Ecology

Defining alpine habitat and mountain regionsWe defined alpine habitat as the area above climatic treeline, including the nival belt, where temperature, wind, drought, snow, or nightly frost limit vegetation growth to shrubs, krummholz, or fragmented tree patches less than 3 m in height3,23,24. Realized treeline can be markedly lower than the climatic treeline due to the absence of continuous forest at lower elevations, or human activities such as logging, burning, and livestock grazing25. While anthropogenically influenced treeline produces habitat reminiscent of alpine meadows, these habitats are not climatically representative of alpine ecosystems and thus they were not included when assembling this dataset. Climatic treeline elevation varies globally based on latitude, topography, aspect, and proximity to the coast (i.e., oceanic influence)11. Therefore, we defined alpine habitat separately for each mountain region based on local climate and published accounts of alpine vegetation. While alpine habitats usually occur above at least 1,500 m elevation globally, at high latitudes ( >55°N or 41°S) this elevation can be as low as ~400 m26 (Fig. 2).Fig. 2The median (triangular points) and range (error bars) of treeline elevation for each of the main mountain regions covered in the dataset (Fig. 1). The mountain regions are arranged from north to south (left to right) and the grey dashed line represents the relative position of the equator. Treeline elevation was derived from different sources depending on the region (see ‘Data sources’ in the dataset). The abbreviation ‘NA’, such as in ‘Northwestern NA’, refers to North America.Full size imageThe alpine habitats we identified broadly align with the ‘lower alpine’, ‘upper alpine’, and ‘nival’ belts mapped by Korner et al.9 and made available by the Global Mountain Biodiversity Assessment project (http://www.mountainbiodiversity.org/explore)27,28. However, certain areas, such as the Sierras de Córdoba, Argentina or the Isthmian Páramo on volcanoes in Central America were classified as ‘upper montane’ by Korner et al.9 based on thermal belts alone. For the purposes of this dataset, we considered these regions alpine habitat based on published measurements of treeline and distinct alpine plant communities facilitated by a mixture of temperature, precipitation, nightly frost, and wind constraints. For example, the Drakensberg range in South Africa was identified as ‘upper montane’ only, but botanical studies have characterized the region as Themeda-Festuca grassland from 1,900–2,800 m and alpine heathlands above 2,800 m13, representing extensive habitat above treeline. As a result, our definition of alpine habitat expands on the thermal belts mapped by Korner et al.9. In this way, the avian communities we identified retain species lineages that are confined to cooler high elevation habitats, representing remnants of more extensive alpine ecosystems from the last glaciation event.We grouped mountain ranges into 12 global regions and 38 subregions based on similar climates and alpine vegetation stemming from shared geographic position (Tables 2, 3; Fig. 1). The ‘Islands’ category represents very limited alpine habitat on four disparate islands that do not easily fit within any other major region, but nevertheless occur in subtropical or tropical realms: Hawaii, Sumatra, Borneo, and the Canary Islands. Alpine breeding birds and life-history traits were identified for each individual region so that future analyses can either include or remove mountain ranges depending on their definition of alpine habitat. This approach also promotes comparisons of avian communities at a finer scale across the full diversity of alpine habitats.Table 2 Description of the major regions and specific mountain ranges in the Americas that are included in the dataset.Full size tableTable 3 Description of the major regions and specific mountain ranges from Eurasia, Africa, and Oceania, plus the miscellaneous mountain ‘Islands’ region.Full size tableAlpine breeding bird speciesFor each region described in Tables 2 and 3, we assembled a list of alpine breeding species from published literature, environmental assessment reports, regional monitoring schemes, bird atlases, and expert knowledge following the most recent International Ornithology Committee taxonomy, version 12.129. An alpine breeding bird is any species that nests above treeline, regardless of how frequently, such that all or a certain proportion of a species is dependent on alpine habitat during the breeding season. Due to certain data-deficiencies underlying existing species range estimates above treeline, using knowledge from regional experts was the most accurate method to assemble a global list of alpine breeding birds for most mountain regions. See the Technical Validation section for specifics on how we validated the use of expert knowledge when assembling species and their traits for the Global Alpine Breeding Bird list.Species traitsWe included species traits that fall under three general topics: 1) alpine breeding propensity, 2) ecological traits, and 3) conservation value. Alpine breeding propensity includes breeding habitat specialization and alpine breeding status, ecological traits include migration behaviour and nest traits, while conservation value encompasses mountain endemism and conservation status. Together, these variables broadly reflect alpine habitat use during the breeding season globally, as well as provide the basis for evaluating the conservation potential and risks for alpine bird communities. We recorded general trait specifications for each species using available resources such as Birds of the World30, the IUCN Red List31, and AVONET21. We then solicited region-specific traits from regional experts and the same review process was conducted for these traits as for alpine breeding evidence (see Technical Validation). All traits were specific to alpine breeding birds whenever possible. The global distribution of each species trait can be visualized in Fig. 3.Fig. 3The global distribution for each trait included in the dataset, including (a–c) alpine breeding propensity, (d–f) ecological traits, and (g–i) traits relevant to conservation status and data uncertainty. In all cases except panel c the y-axis is the proportion of all 1,310 alpine breeding species identified in the dataset. Panel c depicts the elevational breeding distribution expected from the different combinations of breeding specialization and alpine breeding status to visualize the probability of breeding above treeline. In Panel e, ‘BP’ refers to brood parasite. See Table 4 or the metadata for full descriptions of each trait.Full size imageSpecialization for breeding in alpine habitats (hereafter ‘breeding specialization’) and the propensity to breed in alpine habitats (hereafter ‘breeding status’) form a tiered estimate of alpine breeding behaviour. First, we classified each species into one of three breeding specialization categories to differentiate among species that predominantly breed above treeline (alpine specialists), breed both above and below treeline (elevational generalists) or are limited to high latitude tundra habitats (tundra specialists). The latter includes alpine-Arctic or alpine-Antarctic transition zones, where species nest in higher, drier tundra (approximately >400 m elevation) but may also breed in wet tundra at lower or coastal elevations. In this way, we clearly identified species that breed in alpine tundra habitat, but where tundra is the primary driver of breeding presence, not necessarily selection for high elevation. Under breeding status, we quantified the likelihood of breeding above treeline relative to below treeline as common, uncommon, or rare. Alpine specialists are always common alpine breeders (regardless of their density and distribution), but generalists or tundra specialists can be common, uncommon, or rare breeders in alpine habitats depending on whether they are found breeding consistently above treeline, more often breeding below treeline, or only