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80 results for “population shift”
Quantifying changes in fish population stability using statistical early warnings of regime shifts
This data package describes long-term trends in metrics describing population stability and used as statistical early warnings of regime shifts in 29 fish species that inhabit the San Francisco Bay-Delta in central California, USA. Metrics used in this study include spatial synchrony, temporal coefficient of variation (CV), and lag-1 temporal autocorrelation. Trends were measured using ordinary least squares linear regression. These derived data were developed from abundance (as CPUE) time series based on three long-term fish monitoring studies included in https://doi.org/10.6073/pasta/a29a6e674b0f8797e13fbc4b08b92e5b; the Fall Midwater Trawl Survey, Delta Juvenile Monitoring Program, and Bay Study. Selected data were from fall months (September to December) in 1980-2023, from midwater trawl and beach seine surveys for which sampling effort (e.g., tow volume) was recorded. Data on fish exceeding maximum length thresholds for age-0 fish were discarded, except for white sturgeon, where the maximum length threshold corresponded to approximately 10 years of age, the onset of reproductive maturity. Observations from different sampling stations were aggregated into 10 sub-regions (South San Francisco Bay, Central San Francisco Bay, San Pablo Bay, Napa River, Suisun Bay, Delta Confluence, South Delta, North Delta, San Joaquin River, Sacramento River, and midwater trawl samples and beach seine samples were considered separately because the methods sample distinct habitat types. Combinations of sub-region and sampling method were considered distinct spatial units. EWI metrics were measured in 5-year rolling windows to permit assessment of changes over time. The temporal CV and lag-1 autocorrelation were measured on individual spatial unit time series, ignoring windows with >1 year of missing data. The coefficient of variation divides the standard deviation by the mean. Lag-1 autocorrelation was measured as Pearson correlation. Spatial synchrony was measured across spatial un
Population dynamics shifts by Climate Change: High resolution future mid-century trends for South America.
<p>Köppen - Geiger scripts and resulting datasets for the publication entitled "Population dynamics shifts by Climate Change: High resolution future mid-century trends for South America." This scripts can be adapted to any geographic scale and region. Works with climate change scenarios.</p> <p>Original publication: <a href="https://doi.org/10.1016/j.gloplacha.2023.104155">https://doi.org/10.1016/j.gloplacha.2023.104155</a></p> <p>Dataset description</p> <p><strong>Scripts.rar</strong>: R Scripts used in this publication, as well they are reproducible</p> <p><strong>Readme_Köppen.txt</strong>: README file that explain the requisites and data formatting to run the scripts</p> <p><strong>Output datasets.zip</strong>: Output GIS datasets of this publication. Coordinate system GCS WGS 1984</p> <p> </p>
Data from: Seed origin and warming constrain lodgepole pine recruitment, slowing the pace of population range shifts
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Data from: Drastic shift in flowering phenology of F1 hybrids explains the population structure of Imperata cylindrica in Japan
<p>Hybridization is a major source of phenotypic variation and a driving force for evolution. On the other hand, these novel traits can often disrupt adaptive relationships between the parental phenotypes and their environments. However, it remains unclear how new hybrid traits disrupt local adaptation. Here, we report how a new phenotype of hybrids between two ecotypes of Imperata cylindrica contributes to rapid reproductive isolation from their parents and affects hybrid fitness.</p> <p>We analyzed 350 accessions of I. cylindrica collected from the 1980s to the 2010s throughout Japan to explore the genetic population structure of the hybrids. We surveyed flowering periods, seed sets, and germination of two ecotypes and their hybrids in both natural habitats and common gardens.