Philippa Kaur

Prasad, N. G. & Joshi, A. What have two decades of laboratory life-history evolution studies on Drosophila melanogaster taught us?. J. Genet. 82, 45–76 (2003).CAS 
PubMed 

Google Scholar 
MacMillan, H. A., Walsh, J. P. & Sinclair, B. J. The effects of selection for cold tolerance on cross-tolerance to other environmental stressors in Drosophila melanogaster. Insect Sci. 16, 263–276 (2009).
Google Scholar 
Flatt, T. Life-history evolution and the genetics of fitness components in drosophila melanogaster. Genetics 214(1), 3–48. https://doi.org/10.1534/genetics.119.300160 (2020).Article 
CAS 
PubMed 

Google Scholar 
Hoffmann, A. A. & Parsons, P. A. Selection for increased desiccation resistance in Drosophila melanogaster: Additive genetic control and correlated responses for other stresses. Genetics 122, 837–845 (1989).CAS 
PubMed 
PubMed Central 

Google Scholar 
Nghiem, D., Gibbs, A. G., Rose, M. R. & Bradley, T. J. Postponed aging and desiccation resistance in Drosophila melanogaster. Exp. Gerontol. 35, 957–969 (2000).CAS 
PubMed 

Google Scholar 
Hoffmann, A. A., Scott, M., Partridge, L. & Hallas, R. Overwintering in Drosophila melanogaster: Outdoor field cage experiments on clinal and laboratory selected populations help to elucidate traits under selection. J. Evol. Biol. 16, 614–623 (2003).CAS 
PubMed 

Google Scholar 
Bubliy, O. A. & Loeschcke, V. Correlated responses to selection for stress resistance and longevity in a laboratory population of Drosophila melanogaster. J. Evol. Biol. 18, 789–803 (2005).CAS 
PubMed 

Google Scholar 
Bourg, É. L. & Le Bourg, É. A cold stress applied at various ages can increase resistance to heat and fungal infection in aged Drosophila melanogaster flies. Biogerontology 12, 185–193 (2011).PubMed 

Google Scholar 
Sejerkilde, M., Sørensen, J. G. & Loeschcke, V. Effects of cold- and heat hardening on thermal resistance in Drosophila melanogaster. J. Insect Physiol. 49, 719–726 (2003).CAS 
PubMed 

Google Scholar 
Coulson, S. C. & Bale, J. S. Effect of rapid cold hardening on reproduction and survival of offspring in the housefly Musca domestica. J. Insect Physiol. 38, 421–424 (1992).
Google Scholar 
Bayley, M., Petersen, S. O., Knigge, T., Köhler, H.-R. & Holmstrup, M. Drought acclimation confers cold tolerance in the soil collembolan Folsomia candida. J. Insect Physiol. 47, 1197–1204 (2001).CAS 
PubMed 

Google Scholar 
Wu, B. S. et al. Anoxia induces thermotolerance in the locust flight system. J. Exp. Biol. 205, 815–827 (2002).CAS 
PubMed 

Google Scholar 
Phelan, J. P. et al. Breakdown in correlations during laboratory evolution. I. Comparative analyses of Drosophila populations. Evolution 57, 527–535 (2003).PubMed 

Google Scholar 
Hoffmann, A. A. & Harshman, L. G. Desiccation and starvation resistance in Drosophila: Patterns of variation at the species, population and intrapopulation levels. Heredity 83(Pt 6), 637–643 (1999).PubMed 

Google Scholar 
Sinclair, B. J., Nelson, S., Nilson, T. L., Roberts, S. P. & Gibbs, A. G. The effect of selection for desiccation resistance on cold tolerance of Drosophila melanogaster. Physiol. Entomol. 32, 322–327 (2007).
Google Scholar 
Anderson, A. R., Hoffmann, A. A. & McKechnie, S. W. Response to selection for rapid chill-coma recovery in Drosophila melanogaster: Physiology and life-history traits. Genet. Res. 85, 15–22 (2005).PubMed 

Google Scholar 
Kellett, M., Hoffmann, A. A. & Mckechnie, S. W. Hardening capacity in the Drosophila melanogaster species group is constrained by basal thermotolerance. Funct. Ecol. 19, 853–858 (2005).
Google Scholar 
Overgaard, J., Sørensen, J. G., Petersen, S. O., Loeschcke, V. & Holmstrup, M. Reorganization of membrane lipids during fast and slow cold hardening in Drosophila melanogaster. Physiol. Entomol. 31, 328–335 (2006).CAS 

Google Scholar 
Hoffmann, A. A., Hallas, R., Anderson, A. R. & Telonis-Scott, M. Evidence for a robust sex-specific trade-off between cold resistance and starvation resistance in Drosophila melanogaster. J. Evol. Biol. 18, 804–810 (2005).CAS 
PubMed 

Google Scholar 
Singh, K., Kochar, E. & Prasad, N. G. Egg Viability, Mating Frequency and Male Mating Ability Evolve in Populations of Drosophila melanogaster Selected for Resistance to Cold Shock. PLoS ONE 10, e0129992 (2015).PubMed 
PubMed Central 

Google Scholar 
Singh, K., Kochar, E., Gahlot, P., Bhatt, K. & Prasad, N. G. Evolution of reproductive traits have no apparent life-history associated cost in populations of Drosophila melanogaster selected for cold shock resistance. BMC Ecol. Evol. 21, 1–4 (2021).
Google Scholar 
Salehipour-Shirazi, G., Ferguson, L. V. & Sinclair, B. J. Does cold activate the Drosophila melanogaster immune system?. J. Insect Physiol. 96, 29–34 (2017).CAS 
PubMed 

Google Scholar 
Singh, K., Zulkifli, M. & Prasad, N. G. Identification and characterization of novel natural pathogen of Drosophila melanogaster isolated from wild captured Drosophila spp. Microbes Infect. 18, 813–821 (2016).PubMed 

Google Scholar 
Singh, K., Samant, M. A., Tom, M. T. & Prasad, N. G. Evolution of Pre- and Post-Copulatory Traits in Male Drosophila melanogaster as a Correlated Response to Selection for Resistance to Cold Stress. PLoS ONE 11, e0153629 (2016).PubMed 
PubMed Central 

Google Scholar 
Lefevre, G. J. & Jonsson, U. B. The effect of cold shock on D. melanogaster sperm. Drosophila Inf. Serv. 1962(36), 86–876 (1962).
Google Scholar 
Novitski, E. & Rush, G. Viability and fertility of Drosophila exposed to sub-zero temperatures. Biol. Bull. 97, 150–157 (1949).CAS 
PubMed 

