Quantifying forest disturbance regimes within caribou (Rangifer tarandus) range in British Columbia.

Habitat disturbance is a major driver of the decline of woodland caribou (Rangifer tarandus caribou) in Canada. Different disturbance agents and regimes negatively impact caribou populations to different degrees. It is therefore critical that land managers and scientists studying caribou have a detailed understanding of the disturbance regimes affecting caribou habitat. In this work we use recent advances in satellite-based disturbance detection to quantify polygonal forest disturbance regimes affecting caribou ecotypes and herds in British Columbia (BC) from 1985 to 2019. Additionally, we utilize this data to investigate harvesting rates since the implementation of the Species at Risk Act (SARA) and publication of recovery strategies for caribou in BC. Southern Mountain caribou herds are the most threatened yet experienced the highest rates of disturbance, with 22.75% of forested habitat within their ranges disturbed during the study period. Over the study period, we found that in total, 16.4% of forested area was disturbed across all caribou herd ranges. Our findings indicate that caribou in BC face high, and in many cases increasing, levels of habitat disturbance. Our results provide a detailed understanding of the polygonal disturbance regimes affecting caribou in BC at the herd scale, and highlight the need for effective implementation of policies aimed at preserving caribou habitat.

A fast linkage method for population GWAS cohorts with related individuals.

Linkage analysis, a class of methods for detecting co-segregation of genomic segments and traits in families, was used to map disease-causing genes for decades before genotyping arrays and dense SNP genotyping enabled genome-wide association studies in population samples. Population samples often contain related individuals, but the segregation of alleles within families is rarely used because traditional linkage methods are computationally inefficient for larger datasets. Here, we describe Population Linkage, a novel application of Haseman-Elston regression as a method of moments estimator of variance components and their standard errors. We achieve additional computational efficiency by using modern methods for detection of IBD segments and variance component estimation, efficient preprocessing of input data, and minimizing redundant numerical calculations. We also refined variance component models to account for the biases in population-scale methods for IBD segment detection. We ran Population Linkage on four blood lipid traits in over 70,000 individuals from the HUNT and SardiNIA studies, successfully detecting 25 known genetic signals. One notable linkage signal that appeared in both was for low-density lipoprotein (LDL) cholesterol levels in the region near the gene APOE (LOD = 29.3, variance explained = 4.1%). This is the region where the missense variants rs7412 and rs429358, which together make up the ε2, ε3, and ε4 alleles each account for 2.4% and 0.8% of variation in circulating LDL cholesterol. Our results show the potential for linkage analysis and other large-scale applications of method of moments variance components estimation.

Receptor-Mediated ER Export of Lipoproteins Controls Lipid Homeostasis in Mice and Humans.

Efficient delivery of specific cargos in vivo poses a major challenge to the secretory pathway, which shuttles products encoded by ∼30% of the genome. Newly synthesized protein and lipid cargos embark on the secretory pathway via COPII-coated vesicles, assembled by the GTPase SAR1 on the endoplasmic reticulum (ER), but how lipid-carrying lipoproteins are distinguished from the general protein cargos in the ER and selectively secreted has not been clear. Here, we show that this process is quantitatively governed by the GTPase SAR1B and SURF4, a high-efficiency cargo receptor. While both genes are implicated in lipid regulation in humans, hepatic inactivation of either mouse Sar1b or Surf4 selectively depletes plasma lipids to near-zero and protects the mice from atherosclerosis. These findings show that the pairing between SURF4 and SAR1B synergistically operates a specialized, dosage-sensitive transport program for circulating lipids, while further suggesting a potential translation to treat atherosclerosis and related cardio-metabolic diseases.

Loss-of-function genomic variants highlight potential therapeutic targets for cardiovascular disease.

Pharmaceutical drugs targeting dyslipidemia and cardiovascular disease (CVD) may increase the risk of fatty liver disease and other metabolic disorders. To identify potential novel CVD drug targets without these adverse effects, we perform genome-wide analyses of participants in the HUNT Study in Norway (n = 69,479) to search for protein-altering variants with beneficial impact on quantitative blood traits related to cardiovascular disease, but without detrimental impact on liver function. We identify 76 (11 previously unreported) presumed causal protein-altering variants associated with one or more CVD- or liver-related blood traits. Nine of the variants are predicted to result in loss-of-function of the protein. This includes ZNF529:p.K405X, which is associated with decreased low-density-lipoprotein (LDL) cholesterol (P = 1.3 × 10-8) without being associated with liver enzymes or non-fasting blood glucose. Silencing of ZNF529 in human hepatoma cells results in upregulation of LDL receptor and increased LDL uptake in the cells. This suggests that inhibition of ZNF529 or its gene product should be prioritized as a novel candidate drug target for treating dyslipidemia and associated CVD.