incidentally breeding in the alpine, respectively. Together, these variables identify a species’ relative probability of breeding along the elevational gradient and with respect to the treeline (Fig. 3).We used two nest traits to identify the general breeding niche of each species: nest type and nest site. Nest type included three primary category levels (open cup, cavity, domed nest), while nest site was subdivided into seven levels (ground, bank, shrub, tree, rock, cliff, and glacier). Brood parasite species, which will use a range of nest types and sites depending on the host species, were placed in an additional ‘brood parasite’ category for each nest trait. A species with an open cup or domed nest is limited to placing the nest on the ground, in vegetation (e.g., a shrub or stunted tree), or on a cliff, while cavity nesters may be in a bank (i.e., burrow/tunnel), in a rock (e.g., crevice), or in a tree (e.g., natural or excavated cavity). If nest traits were undescribed for a certain species, we inferred nest traits from the most closely related species in similar high elevation habitats (see Data uncertainty).Species were assigned to three migration categories based on their predominant behaviour: resident, short-distance, and long-distance migrants. Resident species remain near their breeding habitat year-round, allowing for occasional, short-term movements in response to extreme weather events. Short-distance migrants conduct seasonal altitudinal migrations, short latitudinal migrations, or nomadic movements where the species remains within the general breeding region (e.g., within the temperate zone). Long-distance migrants travel extensive distances to winter in an entirely different region than their breeding habitat (e.g., temperate breeders to tropical habitats). A general threshold of 3,000 km was used to distinguish between short- and long-distance migrants because it approximates the distance traveled from the Himalayas to the southern coast of India, Northern Europe to the Mediterranean coast, or Alaska to California. In other words, this distance represents a relatively consistent reference across global regions. While there are finer-scale migration designations that could be made, such as partial or altitudinal migration, we lack detailed movement data for most species and regions. Although a global list of potential altitudinal migrants exists that can be incorporated with this alpine breeding bird dataset if desired32, altitudinal migration often co-occurs with short-distance latitudinal movements and there are considerable differences in migration behaviour among subspecies, populations, and even individuals33. We therefore chose to use established migration categories that align with other global trait databases. In fact, our migration designations were largely congruent with those in AVONET21, with the primary difference being between resident and short-distance migrants. We identified ~200 short-distance migrants that were considered sedentary (resident) under the AVONET classification. This difference is to be expected given that we defined migration behaviour for alpine breeding populations compared to global trait values for all populations. For many species, alpine breeding birds will depart higher elevations during winter to avoid severe weather conditions, even though low elevation populations of the same species may be predominantly resident34. Therefore, the three broad categories chosen here are intended to balance available information with sufficient accuracy to provide data useful for large-scale life-history and biogeographic analyses of alpine breeding birds.Mountain endemism refers to a species whose breeding range is restricted by physical, environmental, or biological barriers to a general mountain region and the surrounding low elevation habitat. For example, a species breeding only on the Tibetan Plateau was classified as an endemic species, but a species that breeds across the Tibetan Plateau, the Himalayas, and the Altai Mountains was classified as non-endemic. When possible, we also classified endemism for defined subspecies. Species endemism is a more conservative metric, while subspecies endemism attempts to estimate additional cryptic endemism given that species differentiation is not well-defined for many high elevation birds. For example, the Caucasus Mountains support several distinct subspecies isolated from their primary distributions, including the Great rosefinch (Carpodacus rubicilla rubicilla), Dunnock (Prunella modularis obscura), and Güldenstädt’s redstart (Phoenicurus erythrogastrus erythrogastrus).Finally, conservation status refers to the IUCN Red List designations, version 2022-131. In addition to the traditional IUCN categories (e.g., Least Concern, Near Threatened, Vulnerable, etc.), we also included a Not Assessed (NA) category that generally occurred when a species was recently split. See Table 4 for complete definitions of all traits.Table 4 Definitions of species traits included in the Global Alpine Breeding Bird dataset.Full size tableData uncertaintyGlobally, there is significant variation in accessibility to alpine habitats and funding for alpine research. As a result, uncertainty in alpine breeding status may differ among regions and species. For example, in New Guinea, mist-net surveys and point counts across elevation have identified species that frequently use alpine habitat, but a dearth of breeding biology studies means that there are few nest records above treeline. It is thus necessary to codify this level of uncertainty for each species.To this effect, we included a variable termed ‘Data reliability’, which is a four-level categorical variable from 0 to 3 that is based on the number of reported nests that have been found and described for each species. We used the presence of nest descriptions to evaluate uncertainty because active nests are the must fundamental form of evidence for breeding above treeline, and therefore it is reasonable that a species with less existing knowledge about nest traits or nesting behaviours will have considerably more uncertainty around its designation as an alpine breeding species. For this variable, 0 indicates that nest traits are undescribed for a given species, 1 means less than five nests have been described, 2 indicates more than five nests have been described, but all from a single population, and therefore there is limited understanding of geographic variation, while 3 occurs when nests have been described from multiple populations or regions. If nest traits were undescribed for a species (data reliability = 0), then nest type and site were inferred from the most closely related species with available data, and whenever possible, a congener was selected that also breeds at high elevations or in alpine habitats. While the nest traits of most species have been sufficiently described, there is a significant proportion of alpine breeding birds with less available data (27.0%; Fig. 3i). The relative number of described nests was derived from Birds of the World30. We recognize that these data may not reflect true knowledge of nest traits given that not all species accounts have been recently updated. However, it does represent a consistent data source that allowed us to approximate data reliability sufficiently for our purposes.In combination, data reliability and alpine breeding status fully characterize alpine breeding uncertainty. For example, a species considered a rare alpine breeder with a data reliability of 3, means that there is strong evidence for breeding above treeline, but only incidentally under very specific circumstances. However, a rare alpine breeder with a data reliability of zero (i.e., nest undescribed), means that the likelihood of breeding above treeline may be probable based on behavioural observations, but further confirmation is required. When using this dataset for analyses, one must decide whether to use a conservative approach or consider all potential alpine breeding species with the appropriate caveats (see Usage Notes). More

