Forecasting seasonal peaks in roadkill patterns for improving road management

For several species, roadkill is not spatially aggregated on hotspots, having instead a more diffuse pattern along the roads. For such species, management measures such as road passages may be insufficient for effective mitigation, since a large part of the road crossings is likely to occur outside the influence of those structures. One complementary approach could be to implement temporary mitigation actions, such as traffic calming. This requires understanding when roadkill peaks may occur. We tested the feasibility of predicting seasonal peaks of roadkill using data from a 3-year systematic monitoring (78 surveys over ca. 960 km of roads) from eight non-flying vertebrate species from Mato Grosso do Sul, Brazil, with different body size and life history traits (ca. 6400 records from focal species). We modelled the time-series of the roadkill of these species at large scale (state level) using generalized additive mixed models (GAMMs). We used the data of the first 2 years as training datasets, and the information from the third year of surveys as testing datasets to evaluate the prediction performance of models. Overall, the models of species feed with a higher number of records were able to follow reasonably well the variations of roadkill over time, although they were not able to correctly predict the number of collisions. For species with fewer observations, the models presented a poorer goodness-of-fit and prediction ability. Our results suggest that, at least for those species with higher roadkill rates, it can be possible to forecast periods of higher probability of occurring hot-moments of mortality. Such models can provide valuable information to implement seasonal management actions.info:eu-repo/semantics/publishedVersio

Social media data from two iconic Neotropical big cats: can this translate to action?

IntroductionThere has been a gradual increase in studies of social media data usage in biodiversity conservation. Social media data is an underused source of information with the potential to maximize the outcomes of established conservation measures. In this study, we assessed how structured social media data can provide insight into species conservation through a species conservation plan, based on predefined actions. MethodsWe established a framework centered on a set of steps that go from defining social media platforms and species of interest to applying general analysis of data based on data dimensions—three W’s framework (What, When, Who) and the public engagement that posts received. The final and most important step in our proposed framework is to assess the overlap between social media data outcomes and measures established in conservation plans. In our study, we used the Brazilian National Action Plan (BNAP) for big cats as our model. We extracted posts and metrics about jaguars (Panthera onca) and pumas (Puma concolor) from two social media platforms, Facebook and Twitter. ResultsWe obtained 159 posts for both jaguars and pumas on Facebook (manually) and 23,869 posts for the jaguar and 14,675 posts for the puma on Twitter (through an application user interface). Data were categorized for content and users (only Facebook data) based on analysis of the content obtained and similarities found between posts. We used descriptive statistics for analyzing the metrics extracted for each data dimension (what, when, who, and engagement). We also used algorithms to predict categories in the Twitter database. Our most important findings were based on the development of a matrix summarizing the overlapping actions and dimensions of the data. Our findings revealed that the most prominent category of information for jaguars on Facebook was the sighting of wildlife outside protected areas, while for pumas, it was the trespassing of property by wildlife. From the Twitter dataset, we observed that the most prominent category of information for jaguars was: the sighting of wildlife outside protected areas, while for pumas, it was wildlife depredation by direct or indirect means. We found temporal trends that highlight the importance of categories in understanding information peaks on Facebook and Twitter. DiscussionWhen we analyze online engagement, we see a predominance of positive reactions on Facebook, and on Twitter, we see a balanced reaction between positive and negative. We identified 10 of 41 actions in the BNAP that might benefit from social media data. Most of the actions that could benefit from our dataset were linked to human–wildlife conflicts and threats, such as wildlife–vehicle collisions. Communication and educational actions could benefit from all dimensions of the data. Our results highlight the variety of information on social media to inform conservation programs and their application to conservation actions. We believe that studies on the success of applying data to conservation measures are the next step in this process and could benefit from input from decision-makers

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Catálogo Taxonômico da Fauna do Brasil: setting the baseline knowledge on the animal diversity in Brazil

