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13,064 results for “predictions”
Data from: Eggshell biliverdin concentration does not sufficiently predict eggshell coloration
Avian eggshell coloration may have arisen due to selection on the biological, chemical, or physical properties of the pigments embedded within the eggshell, or due to selection on the coloration that emerges due to pigment deposition. Within both hypothetical frameworks, pigment-based eggshell coloration would be related to metrics of egg quality; however, no one has evaluated the relative strength of coloration and pigment concentration in predicting egg quality. Here, we examined 66 European starling Sturnus vulgaris eggs and quantified eggshell biliverdin concentration (an antioxidant that produces the eggshell's blue coloration) and used 28 different coloration metrics derived from both photographic and spectrophotometric data. We also measured egg size, eggshell thickness, concentration of carotenoids in the yolk, and concentration of lysozyme in the albumen to capture variation in egg quality. We found that throughout the laying sequence, biliverdin concentration increased while eggshell thickness, yolk carotenoid concentration, and lysozyme concentration in the albumen all decreased, but this variation was not captured by any eggshell coloration metric. Both eggshell coloration and biliverdin concentration were negatively associated with yolk carotenoid concentration, but only eggshell biliverdin concentration was negatively associated with yolk mass. Lastly, biliverdin concentration explained, at most, only 46% of the variation for all eggshell coloration metrics. Our results suggest biliverdin concentration is a better predictor of egg quality than egg coloration in European starlings, supporting the hypothesis that eggshell pigment concentration per se may be the target of selection.
Data from: Behavioral tactic predicts preoptic-hypothalamic gene expression more strongly than developmental morph in fish with alternative reproductive tactics
Reproductive success relies on the coordination of social behaviors, such as territory defense, courtship, and mating. Species with extreme variation in reproductive tactics are useful models for identifying the neural mechanisms underlying social behavior plasticity. The plainfin midshipman (Porichthys notatus) is a teleost fish with two male reproductive morphs that follow widely divergent developmental trajectories and display alternative reproductive tactics (ARTs). Type I males defend territories, court females, and provide paternal care, but will resort to cuckoldry if they cannot maintain a territory. Type II males reproduce only through cuckoldry. We sought to disentangle gene expression patterns underlying behavioral tactic, in this case ARTs, from those solely reflective of developmental morph. Using RNA-sequencing, we investigated differential transcript expression in the preoptic area-anterior hypothalamus (POA-AH) of courting type I males, cuckolding type I males, and cuckolding type II males. Unexpectedly, POA-AH differential expression was more strongly coupled to behavioral tactic than morph. This included a suite of transcripts implicated in hormonal regulation of vertebrate social behavior. Our results reveal that divergent expression patterns in a conserved neuroendocrine center known to regulate social-reproductive behaviors across vertebrate lineages may be uncoupled from developmental history to enable plasticity in the performance of reproductive tactics.
Data from: Preoperative falls predict postoperative falls and other adverse patient-reported outcomes
BACKGROUND: Falls are common and linked to morbidity. Our objectives were to characterize postoperative falls, and determine whether preoperative falls independently predicted postoperative falls (primary outcome), functional dependence, quality of life, complications, and readmission. METHODS: This prospective cohort study included 7982 unselected patients undergoing elective surgery. Data were collected from the medical record, a baseline survey, and follow-up surveys approximately 30days and one year after surgery. RESULTS: Fall rates (per 100 person-years) peaked at 175 (hospitalization), declined to 140 (30-day survey), and then to 97 (one-year survey). After controlling for confounders, a history of one, two, and ≥three preoperative falls predicted postoperative falls at 30days (adjusted odds ratios [aOR] 2.3, 3.6, 5.5) and one year (aOR 2.3, 3.4, 6.9). One, two, and ≥three falls predicted functional decline at 30days (aOR 1.2, 2.4, 2.4) and one year (aOR 1.3, 1.5, 3.2), along with in-hospital complications (aOR 1.2, 1.3, 2.0). Fall history predicted adverse outcomes better than commonly-used metrics, but did not predict quality of life deterioration or readmission. CONCLUSIONS: Falls are common after surgery, and preoperative falls herald postoperative falls and other adverse outcomes. A history of preoperative falls should be routinely ascertained.