Kobayashi, H. & Kohshima, S. Unique morphology of the human eye. Nature 387(6635), 767–768 (1997).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Kobayashi, H. & Kohshima, S. Unique morphology of the human eye and its adaptive meaning: Comparative studies on external morphology of the primate eye. J. Hum. Evol. 40(5), 419–435 (2001).CAS 
PubMed 
Article 

Google Scholar 
Mayhew, J. A. & Gómez, J. C. Gorillas with white sclera: A naturally occurring variation in a morphological trait linked to social cognitive functions. Am. J. Primatol. 77, 869–877 (2015).PubMed 
Article 

Google Scholar 
Perea-García, J. O. Quantifying ocular morphologies in extant primates for reliable interspecific comparisons. J. Lang. Evol. 1(2), 151–158 (2016).Article 

Google Scholar 
Perea-García, J. O., Kret, M. E., Monteiro, A. & Hobaiter, C. Scleral pigmentation leads to conspicuous, not cryptic, eye morphology in chimpanzees. Proc. Natl. Acad. Sci. 116(39), 19248–19250 (2019).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Caspar, K., Biggemann, M., Geissmann, T. & Begall, S. Ocular pigmentation in humans, great apes, and gibbons is not suggestive of communicative functions. Sci. Rep. 11, 1–14 (2021).Article 

Google Scholar 
Mearing, A. S. & Koops, K. Quantifying gaze conspicuousness: Are humans distinct from chimpanzees and bonobos?. J. Hum. Evol. 157, 103043. https://doi.org/10.1016/J.JHEVOL.2021.103043 (2021).Article 
PubMed 

Google Scholar 
Perea-García, J. O., Danel, D. P. & Monteiro, A. Diversity in primate external eye morphology: Previously undescribed traits and their potential adaptive value. Symmetry 13, 1270 (2021).ADS 
Article 

Google Scholar 
Banks, M. S., Sprague, W. W., Schmoll, J., Parnell, J. A. & Love, G. D. Why do animal eyes have pupils of different shapes?. Sci. Adv. 1(7), e1500391 (2015).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Corfield, J. R. et al. Anatomical specializations for nocturnality in a critically endangered parrot, the kakapo (Strigops habroptilus). PLoS ONE 6(8), e22945 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lisney, T. J. et al. Ecomorphology of eye shape and retinal topography in waterfowl (Aves: Anseriformes: Anatidae) with different foraging modes. J. Comp. Physiol. A. 199(5), 385–402 (2013).Article 

Google Scholar 
Lisney, T. J., Iwaniuk, A. N., Bandet, M. V. & Wylie, D. R. Eye shape and retinal topography in owls (Aves: Strigiformes). Brain Behav. Evol. 79(4), 218–236 (2012).PubMed 
Article 

Google Scholar 
Duke-Elder, S. S. The eye in evolution. In System of Ophthalmology (ed. Duke-Elder, S. S.) 453 (Henry Kimpton, 1985).
Google Scholar 
-Miller, D., & Sanghvi, S. (1990). Contrast sensitivity and glare testing in corneal disease. In Glare and Contrast Sensitivity for Clinicians (pp. 45–52). Springer.De Broff, B. M. & Pahk, P. J. The ability of periorbitally applied antiglare products to improve contrast sensitivity in conditions of sunlight exposure. Arch. Ophthalmol. 121(7), 997–1001 (2003).Article 

Google Scholar 
Caspar, K. R., Mader, L., Pallasdies, F., Lindenmeier, M. & Begall, S. Captive gibbons (Hylobatidae) use different referential cues in an object-choice task: Insights into lesser ape cognition and manual laterality. PeerJ 6, e5348 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Kaplan, G. & Rogers, L. J. Patterns of gazing in orangutans (Pongo pygmaeus). Int. J. Primatol. 23(3), 501–526 (2002).Article 

Google Scholar 
Kamilar, J. M. & Bradley, B. J. Interspecific variation in primate coat colour supports Gloger’s rule. J. Biogeogr. 38(12), 2270–2277 (2011).Article 

Google Scholar 
Santana, S. E., Lynch Alfaro, J. & Alfaro, M. E. Adaptive evolution of facial colour patterns in Neotropical primates. Proc. R. Soc. B Biol. Sci. 279(1736), 2204–2211 (2012).Article 

Google Scholar 
Santana, S. E., Alfaro, J. L., Noonan, A. & Alfaro, M. E. Adaptive response to sociality and ecology drives the diversification of facial colour patterns in catarrhines. Nat. Commun. 4(1), 1–7 (2013).Article 

Google Scholar 
Delhey, K. A review of Gloger’s rule, an ecogeographical rule of colour: Definitions, interpretations and evidence. Biol. Rev. 94(4), 1294–1316 (2019).PubMed 

Google Scholar 
Zhang, P. & Watanabe, K. Preliminary study on eye colour in Japanese macaques (Macaca fuscata) in their natural habitat. Primates 48(2), 122–129 (2007).PubMed 
Article 

Google Scholar 
Bradley, B. J., Pedersen, A. & Mundy, N. I. Brief communication: blue eyes in lemurs and humans: Same phenotype, different genetic mechanism. Am. J. Phys. Anthropol. 139(2), 269–273 (2009).PubMed 
Article 

