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Fig 4 in Proximate composition and its seasonal variations of the muscle tissue of Channa striata from Krishna river, Andhra Pradesh

Fig 4: Ash content variations in muscle of male and female C. striata

Fig 2 in Proximate composition and its seasonal variations of the muscle tissue of Channa striata from Krishna river, Andhra Pradesh

Fig 2: Fat content variations in muscle of male and female C. striata

Fig 3 in Proximate composition and its seasonal variations of the muscle tissue of Channa striata from Krishna river, Andhra Pradesh

Fig 3: Moisture content variations in muscle of male and female C. striata

Fig 1 in Proximate composition and its seasonal variations of the muscle tissue of Channa striata from Krishna river, Andhra Pradesh

Fig 1: Protein content variations in muscle of male and female C. striata

opencc-by-4.0Sep 2018View details →

Data for: Temporal variation in intertidal habitat use by nekton at seasonal and diel scales

A two-year dataset of nekton community structure, seagrass, temperature, and salinity for seagrass-vegetated and unvegetated habitats in Willapa Bay, Washington State, USA.

opencc-by-4.0Jan 2014View details →

Figure 6 in Biological diversity and seasonal variation of mesozooplankton in the southeastern Black Sea coastal ecosystem

Figure 6. Anchovy production during the sampling period.

opencc-by-4.0May 2015View details →

Figure 1 in Seasonal variations of abundance and live/dead compositions of copepods in Mersin Bay, northeastern Levantine Sea (eastern Mediterranean)

Figure 1. Locations of the sampling stations.

opencc-by-4.0Aug 2018View details →

Figure 1 in Seasonal variation and taxonomic composition of mesozooplankton in the southern Black Sea (off Sinop) between 2005 and 2009

Figure 1. Study area and sampling station location.

opencc-by-4.0Aug 2024View details →

Data for article "Seasonal variation in migration routes of Common Whitethroat Curruca communis"

Abstract This archive contains raw geolocator data and daily positions of analysed data from seven Common Whitethroats tracked by light geolocators from breeding sites in the Czech Republic and Latvia. Country and year of geolocator deployment is indicated in folder names. All geolocators, except for a logger with an ID BG630, are model SOI-GDL2 (Swiss Ornithological Institute) geolocators. Geolocator BG630 is model Intigeo-P50Z11-7-DIP geolocator (Migrate Technology Ltd.).

opencc-by-4.0Dec 2023View details →

Figure 1 in SEASONAL VARIATION IN THE CHIRONOMIDAE (DIPTERA) COMMUNITIES OF TWO FAROESE STREAMS Abstract

Figure 1. Map of the Faroe Islands with the location of the two sampled streams, Matará and Sundsá.

Table 1 in Proximate composition and its seasonal variations of the muscle tissue of Channa striata from Krishna river, Andhra Pradesh

Table 1: Variations of biochemical composition of muscle. C. striata (females)<table><tbody><tr><th>Seasons</th><th>Months Protein 2021-2022</th><th>Fat</th><th>Moisture</th><th>Ash</th></tr></tbody><tbody><tr><th>South West Monsoon</th><td>June</td><td>22.9</td><td>5.1</td><td>81.5</td><td>1.75</td></tr><tr><td>July</td><td>23.5</td><td>4.9</td><td>82.6</td><td>1.87</td></tr><tr><td>August</td><td>24.3</td><td>4.3</td><td>83.8</td><td>1.99</td></tr><tr><td>September</td><td>23.8</td><td>4.5</td><td>83.4</td><td>2.03</td></tr><tr><th>Post Monsoon</th><td>October November</td><td>22.8 21.5</td><td>4.8 5.2</td><td>83 82</td><td>2.12 2.19</td></tr><tr><th>North East Monsoon</th><td>December January February</td><td>20.4 21.5 21.2</td><td>5.4 4.9 4.6</td><td>82.1 82.3 82.2</td><td>2.01 1.96 1.81</td></tr><tr><th>Summer</th><td>March</td><td>21.8</td><td>4.5</td><td>80.2</td><td>1.79</td></tr><tr><td>April</td><td>22.3</td><td>4.6</td><td>81.5</td><td>1.61</td></tr><tr><td>May</td><td>22.4</td><td>5.2</td><td>80.5</td><td>1.75</td></tr><tr><th></th><td>Min.</td><td>20.4</td><td>4.3</td><td>80.2</td><td>1.61</td></tr><tr><th></th><td>Max.</td><td>24.3</td><td>5.4</td><td>83.8</td><td>2.19</td></tr><tr><th></th><td>Avg. 22.36±2.24.83±0.582.09±5.91.90±0.06</td></tr></tbody></table>

