Literature DB >> 35790740

CaliPopGen: A genetic and life history database for the fauna and flora of California.

Joscha Beninde1, Erin M Toffelmier2,3, Aarron Andreas4, Celina Nishioka4, Meryl Slay4, Ashley Soto4, Justin P Bueno4, Germar Gonzalez4, Hannah V Pham4, Molly Posta4, Jordan L Pace4, H Bradley Shaffer5,4.   

Abstract

CaliPopGen is a database of population genetic data for native and naturalized eukaryotic species in California, USA. It summarizes the published literature (1985-2020) for 5,453 unique populations with genetic data from more than 187,394 individuals and 448 species (513 species plus subspecies) across molecular markers including allozymes, RFLPs, mtDNA, microsatellites, nDNA, and SNPs. Terrestrial habitats accounted for the majority (46.4%) of the genetic data. Taxonomic groups with the greatest representation were Magnoliophyta (20.31%), Insecta (13.4%), and Actinopterygii (12.85%). CaliPopGen also reports life-history data for most included species to enable analyses of the drivers of genetic diversity across the state. The large number of populations and wide taxonomic breadth will facilitate explorations of ecological patterns and processes across the varied geography of California. CaliPopGen covers all terrestrial and marine ecoregions of California and has a greater density of species and georeferenced populations than any previously published population genetic database. It is thus uniquely suited to inform conservation management at the regional and state levels across taxonomic groups.
© 2022. The Author(s).

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Year:  2022        PMID: 35790740      PMCID: PMC9256587          DOI: 10.1038/s41597-022-01479-z

Source DB:  PubMed          Journal:  Sci Data        ISSN: 2052-4463            Impact factor:   8.501


Background & Summary

The CaliPopGen database consists of four datasets that contain estimates of population genetic diversity, differentiation, and life history traits for 448 eukaryotic species sampled across California, USA. The state has exceptionally high plant and animal biodiversity, and a correspondingly large number of endangered taxa[1]. It is often divided into 19 terrestrial and three marine ecoregions, reflecting its tremendous geologic and ecological diversity[2,3], including the highest and lowest elevations in the contiguous U.S., extreme deserts and temperate rainforests, and mean annual precipitation ranging from 150 mm–1200 mm[4]. California is the most populous state in the USA, accommodating roughly 12% of the nation’s human population; the third largest state geographically, encompassing 5% of the country’s continental land area; and is a major agricultural producer. This combination of high species richness and human-mediated pressures constitute a persistent threat to the short- and long-term persistence of biodiversity, and has led to California’s inclusion as one of only two global biodiversity hotspots in the USA[5,6]. Perhaps unsurprisingly, California has the greatest number of documented and possibly extinct species of vascular plants[7], and more than twice as many federally protected species (total of 287) as any other state[8]. It has also been the focus of more population genetic studies, including states with similarly high numbers of threatened/endangered species like Florida and Hawaii[1]. However, this wealth of genetic information has never been adequately summarized or made publicly available. The few broadly comparative analyses for California have largely been based on inferences derived from fewer than 10 species[9-12], with the exceptions of one review[13], and one empirical study[14], both of which were restricted to marine taxa. California is a perennial leader in biodiversity management, and our compilation of genetic data for the state aligns with the administrative level at which environmental legislation and biodiversity management is implemented, increasing the likelihood that the CaliPopGen database will inform conservation actions. In compiling CaliPopGen, we examined 4,942 published studies identified by our search criteria in the Web of Science, of which 450 met our final inclusion criteria and are included in the database. The majority of genetic samples represented in this database were collected from 1995–2015 (ranging from 1888–2019), and all studies were published between 1983–2020. CaliPopGen contains information on more than 187,394 individuals from 5,453 unique populations, of which 5,276 are spatially georeferenced. These populations include terrestrial (46.6%), marine (21.9%), freshwater (14.1%), amphibious (9.7%), and diadromous (7.7%) populations of fungi (<2% of unique species), chromists (<2%), plants (23%), and animals (73%; Fig. 1). CaliPopGen includes population level data with broader taxonomic coverage than recent, more global compilations, which have focused on freshwater and marine fishes[15], mammals[16], mammals and amphibians[17], vertebrates[18], and birds, fishes, insects and mammals[19]. Its focus at the regional (state) level is unique. The CaliPopGen database also includes a wider range of molecular markers (Fig. 2), populations and species than these previous population genetic compilations. Molecular markers in our database include RFLPs, AFLPs, allozymes and isozymes, microsatellites, mitochondrial, and other nuclear markers, whereas previously published datasets frequently focussed on one or a few loci (e.g.[15-17,19]) or single marker types (e.g.[18],). Our inclusion of all available marker types both reflects the change in methodological approaches through time (for example, the temporal replacement of allozymes with microsatellites in the early 2000’s), and presents opportunities for quantitative comparisons among different marker types.
Fig. 1

Taxonomic breakdown of species represented in the CaliPopGen database. Values in parentheses represent the total number of species as a percentage of the number of unique species in the database.

Fig. 2

The six predominant marker types included in the CaliPopGen database, demonstrating different publication trends through time. The grey bars in each panel are the total number of published studies across all marker types (and are the same in each panel).

Taxonomic breakdown of species represented in the CaliPopGen database. Values in parentheses represent the total number of species as a percentage of the number of unique species in the database. The six predominant marker types included in the CaliPopGen database, demonstrating different publication trends through time. The grey bars in each panel are the total number of published studies across all marker types (and are the same in each panel). Expressed as a proportion of the study area, CaliPopGen contains at least an order of magnitude more species (0.83/1,000 km²), populations (9.59/1,000 km²), and individuals (284.04/1,000 km2) than the aforementioned studies and databases. This high spatial density of samples across the full ecological scope of California (Fig. 3) should facilitate future analyses of ecological trends at the population level where biological processes actually occur, and is well suited to help identify important mechanisms shaping genetic diversity, connectivity and fragmentation. CaliPopGen should also serve as a point of departure for future studies, providing a genetic baseline against which researchers can contrast and quantify future population genetic impacts resulting from changes in climate or land use. As such, CaliPopGen is an historical antecedent to ongoing genomic initiatives to study the diversity and distribution of California’s flora and fauna, including the California Conservation Genomics Project[20], and other projects using landscape genomic approaches.
Fig. 3

Maps of data contained in the CaliPopGen databases. (A) All unique sampling locations of both the population genetic (Dataset 1[21]) and pairwise comparison (Dataset 2[21]) data. The inset shows the location of California within the contiguous USA. (B) The number of unique populations in CaliPopGen per California ecoregion. Note the relative under-representation of inland desert regions (yellow) and over-representation of coastal ecoregions (purple-blue). (C) The number of unique populations of the populations genetic Dataset 1[21] per 20km raster cell. (D) The number of straight-line pairwise comparisons of Dataset 2[21] per 20km raster cell.