Google Scholar 
Meyer, W. K., Zhang, S., Hayakawa, S., Imai, H. & Przeworski, M. The convergent evolution of blue iris pigmentation in primates took distinct molecular paths. Am. J. Phys. Anthropol. 151(3), 398–407 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Negro, J. J., Blázquez, M. C. & Galván, I. Intraspecific eye color variability in birds and mammals: A recent evolutionary event exclusive to humans and domestic animals. Front. Zool. 14(1), 1–6 (2017).Article 

Google Scholar 
van den Berg, T. J. T. P., IJspeert, J. K. & De Waard, P. W. T. Dependence of intraocular straylight on pigmentation and light transmission through the ocular wall. Vis. Res. 31(7–8), 1361–1367 (1991).PubMed 
Article 

Google Scholar 
Mure, L. S. Intrinsically photosensitive retinal ganglion cells of the human retina. Front. Neurol. 12, 636330 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Wald, L. (2018). Basics in solar radiation at earth surface. ffhal-01676634ff.Workman, L. Blue eyes keep away the winter blues: Is blue eye pigmentation an evolved feature to provide resilience to seasonal affective disorder. OA J. Behav. Sci. Psychol. 1(1), 180002 (2018).MathSciNet 

Google Scholar 
Smith, A. R. Color gamut transform pairs. ACM Siggraph Comput. Graph. 12(3), 12–19 (1978).CAS 
Article 

Google Scholar 
Kamilar, J. M. & Cooper, N. Phylogenetic signal in primate behaviour, ecology and life history. Philos. Trans. R. Soc. B: Biol. Sci. 368(1618), 20120341 (2013).Article 

Google Scholar 
Leutenegger, W. & Kelly, J. T. Relationship of sexual dimorphism in canine size and body size to social, behavioral, and ecological correlates in anthropoid primates. Primates 18(1), 117–136. https://doi.org/10.1007/bf02382954 (1977).Article 

Google Scholar 
Gómez, J. C. (1996). Ostensive behavior in great apes: The role of eye contact. Reaching into thought: The minds of the great apes, 131–151.Dovidio, J. F., & Ellyson, S. L. (1985). Pattern of visual dominance behavior in humans. In Power, Dominance, and Nonverbal Behavior (pp. 129–149). Springer.Nakatsukasa, M. Locomotor differentiation and different skeletal morphologies in mangabeys (Lophocebus and Cercocebus). Folia Primatol. 66(1–4), 15–24 (1996).CAS 
Article 

Google Scholar 
Smith, R. J. & Jungers, W. L. Body mass in comparative primatology. J. Hum. Evol. 32(6), 523–559 (1997).CAS 
PubMed 
Article 

Google Scholar 
Fioletov, V., Kerr, J. B. & Fergusson, A. The UV index: Definition, distribution and factors affecting it. Can. J. Public Health 101(4), I5–I9 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
Jablonski, N. G. & Chaplin, G. Human skin pigmentation as an adaptation to UV radiation. Proc. Natl. Acad. Sci. 107(Supplement 2), 8962–8968 (2010).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Do, M. T. H. & Yau, K. W. Intrinsically photosensitive retinal ganglion cells. Physiol. Rev. (2010).Pickard, G. E. & Sollars, P. J. Intrinsically photosensitive retinal ganglion cells. Rev. Physiol. Bioch. Pharmacol. 162, 59–90 (2012).Goel, N., Terman, M. & Terman, J. S. Depressive symptomatology differentiates subgroups of patients with seasonal affective disorder. Depress. Anxiety 15(1), 34–41 (2002).PubMed 
Article 

Google Scholar 
Münch, M. et al. Blue-enriched morning light as a countermeasure to light at the wrong time: Effects on cognition, sleepiness, sleep, and circadian phase. Neuropsychobiology 74(4), 207–218 (2016).PubMed 
Article 

Google Scholar 
Davidson, G. L., Thornton, A. & Clayton, N. S. Evolution of iris colour in relation to cavity nesting and parental care in passerine birds. Biol. Let. 13(1), 20160783 (2017).Article 

Google Scholar 
Volpato, G. L., Luchiari, A. C., Duarte, C. R. A., Barreto, R. E. & Ramanzini, G. C. Eye color as an indicator of social rank in the fish Nile tilapia. Braz. J. Med. Biol. Res. 36, 1659–1663 (2003).CAS 
PubMed 
Article 

Google Scholar 
Fosbury, R. A. & Jeffery, G. Reindeer eyes seasonally adapt to ozone-blue Arctic twilight by tuning a photonic tapetum lucidum. Proc. R. Soc. B 289(1977), 20221002 (2022).PubMed 
PubMed Central 
Article 

Google Scholar 
Allen, W. L., Stevens, M. & Higham, J. P. Character displacement of Cercopithecini primate visual signals. Nat. Commun. 5(1), 1–10 (2014).Article 

Google Scholar 
Frost, P. European hair and eye color: A case of frequency-dependent sexual selection?. Evol. Hum. Behav. 27(2), 85–103 (2006).Article 

Google Scholar 
Hart, D. (2000). Primates as prey: Ecological, morphological and behavioral relationships between primate species and their predators.Liebal, K., Waller, B. M., Slocombe, K. E. & Burrows, A. M. Primate communication: a multimodal approach. (Cambridge University Press, 2014).
Google Scholar 
Whitham, W., Schapiro, S. J., Troscianko, J. & Yorzinski, J. L. Chimpanzee (Pan troglodytes) gaze is conspicuous at ecologically-relevant distances. Sci. Rep. 12(1), 1–7 (2022).Article 

Google Scholar 
Kano, F., Kawaguchi, Y. & Hanling, Y. Experimental evidence that uniformly white sclera enhances the visibility of eye-gaze direction in humans and chimpanzees. Elife 11, e74086 (2022).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Emery, N. J. The eyes have it: The neuroethology, function and evolution of social gaze. Neurosci. Biobehav. Rev. 24, 581–604 (2000).CAS 
PubMed 
Article 

