Practice makes the expert: The importance of training volunteers in the generation of phenological data from photographs of biodiversity observation platforms.

Phenology studies the time at which events in the life cycle of a species occur sand how they are related to environmental cues. Patterns of change in phenology at different scales can be used as an indicator of ecosystem changes and climate change, but the data necessary to detect these changes can be difficult to obtain due to their temporal and regional dimensions. Citizen science can contribute to generate large amounts of data on phenological changes at wide geographical scales that would be almost impossible for professional scientists to generate, but the quality and reliability of these data are often questioned. The objective of this study was to evaluate the use of a biodiversity observation citizen science platform based on photographic information as a potential source of large-scale phenological information, and to identify the key benefits and limitations of this type of information source. We used the Naturalista photographic databases for two invasive species in a tropical region: Leonotis nepetifolia and Nicotiana glauca. The photographs were classified into different phenophases (initial growth, immature flower, mature flower, dry fruit) by three groups of volunteers: a group of experts, a trained group with information on the biology and phenology of both species, and an untrained group. The degree of reliability of the phenological classifications was estimated for each group of volunteers and each phenophase. The degree of reliability of the phenological classification of the untrained group was generally very low for all phenophases. The group of trained volunteers showed accuracy levels for the reproductive phenophases that equaled the degree of reliability among the expert group, regardless of species, and was consistent across phenophases. We conclude that volunteer classification of photographic information contained in biodiversity observation platforms can provide phenological information with high geographic coverage and an increasing temporal coverage on general phenological patterns of species with wide distributions but has limited applicability in the identification of exact start and end dates. and peaks of the different phenophases.

A statistical assessment of population trends for data deficient Mexican amphibians.

Background. Mexico has the world's fifth largest population of amphibians and the second country with the highest quantity of threatened amphibian species. About 10% of Mexican amphibians lack enough data to be assigned to a risk category by the IUCN, so in this paper we want to test a statistical tool that, in the absence of specific demographic data, can assess a species' risk of extinction, population trend, and to better understand which variables increase their vulnerability. Recent studies have demonstrated that the risk of species decline depends on extrinsic and intrinsic traits, thus including both of them for assessing extinction might render more accurate assessment of threats. Methods. We harvested data from the Encyclopedia of Life (EOL) and the published literature for Mexican amphibians, and used these data to assess the population trend of some of the Mexican species that have been assigned to the Data Deficient category of the IUCN using Random Forests, a Machine Learning method that gives a prediction of complex processes and identifies the most important variables that account for the predictions. Results. Our results show that most of the data deficient Mexican amphibians that we used have decreasing population trends. We found that Random Forests is a solid way to identify species with decreasing population trends when no demographic data is available. Moreover, we point to the most important variables that make species more vulnerable for extinction. This exercise is a very valuable first step in assigning conservation priorities for poorly known species.