The limited temporal completeness and taxonomic accuracy of species lists, made available in a traditional manner in scientific publications, has always represented a problem. These lists are invariably limited to a few taxonomic groups and do not represent up-to-date knowledge of all species and classifications. In this context, the Brazilian megadiverse fauna is no exception, and the Catálogo Taxonômico da Fauna do Brasil (CTFB) (http://fauna.jbrj.gov.br/), made public in 2015, represents a database on biodiversity anchored on a list of valid and expertly recognized scientific names of animals in Brazil. The CTFB is updated in near real time by a team of more than 800 specialists. By January 1, 2024, the CTFB compiled 133,691 nominal species, with 125,138 that were considered valid. Most of the valid species were arthropods (82.3%, with more than 102,000 species) and chordates (7.69%, with over 11,000 species). These taxa were followed by a cluster composed of Mollusca (3,567 species), Platyhelminthes (2,292 species), Annelida (1,833 species), and Nematoda (1,447 species). All remaining groups had less than 1,000 species reported in Brazil, with Cnidaria (831 species), Porifera (628 species), Rotifera (606 species), and Bryozoa (520 species) representing those with more than 500 species. Analysis of the CTFB database can facilitate and direct efforts towards the discovery of new species in Brazil, but it is also fundamental in providing the best available list of valid nominal species to users, including those in science, health, conservation efforts, and any initiative involving animals. The importance of the CTFB is evidenced by the elevated number of citations in the scientific literature in diverse areas of biology, law, anthropology, education, forensic science, and veterinary science, among others

Social media in the context of land mammals conservation: connecting human dimension with the digital universe