Google Scholar 
-Bourjade, M. (2016). Social attention. Int. Encycl. Primatol. 1–2.Petersen, R. M., Dubuc, C. & Higham, J. P. Facial displays of dominance in non-human primates. In The facial displays of leaders (pp. 123–143) (Palgrave Macmillan, Cham, 2018).Laitly, A., Callaghan, C. T., Delhey, K. & Cornwell, W. K. Is color data from citizen science photographs reliable for biodiversity research?. Ecol. Evol. 11(9), 4071–4083 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Chan, I. Z., Stevens, M. & Todd, P. A. PAT-GEOM: A software package for the analysis of animal patterns. Methods Ecol. Evol. 10(4), 591–600 (2019).Article 

Google Scholar 
Felsenstein, J. Phylogenies and the comparative method. Am. Nat. 125(1), 1–15 (1985).Article 

Google Scholar 
Revell, L. J. phytools: An R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3(2), 217–223 (2012).Article 

Google Scholar 
Pagel, M. Inferring the historical patterns of biological evolution. Nature 401, 877–884 (1999).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Orme, D. et al. The caper package: Comparative analysis of phylogenetics and evolution in R. R Pack. Vers. 5(2), 1–36 (2013).
Google Scholar 
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).MATH 
Book 

Google Scholar 
-Williamson, E. A., Maisels, F. G., Groves, C. P., Fruth, B. I., Humle, T., & Morton, F. B. (2013). Handbook of the Mammals of the World Volume 3: Primates. More

The interaction between the two factors (substrates and CRF rates) was statistically significant by the F-test (p  More

Deforestation trajectories and economic driversCambodia has undergone significant forest loss in recent decades—with 2.6 million hectares of forest cover loss occurring since 2001, equating to 29.5% of forest cover7 and 1.45 billion tonnes of CO2 emissions8. The deforestation rates have increased by 76% in the last decade (2011–2021) compared to the previous (2001–2010; Fig. 1b)7. We find forest loss has occurred within three distinct Phases demonstrated by changepoint analysis: (1) Phase 1: steady rise from 2000 to 2009 (average = 0.82%/year), (2) Phase 2: peak years from 2010 to 2013 (average = 2.3%/year), (3) Phase 3: moderate phase from 2014 to 2021 (average = 1.6%/year). Whilst the annual rate of deforestation has declined since the Phase 2, Cambodia currently has the highest country-level annual rate of forest loss globally7, illustrating the relentless deforestation spreading across the landscape. Critically, much of this forest loss and degradation is occurring in mature primary forests (Fig. 1b), which hold significant carbon and are home to rich biodiversity and keystone species17,18,19.
This deforestation in Cambodia has been attributed to the widespread development of Economic Land Concessions (ELCs), the expansion of numerous agricultural frontiers and relentless illegal logging20,21,22. These drivers have been abetted by the establishment of an extensive national road network (Fig. 1a)20—developed to promote economic growth and urban–rural connectivity23. The majority (88.4%) of these roads have been funded by foreign governments (the People’s Republic of China: 38.5%, Japan: 37.9%, and Republic of Korea: 12.0%)18—all of whom have established land concessions within Cambodia’s borders24 through the allocation of state land into private land for long-term industrial plantations22,25. The expansion of ELCs across Cambodia (average addition of 105,000 ha/year of ELC land since 1998) has been directly attributed to up to 40% of the country’s deforestation21, with further indirect impacts due to encroachment into rural community lands (indigenous areas, community forests, subsistence agricultural fields). This results in landlessness, poverty, and land conflicts, forcing communities to migrate in search of arable land, further contributing to the growing degradation and destruction of forests22,26,27,28,29.Strategic government interventionProtected areas expanded across Cambodia in 1993 following a royal decree26; the legal details of which were delineated in the 2008 Protected Areas Law, introducing protected categories, wildlife corridors and strict laws prohibiting development9. While over 80 protected areas currently exist covering 35% of Cambodian land10, they are still under substantial threat30. In further efforts to curb deforestation, the Royal Government of Cambodia ordered the suspension of new ELCs and revocation of a subset of existing ELCs in 2012 (Order 01BB)31. This resulted in a reduction of ELCs from a peak of ~ 2.1 million ha in 2012 to ~ 1.6 million ha from 2014 onward (Fig. 1b), with a significant positive correlation between the quantity of land classified as ELCs and the country-level deforestation rate (R = 0.87, p  More

Seibold, S. et al. Arthropod decline in grasslands and forests is associated with landscape-level drivers. Nature 574, 671–674 (2019).ADS 
CAS 
PubMed 

Google Scholar 
van Klink, R. et al. Meta-analysis reveals declines in terrestrial but increases in freshwater insect abundances. Science 368, 417–420 (2020).ADS 
PubMed 

Google Scholar 
Wagner, D. L. Insect declines in the anthropocene. Annu. Rev. Entomol. 65, 457–480 (2020).CAS 
PubMed 

Google Scholar 
Wilson, E. O. The little things that run the world (the importance and conservation of invertebrates). Conserv. Biol. 1, 344–346 (1987).
Google Scholar 
Isaacs, R., Tuell, J., Fiedler, A., Gardiner, M. & Landis, D. Maximizing arthropod-mediated ecosystem services in agricultural landscapes: The role of native plants. Front. Ecol. Environ. 7, 196–203 (2009).
Google Scholar 
Hallmann, C. A. et al. More than 75 percent decline over 27 years in total flying insect biomass in protected areas. PLoS ONE 12, e0185809 (2017).PubMed 
PubMed Central 

Google Scholar 
Raven, P. H. & Wagner, D. L. Agricultural intensification and climate change are rapidly decreasing insect biodiversity. Proc. Natl. Acad. Sci. U.S.A. 118, e2002548117 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Neves, B. & Pires, I. M. The Mediterranean diet and the increasing demand of the olive oil sector: Shifts and environmental consequences. Region. 5, 101–112 (2018).
Google Scholar 
Silveira, A. et al. The sustainability of agricultural intensification in the early 21st century: Insights from the olive oil production in Alentejo (Southern Portugal). In Changing Societies: Legacies and Challenges. The Diverse Worlds of Sustainability, 247–275 (2018).Myers, N., Mittermeier, R. A., Mittermeier, C. G., da Fonseca, G. A. B. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).ADS 
CAS 
PubMed 