</p> <p>Genetic analyses of population structure revealed that the hybrid populations consisted of only F1 individuals, without post-F1 hybrids. The flowering phenology of the F1 plants was delayed to autumn, 5–6 months later than the parental ecotypes.</p> <p>The drastic shift in flowering phenology prevents F1s from backcrossing. In addition, it changes their seed dispersal time to winter. Germination is inhibited by low temperatures, and the seeds likely decay before the next spring, resulting in the absence of an F2 generation. For the first time in the field, we found environmental mismatch of F1 as a specific mechanism for the maintenance of only F1 populations.</p> <p>Synthesis. We have demonstrated that this flowering phenology mismatch promotes reproductive isolation between the parents and F1s and affects various temporal components of the hybrids, resulting in a unique hybrid population consisting only of F1s. This system sheds light on the importance of hybrid traits in terms of rapid reproductive isolation.</p>
Fig. 1 in On The Limit Of Altitudinal Range Shifts - Population Genetics Of Relict Butterfly Populations
Fig. 1. Neighbour-joining tree based on genetic distances (CAVALLI-SFORZA & EDWARDS 1967) performed on five microsatellite loci, representing the analysed populations of Scandinavia, Finland, eastern Europe (Poland, Lithuania, Romania) and the Vosges. Genetic distances are projected on a map. Solid lines display the genetic distance, arrows show the locations of the sampling sites. Data
Fig. 2 in On The Limit Of Altitudinal Range Shifts - Population Genetics Of Relict Butterfly Populations
Fig. 2. Allele frequency distributions of L. helle populations of the Pyrenees and the western low-altitude mountains (Massif Central, Vosges, Ardennes). The colours in the pie charts indicate the distribution of alleles (white: occurring in several mountain areas, black: exclusive to a single mountain area, grey: exclusively occurring in the respective population). Data taken from FINGER et al. (2009)
Predation risk drives long-term shifts in migratory behavior and demography in a large herbivore population
<p>Migration is an adaptive life-history strategy that helps individuals across taxa maximize fitness by obtaining forage and avoiding predation risk. The mechanisms driving migratory changes are poorly understood, and links between migratory behavior, space use, and demographic consequences are rare. Here, we use a nearly 20-year record of individual-based monitoring of elk (<em>Cervus canadensis</em>) to test hypotheses for changing patterns of migration in a large herbivore, elk (<em>Cervus canadensis</em>), in and adjacent to a large protected area in Banff National Park (BNP), Canada. We test whether bottom-up (forage quality) or top-down (predation risk) factors explained trends in i) the proportion of individuals using 5 different migratory tactics, ii) differences in survival rates of migratory tactics during migration and whilst on summer ranges, iii) cause-specific mortality by wolves and grizzly bears, and iv) population abundance. We found dramatic shifts in migration consistent with behavioral plasticity in individual choice of annual migratory routes. Shifts were inconsistent with exposure to the bottom-up benefits of migration. Instead, exposure to landscape gradients in predation risk caused by exploitation outside the protected area drove migratory shifts. Carnivore exploitation outside the protected area led to higher survival rates for female elk remaining resident or migrating outside the protected area. Cause-specific mortality aligned with exposure to predation risk along migratory routes and summer ranges. Wolf predation risk was higher on migratory routes than summer ranges of montane-migrant tactics, but wolf predation risk traded-off with heightened risk from grizzly bears on summer ranges. A novel eastern migrant tactic emerged following a large forest fire that enhanced forage in an area with lower predation risk outside of the protected area. The changes in migratory behavior translated to population abundance, where abundance of the montane-migratory tactics declined over time. The presence of diverse migratory life histories maintained a higher total population abundance than would have been the case with one migratory tactic in the population. Our study demonstrates the complex ways in which migratory populations change over time through behavioral plasticity and associated demographic consequences because of individuals balancing predation risk and forage trade-offs.</p>