Google Scholar 
Arbogast, R. T. Mortality and Reproduction of Ephestia cautella and Plodia interpunctella 1 Exposed as Pupae to High Temperatures. Environ. Entomol. 10, 708–711 (1981).
Google Scholar 
Saxena, B. P., Sharma, P. R., Thappa, R. K. & Tikku, K. Temperature induced sterilization for control of three stored grain beetles. J. Stored Prod. Res. 28, 67–70 (1992).
Google Scholar 
Collett, J. I. & Jarman, M. G. Adult female Drosophila pseudoobscura survive and carry fertile sperm through long periods in the cold: Populations are unlikely to suffer substantial bottlenecks in overwintering. Evolution 55, 840–845 (2001).CAS 
PubMed 

Google Scholar 
Schnebel, E. M. & Grossfield, J. Mating-temperature range in drosophila. Evolution 38, 1296–1307 (1984).PubMed 

Google Scholar 
Chakir, M., Chafik, A., Moreteau, B., Gibert, P. & David, J. R. Male sterility thermal thresholds in Drosophila: D. simulans appears more cold-adapted than its sibling D. melanogaster. Genetica 114, 195–205 (2002).PubMed 

Google Scholar 
David, J. R. et al. Male sterility at extreme temperatures: A significant but neglected phenomenon for understanding Drosophila climatic adaptations. J. Evol. Biol. 18, 838–846 (2005).CAS 
PubMed 

Google Scholar 
Dolgin, E. S., Whitlock, M. C. & Agrawal, A. F. Male Drosophila melanogaster have higher mating success when adapted to their thermal environment. J. Evol. Biol. 19, 1894–1900 (2006).CAS 
PubMed 

Google Scholar 
David, J. R. Male sterility at high and low temperatures in Drosophila. J. Soc. Biol. 202, 113–117 (2008).PubMed 

Google Scholar 
Zhang, W., Zhao, F., Hoffmann, A. A. & Ma, C.-S. A single hot event that does not affect survival but decreases reproduction in the diamondback moth, plutella xylostella. PLoS ONE 8, e75923 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Tucić, N. Genetic capacity for adaptation to cold resistance at different developmental stages of Drosophila melanogaster. Evolution 33, 350–358 (1979).PubMed 

Google Scholar 
Chen, C.-P. & Walker, V. K. Increase in cold-shock tolerance by selection of cold resistant lines in Drosophila melanogaster. Ecol. Entomol. 18, 184–190 (1993).
Google Scholar 
Ring, R. A. & Danks, H. V. Desiccation and cryoprotection: Overlapping adaptations. Cryo Lett. 15, 181–190 (1994).
Google Scholar 
Ring, R. A. & Danks, H. The role of trehalose in cold-hardiness and desiccation. Cryo Lett. 19, 275–282 (1998).CAS 

Google Scholar 
Singh, K. & Prasad, N. G. Cold stress upregulates the expression of heat shock proteins and Frost genes, but evolution of cold stress resistance is apparently not mediated through either heat shock proteins or Frost genes in the cold stress selected population. bioRxiv https://doi.org/10.1101/2022.03.07.483305 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Bubliy, O. A., Kristensen, T. N., Kellermann, V. & Loeschcke, V. Plastic responses to four environmental stresses and cross-resistance in a laboratory population of Drosophila melanogaster. Funct. Ecol. 26, 245–253 (2012).
Google Scholar 
Kristensen, T. N., Loeschcke, V. & Hoffmann, A. A. Can artificially selected phenotypes influence a component of field fitness? Thermal selection and fly performance under thermal extremes. Proc. Biol. Sci. 274, 771–778 (2007).PubMed 

Google Scholar 
Hoffmann, A. A., Anderson, A. & Hallas, R. Opposing clines for high and low temperature resistance in Drosophila melanogaster. Ecol. Lett. 5, 614–618 (2002).
Google Scholar 
Yi, S.-X. & Lee, R. E. Jr. Detecting freeze injury and seasonal cold-hardening of cells and tissues in the gall fly larvae, Eurosta solidaginis (Diptera: Tephritidae) using fluorescent vital dyes. J. Insect Physiol. 49, 999–1004 (2003).CAS 
PubMed 

Google Scholar 
Macmillan, H. A. & Sinclair, B. J. Mechanisms underlying insect chill-coma. J. Insect Physiol. 57, 12–20 (2011).CAS 
PubMed 

Google Scholar 
Marshall, K. E. & Sinclair, B. J. The sub-lethal effects of repeated freezing in the woolly bear caterpillar Pyrrharctia isabella. J. Exp. Biol. 214, 1205–1212 (2011).PubMed 

Google Scholar 
Sinclair, B. J., Ferguson, L. V., Salehipour-shirazi, G. & MacMillan, H. A. Cross-tolerance and cross-talk in the cold: Relating low temperatures to desiccation and immune stress in insects. Integr. Comp. Biol. 53, 545–556 (2013).PubMed 

Google Scholar 
Roxström-Lindquist, K., Terenius, O. & Faye, I. Parasite-specific immune response in adult Drosophila melanogaster: A genomic study. EMBO Rep. 5, 207–212 (2004).PubMed 
PubMed Central 

Google Scholar 
Pham, L. N., Dionne, M. S., Shirasu-Hiza, M. & Schneider, D. S. A specific primed immune response in Drosophila is dependent on phagocytes. PLoS Pathog. 3, e26 (2007).PubMed 
PubMed Central 

Google Scholar 
Mikonranta, L., Mappes, J., Kaukoniitty, M. & Freitak, D. Insect immunity: Oral exposure to a bacterial pathogen elicits free radical response and protects from a recurring infection. Front. Zool. 11, 23 (2014).PubMed 
PubMed Central 

Google Scholar 
Ramløv, H. & Lee, R. E. Jr. Extreme resistance to desiccation in overwintering larvae of the gall fly Eurosta solidaginis (Diptera, tephritidae). J. Exp. Biol. 203, 783–789 (2000).PubMed 

Google Scholar 
Holmstrup, M., Bayley, M. & Ramløv, H. Supercool or dehydrate? An experimental analysis of overwintering strategies in small permeable arctic invertebrates. Proc. Natl. Acad. Sci. 99, 5716–5720 (2002).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Chippindale, A. K. et al. Resource acquisition and the evolution of stress resistance in drosophila melanogaster. Evolution 52, 1342 (1998).PubMed 

Google Scholar 
Rose, M. R. Laboratory evolution of postponed senescence in Drosophila melanogaster. Evolution 38, 1004–1010 (1984).ADS 
PubMed 

Google Scholar 
Crill, W. D., Huey, R. B. & Gilchrist, G. W. Within- and between-generation effects of temperature on the morphology and physiology of Drosophila melanogaster. Evolution 50, 1205–1218 (1996).PubMed 

