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Fig. 5 in Habitat Preferences And Activity Patterns Of The Larger Mammal Community In Phnom Prich Wildlife Sanctuary, Cambodia

Fig. 5. Mean Relative Abundance Indice (± SEM) for 12 most frequently encountered mammal species in PPWS at camera trap locations closer (black bars) and further (open bars) than 11-km from nearest village. RM red muntjac; EWP Eurasian wild pig; B banteng; E Asian elephant; LIC large Indian civet; EAP east Asian porcupine; L leopard; CPC common palm civet; D dhole; LC leopard cat; G gaur; and PTM pig-tailed macaque.

Fig. 4 in Habitat Preferences And Activity Patterns Of The Larger Mammal Community In Phnom Prich Wildlife Sanctuary, Cambodia

Fig. 4. Mean Relative Abundance Indices (± SEM) for 12 most frequently encountered mammal species in PPWS at camera trap locations in DDF (black bars) and SEGF (open bars). RM red muntjac; EWP Eurasian wild pig; B banteng; E Asian elephant; LIC large Indian civet; EAP east Asian porcupine; L leopard; CPC common palm civet; D dhole; LC leopard cat; G gaur; and PTM pig-tailed macaque.

Fig.6 in Habitat Preferences And Activity Patterns Of The Larger Mammal Community In Phnom Prich Wildlife Sanctuary, Cambodia

Fig.6. Activity patterns, % of all encounters within each hour, of red muntjacs and Eurasian wild pigs from camera-traps in PPWS.

Fig. 2 in Habitat Preferences And Activity Patterns Of The Larger Mammal Community In Phnom Prich Wildlife Sanctuary, Cambodia

Fig. 2. Increase in species richness (species recorded) with total cumulative number of camera trap nights within Phnom Prich Wildlife Sanctuary (Dec 08–Aug 09).

opencc-zeroSep 2022View details →

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Congruence among multiple indices of habitat preference for species facing human-induced rapid environmental change: A case study using the Brewer's sparrow

Accurate evaluations of habitat preference are key to understanding optimal conditions for wildlife survival and reproduction. Habitat selection, however, usually is evaluated using a single index of preference, and congruence among multiple, relevant indices of preference is examined rarely. We assessed the concordance between patterns of habitat preference using three different indices of breeding site preference in a migratory songbird. Specifically, we compared the chronology of territorial establishment, pair formation, and reproductive initiation of the Brewer's sparrow (Spizella breweri) along a gradient of surface disturbance associated with natural gas development in Wyoming, USA during 2019. We expected all three indices to demonstrate a preference for breeding sites with less surface disturbance, where reproductive success typically is higher. By contrast, all indices suggested suboptimal preference with respect to surface disturbance, with some discrepancy among them. The chronology of settlement and pairing did not vary across the disturbance gradient, whereas nest initiation tended to occur earlier at sites with more disturbance. If the pattern of suboptimal selection of breeding sites that we identified is generalizable across other populations of migratory birds affected by energy development, the resultant lower fitness in those areas may exacerbate population declines. Our results suggest that traditional, single-index approaches to the study of habitat selection, if chosen carefully, may provide adequate inference on habitat preferences. Different metrics, however, can lead to at least subtle differences in patterns of habitat selection. The simultaneous examination of multiple indices of preference across a diversity of systems would help clarify the contexts under which preference metrics can become decoupled.

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Data from: Stochastic character mapping, Bayesian model selection, and biosynthetic pathways shed new light on the evolution of habitat preference in cyanobacteria

Cyanobacteria are the only prokaryotes to have evolved oxygenic photosynthesis paving the way for complex life. Studying the evolution and ecological niche of cyanobacteria and their ancestors is crucial for understanding the intricate dynamics of biosphere evolution. These organisms frequently deal with environmental stressors such as salinity and drought, and they employ compatible solutes as a mechanism to cope with these challenges. Compatible solutes are small molecules that help maintain cellular osmotic balance in high-salinity environments, such as marine waters. Their production plays a crucial role in salt tolerance, which, in turn, influences habitat preference. Among the five known compatible solutes produced by cyanobacteria (sucrose, trehalose, glucosylglycerol, glucosylglycerate, and glycine betaine), their synthesis varies between individual strains. In this study, we work in a Bayesian stochastic mapping framework, integrating multiple sources of information about compatible solute biosynthesis in order to predict the ancestral habitat preference of Cyanobacteria. Through extensive model selection analyses and statistical tests for correlation, we identify glucosylglycerol and glucosylglycerate as the most significantly correlated with habitat preference, while trehalose exhibits the weakest correlation. Additionally, glucosylglycerol, glucosylglycerate, and glycine betaine show high loss/gain rate ratios, indicating their potential role in adaptability, while sucrose and trehalose are less likely to be lost due to their additional cellular functions. Contrary to previous findings, our analyses predict that the last common ancestor of Cyanobacteria (living at around 3180 Ma) had a 97% probability of a high salinity habitat preference and was likely able to synthesize glucosylglycerol and glucosylglycerate. Nevertheless, cyanobacteria likely colonized low-salinity environments shortly after their origin, with an 89% probability of the first cyanobacterium with low-salinity habitat preference arising prior to the Great Oxygenation Event (2460 Ma). Stochastic mapping analyses provide evidence of cyanobacteria inhabiting early marine habitats, aiding in the interpretation of the geological record. Our age estimate of ~2590 Ma for the divergence of two major cyanobacterial clades (Macro- and Microcyanobacteria) suggests that these were likely significant contributors to primary productivity in marine habitats in the lead-up to the Great Oxygenation Event, and thus played a pivotal role in triggering the sudden increase in atmospheric oxygen.