Predation risk drives long-term shifts in migratory behavior and demography in a large herbivore population
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Demographic fluctuations lead to rapid and cyclic shifts in genetic structure among populations of an alpine butterfly, Parnassius smintheus
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Data from: Drastic shift in flowering phenology of F1 hybrids explains the population structure of Imperata cylindrica in Japan
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Data Set to 'Sodium-induced population shift drives activation of thrombin'
<p>The data set 10.5281/zenodo.3688506 contains the raw data used for the preparation of the manuscript 'Sodium-induced population shift drives activation of thrombin' (doi:10.1038/s41598-020-57822-0). To limit the required storage space, the trajectories are deposited only as coordinates of the analysed loop. The data set is separated into following parts:</p> <ul> <li>X-ray: Data used in the analysis of the PDB structures of thrombin, including a list of the PDB IDs, results of the PCA and values of the introduced features in the structures.</li> <li>cMD: Input parameters, topologies and starting coordinates of classical MD simulation based on different PDB structures (3bei, 3lu9 – with Na<sup>+</sup> ions and without Na<sup>+</sup> ions), resulting trajectories, projection on X-ray PCA and values of the introduced features during the simulations.</li> <li>TMD: Input parameters, topologies and starting coordinates of the targeted MD simulations, resulting trajectories, projection on X-ray PCA and RMSD values.</li> <li>Seeded-Simulations: Input parameters, topologies and starting coordinates of classical MD simulations that are started from cluster representatives of the TMD simulations and resulting trajectories (3*100 simulations, each lasting 200 ns), separated into simulations with Na+ and without Na+, additionally values of the introduced features during the simulations.</li> <li>MSM: For both systems (with and without Na<sup>+</sup>), files are provided that can be loaded with pyemma (TICA objects, kmeans clusterings, calculated implied timescales and MSMs); additionally TICs of the seeded simulations, psi-dihedrals of the seeded simulations, RMSD values of the E and E<sup>*</sup> states and representative structures of the E and E<sup>*</sup> states</li> </ul> <p>A more detailed description of the included files can be found in the README files in each folder.</p>
Dynamic shifts in social network structure and composition within a breeding hybrid population
1. Mating behavior and the timing of reproduction can inhibit genetic exchange between closely related species; however, these reproductive barriers are challenging to measure within natural populations. Social network analysis provides promising tools for studying the social context of hybridization, and the exchange of genetic variation, more generally. 2. We test how social networks within a hybrid population of California (Callipepla californica) and Gambel's quail (Callipepla gambelii) change over discrete periods of a breeding season. We assess patterns of phenotypic and genotypic assortment, and ask whether altered associations between individuals (association rewiring), or changes to the composition of the population (individual turnover) drive network dynamics. We use genetic data to test whether social associations and relatedness between individuals correlate with patterns of parentage within the hybrid population. 