Ecology

Bat capture, handling, and tag attachment were carried out in accordance with guidelines of American Society of Mammologists33 under permit from the California Department of Fish and Wildlife (# SC-002911). Experimental methods were approved by the Institutional Animal Care and Use Committee of the USDA Forest Service (IACUC 2017-014). We captured bats using 2.6-m high mist nets in a triple-high configuration. We measured forearm length and mass and determined species, age, sex, and reproductive status for each captured individual.We used Vesper Pipistrelle on-board audio-recording devices with an accelerometer (ASD Tech, Haifa Israel) to quantify bat movement throughout the duration of attachment. We used the smallest possible battery (0.5 g) which was sufficient to allow a 3-h recording period on the first night and up to a 4-h recording period on the second night. Tags were programmed to record for 10 s once every 3 min from 23:00 to 02:00 on the first night and for 10 s once every minute from 19:00 to 23:00 on the second night. We recovered tags from bats tagged between September 28th and October 7th. Sunset was at 19:02 on September 29th and 18:47 on October 8th. Unfortunately, the timing mechanism on the tags malfunctioned some of the time, causing only some of the recordings to have synchronous audio and accelerometer data (See Results).We attached Holohil LB-2X VHF transmitter (0.27 g) to the audio tags so we could locate the device once it detached from the bats. We coated the entire tag package (except the microphone opening) with liquid silicone followed by a latex sleeve covering to provide protection from the environment. The total tag package had a mass of 2.9 g which represented 10.6–12.5% of the mass of the bat. Several studies conducted in flight tents and in the field have shown no adverse consequences of payloads up to 15% for short duration deployments16,34. The diversity of natural behaviors that we observed, including prey pursuit, conspecific interaction, and extended flight over multiple nights indicates that hoary bats are capable fliers with this payload, however we cannot rule out the possibility that tags altered the behaviors that were observed.We attached tags to the posterior dorsum of bats using latex surgical adhesive (Torbot Liquid Bonding Cement, Torbot Group Inc. Cranston, Rhode Island). We used the minimum quantity of adhesive that we estimated would be necessary for tags to remain affixed to bats for 2 nights. We recovered tags by using ground- and aircraft-based VHF telemetry to determine the general location of the shed tag, followed by homing in on the VHF signal using ground-based telemetry. Final recovery of tags was achieved using visual searches of the ground.Microphone calibrationWe calibrated on-board microphones to determine the minimum sound pressure level (SPL) at which we could reliably detect micro calls. We did this by broadcasting a series of micro calls from an Avisoft (Glienicke/Nordbahn, Germany) Scanspeak ultrasonic speaker to the on-board tags. The series of micro calls consisted of a single high-quality micro call that was broadcast 30 times with each successive call being 3 dB lower in SPL. The absolute intensity of the broadcast was calibrated by broadcasting the same signal to a G.R.A.S (Holte, Denmark) 40DP 1/8″ microphone, which itself was calibrated with a G.R.A.S 42AB sound calibrator. For both the calibration of the sound playback and the broadcasts to the on-board microphone, the microphones were placed 10 cm from the speaker. We repeated this procedure three times for each of three microphones that had been recovered from the bats and determined the SPL of the lowest amplitude micro call that could be detected on all nine broadcasts. This SPL was used as the minimum detectable level at which our microphones could detect micro calls.Data processingDetermining whether bats are flyingWe used custom MATLAB (Natick, MA) scripts to analyze ultrasound and accelerometer recordings. We first determined whether bats were in flight for each recording. Unfortunately, we were only able to record simultaneous accelerometer and acoustic data for 364 out of 2241 recordings. For these recordings, we independently classified each file as flight or no flight using only the accelerometer data and only the audio data. Accelerometer recordings showed clear and prominent wingbeat oscillations in the dorsoventral, or Z-axis (Fig. S2A). One observer used a custom program (AccelVis) to visualize and manually classify all accelerometer files. We also quantified the magnitude of wingbeat oscillations by measuring the root-mean-square magnitude of signals after applying a high-pass filter of 4 Hz (Bats used wingbeat frequencies of approximately 8 Hz).A different observer classified all audio recordings as flight or no flight based on the presence or absence of low-frequency wind noise generated by the relative motion of the bats flying through the air (Fig. S2). The Individuals conducting the audio and acceleration analyses were blind to one another’s data. As with the accelerometer data, we analyzed all files both qualitatively and quantitatively. For the qualitative analysis, a user visualized files using a custom program (AudioBrowser; available with all data files as supplementary data) and noted presence or absence of low-frequency wind noise. We also quantified this wind noise by measuring the RMS magnitude of signals after applying a 1-Hz low pass filter. This resulted in a distinct bimodal distribution of low frequency magnitudes that corresponded to no wind and wind conditions with the two peaks being separated by approximately 30 dB. A small number of files ( 5 s). This 5 s threshold is twice the longest pulse interval recorded for echolocation calls (Supplementary Information), and therefore represents a conservative threshold for identifying silent periods.High-intensity calls could be identified by their consistently high signal levels. For recordings where no calls were initially detected, the observer made a second examination of the recording using a custom 55–90 kHz bandpass filter setting that highlights micro calls (Fig. 1D). A second observer also examined all files where either no calls or micro calls were detected by the first observer to confirm classification. Recordings were processed both by visualization of spectrograms and by listening to slowed-down recordings through headphones.Hoary bat feeding buzzes have a characteristic pattern involving a rapid increase in calling rate, and progressively decreasing call intensity (Fig. 1B)35,36. In contrast, social interactions involve prolonged (often several seconds) high-intensity echolocation calls produced at a high rate (e.g., 50–100 Hz) with a second bat also producing echolocation calls at a relatively high calling rate14. Echolocation calls of “other” bats (which could be present in any of the recordings) could be distinguished from the calls of the bat with the tag because they were typically recorded at a much lower intensity levels that increased and decreased, presumably as the other bat approached and then withdrew from the focal bat and were temporally out of phase with calling rate of the tagged bat. Calls classified as “other bat” also had lower calling rates compared to social interactions.Statistical analysisAcoustic recordings were organized by individual bat (Table 1) and by time of night (Fig. 2). To determine if bats exhibited consistent differences in the use of high-intensity echolocation, we measured the proportion of recordings including high-intensity echolocation for each bat night. Initial analysis of the data indicated that bats produced high-intensity echolocation during either most or all of the recordings (96–100%, including feeding buzzes) or at a considerably lower rate ( 96%) or low ( More

Chlorophyll plays an important part in the assimilation, transfer and conversion of light energy during photosynthesis. Its content is therefore closely related to the carbon fixation efficiency of photosynthesis and, because photosynthesis provides the energy source for metabolic responses, plays an important role in the drought resistance of plants. Chlorophyll fluorescence is often used to analyze photosynthesis efficiency under stress1. Fm is the fluorescence output when the reaction center of PSII is completely closed, and therefore reflects the maximum electron transfer through PSII40. Fv/Fm represents the energy conversion efficiency of PSII reactions, and can be used to measure the degree of external stress41.The chlorophyll content and Fm of A. squarrosum first increased and then decreased under moderate and severe drought, indicating that A. squarrosum adjusted its energy capture during the early stage of drought, and because electron transfer was relatively stable, normal photosynthesis was maintained. As stress intensified during prolonged drought, chlorophyll degradation accelerated and electron transfer through PSII slowed, which was similar to the effect of drought stress on chlorophyll of A. halodendron1. On 1 August, when the drought treatments began, the leaves of A. squarrosum in the control became noticeably yellow and slightly wilted, and the chlorophyll content and Fm were lower than those in the drought treatments. After re-watering, the chlorophyll content and Fm of A. squarrosum decreased, but they increased with increasing drought intensity. There is a limit to plant demand for water, and both too much and too little water are not conducive to plant growth. As a pioneer species during vegetation succession in sandy land, A. squarrosum is a xerophyte31. The soil moisture content in the control was higher than its requirements, and its photosynthesis was obviously adversely affected by controlling the water content at a higher level than the plants required. There was a significant positive correlation between Fv/Fm and RWC of the two plants, which indicated that water deficit was the main reason for the decrease of Fv/Fm. The chlorophyll fluorescence of A. squarrosum could maintain higher photosynthetic performance under drought stress because of its stronger water holding capacity than that of S. viridis. For A. squarrosum, Fv/Fm decreased with increasing drought duration and intensity. This is because drought reduced the electron transfer capacity of PSII and photochemical activity, leading to excessive accumulation of excitation energy, and adversely affecting photosynthesis. Fv/Fm increased after re-watering on 8 August, when the reduction of stress slowed the inhibition of photosynthesis by drought, by decreasing the inhibition of photosynthesis.For S. viridis, the chlorophyll content, Fm and Fv/Fm decreased with increasing drought duration and intensity, indicating that drought stress hindered the biosynthesis of chlorophyll, and that chlorophyll decomposition increased, leading to decreased chlorophyll content. At the same time, drought resulted in the decrease of PSII photochemical transformation efficiency and photosynthetic activity, and the damage of PSII receptor, which contributed to the damage of photosynthesis and the decrease of electron transfer ability. Fm and Fv/Fm of S. viridis increased after re-watering on 8 August, showing that rehydration relieved the drought stress. In addition, Fv/Fm increased and Fm decreased after re-watering on 14 August, suggesting that the damage to PSII was mitigated by rehydration, but the electron transfer in the PSII reaction center continued to be slower than normal. The chlorophyll content of S. viridis did not return to normal after re-watering, indicating that the leaves of S. viridis were damaged by both prolonged and severe drought stress and that chlorophyll synthesis was significantly affected1.The cell membrane is both a dynamic barrier between the cell interior and its surroundings, and a channel for the exchange of substance and information with its environment42. In particular, it controls water transport between the cell and its environment, leading to changes in RWC. RWC can be used to indicate the degree of dehydration of cells and assess the level of drought suffered by plants43. Under drought stress, the loss of water in plants is directly related to the stability of the cell membrane, and a stable cell membrane is the most basic requirement for maintaining sufficient water to support the cell’s physiological functions. ROS are produced in large quantities under stress, and this can trigger or exacerbate peroxidation of membrane lipids to produce malondialdehyde. Malondialdehyde can damage the membrane and functional molecules such as proteins and nucleic acids in cells, leading to damage or destruction of the membrane’s structure and functions. This, in turn, can increase the permeability of the membrane, leading to growth inhibition or even death. Therefore, changes in membrane permeability and the malondialdehyde content can reflect the degree of membrane lipid peroxidation and cell damage under stress1,3,32. It is consistent with our correlation analysis that RWC of the two species is negatively correlated with malondialdehyde and membrane permeability, and the correlation between RWC and membrane permeability in S. viridis is significant.In A. squarrosum, membrane permeability in the control on 1 August was significantly less than those under moderate and severe drought, but the malondialdehyde content did not differ among the treatments. The change of membrane permeability may have resulted from degreasing of membrane lipids and destruction of the membrane structure after phospholipid dissociation44. From 1 to 13 August, malondialdehyde content of A. squarrosum in the control first decreased and then increased, while membrane permeability increased continuously, indicating that membrane lipid peroxidation was significantly alleviated in wet soil after short-term drought. In contrast, the severe water deficit during the late stage of drought increased peroxidation of membrane lipids and malondialdehyde accumulation, suggesting that the cell membranes in the control had been damaged during the drought process. The malondialdehyde content and membrane permeability of A. squarrosum increased in the control after rehydration on 8 August, but decreased after rehydration on 14 August. This suggests that rehydration during the early stages of drought can exacerbate the peroxidation of membrane lipids and damage the cell membrane, but that rehydration during the late stages of drought mitigated the stress and eased the damage. Many studies showed that membrane permeability and the malondialdehyde content increased synchronously under stress1, but this contradicts our results for A. squarrosum in the control. This may be because the high soil moisture content in the control was not conducive to normal growth of this xerophyte. That is, long-term natural selection in the species’ arid sandy environment would lead to continuous adaptation to its environment, allowing A. squarrosum to become widely distributed in the mobile dunes of the Horqin sandy land45. With increasing drought duration, the malondialdehyde content and membrane permeability of A. squarrosum increased under both moderate and severe drought, indicating that the accumulation of malondialdehyde after drought stress damaged cell membrane and increased its permeability. The RWC values of A. squarrosum in the control were similar, but the membrane permeability fluctuated greatly. This can be due to more than adequate amount of irrigation.Setaria viridis is a late-successional species, and showed different responses to drought. With increasing drought duration and intensity, RWC of S. viridis decreased, while MDA and membrane permeability increased simultaneously. The results indicated that the early occurrence of water stress and membrane peroxidation in S. viridis under stress was one of the main physiological reasons for its inferior drought tolerance to A. squarrosum. Moreover, the damage degree of plants under drought stress should take into account not only the change of membrane permeability, but also the degree of membrane peroxidation and the ability of plant cell membrane to tolerate membrane lipid peroxidation. The chlorophyll content, Fm and Fv/Fm of S. viridis decreased with increasing drought duration and severity, and Fv/Fm of S. viridis was significantly negatively correlated with membrane permeability, which increased with increasing drought stress. This indicated that membrane lipid peroxidation and the accumulation of ROS under drought stress damaged the membrane and inhibited photosynthesis. Re-hydration of S. viridis increased RWC on both dates and in all drought treatments. This was accompanied by decreased malondialdehyde content, particularly after the 14 August re-watering, and by decreased membrane permeability. Rehydration reduced membrane lipid peroxidation, but it did not return to the control level, showing that drought caused a certain degree of damage that may be permanent or that may take some time to be repaired3.Stress can disrupt the balance of ROS metabolism in aerobic plants. When the concentrations of ROS are too high, peroxidation of membrane lipids and the equilibrium for exchanges of cell materials is also disrupted, resulting in a series of physiological and metabolic disorders. To counteract these disorders, plants have evolved protective enzymes during long-term evolution. The enzymes can eliminate O2-, H2O2, OH- and O- and reduce the damage they cause to the plant46. The changes in antioxidant enzyme activities of both species differed under drought stress. SOD played an active role during initial protection against membrane lipid peroxidation and its activity in A. squarrosum increased gradually during the drought. Under natural drought condition, SOD activities of the two species increased gradually, indicating that SOD activity was easily induced by drought stress. At the end of natural drought, the three enzymes of A. squarrosum maintained high level, and the combination of enzymes could resist drought stress, while only POD and SOD in S. viridis were enhanced to alleviate membrane lipid peroxidation. This transformation of the coordination of enzyme activity may be an important physiological mechanism of drought tolerance of A. squarrosum was stronger than that of S. viridis under severe drought. On 7 August, the peroxidase and catalase activities decreased in the control. Because ROS are a metabolism by-product of photorespiration, photosynthesis was inhibited by short-term drought, and the decreased accumulation of ROS caused by protective antioxidant enzymes reduced membrane lipid peroxidation by decreasing levels of malondialdehyde47. On 7 and 13 August, the activities of protective enzymes in A. squarrosum under moderate and severe drought were greater than that in the control. Drought stress led to the accumulation of ROS, and increased membrane lipid peroxidation, as reflected by the malondialdehyde content. At the same time, the accumulated ROS also stimulated the antioxidant enzyme protection system to continuously increase the activities of enzymes, so as to maintain balance of ROS48.Setaria viridis showed different responses. From 1 to 13 August, its peroxidase activity first decreased and then increased, but catalase activity showed the opposite pattern, and SOD activity increased gradually, indicating the existences of coordination among these enzymes under drought stress49. When catalase activity weakened, SOD and peroxidase activities compensated for this weakness to scavenge more ROS and mitigate cell membrane damage. The catalase activity in S. viridis remained less than 50 U g-1 DW min-1 throughout the study. After rehydration, catalase activity in the control was significantly greater than those under moderate and severe drought. There was a close relationship between Fv/Fm and catalase activity in S. viridis. It is possible that the enzyme must be contributing through ROS scavenging. Some of the antioxidant enzymes of both species did not recover after rehydration, which may be related to the possibility that in xerophytes, rehydration did not immediately improve physiological metabolism. It is possible that their antioxidant enzyme systems were so damaged that they would take longer than our study period to return to normal levels, and our samples were collected1 day after rehydration. More