Data from: Detection error influences both temporal seroprevalence predictions and risk factors associations in wildlife disease models
Understanding the prevalence of pathogens in invasive species is essential to guide efforts to prevent transmission to agricultural animals, wildlife, and humans. Pathogen prevalence can be difficult to estimate for wild species due to imperfect sampling and testing (pathogens may not be detected in infected individuals and erroneously detected in individuals that are not infected). The invasive wild pig (Sus scrofa, also referred to as wild boar and feral swine) is one of the most widespread hosts of domestic animal and human pathogens in North America. We developed hierarchical Bayesian models that account for imperfect detection to estimate the seroprevalence of five pathogens (porcine reproductive and respiratory syndrome virus, pseudorabies virus, Influenza A virus in swine, Hepatitis E virus, and Brucella spp.) in wild pigs in the United States using a dataset of over 50,000 samples across nine years. To assess the effect of incorporating detection error in models, we also evaluated models that ignored detection error. Both sets of models included effects of demographic parameters on seroprevalence. We compared our predictions of seroprevalence to 40 published studies, only one of which accounted for imperfect detection. We found a range of seroprevalence among the pathogens with a high seroprevalence of pseudorabies virus, indicating significant risk to livestock and wildlife. Demographics had mostly weak effects, indicating that other variables may have greater effects in predicting seroprevalence. Models that ignored detection error led to different predictions of seroprevalence as well as different inferences on the effects of demographic parameters. Our results highlight the importance of incorporating detection error in models of seroprevalence and demonstrate that ignoring such error may lead to erroneous conclusions about the risk associated with pathogen transmission. When using opportunistic sampling data to model seroprevalence and evaluate risk factors, detection error should be included.
Data from: Negative biotic interactions drive predictions of distributions for species from a grassland community
Understanding the factors that determine species' geographic distributions is important for addressing a wide range of biological questions, including where species will be able to maintain populations following environmental change. New methods for modelling species distributions include the effects of biotic interactions alongside more commonly used abiotic variables such as temperature and precipitation; however, it is not clear which types of interspecific relationship contribute to shaping species distributions and should therefore be prioritised in models. Even if some interactions are known to be influential at local spatial scales, there is no guarantee they will have similar impacts at macroecological scales. Here we apply a novel method based on information theory to determine which types of interspecific relationship drive species distributions. Our results show that negative biotic interactions such as competition have the greatest effect on model predictions for species from a California grassland community. This knowledge will help focus data collection and improve model predictions for identifying at-risk species. Furthermore, our methodological approach is applicable to any kind of species distribution model that can be specified with and without interspecific relationships.
Data from: Aversion to playing God predicts negative moral judgments of technology and science
This research provides, to our knowledge, the first systematic empirical investigation of people's aversion to playing God. Seven studies validate this construct and show its association with negative moral judgements of science and technology. Motivated by three nationally representative archival datasets that demonstrate this relationship, studies 1 and 2 demonstrate that people condemn scientific procedures they perceive to involve playing God. Studies 3–5 demonstrate that dispositional aversion to playing God corresponds to decreased willingness to fund the National Science Foundation and lower donations to organizations that support novel scientific procedures. Studies 6a and 6b demonstrate that people judge a novel (versus established) scientific practice to involve more playing God and to be more morally unacceptable. Finally, study 7 demonstrates that reminding people of an existing incident of playing God reduces concerns towards scientific practices. Together, these findings provide novel evidence for the impact of people's aversion to playing God on science and policy-related decision-making.