Google Scholar 
Kwan, L., Bedhomme, S., Prasad, N. G. & Chippindale, A. K. Sexual conflict and environmental change: Trade-offs within and between the sexes during the evolution of desiccation resistance. J. Genet. 87, 383–394 (2008).PubMed 

Google Scholar  More

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.This is a summary of: Zhang, S. et al. Sustained productivity and agronomic potential of perennial rice. Nat. Sustain. https://doi.org/10.1038/s41893-022-00997-3 (2022). More

Selection of surveys and extraction of dataWe selected published surveys that produced estimates of sperm whale population size or density (see Supplementary Information for methodology; surveys listed in Table 1). We extracted: the type of survey (ship, aerial; acoustic, visual), the years of data collection; the coordinates of the boundary of the study area; the estimates of g(0) and CV (g(0)) used to correct for availability bias, if given; and an estimate of sperm whale population or density in study area with CV. From these we calculated for each survey the survey area with waters greater than 1000 m deep (typical shallow depth limit of sperm whales3). When no value of g(0) was used (8 ship visual surveys) we corrected the population/density estimate using an assumed generic value of g(0) and recalculated the CV to include uncertainty in g(0) (as in Eq. 1 of8). Three ship visual surveys did calculate a single g(0) estimate: 0.62 (CV 0.35)32; 0.57 (CV 0.28)35; 0.61 (CV 0.25)37. These are consistent and suggest a generic g(0) = 0.60 (CV 0.29), also agreeing with g(0) = 0.60 estimated from pooled surveys in the California Current10.Global habitat of sperm whalesTo extrapolate sperm whale densities from surveyed study areas to the sperm whales’ global habitat, we created a one-degree latitude by one-degree longitude grid. We removed the following grid points as not being prime sperm whale habitat1,3,40: points on land or with central depths less than 1000 m; largely ice-covered points in the Beaufort Sea, and the waters north of Svalbard and Russia; the Black Sea and Red Sea both of which have shallow entrances that appear not to be traversable by sperm whales.Generally, food abundance is a good predictor of species distribution. However, this is not possible for sperm whales as we have no good measures of the abundance or distribution of most of their prey, deep-water squid57. Instead, oceanographic measures have been used to describe sperm whale distributions over various spatial scales with a moderate level of success13,14. We follow this approach. Measures that might predict sperm whale density were collected for each grid point, some at just the surface, others at the surface, 500 m depth, 1000 m depth or an average of the measures at the different depths (Supplementary Table S2). Water depth was the strongest predictor in Mediterranean encounters, when compared to slope and distance to shore13. Temperature and salinity have been used as predictors for the distribution of fish and larger marine animals, which could translate into prey availability and thus density for sperm whales58,59. Primary productivity and dissolved oxygen generally dictate the biomass of wildlife in an area, while nitrate and phosphate levels limit the amount of primary productivity in an area60. Eddy kinetic energy is a measure of the dynamism of physical oceanography which is becoming a commonly used predictor of cetacean habitat61. We did not use: latitude and longitude as these primarily describe the general geographic distribution of the study areas, and geographic aggregates of sperm whale catches62 as these proved to have no predictive power. The mean values of the 14 predictor measures were calculated over calendar months for each grid point, and then over the grid points in each study area.To obtain predictors of the sperm whale density at each grid point, we then made quadratic regressions of the density of sperm whales in each study area (i), d(i), on the mean values of the predictor measures, weighting each study area by its surface area. Because the surveys were conducted over different time periods, the densities were corrected based on the estimated trajectory of global sperm whale populations by multiplying d(i) by the ratio of the global population in 1993 over that in the mid-year of the survey (as in Fig. 4). Predictor variables were selected using forward stepwise selection based upon reduction in AIC.Sperm whale population sizeThe population of sperm whales globally, N, was then calculated as follows:$$N=sum_{k}dleft(kright)cdot aleft(kright),$$
(1)
where a{k} are the parameters of the regression; the summation is over k, the grid points; d(k) is the estimated sperm whale density at grid point k from the habitat suitability model; and a(k) is the area of the 1° cell centred on grid point k. Population estimates for other ocean areas (North Atlantic, North Pacific, Southern Hemisphere) were calculated similarly.The CVs of these population estimates were calculated following the methodology in8, (although there is an error in Eq. (3) of8 such that the squareroot symbol covers both the numerator and denominator rather than just the numerator). The error due to uncertain density estimates for the different surveys is:$$CVleft({D}_{T}right)=frac{sqrt{sum_{i}{left(CV({n}_{i})cdot {n}_{i}right)}^{2}}}{sum_{i}{n}_{i}}.$$
(2)
This is combined with the uncertainty in the extrapolation process (output from the linear models), CV(extrap.), to give an overall CV for the population estimate:$$CVleft(Nright)=sqrt{{CV({D}_{T})}^{2}+{CV(mathrm{extrap}.)}^{2}.}$$
(3)
Post-whaling trend in population sizeWe compiled a database of series of surveys producing population estimates of the same study area during the period 1978 (by which time most commercial sperm whaling had ceased) and 2022. Each series had to span at least 10 years, and all of the surveys in the series had to be comparable in terms of area covered throughout the time span. There also had to have been at least 3 surveys for a data set to be included.The data consisted of the survey area, A, the estimated population in area A in year y (for multi-year surveys, y would be the midpoint of the data collection years), nE(A,y), and the provided CV of that estimate, CV(nE(A,y)). The data series used for these analyses are summarized in Table 3.For each survey area, A, we calculated the trend in logarithmic population size, r(A), over time using weighted linear regression:$${text{Log}}left( {n_{E} left( {A,y} right)} right) , sim {text{ constant}}left( A right) , + rleft( A right) cdot y. left[ {{text{weight }} = { 1}/left( {{1} + {text{ CV}}left( {n_{E} left( {A,y} right)} right)} right)^{{2}} } right]$$
(4)
Table 3 also includes other published estimates of sperm whale population trends, from sighting rates or mark-recapture analyses of photoidentification data, with these estimates also having to span at least 10 years of data collection, and include data collected in three or more different years.Population trajectoryTo examine possible trajectories of the global sperm whale population following the start of commercial whaling in 1712, we used a variant of the theta-logistic, a population model that has been employed in other recent analyses of the population trajectories of large cetaceans45,63. The theta-logistic model is:$$nleft(y+1right)=nleft(yright)+rcdot nleft(yright)left(1-{left(frac{nleft(yright)}{nleft(1711right)}right)}^{theta }right)-fleft(yright)cdot cleft(yright).$$
(5)

