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Figure 8 in Necromys lasiurus (Cricetidae: Sigmodontinae) from open areas of the Atlantic Forest of Rio de Janeiro: Population structure and implications for the monitoring of hantaviruses

Figure 8. Plot of the DIYABC Random Forest simulations for the five hypothetical demographic scenarios proposed for the Necromys lasiurus groups (Atlantic Forest ecoregion, Atlantic Forest domain of Rio de Janeiro state, and Arid Diagonal ecoregion), and the location of the observed data, used to validate the best scenario. In this analysis, scenario 1 received 99 "votes", scenario 2, 398 "votes", scenario 3, 229 "votes", and scenario 4, 76 "votes", with 198 "votes" for scenario 5.

Figure 4 in Necromys lasiurus (Cricetidae: Sigmodontinae) from open areas of the Atlantic Forest of Rio de Janeiro: Population structure and implications for the monitoring of hantaviruses

Figure 4. Haplotype network of the Necromys lasiurus Cytochrome b sequences analyzed in the present study, color-coded according to the results of the Bayesian Analysis of Population Structure (BAPS; see Fig. 6). (AF–RJ) Atlantic Forest domain of Rio de Janeiro state. Mutational steps are indicated with stripes.

Figure 5 in Necromys lasiurus (Cricetidae: Sigmodontinae) from open areas of the Atlantic Forest of Rio de Janeiro: Population structure and implications for the monitoring of hantaviruses

Figure 5. Haplotype network of the Necromys lasiurus Cytochrome b sequences obtained in the present study from localities in the Atlantic Forest and Pampa biomes (AF), color-coded by locality. (ARG) Argentina, (MS) Mato Grosso do Sul, (MG) Minas Gerais, (PY) Paraguay, (PR) Paraná, (RJ) Rio de Janeiro, (RS) Rio Grande do Sul, (SC) Santa Catarina, (SP) São Paulo. Mutational steps are indicated with stripes.

Figure 2 in Necromys lasiurus (Cricetidae: Sigmodontinae) from open areas of the Atlantic Forest of Rio de Janeiro: Population structure and implications for the monitoring of hantaviruses

Figure 2. The demographic scenarios formulated for testing in the DIYABC Random Forest analysis. Pop1 = Arid Diagonal ecoregion (AD), Pop2 = Atlantic Forest ecoregion (AF), Pop3 = Atlantic Forest of Rio de Janeiro state (AF-RJ). The scenarios tested here were: (1) AD as the ancestral population of AF, which originates AF-RJ, (2) AD as the ancestral population, which mixes with AF before originating AF-RJ, (3) AF-RJ as the ancestral population, which mixes with AF before originating AD, (4) AD as the ancestral population of AF and AF-RJ, and (5) AD as the ancestral population, mixing with AF-RJ before originating AF.

Figure 3 in Necromys lasiurus (Cricetidae: Sigmodontinae) from open areas of the Atlantic Forest of Rio de Janeiro: Population structure and implications for the monitoring of hantaviruses

Figure 3. Consensus phylogenetic tree produced by the Maximum Likelihood (ML) and Bayesian Inference (BI) analyses of the Cytochrome b sequences of Necromys lasiurus included in the present study. The clades are color-coded according to the results of the Bayesian Analysis of Population Structure (BAPS; see Fig. 6). The samples shaded green are from Atlantic Forest domain of Rio de Janeiro state. The circles at each branch represent the bootstrap values of the ML (left semi-circles) and the posterior probabilities of the BI (right semi-circles). In the left semi-circles, white indicates bootstrap values of 0.40–0.66, while gray represents values of 0.66–0.90, and black, values of over 0.90. In the right semi-circles, white indicates a posterior probability of less than 0.64, with gray representing posterior probabilities of 0.64–0.90, and black, values of over 0.90.

opencc-by-4.0Jul 2024View details →

UAS-SfM data from Unoccupied aerial system (UAS) Structure-from-Motion canopy fuel parameters: Multisite area-based modelling across forests in California, USA