Data from: Genomic predictions and genome-wide association study of resistance against Piscirickettsia salmonis in coho salmon (Oncorhynchus kisutch) using ddRAD sequencing
Piscirickettsia salmonis is one of the main infectious diseases affecting coho salmon (Oncorhynchus kisutch) farming, and current treatments have been ineffective for the control of this disease. Genetic improvement for P. salmonis resistance has been proposed as a feasible alternative for the control of this infectious disease in farmed fish. Genotyping by sequencing (GBS) strategies allow genotyping of hundreds of individuals with thousands of single nucleotide polymorphisms (SNPs), which can be used to perform genome wide association studies (GWAS) and predict genetic values using genome-wide information. We used double-digest restriction-site associated DNA (ddRAD) sequencing to dissect the genetic architecture of resistance against P. salmonis in a farmed coho salmon population and to identify molecular markers associated with the trait. We also evaluated genomic selection (GS) models in order to determine the potential to accelerate the genetic improvement of this trait by means of using genome-wide molecular information. A total of 764 individuals from 33 full-sib families (17 highly resistant and 16 highly susceptible) were experimentally challenged against P. salmonis and their genotypes were assayed using ddRAD sequencing. A total of 9,389 SNPs markers were identified in the population. These markers were used to test genomic selection models and compare different GWAS methodologies for resistance measured as day of death (DD) and binary survival (BIN). Genomic selection models showed higher accuracies than the traditional pedigree-based best linear unbiased prediction (PBLUP) method, for both DD and BIN. The models showed an improvement of up to 95% and 155% respectively over PBLUP. One SNP related with B-cell development was identified as a potential functional candidate associated with resistance to P. salmonis defined as DD.
Data from: Urban landscape genetics: canopy cover predicts gene flow between white-footed mouse (Peromyscus leucopus) populations in New York City
In this study, I examine the influence of urban canopy cover on gene flow between 15 white-footed mouse (Peromyscus leucopus) populations in New York City. Nm calculated from F_ST and recent migration estimated in BayesAss+, but not historic migration estimated in Migrate-n, exhibited significant isolation-by-distance (IBD). Gene flow was also associated with "effective distances" between populations that were calculated based on percent canopy cover using two different approaches: 1) isolation-by-effective-distance (IED) that calculates the single best pathway to minimize passage through high-resistance (i.e. low canopy cover) areas, and 2) isolation-by-resistance (IBR), an implementation of circuit theory that identifies all low-resistance paths through the landscape. IBR, but not IED, models were still significantly associated with all three measures of gene flow after factoring out the influence of IBD using partial Mantel tests. In cases where both IBR and IED explained gene flow independently of IBD, an additional partial Mantel test indicated that the IBR models still explained gene flow after factoring out IED. The IBR models that explained the most variation in recent migration after factoring out IBD (r = 0.70 – 0.90) included landscape cells with at least 60-80% canopy cover as low resistance habitat. These results have implications for understanding the impacts of urbanization trends on native wildlife, as well as for urban reforestation efforts that aim to improve urban ecosystem processes.
Data from: Does population structure predict the rate of speciation? A comparative test across Australia's most diverse vertebrate radiation
Population divergence is the first step in allopatric speciation, as has long been recognized in both theoretical models of speciation and empirical explorations of natural systems. All else being equal, lineages with substantial population differentiation should form new species more quickly than lineages that maintain range-wide genetic cohesion through high levels of gene flow. However, there have been few direct tests of the extent to which population differentiation predicts speciation rates as measured on phylogenetic trees. Here, we explicitly test the links between organismal traits, population-level processes, and phylogenetic speciation rates across a diverse clade of Australian lizards that shows remarkable variation in speciation rate. Using genome-wide ddRAD data from 892 individuals, we generated a comparative dataset on isolation-by-distance and population differentiation across 104 putative species-level lineages (OTUs). We find that species show substantial variation in the extent of population differentiation, and this variation is predicted by organismal traits that are thought to be proxies for dispersal and deme size. However, variation in population structure does not predict variation in speciation rate. Our results suggest that population differentiation is not the rate-limiting step in species formation and that other ecological and historical factors are primary determinants of speciation rates at macroevolutionary scales.