Maps of data contained in the CaliPopGen databases. (A) All unique sampling locations of both the population genetic (Dataset 1[21]) and pairwise comparison (Dataset 2[21]) data. The inset shows the location of California within the contiguous USA. (B) The number of unique populations in CaliPopGen per California ecoregion. Note the relative under-representation of inland desert regions (yellow) and over-representation of coastal ecoregions (purple-blue). (C) The number of unique populations of the populations genetic Dataset 1[21] per 20km raster cell. (D) The number of straight-line pairwise comparisons of Dataset 2[21] per 20km raster cell. To supplement the genetic data in CaliPopGen, we also compiled datasets containing life history information for all plant and animal species in the database, including adult body size, lifespan, reproductive and dispersal traits, and conservation status.

Methods

Population genetic data collection from primary data sources

Figure 4 describes the overall data collection workflow for the four datasets that comprise CaliPopGen. We first identified literature potentially containing population genetic data for California by querying the Web of Science Core Collection (https://webofknowledge.com/) for relevant literature from 1900 to 2020 with the terms: topic = (California*) AND topic = (genetic* OR genomic*) AND topic = (species OR taxa* OR population*). We included only empirical peer-reviewed literature and excluded unreviewed preprints. In using these search terms, our goal was to broadly identify genetic papers focused on California with population or species-level analyses, while avoiding purely phylogenetic studies or those focused on agricultural or model species. This resulted in 4,942 unique records.
Fig. 4

Flow chart of the data collection process that generated the CaliPopGen databases.

Flow chart of the data collection process that generated the CaliPopGen databases. We next screened titles and abstracts to retain articles that: (1) provided data on populations of species which are self-sustaining without anthropogenic involvement; (2) included at least some eukaryote species; (3) included population(s) sampled within California; (4) mentioned measures of genetic diversity or differentiation; and (5) were not reviews (thus restricting our search to only primary literature). We retained 1869 studies after this first pass of literature screening (see Technical Validation for estimate of inter- and intra-screener bias). Our second, more in-depth screening pass involved reading the full text of these 1869 studies. We had two goals. First, we confirmed that retained papers fully met all five of our inclusion criteria (the first screen was very liberal with respect to these criteria, and many papers failed to meet at least one criterion after close reading). Second, we eliminated papers where the data were not presented in a way that allowed us to extract population-level information. For example, many of the more systematics-focused studies pooled samples from large, somewhat ill-defined regions (“Sierra Nevada” or “Southern California”); if such regions were larger than 50 km in a linear dimension, we deemed them unusable for making geographically-informative inferences. Other studies presented summaries of population data, often in the form of phylogenetic networks or trees, but did not include information on actual population genetic parameters and therefore were not relevant to our database. We retained 528 publications after this second pass. From this set of papers, we extracted species, locality, and genetic data for each California population or sampling locality described in each study (Fig. 3A). This included Latin binomial/trinomial, English common name, population identifiers, and geographic coordinates of sampling sites. We also noted population/sampling localities that were interpreted as comprised of interspecific hybrids, and listed both parental species. We collected population genetic diversity and differentiation statistics for each unique genetic marker for each population/sampling locality; as a result, a sampling locality may have multiple entry rows, one for each locus or marker type. Parameters extracted for each population/marker combination include sample size, genetic marker type, gene targets, number of loci, years of sampling, and reported values for effective population size (N), expected (H) and observed (H,) heterozygosity, nucleotide diversity (π, pi), alleles-per-locus (APL), allelic richness (A), percent polymorphic loci (PPL), haplotype diversity (HDIV), inbreeding coefficient (e.g. F, F, G), and pairwise population genetic comparison parameters (F, G, D, Nei’s D, Jost’s D, or phi). We note that while there are technical differences between allelic richness and alleles-per-locus, source literature often used the terms interchangeably, and we include the parameters and their values as named in the source. We define marker type as the general category of genetic marker used (e.g., “microsatellite” or “nuclear”), while gene targets are the specific locus/loci (e.g., “COI”). We present these data in two separate datasets, one containing all population-level genetic summary statistics (Dataset 1[21], see Fig. 3C and detailed description in Table 1) and a second for estimates of pairwise genetic differentiation (Dataset 2[21], see Fig. 3D and detailed description in Table 2).
Table 1

Description of the population genetic data in Dataset 1[21].

Column IDDescription
CitationIDUnique ID assigned to each source article
EntryIDUnique ID assigned to each unique entry in the entire CaliPopGen database
CitationFullCitation information
KingdomKingdom classification for the species
PhylumPhylum classification for the species
TaxonGroupBroadly categorized taxonomic group
ScientificNameCurrently accepted Latin binomial (GBIF)
SubspeciesNameCurrently accepted subspecies epithet (GBIF)
CommonNameCurrently reported English common name (GBIF)
MarkerTypeGeneral category of genetic marker
GeneTargetSpecific genes or markers used
NumMarkersNumber of markers used in the study
SampleSizeNumber of samples used to calculate genetic parameters. Value may be a non-integer if a mean number of samples across a set of loci was reported.
YearStartFirst year of sample collection
YearEndLast year of sample collection
PopNamePopulation or locality name
LatitudeDDLatitude in decimal degrees
LongitudeDDLongitude in decimal degrees
CoordErrorEstimated radius of error in kilometers for coordinates georeferenced by us
AllelicRichnessAllelic richness
HetExpExpected heterozygosity
HetObsObserved heterozygosity
NucDiversityNucleotide diversity, pi
EffectivePopSizeEffective population size
AllelesPerLocusAlleles per locus
PercentPolyLociPercent polymorphic loci
HaploDivHaplotype diversity
InbreedingCoefTypeType of inbreeding coefficient reported
InbreedingCoefValueValue of inbreeding coefficient
SpeciesIDUnique ID assigned to this entry
HabitatTypeMarine, Freshwater, Diadromous, Terrestrial, Amphibious. See text for descriptions
Columns 32–70Animal life history data (see Table 3)
Columns 71–101Plant life history data (see Table 4)
Table 2

Description of the pairwise genetic distance data in Dataset 2[21].