3. To achieve these aims, we combine RFID association data, phenotypic data, and genomic measures with social network analyses. We adopt methods from the ecological network literature to quantify shifts in network structure and to partition changes into those due to individual turnover and association rewiring. We integrate genomic data into networks as node-level attributes (ancestry) and edges (relatedness, parentage) to test links between social and parentage networks. 4. We show that rewiring of associations between individuals that persist across network periods, rather than individual turnover, drives the majority of the changes in network structure throughout the breeding season, and that the traits involved in phenotypic/genotypic assortment were highly dynamic over time. Social networks were randomly assorted based upon genetic ancestry, suggesting weak behavioral reproductive isolation within this hybrid population. Finally, we show that the strength of associations within the social network, but not levels of genetic relatedness, predict patterns of parentage. 5. Social networks play an important role in population processes such as the transmission of disease and information, yet there has been less focus on how networks influence the exchange of genetic variation. By integrating analyses of social structure, phenotypic assortment, and reproductive outcomes within a hybrid zone, we demonstrate the utility of social networks for analyzing links between social context and gene flow within wild populations. 08-Jul-2020
Data for: Shifts in plant-invertebrate interactions between wild and ex-situ conservation populations of a critically endangered tree
<p>Ex-situ conservation is an effective approach to prevent the extinction of endangered species. Biotic interactions (eg herbivory and pollination) are critical to ex-situ conservation success, including plant establishment, survival, and reproduction. However, shifts in biotic interactions between wild and ex-situ populations are still poorly understood. We compared herbivory and pollination characteristics between the only wild population (WP) and three ex-situ populations (LP, local population, nearby WP; NP, north population, ca. 850 km; and SP, south population, ca. 750 km) of a critically endangered tree species (<em>Sinojackia huangmeiensis</em>) to explore the latitudinal changes in plant-invertebrate interactions. Larvae of the Limacodidae family were the dominant herbivores in WP, LP, and NP, while the only herbivore observed in SP was a snail. Compared to WP, the leaf herbivory rate was unchanged in LP but decreased in NP and SP. Leaf defense traits (total phenols, tannins, leaf thickness, and leaf dry matter content) increased or remained unchanged in the three ex-situ populations. A pollinator (<em>Apis cerana</em>) of <em>S. huangmeiensis</em> was present in the four populations. NP and SP lacked some pollinators that were found in both WP and LP, but they shared one pollinator that was not observed in WP and LP. The pollinator visiting frequency increased in SP, while it did not change significantly in LP and NP. Synthesis and applications: Our results suggested that both herbivory and pollination of <em>S. huangmeiensis</em> changed in ex-situ populations, with complete or partial changes in herbivores, leaf herbivory rate, pollinators, pollinator visiting frequency, and fruit set in the two distant ex-situ populations. This work provides a unique empirical study of shifts in both antagonistic and mutualistic biotic interactions between wild and ex-situ populations. We emphasized that it is essential to integrate herbivore and pollinator management in future ex-situ conservation of plant species.</p>
Bioacoustic monitoring reveals shifts in breeding songbird populations and singing behaviour with selective logging in tropical forests
