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opencc-by-4.0Jul 2023View details →

Nonlinear Development of Ecosystem Suppressed by Seasonal Synchronization in the North Pacific

The dataset is to produce the main figures and tables of the work Nonlinear Development  of Ecosystem Suppressed by Seasonal Synchronization in the North Pacific

FIGURE 5 in Observations on growth rate and allometry in the seasonal predatory killifish Nothobranchius ocellatus (Teleostei: Cyprinodontiformes)

FIGURE 5. Graphical representation of the most important allometric growth changes in morphometric parameters as measured for the three subject specimens of Nothobranchius ocellatus, through the sub-adult to young sexually mature phases, at 37, 56 and 84 days after hatching. Data from Table 2.

FIGURE 6 in Observations on growth rate and allometry in the seasonal predatory killifish Nothobranchius ocellatus (Teleostei: Cyprinodontiformes)

FIGURE 6. Comparative morphometry of growth in selected specimens of Nothobranchius ocellatus; a, Score plot of principal component analysis (PCA) on morphometric characters; first vs. second principal components: female #1 (diamond), male #2 (circle), male #4 (inverted triangle); b, loading plot of PCA for female #1; c, loading plot of PCA for male #2; d, loading plot of PCA for male #4. Most important loadings with absolute magnitude greater than 0.4 appear in bold. Key to character abbreviations: TL, Total length; BD, Body depth at pelvic–fin origin; HL, Head length; PA, Preanal length; PD, Predorsal length; PV, Prepelvic length; PP, Prepectoral length; CPL, Caudal peduncle length; CPD, Caudal peduncle depth; DFB, Dorsal-fin base length; AFB, Anal-fin base length; CF, Caudal-fin length; HD, Head depth; PO, Postorbital length; SD, Suborbital depth; ED, Eye diameter; SEL, Snout to eye end length; SL, Snout length.

FIGURE 4 in Observations on growth rate and allometry in the seasonal predatory killifish Nothobranchius ocellatus (Teleostei: Cyprinodontiformes)

FIGURE 4. Sets of photographs (A, B and C) showing the development of sex distinction as reflected in changes in colour pattern, and some general morphological features, in the study specimens of Nothobranchius ocellatus at 37, 58 and 84 days age.

FIGURE 2 in Observations on growth rate and allometry in the seasonal predatory killifish Nothobranchius ocellatus (Teleostei: Cyprinodontiformes)

FIGURE 2. Illustration of the method used for measuring the total length (TL) of Nothobranchius ocellatus specimens on a weekly basis. Specimens were placed in very shallow water in a clear, flat-bottomed glass dish (of a size suited to the size of the specimen) over graph paper and photographed from above. A straight line was then digitally drawn from the terminus of the upper jaw to the posterior margin of the caudal fin. The line was then rotated into alignment with the grid of the graph paper in order to closely approximate TL.

FIGURE 1 in Observations on growth rate and allometry in the seasonal predatory killifish Nothobranchius ocellatus (Teleostei: Cyprinodontiformes)

FIGURE 1. Nothobranchius ocellatus: upper, wild-caught male from neotype locality (field code: Kikongono TAN 95-9); lower, wild-caught female (field code: Kitonga south TAN 97-36); Rufiji River drainage; central coastal region, Tanzania.

opencc-by-4.0Jul 2022View details →

E3SM simulations to assess the projected changes in tropical cyclone activity during active and inactive North Atlantic hurricane seasons

This dataset compiles the results of high resolutions E3SM experiments performed to assess the projected changes in the North Atlantic tropical cyclone activity in a warming climate. The results of these experiments are currently in revision under the title "Future Changes in Active and Inactive Atlantic Hurricane Seasons in the Energy Exascale Earth System Model"  The file tracks_teca.zip compiles the TC tracks detected in the experiments, using the Toolkit for Extreme Climate Analysis (TECA), available at https://teca.readthedocs.io/en/latest/applications.html#teca-tc-stats GPI_LaNinaAMMp.zip and GPI_ElNinoAMMn.zip includes the genesis potential index and its components for each of the experiments. For further information, please contact the author at anasena@iastate.edu Acknowledgements: This research used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility located at Lawrence Berkeley National Laboratory, operated under Contract No. DE-AC02-05CH11231.  E3SM simulations were performed using BER Earth System Modeling program’s Compy computing cluster located at Pacific Northwest National Laboratory. PNNL is operated by Battelle for the U.S. Department of Energy under Contract DE-AC05-76RL01830.  This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.  This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research (BER), Earth and Environmental Systems Modeling (EESM) Program, under Early Career Research Program Award Number DE‐SC0021109.