Column NameDescription
CitationIDUnique ID assigned to each source article.
EntryIDUnique ID assigned to each unique entry in the entire CaliPopGen database
CitationFullReference information
KingdomKingdom classification for the species
PhylumPhylum classification for the species
TaxonGroupBroadly categorized taxonomic group
Pop1ScientificNameCurrently accepted Latin binomial (GBIF)
Pop1SubspeciesNameCurrently accepted subspecies epithet (GBIF)
Pop1CommonNameCurrently reported English common name (GBIF)
Pop1NamePopulation or locality name of first site in pairwise comparison
Pop1LatitudeDDLatitude in decimal degrees of first site
Pop1LongitudeDDLongitude in decimal degrees of first site
Pop2ScientificNameCurrently accepted Latin binomial (GBIF)
Pop2SubspeciesNameCurrently accepted subspecies epithet (GBIF)
Pop2CommonNameCurrently reported English common name (GBIF)
Pop2NamePopulation or locality name of second site in pairwise comparison
Pop2LatitudeDDLatitude in decimal degrees second site
Pop2LongitudeDDLongitude in decimal degrees second site
CoordErrorEstimated radius of error in kilometers for coordinates georeferenced by us
GenDistGenetic distance score (FST, GST, DST, Nei’s D, Jost’s D, phi)
GenDistMetricType of pairwise genetic parameter reported (FST, GST, DST, Nei’s D, Jost’s D, phi)
GenDistMetricMethodName/citation of specific method used to calculate GenDistMetric (if provided)
MarkerTypeGeneral category of genetic marker
GeneTargetSpecific genes or markers used
NumMarkersNumber of markers used
SepAnalysesWhen multiple analyses were conducted, the level by which data were split is noted here (e.g. species or sampling year)
SpecialComparionsTypeIdentifies pairwise comparisons across timescales (“temporal”), at different temporal intervals (“spatio-temporal replicate”), of samples collected before 1920 (“historic”), between species (“interspecific”) or hybrid populations (“hybrid”)
Pop1ComparisonCharacteristicCharacteristic of special comparison
Pop2ComparisonCharacteristicCharacteristic of special comparison
Pop1YearStartFirst year of sample collection
Pop1YearEndLast year of sample collection
Pop2YearStartFirst year of sample collection
Pop2YearEndLast year of sample collection
SpeciesIDUnique ID assigned to this entry
HabitatTypeMarine, Freshwater, Diadromous, Terrestrial, Amphibious. See text for descriptions
Columns 36–74Animal life history data (see Table 3)
Columns 75–101Plant life history data (see Table 4)
Description of the population genetic data in Dataset 1[21]. Description of the pairwise genetic distance data in Dataset 2[21]. All genetic data were extracted directly from the source literature. However, we also updated or added to the metadata for these population genetic values in several ways. We included kingdom, phylum, and a lower-level taxonomic grouping for each species (usually class), and updated scientific and common names based on the currently accepted taxonomy of the Global Biodiversity Information Facility[22]. When geographic coordinates were not provided for a sampling locality, as was frequently the case in the older literature, we used Google Maps (https://www.google.com/maps) to georeference localities based on either in-text descriptions or embedded figure maps guided by permanent landmarks like a bend in a river or administrative boundaries. Because this can only yield approximate coordinates, we recorded estimated accuracy as the radius of our best estimate of possible error in kilometers. If coordinates were provided in degree/minute/seconds, we used Google Maps to translate them to decimal degrees. In cases where coordinates were not provided and locality descriptions were too vague to determine coordinates with less than 50 km estimated coordinate error, we did not attempt to extract coordinates but still provide the genetic data. All coordinates are provided in the web Mercator projection (EPSG:3857). We excluded studies that reported genetic parameter values only for samples aggregated regionally (“Southern California” or “Sierra Nevada”). If marker type was not explicitly included, we classified marker type based on the gene targets reported, if provided.

Life history trait data collection

To increase the utility of CaliPopGen, we also assembled data on life history traits for all animal (Dataset 3[21]) and plant (Dataset 4[21]) species contained in Datasets 1[21] and 2[21]. We assembled trait data that have previously been shown to correlate with genetic diversity, including those related to reproduction, life cycle, and body size, as well as conservation status (e.g.[23-26],). Life history data were compiled by first referencing large online repositories, often specific to taxonomic groups, like the TRY plant trait database[27], and the Royal Botanic Gardens Kew Seed Information Database[28]. If trait data for species of interest were unavailable from these compilations, we conducted keyword literature searches for each combination of species and life history trait, and extracted data from the primary literature. When data were not available for the subspecies or species for which we had genetic data, we report values for the next closest taxonomic level, up to and including family, as available in the literature. For both animals and plants, we defined habitat types as marine, freshwater, diadromous, amphibious, or terrestrial. Marine species include those that are found in brackish or wetland-marine habitats, as well as bird species that primarily reside in marine habitats. Freshwater species include those that are found in wetland-freshwater habitats, as well as species that primarily reside in freshwater. The diadromous category includes fish species that are catadromous or anadromous. We considered species to be amphibious if they have an obligatory aquatic stage in their life cycle, but also spend a significant portion of their life cycle on land. Terrestrial species were defined as those that spend most of their life cycle on land and are not aquatic for any portion of their life cycle. In a few cases (e.g., waterbirds that are both freshwater and marine, semi-aquatic reptiles), a species could reasonably be placed in more than one category, and we did our best to identify the primary life history category for such taxa. If the taxonomic identity of an entry was hybrid between species or subspecies, this was noted in the speciesID column and no life history data were reported. The CaliPopGen Animal Life History Traits Dataset 3[21] (description of dataset in Table 3) includes habitat type, lifespan, fecundity, lifetime reproductive success, age at sexual maturity, number of breeding events per year, mode of reproduction, adult length and mass, California native status, listing status under the US Endangered Species Act (ESA), listing status under the California Endangered Species Act (CESA), and status as a California Species of Special Concern (SSC). For some traits, value ranges were recorded–for example, minimum to maximum lifespan. In other cases, we recorded single values and, when available, a definition of this single value, (for example, minimum, average, or maximum lifespan). We report either the range of the age of sexual maturity (minimum to maximum), or a single value, depending on the available literature. For sexually dimorphic species, we report female adult length and weight when available, because female body size often correlates with fecundity. Across animal taxonomic groups, different measures of body size and length measurements are often used, reflecting community consensus on how to measure size. Given this variation, we report the type of length measurement, if available, as Standard Length (SL), Fork Length (FL), Total Length (TL), Snout-to-Vent Length (SVL), Straight-Line Carapace (SLC), or Wingspan (WS).
Table 3

Description of the animal life-history data in Dataset 3[21].