Table 2 in Proximate composition and its seasonal variations of the muscle tissue of Channa striata from Krishna river, Andhra Pradesh

Table 2: Variations of biochemical composition of muscle. C. striata (males)<table><tbody><tr><th>Seasons</th><th>Months Protein 2021-2022</th><th>Fat</th><th>Moisture</th><th>Ash</th></tr></tbody><tbody><tr><th>South West Monsoon</th><td>June</td><td>23.7</td><td>5.4</td><td>82.4</td><td>1.79</td></tr><tr><td>July</td><td>23.8</td><td>5.1</td><td>83.1</td><td>1.65</td></tr><tr><td>August</td><td>25.2</td><td>4.5</td><td>84.2</td><td>2.05</td></tr><tr><td>September</td><td>25.1</td><td>4.7</td><td>83.8</td><td>2.14</td></tr><tr><th>Post Monsoon</th><td>October November</td><td>24.2 23.5</td><td>4.9 5.3</td><td>82.9 83.2</td><td>2.1 2.25</td></tr><tr><th>North East Monsoon</th><td>December January February</td><td>22.4 22.3 23.2</td><td>5.5 4.6 4.4</td><td>81.2 81.5 80.5</td><td>1.96 1.85 1.72</td></tr><tr><th>Summer</th><td>March</td><td>22.6</td><td>3.8</td><td>80.4</td><td>1.65</td></tr><tr><td>April</td><td>23.5</td><td>3.6</td><td>81.5</td><td>1.84</td></tr><tr><td>May</td><td>22.5</td><td>4.8</td><td>80.6</td><td>1.89</td></tr><tr><th></th><td>Min.</td><td>22.3</td><td>3.6</td><td>80.4</td><td>1.72</td></tr><tr><th></th><td>Max.</td><td>25.2</td><td>5.5</td><td>84.2</td><td>2.25</td></tr><tr><th></th><td>Avg. 23.5±2.54.71±0.582.10±5.81.90±0.05</td></tr></tbody></table>

opencc-by-4.0Oct 2024View details →

Sensitivity of M2 tidal magnetic signals to seasonal and spatial variations of ocean electric conductivity

Gijm_fwd SH coefficients of M2 induced field for monthly conductivities. WOA03: March; WOA06: June; WOA09: September; WOA12: December Schmidt semi-normalization, complex; ordering g10,g11,h11,g20,g21,h21,g22,h22,... Snm relative differences between SH coefficients same ordering as Gijm_fwd Inversion result of synthetic inversion longitude,latitude,conductivity in S/m

opencc-zeroAug 2021View details →

Energy-water and seasonal variations in climate underlie the spatial distribution patterns of gymnosperms species richness in China