Early life learning ability predicts adult social structure, with potential implications for fitness outcomes in the wild
1. Social environments influence important ecological processes and can determine how selection acts on traits. Cognitive abilities can shape these social environments and in turn, affect individuals' fitness. 2. To understand how cognitive abilities evolve, we need to understand the complex interplay between an individual's cognitive abilities, the social environment that they inhabit and the fitness consequences of these relationships. 3. We measured the associative learning ability of pheasant chicks, Phasianus colchicus, then released them into the wild where we quantified their social position by observing their associations at feeding stations and monitored the number of days survived. 4. We observed disassortative mixing by learning performance at the population level, and poor learners had more associates than good learners. Learning was beneficial for survival when focal individuals had fewer than four associates, but survival probability across learning abilities equalized for individuals with more than four associates. 5. While the mechanisms underlying these relationships remain to be determined, the patterns of association exhibited by pheasants at feeders can be predicted by individual variation in cognitive performances and we suspect these patterns are related to differences in information use. Critically, these resulting patterns of association have fitness consequences for individuals that cannot be explained directly by their cognitive ability, but which could mediate selection on cognition.
Data from: Land colonisation by fish is associated with predictable changes in life history
The colonisation of new environments is a central evolutionary process, yet why species make such transitions often remains unknown because of the difficulty in empirically investigating potential mechanisms. The most likely explanation for transitions to new environments is that doing so conveys survival benefits, either in the form of an ecological release or new ecological opportunity. Life history theory makes explicit predictions about how traits linked to survival and reproduction should change with shifts in age-specific mortality. We used these predictions to examine whether a current colonisation of land by fishes might convey survival benefits. We found that blenny species with more terrestrial lifestyles exhibited faster reproductive development and slower growth rates than species with more marine lifestyles; a life history trade off that is consistent with the hypothesis that mortality has become reduced in younger life stages on land. A plausible explanation for such a shift is that an ecological release or opportunity on land has conveyed survival benefits relative to the ancestral marine environment. More generally, our study illustrates how life history theory can be leveraged in novel ways to formulate testable predictions on why organisms might make transitions into novel environments.
Data from: CXCL17 expression predicts poor prognosis and correlates with adverse immune infiltration in hepatocellular carcinoma
CXC ligand 17 (CXCL17) is a novel CXC chemokine whose clinical significance remains largely unknown. In the present study, we characterized the prognostic value of CXCL17 in patients with hepatocellular carcinoma (HCC) and evaluated the association of CXCL17 with immune infiltration. We examined CXCL17 expression in 227 HCC tissue specimens by immunohistochemical staining, and correlated CXCL17 expression patterns with clinicopathological features, prognosis, and immune infiltrate density (CD4 T cells, CD8 T cells, B cells, natural killer cells, neutrophils, macrophages). Kaplan-Meier survival analysis showed that both increased intratumoral CXCL17 (P = 0.015 for overall survival [OS], P = 0.003 for recurrence-free survival [RFS]) and peritumoral CXCL17 (P = 0.002 for OS, P<0.001 for RFS) were associated with shorter OS and RFS. Patients in the CXCL17low group had significantly lower 5-year recurrence rate compared with patients in the CXCL17high group (peritumoral: 53.1% vs. 77.7%, P<0.001, intratumoral: 58.6% vs. 73.0%, P = 0.001, respectively). Multivariate Cox proportional hazards analysis identified peritumoral CXCL17 as an independent prognostic factor for both OS (hazard ratio [HR] = 2.066, 95% confidence interval [CI] = 1.296–3.292, P = 0.002) and RFS (HR = 1.844, 95% CI = 1.218–2.793, P = 0.004). Moreover, CXCL17 expression was associated with more CD68 and less CD4 cell infiltration (both P<0.05). The combination of CXCL17 density and immune infiltration could be used to further classify patients into subsets with different prognosis for RFS. Our results provide the first evidence that tumor-infiltrating CXCL17+ cell density is an independent prognostic factor that predicts both OS and RFS in HCC. CXCL17 production correlated with adverse immune infiltration and might be an important target for anti-HCC therapies.
