: maximize the temporal range of anthropogenic predictors, exclude mismatched occurrences and test for residual data misrepresentations.
Xue, T., S. R. Gadagkar, T. P. Albright, X. Yang, J. Li, C. Xia, J. Wu, and S. Yu. 2021. Prioritizing conservation of biodiversity in an alpine region: Distribution pattern and conservation status of seed plants in the Qinghai-Tibetan Plateau. Global Ecology and Conservation 32: e01885. https://doi.org/10.1016/j.gecco.2021.e01885
The Qinghai-Tibetan Plateau (QTP) harbors abundant and diverse plant life owing to its high habitat heterogeneity. However, the distribution pattern of biodiversity hotspots and their conservation status remain unclear. Based on 148,283 high-resolution occurrence coordinates of 13,450 seed plants, w…
Lwin, N., N. Sukumal, and T. Savini. 2021. Modelling the conservation status of the threatened hoolock gibbon (genus Hoolock) over its range. Global Ecology and Conservation 29: e01726. https://doi.org/10.1016/j.gecco.2021.e01726
Habitat loss, degradation and fragmentation are major threats to all gibbon species, contributing to the dramatic decline of gibbons over the last 30–40 years. The Hoolock gibbon (genus Hoolock) in South and Southeast Asia, particularly those occurring between the Thanlwin River in the east and Brah…
Williamson, J. L., and C. C. Witt. 2021. Elevational niche-shift migration: Why the degree of elevational change matters for the ecology, evolution, and physiology of migratory birds. Ornithology 138. https://doi.org/10.1093/ornithology/ukaa087
Elevational migration can be defined as roundtrip seasonal movement that involves upward and downward shifts in elevation. These shifts incur physiological challenges that are proportional to the degree of elevational change. Larger shifts in elevation correspond to larger shifts in partial pressure…
Li, X., B. Li, G. Wang, X. Zhan, and M. Holyoak. 2020. Deeply digging the interaction effect in multiple linear regressions using a fractional-power interaction term. MethodsX 7: 101067. https://doi.org/10.1016/j.mex.2020.101067
In multiple regression Y ~ β0 + β1X1 + β2X2 + β3X1 X2 + ɛ., the interaction term is quantified as the product of X1 and X2. We developed fractional-power interaction regression (FPIR), using βX1M X2N as the interaction term. The rationale of FPIR is that the slopes of Y-X1 regression along the X2 gr…