opencc-zeroAug 2022View details →

Plant growth over one growing season of Medicago truncatula in competition with conspecifics of different genetic relatedness

Kin recognition and kin selection have long been known to occur in animals where it shapes altruistic behavior towards relatives. More recently, studies have found that kin recognition and altered behavior towards kin can also occur in plants. However, inferring the underlying mechanism responsible for variation in plant performance in experimental studies is challenging as often, results can be explained by alternative and non-exclusive mechanisms such as niche differences, kin competition avoidance, and genetic variation in growth rate and competitive ability. Plant-plant interactions may change with the life stage of plants, and competition is often most intense towards the end of plants growing season. However, changes in plant-plant interaction intensity across plants life cycle are rarely considered in kin interaction studies. Here, we adapt a model of plant growth over time modified to specifically include effects of kin and non-kin competition. The model decompose competitive interactions at different stages during plant growth from initial growth to the end of the growing season. It estimates genotype specific variation in growth rate, and how sensitive individual genotypes are to competition neighbors. Furthermore, it estimates size asymmetry among plants accounting for both variation in growth rate, neighbor relatedness, and resource variation (here water availability). We use this model to analyze the results from a competition experiment where plants grew in mini-communities with neighbor plants that were either kin or non-kin. We find that when applied to our experiments, this approach can disentangle kin effects from other effects caused by genotypic variation in growth rate and competitive response to neighbors, and thus significantly help to detect whether plants exhibit kin-cooperative behavior

opennotspecifiedSep 2022View details →

FIGURE. Petalidium mannheimerae, habitat and habit. A. Plant in flower during a particularly dry season (Aussenkjer [Aussenkehr] 147 Farm, ||Kharas Region, Namibia). B. Plant in full flower (Kosies, Richtersveld, Northern Cape, South Africa). Photographs by L. Nanyeni (A) & M. Koekemoer (B). in Petalidium mannheimerae (Acanthaceae), a new species from Namibia and South Africa, with notes on the taxonomic identity of P. parvifolium

FIGURE. Petalidium mannheimerae, habitat and habit. A. Plant in flower during a particularly dry season (Aussenkjer [Aussenkehr] 147 Farm, ||Kharas Region, Namibia). B. Plant in full flower (Kosies, Richtersveld, Northern Cape, South Africa). Photographs by L. Nanyeni (A) & M. Koekemoer (B).

opencc-zeroSep 2022View details →

Recent population differentiation in the habitat specialist Glossy Antshrike (Aves: Thamnophilidae) across Amazonian seasonally flooded forests: Final SNPs dataset

We assessed population structure and the spatio-temporal pattern of diversification in the Glossy Antshrike Sakesphorus luctuosus (Aves, Thamnophilidae) to understand the processes shaping the evolutionary history of Amazonian floodplains and address unresolved taxonomic controversies surrounding its species limits. By targeting ultraconserved elements (UCEs) from 32 specimens of S. luctuosus, we identified independent lineages and estimated their differentiation, divergence times and migration rates. We also estimated current and past demographic histories for each recovered lineage. We found evidence confirming that S. luctuosus consists of a single species, comprising at least four populations, with some highly admixed individuals and overall similar levels of migration between populations. We confirmed the differentiation of the Araguaia River basin population (S. l. araguayae), and gathered circumstantial evidence indicating that the taxon S. hagmanni may represent a highly introgressed population between 3 distinct phylogroups of S. luctuosus. Divergence time estimates between populations seem to be recent, occurring during the last 183 kya. Signs of population expansions were detected for populations attributed to subspecies S. l. luctuosus, but the S. l. araguayae population had probably maintained its effective size through time. Our results support that S. luctuosus has had a complex population history, resulting from a high dependence on southeastern "clear-water" habitats and their availability through time. Spatial and demographic expansions towards the western "white water" flooded forests might still be ongoing. Our study reinforces the view that isolation due to absence of suitable habitat has been an important driver of population differentiation within Amazonian flooded forests, but also that differences between várzeas ("white water" floodplains, mostly in southwestern Amazonia) and igapós ("clear- water" floodplains, especially located in the east) should be further explored as powerful drivers of micro-evolution.