Alexander DH, Novembre J, Lange K (2009) Fast model-based estimation of ancestry in unrelated individuals. Genome Res 19:1655–1664CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Alexander DH, Shringarpure SS, Novembre J, Lange K (2015) Admixture 1.3 software manual. UCLA Hum Genet Softw Distrib, Los Angeles
Google Scholar 
Angert AL, Bontrager MG, Ågren J (2020) What do we really know about adaptation at range edges? Annu Rev Ecol Evol Syst 51:341–361Article 

Google Scholar 
Araújo MB, Pearson RG, Thuiller W, Erhard M (2005) Validation of species–climate impact models under climate change. Glob Change Biol 11:1504–1513Article 

Google Scholar 
Astle W, Balding DJ (2009) Population structure and cryptic relatedness in genetic association studies. Stat Sci 24:451–471Article 

Google Scholar 
Attard CRM, Beheregaray LB, Möller LM (2018) Genotyping-by-sequencing for estimating relatedness in nonmodel organisms: Avoiding the trap of precise bias. Mol Ecol Resour 18:381–390CAS 
PubMed 
Article 

Google Scholar 
Baird NA, Etter PD, Atwood TS, Currey MC, Shiver AL, Lewis ZA et al. (2008) Rapid SNP discovery and genetic mapping using sequenced RAD markers. PLoS One 3:1–7Article 
CAS 

Google Scholar 
Barbosa S, Mestre F, White TA, Paupério J, Alves PC, Searle JB (2018) Integrative approaches to guide conservation decisions: Using genomics to define conservation units and functional corridors. Mol Ecol 27:3452–3465PubMed 
Article 

Google Scholar 
Beever EA, Brussard PF, Berger J (2003) Patterns of apparent extirpation among isolated populations of pikas (Ochotona princeps) in the Great Basin. J Mammal 84:37–54Article 

Google Scholar 
Beever EA, Ray C, Mote PW, Wilkening JL (2010) Testing alternative models of climate-mediated extirpations. Ecol Appl 20:164–178PubMed 
Article 

Google Scholar 
Beever EA, Ray C, Wilkening JL, Brussard PF, Mote PW (2011) Contemporary climate change alters the pace and drivers of extinction. Glob Change Biol 17:2054–2070Article 

Google Scholar 
Beever EA, Perrine JD, Rickman T, Flores M, Clark JP, Waters C et al. (2016) Pika (Ochotona princeps) losses from two isolated regions reflect temperature and water balance, but reflect habitat area in a mainland region. J Mammal 97:1495–1511Article 

Google Scholar 
Bellard C, Bertelsmeier C, Leadley P, Thuiller W, Courchamp F (2012) Impacts of climate change on the future of biodiversity. Ecol Lett 15:365–377PubMed 
PubMed Central 
Article 

Google Scholar 
Blois JL, Williams JW, Fitzpatrick MC, Jackson ST, Ferrier S (2013) Space can substitute for time in predicting climate-change effects on biodiversity. Proc Natl Acad Sci USA 110:9374–9379CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Browning SR (2008) Missing data imputation and haplotype phase inference for genome-wide association studies. Hum Genet 124:439–450CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Calkins MT, Beever EA, Boykin KG, Frey JK, Andersen MC (2012) Not-so-splendid isolation: modeling climate-mediated range collapse of a montane mammal Ochotona princeps across numerous ecoregions. Ecography 35:780–791Article 

Google Scholar 
Carlson SM, Cunningham CJ, Westley PAH (2014) Evolutionary rescue in a changing world. Trends Ecol Evol 29:521–530PubMed 
Article 

Google Scholar 
Castillo JA, Epps CW, Davis AR, Cushman SA (2014) Landscape effects on gene flow for a climate-sensitive montane species, the American pika. Mol Ecol 23:843–856PubMed 
Article 

Google Scholar 
Castillo JA, Epps CW, Jeffress MR, Ray C, Rodhouse TJ, Schwalm D (2016) Replicated landscape genetic and network analyses reveal wide variation in functional connectivity for American pikas. Ecol Appl 26:1660–1676PubMed 
Article 
PubMed Central 

Google Scholar 
Ceballos G, Ehrlich PR, Raven PH (2020) Vertebrates on the brink as indicators of biological annihilation and the sixth mass extinction. Proc Natl Acad Sci USA 117:13596–13602CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ceballos G, Ehrlich PR, Barnosky AD, García A, Pringle RM, Palmer TM (2015) Accelerated modern human–induced species losses: entering the sixth mass extinction. Sci Adv 1:e1400253PubMed 
PubMed Central 
Article 

Google Scholar 
Chapman JA, Flux JE (1990) Rabbits, hares and pikas: status survey and conservation action plan. IUCN.Chypre M, Zaidi N, Smans K (2012) ATP-citrate lyase: a mini-review. Biochem Biophys Res Commun 422:1–4CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Cornuet JM, Luikart G (1996) Description and power analysis of two tests for detecting recent population bottlenecks from allele frequency data. Genetics 144:2001–2014CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA et al. (2011) The variant call format and VCFtools. Bioinformatics 27:2156–2158CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Diaz HF, Grosjean M, Graumlich L (2003) Climate variability and change in high elevation regions: past, present and future. Clim Change 59:1–4Article 

Google Scholar 
Eckert CG, Samis EK, Lougheed SC (2008) Genetic variation across species’ geographical ranges: the central-marginal hypothesis and beyond. Mol Ecol 17:1170–1188CAS 
PubMed 
Article 

Google Scholar 
Erb LP, Ray C, Guralnick R (2011) On the generality of a climate-mediated shift in the distribution of the American pika (Ochotona princeps). Ecology 92:1730–1735PubMed 
Article 

Google Scholar 
Excoffier L, Foll M, Petit RJ (2009) Genetic consequences of range expansions. Annu Rev Ecol Evol Syst 40:481–501Article 

Google Scholar 
Flanagan SP, Forester BR, Latch EK, Aitken SN, Hoban S (2018) Guidelines for planning genomic assessment and monitoring of locally adaptive variation to inform species conservation. Evol Appl 11:1035–1052PubMed 
Article 
PubMed Central 

Google Scholar 
Foll M, Gaggiotti O (2008) A genome-scan method to identify selected loci appropriate for both dominant and codominant markers: a Bayesian perspective. Genetics 180:977–993PubMed 
PubMed Central 
Article 

Google Scholar 
Franks SJ, Hoffmann AA (2012) Genetics of climate change adaptation. Annu Rev Genet 46:185–208CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Frichot E, François O (2015) LEA: an R package for landscape and ecological association studies. Methods Ecol Evol 6:925–929Article 

Google Scholar 
Frichot E, Schoville SD, Bouchard G, François O (2013) Testing for associations between loci and environmental gradients using latent factor mixed models. Mol Biol Evol 30:1687–1699CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Funk WC, McKay JK, Hohenlohe PA, Allendorf FW (2012) Harnessing genomics for delineating conservation units. Trends Ecol Evol 27:489–496PubMed 
PubMed Central 
Article 

Google Scholar 
Galbreath KE, Hafner DJ, Zamudio KR (2009) When cold is better: climate-driven elevation shifts yield complex patterns of diversification and demography in an Alpine specialist (American Pika, Ochotona Princeps). Evolution 63:2848–2863CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Gautier M (2015) Genome-wide scan for adaptive divergence and association with population-specific covariates. Genetics 201:1555–1579CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Götz S, García-Gómez JM, Terol J, Williams TD, Nagaraj SH, Nueda MJ et al. (2008) High-throughput functional annotation and data mining with the Blast2GO suite. Nucleic Acids Res 36:3420–3435PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Gradogna A, Gavazzo P, Boccaccio A, Pusch M (2017) Subunit‐dependent oxidative stress sensitivity of LRRC8 volume‐regulated anion channels. J Physiol 595:6719–6733CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hafner DJ, Sullivan RM (1995) Historical and ecological biogeography of Nearctic pikas (Lagomorpha: Ochotonidae). J Mammal 76:302–321Article 

Google Scholar 
Hafner DJ, Smith AT (2010) Revision of the subspecies of the American pika, Ochotona princeps (Lagomorpha: Ochotonidae). J Mammal 91:401–417Article 

Google Scholar 
Hanson JO, Marques A, Veríssimo A, Camacho-Sanchez M, Velo-Antón G, Martínez-Solano Í et al. (2020) Conservation planning for adaptive and neutral evolutionary processes. J Appl Ecol 57:2159–2169Article 

Google Scholar 
Harrisson KA, Pavlova A, Telonis-Scott M, Sunnucks P (2014) Using genomics to characterize evolutionary potential for conservation of wild populations. Evol Appl 7:1008–1025PubMed 
PubMed Central 
Article 

Google Scholar 
Heled J, Drummond AJ (2008) Bayesian inference of population size history from multiple loci. BMC Evol Biol 8:289PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Henry P, Russello MA (2013) Adaptive divergence along environmental gradients in a climate-change-sensitive mammal. Ecol Evol 3:3906–3917CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Henry P, Sim Z, Russello MA (2012) Genetic evidence for restricted dispersal along continuous altitudinal gradients in a climate change-sensitive mammal: the American pika. PLoS One 7:e39077CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hewitt G (2000) The genetic legacy of the Quaternary ice ages. Nature 405:907–913CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Hewitt GM (1996) Some genetic consequences of ice ages, and their role in divergence and speciation. Biol J Linn Soc 58:247–276Article 

Google Scholar 
Hinzpeter A, Lipecka J, Brouillard F, Baudoin-Legros M, Dadlez M, Edelman A et al. (2006) Association between Hsp90 and the ClC-2 chloride channel upregulates channel function. Am J Physiol Cell Physiol 290:C45–56CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Hoban S (2018) Integrative conservation genetics: Prioritizing populations using climate predictions, adaptive potential and habitat connectivity. Mol Ecol Resour 18:14–17PubMed 
Article 
PubMed Central 

Google Scholar 
Hoffmann AA, Sgrò CM (2011) Climate change and evolutionary adaptation. Nature 470:479–485CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Holbrook JD, DeYoung RW, Janecka JE, Tewes ME, Honeycutt RL, Young JH (2012) Genetic diversity, population structure, and movements of mountain lions (Puma concolor) in Texas. J Mammal 93:989–1000Article 

Google Scholar 
IPCC (2014) AR5 Synthesis Report: Climate Change 2014. IPCC.Jentsch TJ, Lutter D, Planells-Cases R, Ullrich F, Voss FK (2016) VRAC: molecular identification as LRRC8 heteromers with differential functions. Pflüg Arch Eur J Physiol 468:385–393CAS 
Article 

Google Scholar 
Johnston AN, Bruggeman JE, Beers AT, Beever EA, Christophersen RG, Ransom JI (2019) Ecological consequences of anomalies in atmospheric moisture and snowpack. Ecology 100:e02638PubMed 
Article 
PubMed Central 

Google Scholar 
Johnston KM, Freund KA, Schmitz OJ (2012) Projected range shifting by montane mammals under climate change: implications for Cascadia’s National Parks. Ecosphere 3:art97Article 

Google Scholar 
Kilham L (1958) Territorial behavior in pikas. J Mammal 39:307–307Article 

Google Scholar 
Kimura M (1971) Theoretical foundation of population genetics at the molecular level. Theor Popul Biol 2:174–208CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Klingler KB, Jahner JP, Parchman TL, Ray C, Peacock MM (2021) Genomic variation in the American pika: signatures of geographic isolation and implications for conservation. BMC Ecol Evol 21:2CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lambers JHR (2015) Extinction risks from climate change. Science 348:501–502CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Latch EK, Dharmarajan G, Glaubitz JC, Rhodes OE (2006) Relative performance of Bayesian clustering software for inferring population substructure and individual assignment at low levels of population differentiation. Conserv Genet 7:295–302Article 

Google Scholar 
Latch EK, Scognamillo DG, Fike JA, Chamberlain MJ, Rhodes Jr OE (2008) Deciphering ecological barriers to North American River Otter (Lontra canadensis) gene flow in the Louisiana landscape. J Hered 99:265–274CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Lee KM, Coop G (2017) Distinguishing among modes of convergent adaptation using population genomic data. Genetics 207:1591–1619PubMed 
PubMed Central 
Article 

Google Scholar 
Lee KM, Coop G (2019) Population genomics perspectives on convergent adaptation. Philos Trans R Soc B Biol Sci 374:20180236Article 

Google Scholar 
Lemay MA, Russello MA (2015) Genetic evidence for ecological divergence in kokanee salmon. Mol Ecol 24:798–811CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Li H (2013) Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. ArXiv13033997 Q-Bio.MacArthur RA, Wang LCH (1974) Behavioral thermoregulation in the pika Ochotona princeps: a field study using radiotelemetry. Can J Zool 52:353–358CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
McCain CM (2019) Assessing the risks to United States and Canadian mammals caused by climate change using a trait-mediated model. J Mammal 100:1808–1817
Google Scholar 
Meirmans PG, Van Tienderen PH (2004) GENOTYPE and GENODIVE: two programs for the analysis of genetic diversity of asexual organisms. Mol Ecol Notes 4:792–794Article 

Google Scholar 
Morin PA, Luikart G, Wayne RK, the SNP Workshop Group (2004) SNPs in ecology, evolution and conservation. Trends Ecol Evol 19:208–216Article 

Google Scholar 
Moritz C, Agudo R (2013) The future of species under climate change: resilience or decline? Science 341:504–508CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Moritz C, Patton JL, Conroy CJ, Parra JL, White GC, Beissinger SR (2008) Impact of a century of climate change on small-mammal communities in Yosemite National Park, USA. Science 322:261–264CAS 
PubMed 
Article 

Google Scholar 
Morrison SF, Hik DS (2007) Demographic analysis of a declining pika Ochotona collaris population: linking survival to broad-scale climate patterns via spring snowmelt patterns. J Anim Ecol 76:899–907PubMed 
Article 
PubMed Central 

Google Scholar 
Moyer‐Horner L, Mathewson PD, Jones GM, Kearney MR, Porter WP (2015) Modeling behavioral thermoregulation in a climate change sentinel. Ecol Evol 5:5810–5822PubMed 
PubMed Central 
Article 

Google Scholar 
Mussmann SM, Douglas MR, Chafin TK, Douglas ME (2019) BA3-SNPs: contemporary migration reconfigured in BayesAss for next-generation sequence data. Methods Ecol Evol 10:1808–1813Article 

Google Scholar 
Parmesan C (2006) Ecological and evolutionary responses to recent climate change. Annu Rev Ecol Evol Syst 37:637–669Article 

Google Scholar 
Paz-Vinas I, Loot G, Hermoso V, Veyssière C, Poulet N, Grenouillet G et al. (2018) Systematic conservation planning for intraspecific genetic diversity. Proc R Soc B Biol Sci 285:20172746Article 

Google Scholar 
Peacock MM (1997) Determining natal dispersal patterns in a population of North American pikas (Ochotona princeps) using direct mark-resight and indirect genetic methods. Behav Ecol 8:340–350Article 

Google Scholar 
Peacock MM, Smith AT (1997) Nonrandom mating in pikas Ochotona princeps: evidence for inbreeding between individuals of intermediate relatedness. Mol Ecol 6:801–811CAS 
PubMed 
Article 