Google Scholar 
Salomone, R. & Ioppolo, G. Environmental impacts of olive oil production: A Life Cycle Assessment case study in the province of Messina (Sicily). J. Clean. Prod. 28, 88–100 (2012).
Google Scholar 
Rallo, L. et al. High-density olive plantations. Hortic. Rev. Am. Soc. Hortic. Sci. 41, 303–383 (2013).
Google Scholar 
Santos, S. A. P., Pereira, J. A., Torres, L. M. & Nogueira, A. J. A. Evaluation of the effects, on canopy arthropods, of two agricultural management systems to control pests in olive groves from north-east of Portugal. Chemosphere 67, 131–139 (2007).ADS 
CAS 
PubMed 

Google Scholar 
Gkisakis, V., Volakakis, N., Kollaros, D., Bàrberi, P. & Kabourakis, E. M. Soil arthropod community in the olive agroecosystem: Determined by environment and farming practices in different management systems and agroecological zones. Agric. Ecosyst. Environ. 218, 178–189 (2016).
Google Scholar 
Beaufoy, G. EU Policies for Olive Farming. Unsustainable on all counts (WWF and Birdlife International, Brussels, 2001).
Google Scholar 
EFNCP. The environmental impact of olive oil production in the EU: Practical options for improving the environmental impact. European Forum on Nature Conservation and Pastoralism & Asociación para el Análisis y Reforma de la Política Agro-rural, Brussels. https://ec.europa.eu/environment/agriculture/pdf/oliveoil.pdf (2000).Vanwalleghem, T. Quantifying the effect of historical soil management on soil erosion rates in Mediterranean olive orchards. Agric. Ecosyst. Environ. 142, 341–351 (2011).
Google Scholar 
Simões, M. P., Belo, A. F., Pinto-Cruz, C. & Pinheiro, A. C. Natural vegetation management to conserve biodiversity and soil water in olive orchards. Span. J. Agric. Res. 12, 633–643 (2014).
Google Scholar 
Milgroom, J., Soriano, M. A., Garrido, J. M., Gómez, J. A. & Fereres, E. The influence of a shift from conventional to organic olive farming on soil management and erosion risk in southern Spain. Renew. Agric. Food Syst. 22, 1–10 (2007).
Google Scholar 
Lodolini, E. M. & Neri, D. Organic olive farming. African J. Agric. Res. 8, 6426–6434 (2013).
Google Scholar 
Rallo, L. Iberian olive growing in a time of change. Chron. Horticult. 49, 27–30 (2010).
Google Scholar 
Diez, C. M. et al. Cultivar and tree density as key factors in the long-term performance of super high-density olive orchards. Front. Plant Sci. 7, 1–13 (2016).
Google Scholar 
Allen, H. D., Randall, R. E., Amable, G. S. & Devereux, B. J. The impact of changing olive cultivation practices on the ground flora of olive groves in the Messara and Psiloritis regions, Crete, Greece. L. Degrad. Dev. 17, 249–327 (2006).
Google Scholar 
Herrera, J. M., Costa, P., Medinas, D., Marques, J. T. & Mira, A. Community composition and activity of insectivorous bats in Mediterranean olive farms. Anim. Conserv. 18, 557–566 (2015).
Google Scholar 
Costa, A. et al. Structural simplification compromises the potential of common insectivorous bats to provide biocontrol services against the major olive pest Prays oleae. Agric. Ecosyst. Environ. 287, 106708 (2020).
Google Scholar 
Morgado, R. et al. A Mediterranean silent spring? The effects of olive farming intensification on breeding bird communities. Agric. Ecosyst. Environ. 288, 106694 (2020).
Google Scholar 
Ruano, F. et al. Use of arthropods for the evaluation of the olive-orchard management regimes. Agric. For. Entomol. 6, 111–120 (2004).
Google Scholar 
Jerez-Valle, C., García, P. A., Campos, M. & Pascual, F. A simple bioindication method to discriminate olive orchard management types using the soil arthropod fauna. Appl. Soil Ecol. 76, 42–51 (2014).
Google Scholar 
Carpio, A. J., Castro, J. & Tortosa, F. S. Arthropod biodiversity in olive groves under two soil management systems: Presence versus absence of herbaceous cover crop. Agric. For. Entomol. 21, 58–68 (2018).
Google Scholar 
Rey, P. J. et al. Landscape-moderated biodiversity effects of ground herb cover in olive groves: Implications for regional biodiversity conservation. Agric. Ecosyst. Environ. 277, 61–73 (2019).
Google Scholar 
Mccomb, W. C. & Noble, R. E. Invertebrate use of natural tree cavities and vertebrate nest boxes. Am. Midl. Nat. 107, 163–172 (1982).
Google Scholar 
Bovyn, R. A., Lordon, M. C., Grecco, A. E., Leeper, A. C. & LaMontagne, J. M. Tree cavity availability in urban cemeteries and city parks. J. Urban Ecol. 5, 1–9 (2019).
Google Scholar 
Ribera, I., Dolédec, S., Downie, I. & Foster, G. Effect of land disturbance and stress on species traits of ground beetle assemblages. Ecology 82, 1112–1129 (2001).
Google Scholar 
Barbaro, L. & van Halder, I. Linking bird, carabid beetle and butterfly life-history traits to habitat fragmentation in mosaic landscapes. Ecography 32, 321–333 (2009).
Google Scholar 
Steffan-Dewenter, I. & Tscharntke, T. Butterfly community structure in fragmented habitats. Ecol. Lett. 3, 449–456 (2000).
Google Scholar 
Gámez-Virués, S. et al. Landscape simplification filters species traits and drives biotic homogenization. Nat. Commun. 6, 8568 (2015).ADS 
PubMed 

Google Scholar 
Medinas, D. et al. Road effects on bat activity depend on surrounding habitat type. Sci. Total Environ. 660, 340–347 (2019).ADS 
CAS 
PubMed 