Data for: Unoccupied aerial system (UAS) Structure-from-Motion canopy fuel parameters: Multisite area-based modelling across forests in California, USA Sean Reilly 1, Matthew L. Clark 2, Lika Loechler 2, Jack Spillane 2, Melina Kozanitas 3, Paris Krause 4, David Ackerly 3, Lisa Patrick Bentley 4, and Imma Oliveras Menor 1,5 1 Environmental Change Institute, University of Oxford, Oxford OX1 3QY, UK 2 Center for Interdisciplinary Geospatial Analysis, Department of Geography, Environment, and Planning, Sonoma State University, Rohnert Park, CA 94928, USA 3 Departments of Integrative Biology and Environmental Science, Policy, and Management, University of California, Berkeley, CA 94720, USA 4 Department of Biology, Sonoma State University, Rohnert Park, CA 94928, USA 5 AMAP (Botanique et Modélisation de l’Architecture des Plantes et des Végétations), CIRAD, CNRS, INRA, IRD, Université de Montpellier, Montpellier, France Study abstract: There is a pressing need for well-informed management to reduce wildfire hazard and restore fire’s beneficial ecological role in the Mediterranean- and temperate-climate forests of California, USA. These efforts rely upon the accessibility of high spatial and temporal resolution data on biomass and canopy fuel parameters such as canopy base height (CBH), mean canopy height, canopy bulk density (CBD), canopy cover, and leaf area index (LAI). Remote sensing using unoccupied aerial system Structure-from-Motion (UAS-SfM) presents a promising technology for this application due to its accessibility, relatively low cost, and possibility for high temporal cadence. However, to date, this method has not been studied in the complex mosaic of forest types found across California. In this study we examined the capacity of structural and multispectral information obtained from UAS-SfM, in conjunction with machine learning methods, to model aboveground biomass and forest canopy fuel structural parameters using an area-based approach across multiple sites representing a diversity of forest types in California. Based on correlations with field measurements, fuel parameters separated into vertical (biomass, CBH, and mean height) and horizontal (LAI, CBD, canopy cover) groups. UAS-SfM random forest models performed well for modelling the vertical structure canopy fuels parameters (R2 0.69 – 0.75). These models exhibited strong performance in comparison to ALS, as well as when transferred to a novel site. Vertical structure predictors were prominent in these models, and did not improve with the addition of spectral predictors. UAS-SfM random forest models of horizontal structure parameters mainly used raster-based spectral indices (primarily NDVI) and had relatively low performance (R2 0.49 – 0.59). In addition, these models underperformed ALS and had poor performance when applied to a novel site. When applied to a region with widespread UAS-SfM coverage, models from both groups successfully produced contiguous maps that could be used for modelling fire behavior or in management decision making and monitoring. These findings indicate that UAS-SfM, without the need for multispectral sensors, is well suited for mapping area-based vertical-structure canopy parameters across diverse landscapes supporting a wide range of forest types. In contrast, the identification of spectral mean variables for modelling horizontal structure canopy fuels suggests the potential of multi- or hyperspectral sensors or high-resolution satellite imagery for meeting management information needs.  Published in Remote Sensing of Environment Contents: This repository contains multispectral UAS-SfM data from four sites around California, USA: jcksn: Jackson Demonstration State Forest ltr: LaTour Demonstration State Forest ppwd: Pepperwood Preserve sdlmtn: Saddle Mountain Open Space Preserve Data were collected during a series of campaigns: c1: Pepperwood, 2019-09-01 to 2019-10-15 c3: Jackson, 2020-06-15 to 2020-07-02 c4: LaTour, 2020-07-07 to 2020-07-17 c6: Saddle Mountain, 2020-08-04 to 2020-08-09 c9: Jackson, 2021-07-08 to 2021-07-12 Data are included in three formats: raw: Raw outputs from Pix4D (spectral and las) reg_grnd, reg_cnpy: Las files with merged multispectral data and classified ground, registered to ALS using either ground points (grnd) or, in cases with insufficient ground points for registration, to the canopy (cnpy) hnrm: Height normalized las files, normalization performed using ALS terrain model File naming structure: site_campaign_flightzone_uas_processedstate See accompanying paper for methods on data collection and processing Data are grouped into zipped folder by product type Funding: Funding for this research was supported by CAL FIRE Forest Health and Forest Legacy (8GG18806) and California State University, Agricultural Research Institute (20-01-106) awards to L.P.B and M.L.C. S.R. was funded by the Rhodes Trust and through the University of Oxford Environmental Change Institute Small Grant Scheme. Pepperwood ground data collection was supported by funding from the Gordon and Betty Moore Foundation and National Science Foundation grants 1754475 and 1835086. Citation: <div> <div>Reilly, S., Clark, M.L., Loechler, L., Spillane, J., Kozanitas, M., Krause, P., Ackerly, D., Bentley, L.P., Menor, I.O., 2024. Unoccupied aerial system (UAS) Structure-from-Motion canopy fuel parameters: Multisite area-based modelling across forests in California, USA. Remote Sensing of Environment 312, 114310. <a href="https://doi.org/10.1016/j.rse.2024.114310">https://doi.org/10.1016/j.rse.2024.114310</a></div> </div>