Data from: Colonisation dynamics during range expansion is poorly predicted by dispersal in the core range
The potential ranges of many species are shifting due to changing ecological conditions. Where populations become patchy towards the range edge, the realised distribution emerges from colonisation-persistence dynamics. Therefore, a greater understanding of the drivers of these processes, and the spatial scales over which they operate, presents an opportunity to improve predictions of species range expansion under environmental change. Species reintroductions offer an ideal opportunity to investigate the drivers and spatial scale of colonisation dynamics at the range edge. To this effect, we performed and monitored experimental translocations of water voles to quantify how colonisation and local persistence were influenced by habitat quality and occupancy. We used a novel statistical method to simultaneously consider effects across a range of spatial scales. Densely occupied neighbourhoods were highly persistent and frequently colonised. Persistence was more likely in high quality habitat, whereas influence of habitat quality on colonisation was less clear. Colonisation of suitable habitat in distant, sparsely occupied areas was much less frequent than expected from the well documented high dispersal ability of the species. Persistence of these distant populations was also low, which we attribute to the absence of a rescue effect in sparsely populated neighbourhoods. Our results illustrate a mismatch between the spatial scales of colonisation dynamics in the core and edge of a species range, suggesting that recolonisation dynamics in established populations may be a poor predictor of colonisation dynamics at the range edge. Such a mismatch leads to predictions of long lags between the emergence and colonisation of new habitat, with detrimental consequences for a species realised distribution, conservation status and contribution to ecosystem function. Conservation translocations that also reinforce existing populations at the range edge might stimulate the rescue effect and mitigate lags in expansion.
Data from: Drivers of the spatial scale that best predict primate responses to landscape structure
Understanding the effect of landscape structure on biodiversity is critically needed to improve management strategies. To accurately evaluate such effect, landscape metrics need to be assessed at the correct scale, i.e. considering the spatial extent at which species‐landscape relationship is strongest (scale of effect, SE). Although SE is highly variable, its drivers are poorly known, but of key relevance to understand the way species use the landscape. In this study, we evaluate whether and how species traits, biological responses, landscape variables and the regional context of the study drive SE in Mexican primates. We estimated the relative abundance and immature‐to‐female ratio (a proxy of reproductive success) of howler monkeys (Alouatta palliata and A. pigra) and spider monkeys (Ateles geoffroyi) in 48 forest patches from four rainforest regions (12 patches per region) with different land‐use intensity. We then assessed the composition (forest cover, matrix functionality) and configuration (forest patch density, connectors' density, forest edge density) of local landscapes considering 13 scales (100 to 1300‐m radius) to identify the spatial extent at which each landscape variable best predict each response variable in each species and region. We found that SE did not differ significantly among the drivers evaluated. However, it tended to be lower for connectors' density than for forest patch density and forest edge density, probably because connectors' density is associated with local‐scale processes such as supplementary dynamics. Surprisingly, SE also tended to be higher in the more disturbed region than in the rest of the regions, probably because primates in the more disturbed region used larger areas of the landscape. Our findings therefore suggest that SE depends more strongly on landscape variables and regional context than on species traits and biological responses, and hence, especial caution should be taken when attempting to generalize SE to different explanatory variables and regions.
Data from: Combining high-throughput phenotyping and genomic information to increase prediction and selection accuracy in wheat breeding
Genomics and phenomics have promised to revolutionize the field of plant breeding. The integration of these two fields has just begun and is being driven through big data by advances in next-generation sequencing and developments of field-based high-throughput phenotyping (HTP) platforms. Each year the International Maize and Wheat Improvement Center (CIMMYT) evaluates tens-of-thousands of advanced lines for grain yield across multiple environments. To evaluate how CIMMYT may utilize dynamic HTP data for genomic selection (GS), we evaluated 1170 of these advanced lines in two environments, drought (2014, 2015) and heat (2015). A portable phenotyping system called 'Phenocart' was used to measure normalized difference vegetation index and canopy temperature simultaneously while tagging each data point with precise GPS coordinates. For genomic profiling, genotyping-by-sequencing (GBS) was used for marker discovery and genotyping. Several GS models were evaluated utilizing the 2254 GBS markers along with over 1.1 million phenotypic observations. The physiological measurements collected by HTP, whether used as a response in multivariate models or as a covariate in univariate models, resulted in a range of 33% below to 7% above the standard univariate model. Continued advances in yield prediction models as well as increasing data generating capabilities for both genomic and phenomic data will make these selection strategies tractable for plant breeders to implement increasing the rate of genetic gain.