<b>Description: </b><p>Counts of individual male songbirds, males and females, songs and duets and original WAV audio recordings used to generate them</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/131"><b>Population and behavioral responses of songbirds to logging and rain forest fragmentation</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3366104">here</a></p><p><b>Files: </b>This dataset consists of 13 files: Pillay_et_al_Songbirds_Acoustic_Counts_Vegetation_Cover.xlsx, 2013_B.zip, 2013_D.zip, 2013_E.zip, 2013_F.zip, 2013_OG1.zip, 2013_OG2.zip, 2014_B.zip, 2014_D.zip, 2014_E.zip, 2014_F.zip, 2014_OG1.zip, 2014_OG2.zip</p><p><b>Pillay_et_al_Songbirds_Acoustic_Counts_Vegetation_Cover.xlsx</b></p><p>This file contains dataset metadata and 5 data tables:</p><ol><li><p><b>CountsMale</b> (described in worksheet CountsMale)</p><p>Description: Counts of male individuals of songbird species</p><p>Number of fields: 12</p><p>Number of data rows: 5700</p><p>Fields: </p><ul><li><b>year</b>: Year of Survey (Field type: id)</li><li><b>ftype</b>: Forest Type (Field type: categorical)</li><li><b>block</b>: Unique ID of Blocks within which Sampling Plots are located (Field type: id)</li><li><b>location</b>: Unique ID of Sampling Plots (Field type: location)</li><li><b>fragment</b>: Future fragments within which sampling plots in logged forest plots were located; not applicable for unlogged forest plots (Field type: id)</li><li><b>species</b>: Species Identity (Field type: taxa)</li><li><b>day</b>: Days 1 to 2 of sampling in each plot (Field type: numeric)</li><li><b>date</b>: Date of Sampling (Field type: date)</li><li><b>jul.date</b>: Julian Date of Sampling (Field type: numeric)</li><li><b>time1-6AM</b>: Counts of male individuals for 6:00-6:05 AM; detection histories generated by collapsing counts to 1/0 data (Field type: numeric)</li><li><b>time2-7AM</b>: Counts of male individuals for 7:00-7:05 AM; detection histories generated by collapsing counts to 1/0 data (Field type: numeric)</li><li><b>time3-8AM</b>: Counts of male individuals for 8:00-8:05 AM; detection histories generated by collapsing counts to 1/0 data (Field type: numeric)</li></ul></li><li><p><b>CountsMaleFemale</b> (described in worksheet CountsMaleFemale)</p><p>Description: Counts of male plus female individuals of songbird species</p><p>Number of fields: 12</p><p>Number of data rows: 1000</p><p>Fields: </p><ul><li><b>year</b>: Year of Survey (Field type: id)</li><li><b>ftype</b>: Forest Type (Field type: categorical)</li><li><b>block</b>: Unique ID of Blocks within which Sampling Plots are located (Field type: id)</li><li><b>location</b>: Unique ID of Sampling Plots (Field type: location)</li><li><b>fragment</b>: Future fragments within which sampling plots in logged forest plots were located; not applicable for unlogged forest plots (Field type: id)</li><li><b>species</b>: Species Identity (Field type: taxa)</li><li><b>day</b>: Days 1 to 2 of sampling in each plot (Field type: numeric)</li><li><b>date</b>: Date of Sampling (Field type: date)</li><li><b>jul.date</b>: Julian Date of Sampling (Field type: numeric)</li><li><b>time1-6AM</b>: Counts of male and female individuals for 6:00-6:05 AM; detection histories generated by collapsing counts to 1/0 data (Field type: numeric)</li><li><b>time2-7AM</b>: Counts of male and female individuals for 7:00-7:05 AM; detection histories generated by collapsing counts to 1/0 data (Field type: numeric)</li><li><b>time3-8AM</b>: Counts of male and female individuals for 8:00-8:05 AM; detection histories generated by collapsing counts to 1/0 data (Field type: numeric)</li></ul></li><li><p><b>CountsSong</b> (described in worksheet CountsSong)</p><p>Description: Counts of songs</p><p>Number of fields: 9</p><p>Number of data rows: 2850</p><p>Fields: </p><ul><li><b>year</b>: Year of Survey (Field type: id)</li><li><b>ftype</b>: Forest Type (Field type: categorical)</li><li><b>block</b>: Unique ID of Blocks within which Sampling Plots are located (Field type: id)</li><li><b>location</b>: Unique ID of Sampling Plots (Field type: location)</li><li><b>fragment</b>: Future fragments within which sampling plots in logged forest plots were located; not applicable for unlogged forest plots (Field type: id)</li><li><b>species</b>: Species Identity (Field type: taxa)</li><li><b>day3-6AM</b>: Counts of songs for 6:00-6:05 AM on day 3 or third consecutive day of sampling in each plot (Field type: numeric)</li><li><b>day3-7AM</b>: Counts of songs for 7:00-7:05 AM on day 3 or third consecutive day of sampling in each plot (Field type: numeric)</li><li><b>day3-8AM</b>: Counts of songs for 8:00-8:05 AM on day 3 or third consecutive day of sampling in each plot (Field type: numeric)</li></ul></li><li><p><b>CountsDuet</b> (described in worksheet CountsDuet)</p><p>Description: Counts of duets</p><p>Number of fields: 9</p><p>Number of data rows: 500</p><p>Fields: </p><ul><li><b>year</b>: Year of Survey (Field type: id)</li><li><b>ftype</b>: Forest Type (Field type: categorical)</li><li><b>block</b>: Unique ID of Blocks within which Sampling Plots are located (Field type: id)</li><li><b>location</b>: Unique ID of Sampling Plots (Field type: location)</li><li><b>fragment</b>: Future fragments within which sampling plots in logged forest plots were located; not applicable for unlogged forest plots (Field type: id)</li><li><b>species</b>: Species Identity (Field type: taxa)</li><li><b>day3-6AM</b>: Counts of duets for 6:00-6:05 AM on day 3 or third consecutive day of sampling in each plot (Field type: numeric)</li><li><b>day3-7AM</b>: Counts of duets for 7:00-7:05 AM on day 3 or third consecutive day of sampling in each plot (Field type: numeric)</li><li><b>day3-8AM</b>: Counts of duets for 8:00-8:05 AM on day 3 or third consecutive day of sampling in each plot (Field type: numeric)</li></ul></li><li><p><b>VegetationCover</b> (described in worksheet VegetationCover)</p><p>Description: Vegetation cover data</p><p>Number of fields: 6</p><p>Number of data rows: 50</p><p>Fields: </p><ul><li><b>location</b>: Unique ID of Sampling Plots (Field type: location)</li><li><b>forest.type</b>: Forest Type (Field type: categorical)</li><li><b>udens</b>: Proportion understory cover (Field type: numeric)</li><li><b>cc</b>: Proportion canopy cover (Field type: numeric)</li><li><b>can.ht</b>: Average canopy height (Field type: numeric)</li><li><b>max.canopy</b>: Maximum height of standing vegetation (Field type: numeric)</li></ul></li></ol><p><b>2013_B.zip</b></p><p>Description: WAV files from 2013 for site B</p><p><b>2013_D.zip</b></p><p>Description: WAV files from 2013 for site D</p><p><b>2013_E.zip</b></p><p>Description: WAV files from 2013 for site E</p><p><b>2013_F.zip</b></p><p>Description: WAV files from 2013 for site F</p><p><b>2013_OG1.zip</b></p><p>Description: WAV files from 2013 for site OG1</p><p><b>2013_OG2.zip</b></p><p>Description: WAV files from 2013 for site OG2</p><p><b>2014_B.zip</b></p><p>Description: WAV files from 2014 for site B</p><p><b>2014_D.zip</b></p><p>Description: WAV files from 2014 for site D</p><p><b>2014_E.zip</b></p><p>Description: WAV files from 2014 for site E</p><p><b>2014_F.zip</b></p><p>Description: WAV files from 2014 for site F</p><p><b>2014_OG1.zip</b></p><p>Description: WAV files from 2014 for site OG1</p><p><b>2014_OG2.zip</b></p><p>Description: WAV files from 2014 for site OG2</p><p><b>Date range: </b>2013-04-09 to 2014-07-26</p><p><b>Latitudinal extent: </b>4.6881 to 4.7530</p><p><b>Longitudinal extent: </b>116.9477 to 117.6249</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div> -  Animalia <br> -  -  Chordata <br> -  -  -  Aves <br> -  -  -  -  Passeriformes <br> -  -  -  -  -  Timaliidae <br> -  -  -  -  -  -  <i>Stachyris</i> <br> -  -  -  -  -  -  -  <i>Stachyris maculata</i> <br> -  -  -  -  -  -  -  <i>Stachyris erythroptera</i> <br> -  -  -  -  -  -  -  <i>Stachyris poliocephala</i> <br> -  -  -  -  -  -  <i>Macronus</i> <br> -  -  -  -  -  -  -  <i>Macronus bornensis</i> <br> -  -  -  -  -  -  -  <i>Macronus ptilosus</i> (as synonym: <i>Macronous ptilosus</i>)<br> -  -  -  -  -  -  <i>Stachyridopsis</i> <br> -  -  -  -  -  -  -  <i>Stachyridopsis rufifrons</i> (as synonym: <i>Stachyris rufifrons</i>)<br> -  -  -  -  -  -  <i>Pomatorhinus</i> <br> -  -  -  -  -  -  -  <i>Pomatorhinus montanus</i> <br> -  -  -  -  -  Pellorneidae <br> -  -  -  -  -  -  <i>Trichastoma</i> <br> -  -  -  -  -  -  -  <i>Trichastoma bicolor</i> <br> -  -  -  -  -  -  <i>Alcippe</i> <br> -  -  -  -  -  -  -  <i>Alcippe brunneicauda</i> <br> -  -  -  -  -  -  <i>Pellorneum</i> <br> -  -  -  -  -  -  -  <i>Pellorneum capistratum</i> <br> -  -  -  -  -  -  <i>Malacocincla</i> <br> -  -  -  -  -  -  -  <i>Malacocincla malaccensis</i> <br> -  -  -  -  -  -  <i>Malacopteron</i> <br> -  -  -  -  -  -  -  <i>Malacopteron magnirostre</i> <br> -  -  -  -  -  -  -  <i>Malacopteron magnum</i> <br> -  -  -  -  -  -  -  <i>Malacopteron cinereum</i> <br> -  -  -  -  -  -  -  <i>Malacopteron affine</i> <br> -  -  -  -  -  Pycnonotidae <br> -  -  -  -  -  -  <i>Alophoixus</i> <br> -  -  -  -  -  -  -  <i>Alophoixus bres</i> <br> -  -  -  -  -  -  -  <i>Alophoixus phaeocephalus</i> <br> -  -  -  -  -  -  <i>Tricholestes</i> <br> -  -  -  -  -  -  -  <i>Tricholestes criniger</i> <br> -  -  -  -  -  -  <i>Iole</i> <br> -  -  -  -  -  -  -  <i>Iole olivacea</i> <br> -  -  -  -  -  -  <i>Pycnonotus</i> <br> -  -  -  -  -  -  -  <i>Pycnonotus atriceps</i> <br> -  -  -  -  -  -  -  <i>Pycnonotus simplex</i> <br> -  -  -  -  -  -  -  <i>Pycnonotus eutilotus</i> <br> -  -  -  -  -  -  -  <i>Pycnonotus brunneus</i> <br> -  -  -  -  -  -  -  <i>Pycnonotus erythropthalmos</i> <br> -  -  -  -  -  Stenostiridae <br> -  -  -  -  -  -  <i>Culicicapa</i> <br> -  -  -  -  -  -  -  <i>Culicicapa ceylonensis</i> <br> -  -  -  -  -  Muscicapidae <br> -  -  -  -  -  -  <i>Cyornis</i> <br> -  -  -  -  -  -  -  <i>Cyornis superbus</i> <br> -  -  -  -  -  -  -  <i>Cyornis unicolor</i> <br> -  -  -  -  -  -  <i>Rhinomyias</i> <br> -  -  -  -  -  -  -  <i>Rhinomyias umbratilis</i> <br> -  -  -  -  -  -  <i>Trichixos</i> <br> -  -  -  -  -  -  -  <i>Trichixos pyrropygus</i> <br> -  -  -  -  -  -  <i>Copsychus</i> <br> -  -  -  -  -  -  -  <i>Copsychus stricklandii</i> <br> -  -  -  -  -  Monarchidae <br> -  -  -  -  -  -  <i>Terpsiphone</i> <br> -  -  -  -  -  -  -  <i>Terpsiphone paradisi</i> <br> -  -  -  -  -  -  <i>Hypothymis</i> <br> -  -  -  -  -  -  -  <i>Hypothymis azurea</i> <br></div><p></p>
Data from: Combining niche-shift and population genetic analyses predicts rapid phenotypic evolution during invasion
Rapid evolution of non-native species can facilitate invasion success, but recent reviews indicate that such microevolution rarely yields expansion of the climatic niche in the introduced habitats. However, because some invasions originate from a geographically restricted portion of the native species range and its climatic niche, it is possible that the frequency, direction and magnitude of phenotypic evolution during invasion has been underestimated. We explored the utility of niche-shift analyses in the red seaweed Gracilaria vermiculophylla, which expanded from the northeastern coastline of Japan to North America, Europe and northwestern Africa within the last 100 years. A genetically-informed climatic niche shift analysis indicates that native source populations occur in colder and highly seasonal habitats, while most non-native populations typically occur in warmer, less seasonal habitats. This climatic niche expansion predicts that non-native populations evolved greater tolerance for elevated heat conditions relative to native source populations. We assayed 935 field-collected and 325 common-garden thalli from 40 locations and as predicted, non-native populations had greater tolerance for ecologically-relevant extreme heat (40ºC) than did Japanese source populations. Non-native populations also had greater tolerance for cold and low-salinity stresses relative to source populations. The importance of local adaptation to warm temperatures during invasion was reinforced by evolution of parallel clines: populations from warmer, lower-latitude estuaries had greater heat tolerance than did populations from colder, higher-latitude estuaries in both Japan and eastern North America. We conclude that rapid evolution plays an important role in facilitating the invasion success of this and perhaps other non-native marine species. Genetically-informed ecological niche analyses readily generate clear predictions of phenotypic shifts during invasions, and may help to resolve debate over the frequency of niche conservatism versus rapid adaptation during invasion.