Google Scholar 
INE. Estatísticas Agrícolas – 2018. Lisboa. Instituto Nacional de Estatística. https://www.ine.pt/xportal/xmain?xpid=INE&xpgid=ine_publicacoes&PUBLICACOESpub_boui=358629204&PUBLICACOESmodo=2 (2019).Rodríguez-Cohard, J. C., Sánchez-Martínez, J. D. & Garrido-Almonacid, A. Strategic responses of the European olive-growing territories to the challenge of globalization. Eur. Plan. Stud. 28, 2261–2283 (2020).
Google Scholar 
Reis, P. O olival em Portugal. Dinâmicas, tecnologias e relação com o desenvolvimento rural. Instituto Nacional de Investigação Agrária e Veterinária. http://www.iniav.pt/fotos/editor2/caderno_olivalemportugal.pdf (2014).Yi, Z., Jinchao, F., Dayuan, X., Weiguo, S. & Axmacher, J. C. A comparison of terrestrial arthropod sampling methods. J. Resour. Ecol. 3, 174–182 (2012).
Google Scholar 
Leather, S. R. Insect Sampling in Forest Ecosystems (Wiley-Blackwell, New Jersey, 2008).
Google Scholar 
Paredes, D., Cayuela, L. & Campos, M. Synergistic effects of ground cover and adjacent vegetation on natural enemies of olive insect pests. Agric. Ecosyst. Environ. 173, 72–80 (2013).
Google Scholar 
Porcel, M., Cotes, B., Castro, J. & Campos, M. The effect of resident vegetation cover on abundance and diversity of green lacewings (Neuroptera: Chrysopidae) on olive trees. J. Pest Sci. 90, 195–206 (2017).
Google Scholar 

Álvarez, H. A. et al. Semi-natural habitat complexity affects abundance and movement of natural enemies in organic olive orchards. Agric. Ecosyst. Environ. 285, 106618 (2019).
Google Scholar 
Paredes, D., Cayuela, L., Gurr, G. M. & Campos, M. Is ground cover vegetation an effective biological control enhancement strategy against olive pests?. PLoS ONE 10, e0117265 (2015).PubMed 
PubMed Central 

Google Scholar 
Gkisakis, V. D. et al. Olive canopy arthropods under organic, integrated, and conventional management. The effect of farming practices, climate and landscape. Agroecol. Sustain. Food Syst. 42, 843–858 (2018).
Google Scholar 
Sanz-Cortés, F. et al. Phenological growth stages of olive trees (Olea europaea). Ann. Appl. Biol. 140, 151–157 (2002).
Google Scholar 
Rodríguez, E., González, B. & Campos, M. Natural enemies associated with cereal cover crops in olive groves. Bullet. Insectol. 65, 43–49 (2012).
Google Scholar 
Morente, M., Campos, M. & Ruano, F. Evaluation of two different methods to measure the effects of the management regime on the olive-canopy arthropod community. Agric. Ecosyst. Environ. 259, 111–118 (2018).
Google Scholar 
Cardenas, M., Pascual, F., Campos, M. & Pekar, S. The spider assemblage of olive groves under three management systems. Environ. Entomol. 44, 509–518 (2015).PubMed 

Google Scholar 
Hegazi, E. M. et al. Seasonality in the occurrence of two lepidopterous olive pests in Egypt. Insect Sci. 18, 565–574 (2011).
Google Scholar 
Markó, V., Keresztes, B., Fountain, M. T. & Cross, J. V. Prey availability, pesticides and the abundance of orchard spider communities. Biol. Control 48, 115–124 (2009).
Google Scholar 
Picchi, M. S., Marchi, S., Albertini, A. & Petacchi, R. Organic management of olive orchards increases the predation rate of overwintering pupae of Bactrocera oleae (Diptera: Tephritidae). Biol. Control 108, 9–15 (2017).
Google Scholar 
Caruso, T. & Migliorini, M. Micro-arthropod communities under human disturbance: Is taxonomic aggregation a valuable tool for detecting multivariate change? Evidence from Mediterranean soil oribatid coenoses. Acta Oecol. 30, 46–53 (2006).ADS 

Google Scholar 
Schipper, A. M., Lotterman, K., Geertsma, M., Leuven, R. S. E. W. & Hendriks, A. J. Using datasets of different taxonomic detail to assess the influence of floodplain characteristics on terrestrial arthropod assemblages. Biodivers. Conserv. 19, 2087–2110 (2010).
Google Scholar 
Timms, L. L., Bowden, J. J., Summerville, K. S. & Buddle, C. M. Does species-level resolution matter? Taxonomic sufficiency in terrestrial arthropod biodiversity studies. Insect Conserv. Divers. 6, 453–462 (2013).
Google Scholar 
Unwin, D. M. A Key to the Families of British Beetles (Field Studies Council, 1984).Goulet, H. & Huber, J. Hymenoptera of the World: An identification Guide to Families. (Agriculture Canada publication, 1993).Johnson, N. F. & Triplehorn, C. A. Borror and DeLong’s Introduction to the Study of Insects 7th edn. (Thomson Brooks/Cole, Belmont, 2005).
Google Scholar 
Fletcher, M. J., and updates. Identification keys and checklists for the leafhoppers, planthoppers and their relatives occurring in Australia and neighbouring areas (Hemiptera: Auchenorrhyncha). https://idtools.dpi.nsw.gov.au/keys/auch/index.html (2009).Mata, L. & Goula, M. Clave de familias de Heterópteros de la Península Ibérica (Insecta, Hemiptera, Heteroptera). Versión 1. Publicaciones del Centre de Recursos de Biodiversitat Animal, Universitat de Barcelona. http://www.ub.edu/crba/publicacions/Clau%20heteropters/Volum4_Clave_de_Familias_de_Heteropteros_de_la_P.Iberica.pdf (2011).Oosterbroek, P. The European families of the Diptera. Identification, diagnosis, biology. (Royal Dutch Society for Natural History (KNNV) Publishing, Utrecht, 2015).World Spider Catalog. Version 19. Natural History Museum Bern. http://wsc.nmbe.ch (2018).Campos, M. Lacewing in Andalusian olive orchards. In Lacewing in the Crop Environment (eds McEwen, P. et al.) 492–497 (Cambridge University Press, Cambridge, 2001).
Google Scholar 
Wilson, E. O. & Hölldobler, B. The rise of the ants: A phylogenetic and ecological explanation. Proc. Natl. Acad. Sci. U. S. A. 102, 7411–7414 (2005).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Martínez-Núñez, C. et al. Ant community potential for pest control in olive groves: Management and landscape effects. Agric. Ecosyst. Environ 305, 107185 (2021).
Google Scholar 
Bianchi, F. J. J. A., Booij, C. J. H. & Tscharntke, T. Sustainable pest regulation in agricultural landscapes: A review on landscape composition, biodiversity and natural pest control. Proc. R. Soc. B. 273, 1715–1727 (2006).CAS 
PubMed 
PubMed Central 

