Dataset Information

When the absence of evidence is not the evidence of absence: Nasa (Loasaceae) rediscoveries from Peru and Ecuador, and the contribution of community science networks.

ABSTRACT: Documentation of plant taxa has long been subject to the temporal and spatial selectivity of professional research expeditions, especially in tropical regions. Therefore, rare and/or narrowly endemic species are sometimes known only from very few and very old herbarium specimens. However, these taxa are very important from a conservation perspective. The lack of observations of living plants and confirmation of the actual occurrence of taxa hinders the planning and implementation of effective conservation measures. Community science networks have recently made tremendous contributions to documenting biodiversity in many regions across the globe. The rediscovery of six species of Nasa (Loasaceae) from Peru and Ecuador primarily via the platform iNaturalist, is reported.

SUBMITTER: Henning T

PROVIDER: S-EPMC10838191 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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When the absence of evidence is not the evidence of absence: <i>Nasa</i> (Loasaceae) rediscoveries from Peru and Ecuador, and the contribution of community science networks.

Henning Tilo T Acuña-Castillo Rafael R Cornejo Xavier X Gonzáles Paúl P Segovia Edgar E Wong Sato Akira Armando AA Weigend Maximilian M

PhytoKeys 20230630

Documentation of plant taxa has long been subject to the temporal and spatial selectivity of professional research expeditions, especially in tropical regions. Therefore, rare and/or narrowly endemic species are sometimes known only from very few and very old herbarium specimens. However, these taxa are very important from a conservation perspective. The lack of observations of living plants and confirmation of the actual occurrence of taxa hinders the planning and implementation of effective co ...[more]

PMID: 38313362

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Project description:ObjectivesUnderstanding human mobility's role on malaria transmission is critical to successful control and elimination. However, common approaches to measuring mobility are ill-equipped for remote regions such as the Amazon. This study develops a network survey to quantify the effect of community connectivity and mobility on malaria transmission.DesignA community-level network survey.SettingWe collect data on community connectivity along three river systems in the Amazon basin: the Pastaza river corridor spanning the Ecuador-Peru border; and the Amazon and Javari river corridors spanning the Brazil-Peru border.ParticipantsWe interviewed key informants in Brazil, Ecuador, and Peru, including from indigenous communities: Shuar, Achuar, Shiwiar, Kichwa, Ticuna, and Yagua. Key informants are at least 18 years of age and are considered community leaders.Primary outcomeWeekly, community-level malaria incidence during the study period.MethodsWe measure community connectivity across the study area using a respondent driven sampling design. Forty-five communities were initially selected: 10 in Brazil, 10 in Ecuador, and 25 in Peru. Participants were recruited in each initial node and administered a survey to obtain data on each community's mobility patterns. Survey responses were ranked and the 2-3 most connected communities were then selected and surveyed. This process was repeated for a third round of data collection. Community network matrices will be linked with eadch country's malaria surveillance system to test the effects of mobility on disease risk.FindingsTo date, 586 key informants were surveyed from 126 communities along the Pastaza river corridor. Data collection along the Amazon and Javari river corridors is ongoing. Initial results indicate that network sampling is a superior method to delineate migration flows between communities.ConclusionsOur study provides measures of mobility and connectivity in rural settings where traditional approaches are insufficient, and will allow us to understand mobility's effect on malaria transmission.

Project description:Community science image libraries offer a massive, but largely untapped, source of observational data for phenological research. The iNaturalist platform offers a particularly rich archive, containing more than 49 million verifiable, georeferenced, open access images, encompassing seven continents and over 278,000 species. A critical limitation preventing scientists from taking full advantage of this rich data source is labor. Each image must be manually inspected and categorized by phenophase, which is both time-intensive and costly. Consequently, researchers may only be able to use a subset of the total number of images available in the database. While iNaturalist has the potential to yield enough data for high-resolution and spatially extensive studies, it requires more efficient tools for phenological data extraction. A promising solution is automation of the image annotation process using deep learning. Recent innovations in deep learning have made these open-source tools accessible to a general research audience. However, it is unknown whether deep learning tools can accurately and efficiently annotate phenophases in community science images. Here, we train a convolutional neural network (CNN) to annotate images of Alliaria petiolata into distinct phenophases from iNaturalist and compare the performance of the model with non-expert human annotators. We demonstrate that researchers can successfully employ deep learning techniques to extract phenological information from community science images. A CNN classified two-stage phenology (flowering and non-flowering) with 95.9% accuracy and classified four-stage phenology (vegetative, budding, flowering, and fruiting) with 86.4% accuracy. The overall accuracy of the CNN did not differ from humans (p = 0.383), although performance varied across phenophases. We found that a primary challenge of using deep learning for image annotation was not related to the model itself, but instead in the quality of the community science images. Up to 4% of A. petiolata images in iNaturalist were taken from an improper distance, were physically manipulated, or were digitally altered, which limited both human and machine annotators in accurately classifying phenology. Thus, we provide a list of photography guidelines that could be included in community science platforms to inform community scientists in the best practices for creating images that facilitate phenological analysis.

Dataset Information

When the absence of evidence is not the evidence of absence: Nasa (Loasaceae) rediscoveries from Peru and Ecuador, and the contribution of community science networks.

Publications

When the absence of evidence is not the evidence of absence: <i>Nasa</i> (Loasaceae) rediscoveries from Peru and Ecuador, and the contribution of community science networks.

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OmicsDI is part of the ELIXIR infrastructure

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Dataset Information

When the absence of evidence is not the evidence of absence: Nasa (Loasaceae) rediscoveries from Peru and Ecuador, and the contribution of community science networks.

Publications

﻿When the absence of evidence is not the evidence of absence: <i>Nasa</i> (Loasaceae) rediscoveries from Peru and Ecuador, and the contribution of community science networks.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

When the absence of evidence is not the evidence of absence: <i>Nasa</i> (Loasaceae) rediscoveries from Peru and Ecuador, and the contribution of community science networks.