Here, n(y) is the population of sperm whales in year y, r is the maximum potential rate of increase of a sperm whale population, and θ describes how the rate of increase varies with population size relative to its basal level before whaling in 1711, n(1711). The recorded catch in year y is c(y) and f(y) is a correction for bias in recorded catches.Whaling reduced the proportion of large breeding males64, likely disrupted the social cohesion of the females3, and may have had other lingering effects which reduced pregnancy or survival, and thus the rate of increase. Poaching has been found to reduce the reproductive output of African elephants, Loxodonta Africana, which have a similar social system to the sperm whales3, and this effect lingers well beyond the effective cessation of poaching46. There is some evidence for these effects of what we call “social disruption” on sperm whale population dynamics20,46,65. We added a term to the theta-logistic to account for such effects:$$nleft(y+1right)=nleft(yright)left[1+rcdot left(1-{left(frac{nleft(yright)}{nleft(1711right)}right)}^{theta }right)-qcdot frac{sum_{t=y-T}^{y}f(t)cdot c(t)}{nleft(y-Tright)}right]-f(y)cdot c(y).$$
(6)

Here, (frac{sum_{t=y-T}^{y}f(t)cdot c(t)}{nleft(y-Tright)}) is the proportion of the population killed over the last T years, and q is the reduction in the rate of increase when almost all the whales have been killed. This reduction is modelled to fall linearly as the proportion killed declines to zero.The global sperm whale population has some geographic structure18. Females appear to rarely move between ocean basins, and males seem to largely stay within one basin. Furthermore, sperm whaling was progressive, moving from ocean area to ocean area as numbers were depleted4. We model this by assuming K largely separate sperm whale subpopulations of equal size. Exploitation in 1712 starts in subpopulation 1 and moves to subpopulations 1 and 2 when the population 1 falls to α% of its initial value, and so on for the other ocean areas. The catch in each year in each area being exploited is pro-rated by the sizes of the different subpopulations being exploited. The population model for subpopulation k, which is one of the KE subpopulations being exploited in year y, is:$$nleft(k,y+1right)=nleft(k,yright)left[1+rcdot left(1-{left(frac{nleft(k,yright)}{nleft(k,1711right)}right)}^{theta }right)-qcdot frac{sum_{t=y-T}^{y}C(k,t)}{nleft(k,y-Tright)}right]-Cleft(k,yright),$$
(7)
where the estimated catch in year y in subpopulation k is given by: (Cleft(k,yright)=f(y)cdot c(y)cdot n(k,y)/sum_{{k}^{mathrm{^{prime}}}= More

Seguin, R. et al. Nat. Sustain. https://doi.org/10.1038/s41893-022-00981-x (2022).Article 

Google Scholar 
Roberts, C. M. & Polunin, N. V. C. Rev. Fish Biol. Fish. 1, 65–91 (1991).Article 

Google Scholar 
Cinner, J. E. et al. Soc. Nat. Resour. 27, 994–1005 (2014).Article 

Google Scholar 
MacNeil, M. A. et al. Nature 520, 341–344 (2015).Article 
CAS 

Google Scholar 
Morais, R. A. & Bellwood, D. R. Coral Reefs 39, 1221–1231 (2020).Article 

Google Scholar 
Morais, R. A., Connolly, S. R. & Bellwood, D. R. Glob. Change Biol. 26, 1295–1305 (2020).Article 

Google Scholar 
Di Lorenzo, M. et al. Fish Fish. 21, 906–915 (2020).Article 

Google Scholar 
Ban, N. C. et al. Nat. Sustain. 2, 524–532 (2019).Article 

Google Scholar 
Rogers, A. et al. Ecology 99, 450–463 (2018).Article 

Google Scholar 
Robinson, J. P. W. et al. Nat. Ecol. Evol. 3, 183–190 (2019).Article 

Google Scholar  More

Balvanera, P. et al. Bioscience 64, 49–57 (2014).Article 

Google Scholar 
Hooper, D. U. et al. Ecol. Monogr. 75, 3–35 (2005).Article 

Google Scholar 
Balvanera, P. et al. Ecol. Lett. 9, 1146–1156 (2006).Article 
PubMed 

Google Scholar 
Cardinale, B. J. et al. Am. J. Bot. 98, 572–592 (2011).Article 
PubMed 

Google Scholar 
Cardinale, B. J. B. J. et al. Nature 486, 59–67 (2012).Article 
CAS 
PubMed 

Google Scholar 
Manning, P. et al. in Advances in Ecological Research (eds Eisenhauer N. et al.) 323–356 (Academic, 2019).Le Provost, G. et al. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-022-01918-5 (2022).Felipe-Lucia, M. R. et al. Proc. Natl Acad. Sci. USA 117, 28140–28149 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Foley, J. A. et al. Science 309, 570–574 (2005).Article 
CAS 
PubMed 

Google Scholar 
Cardinale, B. J. et al. Ecology 94, 1697–1707 (2013).Article 
PubMed 

Google Scholar 
Teles da Mota, V. & Pickering, C. J. Outdoor Recreat. Tour. 30, 100295 (2020).Article 

Google Scholar 
Mitchell, M. G. E. et al. Trends Ecol. Evol. 30, 190–198 (2015).Article 
PubMed 

Google Scholar 
Raudsepp-Hearne, C. & Peterson, G. D. Ecol. Soc. 21, 16 (2016).Article 

Google Scholar 
Chaplin-Kramer, R. & Kremen, C. Ecol. Appl. 22, 1936–1948 (2012).Article 
PubMed 

Google Scholar  More

Hug, L. A. et al. A new view of the tree of life. Nat. Microbiol. 1, 16048 (2016).Article 
PubMed 
CAS 

Google Scholar 
Spang, A. et al. Complex archaea that bridge the gap between prokaryotes and eukaryotes. Nature 521, 173–179 (2015).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Tyson, G. W. et al. Community structure and metabolism through reconstruction of microbial genomes from the environment. Nature 428, 37–43 (2004).Article 
PubMed 
CAS 

Google Scholar 
Anantharaman, K. et al. Thousands of microbial genomes shed light on interconnected biogeochemical processes in an aquifer system. Nat. Commun. 7, 13219 (2016).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Parks, D. H. et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat. Microbiol. 2, 1533–1542 (2017).Article 
PubMed 
CAS 

Google Scholar 
Tully, B. J. & Graham, E. D. & Heidelberg, J. F. The reconstruction of 2,631 draft metagenome-assembled genomes from the global oceans. Sci. Data 5, 170203 (2018).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Stewart, R. D. et al. Assembly of 913 microbial genomes from metagenomic sequencing of the cow rumen. Nat. Commun. 9, 870 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Pasolli, E. et al. Extensive unexplored human microbiome diversity revealed by over 150,000 genomes from metagenomes spanning age, geography and lifestyle. Cell 176, 649–662 (2019).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Nayfach, S. et al. A genomic catalog of Earth’s microbiomes. Nat. Biotechnol. 39, 499–509, https://doi.org/10.1038/s41587-020-0718-6 (2021).Article 
PubMed 
CAS 

