12 documents found, page 1 of 2
Primate extinction, the legacy of 40 years' road expansion in Colombia
Ceia-Hasse, Ana; Thomson, M. A.; Noguera‐Urbano, E. A.; Carrillo‐Restrepo, J. C.; Cruz‐Rodríguez, C. A.; Correa‐Ayram, C. A.; Ochoa‐Quintero, J. M.Primates are declining worldwide and rapid infrastructure expansion, particularly roads, threatens their habitat. New roads fragment habitats allowing anthropogenic activities to occur in once pristine ecosystems; this is particularly impactful in tropical areas with high endemic biodiversity, as is occurring with primates in Colombia. However, temporal assessments of how roads impact local biodiversity are rar...
Using citizen science data for predicting the timing of ecological phenomena ac...
Capinha, César; Ceia-Hasse, Ana; de-Miguel, Sergio; Vila-Viçosa, Carlos; Porto, Miguel; Jarić, Ivan; Tiago, Patricia; Fernández, Néstor; Valdez, JoseThe scarcity of long-term observational data has limited the use of statistical or machine-learning techniques for predicting intraannual ecological variation. However, time-stamped citizen-science observation records, supported by media data such as photographs, are increasingly available. In the present article, we present a novel framework based on the concept of relative phenological niche, using machine-le...
Supporting early detection of biological invasions through short-term spatial f...
Capinha, César; Monteiro, Antonio; Ceia-Hasse, AnaEarly detection of invasive species is crucial to prevent biological invasions. To increase the success of detection efforts, it is often essential to know when key phenological stages of invasive species are reached. This includes knowing, for example, when invasive insect species are in their adult phase, invasive plants are flowering or invasive mammals have finished their hibernation. Unfortunately, this ki...
Using citizen science data for predicting the timing of ecological phenomena ac...
Capinha, César; Ceia-Hasse, Ana; de-Miguel, Sergio; Vila-Viçosa, Carlos; Porto, Miguel; Jarić, Ivan; Tiago, Patrícia; Fernández, Néstor; Valdez, JoseThe scarcity of long-term observational data has limited the use of statistical or machine-learning techniques for predicting intraannual ecological variation. However, time-stamped citizen-science observation records, supported by media data such as photographs, are increasingly available. In the present article, we present a novel framework based on the concept of relative phenological niche, using machine-le...
Forecasting the abundance of disease vectors with deep learning
Ceia-Hasse, Ana; Sousa, Carla A.; Gouveia, Bruna R.; Capinha, CésarArboviral diseases such as dengue, Zika, chikungunya or yellow fever are a worldwide concern. The abundance of vector species plays a key role in the emergence of outbreaks of these diseases, so forecasting these numbers is fundamental in preventive risk assessment. Here we describe and demonstrate a novel approach that uses state-of-the-art deep learning algorithms to forecast disease vector abundances. Unlike...
Drivers of Insect Community Change along the Margins of Mountain Streams in Ser...
Ceia-Hasse, Ana; Boieiro, Mário; Soares, Albano; Antunes, Sandra; Figueiredo, Hugo; Rego, Carla; Borges, Paulo A. V.; Conde, José; Serrano, Artur R. M.Mountain ecosystems are important biodiversity hotspots and valuable natural laboratories to study community assembly processes. Here, we analyze the diversity patterns of butterflies and odonates in a mountainous area of high conservation value—Serra da Estrela Natural Park (Portugal)—and we assess the drivers of community change for each of the two insect groups. The butterflies and odonates were sampled alon...
Drivers of Insect Community Change along the Margins of Mountain Streams in Ser...
Ceia-Hasse, Ana; Boieiro, Mário; Soares, Albano; Antunes, Sandra; Figueiredo, Hugo; Rego, Carla; Borges, Paulo A.V.; Conde, José; Serrano, ArturMountain ecosystems are important biodiversity hotspots and valuable natural laboratories to study community assembly processes. Here, we analyze the diversity patterns of butterflies and odonates in a mountainous area of high conservation value—Serra da Estrela Natural Park (Portugal)—and we assess the drivers of community change for each of the two insect groups. The butterflies and odonates were sampled alon...
Forecasting the abundance of disease vectors with deep learning
Ceia-Hasse, Ana; Sousa, Carla A.; Gouveia, Bruna R.; Capinha, CésarArboviral diseases such as dengue, Zika, chikungunya or yellow fever are a worldwide concern. The abundance of vector species plays a key role in the emergence of outbreaks of these diseases, so forecasting these numbers is fundamental in preventive risk assessment. Here we describe and demonstrate a novel approach that uses state-of-the-art deep learning algorithms to forecast disease vector abundances. Unlike...
Local environmental variables are key drivers of ant taxonomic and functional b...
Wendt, Clara Frasconi; Ceia-Hasse, Ana; Nunes, Alice; Verble, Robin; Santini, Giacomo; Boieiro, Mário; Branquinho, CristinaThe decomposition of beta-diversity (β-diversity) into its replacement (βrepl) and richness (βrich) components in combination with a taxonomic and functional approach, may help to identify processes driving community composition along environmental gradients. We aimed to understand which abiotic and spatial variables influence ant β-diversity and identify which processes may drive ant β-diversity patterns in Me...
Deep learning for supervised classification of temporal data in ecology
Capinha, César; Ceia-Hasse, Ana; Kramer, Andrew M.; Meijer, ChristiaanTemporal data is ubiquitous in ecology and ecologists often face the challenge of accurately differentiating these data into predefined classes, such as biological entities or ecological states. The usual approach consists of transforming the time series into user-defined features and then using these features as predictors in conventional statistical or machine learning models. Here we suggest the use of deep ...