Data from: Continental divide: predicting climate-mediated fragmentation and biodiversity loss in the boreal forest
Climate change threatens natural landscapes through shifting distribution and abundance of species and attendant change in the structure and function of ecosystems. However, it remains unclear how climate-mediated variation in species' environmental niche space may lead to large-scale fragmentation of species distributions, altered meta-population dynamics and gene flow, and disrupted ecosystem integrity. Such change may be especially relevant when species distributions are restricted either spatially or to a narrow environmental niche, or when environments are rapidly changing. Here, we use range-wide environmental niche models to posit that climate-mediated range fragmentation aggravates the direct effects of climate change on species in the boreal forest of North America. We show that climate change will directly alter environmental niche suitability for boreal-obligate species of trees, birds and mammals (n=12), with most species ranges becoming smaller and shifting northward through time. Importantly, species distributions will become increasingly fragmented, as characterized by smaller mean size and greater isolation of environmentally-suitable landscape patches. This loss is especially pronounced along the Ontario-Québec border, where the boreal forest is narrowest and roughly 78% of suitable niche space could disappear by 2080. Despite the diversity of taxa surveyed, patterns of range fragmentation are remarkably consistent, with our models predicting that spruce grouse (Dendragapus canadensis), boreal chickadee (Poecile hudsonicus), moose (Alces americanus) and caribou (Rangifer tarandus) could have entirely disjunct east-west population segments in North America. These findings reveal potentially dire consequences of climate change on population continuity and species diversity in the boreal forest, highlighting the need to better understand: 1) extent and primary drivers of anticipated climate-mediated range loss and fragmentation; 2) diversity of species to be affected by such change; 3) potential for rapid adaptation in the most strongly-affected areas; and 4) potential for invasion by replacement species.
Data from: Seedling recruitment in subalpine grassland forbs: predicting field regeneration behaviour from lab germination responses
Environmental cueing that restricts seed germination onto times and places where mortality risk is relatively low may have considerable selective advantage. The predictive power of lab germination responses for field regeneration behaviour is rarely tested. We screened 11 alpine grassland forbs for germination behaviours predictive of microsite and seasonal selectivity, and seed carry-over across years. The predictions were tested in a field experiment. Germination in the lab ranged from 0.05% to 67.9%, and was affected by light (5 species), temperature (6), fluctuating temperatures (4), moist chilling prior to germination (cold-stratification) (6), and dormancy-breaking by means of gibberellic acid (8). Seedling emergence in the field varied from 0.1% to 14.1%, and increased in low-competition microsites (bare-ground gaps and cut vegetation; 7 species), and showed seasonal timing (1 in autumn and 1 in spring), and seed carry-over across years (7). Lab germination responses successfully predicted microsite selectivity in the field and to some extent seed carry-over across years but not seasonal timing of germination. Gap-detecting species were generally small-seeded, low-growing, and found in unproductive habitats. Larger-seeded species germinated in all microsites but experienced increased mortality in high-competition microsites. Seed carry-over across years was lower in alpine specialists than in more widely-distributed species.