opencc-zeroSep 2022View details →

Data from: A camera trap based assessment of climate-driven phenotypic plasticity of seasonal moulting in an endangered carnivore

For many species, the ability to rapidly adapt to changes in seasonality is essential for long-term survival. In the Arctic, seasonal moulting is a key life history event that provides year-round camouflage and thermal protection. However, increased seasonal variability can lead to phenological mismatch. In this study, we investigated whether winter-white (white morph) and winter-brown (blue morph) Arctic foxes (Vulpes lagopus) could adjust their winter-to-summer moult to match local environmental conditions. We used camera trap images spanning an eight-year period to quantify the timing and rate of fur change in a polymorphic subpopulation in south-central Norway. Seasonal snow cover duration and temperature governed the phenology of the spring moult. We observed a later onset and longer moulting duration with decreasing temperature and longer snow season. Additionally, white foxes moulted earlier than blue in years with shorter periods of snow cover and warmer temperatures. These results suggest that phenotypic plasticity allows Arctic foxes to modulate the timing and rate of their spring moult as snow conditions and temperatures fluctuate. With the Arctic warming at an unprecedented rate, understanding the capacity of polar species to physiologically adapt to a changing environment is urgently needed in order to develop adaptive conservation efforts. Moreover, we provide the first evidence for variations in the moulting phenology of blue and white Arctic foxes. Our study underlines the different intraspecific selective pressures that can exist in populations where several morphs co-occur, and illustrates the importance of integrating morph-based differences in future management strategies of such polymorphic species.

opencc-zeroOct 2022View details →

Effects of El Niño drought on seedling dynamics in a seasonally dry tropical forest in northern Thailand

As El Niño is predicted to become stronger and more frequent in the future, it is crucial to understand how El Niño-induced droughts will affect tropical forests. Although many studies have focused on tropical rainforests, there is a paucity of studies on them, particularly in Asia, and few studies have focused on seedling dynamics, which are expected to be strongly affected by drought. Seedlings in seasonally dry tropical forests (SDTFs) are generally more drought-tolerant than those in the rainforests, and the effects of El Niño-induced droughts may differ between SDTF and tropical rainforests. In this study, we explored the impact of El Niño-induced drought at an SDTF in northern Thailand by monitoring the seedling dynamics at monthly intervals for seven years, including a period of strong El Niño. The effects were compared between two forest types in an SDTF: a deciduous dipterocarp forest (DDF), dominated by deciduous species, and an adjacent lower montane forest (LMF) with more evergreen species. El Niño-induced drought increased seedling mortality in both forest types. The effect of drought was stronger in evergreen than in the deciduous species, resulting in higher mortality in the LMF during El Niño. However, El Niño increased seedling recruitment only in the DDF, mainly because of the massive recruitment of the deciduous oak, Quercus brandisiana (Fagaceae), which compensated for the mortality of seedlings in the DDF. As a result, El Niño increased seedling density in the DDF and decreased it in the LMF. This is the first long-term study to identify the differences in the impacts of El Niño on seedlings between the two forest types, DDF and LMF, and two leaf habits, evergreen and deciduous, in Southeast Asia. Our findings suggest that future climate change may alter the species composition and spatial distribution of seedlings in Asian SDTFs.

opencc-by-4.0Jan 2019View details →

Alien plants and flower visitors disrupt the seasonal dynamics of mutualistic networks - Dataset

Dataset associated with the manuscript "Alien plants and flower visitors disrupt the seasonal dynamics of mutualistic networks" (Arroyo-Correa et al. 2019)

opencc-by-4.0Dec 2016View details →

Dataset Disruptive effects of light pollution on sleep in free-living birds: season and/or light intensity-dependent?

Dataset belonging to the article Disruptive effects of light pollution on sleep in free-living birds: season and/or light intensity-dependent?

opennotspecifiedOct 2017View details →

FIGURE 4 in Seasonal and geographical adaption of two field crickets in China (Orthoptera: Grylloidea: Gryllidae: Gryllinae: Teleogryllus)

FIGURE 4. RelatiOnsHip between Hind femur lengtH and Original latitude (A T. emma B T. occipitalis THe dOtted line represents tHe female, THe sOlid line represents tHe male).