Column NameDescriptionTotal entries
SpeciesIDUnique ID assigned to this entry432
TaxonGroupBroadly categorized taxonomic group432
ScientificNameCurrently accepted Latin species binomial (GBIF)432
SubspeciesNameCurrently accepted subspecies epithet (GBIF)88
CommonNameCurrently reported English common name (GBIF)372
HabitatTypeMarine, Freshwater, Diadromous, Terrestrial, Amphibious. See text for descriptions429
LifespanMinMinimum value for reported lifespan range90
LifespanMaxMaximum value for reported lifespan range131
LifespanOtherValue of lifespan if not reported as a range147
LifespanOtherTypeValue type of “LifespanOther” (average, minimum or maximum)147
FecundityThe number of offspring or eggs per reproductive event216
LifetimeReprodOutputTotal lifetime reproductive output24
AgeSexMatMinThe minimum age for an individual to reach sexual maturity, in years92
AgeSexMatMaxThe maximum age for an individual to reach sexual maturity, in years79
AgeSexMatOtherSingle values for age of sexual maturity in years if not reported as a range121
AgeSexMatOtherTypeValue type of “AgeSexMatOther” (average, minimum or maximum)121
NumBreedingEventsNumber of breeding events per year146
ReprodModeMode of reproduction (asexual, sexual, both)312
BodyLengthAdult body length reported in centimeters (cm)333
BodyLengthTypeAdult body length measurement type: SL (standard length) or PCL (precaudal standard length), FL (fork length), TL (total length), WS (wingspan), SCL (straight-line carapace), SVL (snout-to-vent length)254
BodyLengthSexThe gender of the adult length reported248
AdultMassAdult mass, reported in kilograms (kg)178
AdultMassSexThe gender of the adult mass reported124
CANativeStatusNative/non-native: whether the species is known to be native to California329
CESAStatusCalifornia Endangered Species Act listing status, if any39
SSCStatusCalifornia Species of Special Concern listing status, if any49
ESAStatusFederal Endangered Species Act (ESA) listing status, if any52
TaxonDataLevelThe taxonomic level at which collected data was obtained, if not for the species or subspecies in question16
SpeciesSynonymsList of species synonyms used to acquire information (GBIF)15
Columns 30–45Reference sources for trait data
Description of the animal life-history data in Dataset 3[21]. The CaliPopGen Plant Life History Traits Dataset 4[21] (description of dataset in Table 4) includes habitat type, lifespan, life cycle, adult height, self-compatibility, monoecious or dioecious, mode of reproduction, pollination and seed dispersal modes, mass per seed, California native status, NatureServe[29] element ranks (global and state ranks, see Table 5 for definitions), listing status under the Federal Endangered Species Act (ESA), and listing status under the California Endangered Species Act (CESA). In contrast to most animal species, plant lifespan was typically reported as a single value. We define life cycles as the following: Annual: completes full life cycle in one year; Biennial: completes full life cycle in two years; Perennial: completes full life cycle in more than two years; Perennial-Evergreen: perennial and retains functional leaves throughout the year; Perennial-Deciduous: perennial and loses all leaves synchronously for part of the year. Some species are variable (for example, have annual and biennial individuals), and in those cases we attempted to characterize the most common modality.
Table 4

Description of the plant life-history data in Dataset 4[21].

Column NameDescriptionTotal entries
SpeciesIDUnique ID assigned to this entry177
TaxonGroupBroadly categorized taxonomic group177
ScientificNameCurrently accepted Latin binomial (GBIF)177
SubspeciesNameCurrently accepted subspecies epithet (GBIF)34
CommonNameCurrently reported English common name (GBIF)144
HabitatTypeMarine, Freshwater, Terrestrial. See text for descriptions116
LifespanReported only for perennial species. Maximum lifespan value reported or highest value of reported lifespan range (years)61
LifeCycleAnnual, Biennial, Perennial, Perennial-Evergreen, Perennial-Deciduous. See text for descriptions152
AdultHeightMaximum height value reported or highest value of reported height range in meters (m)145
SelfCompatibilityIndicates whether species is self-compatible98
MonoeciousDioeciousMonoecious: individuals bear both male and female flowers; Dioecious: individuals bear either male or female flowers, but not both78
AsexualIndicates whether primary mode of reproduction is asexually21
PollinationModePrimary pollination mode: wind, animal, water120
SeedDispModeSeed dispersal mode: wind, animal, gravity, water, human94
MassPerSeedFecundity as measured by mass per seed in milligrams (mg)83
CANativeStatusNative/non-native: whether the species is known to be native to the state of California164
CAEndemicStatusEndemic, near-endemic or distributed only in California & Baja California62
InvasiveRatingCalifornia Invasive Plant Council rating of invasiveness (non-native species only)27
CESAStatusCalifornia Endangered Species Act listing status, if any18
CNDDBStatusHeritage rank as defined by the California Natural Diversity Database. See Table 5 for ranking descriptions.139
ESAStatusFederal Endangered Species Act (ESA) listing status, if any19
TaxonDataLevelThe taxonomic level at which collected data was obtained, if not for the species or subspecies in question63
SpeciesSynonymsList of species synonyms used to acquire information (GBIF)21
Columns 24–37Reference sources for trait data
Table 5

Description of the Conservation status (Heritage Rank) from California Natural Diversity Database[29].

Global/State rankDescription
GX/SXPresumed extirpated
GH/SHPossibly extirpated; known only from historical occurrences but there is still some hope of rediscovery.
G1/S1Critically imperiled; at very high risk of extirpation in the jurisdiction due to very restricted range, very few populations or occurrences, very steep declines, severe threats, or other factors.
G2/S2Imperiled; at high risk of extirpation in the jurisdiction due to restricted range, few populations or occurrences, steep declines, severe threats, or other factors.
G3/S3Vulnerable; at moderate risk of extirpation in the jurisdiction due to a fairly restricted range, relatively few populations or occurrences, recent and widespread declines, threats, or other factors.
G4/S4Apparently secure; at a fairly low risk of extirpation in the jurisdiction due to an extensive range and/or many populations or occurrences, but with possible vii cause for some concern as a result of local recent declines, threats, or other factors.
G5/S5Secure; at very low or no risk of extirpation in the jurisdiction due to a very extensive range, abundant populations or occurrences, with little to no concern from declines or threats.

The Global rank (G rank) is a reflection of the overall status of a species throughout its global range. The State rank (S rank) is assigned much the same way as the Global rank, but State ranks refer to the imperilment status only within California’s state boundaries.

Description of the plant life-history data in Dataset 4[21]. Description of the Conservation status (Heritage Rank) from California Natural Diversity Database[29]. The Global rank (G rank) is a reflection of the overall status of a species throughout its global range. The State rank (S rank) is assigned much the same way as the Global rank, but State ranks refer to the imperilment status only within California’s state boundaries. Because of the paucity of data available for chromists and fungi, we did not extract life history trait data for the relatively few species in these taxonomic groups.

Data visualization and summary

We used the R-package raster (v3.1–5) to visualize the spatial extent of the data in CaliPopGen in Fig. 3. Panel (A) shows a summary plot of all unique populations of both the Population Genetic Diversity in Dataset 1[21] and the Pairwise Population Differentiation in Dataset 2[21]. Panel (B) shows the total number of unique populations in each California terrestrial ecoregion. Panel (C) depicts all data entries of Population Genetic Diversity Dataset 1[21], summed for each 20x20 km grid cell. Panel (D) shows the density of pairwise straight lines drawn between pairs of localities in the Pairwise Population Differentiation Dataset 2[21], depicted as the total number of lines per 20x20 km grid cell. The number of populations and species of both Datasets 1[21] & 2[21] are summarized for each marine and terrestrial ecoregion in Table 6.
Table 6

Summary of total numbers of populations and species per California ecoregion, separately for population genetic and pairwise datasets.

Ecoregiontypearea (km²)N species PopGenN populations PopGenN species PairwiseN populations Pairwise
Oregon, Washington, Vancouver Coast and Shelfmarine23341169
Northern Californiamarine9324743273
Southern California Bightmarine7924828223
Central California Coastterrestrial13,72616140174905
Central Valley Coast Rangesterrestrial24,852377517211
Colorado Desertterrestrial11,852184615165
Great Valleyterrestrial49,1766734843567
Klamath Mountainsterrestrial22,568379616261
Modoc Plateauterrestrial14,3091931640
Mojave Desertterrestrial66,832236312164
Monoterrestrial7,98415458129
Northern California Coastterrestrial17,1358341951758
Northern California Coast Rangesterrestrial15,5244112122390
Northern California Interior Coast Rangesterrestrial7,494151610145
North-western Basin and Rangeterrestrial5,2247900
Sierra Nevadaterrestrial51,5936735823511
Sierra Nevada Foothillsterrestrial18,191288717336
Sonoran Desertterrestrial12,87846322
South-eastern Great Basinterrestrial11,038715242
Southern California Coastterrestrial14,47317764577920
Southern California Mountains and Valleysterrestrial27,5517834043619
Southern Cascadesterrestrial17,025288113212

The first three are marine, followed by the 19 USDA-defined ecoregions.

