Leaf beetles attracted to light in a tropical dry forest of northern Mexico (Coleoptera: Chrysomelidae)

This is the first study of the composition of leaf beetles attracted to light in Mexico. We analyze the richness, temporal abundance and similarity between four localities of tropical dry forest in Sierra de San Javier, Sonora. A total of 3217 individuals belonging to 45 morphospecies, 31 genera and 10 tribes within five subfamilies of Chrysomelidae were collected in light traps in the tropical dry forest of Sierra de San Javier, Sonora. Galerucinae was the subfamily with the greatest number of genera and species. The genera with the highest number of species were Pachybrachis Chevrolat and Alagoasa Bechyné. Eight genera were represented by two species and 21 genera by only one species. Metrioidea rugipennis (Blake) was the species with the highest number of individuals (80% of the abundance recorded). According to the taxonomic and species similarity analysis, the sites San Javier and La Barranca had the highest similarity, while the lowest values were recorded at Rancho Las Peñitas and Cañón de Lo de Campa. The low similarity between sites cannot be explained by the geographic distance since it was not a determining factor to explain the low similarity in Sierra de San Javier.

Optimizing speleological monitoring efforts: insights from long-term data for tropical iron caves

Understanding the factors underpinning species abundance patterns in space and time is essential to implement effective cave conservation actions. Yet, the methods employed to monitor cave biodiversity still lack standardization, and no quantitative assessment has yet tried to optimize the amount and type of information required to efficiently identify disturbances in cave ecosystems. Using a comprehensive monitoring dataset for tropical iron caves, comprising abundance measurements for 33 target taxa surveyed across 95 caves along four years, here we provide the first evidence-based recommendations to optimize monitoring programs seeking to follow target species abundance through time. We found that seasonality did not influence the ability to detect temporal abundance trends. However, in most species, abundance estimates assessed during the dry season resulted in a more accurate detection of temporal abundance trends, and at least three surveys were required to identify global temporal abundance trends. Finally, we identified a subset of species that could potentially serve as short-term disturbance indicators. Results suggest that iron cave monitoring programs implemented in our study region could focus sampling efforts in the dry season, where detectability of target species is higher, while assuring data collection for at least three years. More generally, our study reveals the importance of long-term cave monitoring programs for detecting possible disturbances in subterranean ecosystems, and for using the generated information to optimize future monitoring efforts.

Subpixel Change Detection Based on Radial Basis Function with Abundance Image Difference Measure for Remote Sensing Images

Recently, land cover change detection has become a research focus of remote sensing. To obtain the change information from remote sensing images at fine spatial and temporal resolutions, subpixel change detection is widely studied and applied. In this paper, a new subpixel change detection method based on radial basis function (RBF) for remote sensing images is proposed, in which the abundance image difference measure (AIDM) is designed and utilized to enhance the subpixel mapping (SPM) by borrowing the fine spatial distribution of the fine spatial resolution image to decrease the influence of the spectral unmixing error. First, the fine and coarse spatial resolution images are used to develop subpixel change detection. Second, linear spectral mixing modeling and the degradation procedure are conducted on the coarse and fine spatial resolution image to produce two temporal abundance images, respectively. Then, the designed AIDM is utilized to enhance the RBF-based SPM by comparing the two temporal abundance images. At last, the proposed RBF-AIDM method is applied for SPM and subpixel change detection. The synthetic images based on Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and real case images based on two temporal Landsat-8 Operational Land Imager (OLI) images and one Moderate Resolution Imaging Spectroradiometer (MODIS) image are undertaken to validate the proposed method. The experimental results indicate that the proposed method can sufficiently decrease the influence of the spectral unmixing error and improve the subpixel change detection results.

Spatial and temporal distribution of phytoplankton with emphasis on the harmful and toxic algae in the Limboto Lake

This study aims to describe Limboto Lake's condition based on the spatial-temporal abundance of phytoplankton and the presence of harmful algae species. Sampling was conducted in February, March, and April 2018 at six stations. The phytoplankton found in Limboto Lake consisted of 7 divisions from 80 species, namely Chlorophyta (35 species), Euglenophyta (17 species), Bacillarophyta (13 species), Cyanophyta (7 species), Cryptophyta (4 species), Dinophyta (3 species) and Chyrosphyta (1 species). The results of the phytoplankton distribution analysis in Limboto Lake varied on average among stations as well as observation times. The results show that the average of phytoplankton abundance at stations 1, stations 2, stations 3, and stations 4 categorized as medium polluted waters with 4,903 ind/L, 5,000 ind/ L, 9,418 ind/L, and 10,049 ind/L, respectively. The abundance at station five is included in the lightly polluted category with an average phytoplankton abundance of 1,541 ind/L, while station 6 is in the heavily polluted category with the average value of 20,894 ind/L. Species that can be used as indicator species for pollution are those with the highest abundance value, namely Microcystis sp., which indicates that Limboto Lake contains high nitrate and phosphate.

Composición taxonómica y abundancia espacio-temporal de la ictiofauna del Río Presidio, Sinaloa, México

Taxonomic composition and spatio-temporal abundance of the ichthyofauna in Presidio River, Sinaloa, Mexico. The Rio Presidio in the southern Sinaloa, Mexico belongs to Nearctic-Neotropical zoogeographical transition zone, where its fish fauna could manifest changes in the abundance and distribution of the species during rainy and dry periods. The main objective of this study was to describe the taxonomic composition and spatial and temporal abundance of the fish fauna from the lower and middle Rio Presidio, by means of bimonthly samplings from April 2008 to February 2009. Forty fish species (39 native and 1 exotic) belonging to 32 genera and 19 families were registered. Two families (Gerreidae and Poeciliidae) contained the highest numbers of species (five and four, respectively). Six species (Lile stolifera, Oreochromis aureus, Awaous banana, Anchoa analis, Gobiomorus maculatus and Atherinella crystallina) in this order of importance contributed with almost 49 % of the total fish abundance. Species richness and salinity showed a direct relationship and an inverse relationship with the altitude. Based on the coefficient of similarity of species among sites two fish assemblages were identified. The first assemblage formed by species from marine derivation (peripheral) occurring in the lower part of the river, and the second assemblage by secondary freshwater species in the middle part. The higher fish species similarity observed in August and September was under the influence of the highest flows of the rainy period. Twenty-seven 27 species showed affinity to the Tropical Eastern Pacific Region, six species to the Neotropical region and two to the Nearctic region (Dorosoma smithi and Ictalurus cf. pricei ); one species has an Amphiatlantic distribution (Mugil curema), another is circumtropical (Mugil cephalus), two species have Amphiamerican distribution (A. monticola and G. cinereus), and one is exotic (O. aureus). Rev. Biol. Trop. 66(2): 848-862. Epub 2018 June 01.