A shift to shorter cuticular hydrocarbons accompanies sexual isolation among Drosophila americana group populations
<p>Because sensory signals often evolve rapidly, they could be instrumental in the emergence of reproductive isolation between species. However, pinpointing their specific contribution to isolating barriers, and the mechanisms underlying their divergence, remains challenging. Here we demonstrate sexual isolation due to divergence in chemical signals between two populations of <i>Drosophila americana</i> (SC and NE) and one population of <i>D. novamexicana</i>, and dissect its underlying phenotypic and genetic mechanisms. Mating trials revealed strong sexual isolation between <i>Drosophila novamexicana</i> males and SC <i>Drosophila americana</i> females, as well as more moderate bi-directional isolation between <i>D. americana</i> populations. Mating behavior data indicates SC <i>D. americana</i> males have the highest courtship efficiency and, unlike males of the other populations, are accepted by females of all species. Quantification of cuticular hydrocarbon (CHC) profiles—chemosensory signals that are used for species recognition and mate finding in <i>Drosophila</i>—shows that the SC <i>D. americana</i> population differs from the other populations primarily on the basis of compound carbon chain-length. Moreover, manipulation of male CHC composition via heterospecific perfuming—specifically perfuming <i>D. novamexicana</i> males with SC <i>D. americana</i> males—abolishes their sexual isolation from these <i>D. americana</i> females. Of a set of candidates, a single gene—elongase CG17821—had patterns of gene expression consistent with a role in CHC differences between species. Sequence comparisons indicate <i>D. novamexicana</i> and our Nebraska (NE) <i>D. americana</i> population share a derived CG17821 truncation mutation that could also contribute to their shared "short" CHC phenotype. Together, these data suggest an evolutionary model for the origin and spread of this allele and its consequences for CHC divergence and sexual isolation in this group.</p>
Distinct responses and range shifts of lizards populations across an elevational gradient under climate change
<p><span>Ongoing climate change has profoundly affected global biodiversity, but its impacts on populations across elevations remain understudied. </span><span>Using a mechanistic niche model incorporating species traits, we predicted ecophysiological responses (activity times, oxygen consumption and evaporative water loss) for lizard populations at high-elevation (< 3600 m asl) and extra-high-elevation (> 3600 m asl) under recent (1970–2000) and future (2081–2100) climates. Compared with their high-elevation counterparts, lizards from extra-high-elevations are predicted to experience a greater increase in activity time and oxygen consumption but a similar increase in evaporative water loss. By integrating these ecophysiological traits into a hybrid species distribution model (HSDM), we were able to make the following predictions under two warming scenarios (SSP1-2.6, SSP5-8.5). By 2081–2100, we predict that lizards at both high- and extra-high-elevations will shift upslope; lizards at extra-high-elevations will gain more and lose less habitat than will their high-elevation congeners. We therefore advocate the conservation of high-elevation species in the context of climate change, especially for those populations living close to their lower elevational range limits. In addition, b</span><span>y comparing the results from </span><span>HSDM and traditional species distribution models, we highlight the importance of </span><span>considering intraspecific variation and local adaptation in physiological traits along elevational gradients when forecasting species' future distributions under climate change</span><span>. </span></p>
Distinct responses and range shifts of lizards populations across an elevational gradient under climate change
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Data from: Shifting precipitation regimes influence optimal germination strategies and population dynamics in bet-hedging desert annuals
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Data from: Combining niche-shift and population genetic analyses predicts rapid phenotypic evolution during invasion
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