Summary of total numbers of populations and species per California ecoregion, separately for population genetic and pairwise datasets. The first three are marine, followed by the 19 USDA-defined ecoregions.

Data Records

The CaliPopGen database comprises four datasets, which are hosted at Figshare and can be downloaded as XLSX, TSV and CSV files. For convenience, the life history trait data for both animals (Dataset 3[21]) and plants (Dataset 4[21]) have also been included in Dataset 1[21] and Dataset 2[21]. We combined the genetic and life history data under the assumption that potential users may want to examine correlations between these two classes of data. Dataset 1[21]: The Population Genetic Diversity dataset consists of 101 columns, described in Table 1, and is comprised of data from 401 studies on 446 (sub-)species and 4,697 unique species-population-marker type combinations, with the latter equaling the number of rows in the dataset. The first 31 columns summarize taxonomic, population, marker type, and genetic data, while the remaining 70 columns contain data on animal and plant life history (Dataset 3[21] and Dataset 4[21], respectively, see below). Dataset 2[21]: The Pairwise Population Differentiation dataset consists of 106 columns, described in Table 2, and is comprised of data from 199 studies on 197 (sub-)species and 14,703 pairwise population comparisons, with the latter equaling the number of rows. The first 36 columns summarize taxonomic, population, marker type, and pairwise population comparison data, while the remaining 70 columns contain data on animal and plant life history (Dataset 3[21] and Dataset 4[21], respectively, see below). Dataset 3[21]: The Animal Life History Traits dataset consists of 45 columns, containing data for 432 species and subspecies, and is described in Table 3. The first 29 columns describe the life history of species and subspecies, and give details on their conservation status, while columns 30–45 provide information on the sources of these data. Dataset 4[21]: The Plant Life History Traits dataset consists of 37 columns containing data for 177 species and is described in Table 4. The first 23 columns describe the life-history of species and subspecies, and give details on their conservation status, while columns 24–37 provide sources of data. Total species numbers of Dataset 3[21] & 4[21] are higher than the number of species of Dataset 1[21] & 2[21] because we left species in these datasets even though their genetic entries may have been excluded based on the criteria set out in the Methods.

Technical Validation

Article classification

During the first step in our screening protocol based on titles and abstracts (see Fig. 4), we examined the repeatability (intra-individual variation), and reproducibility (inter-individual variation) of article classification. Given that multiple individuals were doing the article screening, we recognize that understanding variation at both of these levels is important. During this first screening, six screeners assigned a non-overlapping set of articles into three broad categories (“reject”, “include”, or “possibly include”), based on our five screening criteria (see Methods); we used “possibly include” if it was unclear from the title and abstract if a paper contained appropriate data. Each screener independently evaluated 777–782 articles (total screened = 4,942). To quantify the repeatability of our screeners, all of whom were UCLA undergraduates, each individual re-screened a subset of their original set of articles. 54 randomly selected papers were re-screened by the same person (6 screeners, range 6–13 papers per person, mean = 10.8 papers re-screened/screener). We allowed 10 weeks between the initial and re-screening procedures, which all screeners felt was a sufficiently long time that they would not remember their initial classification, and papers were randomly chosen by the senior authors. To quantify the reproducibility of the screening process across individuals, 421 papers were re-screened by a different individual than the original screener (8 re-screeners, range 46–60 per person, mean = 50.33 re-screened/screener). Each of the 421 papers was re-screened by exactly one new person. This procedure included JB and EMT in addition to the original six undergraduates. As might be expected, intra-individual repeatability (agreement between the initial and re-screened classification of a paper screened by the same person) was higher than inter-individual reproducibility (agreement between the initial and re-screened classification of a paper screened by two different people): 92.6% (50/54) of papers re-screened by the same individual received an identical score whereas 74.8% (315/421) of papers re-screened by a different individual received an identical score. Across both of these exercises, 17.5% of articles that were re-screened by either the same or different individual (total = 475) were assigned to different categories between the first and second screening. For the inter-individual analyses, 27.7% of “possibly include” articles changed status when screened by different individuals, while only 16.0% of “reject” and 16.3% of “include” decisions changed. However, when we subsequently attempted to extract data during the Data Collection Phase, we did so from both “include” and “possibly include” papers, so the relatively low change of “reject” status makes us comfortable that screener variability and its potential bias had at most a very limited impact in our decision pipeline.

Data validation

To identify and correct potential recording errors in the datasets after the initial round of data extraction, we flagged numerical outliers and values outside of theoretical expectations for all genetic parameters and life history traits. Both outliers and values outside theoretical bounds may represent values as reported in the original publication, or they may be transcription errors as we compiled datasets. To increase the likelihood of identifying errors via outlier analysis, we examined each genetic parameter distribution separately for each marker type and taxonomic group (for example, H of microsatellite markers in Aves was examined separately from H of mitochondrial markers in Reptilia), and we examined life history trait distributions separately for each taxonomic group. In all cases, we identified outliers as values greater or less than the upper or lower quartiles +/− 1.5 * IQR (IQR = inter-quartile range), using the function boxplot.stats in the R-package grDevices. For all identified outliers we returned to the original source publication to confirm that values were as reported, or corrected them if they were a data-entry error. Correctly transcribed values falling outside of their theoretical bounds (H, H, π, F, G, D are constrained between zero and 1, F is bounded by −1 and 1, N must be greater than zero) were left unaltered, which users of the CaliPopGen databases should consider carefully in using these results.
Measurement(s)genetic variation
Technology Type(s)DNA sequencing
Factor Type(s)Kingdom • Phylum • TaxonGroup • MarkerType • SampleSize • GeneTarget • NumMarkers • YearStart • YearEnd • PopName • LongitudeDD • LatitudeDD • CoordError • HabitatType • Lifespan • Fecundity • LifetimeReprodOutput • AgeSexMat • NumBreedingEvents • ReprodMode • BodyLength • AdultMass • CANativeStatus • CESAStatus • SSCStatus • ESAStatus • LifeCycle • AdultHeight • SelfCompatibility • MonoeciousDioecious • Asexual • PollinationMode • SeedDispMode • MassPerSeed • CAEndemicStatus
Sample Characteristic - Organismeukaryota
Sample Characteristic - LocationCalifornia
  17 in total

1.  Geographic Distribution of Endangered Species in the United States

Authors: 
Journal:  Science       Date:  1997-01-24       Impact factor: 47.728

2.  An Anthropocene map of genetic diversity.

Authors:  Andreia Miraldo; Sen Li; Michael K Borregaard; Alexander Flórez-Rodríguez; Shyam Gopalakrishnan; Mirnesa Rizvanovic; Zhiheng Wang; Carsten Rahbek; Katharine A Marske; David Nogués-Bravo
Journal:  Science       Date:  2016-09-30       Impact factor: 47.728