Google Scholar 
Pew J, Muir PH, Wang J, Frasier TR (2015) related: an R package for analysing pairwise relatedness from codominant molecular markers. Mol Ecol Resour 15:557–561PubMed 
Article 

Google Scholar 
Pickett STA (1989) Space-for-time substitution as an alternative to long-term studies. In: Likens GE (ed) Long-term studies in ecology. Springer New York, New York, NY, p 110–135Chapter 

Google Scholar 
Piry S, Alapetite A, Cornuet J-M, Paetkau D, Baudouin L, Estoup A (2004) GENECLASS2: a software for genetic assignment and first-generation migrant detection. J Hered 95:536–539CAS 
PubMed 
Article 

Google Scholar 
R Core Team (2019) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, http://www.R-project.org/Rankin AM, Galbreath KE, Teeter KC (2017) Signatures of adaptive molecular evolution in American pikas (Ochotona princeps). J Mammal 98:1156–1167Article 

Google Scholar 
Rannala B, Mountain JL (1997) Detecting immigration by using multilocus genotypes. Proc Natl Acad Sci USA 94:9197–9201CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Razgour O, Taggart JB, Manel S, Juste J, Ibáñez C, Rebelo H et al. (2018) An integrated framework to identify wildlife populations under threat from climate change. Mol Ecol Resour 18:18–31PubMed 
Article 

Google Scholar 
Rellstab C, Gugerli F, Eckert AJ, Hancock AM, Holderegger R (2015) A practical guide to environmental association analysis in landscape genomics. Mol Ecol 24:4348–4370PubMed 
Article 

Google Scholar 
Ritland K (1996) Estimators for pairwise relatedness and individual inbreeding coefficients. Genet Res 67:175–185Article 

Google Scholar 
Robson KM, Lamb CT, Russello MA (2016) Low genetic diversity, restricted dispersal, and elevation-specific patterns of population decline in American pikas in an atypical environment. J Mammal 97:464–472Article 

Google Scholar 
Rochette NC, Rivera‐Colón AG, Catchen JM (2019) Stacks 2: analytical methods for paired-end sequencing improve RADseq-based population genomics. Mol Ecol 28:4737–4754CAS 
PubMed 
Article 

Google Scholar 
Rousset F (2008) GENEPOP’007: a complete re-implementation of the GENEPOP software for Windows and Linux. Mol Ecol Resour 8:103–106PubMed 
Article 

Google Scholar 
Rubidge EM, Patton JL, Lim M, Burton AC, Brashares JS, Moritz C (2012) Climate-induced range contraction drives genetic erosion in an alpine mammal. Nat Clim Change 2:285–288Article 

Google Scholar 
Russello MA, Waterhouse MD, Etter PD, Johnson EA (2015) From promise to practice: pairing non-invasive sampling with genomics in conservation. PeerJ 3:e1106PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Savolainen O, Lascoux M, Merilä J (2013) Ecological genomics of local adaptation. Nat Rev Genet 14:807–820CAS 
PubMed 
Article 

Google Scholar 
Sjodin BMF, Galbreath KE, Lanier HC, Russello MA (2021) Chromosome-level reference genome assembly for the American pika (Ochotona princeps). J Hered https://doi.org/10.1093/jhered/esab031Smith AT (1974b) The distribution and dispersal of pikas: influences of behavior and climate. Ecology 55:1368–1376Article 

Google Scholar 
Smith AT (1974a) The distribution and dispersal of pikas: consequences of insular population structure. Ecology 55:1112–1119Article 

Google Scholar 
Smith AT (2020) Conservation status of American pikas (Ochotona princeps). J Mammal 101:1466–1488Article 

Google Scholar 
Smith AT, Ivins BL (1983) Colonization in a pika population: dispersal vs philopatry. Behav Ecol Sociobiol 13:37–47Article 

Google Scholar 
Smith AT, Weston ML (1990) Ochotona princeps. Mamm Species 352:1–8.Article 

Google Scholar 
Smith AT, Millar CI (2018) American pika (Ochotona princeps) population survival in winters with low or no snowpack. West North Am Nat 78:126–132Article 

Google Scholar 
La Sorte FA, Jetz W (2010) Projected range contractions of montane biodiversity under global warming. Proc R Soc B Biol Sci 277:3401–3410Article 

Google Scholar 
Stern DL (2013) The genetic causes of convergent evolution. Nat Rev Genet 14:751–764CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Stewart JAE, Wright DH, Heckman KA (2017) Apparent climate-mediated loss and fragmentation of core habitat of the American pika in the Northern Sierra Nevada, California, USA. PLoS One 12:e0181834PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Stewart JAE, Perrine JD, Nichols LB, Thorne JH, Millar CI, Goehring KE et al. (2015) Revisiting the past to foretell the future: summer temperature and habitat area predict pika extirpations in California. J Biogeogr 42:880–890Article 

Google Scholar 
Varner J, Dearing MD (2014) The importance of biologically relevant microclimates in habitat suitability assessments. PLoS One 9:e104648PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Waldvogel A-M, Feldmeyer B, Rolshausen G, Exposito‐Alonso M, Rellstab C, Kofler R et al. (2020) Evolutionary genomics can improve prediction of species’ responses to climate change. Evol Lett 4:4–18PubMed 
PubMed Central 
Article 

Google Scholar 
Wang T, Hamann A, Spittlehouse DL, Murdock TQ (2012) ClimateWNA—high-resolution spatial climate data for Western North America. J Appl Meteorol Climatol 51:16–29Article 

Google Scholar 
Waterhouse MD, Erb LP, Beever EA, Russello MA (2018) Adaptive population divergence and directional gene flow across steep elevational gradients in a climate-sensitive mammal. Mol Ecol 27:2512–2528PubMed 
Article 
PubMed Central 

Google Scholar 
Weir BS, Cockerham CC (1984) Estimating F-statistics for the analysis of population structure. Evolution 38:1358–1370CAS 
PubMed 

Google Scholar 
Wiens JJ (2016) Climate-related local extinctions are already widespread among plant and animal species. PLoS Biol 14:e2001104PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Wilkening JL, Ray C (2016) Characterizing predictors of survival in the American pika (Ochotona princeps). J Mammal 97:1366–1375Article 

Google Scholar 
Wilkening JL, Ray C, Beever EA, Brussard PF (2011) Modeling contemporary range retraction in Great Basin pikas (Ochotona princeps) using data on microclimate and microhabitat. Quat Int 235:77–88Article 

Google Scholar 
Wilson GA, Rannala B (2003) Bayesian inference of recent migration rates using multilocus genotypes. Genetics 163:1177–1191PubMed 
PubMed Central 
Article 

Google Scholar 
Wogan GOU, Wang IJ (2018) The value of space-for-time substitution for studying fine-scale microevolutionary processes. Ecography 41:1456–1468Article 

Google Scholar 
Yandow LH, Chalfoun AD, Doak DF (2015) Climate tolerances and habitat requirements jointly shape the elevational distribution of the American pika (Ochotona princeps), with implications for climate change effects. PLoS One 10:e0131082PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Zgurski JM, Hik DS (2012) Polygynandry and even-sexed dispersal in a population of collared pikas, Ochotona collaris. Anim Behav 83:1075–1082Article 

Google Scholar 
Zhang D, Pan J, Cao J, Cao Y, Zhou H (2020) Screening of drought-resistance related genes and analysis of promising regulatory pathway in camel renal medulla. Genomics 112:2633–2639CAS 
PubMed 
Article 

Google Scholar 
Zhang Z, Schwartz S, Wagner L, Miller W (2000) A greedy algorithm for aligning DNA sequences. J Comput Biol 7:203–214CAS 
PubMed 
Article 

Google Scholar  More

1.Darwin, C. On the origin of species. (D. Appleton and Co., 1871). https://doi.org/10.5962/bhl.title.28875.2.Thompson, J. N. Mutualistic webs of species. Science (80–) 312, 372–373 (2006).CAS 
Article 

Google Scholar 
3.Bronstein, J. L. The exploitation of mutualisms. Ecol. Lett. 4, 277–287 (2001).Article 

Google Scholar 
4.Bshary, R. & Grutter, A. S. Experimental evidence that partner choice is a driving force in the payoff distribution among cooperators or mutualists: The cleaner fish case. Ecol. Lett. 5, 130–136 (2002).Article 

Google Scholar 
5.Kiers, E. T., Rousseau, R. A., West, S. A. & Denison, R. F. Host sanctions and the legume–rhizobium mutualism. Nature 425, 78–81 (2003).ADS 
CAS 
Article 

Google Scholar 
6.Heil, M., Barajas-Barron, A., Orona-Tamayo, D., Wielsch, N. & Svatos, A. Partner manipulation stabilises a horizontally transmitted mutualism. Ecol. Lett. 17, 185–192 (2014).Article 

Google Scholar 
7.Hindsbo, O. Effects of Polymorphus (Acanthocephala) on colour and behaviour of Gammarus lacustris. Nature 238, 333 (1972).ADS 
Article 

Google Scholar 
8.Thomas, F., Renaud, F., de Meeus, T. & Poulin, R. Manipulation of host behaviour by parasites: Ecosystem engineering in the intertidal zone?. Proc. R. Soc. B Biol. Sci. 265, 1091–1096 (1998).Article 

Google Scholar 
9.Thomas, F. et al. Do hairworms (Nematomorpha) manipulate the water seeking behaviour of their terrestrial hosts?. J. Evol. Biol. 15, 356–361 (2002).Article 

Google Scholar 
10.Kadoya, E. Z., Ishii, H. S. & Williams, N. M. Host manipulation of bumble bee queens by Sphaerularia nematodes indirectly affects foraging of non-host workers. Ecology 96, 1361–1370 (2015).Article 

Google Scholar 
11.Hojo, M. K., Pierce, N. E. & Tsuji, K. Lycaenid caterpillar secretions manipulate attendant ant behavior. Curr. Biol. 25, 2260–2264 (2015).CAS 
Article 

Google Scholar 
12.Poulin, R., Brodeur, J. & Moore, J. Parasite manipulation of host behaviour: Should hosts always lose?. Oikos 70, 479 (1994).Article 

Google Scholar 
13.Heil, M. et al. Divergent investment strategies of Acacia myrmecophytes and the coexistence of mutualists and exploiters. Proc. Natl. Acad. Sci. USA. 106, 18091–18096 (2009).ADS 
CAS 
Article 

Google Scholar 
14.Watanabe, S., Murakami, T., Yoshimura, J. & Hasegawa, E. Color polymorphism in an aphid is maintained by attending ants. Sci. Adv. 2, (2016).15.Watanabe, S., Yoshimura, J. & Hasegawa, E. Ants improve the reproduction of inferior morphs to maintain a polymorphism in symbiont aphids. Sci. Rep. 8, (2018).16.Sakata, H. Density-dependent predation of the ant Lasius niger (Hymenoptera: Formicidae) on two attended aphids Lachnus tropicalis and Myzocallis kuricola (Homoptera: Aphididae). Res. Popul. Ecol. (Kyoto) 37, 159–164 (1995).Article 

Google Scholar 
17.Evans, P. D. Biogenic Amines in the insect nervous system. Adv. In Insect Phys. 15, 317–473 (1980).CAS 
Article 

Google Scholar 
18.Aonuma, H. & Watanabe, T. Octopaminergic system in the brain controls aggressive motivation in the ant Formica japonica. Acta Biol. Hung. 63, 63–68 (2012).Article 

Google Scholar 
19.Stevenson, P. A., Dyakonova, V., Rillich, J. & Schildberger, K. Octopamine and experience-dependent modulation of aggression in crickets. J. Neurosci. 25, 1431–1441 (2005).CAS 
Article 

Google Scholar 
20.Kostowski, W. & Tarchalska, B. The effects of some drugs affecting brain 5-HT on the aggressive behaviour and spontaneous electrical activity of the central nervous system of the ant Formica rufa. Brain Res. 38, 143–149 (1972).CAS 
Article 

Google Scholar 
21.Szczuka, A. et al. The effects of serotonin, dopamine, octopamine and tyramine on behavior of workers of the ant Formica polyctena during dyadic aggression tests. Acta Neurobiol. Exp. (Wars) 73, 495–520 (2013).
Google Scholar 
22.Way, M. J. Mutualism between ants and honeydew-producing homoptera. Annu. Rev. Entomol. 8, 307–344 (1963).Article 

Google Scholar 
23.Hafer-Hahmann, N. Behavior out of control: Experimental evolution of resistance to host manipulation. Ecol. Evol. 9, 7237–7245 (2019).Article 

Google Scholar 
24.Martinez, J., Fleury, F. & Varaldi, J. Heritable variation in an extended phenotype: The case of a parasitoid manipulated by a virus. J. Evol. Biol. 25, 54–65 (2012).Article 

Google Scholar 
25.Engelstädter, J. & Hurst, G. D. D. The ecology and evolution of microbes that manipulate host reproduction. Annu. Rev. Ecol. Evol. Syst. 40, 127–149 (2009).Article 

Google Scholar 
26.Rosenthal, G. G. & Servedio, M. R. Chase-away sexual selection: Resistance to ‘resistance’. Evolution (N.Y.) 53, 296 (1999).
Google Scholar 
27.Woodring, J., Wiedemann, R., Fischer, M. K., Hoffmann, K. H. & Völkl, W. Honeydew amino acids in relation to sugars and their role in the establishment of ant-attendance hierarchy in eight species of aphids feeding on tansy (Tanacetum vulgare). Physiol. Entomol. 29, 311–319 (2004).CAS 
Article 

Google Scholar 
28.Stadler, B. & Dixon, A. F. G. Ecology and evolution of aphid-ant interactions. Annu. Rev. Ecol. Evol. Syst. 36, 345–372 (2005).Article 

Google Scholar 
29.Tsuji, K. & Dobata, S. Social cancer and the biology of the clonal ant Pristomyrmex punctatus (Hymenoptera: Formicidae). Myrmecological News 15, 91–99 (2011).
Google Scholar 
30.Vellend, M. Conceptual synthesis in community ecology. Q. Rev. Biol. 85, 183–206 (2010).Article 

Google Scholar 
31.Agawa, H. & Kawata, M. The effect of color polymorphism on mortality in the aphid Macrosiphoniella yomogicola. Ecol. Res. 10, 301–306 (1995).Article 

Google Scholar 
32.Watanabe, S., Murakami, Y. & Hasegawa, E. Effects of attending ant species on the fate of colonies of an aphid, Macrosiphoniella yomogicola (Matsumura) (Homoptera: Aphididae), in an ant-aphid symbiosis. Entomol. News 128, 325 (2019).Article 

Google Scholar 
33.Wada-Katsumata, A., Yamaoka, R. & Aonuma, H. Social interactions influence dopamine and octopamine homeostasis in the brain of the ant Formica japonica. J. Exp. Biol. 214, 1707–1713 (2011).CAS 
Article 

Google Scholar 
34.Aonuma, H. & Watanabe, T. Changes in the content of brain biogenic amine associated with early colony establishment in the queen of the ant, formica japonica. PLoS One 7, (2012).35.Aonuma, H. Serotonergic control in initiating defensive responses to unexpected tactile stimuli in the trap-jaw ant Odontomachus kuroiwae. J. Exp. Biol. 223, jeb228874 (2020).Article 

Google Scholar 
36.R Core Team. R: A Language and Environment for Statistical Computing. (2020).37.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using {lme4}. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
38.Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S. (Springer, 2002).39.Hothorn, T. & Hornik, K. exactRankTests: Exact Distributions for Rank and Permutation Tests. (2019). More