Changing spring snow cover dynamics and early season forage availability affect the behavior of a large carnivore.

Changing climates are altering wildlife habitats and wildlife behavior in complex ways. Here, we examine how changing spring snow cover dynamics and early season forage availability are altering grizzly bear (Ursus arctos) behavior postden emergence. Telemetry data were used to identify spring activity dates for 48 individuals in the Yellowhead region of Alberta, Canada. Spring activity date was related to snow cover dynamics using a daily percent snow cover dataset. Snow melt end date, melt rate, and melt consistency explained 45% of the variation in spring activity date. We applied this activity date model across the entire Yellowhead region from 2000 to 2016 using simulated grizzly bear home ranges. Predicted spring activity date was then compared with a daily spring forage availability date dataset, resulting in "wait time" estimates for four key early season forage species. Temporal changes in both spring activity date and early season forage "wait times" were assessed using non-parametric regression. Grizzly bear activity date was found to have either remained constant (95%) or become earlier (5%) across the study area; virtually no areas with significantly later spring activity dates were detected. Similarly, the majority of "wait times" did not change (85%); however, the majority of significant changes in "wait times" for the four early season forage species indicated that "wait times" were lessening where changes were detected. Our results show that spring activity date is largely dictated by snow melt characteristics and that changing snow melt conditions may result in earlier spring activity. However, early season food stress conditions are likely to remain unchanged or improve as vegetation phenology also becomes earlier. Our findings extend the recent work examining animal movement in response to changing phenology from migratory birds and ungulates to an apex predator, further demonstrating the potential effects of changing climates on wildlife species.

Association Analysis and Meta-Analysis of Multi-Allelic Variants for Large-Scale Sequence Data.

There is great interest in understanding the impact of rare variants in human diseases using large sequence datasets. In deep sequence datasets of >10,000 samples, ~10% of the variant sites are observed to be multi-allelic. Many of the multi-allelic variants have been shown to be functional and disease-relevant. Proper analysis of multi-allelic variants is critical to the success of a sequencing study, but existing methods do not properly handle multi-allelic variants and can produce highly misleading association results. We discuss practical issues and methods to encode multi-allelic sites, conduct single-variant and gene-level association analyses, and perform meta-analysis for multi-allelic variants. We evaluated these methods through extensive simulations and the study of a large meta-analysis of ~18,000 samples on the cigarettes-per-day phenotype. We showed that our joint modeling approach provided an unbiased estimate of genetic effects, greatly improved the power of single-variant association tests among methods that can properly estimate allele effects, and enhanced gene-level tests over existing approaches. Software packages implementing these methods are available online.

Integrating Mouse and Human Genetic Data to Move beyond GWAS and Identify Causal Genes in Cholesterol Metabolism.

Identifying the causal gene(s) that connects genetic variation to a phenotype is a challenging problem in genome-wide association studies (GWASs). Here, we develop a systematic approach that integrates mouse liver co-expression networks with human lipid GWAS data to identify regulators of cholesterol and lipid metabolism. Through our approach, we identified 48 genes showing replication in mice and associated with plasma lipid traits in humans and six genes on the X chromosome. Among these 54 genes, 25 have no previously identified role in lipid metabolism. Based on functional studies and integration with additional human lipid GWAS datasets, we pinpoint Sestrin1 as a causal gene associated with plasma cholesterol levels in humans. Our validation studies demonstrate that Sestrin1 influences plasma cholesterol in multiple mouse models and regulates cholesterol biosynthesis. Our results highlight the power of combining mouse and human datasets for prioritization of human lipid GWAS loci and discovery of lipid genes.

Spatial transcriptomics identifies spatially dysregulated expression of GRM3 and USP47 in amyotrophic lateral sclerosis.