Google Scholar 
Gilbert, J. A., Jansson, J. K. & Knight, R. The Earth Microbiome project: successes and aspirations. BMC Biol 12, 69 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Saheb Kashaf, S., Almeida, A., Segre, J. A. & Finn, R. D. Recovering prokaryotic genomes from host-associated, short-read shotgun metagenomic sequencing data. Nat. Protoc. 16, 2520–2541 (2021).Article 
PubMed 
CAS 

Google Scholar 
Chong, J., Liu, P., Zhou, G. & Xia, J. Using MicrobiomeAnalyst for comprehensive statistical, functional, and meta-analysis of microbiome data. Nat. Protoc. 15, 799–821 (2020).Article 
PubMed 
CAS 

Google Scholar 
Arkin, A. P. et al. KBase: The United States Department of Energy Systems Biology Knowledgebase. Nat. Biotechnol. 36, 566–569 (2018).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Sayers, E. W. et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 49, D10–D17 (2021).Article 
PubMed 
CAS 

Google Scholar 
Kluyver, T., et al. Jupyter Notebooks – a publishing format for reproducible computational workflows. In: Loizides F, Schmidt B, editors. Positioning and Power in Academic Publishing: Players, Agents and Agendas. p. 87–90 (2016).Banfield, J. Development of a Knowledgebase to Integrate, Analyze, Distribute, and Visualize Microbial Community Systems Biology Data. (2015). Report number: DOE-UCB-4918, OSTI ID: 1167269.Chen, I.-M. A. et al. IMG/M v.5.0: an integrated data management and comparative analysis system for microbial genomes and microbiomes. Nucleic Acids Res 47, D666–D677 (2019).Article 
PubMed 
CAS 

Google Scholar 
Afgan, E. et al. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update. Nucleic Acids Res 44, W3–W10 (2016).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Devisetty, U. K., Kennedy, K., Sarando, P., Merchant, N. & Lyons, E. Bringing your tools to CyVerse discovery environment using Docker. F1000Res. 5, 1442 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Wang, L., Lu, Z., Van Buren, P. & Ware, D. SciApps: a bioinformatics workflow platform powered by XSEDE and CyVerse. in Proceedings of the Practice and Experience on Advanced Research Computing 1–5 (Association for Computing Machinery, 2018).Eren, A. M. et al. Community-led, integrated, reproducible multi-omics with anvi’o. Nat. Microbiol. 6, 3–6 (2021).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Wattam, A. R. et al. Improvements to PATRIC, the all-bacterial bioinformatics database and analysis resource center. Nucleic Acids Res 45, D535–D542 (2017).Article 
PubMed 
CAS 

Google Scholar 
Mitchell, A. L. et al. MGnify: the microbiome analysis resource in 2020. Nucleic Acids Res. 48, D570–D578 (2020).PubMed 
CAS 

Google Scholar 
Wu, Y.-W. et al. Ionic liquids impact the bioenergy feedstock-degrading microbiome and transcription of enzymes relevant to polysaccharide hydrolysis. mSystems 1, e00120–16 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Rajeev, L. et al. Dynamic cyanobacterial response to hydration and dehydration in a desert biological soil crust. ISME J 7, 2178–2191 (2013).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Foster, I. Globus Online: accelerating and democratizing science through cloud-based services. IEEE Internet Comput 15, 70–73 (2011).Article 

Google Scholar 
Nurk, S., Meleshko, D., Korobeynikov, A. & Pevzner, P. A. metaSPAdes: a new versatile metagenomic assembler. Genome Res 27, 824–834 (2017).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Zhang, H. et al. dbCAN2: a meta server for automated carbohydrate-active enzyme annotation. Nucleic Acids Res 46, W95–W101 (2018).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Chaumeil, P.-A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 36, 1925–1927 (2019).PubMed Central 

Google Scholar 
Camacho, C. et al. BLAST+: architecture and applications. BMC Bioinforma 10, 421 (2009).Article 

Google Scholar 
Nordberg, H. et al. The genome portal of the Department of Energy Joint Genome Institute: 2014 updates. Nucleic Acids Res 42, D26–D31 (2014).Article 
PubMed 
CAS 

Google Scholar 
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J. 17, 10–12 (2011).Article 

Google Scholar 
Menzel, P., Ng, K. L. & Krogh, A. Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat. Commun. 7, 11257 (2016).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Freitas, T. A. K., Li, P.-E., Scholz, M. B. & Chain, P. S. G. Accurate read-based metagenome characterization using a hierarchical suite of unique signatures. Nucleic Acids Res 43, e69 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Wood, D. E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biol 20, 257 (2019).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Truong, D. T. et al. MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nat. Methods 12, 902–903 (2015).Article 
PubMed 
CAS 

Google Scholar 
Milanese, A. et al. Microbial abundance, activity and population genomic profiling with mOTUs2. Nat. Commun. 10, 2014 (2019).Article 

Google Scholar 
Youngblut, N. D. & Ley, R. E. Struo2: efficient metagenome profiling database construction for ever-expanding microbial genome datasets. Peer J 9, e12198 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Ondov, B. D., Bergman, N. H. & Phillippy, A. M. Interactive metagenomic visualization in a Web browser. BMC Bioinform 12, 385 (2011).Article 

Google Scholar 
Li, D., Liu, C.-M., Luo, R., Sadakane, K. & Lam, T.-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015).Article 
PubMed 
CAS 

Google Scholar 
Peng, Y., Leung, H. C. M., Yiu, S. M. & Chin, F. Y. L. IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics 28, 1420–1428 (2012).Article 
PubMed 
CAS 

Google Scholar 
Orakov, A. et al. GUNC: detection of chimerism and contamination in prokaryotic genomes. Genome Biol 22, 178 (2021).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Gurevich, A., Saveliev, V., Vyahhi, N. & Tesler, G. QUAST: quality assessment tool for genome assemblies. Bioinformatics 29, 1072–1075 (2013).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Wu, Y.-W., Simmons, B. A. & Singer, S. W. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 32, 605–607 (2016).Article 
PubMed 
CAS 

Google Scholar 
Kang, D. D. et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 7, e7359 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Alneberg, J. et al. Binning metagenomic contigs by coverage and composition. Nat. Methods 11, 1144–1146 (2014).Article 
PubMed 
CAS 