O volume de dados de redes sociais e seus usos têm crescido em número e complexidade e vem sendo utilizado nas mais variadas áreas, de marketing a pesquisas de opinião. Estratégias para sua obtenção, tratamento e análise são necessárias já que variam enormemente em relação a características (dimensões) dos dados. Este conjunto de dados apresenta forte potencial para contribuir, por exemplo, com o monitoramento da opinião pública em relação a eventos relacionados à conservação. Apesar da crescente popularidade das redes sociais, estudos que abordam seu potencial como fonte de informação para conservação e manejo de fauna silvestre continuam novos e merecem uma atenção especial. Assim, objetivamos neste estudo explorar dados de redes sociais sob a ótica de cinco dimensões: do que se trata, quando foi gerado, onde foi gerado, quem gerou e o engajamento popular (i.e., interação pública com conteúdo online através de recursos dispobilizados por redes sociais) que recebeu, afim de explorarmos o potencial de aprendizado com conjuntos de dados menores e aplicações em processos automatizados. Obtivemos dados a partir de duas plataformas de busca: Facebook e Twiter, relacionados a quatro espécies icônicas da fauna brasileira: onça-pintada (Panthera onca), onça-parda (Puma concolor), bugio (Alouatta spp.) e bicho-preguiça (Bradypus spp.; Choloepus spp.). Utilizamos a combinação de busca \"espécie+local\" no Facebook e apenas \"espécie\" no Twitter, através de API. Obtivemos 317 postagens para as espécies no Facebook e 38.516 no Twitter (com uma estimativa de 10% de postagens não correspondendo às espécies). Classificamos os dados em 12 categorias, sendo que por espécie, observamos que as categorias mais proeminentes foram \"avistamento de fauna fora de área protegida\", \"cativeiro\", \"resgate/tratamento e soltura de fauna\" e \"saúde de fauna silvestre\". A análise específica do texto por espécie revelou palavras principalmente relacionadas a estas categorias no Facebook, e no Twitter a inclusão de termos relacionados ao bioma Pantanal e outras áreas. Observamos padrões temporais que destacam a importância das categorias para entender picos de informação no Facebook. No Twitter, os padrões temporais parecem estar relacionados às mesmas categorias, mas utilizamos ranking de palavras para entender picos de informação. Palavras como \"Juma\" (onça-pintada abatida logo antes dos jogos olímpicos de 2016) foram importantes para determinar eventos que geraram picos de postagens. De modo geral, a distribuição espacial das postagens se concentrou no município de São Paulo, com poucos dados apresentando localização precisa de eventos. Encontramos 9 categorias de tipos de usuários para o Facebook, onde páginas pessoais, páginas de notícias, páginas de negócios e zoológicos e aquários foram as categorias com mais postagens encontradas. No Twitter, observamos grande variação em relação aos valores obtidos para seguidores e amigos de usuários que postaram sobre as espécies de interesse deste estudo. Quando analisamos as variáveis de engajamento online, observamos a predominância de reações positivas no Facebook e altos valores de compartilhamento e comentários em postagens, com exceção do bugio, que apresentou reações negativas. Identificamos a importância de temas como a febre amarela na determinação de engajamento. Além disso, encontramos variáveis com associação positiva com engajamento através de regressão linear múltipla, como \"avistamento de fauna em área protegida\" e o tipo de usuário \"instituição Governamental\". A partir de algoritmos de aprendizagem de máquina conseguimos resultados variando entre 0,58 e 0,71 de acurácia para a classificação automatizada na base do Twitter. Este trabalho teve como principal conclusão a importância da utilização de agrupamento de dados menores para lidar com grandes conjuntos de dados de redes sociais no contexto da conservação de espécies, utilizando o Brasil e estado de São Paulo como áreas de estudo, além da necessidade de expandir pesquisas neste segmento no Brasil que contribuam com conservação aplicada.The volume of data from social media and their uses have grown in number and complexity and have been used in the most diverse areas, from marketing to opinion monitoring. Strategies for obtaining, treating and analyzing them are necessary, since they vary enormously in relation to its characteristics (dimensions) of data. These data have a strong potential to contribute, for example, to the monitoring of public opinion in relation to conservation-related events. Despite the growing popularity of social media, studies that address its potential as a source of information for conservation and management of wildlife remain new and deserve special attention. Thus, in this study we aim to explore data from social media from the perspective of five different dimensions: what is it about, when was it generated, where was it generated, who generated it and engagement (i.e., public interaction with online content through metrics avaiable by social media) it received, in order to explore and learn from smaller data sets and apply it in automated processes. We obtained data from two search platforms: Facebook and Twiiter, related to four central species from brazilian wildlife: jaguar (Panthera onca), puma (Puma concolor), howler monkey (Alouatta spp.) and sloth (Bradypus spp.; Choloepus spp.). We use the combination of \"species + local\" search on Facebook and only \"species\" on Twitter through API (Application Programming Inteface) use. We obtained 317 posts for the species on Facebook and 38,516 on Twitter (with an estimated 10% of posts not corresponding to the species) on Twitter. We classified data in 12 categories, we observed that the most prominent categories were \"sighting of fauna outside protected áreas\", \"captivity\", \"rescue / treatment and release of wildlife\" and \"wildlife health\". The specific analysis of the text by species revealed words mainly related to these categories on Facebook with the inclusion of terms related to the Pantanal biome and other areas on Twitter. We observed temporal patterns that highlight the importance of categories to understand information peaks on Facebook. On Twitter, temporal patterns seem to be related to the same categories found in Facebook, but we use word ranking to understand peaks of information. Words like \"Juma\" (jaguar shot dead before 2016 Brazil Olympic Games) were important in determining events that generated peaks in posting. In general, the spatial distribution of posts was concentrated in the municipality of São Paulo with little data showing precise location of events. We found 9 categories of users types on Facebook, where personal pages, news pages, business and zoos and aquariums were the categories with most posts found. On Twitter, we observed a great variation in relation to the values obtained for followers and friends of users who posted about the species of interest in this study. When we analyze engagement variables online, we see a predominance of positive reactions on Facebook and high values for sharing and commenting on posts, with the exception of the howler monkey, who showed negative reactions. We identified the importance of topics such as yellow fever in determining engagement. In addition, we found variables with a positive association with engagement through multiple linear regression, such as \"wildlife sighting in a protected área\" and the type of user \"government institution\". Using machine learning algorithms, we obtained results varying between 0.58 and 0.71 of accuracy for the automated classification performed on Twitter database. The main conclusion of this work was the importance of using smaller data clusters to deal with large social network data sets in the context of species conservation using Brazil and the state of São Paulo as areas of study. In addition, we found the need to expand research in this segment in Brazil that contribute to applied conservation