3.  Comparative population genomics in animals uncovers the determinants of genetic diversity.

Authors:  J Romiguier; P Gayral; M Ballenghien; A Bernard; V Cahais; A Chenuil; Y Chiari; R Dernat; L Duret; N Faivre; E Loire; J M Lourenco; B Nabholz; C Roux; G Tsagkogeorga; A A-T Weber; L A Weinert; K Belkhir; N Bierne; S Glémin; N Galtier
Journal:  Nature       Date:  2014-08-20       Impact factor: 49.962

4.  TRY plant trait database - enhanced coverage and open access.

Authors:  Jens Kattge; Gerhard Bönisch; Sandra Díaz; Sandra Lavorel; Iain Colin Prentice; Paul Leadley; Susanne Tautenhahn; Gijsbert D A Werner; Tuomas Aakala; Mehdi Abedi; Alicia T R Acosta; George C Adamidis; Kairi Adamson; Masahiro Aiba; Cécile H Albert; Julio M Alcántara; Carolina Alcázar C; Izabela Aleixo; Hamada Ali; Bernard Amiaud; Christian Ammer; Mariano M Amoroso; Madhur Anand; Carolyn Anderson; Niels Anten; Joseph Antos; Deborah Mattos Guimarães Apgaua; Tia-Lynn Ashman; Degi Harja Asmara; Gregory P Asner; Michael Aspinwall; Owen Atkin; Isabelle Aubin; Lars Baastrup-Spohr; Khadijeh Bahalkeh; Michael Bahn; Timothy Baker; William J Baker; Jan P Bakker; Dennis Baldocchi; Jennifer Baltzer; Arindam Banerjee; Anne Baranger; Jos Barlow; Diego R Barneche; Zdravko Baruch; Denis Bastianelli; John Battles; William Bauerle; Marijn Bauters; Erika Bazzato; Michael Beckmann; Hans Beeckman; Carl Beierkuhnlein; Renee Bekker; Gavin Belfry; Michael Belluau; Mirela Beloiu; Raquel Benavides; Lahcen Benomar; Mary Lee Berdugo-Lattke; Erika Berenguer; Rodrigo Bergamin; Joana Bergmann; Marcos Bergmann Carlucci; Logan Berner; Markus Bernhardt-Römermann; Christof Bigler; Anne D Bjorkman; Chris Blackman; Carolina Blanco; Benjamin Blonder; Dana Blumenthal; Kelly T Bocanegra-González; Pascal Boeckx; Stephanie Bohlman; Katrin Böhning-Gaese; Laura Boisvert-Marsh; William Bond; Ben Bond-Lamberty; Arnoud Boom; Coline C F Boonman; Kauane Bordin; Elizabeth H Boughton; Vanessa Boukili; David M J S Bowman; Sandra Bravo; Marco Richard Brendel; Martin R Broadley; Kerry A Brown; Helge Bruelheide; Federico Brumnich; Hans Henrik Bruun; David Bruy; Serra W Buchanan; Solveig Franziska Bucher; Nina Buchmann; Robert Buitenwerf; Daniel E Bunker; Jana Bürger; Sabina Burrascano; David F R P Burslem; Bradley J Butterfield; Chaeho Byun; Marcia Marques; Marina C Scalon; Marco Caccianiga; Marc Cadotte; Maxime Cailleret; James Camac; Jesús Julio Camarero; Courtney Campany; Giandiego Campetella; Juan Antonio Campos; Laura Cano-Arboleda; Roberto Canullo; Michele Carbognani; Fabio Carvalho; Fernando Casanoves; Bastien Castagneyrol; Jane A Catford; Jeannine Cavender-Bares; Bruno E L Cerabolini; Marco Cervellini; Eduardo Chacón-Madrigal; Kenneth Chapin; F Stuart Chapin; Stefano Chelli; Si-Chong Chen; Anping Chen; Paolo Cherubini; Francesco Chianucci; Brendan Choat; Kyong-Sook Chung; Milan Chytrý; Daniela Ciccarelli; Lluís Coll; Courtney G Collins; Luisa Conti; David Coomes; Johannes H C Cornelissen; William K Cornwell; Piermaria Corona; Marie Coyea; Joseph Craine; Dylan Craven; Joris P G M Cromsigt; Anikó Csecserits; Katarina Cufar; Matthias Cuntz; Ana Carolina da Silva; Kyla M Dahlin; Matteo Dainese; Igor Dalke; Michele Dalle Fratte; Anh Tuan Dang-Le; Jirí Danihelka; Masako Dannoura; Samantha Dawson; Arend Jacobus de Beer; Angel De Frutos; Jonathan R De Long; Benjamin Dechant; Sylvain Delagrange; Nicolas Delpierre; Géraldine Derroire; Arildo S Dias; Milton Hugo Diaz-Toribio; Panayiotis G Dimitrakopoulos; Mark Dobrowolski; Daniel Doktor; Pavel Dřevojan; Ning Dong; John Dransfield; Stefan Dressler; Leandro Duarte; Emilie Ducouret; Stefan Dullinger; Walter Durka; Remko Duursma; Olga Dymova; Anna E-Vojtkó; Rolf Lutz Eckstein; Hamid Ejtehadi; James Elser; Thaise Emilio; Kristine Engemann; Mohammad Bagher Erfanian; Alexandra Erfmeier; Adriane Esquivel-Muelbert; Gerd Esser; Marc Estiarte; Tomas F Domingues; William F Fagan; Jaime Fagúndez; Daniel S Falster; Ying Fan; Jingyun Fang; Emmanuele Farris; Fatih Fazlioglu; Yanhao Feng; Fernando Fernandez-Mendez; Carlotta Ferrara; Joice Ferreira; Alessandra Fidelis; Bryan Finegan; Jennifer Firn; Timothy J Flowers; Dan F B Flynn; Veronika Fontana; Estelle Forey; Cristiane Forgiarini; Louis François; Marcelo Frangipani; Dorothea Frank; Cedric Frenette-Dussault; Grégoire T Freschet; Ellen L Fry; Nikolaos M Fyllas; Guilherme G Mazzochini; Sophie Gachet; Rachael Gallagher; Gislene Ganade; Francesca Ganga; Pablo García-Palacios; Verónica Gargaglione; Eric Garnier; Jose Luis Garrido; André Luís de Gasper; Guillermo Gea-Izquierdo; David Gibson; Andrew N Gillison; Aelton Giroldo; Mary-Claire Glasenhardt; Sean Gleason; Mariana Gliesch; Emma Goldberg; Bastian Göldel; Erika Gonzalez-Akre; Jose L Gonzalez-Andujar; Andrés González-Melo; Ana González-Robles; Bente Jessen Graae; Elena Granda; Sarah Graves; Walton A Green; Thomas Gregor; Nicolas Gross; Greg R Guerin; Angela Günther; Alvaro G Gutiérrez; Lillie Haddock; Anna Haines; Jefferson Hall; Alain Hambuckers; Wenxuan Han; Sandy P Harrison; Wesley Hattingh; Joseph E Hawes; Tianhua He; Pengcheng He; Jacob Mason Heberling; Aveliina Helm; Stefan Hempel; Jörn Hentschel; Bruno Hérault; Ana-Maria Hereş; Katharina Herz; Myriam Heuertz; Thomas Hickler; Peter Hietz; Pedro Higuchi; Andrew L Hipp; Andrew Hirons; Maria Hock; James Aaron Hogan; Karen Holl; Olivier Honnay; Daniel Hornstein; Enqing Hou; Nate Hough-Snee; Knut Anders Hovstad; Tomoaki Ichie; Boris Igić; Estela Illa; Marney Isaac; Masae Ishihara; Leonid Ivanov; Larissa Ivanova; Colleen M Iversen; Jordi Izquierdo; Robert B Jackson; Benjamin Jackson; Hervé Jactel; Andrzej M Jagodzinski; Ute Jandt; Steven Jansen; Thomas Jenkins; Anke Jentsch; Jens Rasmus Plantener Jespersen; Guo-Feng Jiang; Jesper Liengaard Johansen; David Johnson; Eric J Jokela; Carlos Alfredo Joly; Gregory J Jordan; Grant Stuart Joseph; Decky Junaedi; Robert R Junker; Eric Justes; Richard Kabzems; Jeffrey Kane; Zdenek Kaplan; Teja Kattenborn; Lyudmila Kavelenova; Elizabeth Kearsley; Anne Kempel; Tanaka Kenzo; Andrew Kerkhoff; Mohammed I Khalil; Nicole L Kinlock; Wilm Daniel Kissling; Kaoru Kitajima; Thomas Kitzberger; Rasmus Kjøller; Tamir Klein; Michael Kleyer; Jitka Klimešová; Joice Klipel; Brian Kloeppel; Stefan Klotz; Johannes M H Knops; Takashi Kohyama; Fumito Koike; Johannes Kollmann; Benjamin Komac; Kimberly Komatsu; Christian König; Nathan J B Kraft; Koen Kramer; Holger Kreft; Ingolf Kühn; Dushan Kumarathunge; Jonas Kuppler; Hiroko Kurokawa; Yoko Kurosawa; Shem Kuyah; Jean-Paul Laclau; Benoit Lafleur; Erik