Google Scholar 
Sieber, C. M. K. et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat. Microbiol. 3, 836–843 (2018).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res 25, 1043–1055 (2015).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Delcher, A. L., Salzberg, S. L. & Phillippy, A. M. Using MUMmer to identify similar regions in large sequence sets. Curr. Protoc. Bioinform. Chapter 10, Unit 10.3 (2003).
Google Scholar 
Darling, A. C. E., Mau, B., Blattner, F. R. & Perna, N. T. Mauve: multiple alignment of conserved genomic sequence with rearrangements. Genome Res 14, 1394–1403 (2004).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Parks, D. H. et al. GTDB: an ongoing census of bacterial and archaeal diversity through a phylogenetically consistent, rank normalized and complete genome-based taxonomy. Nucleic Acids Res 50, D785–D794 (2022).Article 
PubMed 
CAS 

Google Scholar 
Bowers, R. M. et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat. Biotechnol. 35, 725–731 (2017).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Brettin, T. et al. RASTtk: a modular and extensible implementation of the RAST algorithm for building custom annotation pipelines and annotating batches of genomes. Sci. Rep. 5, 8365 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Overbeek, R. et al. The SEED and the rapid annotation of microbial genomes using Subsystems Technology (RAST). Nucleic Acids Res 42, D206–D214 (2014).Article 
PubMed 
CAS 

Google Scholar 
Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069 (2014).Article 
PubMed 
CAS 

Google Scholar 
Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform 11, 119 (2010).Article 

Google Scholar 
Parks, D. H. et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat. Biotechnol. 36, 996–1004 (2018).Article 
PubMed 
CAS 

Google Scholar 
Rinke, C. et al. A standardized archaeal taxonomy for the Genome Taxonomy Database. Nat. Microbiol. 6, 946–959 (2021).Article 
PubMed 
CAS 

Google Scholar 
Haft, D. H. et al. RefSeq: an update on prokaryotic genome annotation and curation. Nucleic Acids Res 46, D851–D860 (2018).Article 
PubMed 
CAS 

Google Scholar 
Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2–approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
Shaffer, M. et al. DRAM for distilling microbial metabolism to automate the curation of microbiome function. Nucleic Acids Res 48, 8883–8900 (2020).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Galperin, M. Y., Makarova, K. S., Wolf, Y. I. & Koonin, E. V. Expanded microbial genome coverage and improved protein family annotation in the COG database. Nucleic Acids Res 43, D261–D269 (2015). (Database Issue).Article 
PubMed 
CAS 

Google Scholar 
El-Gebali, S. et al. The Pfam protein families database in 2019. Nucleic Acids Res 47, D427–D432 (2019).Article 
PubMed 
CAS 

Google Scholar 
Haft, D. H. et al. TIGRFAMs and Genome Properties in 2013. Nucleic Acids Res 41, D387–D395 (2013). (Database issue).Article 
PubMed 
CAS 

Google Scholar 
Eddy, S. R. Accelerated Profile HMM Searches. PLoS Comput. Biol. 7, e1002195 (2011).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Lombard, V., Golaconda Ramulu, H., Drula, E., Coutinho, P. M. & Henrissat, B. The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res 42, D490–D495 (2014).Article 
PubMed 
CAS 

Google Scholar 
Chivian, D., Dehal, P. S., Keller, K. & Arkin, A. P. MetaMicrobesOnline: phylogenomic analysis of microbial communities. Nucleic Acids Res 41, D648–D654 (2013).Article 
PubMed 
CAS 

Google Scholar 
Karaoz, U. & Brodie, E. L. microTrait: a toolset for a trait-based representation of microbial genomes. Front. Bioinform. https://doi.org/10.3389/fbinf.2022.918853 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Wood-Charlson, E. M. et al. The National Microbiome Data Collaborative: enabling microbiome science. Nat. Rev. Microbiol. 18, 313–314 (2020).Article 
PubMed 
CAS 

Google Scholar 
Hofmeyr, S. et al. Terabase-scale metagenome coassembly with MetaHipMer. Sci. Rep. 10, 10689 (2020).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Kolmogorov, M. et al. metaFlye: scalable long-read metagenome assembly using repeat graphs. Nat. Methods 17, 1103–1110 (2020).Article 
PubMed 
CAS 

Google Scholar 
Koren, S. et al. Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. Genome Res 27, 722–736 (2017).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Bertrand, D. et al. Hybrid metagenomic assembly enables high-resolution analysis of resistance determinants and mobile elements in human microbiomes. Nat. Biotechnol. 37, 937–944 (2019).Article 
PubMed 
CAS 

Google Scholar 
Chen, L.-X. et al. Accurate and complete genomes from metagenomes. Genome Res 30, 315–333 (2020).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Lui, L. M., Nielsen, T. N. & Arkin, A. P. A method for achieving complete microbial genomes and improving bins from metagenomics data. PLoS Comput Biol 17, e1008972 (2021).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Miller, C. S., Baker, B. J., Thomas, B. C., Singer, S. W. & Banfield, J. F. EMIRGE: reconstruction of full-length ribosomal genes from microbial community short read sequencing data. Genome Biol 12, R44 (2011).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Chivian, D. et al. Genome extraction from shotgun metagenome sequence data. KBase n/33233/628 https://doi.org/10.25982/33233.606/1831502 (2022).Article 

Google Scholar 
Chivian, D., et al. Moab desert crust – sample 4E. KBase n/62384/334 (2022). https://doi.org/10.25982/62384.253/1831503Jain, C., Rodriguez-R, L. M., Phillippy, A. M., Konstantinidis, K. T. & Aluru, S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat. Commun. 9, 5114 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Matsen, F. A., Kodner, R. B. & Armbrust, E. V. pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree. BMC Bioinform 11, 538 (2010).Article 

Google Scholar 
Benson, D. A. et al. GenBank. Nucleic Acids Res 46, D41–D47 (2018).Article 
PubMed 
CAS 

Google Scholar 
Ewing, B. & Green, P. Base-calling of automated sequencer traces using phred. II. Error probabilities. Genome Res. 8, 186–194 (1998).Article 
PubMed 
CAS 

Google Scholar 
Teiling, C. BaseSpace: Simplifying metagenomic analysis. 26th European Congress of Clinical Microbiology and Infectious Diseases (2016) 10.26226/morressier.56d5ba2ed462b80296c9509dReich, M. et al. The GenePattern notebook environment. Cell Syst 5, 149–151.e1 (2017).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Uritskiy, G. V., DiRuggiero, J. & Taylor, J. MetaWRAP-a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome 6, 158 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Karp, P. D. et al. A comparison of microbial genome web portals. Front. Microbiol. 10, 208 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Yue, Y. et al. Evaluating metagenomics tools for genome binning with real metagenomic datasets and CAMI datasets. BMC Bioinform 21, 334 (2020).Article 
CAS 

