Among-species variation in flower size determines florivory in the largest tropical wetland.

PREMISE: Flower damage caused by florivores often has negative consequences for plant reproduction. However, the factors affecting plant-florivore interactions are still poorly understood, especially the role of abiotic factors and interspecific variation in florivory within ecosystems. Thus, the patterns of florivory levels and its consequences for plant communities need to be investigated further. METHODS: We assessed the influence of abiotic factors related to climatic seasonality, of phylogenetic relationships among plants, and of functional attributes associated with attractiveness to pollinators on florivory incidence and intensity in the Pantanal, the world's largest tropical wetland. Between December 2020 and November 2021, the percentage of flowers attacked (incidence) and petal area removed (intensity) by florivores were examined in 51 species from 25 families, considering flowering season, the substrate where the plants occur, and flower attributes as potentially determining factors on florivory levels. RESULTS: Phylogeny and environmental factors did not have a significant influence on florivory. The only determinant of interspecific variation in florivory incidence and intensity was flower size, where larger flowers experienced higher florivory levels regardless of season and substrate, while flower arrangement and color were not significant factors. CONCLUSIONS: Our study is one of the first to estimate the community-wide effects of biotic and abiotic factors on both the incidence and the intensity of florivory. The magnitude of this plant-florivore interaction may reduce reproductive success and entail selective pressures on plant attractiveness to pollinators.

Atlantic flower-invertebrate interactions: A data set of occurrence and frequency of floral visits.

Encounters between flowers and invertebrates are key events for the functioning of tropical forests. Assessing the structure of networks composed of the interactions between those partners leads to a better understanding of ecosystem functioning and the effects of environmental factors on ecological processes. Gathering such data is, however, costly and time-consuming, especially in the highly diverse tropics. We aimed to provide a comprehensive repository of available flower-invertebrate interaction information for the Atlantic Forest, a South American tropical forest domain. Data were obtained from published works and "gray literature," such as theses and dissertations, as well as self-reports by co-authors. The data set has ~18,000 interaction records forming 482 networks, each containing between one and 1061 interaction links. Each network was sampled for about 200 h or less, with few exceptions. A total of 641 plant genera within 136 different families and 39 orders were reported, with the most abundant and rich families being Asteraceae, Fabaceae, and Rubiaceae. Invertebrates interacting with these plants were all arthropods from 10 orders, 129 families, and 581 genera, comprising 2419 morphotypes (including 988 named species). Hymenoptera was the most abundant and diverse order, with at least six times more records than the second-ranked order (Lepidoptera). The complete data set shows Hymenoptera interacting with all plant orders and also shows Diptera, Lepidoptera, Coleoptera, and Hemiptera to be important nodes. Among plants, Asterales and Fabales had the highest number of interactions. The best sampled environment was forest (~8000 records), followed by pastures and crops. Savanna, grasslands, and urban environments (among others) were also reported, indicating a wide range of approaches dedicated to collecting flower-invertebrate interaction data in the Atlantic Forest domain. Nevertheless, most reported data were from forest understory or lower strata, indicating a knowledge gap about flower-invertebrate interactions at the canopy. Also, access to remote regions remains a limitation, generating sampling bias across the geographical range of the Atlantic Forest. Future studies in these continuous and hard-to-access forested areas will yield important new information regarding the interactions between flowers and invertebrates in the Atlantic Forest. There are no copyright restrictions on the data set. Please cite this data paper if the data are used in publications and teaching events.

Mauritia flexuosa palm trees airborne mapping with deep convolutional neural network.

Accurately mapping individual tree species in densely forested environments is crucial to forest inventory. When considering only RGB images, this is a challenging task for many automatic photogrammetry processes. The main reason for that is the spectral similarity between species in RGB scenes, which can be a hindrance for most automatic methods. This paper presents a deep learning-based approach to detect an important multi-use species of palm trees (Mauritia flexuosa; i.e., Buriti) on aerial RGB imagery. In South-America, this palm tree is essential for many indigenous and local communities because of its characteristics. The species is also a valuable indicator of water resources, which comes as a benefit for mapping its location. The method is based on a Convolutional Neural Network (CNN) to identify and geolocate singular tree species in a high-complexity forest environment. The results returned a mean absolute error (MAE) of 0.75 trees and an F1-measure of 86.9%. These results are better than Faster R-CNN and RetinaNet methods considering equal experiment conditions. In conclusion, the method presented is efficient to deal with a high-density forest scenario and can accurately map the location of single species like the M. flexuosa palm tree and may be useful for future frameworks.