Lallai; Eric Lamb; Andrea Lamprecht; Daniel J Larkin; Daniel Laughlin; Yoann Le Bagousse-Pinguet; Guerric le Maire; Peter C le Roux; Elizabeth le Roux; Tali Lee; Frederic Lens; Simon L Lewis; Barbara Lhotsky; Yuanzhi Li; Xine Li; Jeremy W Lichstein; Mario Liebergesell; Jun Ying Lim; Yan-Shih Lin; Juan Carlos Linares; Chunjiang Liu; Daijun Liu; Udayangani Liu; Stuart Livingstone; Joan Llusià; Madelon Lohbeck; Álvaro López-García; Gabriela Lopez-Gonzalez; Zdeňka Lososová; Frédérique Louault; Balázs A Lukács; Petr Lukeš; Yunjian Luo; Michele Lussu; Siyan Ma; Camilla Maciel Rabelo Pereira; Michelle Mack; Vincent Maire; Annikki Mäkelä; Harri Mäkinen; Ana Claudia Mendes Malhado; Azim Mallik; Peter Manning; Stefano Manzoni; Zuleica Marchetti; Luca Marchino; Vinicius Marcilio-Silva; Eric Marcon; Michela Marignani; Lars Markesteijn; Adam Martin; Cristina Martínez-Garza; Jordi Martínez-Vilalta; Tereza Mašková; Kelly Mason; Norman Mason; Tara Joy Massad; Jacynthe Masse; Itay Mayrose; James McCarthy; M Luke McCormack; Katherine McCulloh; Ian R McFadden; Brian J McGill; Mara Y McPartland; Juliana S Medeiros; Belinda Medlyn; Pierre Meerts; Zia Mehrabi; Patrick Meir; Felipe P L Melo; Maurizio Mencuccini; Céline Meredieu; Julie Messier; Ilona Mészáros; Juha Metsaranta; Sean T Michaletz; Chrysanthi Michelaki; Svetlana Migalina; Ruben Milla; Jesse E D Miller; Vanessa Minden; Ray Ming; Karel Mokany; Angela T Moles; Attila Molnár; Jane Molofsky; Martin Molz; Rebecca A Montgomery; Arnaud Monty; Lenka Moravcová; Alvaro Moreno-Martínez; Marco Moretti; Akira S Mori; Shigeta Mori; Dave Morris; Jane Morrison; Ladislav Mucina; Sandra Mueller; Christopher D Muir; Sandra Cristina Müller; François Munoz; Isla H Myers-Smith; Randall W Myster; Masahiro Nagano; Shawna Naidu; Ayyappan Narayanan; Balachandran Natesan; Luka Negoita; Andrew S Nelson; Eike Lena Neuschulz; Jian Ni; Georg Niedrist; Jhon Nieto; Ülo Niinemets; Rachael Nolan; Henning Nottebrock; Yann Nouvellon; Alexander Novakovskiy; Kristin Odden Nystuen; Anthony O'Grady; Kevin O'Hara; Andrew O'Reilly-Nugent; Simon Oakley; Walter Oberhuber; Toshiyuki Ohtsuka; Ricardo Oliveira; Kinga Öllerer; Mark E Olson; Vladimir Onipchenko; Yusuke Onoda; Renske E Onstein; Jenny C Ordonez; Noriyuki Osada; Ivika Ostonen; Gianluigi Ottaviani; Sarah Otto; Gerhard E Overbeck; Wim A Ozinga; Anna T Pahl; C E Timothy Paine; Robin J Pakeman; Aristotelis C Papageorgiou; Evgeniya Parfionova; Meelis Pärtel; Marco Patacca; Susana Paula; Juraj Paule; Harald Pauli; Juli G Pausas; Begoña Peco; Josep Penuelas; Antonio Perea; Pablo Luis Peri; Ana Carolina Petisco-Souza; Alessandro Petraglia; Any Mary Petritan; Oliver L Phillips; Simon Pierce; Valério D Pillar; Jan Pisek; Alexandr Pomogaybin; Hendrik Poorter; Angelika Portsmuth; Peter Poschlod; Catherine Potvin; Devon Pounds; A Shafer Powell; Sally A Power; Andreas Prinzing; Giacomo Puglielli; Petr Pyšek; Valerie Raevel; Anja Rammig; Johannes Ransijn; Courtenay A Ray; Peter B Reich; Markus Reichstein; Douglas E B Reid; Maxime Réjou-Méchain; Victor Resco de Dios; Sabina Ribeiro; Sarah Richardson; Kersti Riibak; Matthias C Rillig; Fiamma Riviera; Elisabeth M R Robert; Scott Roberts; Bjorn Robroek; Adam Roddy; Arthur Vinicius Rodrigues; Alistair Rogers; Emily Rollinson; Victor Rolo; Christine Römermann; Dina Ronzhina; Christiane Roscher; Julieta A Rosell; Milena Fermina Rosenfield; Christian Rossi; David B Roy; Samuel Royer-Tardif; Nadja Rüger; Ricardo Ruiz-Peinado; Sabine B Rumpf; Graciela M Rusch; Masahiro Ryo; Lawren Sack; Angela Saldaña; Beatriz Salgado-Negret; Roberto Salguero-Gomez; Ignacio Santa-Regina; Ana Carolina Santacruz-García; Joaquim Santos; Jordi Sardans; Brandon Schamp; Michael Scherer-Lorenzen; Matthias Schleuning; Bernhard Schmid; Marco Schmidt; Sylvain Schmitt; Julio V Schneider; Simon D Schowanek; Julian Schrader; Franziska Schrodt; Bernhard Schuldt; Frank Schurr; Galia Selaya Garvizu; Marina Semchenko; Colleen Seymour; Julia C Sfair; Joanne M Sharpe; Christine S Sheppard; Serge Sheremetiev; Satomi Shiodera; Bill Shipley; Tanvir Ahmed Shovon; Alrun Siebenkäs; Carlos Sierra; Vasco Silva; Mateus Silva; Tommaso Sitzia; Henrik Sjöman; Martijn Slot; Nicholas G Smith; Darwin Sodhi; Pamela Soltis; Douglas Soltis; Ben Somers; Grégory Sonnier; Mia Vedel Sørensen; Enio Egon Sosinski; Nadejda A Soudzilovskaia; Alexandre F Souza; Marko Spasojevic; Marta Gaia Sperandii; Amanda B Stan; James Stegen; Klaus Steinbauer; Jörg G Stephan; Frank Sterck; Dejan B Stojanovic; Tanya Strydom; Maria Laura Suarez; Jens-Christian Svenning; Ivana Svitková; Marek Svitok; Miroslav Svoboda; Emily Swaine; Nathan Swenson; Marcelo Tabarelli; Kentaro Takagi; Ulrike Tappeiner; Rubén Tarifa; Simon Tauugourdeau; Cagatay Tavsanoglu; Mariska Te Beest; Leho Tedersoo; Nelson Thiffault; Dominik Thom; Evert Thomas; Ken Thompson; Peter E Thornton; Wilfried Thuiller; Lubomír Tichý; David Tissue; Mark G Tjoelker; David Yue Phin Tng; Joseph Tobias; Péter Török; Tonantzin Tarin; José M Torres-Ruiz; Béla Tóthmérész; Martina Treurnicht; Valeria Trivellone; Franck Trolliet; Volodymyr Trotsiuk; James L Tsakalos; Ioannis Tsiripidis; Niklas Tysklind; Toru Umehara; Vladimir Usoltsev; Matthew Vadeboncoeur; Jamil Vaezi; Fernando Valladares; Jana Vamosi; Peter M van Bodegom; Michiel van Breugel; Elisa Van Cleemput; Martine van de Weg; Stephni van der Merwe; Fons van der Plas; Masha T van der Sande; Mark van Kleunen; Koenraad Van Meerbeek; Mark Vanderwel; Kim André Vanselow; Angelica Vårhammar; Laura Varone; Maribel Yesenia Vasquez Valderrama; Kiril Vassilev; Mark Vellend; Erik J Veneklaas; Hans Verbeeck; Kris Verheyen; Alexander Vibrans; Ima Vieira; Jaime Villacís; Cyrille Violle; Pandi Vivek; Katrin Wagner; Matthew Waldram; Anthony Waldron; Anthony P Walker; Martyn Waller; Gabriel Walther; Han Wang; Feng Wang; Weiqi Wang; Harry Watkins; James Watkins; Ulrich Weber; James T Weedon; Liping Wei; Patrick Weigelt; Evan Weiher; Aidan W Wells; Camilla Wellstein; Elizabeth Wenk; Mark Westoby; Alana Westwood; Philip John White; Mark Whitten; Mathew Williams; Daniel E Winkler; Klaus Winter; Chevonne Womack; Ian J Wright; S Joseph Wright; Justin Wright; Bruno X Pinho; Fabiano Ximenes; Toshihiro Yamada; Keiko Yamaji; Ruth Yanai; Nikolay Yankov; Benjamin Yguel; Kátia Janaina Zanini; Amy E Zanne; David Zelený; Yun-Peng Zhao; Jingming Zheng; Ji Zheng; Kasia Ziemińska; Chad R Zirbel; Georg Zizka; Irié Casimir Zo-Bi; Gerhard Zotz; Christian Wirth
Journal:  Glob Chang Biol       Date:  2019-12-31       Impact factor: 10.863