Google Scholar 
Nelson, W. C., Tully, B. J. & Mobberley, J. M. Biases in genome reconstruction from metagenomic data. PeerJ 8, e10119 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Olm, M. R., Brown, C. T., Brooks, B. & Banfield, J. F. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J 11, 2864–2868 (2017).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Li, L., Stoeckert, C. J. Jr & Roos, D. S. OrthoMCL: identification of ortholog groups for eukaryotic genomes. Genome Res 13, 2178–2189 (2003).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res 32, 1792–1797 (2004).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Kim, D., Paggi, J. M., Park, C., Bennett, C. & Salzberg, S. L. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat. Biotechnol. 37, 907–915 (2019).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Pertea, M. et al. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol. 33, 290–295 (2015).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15, 550 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Kumari, S. et al. A KBase case study on genome-wide transcriptomics and plant primary metabolism in response to drought stress in sorghum. Curr. Plant Biol. 28, 100229 (2021).Article 
CAS 

Google Scholar 
Seaver, S. M. D. et al. The ModelSEED biochemistry database for the integration of metabolic annotations and the reconstruction, comparison and analysis of metabolic models for plants, fungi and microbes. Nucleic Acids Res 49, D575–D588 (2021).Article 
PubMed 
CAS 

Google Scholar 
Schloss, P. D. et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar  More

Réale, D., Reader, S. M., Sol, D., McDougall, P. T. & Dingemanse, N. J. Integrating animal temperament within ecology and evolution. Biol. Rev. 82, 291–318 (2007).PubMed 

Google Scholar 
Kaiser, M. I. & Müller, C. What is an animal personality?. Biol. Philos. 36, 1 (2021).
Google Scholar 
Sih, A., Bell, A. & Johnson, J. C. Behavioral syndromes: An ecological and evolutionary overview. Trends Ecol. Evol. 19, 372–378 (2004).PubMed 

Google Scholar 
Gosling, S. D. From mice to men: What can we learn about personality from animal research?. Psychol. Bull. 127, 45–86 (2001).PubMed 

Google Scholar 
Biro, P. A. & Stamps, J. A. Are animal personality traits linked to life-history productivity?. Trends Ecol. Evol. 23, 361–368 (2008).PubMed 

Google Scholar 
Koolhaas, J. M. Coping style and immunity in animals: Making sense of individual variation. Brain Behav. Immun. 22, 662–667 (2008).PubMed 

Google Scholar 
Réale, D. et al. Personality and the emergence of the pace-of-life syndrome concept at the population level. Philos. Trans. R. Soc. B Biol. Sci. 365, 4051–4063 (2010).
Google Scholar 
Stamps, J. A. Growth-mortality tradeoffs and ‘personality traits’ in animals. Ecol. Lett. 10, 355–363 (2007).PubMed 

Google Scholar 
Finkemeier, M. A., Langbein, J. & Puppe, B. Personality research in mammalian farm animals: Concepts, measures, and relationship to welfare. Front Vet. Sci. 10(5), 355–363 (2018).
Google Scholar 
Murphy, E., Nordquist, R. E. & van der Staay, F. J. A review of behavioural methods to study emotion and mood in pigs. Sus. Scrofa. Appl. Anim. Behav. Sci 159, 9–28 (2014).
Google Scholar 
Lauber, M. C. Y., Hemsworth, P. H. & Barnett, J. L. The effects of age and experience on behavioural development in dairy calves. Appl. Anim. Behav. Sci. 99, 41–52 (2006).
Google Scholar 
Neave, H. W., Costa, J. H. C., Weary, D. M. & von Keyserlingk, M. A. G. Personality is associated with feeding behavior and performance in dairy calves. J. Dairy Sci. 101, 7437–7449 (2018).PubMed 

Google Scholar 
Foris, B., Zebunke, M., Langbein, J. & Melzer, N. Evaluating the temporal and situational consistency of personality traits in adult dairy cattle. Plos One 13, e0204619 (2018).PubMed 
PubMed Central 

Google Scholar 
Dingemanse, N. J. & Dochtermann, N. A. Quantifying individual variation in behaviour: Mixed-effect modelling approaches. J. Anim. Ecol. 82, 39–54 (2013).PubMed 

Google Scholar 
Dingemanse, N. J., Kazem, A. J. N., Réale, D. & Wright, J. Behavioural reaction norms: Animal personality meets individual plasticity. Trends Ecol. Evol. 25, 81–89 (2010).PubMed 

Google Scholar 
Nakagawa, S. & Schielzeth, H. Repeatability for Gaussian and non-Gaussian data: A practical guide for biologists. Biol. Rev. https://doi.org/10.1111/j.1469-185X.2010.00141.x (2010).Article 
PubMed 

Google Scholar 
Bell, A. M., Hankison, S. J. & Laskowski, K. L. The repeatability of behaviour: A meta-analysis. Anim. Behav. 77, 771–783 (2009).PubMed 
PubMed Central 

Google Scholar 
Neave, H. W., Costa, J. H. C., Benetton, J. B., Weary, D. M. & von Keyserlingk, M. A. G. Individual characteristics in early life relate to variability in weaning age, feeding behavior, and weight gain of dairy calves automatically weaned based on solid feed intake. J. Dairy Sci. 102, 10250–10265 (2019).PubMed 

Google Scholar 
Berckmans, D. Precision livestock farming technologies for welfare management in intensive livestock systems. Rev. Sci. Tech. OIE 33, 189–196 (2014).
Google Scholar 
Carslake, C., Vázquez-Diosdado, J. A. & Kaler, J. Machine learning algorithms to classify and quantify multiple behaviours in dairy calves using a sensor: Moving beyond classification in precision livestock. Sensors 21, 88 (2020).ADS 
PubMed Central 

Google Scholar 
Hertel, A. G., Niemelä, P. T., Dingemanse, N. J. & Mueller, T. A guide for studying among-individual behavioral variation from movement data in the wild. Mov. Ecol. 8(1), 1–18 (2020).
Google Scholar 
Occhiuto, F., Vázquez-Diosdado, J. A., Carslake, C. & Kaler, J. Personality and predictability in farmed calves using movement and space-use behaviours quantified by ultra-wideband sensors. R. Soc. Open Sci. 9, 212019 (2022).ADS 
PubMed 
PubMed Central 

Google Scholar 
Carslake, C., Occhiuto, F., Vázquez-Diosdado, J. A. & Kaler, J. Repeatability and predictability of calf feeding behaviors—quantifying between- and within-individual variation for precision livestock farming. Front. Vet. Sci. https://doi.org/10.3389/fvets.2022.827124 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Tolkamp, B. J. & Kyriazakis, I. To split behaviour into bouts, log-transform the intervals. Anim. Behav. 57, 807–817 (1999).PubMed 