1.Paerl, H. W. & Otten, T. G. Harmful cyanobacterial blooms: causes, consequences, and controls. Microb. Ecol. 65, 995–1010 (2013).CAS 
PubMed 
Article 

Google Scholar 
2.Visser, P. M. et al. How rising CO2 and global warming may stimulate harmful cyanobacterial blooms. Harmful Algae 54, 145–159 (2016).CAS 
PubMed 
Article 

Google Scholar 
3.Lürling, M., Mendes e Mello, M., van Oosterhout, F., de Senerpont Domis, L. & Marinho, M. M. Response of natural cyanobacteria and algae assemblages to a nutrient pulse and elevated temperature. Front. Microbiol. 9, 1851 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
4.Low-Décarie, E., Fussmann, G. F. & Bell, G. Aquatic primary production in a high-CO2 world. Trends Ecol. Evol. 29, 223–232 (2014).PubMed 
Article 

Google Scholar 
5.Stanley, S. M. Estimates of the magnitudes of major marine mass extinctions in earth history. PNAS 113, E6325–E6334 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
6.Sun, Y. D. et al. Lethally hot temperatures during the Early Triassic Greenhouse. Science 338, 366–370 (2012).ADS 
CAS 
PubMed 
Article 

Google Scholar 
7.Frank, T. D. et al. Pace, magnitude, and nature of terrestrial climate change through the end Permian extinction in southeastern Gondwana. Geology 49, https://doi.org/10.1130/G48795.1 (2021).8.Wu, Y. et al. Six-fold increase of atmospheric pCO2 during the Permian–Triassic mass extinction. Nat. Commun. 12, 2137 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
9.Burgess, S. D., Muirhead, J. D. & Bowring, S. A. Initial pulse of Siberian Traps sills as the trigger of the end-Permian mass extinction. Nat. Commun. 8, 164 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
10.Mays, C. et al. Refined Permian–Triassic floristic timeline reveals early collapse and delayed recovery of south polar terrestrial ecosystems. GSA Bull. 132, 1489–1513 (2020).CAS 
Article 

Google Scholar 
11.Chu, D. et al. Ecological disturbance in tropical peatlands prior to marine Permian-Triassic mass extinction. Geology 48, 288–292 (2020).ADS 
Article 

Google Scholar 
12.Retallack, G. J., Veevers, J. J. & Morante, R. Global coal gap between Permian–Triassic extinction and Middle Triassic recovery of peat-forming plants. GSA Bull. 108, 195–207 (1996).CAS 
Article 

Google Scholar 
13.Fielding, C. R. et al. Age and pattern of the southern high-latitude continental end-Permian extinction constrained by multiproxy analysis. Nat. Commun. 10, 385 (2019).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
14.Fielding, C. R. et al. Sedimentology of the continental end-Permian extinction event in the Sydney Basin, eastern Australia. Sedimentology 68, 30–62 (2021).CAS 
Article 

Google Scholar 
15.Metcalfe, I., Crowley, J. L., Nicoll, R. S. & Schmitz, M. High-precision U-Pb CA-TIMS calibration of Middle Permian to Lower Triassic sequences, mass extinction and extreme climate-change in eastern Australian Gondwana. Gondwana Res. 28, 61–81 (2015).ADS 
CAS 
Article 

Google Scholar 
16.Vajda, V. et al. End-Permian (252 Mya) deforestation, wildfires and flooding—an ancient biotic crisis with lessons for the present. Earth Planet. Sci. Lett. 529, 115875 (2020).CAS 
Article 

Google Scholar 
17.McLoughlin, S. et al. Dwelling in the dead zone—vertebrate burrows immediately succeeding the end-Permian extinction event in Australia. Palaios 35, 342–357 (2020).ADS 
Article 

Google Scholar 
18.Lamb, A. L., Wilson, G. P. & Leng, M. J. A review of coastal palaeoclimate and relative sea-level reconstructions using δ13C and C/N ratios in organic material. Earth-Sci. Rev. 75, 29–57 (2006).ADS 
CAS 
Article 

Google Scholar 
19.Mays, C., Vajda, V. & McLoughlin, S. Permian–Triassic non-marine algae of Gondwana—distributions, natural affinities and ecological implications. Earth-Sci. Rev. 212, 103382 (2021).CAS 
Article 

Google Scholar 
20.McLoughlin, S. et al. Age and paleoenvironmental significance of the Frazer Beach Member—a new lithostratigraphic unit overlying the end-Permian extinction horizon in the Sydney Basin, Australia. Front. Earth Sci. 8, 600976 (2021).Article 

Google Scholar 
21.Huber, J. K. A postglacial pollen and nonsiliceous algae record from Gegoka Lake, Lake County, Minnesota. J. Paleolimnol. 16, 23–35 (1996).ADS 
Article 

Google Scholar 
22.Woodward, C. A. & Shulmeister, J. A Holocene record of human induced and natural environmental change from Lake Forsyth (Te Wairewa), New Zealand. J. Paleolimnol. 34, 481–501 (2005).ADS 
Article 

Google Scholar 
23.Pacton, M., Gorin, G. & Fiet, N. Occurrence of photosynthetic microbial mats in a Lower Cretaceous black shale (central Italy): a shallow-water deposit. Facies 55, 401–419 (2009).Article 

Google Scholar 
24.Pacton, M., Gorin, G. E. & Vasconcelos, C. Amorphous organic matter—Experimental data on formation and the role of microbes. Rev. Palaeobot. Palynol. 166, 253–267 (2011).Article 

Google Scholar 
25.Tyson, R. V. Sedimentary Organic Matter: Organic Facies and Palynofacies (Chapman & Hall, 1995).26.Retallack, G. J. Earliest Triassic claystone breccias and soil-erosion crisis. J. Sediment. Res. 75, 679–695 (2005).ADS 
Article 

Google Scholar 
27.Augland, L. E. et al. The main pulse of the Siberian Traps expanded in size and composition. Sci. Rep. 9, 18723 (2019).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
28.Retallack, G. J. Post-apocalyptic greenhouse paleoclimate revealed by earliest Triassic paleosols in the Sydney Basin. Aust. GSA Bull. 111, 52–70 (1999).CAS 
Article 

Google Scholar 
29.Woodward, C., Shulmeister, J., Larsen, J., Jacobsen, G. E. & Zawadzki, A. The hydrological legacy of deforestation on global wetlands. Science 346, 844–847 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
30.Stevenson, R. J. & Smol, J. P. In Freshwater Algae of North America: Ecology and Classification (eds Wehr, J. D., Sheath, R. G. & Kociolek, P.) Ch. 21, 921–962 (Academic Press, 2015).31.Lindström, S., Bjerager, M., Alsen, P., Sanei, H. & Bojesen-Koefoed, J. The Smithian–Spathian boundary in North Greenland: implications for extreme global climate changes. Geol. Mag. 157, 1547–1567 (2020).ADS 
Article 
CAS 

Google Scholar 
32.de Leeuw, J. W., Versteegh, G. J. M. & van Bergen, P. F. in Plants and Climate Change, Plant Ecology (eds Rozema, J., Aerts, R. & Cornelissen, H.) Vol. 182, 209–233 (Springer, 2006).33.Baudelet, P.-H., Ricochon, G., Linder, M. & Muniglia, L. A new insight into cell walls of Chlorophyta. Algal Res 25, 333–371 (2017).Article 

Google Scholar 
34.Graham, L. E. & Gray, J. In Plants Invade the Land: Evolutionary and Environmental Perspectives (eds Gensel, P. G. & Edwards, D.) 140–158 (Columbia University Press, 2001).35.Demura, M., Ioki, M., Kawachi, M., Nakajima, N. & Watanabe, M. M. Desiccation tolerance of Botryococcus braunii (Trebouxiophyceae, Chlorophyta) and extreme temperature tolerance of dehydrated cells. J. Appl. Phycol. 26, 49–53 (2014).CAS 
PubMed 
Article 

Google Scholar 
36.Del Cortona, A. et al. Neoproterozoic origin and multiple transitions to macroscopic growth in green seaweeds. PNAS 117, 2551–2559 (2020).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
37.Wheeler, A., Van de Wetering, N., Esterle, J. S. & Götz, A. E. Palaeoenvironmental changes recorded in the palynology and palynofacies of a Late Permian Marker Mudstone (Galilee Basin, Australia). Palaeoworld 29, 439–452 (2020).Article 

Google Scholar 
38.Reynolds, C. S., Huszar, V., Kruk, C., Naselli-Flores, L. & Melo, S. Towards a functional classification of the freshwater phytoplankton. J. Plankton Res. 24, 417–428 (2002).Article 

Google Scholar 
39.Low-Décarie, E., Fussmann, G. F. & Bell, G. The effect of elevated CO2 on growth and competition in experimental phytoplankton communities. Glob. Change Biol. 17, 2525–2535 (2011).ADS 
Article 

Google Scholar 
40.von Alvensleben, N., Magnusson, M. & Heimann, K. Salinity tolerance of four freshwater microalgal species and the effects of salinity and nutrient limitation on biochemical profiles. J. Appl. Phycol. 28, 861–876 (2016).Article 
CAS 

Google Scholar 
41.Chu, D. et al. Microbial mats in the terrestrial Lower Triassic of North China and implications for the Permian–Triassic mass extinction. Palaeogeog. Palaeoclimatol. Palaeoecol. 474, 214–231 (2017).ADS 
Article 

Google Scholar 
42.Guo, W. et al. Secular variations of ichnofossils from the terrestrial Late Permian–Middle Triassic succession at the Shichuanhe section in Shaanxi Province, North China. Glob. Planet. Change 181, 102978 (2019).Article 

Google Scholar 
43.Lee, J. Y. et al. Future global climate: Scenario-based projections and near-term information. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (eds Masson-Delmotte, V., et al.) 195 pp. (Cambridge University Press, 2021).44.de Jersey, N. J. Palynology of the Permian-Triassic transition in the western Bowen Basin. Geol. Surv. Qld. Publ. 374, 1–39 (1979).
Google Scholar 
45.Lindström, S. & McLoughlin, S. Synchronous palynofloristic extinction and recovery after the end-Permian event in the Prince Charles Mountains, Antarctica: Implications for palynofloristic turnover across Gondwana. Rev. Palaeobot. Palynol. 145, 89–122 (2007).Article 

Google Scholar 
46.Grebe, H. Permian plant microfossils from the Newcastle Coal Measures/Narrabeen Group Boundary, Lake Munmorah, New South Wales. Rec. Geol. Surv. NSW 12, 125–136 (1970).
Google Scholar 
47.Mishra, S. et al. A new acritarch spike of Leiosphaeridia dessicata comb. nov. emend. from the Upper Permian and Lower Triassic sequence of India (Pranhita-Godavari Basin): its origin and palaeoecological significance. Palaeogeog. Palaeoclimatol. Palaeoecol. 567, 110274 (2021).ADS 
Article 

Google Scholar 
48.Grice, K. et al. Photic zone euxinia during the Permian-Triassic superanoxic event. Science 307, 706–709 (2005).ADS 
CAS 
PubMed 
Article 

Google Scholar 
49.Kershaw, S. et al. Microbialites and global environmental change across the Permian–Triassic boundary: a synthesis. Geobiology 10, 25–47 (2012).CAS 
PubMed 
Article 

Google Scholar 
50.Schneebeli-Hermann, E. et al. Palynofacies analysis of the Permian–Triassic transition in the Amb section (Salt Range, Pakistan): implications for the anoxia on the South Tethyan Margin. J. Asian Earth Sci. 60, 225–234 (2012).ADS 
Article 

Google Scholar 
51.van Soelen, E. E. & Kürschner, W. M. Late Permian to Early Triassic changes in acritarch assemblages and morphology in the Boreal Arctic: new data from the Finnmark Platform. Palaeogeog. Palaeoclimatol. Palaeoecol. 505, 120–127 (2018).ADS 
Article 

Google Scholar 
52.Spina, A., Cirilli, S., Utting, J. & Jansonius, J. Palynology of the Permian and Triassic of the Tesero and Bulla sections (Western Dolomites, Italy) and consideration about the enigmatic species Reduviasporonites chalastus. Rev. Palaeobot. Palynol. 218, 3–14 (2015).Article 

Google Scholar 
53.Thomas, B. M. et al. Unique marine Permian‐Triassic boundary section from Western Australia. Aust. J. Earth Sci. 51, 423–430 (2004).ADS 
Article 

Google Scholar 
54.Schneebeli-Hermann, E. & Bucher, H. Palynostratigraphy at the Permian-Triassic boundary of the Amb section, Salt Range, Pakistan. Palynology 39, 1–18 (2015).Article 

Google Scholar 
55.Lei, Y. et al. Phytoplankton (acritarch) community changes during the Permian-Triassic transition in South China. Palaeogeog. Palaeoclimatol. Palaeoecol. 519, 84–94 (2019).ADS 
Article 

Google Scholar 
56.Algeo, T. J. et al. Plankton and productivity during the Permian–Triassic boundary crisis: An analysis of organic carbon fluxes. Glob. Planet. Change 105, 52–67 (2013).ADS 
Article 

Google Scholar 
57.van Soelen, E. E., Twitchett, R. J. & Kürschner, W. M. Salinity changes and anoxia resulting from enhanced run-off during the late Permian global warming and mass extinction event. Climate 14, 441–453 (2018).
Google Scholar 
58.Kaiho, K. et al. Effects of soil erosion and anoxic–euxinic ocean in the Permian–Triassic marine crisis. Heliyon 2, e00137 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
59.Bond, D. P. G. & Grasby, S. E. On the causes of mass extinctions. Palaeogeog. Palaeoclimatol. Palaeoecol. 478, 3–29 (2017).ADS 
Article 

Google Scholar 
60.Lindström, S., Erlström, M., Piasecki, S., Nielsen, L. H. & Mathiesen, A. Palynology and terrestrial ecosystem change of the Middle Triassic to lowermost Jurassic succession of the eastern Danish Basin. Rev. Palaeobot. Palynol. 244, 65–95 (2017).Article 

Google Scholar 
61.Garel, S. et al. Paleohydrological and paleoenvironmental changes recorded in terrestrial sediments of the Paleocene–Eocene boundary (Normandy, France). Palaeogeog. Palaeoclimatol. Palaeoecol. 376, 184–199 (2013).ADS 
Article 

Google Scholar 
62.van de Schootbrugge, B. & Gollner, S. In Ecosystem Paleobiology and Geobiology, The Paleontological Society Papers (eds Bush, A. M., Pruss, S. B. & Payne, J. L.) 19, 87–114 (The Paleontological Society, 2013).63.Mata, S. A. & Bottjer, D. J. Microbes and mass extinctions: paleoenvironmental distribution of microbialites during times of biotic crisis. Geobiology 10, 3–24 (2012).CAS 
PubMed 
Article 

Google Scholar 
64.Peterffy, O., Calner, M. & Vajda, V. Early Jurassic microbial mats—a potential response to reduced biotic activity in the aftermath of the end-Triassic mass extinction event. Palaeogeog. Palaeoclimatol. Palaeoecol. 464, 76–85 (2016).ADS 
Article 

Google Scholar 
65.Schoene, B. et al. U-Pb constraints on pulsed eruption of the Deccan Traps across the end-Cretaceous mass extinction. Science 3636, 862–866 (2019).ADS 
Article 
CAS 

Google Scholar 
66.Hull, P. M. et al. On impact and volcanism across the Cretaceous-Paleogene boundary. Science 367, 266–272 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
67.Vajda, V., Ocampo, A., Ferrow, E. & Bender Koch, C. Nano particles as the primary cause for long-term sunlight suppression at high southern latitudes following the Chicxulub impact—evidence from ejecta deposits in Belize and Mexico. Gondwana Res. 27, 1079–1088 (2015).ADS 
CAS 
Article 