Assessment of CNN-Based Methods for Individual Tree Detection on Images Captured by RGB Cameras Attached to UAVs.

Detection and classification of tree species from remote sensing data were performed using mainly multispectral and hyperspectral images and Light Detection And Ranging (LiDAR) data. Despite the comparatively lower cost and higher spatial resolution, few studies focused on images captured by Red-Green-Blue (RGB) sensors. Besides, the recent years have witnessed an impressive progress of deep learning methods for object detection. Motivated by this scenario, we proposed and evaluated the usage of Convolutional Neural Network (CNN)-based methods combined with Unmanned Aerial Vehicle (UAV) high spatial resolution RGB imagery for the detection of law protected tree species. Three state-of-the-art object detection methods were evaluated: Faster Region-based Convolutional Neural Network (Faster R-CNN), YOLOv3 and RetinaNet. A dataset was built to assess the selected methods, comprising 392 RBG images captured from August 2018 to February 2019, over a forested urban area in midwest Brazil. The target object is an important tree species threatened by extinction known as Dipteryx alata Vogel (Fabaceae). The experimental analysis delivered average precision around 92% with an associated processing times below 30 miliseconds.

Escape o sea presa: nuevos registros y conocimiento actual sobre depredadores de ranas Pseudinae (Anura: Hylidae) en América del sur / Escape or be preyed: new records and current knowledge on predators of pseudinae frogs (anura: hylidae) in south america

ABSTRACT Neotropical amphibians play important roles as preys and predators in freshwater and terrestrial ecosystems. The subfamily Pseudinae includes small and medium-sized frogs within three genera representing 13 valid species. Most published records of predation on Pseudinae frogs are anecdotic and scarce. Herein, we provide five new reports of predation and presenting a detailed literature review on Pseudinae predation, with 15 studies published between 1983-2017. Pseudinae species were preyed at both day and night by a wide variety of predators, principally birds. Adults were preyed upon more frequently than juveniles and tadpoles. In the present study, most predators were diurnal, with birds accounting for most records. A variety of invertebrates prey on anurans, but water bugs and spiders are the most common ones. In the present study, water bugs comprised 75 % of the Pseudinae predation records by invertebrates. However, anuran predation by invertebrates remains poorly documented. Thus, the description of new cases of predation accompanied by a review of the data available in the literature is crucial to understanding Neotropical food webs.

RESUMEN Los anfibios neotropicales desempeñan papeles importantes como depredadores y presas en los ecosistemas dulce acuícolas y terrestres. La subfamilia Pseudinae incluye ranas pequeñas y medianas, distribuidas en tres géneros y representando 13 especies válidas. Los registros de depredación de Pseudinae son escasos y anecdóticos. Por lo tanto, el conocimiento de nuevos casos puede mejorar nuestra comprensión de las relaciones depredador-presa entre estas ranas y su papel en la cadena trófica. Presentamos cinco nuevos registros de depredación y revisamos los casos de depredación de la literatura, que incluyen 15 estudios publicados entre 1983 y 2017. Las especies de Pseudinae son depredadas, tanto durante el día como en la noche, por una gran variedad de depredadores, principalmente aves. Los adultos son más frecuentemente depredados que los juveniles y los renacuajos. En el presente estudio, las chinches de agua representan 75 % de los registros de depredación de Pseudinae por invertebrados. Sin embargo, depredación de anuros por invertebrados permanece poco documentada. Por lo tanto, la descripción de nuevos casos de depredación, junto a una revisión de los datos disponibles en la literatura es crucial para entender redes tróficas neotropicales.