Google Scholar 
Houslay, T. M. & Wilson, A. J. Avoiding the misuse of BLUP in behavioural ecology. Behav. Ecol. 28, 948 (2017).PubMed 
PubMed Central 

Google Scholar 
R Core Team. R. Preprint at (2021).Bürkner, P.-C. Advanced bayesian multilevel modeling with the R package brms. R. J. 10, 395 (2018).
Google Scholar 
Dancey, C. P. & Reidy, J. Statistics without maths for psychology (Pearson education, 2007).
Google Scholar 
von Keyserlingk, M. A. G., Brusius, L. & Weary, D. M. Competition for teats and feeding behavior by group-housed dairy calves. J. Dairy Sci. 87, 4190–4194 (2004).
Google Scholar 
Fraley, R. C. & Roberts, B. W. Patterns of continuity: A dynamic model for conceptualizing the stability of individual differences in psychological constructs across the life course. Psychol. Rev. 112, 60–74 (2005).PubMed 

Google Scholar 
Ashcroft, J., Semmler, C., Carnell, S., van Jaarsveld, C. H. M. & Wardle, J. Continuity and stability of eating behaviour traits in children. Eur. J. Clin. Nutr. 62, 985–990 (2008).PubMed 

Google Scholar 
Neave, H. W., Costa, J. H. C., Weary, D. M. & von Keyserlingk, M. A. G. Long-term consistency of personality traits of cattle. R. Soc. Open Sci. 7, 191849 (2020).ADS 
PubMed 
PubMed Central 

Google Scholar 
Müller, R. & von Keyserlingk, M. A. G. Consistency of flight speed and its correlation to productivity and to personality in Bos taurus beef cattle. Appl. Anim. Behav. Sci. 99, 193–204 (2006).
Google Scholar 
Neja, W., Sawa, A., Jankowska, M., Bogucki, M. & Krężel-Czopek, S. Effect of the temperament of dairy cows on lifetime production efficiency. Arch. Anim. Breed 58, 193–197 (2015).
Google Scholar 
Haskell, M. J., Simm, G. & Turner, S. P. Genetic selection for temperament traits in dairy and beef cattle. Front Genet. https://doi.org/10.3389/fgene.2014.00368 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Whalin, L., Neave, H. W., Føske Johnsen, J., Mejdell, C. M. & Ellingsen-Dalskau, K. The influence of personality and weaning method on early feeding behavior and growth of Norwegian red calves. J. Dairy Sci. 105, 1369–1386 (2022).PubMed 

Google Scholar 
Dammhahn, M., Dingemanse, N. J., Niemelä, P. T. & Réale, D. Pace-of-life syndromes: A framework for the adaptive integration of behaviour, physiology and life history. Behav. Ecol. Sociobiol. 72(3), 1–8 (2018).
Google Scholar 
Kelly, D. N. et al. Large variability in feeding behavior among crossbred growing cattle. J. Anim. Sci. 98, 1–10 (2020).
Google Scholar 
Neave, H. W., Weary, D. M. & von Keyserlingk, M. A. G. Review: Individual variability in feeding behaviour of domesticated ruminants. Animal 12, S419–S430 (2018).PubMed 

Google Scholar 
DeVries, T. J., von Keyserlingk, M. A. G., Weary, D. M. & Beauchemin, K. A. Measuring the feeding behavior of lactating dairy cows in early to peak lactation. J. Dairy Sci. 86, 3354–3361 (2003).PubMed 

Google Scholar 
Kelly, D. N., Sleator, R. D., Murphy, C. P., Conroy, S. B. & Berry, D. P. Phenotypic and genetic associations between feeding behavior and carcass merit in crossbred growing cattle. J. Anim. Sci. 99, skab285 (2021).PubMed 

Google Scholar 
Weary, D. M., Huzzey, J. M. & von Keyserlingk, M. A. G. Board-invited review: Using behavior to predict and identify ill health in animals. J. Anim. Sci. 87, 770–777 (2009).PubMed 

Google Scholar 
Carter, A. J., Feeney, W. E., Marshall, H. H., Cowlishaw, G. & Heinsohn, R. Animal personality: What are behavioural ecologists measuring?. Biol. Rev. 88, 465–475 (2013).PubMed 

Google Scholar 
Biro, P. A. Do rapid assays predict repeatability in labile (behavioural) traits?. Anim Behav 83, 1295–1300 (2012).
Google Scholar 
Percie du Sert, N. et al. Reporting animal research: Explanation and elaboration for the ARRIVE guidelines 20. Plos Biol. 18, e3000411 (2020).PubMed 
PubMed Central 

Google Scholar  More

The plots of Igeo, PERI, and PLI of HMs in the topsoil of the tourist area of Sayram Lake (Fig. 5) reveal the degree of HM pollution and eco-risk in this study area on the one hand and, on the other hand, indicate the direction for the relevant agencies to target soil environmental protection and HM pollution prevention and control measures. In this study, the Igeo results showed that Cd was the most highly enriched HM, and Pb, Zn, Cd, and Ni were slightly enriched in a few sample sites. The unnatural accumulation of these elements is usually closely associated with human activities in the area34. Tourism is the main economic activity in the district, and published studies have reported that tourism infrastructure construction (e.g., roads, buildings, etc.) and tourism wastes (e.g., plastic bags, batteries, hotel wastewater) release Cd into the soil35. Additionally, the accumulation of Pb, Zn, Cu and Ni in soils is usually associated with traffic emissions36. The PERI showed that the study area was at low risk overall, with only point ss04 exhibiting medium risk; however, this result was caused by the abnormally high Cd concentration value (Fig. 4) at point ss04 (Cd (concentration): 1.08 mg/kg, Cd (background): 0.34 mg/kg). This anomalous concentration value has a large influence on the PERI calculated based on the measured concentration, the background value and the toxicity coefficient. Therefore, references to this point can be appropriately removed when considering eco-risk. The PLI of each sampling point was greater than 1 and less than 2, which means that the area was in a moderately contaminated state. In general, the degree of soil HM contamination in this area was low; however, due to HM toxicity, bioaccumulation, and persistence37, the HM contamination of this area still requires sustained attention.Figure 5Contamination and ecological risk indices: (a) geoaccumulation index (Igeo) of HMs; (b) ecological risk of individual HMs; (c) potential ecological risk index (PERI) of HMs; (d) pollution load index (PLI) of HMs.Full size imageCorrelation analysis is an efficient way to reveal correlations among HMs through Pearson correlation coefficients, and HMs with significant correlations may originate from the same source38. As shown in Table S5, the elemental pairs Cd-Cu (p  More