5.  Comparative phylogeography of woodland reptiles in California: repeated patterns of cladogenesis and population expansion.

Authors:  Chris R Feldman; Greg S Spicer
Journal:  Mol Ecol       Date:  2006-07       Impact factor: 6.185

6.  Landscape genomics to enable conservation actions: The California Conservation Genomics Project.

Authors:  H Bradley Shaffer; Erin Toffelmier; Russ B Corbett-Detig; Merly Escalona; Bjorn Erickson; Peggy Fiedler; Mark Gold; Ryan J Harrigan; Scott Hodges; Tara K Luckau; Courtney Miller; Daniel R Oliveira; Kevin E Shaffer; Beth Shapiro; Victoria L Sork; Ian J Wang
Journal:  J Hered       Date:  2022-04-08       Impact factor: 2.645

7.  Genomic Flatlining in the Endangered Island Fox.

Authors:  Jacqueline A Robinson; Diego Ortega-Del Vecchyo; Zhenxin Fan; Bernard Y Kim; Bridgett M vonHoldt; Clare D Marsden; Kirk E Lohmueller; Robert K Wayne
Journal:  Curr Biol       Date:  2016-04-21       Impact factor: 10.834

8.  Evolutionary history and past climate change shape the distribution of genetic diversity in terrestrial mammals.

Authors:  Spyros Theodoridis; Damien A Fordham; Stuart C Brown; Sen Li; Carsten Rahbek; David Nogues-Bravo
Journal:  Nat Commun       Date:  2020-05-22       Impact factor: 14.919

9.  Geo-referenced population-specific microsatellite data across American continents, the MacroPopGen Database.

Authors:  Elizabeth R Lawrence; Javiera N Benavente; Jean-Michel Matte; Kia Marin; Zachery R R Wells; Thaïs A Bernos; Nia Krasteva; Andrew Habrich; Gabrielle A Nessel; Ramela Arax Koumrouyan; Dylan J Fraser
Journal:  Sci Data       Date:  2019-04-03       Impact factor: 6.444

10.  Global determinants of freshwater and marine fish genetic diversity.

Authors:  Stéphanie Manel; Pierre-Edouard Guerin; David Mouillot; Simon Blanchet; Laure Velez; Camille Albouy; Loïc Pellissier
Journal:  Nat Commun       Date:  2020-02-10       Impact factor: 14.919

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