Plant‐O‐Matic : a dynamic and mobile guide to all plants of the Americas

How deregulation, drought and increasing fire impact Amazonian biodiversity

Biodiversity contributes to the ecological and climatic stability of the Amazon Basin^1,2, but is increasingly threatened by deforestation and fire^3,4. Here we quantify these impacts over the past two decades using remote-sensing estimates of fire and deforestation and comprehensive range estimates of 11,514 plant species and 3,079 vertebrate species in the Amazon. Deforestation has led to large amounts of habitat loss, and fires further exacerbate this already substantial impact on Amazonian biodiversity. Since 2001, 103,079-189,755 km² of Amazon rainforest has been impacted by fires, potentially impacting the ranges of 77.3-85.2% of species that are listed as threatened in this region⁵. The impacts of fire on the ranges of species in Amazonia could be as high as 64%, and greater impacts are typically associated with species that have restricted ranges. We find close associations between forest policy, fire-impacted forest area and their potential impacts on biodiversity. In Brazil, forest policies that were initiated in the mid-2000s corresponded to reduced rates of burning. However, relaxed enforcement of these policies in 2019 has seemingly begun to reverse this trend: approximately 4,253-10,343 km² of forest has been impacted by fire, leading to some of the most severe potential impacts on biodiversity since 2009. These results highlight the critical role of policy enforcement in the preservation of biodiversity in the Amazon.

Demographic trade-offs predict tropical forest dynamics

Understanding tropical forest dynamics and planning for their sustainable management require efficient, yet accurate, predictions of the joint dynamics of hundreds of tree species. With increasing information on tropical tree life histories, our predictive understanding is no longer limited by species data but by the ability of existing models to make use of it. Using a demographic forest model, we show that the basal area and compositional changes during forest succession in a neotropical forest can be accurately predicted by representing tropical tree diversity (hundreds of species) with only five functional groups spanning two essential trade-offs-the growth-survival and stature-recruitment trade-offs. This data-driven modeling framework substantially improves our ability to predict consequences of anthropogenic impacts on tropical forests.

The commonness of rarity: Global and future distribution of rarity across land plants

A key feature of life's diversity is that some species are common but many more are rare. Nonetheless, at global scales, we do not know what fraction of biodiversity consists of rare species. Here, we present the largest compilation of global plant diversity to quantify the fraction of Earth's plant biodiversity that are rare. A large fraction, ~36.5% of Earth's ~435,000 plant species, are exceedingly rare. Sampling biases and prominent models, such as neutral theory and the k-niche model, cannot account for the observed prevalence of rarity. Our results indicate that (i) climatically more stable regions have harbored rare species and hence a large fraction of Earth's plant species via reduced extinction risk but that (ii) climate change and human land use are now disproportionately impacting rare species. Estimates of global species abundance distributions have important implications for risk assessments and conservation planning in this era of rapid global change.

Temperature shapes opposing latitudinal gradients of plant taxonomic and phylogenetic β diversity

Latitudinal and elevational richness gradients have received much attention from ecologists but there is little consensus on underlying causes. One possible proximate cause is increased levels of species turnover, or β diversity, in the tropics compared to temperate regions. Here, we leverage a large botanical dataset to map taxonomic and phylogenetic β diversity, as mean turnover between neighboring 100 × 100 km cells, across the Americas and determine key climatic drivers. We find taxonomic and tip-weighted phylogenetic β diversity is higher in the tropics, but that basal-weighted phylogenetic β diversity is highest in temperate regions. Supporting Janzen's 'mountain passes' hypothesis, tropical mountainous regions had higher β diversity than temperate regions for taxonomic and tip-weighted metrics. The strongest climatic predictors of turnover were average temperature and temperature seasonality. Taken together, these results suggest β diversity is coupled to latitudinal richness gradients and that temperature is a major driver of plant community composition and change.

Dragonfly Hunter CZ: Mobile application for biological species recognition in citizen science

Citizen science and data collected from various volunteers have an interesting potential in aiding the understanding of many biological and ecological processes. We describe a mobile application that allows the public to map and report occurrences of the odonata species (dragonflies and damselflies) found in the Czech Republic. The application also helps in species classification based on observation details such as date, GPS coordinates, and the altitude, biotope, suborder, and colour. Dragonfly Hunter CZ is a free Android application built on the open-source framework NativeScript using the JavaScript programming language which is now fully available on Google Play. The server side is powered by Apache Server with PHP and MariaDB SQL database. A mobile application is a fast and accurate way to obtain data pertaining to the odonata species, which can be used after expert verification for ecological studies and conservation basis like Red Lists and policy instruments. We expect it to be effective in encouraging Citizen Science and in promoting the proactive reporting of odonates. It can also be extended to the reporting and monitoring of other plant and animal species.

Recommending plant taxa for supporting on-site species identification

BACKGROUND

Predicting a list of plant taxa most likely to be observed at a given geographical location and time is useful for many scenarios in biodiversity informatics. Since efficient plant species identification is impeded mainly by the large number of possible candidate species, providing a shortlist of likely candidates can help significantly expedite the task. Whereas species distribution models heavily rely on geo-referenced occurrence data, such information still remains largely unused for plant taxa identification tools.

RESULTS

In this paper, we conduct a study on the feasibility of computing a ranked shortlist of plant taxa likely to be encountered by an observer in the field. We use the territory of Germany as case study with a total of 7.62M records of freely available plant presence-absence data and occurrence records for 2.7k plant taxa. We systematically study achievable recommendation quality based on two types of source data: binary presence-absence data and individual occurrence records. Furthermore, we study strategies for aggregating records into a taxa recommendation based on location and date of an observation.

CONCLUSION

We evaluate recommendations using 28k geo-referenced and taxa-labeled plant images hosted on the Flickr website as an independent test dataset. Relying on location information from presence-absence data alone results in an average recall of 82%. However, we find that occurrence records are complementary to presence-absence data and using both in combination yields considerably higher recall of 96% along with improved ranking metrics. Ultimately, by reducing the list of candidate taxa by an average of 62%, a spatio-temporal prior can substantially expedite the overall identification problem.

Incorporating explicit geospatial data shows more species at risk of extinction than the current Red List

The IUCN (International Union for Conservation of Nature) Red List classifies species according to their risk of extinction, informing global to local conservation decisions. Unfortunately, important geospatial data do not explicitly or efficiently enter this process. Rapid growth in the availability of remotely sensed observations provides fine-scale data on elevation and increasingly sophisticated characterizations of land cover and its changes. These data readily show that species are likely not present within many areas within the overall envelopes of their distributions. Additionally, global databases on protected areas inform how extensively ranges are protected. We selected 586 endemic and threatened forest bird species from six of the world's most biodiverse and threatened places (Atlantic Forest of Brazil, Central America, Western Andes of Colombia, Madagascar, Sumatra, and Southeast Asia). The Red List deems 18% of these species to be threatened (15 critically endangered, 29 endangered, and 64 vulnerable). Inevitably, after refining ranges by elevation and forest cover, ranges shrink. Do they do so consistently? For example, refined ranges of critically endangered species might reduce by (say) 50% but so might the ranges of endangered, vulnerable, and nonthreatened species. Critically, this is not the case. We find that 43% of species fall below the range threshold where comparable species are deemed threatened. Some 210 bird species belong in a higher-threat category than the current Red List placement, including 189 species that are currently deemed nonthreatened. Incorporating readily available spatial data substantially increases the numbers of species that should be considered at risk and alters priority areas for conservation.