AIMS: The mechanisms underlying the selective degeneration of motor neurones in amyotrophic lateral sclerosis (ALS) are poorly understood. The aim of this study was to implement spatially resolved RNA sequencing in human post mortem cortical tissue from an ALS patient harbouring the C9orf72 hexanucleotide repeat expansion to identify dysregulated transcripts that may account for differential vulnerabilities of distinct (i) cell types and (ii) brain regions in the pathogenesis of ALS. METHODS: Using spatial transcriptomics (ST) we analysed the transcriptome of post mortem brain tissue, with spatial resolution down to 100 µm. Validation of these findings was then performed using BaseScope, an adapted, in situ hybridization technique with single-transcript single-cell-resolution, providing extensive regional and cell-type specific confirmation of these dysregulated transcripts. The validation cohort was then extended to include multiple post mortem brain regions and spinal cord tissue from an extended cohort of C9orf72, sporadic ALS (sALS) and SOD1 ALS cases. RESULTS: We identified sixteen dysregulated transcripts of proteins that have roles within six disease-related pathways. Furthermore, these complementary molecular pathology techniques converged to identify two spatially dysregulated transcripts, GRM3 and USP47, that are commonly dysregulated across sALS, SOD1 and C9orf72 cases alike. CONCLUSIONS: This study presents the first description of ST in human post mortem cortical tissue from an ALS patient harbouring the C9orf72 hexanucleotide repeat expansion. These data taken together highlight the importance of preserving spatial resolution, facilitating the identification of genes whose dysregulation may in part underlie regional susceptibilities to ALS, crucially highlighting potential therapeutic and diagnostic targets.

Neuronal clusterin expression is associated with cognitive protection in amyotrophic lateral sclerosis.

AIMS: Clusterin is a topologically dynamic chaperone protein with the ability to participate in both intra- and extacellular proteostasis. Clusterin has been shown to be upregulated in the spinal cord of patients with amyotrophic lateral sclerosis (ALS) and has been shown to protect against TDP-43 protein misfolding in animal and cell models. Previous studies have demonstrated an association between the pathological burden of TDP-43 misfolding and cognitive deficits in ALS, demonstrating high specificity, but correspondingly low sensitivity owing to a subset of individuals with no evidence of cognitive deficits despite a high burden of TDP-43 pathology, called mismatch cases. METHODS: Hypothesizing that differences in the ability to cope with protein misfolding in these cases may be due to differences in expression of protective mechanisms such as clusterin expression, we assessed the spatial expression of clusterin and another chaperone protein, HspB8, in post mortem brain tissue of mismatch cases. We employed a modified in situ hybridization technique called BaseScope, with single cell, single transcript resolution. RESULTS: Mismatch cases demonstrated differential spatial expression of clusterin, with a predominantly neuronal pattern, compared to cases with cognitive manifestations of their TDP-43 pathology who demonstrated a predominantly glial distribution of expression. CONCLUSIONS: Our data suggest that, in individuals with TDP-43 pathology, predominantly neuronal expression of clusterin in extra-motor brain regions may indicate a cell protective mechanism delaying clinical manifestations such as cognitive dysfunction.

Estimation of DNA contamination and its sources in genotyped samples.

Array genotyping is a cost-effective and widely used tool that enables assessment of up to millions of genetic markers in hundreds of thousands of individuals. Genotyping array data are typically highly accurate but sensitive to mixing of DNA samples from multiple individuals before or during genotyping. Contaminated samples can lead to genotyping errors and consequently cause false positive signals or reduce power of association analyses. Here, we propose a new method to identify contaminated samples and the sources of contamination within a genotyping batch. Through analysis of array intensity and genotype data from intentionally mixed samples and 22,366 samples of the Michigan Genomics Initiative, an ongoing biobank-based study, we show that our method can reliably estimate contamination. We also show that identifying sources of contamination can implicate problematic sample processing steps and guide process improvements. Compared to existing methods, our approach can estimate the proportion of contaminating DNA more accurately, eliminate the need for external databases of allele frequencies, and provide contamination estimates that are more robust to the ancestral origin of the contaminating sample.

Genes for Good: Engaging the Public in Genetics Research via Social Media.