Supplementary material 9 from: Saarela JM, Bull RD, Paradis MJ, Ebata SN, Peterson PM, Soreng RJ, Paszko B (2017) Molecular phylogenetics of cool-season grasses in the subtribes Agrostidinae, Anthoxanthinae, Aveninae, Brizinae, Calothecinae, Koeleriinae and Phalaridinae (Poaceae, Pooideae, Poeae, Poeae chloroplast group 1). PhytoKeys 87: 1-139. https://doi.org/10.3897/phytokeys.87.12774

Maximum likelihood phylogram inferred from psbK–psbI sequence data : Explanation note: ML bootstrap support is recorded along branches. No support is shown for branches with bootstrap support <50%.

Supplementary material 8 from: Saarela JM, Bull RD, Paradis MJ, Ebata SN, Peterson PM, Soreng RJ, Paszko B (2017) Molecular phylogenetics of cool-season grasses in the subtribes Agrostidinae, Anthoxanthinae, Aveninae, Brizinae, Calothecinae, Koeleriinae and Phalaridinae (Poaceae, Pooideae, Poeae, Poeae chloroplast group 1). PhytoKeys 87: 1-139. https://doi.org/10.3897/phytokeys.87.12774

Maximum likelihood phylogram inferred from atpF–atpH sequence data : Explanation note: ML bootstrap support is recorded along branches. No support is shown for branches with bootstrap support <50%.

Supplementary material 2 from: Saarela JM, Bull RD, Paradis MJ, Ebata SN, Peterson PM, Soreng RJ, Paszko B (2017) Molecular phylogenetics of cool-season grasses in the subtribes Agrostidinae, Anthoxanthinae, Aveninae, Brizinae, Calothecinae, Koeleriinae and Phalaridinae (Poaceae, Pooideae, Poeae, Poeae chloroplast group 1). PhytoKeys 87: 1-139. https://doi.org/10.3897/phytokeys.87.12774

Supplementary material 2 from: Saarela JM, Bull RD, Paradis MJ, Ebata SN, Peterson PM, Soreng RJ, Paszko B (2017) Molecular phylogenetics of cool-season grasses in the subtribes Agrostidinae, Anthoxanthinae, Aveninae, Brizinae, Calothecinae, Koeleriinae and Phalaridinae (Poaceae, Pooideae, Poeae, Poeae chloroplast group 1). PhytoKeys 87: 1-139. https://doi.org/10.3897/phytokeys.87.12774

Supplementary material 19 from: Saarela JM, Bull RD, Paradis MJ, Ebata SN, Peterson PM, Soreng RJ, Paszko B (2017) Molecular phylogenetics of cool-season grasses in the subtribes Agrostidinae, Anthoxanthinae, Aveninae, Brizinae, Calothecinae, Koeleriinae and Phalaridinae (Poaceae, Pooideae, Poeae, Poeae chloroplast group 1). PhytoKeys 87: 1-139. https://doi.org/10.3897/phytokeys.87.12774

Supplementary material 19 from: Saarela JM, Bull RD, Paradis MJ, Ebata SN, Peterson PM, Soreng RJ, Paszko B (2017) Molecular phylogenetics of cool-season grasses in the subtribes Agrostidinae, Anthoxanthinae, Aveninae, Brizinae, Calothecinae, Koeleriinae and Phalaridinae (Poaceae, Pooideae, Poeae, Poeae chloroplast group 1). PhytoKeys 87: 1-139. https://doi.org/10.3897/phytokeys.87.12774

Supplementary material 4 from: Saarela JM, Bull RD, Paradis MJ, Ebata SN, Peterson PM, Soreng RJ, Paszko B (2017) Molecular phylogenetics of cool-season grasses in the subtribes Agrostidinae, Anthoxanthinae, Aveninae, Brizinae, Calothecinae, Koeleriinae and Phalaridinae (Poaceae, Pooideae, Poeae, Poeae chloroplast group 1). PhytoKeys 87: 1-139. https://doi.org/10.3897/phytokeys.87.12774

Maximum likelihood phylogram inferred from ITS sequence data : Explanation note: ML bootstrap support is recorded along branches. No support is shown for branches with bootstrap support <50%. Source information for previously published sequences is provided in Suppl. material 2.