opencc-by-4.0Nov 2015View details →

Fig. 2 in Spatial variation of dung beetle assemblages associated with forest structure in remnants of southern Brazilian Atlantic Forest

Fig. 2. Principal coordinates analysis (PCoA) of dung beetle species based on Bray–Curtis similarity and environmental variables based on Euclidean distance. The analysis was performed using presence–absence (a), abundance (b) and biomass (c) data of dung beetles, and 15 environmental variables (d). ANH: Anhatomirim Environmental Protection Area; ITA: Permanent Protection Areas of Itapema; PER: Peri Lagoon Municipal Park; RAT: Permanent Protection Areas of Ratones.

opencc-by-4.0Jan 2015View details →

Figure 1 in Effects of forest conversion on tce assemblages' structure of aquatic insects in subtropical regions

Figure 1. Location of tce micro-basin and sampled streams in forested area (F1, F2, and F3) and converted area (C1, C2, and C3) at Parque Estadual do Turvo and adjacent areas, in soutcern Brazil.

opencc-by-4.0Jan 2015View details →

Figure 3 in Effects of forest conversion on tce assemblages' structure of aquatic insects in subtropical regions

Figure 3. Ordination diagram of NMDS of Epcemeroptera, Plecoptera, and Triccoptera assemblages at streams in forested area (F) and converted area (C). Numbers 1-3 refer to tce stream; R refers to rocky bottom substrate, and L refers to leaf litter substrate.

opencc-by-4.0Jan 2016View details →

Figure 2 in Structure of summer bat assemblages in forests in European Russia

Figure 2. Location of main mist-netting site in the southeast part of the Voronezhsky State Nature Biosphere Reserve.

Fig. 5 in Environmental heterogeneity causes differences in the amphibian assemblage structure of an undisturbed montane cloud forest in southern Mexico

Fig. 5. Canonical Correspondence Analysis of the most common amphibians. The arrow orientation and length represent the association, direction, and strength between the environmental variables and the ordination axis. Species names correspond to: Crm (C. matudai), Plm (Pl. matudai), Pls (Pl. sagorum), Pte (Pt. euthysanota), Bof (B. franklini), Boo (B. occidentalis), and Dex (D. xolocalcae) Environmental acronyms correspond to: Hum (Humidity), Understory_Den (Under story density), Le_Li_depth (leaf litter depth), and Temp (temperature).

Fig. 4 in Environmental heterogeneity causes differences in the amphibian assemblage structure of an undisturbed montane cloud forest in southern Mexico

Fig. 4. (a) Principal Component Analysis, grouping the eight sites present in the core zones according to eight environmental variables taken in each site. Blue triangles: TCZ (El Triunfo core zone) sites; pink circles: QCZ (El Quetzal core zone) site. (b) Eight environmental variables measured in the eight sites (four per core zone). Median (solid line), 25th and 75th percentiles (boundaries of boxes), minimum and maximum (lines).