Data from: Which landscape size best predicts the influence of forest cover on restoration success? – A global meta-analysis on the scale of effect
Landscape context is a strong predictor of species persistence, abundance and distribution, yet its influence on the success of ecological restoration remains unclear. Thus, a primary question arises: which landscape size best predicts the effects of forest cover on restoration success? To answer this question, we conducted a global meta-analysis for biodiversity (mammals, birds, invertebrates, herpetofauna and plants) and measures of vegetation structure (cover, density, height, biomass and litter). Response ratios were calculated for comparisons between reference (e.g. old-growth forest) and disturbed sites (degraded or restored). Using an information-theoretic approach, mean response ratio (restoration success) and response ratio variance (restoration predictability) within each study landscape were regressed against the percentage of overall (summed forest cover) and contiguous (summed pixels of ≥60% forest cover) forest within eight different buffer sizes of radius 5–200 km (at 1-km resolution). We included 247 studies encompassing 196 study landscapes and 4360 quantitative comparisons. The best buffer (landscape) size varied for the following: (i) overall and contiguous forest cover, (ii) biodiversity and vegetation structure and (iii) mean response ratio and response ratio variance. Only plant biodiversity was influenced by overall forest cover (buffer size of 5, 10 and 200 km radii), while plants (10 and 200 km radii), mammals (5, 10 and 50–200 km radii), invertebrates (5 and 10 km radii), cover (5 km radii), height (5 km radii) and litter (100 km radii) were influenced by contiguous forest cover. Overall, mean response ratio and response ratio variance were positively and negatively nonlinearly related with both overall and contiguous forest cover, respectively. We reveal for the first time a clear pattern of increasing restoration success and decreasing uncertainty as contiguous forest cover increases. We also indicate preliminary recommended buffer sizes for investigating landscape restoration effects on biodiversity and vegetation structure. However, the coarse grain and variability in the data mean the optimal landscape size may not have been detected; thus, further research is needed. Synthesis and applications. When setting targets for ecological restoration, policymakers and restoration practitioners should account for the following: (i) the landscape context, particularly the amount of contiguous habitat up to 10 km around a disturbed site, and (ii) the uncertainty in restoration success, as it increases when contiguous forest cover falls below about 50%.
Data from: Ocean circulation model predicts high genetic structure in a long-lived pelagic developer
Understanding the movement of genes and individuals across marine seascapes is a long-standing challenge in marine ecology, and can inform our understanding of local adaptation, the persistence and movement of populations, and the spatial scale of effective management. Patterns of gene flow in the ocean are often inferred based on population genetic analyses coupled with knowledge of species' dispersive life histories. However, genetic structure is the result of time-integrated processes, and may not capture present-day connectivity between populations. Here we use a high-resolution oceanographic circulation model to predict larval dispersal along the complex coastline of western Canada that includes the transition between two well-studied zoogeographic provinces. We simulate dispersal in a benthic sea star with a 6-10 week pelagic larval phase, and test predictions of this model against previously observed genetic structure including a strong phylogeographic break within the zoogeographical transition zone. We also test predictions with new genetic sampling in a site within the phylogeographic break. We find that the coupled genetic and circulation model predicts the high degree of genetic structure observed in this species, despite its long pelagic duration. High genetic structure on this complex coastline can thus be explained through ocean circulation patterns which tend to retain passive larvae within 20 - 50 km of their parents, suggesting a necessity for close-knit design of Marine Protected Area networks.
Data from: Genetic constraints predict evolutionary divergence in Dalechampia blossoms
If genetic constraints are important, then rates and direction of evolution should be related to trait evolvability. Here we use recently developed measures of evolvability to test the genetic constraint hypothesis with quantitative genetic data on floral morphology from the Neotropical vine Dalechampia scandens (Euphorbiaceae). These measures were compared against rates of evolution and patterns of divergence among 24 populations in two species in the D. scandens species complex. We found clear evidence for genetic constraints, particularly among traits that were tightly phenotypically integrated. This relationship between evolvability and evolutionary divergence is puzzling, because the estimated evolvabilities seem too large to constitute real constraints. We suggest that this paradox can be explained by a combination of weak stabilizing selection around moving adaptive optima and small realized evolvabilities relative to the observed additive genetic variance.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.