Google Scholar 
68.Sepúlveda, J., Wendler, J. E., Summons, R. E. & Hinrichs, K.-U. Rapid resurgence of marine productivity after the Cretaceous-Paleogene mass extinction. Science 326, 129–132 (2009).ADS 
PubMed 
Article 
CAS 

Google Scholar 
69.Bralower, T. J. et al. Origin of a global carbonate layer deposited in the aftermath of the Cretaceous-Paleogene boundary impact. Earth Planet. Sci. Lett. 548, 116476 (2020).CAS 
Article 

Google Scholar 
70.Schaefer, B. et al. Microbial life in the nascent Chicxulub crater. Geology 48, 328–332 (2020).ADS 
CAS 
Article 

Google Scholar 
71.Milligan, J. N., Royer, D. L., Franks, P. J., Upchurch, G. R. & McKee, M. L. No evidence for a large atmospheric CO2 spike across the Cretaceous‐Paleogene boundary. Geophys. Res. Lett. 46, 3462–3472 (2019).ADS 
CAS 
Article 

Google Scholar 
72.Strother, P. K. & Wellman, C. H. The Nonesuch Formation Lagerstätte: a rare window into freshwater life one billion years ago. J. Geol. Soc. 178, jgs2020–jgs2133 (2021).Article 

Google Scholar 
73.Sepkoski, J. J., Bambach, R. K. & Droser, M. L. In Cycles and Events in Stratigraphy (eds Einsele, G., Ricken, W. & Seilacher, A.) 298–312 (Springer-Verlag, 1991).74.Tyson, R. V. Calibration of hydrogen indices with microscopy: a review, reanalysis and new results using the fluorescence scale. Org. Geochem. 37, 45–63 (2006).CAS 
Article 

Google Scholar 
75.Benninghoff, W. S. Calculation of pollen and spore density in sediments by addition of exotic pollen in known quantities. Pollen et. Spores 4, 332–333 (1962).
Google Scholar 
76.Maher, L. J. Statistics for microfossil concentration measurements employing samples spiked with marker grains. Rev. Palaeobot. Palynol. 32, 153–191 (1981).Article 

Google Scholar 
77.Simpson, M. G. Plant Systematics (Academic Press, 2019).78.Evitt, W. R. A discussion and proposals concerning fossil dinoflagellates, hystrichospheres, and acritarchs, II. PNAS 49, 298–302 (1963).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
79.Rampino, M. R. & Eshet, Y. The fungal and acritarch events as time markers for the latest Permian mass extinction: an update. Geosci. Front. 9, 147–154 (2018).Article 

Google Scholar 
80.Combaz, A. Les palynofaciès. Rev. Micropaléontol. 7, 205–218 (1964).
Google Scholar 
81.Hammer, Ø., Harper, D. A. T. & Ryan, P. D. PAST: paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4, 4 (2001).
Google Scholar 
82.Wei, W. & Algeo, T. J. Elemental proxies for paleosalinity analysis of ancient shales and mudrocks. Geochim. Cosmochim. Acta 287, 341–366 (2020).ADS 
CAS 
Article 

Google Scholar 
83.Rowe, H., Hughes, N. & Robinson, K. The quantification and application of handheld energy-dispersive x-ray fluorescence (ED-XRF) in mudrock chemostratigraphy and geochemistry. Chem. Geol. 324–325, 122–131 (2012).ADS 
Article 
CAS 

Google Scholar 
84.Blakey, R. C. Global paleogeography and tectonics in deep time. https://deeptimemaps.com/global-series-details/. Accessed 16 June 2020 (2016).85.Zhuravlev, A. Y. & Wood, R. A. Anoxia as the cause of the mid-Early Cambrian (Botomian) extinction event. Geology 24, 311–314 (1996).ADS 
CAS 
Article 

Google Scholar 
86.Zhang, W., Shi, X., Jiang, G., Tang, D. & Wang, X. Mass-occurrence of oncoids at the Cambrian Series 2–Series 3 transition: Implications for microbial resurgence following an Early Cambrian extinction. Gondwana Res. 28, 432–450 (2015).ADS 
Article 
CAS 

Google Scholar 
87.Vecoli, M. Fossil microphytoplankton dynamics across the Ordovician–Silurian boundary. Rev. Palaeobot. Palynol. 148, 91–107 (2008).Article 

Google Scholar 
88.Xie, S. et al. Contrasting microbial community changes during mass extinctions at the Middle/Late Permian and Permian/Triassic boundaries. Earth Planet. Sci. Lett. 460, 180–191 (2017).ADS 
CAS 
Article 

Google Scholar 
89.Eshet, Y., Rampino, M. R. & Visscher, H. Fungal event and palynological record of ecological crisis and recovery across the Permian-Triassic boundary. Geology 23, 967–970 (1995).ADS 
Article 

Google Scholar 
90.Richoz, S. et al. Hydrogen sulphide poisoning of shallow seas following the end-Triassic extinction. Nat. Geosci. 5, 662–667 (2012).ADS 
CAS 
Article 

Google Scholar 
91.Lindström, S. et al. No causal link between terrestrial ecosystem change and methane release during the end-Triassic mass extinction. Geology 40, 531–534 (2012).ADS 
Article 
CAS 

Google Scholar 
92.van de Schootbrugge, B. et al. End-Triassic calcification crisis and blooms of organic-walled “disaster species”. Palaeogeog. Palaeoclimatol. Palaeoecol. 244, 126–141 (2007).ADS 
Article 

Google Scholar 
93.Slater, S. M., Twitchett, R. J., Danise, S. & Vajda, V. Substantial vegetation response to Early Jurassic global warming with impacts on oceanic anoxia. Nat. Geosci. 12, 462–467 (2019).ADS 
CAS 
Article 

Google Scholar 
94.Polgári, M. et al. Mineral and chemostratigraphy of a Toarcian black shale hosting Mn-carbonate microbialites (Úrkút, Hungary). Palaeogeog. Palaeoclimatol. Palaeoecol. 459, 99–120 (2016).ADS 
Article 

Google Scholar 
95.Xu, W. et al. Carbon sequestration in an expanded lake system during the Toarcian oceanic anoxic event. Nat. Geosci. 10, 129–134 (2017).ADS 
CAS 
Article 

Google Scholar 
96.Kashiyama, Y. et al. Diazotrophic cyanobacteria as the major photoautotrophs during mid-Cretaceous oceanic anoxic events: nitrogen and carbon isotopic evidence from sedimentary porphyrin. Org. Geochem. 39, 532–549 (2008).CAS 
Article 

Google Scholar 
97.Jarvis, I. et al. Microfossil assemblages and the Cenomanian-Turonian (late Cretaceous) oceanic anoxic event. Cretac. Res 9, 3–103 (1988).Article 

Google Scholar 
98.Layeb, M., Ben Fadhel, M., Layeb-Tounsi, Y. & Ben Youssef, M. First microbialites associated to organic-rich facies of the Oceanic Anoxic Event 2 (Northern Tunisia, Cenomanian–Turonian transition). Arab. J. Geosci. 7, 3349–3363 (2014).CAS 
Article 

Google Scholar 
99.Pearce, M. A., Jarvis, I. & Tocher, B. A. The Cenomanian–Turonian boundary event, OAE2 and palaeoenvironmental change in epicontinental seas: new insights from the dinocyst and geochemical records. Palaeogeog. Palaeoclimatol. Palaeoecol. 280, 207–234 (2009).ADS 
Article 

Google Scholar 
100.Kuypers, M. M. M., Pancost, R. D., Nijenhuis, I. A. & Sinninghe Damsté, J. S. Enhanced productivity led to increased organic carbon burial in the euxinic North Atlantic basin during the late Cenomanian oceanic anoxic event. Paleoceanography 17, 1051 (2002).ADS 
Article 

Google Scholar 
101.Dodsworth, P., Eldrett, J. S. & Hart, M. B. Cretaceous Oceanic Anoxic Event 2 in eastern England: further palynological and geochemical data from Melton Ross. figshare https://doi.org/10.6084/m9.figshare.c.4987205.v3 (2020).102.Schwab, K. W., Bayliss, G. S., Smith, M. A. & Yoder, N. B. Mushroom and broccoli-head shaped algal fragments from the Eagle Ford Shale of south Texas and Coahuila, Mexico. Search and Discovery 70134 (2013).103.Lyson, T. R. et al. Exceptional continental record of biotic recovery after the Cretaceous–Paleogene mass extinction. Science 366, 977–983 (2019).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
104.Scasso, R. A. et al. A high-resolution record of environmental changes from a Cretaceous-Paleogene section of Seymour Island. Antarctica. Palaeogeog. Palaeoclimatol. Palaeoecol. 555, 109844 (2020).ADS 
Article 

Google Scholar 
105.Sosa-Montes de Oca, C. et al. Minor changes in biomarker assemblages in the aftermath of the Cretaceous-Paleogene mass extinction event at the Agost distal section (Spain). Palaeogeog. Palaeoclimatol. Palaeoecol. 569, 110310 (2021).ADS 
Article 

Google Scholar 
106.Sluijs, A. et al. Environmental precursors to rapid light carbon injection at the Palaeocene/Eocene boundary. Nature 450, 1218–1222 (2007).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
107.Junium, C. K., Dickson, A. J. & Uveges, B. T. Perturbation to the nitrogen cycle during rapid Early Eocene global warming. Nat. Commun. 9, 3186 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
108.Pagani, M. et al. Arctic hydrology during global warming at the Palaeocene/Eocene thermal maximum. Nature 442, 671–675 (2006).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
109.Kender, S. et al. Marine and terrestrial environmental changes in NW Europe preceding carbon release at the Paleocene–Eocene transition. Earth Planet. Sci. Lett. 353–354, 108–120 (2012).ADS 
Article 
CAS 

Google Scholar 
110.Huisman, J. et al. Cyanobacterial blooms. Nat. Rev. Microbiol. 16, 471–483 (2018).CAS 
PubMed 
Article 

Google Scholar 
111.Paerl, H. W., Hall, N. S. & Calandrino, E. S. Controlling harmful cyanobacterial blooms in a world experiencing anthropogenic and climatic-induced change. Sci. Tot. Environ. 409, 1739–1745 (2011).CAS 
Article 