Studying the pattern of species richness is crucial in understanding the diversity and distribution of organisms in the earth. Climate and human influences are the major driving factors that directly influence the large-scale distributions of plant species, including gymnosperms. Understanding how gymnosperms respond to climate, topography, and human-induced changes is useful in predicting the impacts of global change. Here, we attempt to evaluate how climatic and human-induced processes could affect the spatial richness patterns of gymnosperms in China. Initially, we divided a map of the country into grid cells of 50 × 50 km2 spatial resolution and plotted the geographical coordinate distribution occurrence of 236 native gymnosperm taxa. The gymnosperm taxa were separated into three response variables: (i) all species, (ii) endemic species, and (iii) non-endemic species, based on their distribution. The species richness patterns of these response variables to four predictor sets were also evaluated: (i) energy-water, (ii) climatic seasonality, (iii) habitat heterogeneity, and (iv) human influences. We performed generalized linear models (GLMs) and variation partitioning analyses to determine the effect of predictors on spatial richness patterns. The results showed that the distribution pattern of species richness was highest in the southwestern mountainous area and Taiwan in China. We found a significant relationship between the predictor variable set and species richness pattern. Further, our findings provide evidence that climatic seasonality is the most important factor in explaining distinct fractions of variations in the species richness patterns of all studied response variables. Moreover, it was found that energy-water was the best predictor set to determine the richness pattern of all species and endemic species, while habitat-heterogeneity has a better influence on non-endemic species. Therefore, we conclude that with the current climate fluctuations as a result of climate change and increasing human activities, gymnosperms might face a high risk of extinction.

opencc-zeroSep 2021View details →

Genetic differentiation underlies seasonal variation in thermal tolerance, body size, and plasticity in a short-lived copepod

Organisms experience variation in the thermal environment on several different temporal scales, with seasonality being particularly prominent in temperate regions. For organisms with short generation times, seasonal variation is experienced across, rather than within, generations. How this variation affects the seasonal evolution of thermal tolerance and phenotypic plasticity is understudied, but has direct implications for the thermal ecology of these organisms. Here we document intra-annual patterns of thermal tolerance in two species of Acartia copepods (Crustacea) from a highly seasonal estuary, showing strong variation across the annual temperature cycle. Common garden, split-brood experiments indicate that this seasonal variation in thermal tolerance, along with seasonal variation in body size and phenotypic plasticity, is likely affected by genetic polymorphism. Our results show that adaptation to seasonal variation is important to consider when predicting how populations may respond to ongoing climate change.

opencc-zeroSep 2021View details →

Seasonal variation in age, sex, and reproductive status of Mexican free-tailed bats

In North America, Mexican free-tailed bats (Tadarida brasiliensis mexicana) consume vast numbers of insects contributing to the economic well-being of society. Mexican free-tailed bats have declined due to historic guano mining, roost destruction, and bioaccumulation of organochlorine pesticides. Long-distance migrations and dense congregations at roosts exacerbate these declines. Wind energy development further threatens bat communities worldwide and presents emerging challenges to bat conservation. Effective mitigation of bat mortality at wind energy facilities requires baseline data on the biology of affected populations. We collected data on age, sex, and reproductive condition of Mexican free-tailed bats at a cave roost in eastern Nevada located six km from a 152-megawatt industrial wind energy facility. Over five years, we captured 46,353 Mexican free-tailed bats. Although just over half of the caught individuals were non-reproductive adult males (53.6%), 826 pregnant, 892 lactating, 10,101 post-lactating, and 4,327 non-reproductive adult females were captured. Juveniles comprised 11.5% of captures. Female reproductive phenology was delayed relative to conspecific roosts at lower latitudes, likely due to cooler temperatures. Roost use by reproductive females and juvenile bats demonstrates this site is a maternity roost, with significant ecological and conservation value. To our knowledge, no other industrial scale wind energy facilities exist in such close proximity to a heavily used bat roost in North America. Given the susceptibility of Mexican free-tailed bats to wind turbine mortality and the proximity of this roost to a wind energy facility, these data provide a foundation from which differential impacts on demographic groups can be assessed.

opencc-zeroNov 2022View details →

Intra‐season variations in distribution and abundance of humpback whales in the West Antarctic Peninsula using cruise vessels as opportunistic platforms