Fig. 1 in Environmental heterogeneity causes differences in the amphibian assemblage structure of an undisturbed montane cloud forest in southern Mexico

Fig. 1. Location of the two sampled zones, El Triunfo core zone [TCZ] (1) and the El Quetzal core zone [QCZ] (3), in the El Triunfo Biosphere Reserve (ETBR), Sierra Madre de Chiapas, Mexico, and illustration of the sample design (core zones, sites, and plots).

Fig. 3 in Environmental heterogeneity causes differences in the amphibian assemblage structure of an undisturbed montane cloud forest in southern Mexico

Fig. 3. (a) Rank-abundance Curves for the El Triunfo core zone [TCZ] and Quetzal core zone [QCZ] in the El Triunfo Biosphere Reserve. Letters on the Rank-abundance Curves correspond to Crm (C. matudai), Crs (C. stuarti), Pll (Pl. lacertosa), Plh (Pl. hartwegii), Plm (Pl. matudai), Pls (Pl. sagorum), Dus (D. schmidtorum), Pte (Pt. euthysanota), Exs (E. sumichrasti), Lim (L. maculatus), Bof (B. franklini), Boo (B. occidentalis), Bofl (B. flavimembris), and Dex (D. xolocalcae). (b) Nonmetric multidimensional scaling of the eight sites within the core zones in the ETBR. Blue triangles: TCZ sites, pink circles: QCZ sites. (c) Dendrogram of functional groups of the El Triunfo core zone amphibian species, using Euclidian Distance, and tested functional groups by ANOSIM are highlighted in different colors (FG1: green; FG2: brown; FG3: blue; FG4: red, and FG5: yellow).

Fig. 2 in Environmental heterogeneity causes differences in the amphibian assemblage structure of an undisturbed montane cloud forest in southern Mexico

Fig. 2. Box plots of amphibian species diversity in the El Triunfo Biosphere Reserve (ETBR), Chiapas, Mexico, showing the median (solid line), 25th and 75th percentiles (boundaries of boxes), and minimum and maximum (lines). (a) Number of individuals, (b) Species richness (0D), (c) Common species (1D), and (d) Dominant species (2D).

opencc-by-4.0Aug 2021View details →

Fig. 3 in Composition and structure of the helminth community of rodents in matrix habitat areas of the Atlantic forest of southeastern Brazil

Fig. 3. Bipartite plot of the interactions between the mammal hosts and the helminth parasites identified in the present study.

opencc-by-4.0Aug 2021View details →

Fig. 2 in Composition and structure of the helminth community of rodents in matrix habitat areas of the Atlantic forest of southeastern Brazil

Fig. 2. Species accumulation curve of the helminths recorded in each mammalian host: a. Akodon cursor b. Mus musculus c. Necromys lasiurus.

opencc-by-4.0Aug 2021View details →

Fig. 1 in Composition and structure of the helminth community of rodents in matrix habitat areas of the Atlantic forest of southeastern Brazil

Fig. 1. Location of the sampling sites within the REBIO Poço das Antas and the APA-BRSJ in Rio de Janeiro state (RJ), southeastern Brazil, showing the distribution of the different vegetation types and the canals that separate the two reserves.

opencc-by-4.0Dec 2019View details →

Fig. 2 in Richness of Chrysomelidae (Coleoptera) depends on the area and habitat structure in semideciduous forest remnants

Fig. 2. Rarefaction and extrapolation curve (a) and sample-coverage curve (B) of Chrysomelidae assemblage from remnant forest fragments in Dourados, Mato Grosso do Sul, Brazil. In both figures, solid lines represent observed data, dashed lines represent the extrapolation (900 individuals) and shaded areas the 95% confidence intervals (based on a bootstrap method with 1,000 replications).