opencc-zeroMay 2024View details →

opencc-by-4.0Feb 2008View details →

Fig. 1 in Habitat Preference In Territories Of The Red-Backed Shrike Lanius Collurio And Their Food Richness In An Extensive Agriculture Landscape

Fig. 1. Mean numbers of more important invertebrate taxa of 4–10 mm in length in four types of habitat

opencc-by-4.0Dec 2010View details →

Fig. 3 in Habitat Preference And Prey Selection Of Marsh Harrier (Circus Aeruginosus) In Overwintering Area Of Southeast China

Fig. 3. Abundances of passerines (), pheasant () and marsh harrier (+) of the four years in the four habitats in Shahu Nature Reserve, China, with line transects 2000 m × 200 m (A, autumn; W,

opencc-by-4.0Dec 2010View details →

Fig. 2 in Habitat Preference And Prey Selection Of Marsh Harrier (Circus Aeruginosus) In Overwintering Area Of Southeast China

Fig. 2. Wintering marsh harrier's abundance in different habitats in Shahu Nature Reserve, China in autumn and winter of 2001, 2003, 2004 and 2006

opencc-by-4.0Dec 2010View details →

Fig. 1 in Habitat Preference And Prey Selection Of Marsh Harrier (Circus Aeruginosus) In Overwintering Area Of Southeast China

Fig. 1. Shahu Nature Reserve (SNR, autumn and winter). The up left shows the location of SNR; HB, Hubei Province; BWH, Beiwu Lake; NWH, Nanwu Lake; DC, Daocao Lake; DJ, Dongji River; YR, Yangtze River

opencc-by-4.0Dec 2013View details →

Fig. 5 in Habitat preferences of common native fishes in a tropical river in Southeastern Brazil

Fig. 5. Substrate preferences of seven dominant native fish species in the Guandu River. Type of Substrate: 1, clay; 2, mud; 3, sand; 4, boulder/cobble/gravel; 5, bedrock.

opencc-by-4.0Dec 2013View details →

Fig. 2 in Habitat preferences of common native fishes in a tropical river in Southeastern Brazil

Fig. 2. Distribution of sampling by habitat availability (%) in terms of depth (m), velocity (m s-1), and type of substrate. (1, clay; 2, mud; 3, sand; 4, boulder/cobble/gravel; 5, bedrock).

opencc-by-4.0Dec 2013View details →

Fig. 1 in Habitat preferences of common native fishes in a tropical river in Southeastern Brazil

Fig. 1. Study area, Guandu River, indicating the four sampled stretches. WTP, Water Treatment Plant.

Fig. 3 in Short-term movements and habitat preferences of sailfish, Istiophorus platypterus (Istiophoridae), along the southeast coast of Brazil

Fig. 3. Minimum and maximum daily temperature and depth experienced by sailfish I, II and III. Box-plots represent the depths experienced by tagged sailfish.

Fig. 2 in Short-term movements and habitat preferences of sailfish, Istiophorus platypterus (Istiophoridae), along the southeast coast of Brazil

Fig. 2. Temperature histograms, showing the relative frequency of time spent at temperature for each tagged sailfish individual. Error bars indicates the standard errors around mean values.

Fig. 1 in Short-term movements and habitat preferences of sailfish, Istiophorus platypterus (Istiophoridae), along the southeast coast of Brazil

Fig. 1. Depth histograms, showing relative frequency of time spent at depth for each tagged sailfish. Error bars indicates the standard errors around mean values.

Fig. 4. Most-probable tracks for sailfish I, II in Short-term movements and habitat preferences of sailfish, Istiophorus platypterus (Istiophoridae), along the southeast coast of Brazil

Fig. 4. Most-probable tracks for sailfish I, II, III, and IV fitted with Kalman Filter State-Space Model.

opencc-by-4.0Dec 2018View details →

Figure 3. CCA showing the relationship between 16 in Assessments of environmental variables affecting the spatiotemporal distribution and habitat preferences of living Ostracoda (Crustacea) species in the Enez Lagoon Complex (Enez-Evros Delta, Turkey)

Figure 3. CCA showing the relationship between 16 species (red triangles) and 9 environmental variables (red arrows). See Tables 2 and 4 for an explanation of abbreviations and variables.