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Figure 1 in First data on population estimates and dispersal of Montenegrina subcristata - a field study at Virpazar, Montenegro
Figure 1. Study area with three study sites (A, B, C). Note that due to the perspective view site B and C appear much smaller.
Fig. 5 in Comparison of sampling methodologies and estimation of population parameters for a temporary fish ectoparasite
Fig. 5. Histograms of emergence counts from the time-series emergence traps. Count bars for each day are subdivided by individual trap.
Fig. 4 in Comparison of sampling methodologies and estimation of population parameters for a temporary fish ectoparasite
Fig. 4. Scatterplot showing total body length in mm versus estimated volume of blood and plasma extracted in Ml. The box-and-whisker plots are centered on the mean body length for each of the three juvenile stages. The box edges are placed at the 2nd and 3rd quartiles for volume estimates and the whiskers show extreme minimum and maximum volumes. The mean estimate of extracted volume by juvenile stage is shown as a labeled dashed-red horizontal line. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 1 in Comparison of sampling methodologies and estimation of population parameters for a temporary fish ectoparasite
Fig. 1. Traps used in the first study. (A) Small emergence trap, (B) fish-baited emergence trap, (C) fish-baited tripod, (D) open-mesh fish-baited trap and (E) lighted plankton trap. Note that the sample container holding a small French grunt fish for the fish-baited emergence trap (B) and the fish-baited tripod trap (C) are identical units other than the sealed floats attached to the top of the sample container when used with the fish-baited emergence trap.
Fig. 2 in Comparison of sampling methodologies and estimation of population parameters for a temporary fish ectoparasite
Fig. 2. Traps used in the second study. The lighted plankton trap, in the left foreground, stands on short legs—four large emergence traps can be seen in the middleground to the right of the lighted plankton trap. A second lighted plankton trap in the background can be seen towards the center of the frame.
Fig. 3 in Comparison of sampling methodologies and estimation of population parameters for a temporary fish ectoparasite
Fig. 3. Scatterplots of total body length in mm plotted against eye length in mm along the long axis. The upper plot shows measurements for zuphea and the lower plot for praniza. The body length cutoff values separating juvenile stages are shown as a dotted-green line. Gnathiids collected from emergence traps are seen as gold-filled squares and those collected from light traps are presented as purple-filled triangles. Differences in the ontological sampling bias of these two trap designs can be seen by comparing the two scatterplots. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 6 in Comparison of sampling methodologies and estimation of population parameters for a temporary fish ectoparasite
Fig. 6. Histograms of trap counts by sample day and juvenile stage. The upper histograms show counts from emergence traps and the lower histograms show counts from light traps. Mean count for each histogram is shown as a dashed horizontal line. See text for an explanation of the number of sampling days shown in each plot.
Figure. Phylogram showing phylogenetic relationships estimated using maximum likelihood analysis of 16S rRNA and COXI gene revealed the grouping of Orthochirus iranus, O. farzanpay, O. stockwelli, O. zagrosensis, O. innesi (JQ514244.1 Morocco), and O. bicolor (KT716038.1 India), with the outgroup species Androctonus crassicauda (FJ217732). in A study of genetic diversity among different population of Orthochirus sp. based on cytochrome C oxidase subunit I and 16srRNA sequencing
Figure. Phylogram showing phylogenetic relationships estimated using maximum likelihood analysis of 16S rRNA and COXI gene revealed the grouping of Orthochirus iranus, O. farzanpay, O. stockwelli, O. zagrosensis, O. innesi (JQ514244.1 Morocco), and O. bicolor (KT716038.1 India), with the outgroup species Androctonus crassicauda (FJ217732).
Fig. 2 in Conservation Of The Javan Gibbon Hylobates Moloch: Population Estimates, Local Extinctions, And Conservation Priorities
Fig. 2. Relation between number of groups calling per day and number of census days in the Telaga Warna Nature Reserve (Sept 1999) and Lingo Asri, Dieng mountains (Sept 1998).
Fig. 1 in Conservation Of The Javan Gibbon Hylobates Moloch: Population Estimates, Local Extinctions, And Conservation Priorities
Fig. 1. Current distribution of the Javan gibbon Hylobates moloch. Based on Kappeler, 1984, Asquith et al., 1995, Nijman 2001b, and present study. All areas where the species' presence has been confirmed are indicated in black; areas where the species' possible presence was reported by Andayani et al. (1999) are indicated in white. The three main study areas are: A, Telaga Warna Nature Reserve; B, Gunung Gede Pangrango National Park; C, Dieng mountains. The insert shows Java with all remaining forest patches on the island.
Fig. 4 in Population Estimates And Distribution Patterns Of Irrawaddy Dolphins (Orcaella Brevirostris) And Indo-Pacific Finless Porpoises (Neophocaena Phocaenoides) In The Kuching Bay, Sarawak
Fig. 4. Mapping of selected re-sighted Irrawaddy dolphins represented by photographs of the right sides of their dorsal fins in the Kuching Bay area.