The Genes for Good study uses social media to engage a large, diverse participant pool in genetics research and education. Health history and daily tracking surveys are administered through a Facebook application, and participants who complete a minimum number of surveys are mailed a saliva sample kit ("spit kit") to collect DNA for genotyping. As of March 2019, we engaged >80,000 individuals, sent spit kits to >32,000 individuals who met minimum participation requirements, and collected >27,000 spit kits. Participants come from all 50 states and include a diversity of ancestral backgrounds. Rates of important chronic health indicators are consistent with those estimated for the general U.S. population using more traditional study designs. However, our sample is younger and contains a greater percentage of females than the general population. As one means of verifying data quality, we have replicated genome-wide association studies (GWASs) for exemplar traits, such as asthma, diabetes, body mass index (BMI), and pigmentation. The flexible framework of the web application makes it relatively simple to add new questionnaires and for other researchers to collaborate. We anticipate that the study sample will continue to grow and that future analyses may further capitalize on the strengths of the longitudinal data in combination with genetic information.

Plasticity in elk migration timing is a response to changing environmental conditions.

Migration is an effective behavioral strategy for prolonging access to seasonal resources and may be a resilient strategy for ungulates experiencing changing climatic conditions. In the Greater Yellowstone Ecosystem (GYE), elk are the primary ungulate, with approximately 20,000 individuals migrating to exploit seasonal gradients in forage while also avoiding energetically costly snow conditions. How climate-induced changes in plant phenology and snow accumulation are influencing elk migration timing is unknown. We present the most complete record of elk migration across the GYE, spanning 9 herds and 414 individuals from 2001 to 2017, to evaluate the drivers of migration timing and test for temporal shifts. The timing of elk departure from winter range involved a trade-off between current and anticipated forage conditions, while snow melt governed summer range arrival date. Timing of elk departure from summer range and arrival on winter range were both influenced by snow accumulation and exposure to hunting. At the GYE scale, spring and fall migration timing changed through time, most notably with winter range arrival dates becoming almost 50 days later since 2001. Predicted herd-level changes in migration timing largely agreed with observed GYE-wide changes-except for predicted winter range arrival dates which did not reflect the magnitude of change detected in the elk telemetry data. Snow melt, snow accumulation, and spring green-up dates all changed through time, with different herds experiencing different rates and directions of change. We conclude that elk migration is plastic, is a direct response to environmental cues, and that these environmental cues are not changing in a consistent manner across the GYE. The impacts of changing elk migration timing on predator-prey dynamics, carnivore-livestock conflict, disease ecology, and harvest management across the GYE are likely to be significant and complex.

Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use.

Tobacco and alcohol use are leading causes of mortality that influence risk for many complex diseases and disorders1. They are heritable2,3 and etiologically related4,5 behaviors that have been resistant to gene discovery efforts6-11. In sample sizes up to 1.2 million individuals, we discovered 566 genetic variants in 406 loci associated with multiple stages of tobacco use (initiation, cessation, and heaviness) as well as alcohol use, with 150 loci evidencing pleiotropic association. Smoking phenotypes were positively genetically correlated with many health conditions, whereas alcohol use was negatively correlated with these conditions, such that increased genetic risk for alcohol use is associated with lower disease risk. We report evidence for the involvement of many systems in tobacco and alcohol use, including genes involved in nicotinic, dopaminergic, and glutamatergic neurotransmission. The results provide a solid starting point to evaluate the effects of these loci in model organisms and more precise substance use measures.

Proper conditional analysis in the presence of missing data: Application to large scale meta-analysis of tobacco use phenotypes.

Meta-analysis of genetic association studies increases sample size and the power for mapping complex traits. Existing methods are mostly developed for datasets without missing values, i.e. the summary association statistics are measured for all variants in contributing studies. In practice, genotype imputation is not always effective. This may be the case when targeted genotyping/sequencing assays are used or when the un-typed genetic variant is rare. Therefore, contributed summary statistics often contain missing values. Existing methods for imputing missing summary association statistics and using imputed values in meta-analysis, approximate conditional analysis, or simple strategies such as complete case analysis all have theoretical limitations. Applying these approaches can bias genetic effect estimates and lead to seriously inflated type-I or type-II errors in conditional analysis, which is a critical tool for identifying independently associated variants. To address this challenge and complement imputation methods, we developed a method to combine summary statistics across participating studies and consistently estimate joint effects, even when the contributed summary statistics contain large amounts of missing values. Based on this estimator, we proposed a score statistic called PCBS (partial correlation based score statistic) for conditional analysis of single-variant and gene-level associations. Through extensive analysis of simulated and real data, we showed that the new method produces well-calibrated type-I errors and is substantially more powerful than existing approaches. We applied the proposed approach to one of the largest meta-analyses to date for the cigarettes-per-day phenotype. Using the new method, we identified multiple novel independently associated variants at known loci for tobacco use, which were otherwise missed by alternative methods. Together, the phenotypic variance explained by these variants was 1.1%, improving that of previously reported associations by 71%. These findings illustrate the extent of locus allelic heterogeneity and can help pinpoint causal variants.