Following the near collapse of several whale populations in the Southern Ocean, some baleen whale stocks are on the rise again. Combined with the recent increase in fishery of Antarctic Krill (Euphausiia superba) around the Western Antarctic Peninsula (WAP) there is a growing need to quantify several aspects of some of these whale species in this area. In this study we use data collected from tourist vessels performing several trips during the Austral summer to quantify the beginning of the foraging season for Antarctic Humpback whales, estimate abundance, as well as use predictive habitat model to identify potential areas for interaction between this species and fishing vessels. The following dataset includes the GPS track of both vessels and all marine mammal and seabird observations collected on two ships between late November 2019 and mid-January 2020. These data were gathered following standard Distance Sampling protocols, recorded in Logger2010 software (<a href="http://www.marineconservationresearch.co.uk/downloads/logger-2000-rainbowclick-software-downloads/">http://www.marineconservationresearch.co.uk/downloads/logger-2000-rainbowclick-software-downloads/</a>), stored in MS Access database files and subset in .RData files for analysis.

opencc-zeroJan 2023View details →

Seasonal variation of behavior and brain size in a freshwater fish

Teleost fishes occupy a range of ecosystem and habitat types subject to large seasonal fluctuations. Temperate fishes in particular, survive large seasonal shifts in temperature, light availability, and access to certain habitats. Mobile species like lake trout (Salvelinus namaycush) can behaviorally respond to seasonal variation by shifting their habitat deeper and further offshore in response to warmer surface water temperatures during the summer. During cooler seasons, use of more structurally complex nearshore zones by lake trout could increase cognitive demands and potentially result in a larger relative brain size during those periods. Yet, there is limited understanding of how such behavioral responses to a seasonally shifting environment might shape, or be shaped by, the nervous system. Here we quantified variation in relative brain size and the size of five externally visible brain regions in lake trout, across six consecutive seasons in two different lakes. Acoustic telemetry data from one of our study lakes was collected during the study period from a different subset of individuals and used to infer relationships between brain size and seasonal behaviors (habitat use and movement rate). Our results indicated that lake trout relative brain size was larger in the fall and winter compared to the spring and summer in both lakes. Larger brains coincided with increased use of nearshore habitats and increased horizontal movement rates in the fall and winter based on acoustic telemetry. The telencephalon followed the same pattern as whole brain size, while the other brain regions (cerebellum, optic tectum, olfactory bulbs, hypothalamus) were only smaller in the spring. These findings provide evidence that flexibility in brain size could underpin shifts in behavior, which could potentially subserve functions associated with differential habitat use during cold and warm seasons and allow fish to succeed in seasonally variable environments.

opencc-by-4.0Feb 2023View details →

Mediterranean songbirds show pronounced seasonal variation in thermoregulatory traits

Addressing the patterns of variation in thermal traits is crucial to better predict the potential effects of climate change on organisms. Here, we assessed seasonal (winter vs summer) adjustments in key thermoregulatory traits in eight Mediterranean-resident songbirds. Overall, songbirds increased whole-animal (by 8%) and mass-adjusted (by 9%) basal metabolic rate and decreased (by 56%) thermal conductance below the thermoneutral zone during winter. The magnitude of these changes was within the lower values found in songbirds from northern temperate areas. Moreover, songbirds increased (by 11%) evaporative water loss within the thermoneutral zone during summer, while its rate of increase above the inflection point of evaporative water loss (i.e., the slope of evaporative water loss versus temperature) decreased by 35% during summer — a value well above that reported for other temperate and tropical songbirds. Finally, body mass increased by 5% during winter, a pattern similar to that found in many northern temperate species. Our findings support the idea that physiological adjustments might enhance the resilience of Mediterranean songbirds to environmental changes, with short-term benefits by saving energy and water under thermally stressful conditions. Nevertheless, not all species showed the same patterns, suggesting different strategies in their thermoregulatory adaptations to seasonal environments.