Google Scholar  More

Macro- and micro-evolutionary integration between jaw complexesWe examined phenotypic associations between the lower oral and pharyngeal jaws (LOJ and LPJ, respectively) of 88 cichlid species from across Africa, primarily sampling from lakes in the East African Rift Valley: lakes Malawi, Tanganyika, and Victoria (Supplementary Data 1). We characterized jaw shapes based on 107 individuals using 3D geometric morphometrics by placing landmarks in positions that capture functionally (e.g., bony processes, sutures, etc.) and developmentally (e.g., distinct cellular origins) important aspects of morphology, including placing mirrored landmarks across midlines to gain symmetric configurations (Fig. 1e, Supplementary Fig. 1). We conducted a Procrustes superimposition, removed the effects of allometry to account for size differences, and then removed the effects of asymmetry to account for developmental noise. We performed a two-block partial least squares (PLS) analysis on the species mean landmark configurations and corrected for phylogenetic non-independence using a Bayesian time-calibrated tree31. We found the LOJ and LPJ were evolutionarily correlated (r-PLS = 0.482, P = 0.002, effect size (Z) = 2.585), but some taxa, particularly those with unique diets and/or modes of feeding, appeared to deviate from the best-fit line, indicating lower levels (or different patterns) of integration between jaws (Fig. 2a). Indeed, we found numerous taxa, typically from Lake Malawi, whereby covariation between the LOJ and LPJ appeared much different relative to other African cichlids. Taxa placed far from the best-fit line either (1) exhibited a specialized feeding morphology to better exploit an foraging niche shared with many competitors (i.e., Labeotropheus, algae; Copadichromis, zooplankton; Taeniolethrinops, insects), or (2) exhibited a specialized feeding morphology to take advantage of a more challenging food source (i.e., Trematocranus, snails). However, not all taxa that consume specialized prey were far from the best-fit line; Pungu, (primarily a sponge-feeder) and Perissodus (a scale-feeder), while exhibiting specialized feeding apparatuses to consume such prey, exhibited a relationship between their LOJ and LPJ that was in-line with other African cichlids (Supplementary Fig. 2). We also noted, that while Malawi cichlids exhibit a range of LOJ-LPJ relationships (from weak to strong), most Tanganyikan cichlids reside close to the best-fit line. However, when we examine the strength of integration in the Tanganyika group (n = 29, r-PLS = 0.698, P = 0.001, Z = 2.954) and Malawi group (n = 40, r-PLS = 0.541, P = 0.020, Z = 2.155), despite Tangyanika cichlids exhibiting higher Z-scores, consistent with stronger integration, a statistical comparison between groups finds no significant difference (Z pairwise = 1.188, P = 0.235). Comparisons between Tanganyikan and Malawi cichlids should not be influenced by sampling bias, as principal components analyses (PCA) on the LOJ and LPJ landmark data (Supplementary Data 2 and 3) showed that our sampling of Tanganyikan cichlids includes many species with extreme morphologies that reside at the outer edges of LOJ and LPJ morphospace (Supplementary Fig. 3). Indeed, cichlids from Lake Tanganyika exhibited similar LOJ morphological disparity (Malawi Procrustes variance (PV) = 0.074; Tanganyika PV = 0.057, P = 0.253) and greater LPJ morphological disparity (Malawi PV = 0.015; Tanganyika PV = 0.023, P = 0.012), relative to cichlids from Lake Malawi. Taken together, this indicates that while Tanganyikan cichlids exhibit comparable (i.e., LOJ), or greater (i.e., LPJ) morphological variation compared to Malawi cichlids, covariation between LOJ and LPJ shapes was generally similar between groups.Fig. 2: Phylogenetic two-block partial least squares analysis to assess macroevolutionary associations between lower oral and pharyngeal jaws.a Jaw shape associations across a broad sample of African cichlids (n = 88). Taxa from Lake Malawi are placed into two groups based on phylogenetic position: an mbuna ‘rock-dwellers’ group, and a non-mbuna group consisting of the utaka ‘sand-dwellers’ alongside other benthic species88. b Jaw shape associations across the Tropheops sp. species complex from across a depth gradient (n = 22). Oral and pharyngeal jaw wireframes denote morphologies at either end of the correlational axis. Source data are provided as a Source Data file.Full size imageWe next investigated the degree of integration at lower taxonomic levels. First, we analyzed the jaws of cichlids within the Tropheops species complex from Lake Malawi that is diverse and known to partition habitat by depth32,33. While Tropheops exhibited strong integration between jaws in on our macroevolutionary assessment, species within this genus occupy a broader niche space. Investigating integration within such a species complex provided an opportunity to understand whether habitat differences could lead to differences in integration between jaw complexes. Using the same landmarking procedure as described above, we characterized shape variation in the LOJs and LPJs of 22 wild-caught Tropheops taxa from 60 individuals, concentrating on members from localities across the southern portion of Lake Malawi (Supplementary Data 4). We again performed a two-block PLS analysis on the mean landmark configurations and accounted for phylogenetic non-independence using an amplified fragment length polymorphism tree33. Again, we found the LOJ and LPJ were evolutionarily correlated (r-PLS = 0.795, P = 0.006, Z = 2.521), indicating jaw integration does not appear to vary by habitat (Fig. 2b).Finally, we measured and compared integration between a species pair that exhibited relatively strong versus weak covariation between LOJ and LPJ shapes in our macroevolutionary assessments, Tropheops sp. ‘red cheek’ (TRC, relatively stronger integration) and Labeotropheus fuelleborni (LF, relatively weaker integration). Using the same landmarking protocol we performed separate two-block PLS analyses between LOJs and LPJs of LF and TRC (Supplementary Data 5). Notably, we found strong and significant integration between jaw complexes in TRC (n = 11, r-PLS = 0.957, P = 0.001, Z = 3.038; Fig. 3a) relative to LF (n = 17, r-PLS = 0.669, P = 0.22, Z = 0.794; Fig. 3b). Further, we found the effect sizes of jaw integration within TRC and LF to be statistically distinct (Z pairwise = 1.678, P = 0.047). Altogether, our data support the assertion that the LOJ and LPJ are evolutionarily integrated at multiple taxonomic levels, but they also appear to indicate that certain taxa, such as Labeotropheus, can more readily generate adaptive morphological variation in each jaw complex independently.Fig. 3: Two-block partial least squares analysis to assess microevolutionary associations between lower oral and pharyngeal jaws.a Shape associations among Tropheops sp. “red cheek” (TRC) individuals (n = 11). b Shape associations among Labeotropheus fuelleborni (LF) individuals (n = 17). c Shape associations among members of a hybrid cross between TRC and LF (n = 409). Oral and pharyngeal jaw wireframes denote morphologies at either end of the correlational axis. Source data are provided as a Source Data file.Full size imageGenetic basis for oral and pharyngeal jaw shape covariationTo understand whether phenotypic covariation between the LOJ and LPJ is genetically determined we performed a quantitative trait loci (QTL) analysis to identify prospective genomic regions involved in jaw shape variation for both the LOJ and LPJ. Specifically, we extended an existing genetic cross between the more strongly integrated TRC and the more weakly integrated LF to the F5 generation. Details of the pedigree may be found in34 and in the supplement. For this experiment, we genotyped 636 F5 hybrids and produced a genetic map containing 812 single-nucleotide polymorphisms (SNPs) spread across 24 linkage groups (Supplementary Data 6). With a total length of 1431 cM, our high-resolution linkage map contained a marker every 1.83 cM, on average, allowing us to leverage the increased number of recombination events that occurred to reach an F5 population. We then characterized LOJ and LPJ shape in 409 F5 hybrids using the same landmarking scheme described above, and performed a two-block PLS analysis. In concordance with our findings from natural populations, we documented an association between jaw complexes in this laboratory pedigree (r-PLS = 0.491, P = 0.001, effect size = 6.189; Fig. 3c).We next performed a PCA on the hybrid landmark configurations to distill the data down to a series of orthogonal axes that best explain shape variation among individuals. We extracted the first two PCs from the LOJ and LPJ as each axis represented more than 10% of the shape variation (Supplementary Data 7; Supplementary Figs. 4 and 5). The first axis of the LOJ reflected more general variation in depth, width, and length of the element (41.8% of variation), while the second axis reflected more specific variation in the length of the ascending arm of the articular––the process for which jaw closing muscles attach (12.7% of variation). The first axis of the LPJ reflected width, length, and wing process size (33.7% of variation), while the second axis reflected depth and the size of the anterior keel – the process for which the pharyngeal jaw pharyngohyoideus muscle attaches and controls jaw adduction (14.2% of variation). We then utilized these PC scores as traits to run in our QTL analyses to investigate the genetic basis for variation in these structures.QTL mapping implicates pleiotropic control of LOJ and LPJ shape variationIntegration between LOJ and LJP shapes in the F5 predicts that this pattern of covariation will be reflected in the genotype-phenotype map. Specifically, we predict that we will find overlapping QTL for both jaws. We used a multiple QTL mapping (MQM) approach to test this prediction. Specifically, we performed QTL scans for all four traits and quantitatively assessed the evidence for significant QTL marker(s) using a permutation procedure that reshuffles the phenotypic data relative to genotypic data 1000 times to generate a null distribution, disassociating any possible relationship between genotype and phenotype, to then compare the empirical distribution against35. Once candidate QTL markers were identified, we calculated an approximate Bayesian credible interval to determine the region in which a potential candidate locus would reside. We uncovered a total of five QTL for LOJ traits, and four QTL for LPJ traits (Fig. 4a; Supplementary Data 8). While most QTL localize to different linkage groups, we also identified some QTL that colocalized. Two traits (LOJ PC1, LPJ PC1) share a marker on LG4, while three traits (LOJ PC1, LOJ PC2, LPJ PC1) colocalized to the same markers on LG7. These data are consistent with pleiotropy on LG7 and possibly LG4.Fig. 4: Genetic analyses to identify regions of the genome responsible for major changes in jaw shape.All plots are based on 409 LFxTRC F5 hybrids. a QTL analysis to identify positions in the genome most associated with each trait. b Pleiotropy analysis on linkage group seven to determine whether the oral jaw PC1 trait colocalizes to the same region as the pharyngeal jaw PC1 trait. Significance was determined using a likelihood ratio test (LLRT). c Pleiotropy analysis on linkage group seven to determine whether the oral jaw PC2 trait colocalizes to the same region as the pharyngeal jaw PC1 trait. Significance was determined using a LLRT. d Fine mapping all traits across the entirety of LG7. Values furthest from 0 reflect the largest differences between hybrids with LF and TRC genotypes at a given marker. We find peak genotype-phenotype association at ~50 mb that coincides with our Bayes credible interval (grey bar). Intervals that surround the average phenotypic effect line denote standard error of the mean. e Fine mapping all traits across the Bayes credible interval. Population level genetic diversity (FST) data are applied to the map (black dots) with the opacity of each SNP dependent on the degree of segregation between LF and TRC, with those falling above an empirical Z-score threshold of 0.6 determined to be significant, and those above 0.9 deemed highly significant (green lines). Within the credible interval there are four SNPs with FST values of 1.0, but a single SNP that falls within a genotype-phenotype peak residing within an intron of dym (black circle). Source data are provided as a Source Data file.Full size imageWe then quantitatively assessed the evidence for pleiotropy using a likelihood ratio test (LLRT) to compare the null hypothesis of a common pleiotropic QTL to the alternative hypothesis that they are affected by separate QTL36,37. The overlap on LG4 at a single marker (43.57 cM) was deemed significant (LLRT = 1.85, P = 0.03), indicating that we can reject the null hypothesis and that these peaks likely represent separate QTL for each trait (Supplementary Fig. 6). The three traits that overlap on LG7 spanned two markers (19.12 cM–28.04 cM) and were all deemed non-significant (LOJpc1-LPJpc1: LLRT = 0.02, P = 0.66, Fig. 4b; LOJpc2-LPJpc1: LLRT = 0.20, P = 0.41, Fig. 4c), leading us to accept the null hypothesis and conclude that this interval likely contains a single pleiotropic QTL. Whether a single gene, or multiple closely linked genes drive this pleiotropic signal requires a fine-mapping approach.Notably, this locus on LG7 has been implicated previously in underlying LOJ and LPJ shape in another Lake Malawi cichlid cross between LF and Maylandia zebra38,39. Maylandia species, like Tropheops, were generally more integrated in our macroevolutionary analysis (Fig. 2a), and thus another cross between LF and a species with higher integration values point to the same locus. This suggests that the genetic mechanism of integration may be conserved.Fine mapping identifies two candidate genes critical for bone formationTo gain insights into which gene(s) may be pleiotropically regulating LOJ and LPJ jaw shape variation on LG7 we constructed a fine map with greater marker density to investigate genotype-phenotype associations with greater resolution. To that end, we anchored QTL intervals to particular stretches of physical sequence of the Maylandia zebra genome40. We then identified additional RAD-seq SNPs across the linkage group of interest and genotyped them in the F5. Based on this we created two fine maps: the first spanned the entirety of LG7 group with an average spacing of around one marker every 490 kb (Supplementary Data 9), the second matched the QTL marker range revealed by the Bayesian credible interval analysis with an average spacing of around one marker every 180 kb (Supplementary Data 10). We also calculated FST from a panel of wild-caught LF (n = 20) and TRC (n = 20), and primarily focused on FST values of 1.0 that would indicate complete segregation of a SNP between LF and TRC. At every marker on our LG7 fine maps, we calculated the difference in the values of our three colocalized traits between those hybrids homozygous for the LF allele and those homozygous for the TRC allele.We identified a small region on LG7 that exhibited large differences in the average phenotypic effect of those hybrids with either LF or TRC alleles. In our full LG7 map we identified a ~2 mb region (46.7 mb–48.7 mb) that peaked in all three traits (Fig. 4d; Supplementary Data 11). Notably, the traces of all three traits across our LG7 fine maps track together in an almost identical fashion. In our fine map that centered on the Bayes credible interval, we found evidence for both large phenotypic effects among all traits, and the presence of several FST markers approaching or equal to 1.0 (Fig. 4e; Supplementary Data 12). One marker combined an FST score of 1.0, indicating complete segregation of that allele between LF and TRC, with high average phenotypic effects across all traits (Supplementary Fig. 7). This SNP fell within an intron of dymeclin (dym), a gene that is necessary for correct organization of Golgi apparatus and controls endochondral bone formation during early development. Dym is critical for chondrocyte development and previous research using the zebrafish model found an expression pattern that spanned the presumptive mandibular and ceratobranchial regions at larval stages41. Mutations in this gene lead to profound effects on the size and shape of bones due to misregulated chondrocyte development42. Just downstream (8 kb, Supplementary Fig. 7) of dym is mothers against decapentaplegic homolog 7 (smad7), an antagonist of both TGF-β and BMP signaling and a suppressor of bone formation. As an inhibitory Smad, smad7 negatively regulates genes from the BMP and TGF-β signaling pathways (i.e., bmp-2, -4, -7, nodal, etc.) that are known to shape phenotypic differences in the craniofacial skeleton across a wide range of taxa including cichlids25,38,43, Geospiza finches44,45, and Anolis lizards46, primarily because these genes have the capacity to influence size in structures of trophic importance such as the mandible47. Both of these genes represent good candidates for controlling shape variation in the LOJ and the LPJ simultaneously. While two of the three traits peak at the dym SNP, when considering markers just outside the credible interval another peak is visible (especially for LOJ PC1) that sits close to notch1a, a gene involved in skeletal development and homeostasis. Notch1a is flanked by two fully segregated FST markers. The upstream marker is around ~60 kb from the promoter region, while the downstream marker resides around ~52 kb away from the gene within an intron of kcnt1, a gene involved in potassium channel development that appears to regulate brain function48. While kcnt1 reflects a poor candidate gene for our analysis, the intronic SNP could act as a distant enhancer of notch1a. Thus, given the combined results from QTL and fine-mapping, dym and smad7 represent strong candidates, but we cannot rule out notch1a.Correlated expression of key genes between LOJ and LPJWe used quantitative real-time PCR (qPCR) to assess and compare the expression levels of dym, smad7, and notch1a in the LOJ and LPJ of three mbuna genera from lake Malawi (Tropheops n = 6, Labeotropheus n = 8, Maylandia n = 8). We used Labeotropheus and Tropheops to complement our quantitative genetic analysis, and all three taxa were represented in our phenotypic assessments of integration, permitting a comparison between macroevolutionary associations of the LOJ and LPJ with the underlying genetic architecture and expression for jaw complex correlation. We collected tissue samples from young juveniles of these four taxa, taking the LOJ and LPJ, alongside the caudal fin to act as an internal control, and performed a phenol/chloroform RNA extraction. We designed primers with high amplification efficiency ( >90%) for our three genes (Supplementary Data 13), and used β-actin as our control gene. We calculated relative expression of the LOJ and LPJ using the 2-ΔΔCT method49, and compared expression across taxa and between tissues (Supplementary Data 14 and 15).We initially compared tissue level expression levels between Labeotropheus and Tropheops and found small differences in dym expression, with LF typically exhibiting slightly higher levels (t-test LOJ t = 2.863, P = 0.014; LPJ t = 1.212, P = 0.249; Fig. 5a). These results are consistent with previous expression studies that demonstrated how Labeotropheus typically has up-regulated bone and collagen markers and as a consequence has greater bone deposition and a more robust craniofacial skeleton50,51. Expression level differences were also noted for notch1a and smad7 (Fig. 5b-c); both showed reduced expression in LF, which is expected based on each genes role as negative regulators of bone formation52,53. While the differences between species were fairly small in smad7 between taxa (t-test LOJ t = −1.869, P = 0.086; LPJ t = −0.359, P = 0.726), they were more notable in notch1a (t-test LOJ t = −1.947, P = 0.080; LPJ t = −3.221, P = 0.009). Notch1a is involved in skeletal remodeling, previous research has shown LF exhibits a minimal plastic response to environmental stimuli51. Thus, the relatively low expression of notch1a in Labeotropheus compared to Tropheops is consistent with this observation. While only representing a single life-history stage, the expression differences between species suggest that all three genes may underlie the development of species-specific shapes for the LOJ and/or LPJ. However, visualizing the data this way cannot speak to whether one or more of these loci underlie the covariation of the jaws.Fig. 5: Comparing expression levels of dym, smad7, and notch1a via qPCR in the oral and pharyngeal jaws.a dym bar plot; (b) notch1a bar plot; (c), smad7 bar plot; (d), dym scatter plot; (e), notch1a scatter plot; (f), smad7 scatter plot. a–c bar plots depict mean relative expression levels, error bars denote standard error. d–f Scatterplots depict relative expression levels of the LOJ and LPJ, error bounds surrounding the linear regression line denote standard error. e inset, linear regression for each genus. Three cichlid taxa were included: Labeotropheus n = 8, Tropheops n = 6, Maylandia n = 8. Bar plot significance determined via t-tests: ●P  More

1.Gause, G. F. & Witt, A. A. Behavior of mixed populations and the problem of natural selection. Am. Nat. 69, 596–609 (1935).Article 

Google Scholar 
2.Hairston, N. G., Smith, F. E. & Slobodkin, L. B. Community structure, population control, and competition. Am. Nat. 94, 421–425 (1960).Article 

Google Scholar 
3.Ayala, F. J. Experimental invalidation of the principle of competitive exclusion. Nature 224, 1076–1079 (1969).ADS 
CAS 
PubMed 
Article 

Google Scholar 
4.Bengtsson, J. Interspecific competition increases local extinction rate in a metapopulation system. Nature 340, 713–715 (1989).ADS 
Article 

Google Scholar 
5.Bolnick, D. I. Intraspecific competition favours niche width expansion in Drosophila melanogaster. Nature 410, 463–466 (2001).ADS 
CAS 
PubMed 
Article 

Google Scholar 
6.Collins, S. Competition limits adaptation and productivity in a photosynthetic alga at elevated CO2. Proc. Biol. Sci. 278, 247–255 (2011).PubMed 

Google Scholar 
7.Osmond, M. M. & de Mazancourt, C. How competition affects evolutionary rescue. Philos. Trans. R. Soc. B 368, 20120085 (2013).Article 

Google Scholar 
8.Birch, L. C. Selection in Drosophila pseudoobscura in relation to crowding. Evolution 9, 389–399 (1955).Article 

Google Scholar 
9.Martin, M. J., Perez-Tome, J. M. & Toro, M. A. Competition and genotypic variability in Drosophila melanogaster. Heredity 60, 119–123 (1988).PubMed 
Article 

Google Scholar 
10.Harvey, J. A., Poelman, E. H. & Tanaka, T. Intrinsic inter- and intraspecific competition in parasitoid wasps. Annu. Rev. Entomol. 58, 333–351 (2013).CAS 
PubMed 
Article 

Google Scholar 
11.Pennacchio, F. & Strand, M. R. Evolution of developmental strategies in parasitic hymenoptera. Annu. Rev. Entomol. 51, 233–258 (2006).CAS 
PubMed 
Article 