A new, high-resolution global mass coral bleaching database.

Episodes of mass coral bleaching have been reported in recent decades and have raised concerns about the future of coral reefs on a warming planet. Despite the efforts to enhance and coordinate coral reef monitoring within and across countries, our knowledge of the geographic extent of mass coral bleaching over the past few decades is incomplete. Existing databases, like ReefBase, are limited by the voluntary nature of contributions, geographical biases in data collection, and the variations in the spatial scale of bleaching reports. In this study, we have developed the first-ever gridded, global-scale historical coral bleaching database. First, we conducted a targeted search for bleaching reports not included in ReefBase by personally contacting scientists and divers conducting monitoring in under-reported locations and by extracting data from the literature. This search increased the number of observed bleaching reports by 79%, from 4146 to 7429. Second, we employed spatial interpolation techniques to develop annual 0.04° × 0.04° latitude-longitude global maps of the probability that bleaching occurred for 1985 through 2010. Initial results indicate that the area of coral reefs with a more likely than not (>50%) or likely (>66%) probability of bleaching was eight times higher in the second half of the assessed time period, after the 1997/1998 El Niño. The results also indicate that annual maximum Degree Heating Weeks, a measure of thermal stress, for coral reefs with a high probability of bleaching increased over time. The database will help the scientific community more accurately assess the change in the frequency of mass coral bleaching events, validate methods of predicting mass coral bleaching, and test whether coral reefs are adjusting to rising ocean temperatures.

Estimating changes in lichen mat volume through time and related effects on barren ground caribou (Rangifer tarandus groenlandicus) movement.

Lichens form a critical portion of barren ground caribou (Rangifer tarandus groenlandicus) diets, especially during winter months. Here, we assess lichen mat volume across five herd ranges in the Northwest Territories and Nunavut, Canada, using newly developed composite Landsat imagery. The lichen volume estimator (LVE) was adapted for use across 700 000 km2 of barren ground caribou habitat annually from 1984-2012. We subsequently assessed how LVE changed temporally throughout the time series for each pixel using Theil-Sen's slopes, and spatially by assessing whether slope values were centered in local clusters of similar values. Additionally, we assessed how LVE estimates resulted in changes in barren ground caribou movement rates using an extensive telemetry data set from 2006-2011. The Ahiak/Beverly herd had the largest overall increase in LVE (median = 0.033), while the more western herds had the least (median slopes below zero in all cases). LVE slope pixels were arranged in significant clusters across the study area, with the Cape Bathurst, Bathurst, and Bluenose East herds having the most significant clusters of negative slopes (more than 20% of vegetated land in each case). The Ahiak/Beverly and Bluenose West had the most significant positive clusters (16.3% and 18.5% of vegetated land respectively). Barren ground caribou displayed complex reactions to changing lichen conditions depending on season; the majority of detected associations with movement data agreed with current understanding of barren ground caribou foraging behavior (the exception was an increase in movement velocity at high lichen volume estimates in Fall). The temporal assessment of LVE identified areas where shifts in ecological conditions may have resulted in changing lichen mat conditions, while assessing the slope estimates for clustering identified zones beyond the pixel scale where forage conditions may be changing. Lichen volume estimates associated with barren ground caribou movement metrics in an expected manner and, as such, show value for future habitat assessments.

Barren-ground caribou (Rangifer tarandus groenlandicus) behaviour after recent fire events; integrating caribou telemetry data with Landsat fire detection techniques.