Fig. 3 in Population Estimates And Distribution Patterns Of Irrawaddy Dolphins (Orcaella Brevirostris) And Indo-Pacific Finless Porpoises (Neophocaena Phocaenoides) In The Kuching Bay, Sarawak
Fig. 3. Relative densities for Irrawaddy dolphins (a) and finless porpoises (b). Densities are represented as the number of sightings per km searched in 2 × 2 km grid cells. This includes all on-effort sightings and all effort tracks from the start of the project in Jun.2008 through Oct. 2011.
Fig. 2 in Population Estimates And Distribution Patterns Of Irrawaddy Dolphins (Orcaella Brevirostris) And Indo-Pacific Finless Porpoises (Neophocaena Phocaenoides) In The Kuching Bay, Sarawak
Fig. 2. Distribution of on-effort sightings made during 2010–2011 DISTANCE surveys of the Kuching area. Sea conditions and logistical constraints limited survey coverage of the upper Northwestern most corner of the Santubong-Salak block.
Fig. 1 in Population Estimates And Distribution Patterns Of Irrawaddy Dolphins (Orcaella Brevirostris) And Indo-Pacific Finless Porpoises (Neophocaena Phocaenoides) In The Kuching Bay, Sarawak
Fig. 1. Kuching area survey "strata". Shapes for areas were created in Google Earth, creating slight mis-match with the base maps used in ArcGIS.
Fig. 1 in Estimating Population Size And Distribution Of Hume'S Pheasant In Northern Thailand
Fig. 1. Study area is covered by two LANDSAT 7/ETM+ images, path 131/row 46 and path 131/row 47, which were acquired in Feb.2002.
Fig. 1 in Using a spatial mark-resight model to estimate the parameters of a wild pig (Sus scrofa) population in Singapore
Fig. 1. Map showing the location of the Central Catchment Nature Reserve on mainland Singapore. All 27 camera points are indicated with a red circle. Black squares indicate the three camera points added to the 1 km2 grid. The six cage traps are marked with a blue cross. Dotted circles indicate areas the last remaining patches of primary forest in Singapore.
Fig. 3 in Using a spatial mark-resight model to estimate the parameters of a wild pig (Sus scrofa) population in Singapore
Fig. 3. Map of the Central Catchment Nature Reserve showing the day and night fixes of the collared pig. The home ranges are calculated from the monthly 95% Kernel Density Estimate (KDE), while the aggregate home range was calculated from the 99% KDE from all six months. The Seletar Expressway (SLE) is pointed out on the map and the satellite overlay was adapted from Google Earth.
Fig. 2 in Using a spatial mark-resight model to estimate the parameters of a wild pig (Sus scrofa) population in Singapore
Fig. 2. The density map showing the number of activity centres per kilometer square, the locations of the camera points (circles), 143 out of 856 GPS locations from the collared pig (black dots) and the boundary of the Central Catchment Nature Reserve. Only a fraction of the GPS locations was plotted to prevent the colored pixels from being obscured. Each pixel is 1 km2. X and Y coordinates are in kilometers.
Acclimation capacity of critical thermal maximum varies among populations: Consequences for estimates of vulnerability
<p>Adaptive plasticity in thermal tolerance traits may buffer organisms against changing temperatures, making such responses of particular interest in the face of global climate change. Although population variation is integral to the evolvability of this trait, many studies inferring proxies of physiological vulnerability from thermal tolerance traits extrapolate data from one or few populations to represent the species. Estimates of physiological vulnerability can be further complicated by methodological effects associated with experimental design. We evaluated how populations varied in their acclimation capacity (i.e., the magnitude of plasticity) for critical thermal maximum (CT<sub>max</sub>) in two species of tailed frogs (Ascaphidae), cold-stream specialists. We used the estimates of acclimation capacity to infer physiological vulnerability to future warming. We performed CT<sub>max</sub> experiments on tadpoles from 14 populations using a fully factorial experimental design of two holding temperatures (8℃, 15℃) and two experimental starting temperatures (8℃, 15℃). This design allowed us to investigate the acute effects of transferring organisms from one holding temperature to a different experimental starting temperature, as well as fully acclimated responses by using the same holding and starting temperature. We found that most populations exhibited beneficial acclimation, where CT<sub>max</sub> was higher in tadpoles held at a warmer temperature, but populations varied markedly in magnitude of the response and the inferred physiological vulnerability to future warming. We also found that the response of transferring organisms to different starting temperatures varied substantially among populations, although accounting for acute effects did not greatly alter estimates of physiological vulnerability at the species-level or for most populations. These results underscore the importance of sampling widely among populations when inferring physiological vulnerability, as population variation in acclimation capacity and thermal sensitivity may be critical when assessing vulnerability to future warming. </p>
Acclimation capacity of critical thermal maximum varies among populations: Consequences for estimates of vulnerability
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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