Google Scholar 
12.Van Alphen, J. J. & Visser, M. E. Superparasitism as an adaptive strategy for insect parasitoids. Annu. Rev. Entomol. 35, 59–79 (1990).PubMed 
Article 

Google Scholar 
13.Varaldi, J. et al. Infectious behavior in a parasitoid. Science 302, 1930–1930 (2003).CAS 
PubMed 
Article 

Google Scholar 
14.Dorn, S. & Beckage, N. E. Superparasitism in gregarious hymenopteran parasitoids: ecological, behavioural and physiological perspectives. Physiol. Entomol. 32, 199–211 (2007).Article 

Google Scholar 
15.Gandon, S., Rivero, A. & Varaldi, J. Superparasitism evolution: adaptation or manipulation? Am. Nat. 167, E1–E22 (2006).PubMed 
Article 

Google Scholar 
16.Speirs, D. C., Sherratt, T. N. & Hubbard, S. F. Parasitoid diets: does superparasitism pay? Trends Ecol. Evol. 6, 22–25 (1991).CAS 
PubMed 
Article 

Google Scholar 
17.Tracy Reynolds, K. & Hardy, I. C. Superparasitism: a non-adaptive strategy? Trends Ecol. Evol. 19, 347–348 (2004).CAS 
PubMed 
Article 

Google Scholar 
18.Pan, M., Liu, T. & Nansen, C. Avoidance of parasitized host by female wasps of Aphidius gifuensis (Hymenoptera: Braconidae): the role of natal rearing effects and host availability? Insect Sci. 25, 1035–1044 (2018).PubMed 
Article 

Google Scholar 
19.Potting, R. P. J., Snellen, H. M. & Vet, L. E. M. Fitness consequences of superparasitism and mechanism of host discrimination in the stem borer parasitoid Cotesia flavipes. Entomol. Exp. Appl. 82, 341–348 (1997).Article 

Google Scholar 
20.Mackauer, B. B. Influence of superparasitism on development rate and adult size in a solitary parasitoid wasp, Aphidius ervi. Funct. Ecol. 6, 302–307 (1992).Article 

Google Scholar 
21.Keasar, T. et al. Costs and consequences of superparasitism in the polyembryonic parasitoid Copidosoma koehleri (Hymenoptera: Encyrtidae). Ecol. Entomol. 31, 277–283 (2006).Article 

Google Scholar 
22.Silva-Torres, C. S. A., Ramos, I. T., Torres, J. B. & Barros, R. Superparasitism and host size effects in Oomyzus sokolowskii, a parasitoid of diamondback moth. Entomol. Exp. Appl. 133, 65–73 (2009).Article 

Google Scholar 
23.Wylie, H. G. Delayed development of Microctonus vittatae (Hymenoptera: Braconidae) in superparasitized adults of Phyllotreta cruciferae (Coleoptera: Chrysomelidae). Can. Entomol. 115, 441–442 (1983).Article 

Google Scholar 
24.White, J. A. & Andow, D. A. Benefits of self-superparasitism in a polyembryonic parasitoid. Biol. Control 46, 133–139 (2008).PubMed 
PubMed Central 
Article 

Google Scholar 
25.Yamada, Y. Y. & Sugaura, K. Evidence for adaptive self-superparasitism in the dryinid parasitoid Haplogonatopus atratus when conspecifics are present. Oikos 103, 175–181 (2003).Article 

Google Scholar 
26.Varaldi, J., Fouillet, P., Bouletreau, M. & Fleury, F. Superparasitism acceptance and patch-leaving mechanisms in parasitoids: a comparison between two sympatric wasps. Anim. Behav. 69, 1227–1234 (2005).Article 

Google Scholar 
27.Varaldi, J., Patot, S., Nardin, M. & Gandon, S. A virus-shaping reproductive strategy in a Drosophila parasitoid. Adv. Parasitol. 70, 333–363 (2009).PubMed 
Article 

Google Scholar 
28.Carton, Y., Bouletreau, M., van Alphen, J. J. M. & van Lenteren, J. C. The Drosophila parasitic wasps. in The Genetics and Biology of Drosophila (eds Ashburner, M., Carson, H. L. & Thompson, J. N.) 347–394 (Academic Press, 1986).29.Kacsoh, B. Z., Lynch, Z. R., Mortimer, N. T. & Schlenke, T. A. Fruit flies medicate offspring after seeing parasites. Science 339, 947–950 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
30.Krzemien, J. et al. Control of blood cell homeostasis in Drosophila larvae by the posterior signalling centre. Nature 446, 325–328 (2007).ADS 
CAS 
PubMed 
Article 

Google Scholar 
31.Kraaijeveld, A. R. & Godfray, H. C. Trade-off between parasitoid resistance and larval competitive ability in Drosophila melanogaster. Nature 389, 278–280 (1997).ADS 
CAS 
PubMed 
Article 

Google Scholar 
32.Hwang, R. Y. et al. Nociceptive neurons protect Drosophila larvae from parasitoid wasps. Curr. Biol. 17, 2105–2116 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
33.Mortimer, N. T. et al. Parasitoid wasp venom SERCA regulates Drosophila calcium levels and inhibits cellular immunity. Proc. Natl Acad. Sci. USA 110, 9427–9432 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
34.Huang, J. et al. Two novel venom proteins underlie divergent parasitic strategies between a generalist and a specialist parasite. Nat. Commun. 12, 234 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
35.Martinson, E. O., Mrinalini, Kelkar, Y. D., Chang, C. H. & Werren, J. H. The evolution of venom by co-option of single-copy genes. Curr. Biol. 27, 2007–2013 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
36.Jaffe, A. B. & Hall, A. Rho GTPases: biochemistry and biology. Annu. Rev. Cell. Dev. Biol. 21, 247–269 (2005).CAS 
PubMed 
Article 

Google Scholar 
37.Moon, S. Y. & Zheng, Y. Rho GTPase-activating proteins in cell regulation. Trends Cell Biol. 13, 13–22 (2003).CAS 
PubMed 
Article 

Google Scholar 
38.Xu, J. et al. RhoGAPs attenuate cell proliferation by direct interaction with p53 tetramerization domain. Cell Rep. 3, 1526–1538 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
39.Hinge, A. et al. p190-B RhoGAP and intracellular cytokine signals balance hematopoietic stem and progenitor cell self-renewal and differentiation. Nat. Commun. 8, 14382 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
40.Werner, E. GTPases and reactive oxygen species: switches for killing and signaling. J. Cell Sci. 117, 143–153 (2004).CAS 
PubMed 
Article 

Google Scholar 
41.Bailey, A. P. et al. Antioxidant role for lipid droplets in a stem cell niche of Drosophila. Cell 163, 340–353 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
42.Boguski, M. S. & McCormick, F. Proteins regulating Ras and its relatives. Nature 366, 643–654 (1993).ADS 
CAS 
PubMed 
Article 

Google Scholar 
43.Rittinger, K. et al. Crystal structure of a small G protein in complex with the GTPase-activating protein rhoGAP. Nature 388, 693–697 (1997).ADS 
CAS 
PubMed 
Article 

Google Scholar 
44.Simanshu, D. K., Nissley, D. V. & McCormick, F. RAS proteins and their regulators in human disease. Cell 170, 17–33 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
45.Outreman, Y., Le Ralec, A., Plantegenest, M., Chaubet, B. & Pierre, J. S. Superparasitism limitation in an aphid parasitoid: cornicle secretion avoidance and host discrimination ability. J. Insect Physiol. 47, 339–348 (2001).CAS 
PubMed 
Article 

Google Scholar 
46.Hofsvang, T. Discrimination between unparasitized and parasitized hosts in hymenopterous parasitoids. Acta Entomol. Bohemosl 87, 161–175 (1990).
Google Scholar 
47.van Lenteren, J. C. in Semiochemicals: Their Role in Pest Control (eds Nordlund, D. A., Jones, R. L. & Lewis, W. J.) 153–179 (Wiley and Sons, 1981).48.Ganesalingam, V. K. Mechanism of discrimination between parasitized and unparasitized hosts by Venturia canescens (hymenoptera: Ichneumonidae). Entomol. Exp. Appl. 17, 36–44 (2011).Article 

Google Scholar 
49.Hoffmeister, T. S. & Roitberg, B. D. To mark the host or the patch: decisions of a parasitoid searching for concealed host larvae. Evol. Ecol. 11, 145–168 (1997).Article 

Google Scholar 
50.Agboka, K. et al. Self-, intra-, and interspecific host discrimination in Telenomus busseolae Gahan and T. isis Polaszek (Hymenoptera: Scelionidae), sympatric egg parasitoids of the African cereal stem borer Sesamia calamistis Hampson (Lepidoptera: Noctuidae). J. Insect Behav. 15, 1–12 (2002).Article 

Google Scholar 
51.Liang, Q., Jia, Y. & Liu, T. Self- and conspecific discrimination between unparasitized and parasitized green peach aphid (Hemiptera: Aphididae), by Aphelinus asychis (Hymenoptera: Aphelinidae). J. Econ. Entomol. 110, 430–437 (2017).PubMed 

Google Scholar 
52.Gandon, S., Varaldi, J., Fleury, F. & Rivero, A. Evolution and manipulation of parasitoid egg load. Evolution 63, 2974–2984 (2009).PubMed 
Article 

Google Scholar 
53.Hughes, D. P. & Libersat, F. Neuroparasitology of parasite-insect associations. Annu. Rev. Entomol. 63, 471–487 (2018).CAS 
PubMed 
Article 

Google Scholar 
54.Sberro, H. et al. Large-scale analyses of human microbiomes reveal thousands of small, novel genes. Cell 178, 1245–1259 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
55.Zuzarte-Luis, V. & Mota, M. M. Parasite sensing of host nutrients and environmental cues. Cell Host Microbe 23, 749–758 (2018).CAS 
PubMed 
Article 

Google Scholar 
56.Cox, F. E. G. Parasites affect behavior of mice. Nature 294, 515–515 (1981).ADS 
CAS 
PubMed 
Article 

Google Scholar 
57.Elya, C. et al. Robust manipulation of the behavior of Drosophila melanogaster by a fungal pathogen in the laboratory. eLife 7, e34414 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
58.Hoover, K. et al. A gene for an extended phenotype. Science 333, 1401–140 (2011).ADS 
CAS 
PubMed 
Article 

Google Scholar 
59.Mcallister, M. K. & Roitberg, B. D. Adaptive suicidal-behavior in pea aphids. Nature 328, 797–799 (1987).ADS 
Article 

Google Scholar 
60.Maure, F., Brodeur, J., Droit, A., Doyon, J. & Thomas, F. Bodyguard manipulation in a multipredator context: different processes, same effect. Behav. Process. 99, 81–86 (2013).Article 

Google Scholar 
61.Mohan, P. & Sinu, P. A. Parasitoid wasp usurps its host to guard its pupa against hyperparasitoids and induces rapid behavioral changes in the parasitized host. PLoS ONE 12, e0178108 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
62.Muller, C. B. & Schmidhempel, P. Exploitation of cold temperature as defense against parasitoids in bumblebees. Nature 363, 65–67 (1993).ADS 
Article 

Google Scholar 
63.Noubade, R. et al. NRROS negatively regulates reactive oxygen species during host defence and autoimmunity. Nature 509, 235–239 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
64.Louradour, I. et al. Reactive oxygen species-dependent Toll/NF-κB activation in the Drosophila hematopoietic niche confers resistance to wasp parasitism. eLife 6, e25496 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
65.Sinenko, S. A., Shim, J. & Banerjee, U. Oxidative stress in the haematopoietic niche regulates the cellular immune response in. Drosoph. EMBO Rep. 13, 83–89 (2012).CAS 
Article 

Google Scholar 
66.Wang, Y. et al. Superoxide dismutases: dual roles in controlling ROS damage and regulating ROS signaling. J. Cell Biol. 217, 1915–1928 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
67.Colinet, D. et al. Extracellular superoxide dismutase in insects: characterization, function, and interspecific variation in parasitoid wasp venom. J. Biol. Chem. 286, 40110–40121 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
68.Colinet, D. et al. Extensive inter- and intraspecific venom variation in closely related parasites targeting the same host: the case of Leptopilina parasitoids of Drosophila. Insect Biochem. Mol. Biol. 43, 601–611 (2013).CAS 
PubMed 
Article 

Google Scholar 
69.Carton, Y., Frey, F. & Nappi, A. Genetic determinism of the cellular immune reaction in Drosophila melanogaster. Heredity 69, 393–399 (1992).PubMed 
Article 

Google Scholar 
70.Colinet, D., Schmitz, A., Depoix, D., Crochard, D. & Poirie, M. Convergent use of RhoGAP toxins by eukaryotic parasites and bacterial pathogens. PLoS Pathog. 3, 2029–2037 (2007).CAS 
Article 

Google Scholar 
71.Colinet, D. et al. The origin of intraspecific variation of virulence in an eukaryotic immune suppressive parasite. PLoS Pathog. 6, e1001206 (2010).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
72.Schlenke, T. A., Morales, J., Govind, S. & Clark, A. G. Contrasting infection strategies in generalist and specialist wasp parasitoids of Drosophila melanogaster. PLoS Pathog. 3, 1486–1501 (2007).PubMed 
Article 
CAS 

Google Scholar 
73.Anderl, I. et al. Transdifferentiation and proliferation in two distinct hemocyte lineages in Drosophila melanogaster larvae after wasp infection. PLoS Pathog. 12, e1005746 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
74.Forbes, A. A. et al. Revisiting the particular role of host shifts in initiating insect speciation. Evolution 71, 1126–1137 (2017).PubMed 
Article 

Google Scholar 
75.Allio, R. et al. Genome-wide macroevolutionary signatures of key innovations in butterflies colonizing new host plants. Nat. Commun. 12, 354 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
76.Araújo, M. S., Bolnick, D. I. & Layman, C. A. The ecological causes of individual specialisation. Ecol. Lett. 14, 948–958 (2011).PubMed 
Article 

Google Scholar 
77.Svanbäck, R. & Bolnick, D. I. Intraspecific competition drives increased resource use diversity within a natural population. P. Roy. Soc. B-Biol. Sci. 274, 839–844 (2007).
Google Scholar 
78.Laskowski, K. L. & Bell, A. M. Competition avoidance drives individual differences in response to a changing food resource in sticklebacks. Ecol. Lett. 16, 746–753 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
79.Huang, J., Reilein, A. & Kalderon, D. Yorkie and Hedgehog independently restrict BMP production in escort cells to permit germline differentiation in the Drosophila ovary. Development 144, 2584–2594 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
80.Marçais, G. & Kingsford, C. A fast, lock-free approach for efficient parallel counting of occurrences of k-mers. Bioinformatics 27, 764–770 (2011).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
81.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).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
82.Kolmogorov, M., Yuan, J., Lin, Y. & Pevzner, P. A. Assembly of long, error-prone reads using repeat graphs. Nat. Biotechnol. 37, 540–546 (2019).CAS 
PubMed 
Article 

Google Scholar 
83.Walker, B. J. et al. Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS ONE 9, e112963 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
84.Parra, G., Bradnam, K. & Korf, I. CEGMA: a pipeline to accurately annotate core genes in eukaryotic genomes. Bioinformatics 23, 1061–1067 (2007).CAS 
PubMed 
Article 

Google Scholar 
85.Waterhouse, R. M. et al. BUSCO applications from quality assessments to gene prediction and phylogenomics. Mol. Phylogenet. Evol. 35, 543–548 (2017).Article 
CAS 

Google Scholar 
86.Jurka, J. et al. Repbase Update, a database of eukaryotic repetitive elements. Cytogenet Genome Res. 110, 462–467 (2005).CAS 
PubMed 
Article 