Fire regimes are changing throughout the North American boreal forest in complex ways. Fire is also a major factor governing access to high-quality forage such as terricholous lichens for barren-ground caribou (Rangifer tarandus groenlandicus). Additionally, fire alters forest structure which can affect barren-ground caribou's ability to navigate in a landscape. Here, we characterize how the size and severity of fires are changing across five barren-ground caribou herd ranges in the Northwest Territories and Nunavut, Canada. Additionally, we demonstrate how time since fire, fire severity, and season result in complex changes in caribou behavioural metrics estimated using telemetry data. Fire disturbances were identified using novel gap-free Landsat surface reflectance composites from 1985 to 2011 across all herd ranges. Burn severity was estimated using the differenced normalized burn ratio. Annual area burned and burn severity were assessed through time for each herd and related to two behavioural metrics: velocity and relative turning angle. Neither annual area burned nor burn severity displayed any temporal trend within the study period. However, certain herds, such as the Ahiak/Beverly, have more exposure to fire than other herds (i.e. Cape Bathurst had a maximum forested area burned of less than 4 km2 ). Time since fire and burn severity both significantly affected velocity and relative turning angles. During fall, winter, and spring, fire virtually eliminated foraging-focused behaviour for all 26 years of analysis while more severe fires resulted in a marked increase in movement-focused behaviour compared to unburnt patches. Between seasons, caribou used burned areas as early as 1-year postfire, demonstrating complex, nonlinear reactions to time since fire, fire severity, and season. In all cases, increases in movement-focused behaviour were detected postfire. We conclude that changes in caribou behaviour immediately postfire are primarily driven by changes in forest structure rather than changes in terricholous lichen availability.

Depression and Disturbed Bone Metabolism: A Narrative Review of the Epidemiological Findings and Postulated Mechanisms.

Major depressive disorder (MDD) is a pervasive chronic condition that contributes substantially to the global burden of disease and disability. Adding to the complexity of this disorder are numerous associated medical comorbidities with a bidirectional impact on morbidity and mortality. In recent years, osteoporosis has been increasingly identified as a significant comorbidity of MDD. This narrative review examines the literature to summarize key epidemiological studies and discuss postulated mechanisms of interaction. Epidemiological studies have repeatedly shown an increased co-prevalence of fractures and decreased bone mineral density (BMD) in MDD. The pathophysiological mechanism underlying this interaction is undoubtedly complex and multifactorial, and proposed pathways have varying levels of evidence from preclinical and clinical models. Conceptually, the mechanisms by which depression might influence bone metabolism can be categorized into biological, behavioral, iatrogenic, and comorbidity-related factors. Biological factors include the inflammatory-mood pathway, hypothalamic-pituitary-adrenal (HPA) axis dysregulation, metabolic dysfunction, and serotonin's direct and indirect effects on bone cells. Behavioral factors incorporate lifestyle choices typical in depressed patients, such as increased tobacco use or limited exercise. The prominent iatrogenic factor is the independent effects of anti-depressants on bone metabolism. Psychiatric and medical comorbidities common to both osteoporosis and MDD are also important to consider. Physical activity promotion, vitamin D supplementation, and routine BMD screening of MDD patients are simple interventions that might lead to improved outcomes for both conditions. An improved understanding of the underlying mechanisms may yield insights into novel prevention and treatment strategies to target osteoporosis and fractures in the MDD population.

The inconstancy of the transient climate response parameter under increasing CO2.

In the Coupled Model Intercomparison Project Phase 5 (CMIP5), the model-mean increase in global mean surface air temperature T under the 1pctCO2 scenario (atmospheric CO(2) increasing at 1% yr(-1)) during the second doubling of CO(2) is 40% larger than the transient climate response (TCR), i.e. the increase in T during the first doubling. We identify four possible contributory effects. First, the surface climate system loses heat less readily into the ocean beneath as the latter warms. The model spread in the thermal coupling between the upper and deep ocean largely explains the model spread in ocean heat uptake efficiency. Second, CO(2) radiative forcing may rise more rapidly than logarithmically with CO(2) concentration. Third, the climate feedback parameter may decline as the CO(2) concentration rises. With CMIP5 data, we cannot distinguish the second and third possibilities. Fourth, the climate feedback parameter declines as time passes or T rises; in 1pctCO2, this effect is less important than the others. We find that T projected for the end of the twenty-first century correlates more highly with T at the time of quadrupled CO(2) in 1pctCO2 than with the TCR, and we suggest that the TCR may be underestimated from observed climate change.