Google Scholar 
87.Cantarel, B. L. et al. MAKER: an easy-to-use annotation pipeline designed for emerging model organism genomes. Genome Res. 18, 188–196 (2008).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
88.Li, H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34, 3094–3100 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
89.Kim, D., Langmead, B. & Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360 (2015).CAS 
PubMed 
PubMed Central 
Article 

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

Google Scholar 
91.Werren, J. H. et al. Functional and evolutionary insights from the genomes of three parasitoid Nasonia species. Science 327, 343–348 (2010).CAS 
PubMed 
Article 

Google Scholar 
92.Consortium, H. G. S. Insights into social insects from the genome of the honeybee Apis mellifera. Nature 443, 931–949 (2006).ADS 
Article 
CAS 

Google Scholar 
93.Geib, S. M., Liang, G. H., Murphy, T. D. & Sim, S. B. Whole genome sequencing of the braconid parasitoid wasp Fopius arisanus, an important biocontrol agent of pest tepritid fruit flies. G3-Genes Genom. Genet. 7, 2407–2411 (2017).CAS 

Google Scholar 
94.Standage, D. S. et al. Genome, transcriptome and methylome sequencing of a primitively eusocial wasp reveal a greatly reduced DNA methylation system in a social insect. Mol. Ecol. 25, 1769–1784 (2016).CAS 
PubMed 
Article 

Google Scholar 
95.Lindsey, A. R. et al. Comparative genomics of the miniature wasp and pest control agent Trichogramma pretiosum. BMC Biol. 16, 54 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
96.Stanke, M. et al. AUGUSTUS: ab initio prediction of alternative transcripts. Nucleic Acids Res. 34, W435–W439 (2006).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
97.Korf, I. Gene finding in novel genomes. BMC Biol. 5, 59 (2004).
Google Scholar 
98.Marchler-Bauer, A. et al. CDD: NCBI’s conserved domain database. Nucleic Acids Res. 43, D222–D226 (2015).CAS 
PubMed 
Article 

Google Scholar 
99.Hunter, S. et al. InterPro: the integrative protein signature database. Nucleic Acids Res. 37, D211–D215 (2008).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
100.Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
101.Corpet, F. Multiple sequence alignment with hierarchical clustering. Nucleic Acids Res. 16, 10881–10890 (1988).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
102.Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
103.Birney, E., Clamp, M. & Durbin, R. GeneWise and genomewise. Genome Res. 14, 988–995 (2004).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
104.Livak, K. J. & Schmittgen, T. D. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C (T)) method. Methods 25, 402–408 (2001).CAS 
Article 

Google Scholar 
105.Zhang, X. S., Wang, T., Lin, X. W., Denlinger, D. L. & Xu, W. H. Reactive oxygen species extend insect life span using components of the insulin-signaling pathway. Proc. Natl Acad. Sci. USA 114, 7832–7840 (2017).Article 
CAS 

Google Scholar  More

1.Gaston, K. J. The Structure and Dynamics of Geographic Ranges (Oxford University Press, 2003).
Google Scholar 
2.Sexton, J. P., McIntyre, P., Angert, A. L. & Rice, K. J. Evolution and ecology of species range limits. Annu. Rev. Ecol. Evol. Syst. 40, 415–436 (2009).Article 

Google Scholar 
3.Holt, R. D., Keitt, T. H., Lewis, M. A., Maurer, B. A. & Taper, M. L. Theoretical models of species’ borders: single species approaches. Oikos 108, 18–27 (2005).Article 

Google Scholar 
4.Gaston, K. J. Geographic range limits: Achieving synthesis. Proc. R. Soc. Lond. B 276, 1395–1406 (2009).
Google Scholar 
5.Parmesan, C. et al. Empirical perspectives on species borders: From traditional biogeography to global change. Oikos 108, 58–75 (2005).Article 

Google Scholar 
6.Travis, J. M. J. & Dytham, C. In Dispersal Ecology and Evolution (eds Clobert, J. et al.) 337–348 (Oxford University Press, 2012).Chapter 

Google Scholar 
7.Kirkpatrick, M. & Barton, N. H. Evolution of a species’ range. Am. Nat. 150, 1–23 (1997).PubMed 
Article 
CAS 

Google Scholar 
8.Case, T. J. & Taper, M. L. Interspecific competition, environmental gradients, gene flow, and the coevolution of species’ borders. Am. Nat. 155, 583–605 (2000).PubMed 
Article 
CAS 

Google Scholar 
9.Rahbek, C. The role of spatial scale and the perception of large-scale species-richness patterns. Ecol. Lett. 8, 224–239 (2005).Article 

Google Scholar 
10.Hargreaves, A. L., Eckert, C. G. & Bailey, J. Evolution of dispersal and mating systems along geographic gradients. Implications for shifting ranges. Funct. Ecol. 28, 5–21 (2014).Article 

Google Scholar 
11.Hille, S. M. & Cooper, C. B. Elevational trends in life histories. Revising the pace-of-life framework. Biol. Rev. Camb. Philos. Soc. 90, 204–213 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
12.Boyle, W. A., Sandercock, B. K. & Martin, K. Patterns and drivers of intraspecific variation in avian life history along elevational gradients: A meta-analysis. Biol. Rev. 91, 469–482 (2016).Article 

Google Scholar 
13.Badyaev, A. V. & Ghalambor, C. K. Evolution of life histories along elevational gradients: Trade-off between parental care and fecundity. Ecology 82, 2948–2960 (2001).Article 

Google Scholar 
14.Bears, H., Martin, K. & White, G. C. Breeding in high-elevation habitat results in shift to slower life-history strategy within a single species. J. Anim. Ecol. 78, 365–375 (2009).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
15.Caro, L. M., Caycedo-Rosales, P. C., Bowie, R. C. K., Slabbekoorn, H. & Cadena, C. D. Ecological speciation along an elevational gradient in a tropical passerine bird?. J. Evol. Biol. 26, 357 (2013).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
16.Branch, C. L., Jahner, J. P., Kozlovsky, D. Y., Parchman, T. L. & Pravosudov, V. V. Absence of population structure across elevational gradients despite large phenotypic variation in mountain chickadees (Poecile gambeli). R. Soc. Open Sci. 4, 170057. https://doi.org/10.1098/rsos.170057 (2017).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
17.Chamberlain, D. E. et al. The altitudinal frontier in avian climate impact research. Ibis 154, 205–209 (2012).Article 

Google Scholar 
18.Hargreaves, A. L., Samis, K. E. & Eckert, C. G. Are species’ range limits simply niche limits writ large? A review of transplant experiments beyond the range. Am. Nat. 183, 157–173 (2014).PubMed 
Article 

Google Scholar 
19.Graham, C. H., Silva, N. & Velásquez-Tibatá, J. Evaluating the potential causes of range limits of birds of the Colombian Andes. J. Biogeogr. 37, 1863–1875 (2010).
Google Scholar 
20.Popy, S., Bordignon, L. & Prodon, R. A weak upward elevational shift in the distributions of breeding birds in the Italian Alps. J. Biogeogr. 37, 57–67 (2010).Article 

Google Scholar 
21.Chen, I.-C., Hill, J. K., Ohlemüller, R., Roy, D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science 333, 1024–1026 (2011).ADS 
PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
22.Maggini, R. et al. Are Swiss birds tracking climate change?. Ecol. Model. 222, 21–32 (2011).Article 

Google Scholar 
23.Pearce-Higgins, J. W. & Green, R. E. Climate Change and Birds: Impacts and Conservation Responses (Cambridge University Press, 2014).Book 

Google Scholar 
24.Knaus, P. et al. Schweizer Brutvogelatlas 2013–2016. Verbreitung und Bestandsentwicklung der Vögel in der Schweiz und im Fürstentum Liechtenstein (Schweizerische Vogelwarte, 2018).
Google Scholar 
25.Chamberlain, D. E., Fuller, R. J., Bunce, R. G. H., Duckworth, J. C. & Shrubb, M. Changes in the abundance of farmland birds in relation to the timing of agricultural intensification in England and Wales. J. Appl. Ecol. 37, 771–788 (2000).Article 

Google Scholar 
26.Chamberlain, D. & Pearce-Higgins, J. Impacts of climate change on upland birds. Complex interactions, compensatory mechanisms and the need for long-term data. Ibis 155, 451–455 (2013).Article 

Google Scholar 
27.Sergio, F. & Newton, I. Occupancy as a measure of territory quality. J. Anim. Ecol. 72, 857–865 (2003).Article 

Google Scholar 
28.Fretwell, S. D. & Lucas, H. L. On territorial behavior and other factors influencing habitat distribution in birds. Acta Biotheor. 19, 16–36 (1969).Article 

Google Scholar 
29.Grüebler, M. U., Korner-Nievergelt, F. & von Hirschheydt, J. The reproductive benefits of livestock farming in barn swallows Hirundo rustica: Quality of nest site or foraging habitat?. J. Appl. Ecol. 47, 1340–1347 (2010).Article 

Google Scholar 
30.Schaub, M. & von Hirschheydt, J. Effects of current reproduction on apparent survival, breeding dispersal, and future reproduction in barn swallows assessed by multistate capture-recapture models. J. Anim. Ecol. 78, 625–635 (2009).PubMed 
Article 

Google Scholar 
31.Furrer, R. D. & Pasinelli, G. Empirical evidence for source-sink populations: A review on occurrence, assessments and implications. Biol. Rev. Camb. Philos. Soc. 91, 782–795 (2016).PubMed 
Article 

Google Scholar 
32.Plard, F., Turek, D., Grüebler, M. U. & Schaub, M. IPM2: Toward better understanding and forecasting of population dynamics. Ecol. Monogr. 8, e01364. https://doi.org/10.1002/ecm.1364 (2019).Article 

Google Scholar 
33.Grüebler, M. U. & Naef-Daenzer, B. Fitness consequences of pre- and post-fledging timing decisions in a double-brooded passerine. Ecology 89, 2736–2745 (2008).PubMed 
Article 

Google Scholar 
34.Grüebler, M. U., Morand, M. & Naef-Daenzer, B. A predictive model of the density of airborne insects in agricultural environments. Agric. Ecosyst. Environ. 123, 75–80 (2008).Article 

Google Scholar 
35.Jenni-Eiermann, S., Glaus, E., Grüebler, M. U., Schwabl, H. & Jenni, L. Glucocorticoid response to food availability in breeding barn swallows (Hirundo rustica). Gen. Comp. Endocrinol. 155, 558–565 (2008).PubMed 
Article 
CAS 

Google Scholar 
36.Schifferli, L., Grüebler, M. U., Meijer, H. A. J., Visser, G. H. & Naef-Daenzer, B. Barn Swallow Hirundo rustica parents work harder when foraging conditions are good. Ibis 156, 777–787 (2014).Article 

Google Scholar 
37.Shields, W. M. Factors Affecting nest and site fidelity in Adirondack barn swallows (Hirundo rustica). Auk 101, 780–789 (1984).Article 

Google Scholar 
38.Saino, N., Calza, S., Ninni, P. & Møller, A. P. Barn swallows trade survival against offspring condition and immunocompetence. J. Anim. Ecol. 68, 999–1009 (1999).Article 

Google Scholar 
39.Turner, A. The Barn Swallow (T & A D Poyser, 2006).
Google Scholar 
40.Newton, I. The Migration Ecology of Birds 1st edn. (Academic Press, 2007).
Google Scholar 
41.Ambrosini, R. & Saino, N. Environmental effects at two nested spatial scales on habitat choice and breeding performance of barn swallow. Evol. Ecol. 24, 491–508 (2010).Article 

Google Scholar 
42.Ambrosini, R. et al. The distribution and colony size of barn swallows in relation to agricultural land use. J. Appl. Ecol. 39, 524–534 (2002).Article 

Google Scholar 
43.Evans, K. L., Bradbury, R. B. & Wilson, J. D. Selection of hedgerows by Swallows Hirundo rustica foraging on farmland: the influence of local habitat and weather. Bird Study 50, 8–14 (2003).Article 

Google Scholar 
44.Newton, I. Population Limitation in Bird (Academic Press, 1998).
Google Scholar 
45.Paradis, E., Baillie, S. R., Sutherland, W. J. & Gregory, R. D. Patterns of natal and breeding dispersal in birds. J. Anim. Ecol. 67, 518–536 (1998).Article 

Google Scholar 
46.Scandolara, C. et al. Context-, phenotype-, and kin-dependent natal dispersal of barn swallows (Hirundo rustica). Behav. Ecol. 25, 180–190 (2014).Article 

Google Scholar 
47.Schaub, M., von Hirschheydt, J. & Grüebler, M. U. Differential contribution of demographic rate synchrony to population synchrony in barn swallows. J. Anim. Ecol. 84, 1530–1541 (2015).PubMed 
Article 

Google Scholar 
48.Camfield, A. F., Pearson, S. F. & Martin, K. Life history variation between high and low elevation subspecies of horned larks Eremophila spp. J. Avian Biol. 41, 273–281 (2010).Article 

Google Scholar 
49.Møller, A. P. Phenotype-dependent arrival time and its consequences in a migratory bird. Behav. Ecol. Sociobiol. 35, 115–122 (1994).Article 

Google Scholar 
50.Møller, A. P. Sexual Selection and the Barn Swallow (Oxford University Press, 1994).
Google Scholar 
51.Lerche-Jørgensen, M., Korner-Nievergelt, F., Tøttrup, A. P., Willemoes, M. & Thorup, K. Early returning long-distance migrant males do pay a survival cost. Ecol. Evol. 8, 11434–11449. https://doi.org/10.1002/ece3.4569 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
52.Pulliam, H. R. On the relationship between niche and distribution. Ecol. Lett. 3, 349–361 (2000).Article 

Google Scholar 
53.Pulliam, H. R. Sources, sinks, and population regulation. Am. Nat. 132, 652–661 (1988).Article 

Google Scholar 
54.Møller, A. P., de Lope, F. & Saino, N. Parasitism, immunity, and arrival date in a migratory bird, the barn swallow. Ecology 85, 206–219 (2004).Article 

Google Scholar 
55.Huntley, B., Green, R. E., Collingham, Y. C. & Willis, S. G. A Climatic Atlas of European Breeding Birds (Lynx Edicions, 2007).
Google Scholar 
56.Scridel, D. et al. A review and meta-analysis of the effects of climate change on Holarctic mountain and upland bird populations. Ibis 160, 489–515 (2018).Article 

Google Scholar 
57.Cormack, R. M. Estimates of survival from the sighting of marked animals. Biometrika 51, 429–438 (1964).MATH 
Article 

Google Scholar 
58.Jolly, G. Explicit estimates from capture-recapture data with both death and immigration-stochastic model. Biometrika 52, 225–247 (1965).MathSciNet 
PubMed 
MATH 
Article 
CAS 
PubMed Central 

Google Scholar 
59.Seber, G. A. F. A note on the multiple-recapture census. Biometrika 52, 249–259 (1965).MathSciNet 
PubMed 
MATH 
Article 
CAS 
PubMed Central 

Google Scholar 
60.Lebreton, J.-D., Burnham, K. P., Clobert, J. & Anderson, D. R. Modeling survival and testing biological hypotheses using marked animals: A unified approach with case studies. Ecol. Monogr. 62, 67–118 (1992).Article 

Google Scholar 
61.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

Google Scholar 
62.Saino, N., Martinelli, R. & Romano, M. Ecological and phenological covariates of offspring sex ratio in barn swallows. Evol. Ecol. 22, 659–674 (2008).Article 

Google Scholar 
63.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2017).64.Gelman, A. & Hill, J. Data Analysis Using Regression and Multilevel/Hierarchical Models (Cambridge University Press, 2007).
Google Scholar  More