Google Scholar 
Holland, J. M. et al. Semi-natural habitats support biological control, pollination and soil conservation in Europe. A review. Agron. Sustain. Dev. 37, 31 (2017).
Google Scholar 

Paredes, D. et al. Landscape simplification increases Bactrocera oleae abundance in olive groves: Adult population dynamics in different land uses. J. Pest Sci. https://doi.org/10.1007/s10340-022-01489-1 (2022).Article 

Google Scholar 
Thies, C., Roschewitz, I. & Tscharntke, T. The landscape context of cereal aphid–parasitoid interactions. Proc. R. Soc. B. 285, 203–210 (2005).
Google Scholar 
Pinto-Correia, T., Ribeiro, N. & Sá-Sousa, P. Introducing the montado, the cork and holm oak agroforestry system of Southern Portugal. Agrofor. Syst. 82, 99–104 (2011).
Google Scholar 
Morgado, R. et al. Drivers of irrigated olive grove expansion in Mediterranean landscapes and associated biodiversity impacts. Landsc. Urban Plan. 225, 104429 (2022).
Google Scholar 
Direção-Geral do Território. Carta de Uso e Ocupação do Solo de Portugal Continental para 2015 (COS2015). http://www.dgterritorio.pt/dados_abertos/cos/ (2015).Roswell, M., Dushoff, J. & Winfree, R. A conceptual guide to measuring species diversity. Oikos 130, 321–338 (2021).
Google Scholar 
Hsieh, T. C., Ma, K. H. & Chao, A. iNEXT: An R package for rarefaction and extrapolation of species diversity (Hill numbers). Methods Ecol. Evol. 7, 1451–1456 (2016).
Google Scholar 
Penado, A. et al. From pastures to forests: Changes in Mediterranean wild bee communities after rural land abandonment. Insect Conserv. Divers. 15, 325–336 (2022).
Google Scholar 
Ovaskainen, O. & Abrego, N. Joint Species Distribution Modelling. With Applications in R. (Cambridge University Press, 2020).Dormann, C. F. et al. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46 (2013).
Google Scholar 
Macgregor-Fors, I. & Payton, M. E. Contrasting diversity values: Statistical inferences based on overlapping confidence intervals. PLoS ONE 8, e56794 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Tikhonov, G. et al. Joint species distribution modelling with the r-package Hmsc. Methods Ecol. Evol. 11, 442–447 (2019).
Google Scholar 
R Development Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, Austria, 2021).
Google Scholar 
Wickramasinghe, L. P., Harris, S. H., Jones, G. & Jennings, N. V. Abundance and species richness of nocturnal insects on organic and conventional farms: Effects of agricultural intensification on bat foraging. Conserv. Biol. 8, 1283–1292 (2004).
Google Scholar 
Galloway, A. D., Seymour, C. L., Gaigher, R. & Pryke, J. S. Organic farming promotes arthropod predators, but this depends on neighbouring patches of natural vegetation. Agric. Ecosyst. Environ. 310, 107295 (2021).
Google Scholar 
Hevia, V., Ortega, J., Azcárate, F. M., López, C. A. & González, J. A. Exploring the effect of soil management intensity on taxonomic and functional diversity of ants in Mediterranean olive groves. Agric. For. Entomol. 21, 109–118 (2019).
Google Scholar 
Vitanović, E. et al. Arthropod communities within the olive canopy as bioindicators of different management systems. Span. J. Agric. Res. 16, e0301 (2018).
Google Scholar 
Vasconcelos, S. et al. Long-term consequences of agricultural policy decisions: How are forests planted under EEC regulation 2080/92 affecting biodiversity 20 years later?. Biol. Conserv. 236, 393–403 (2019).
Google Scholar 
Tscharntke, T. et al. When natural habitat fails to enhance biological pest control—five hypotheses. Biol. Conserv. 204, 449–458 (2016).
Google Scholar 
Ortega, M., Pascual, S. & Rescia, A. J. Spatial structure of olive groves and scrublands affects Bactrocera oleae abundance: A multi-scale analysis. Basic Appl. Ecol. 17, 696–705 (2016).
Google Scholar 
Martínez-Núñez, C. et al. Direct and indirect effects of agricultural practices, landscape complexity and climate on insectivorous birds, pest abundance and damage in olive groves. Agric. Ecosyst. Environ. 304, 107145 (2020).
Google Scholar 
Paredes, D., Karp, D. S., Chaplin-Kramer, R., Benítez, E. & Campos, M. Natural habitat increases natural pest control in olive groves: Economic implications. J. Pest Sci. 92, 1111–1121 (2019).
Google Scholar 
Attwood, S. J., Maron, M., House, P. N. & Zammit, C. Do arthropod assemblages display globally consistent responses to intensified agricultural land use and management?. Glob. Ecol. Biogeogr. 17, 585–599 (2008).
Google Scholar 
Miranda, M. A., Miquel, M., Terrassa, J., Melis, N. & Monerris, M. Parasitism of Bactrocera oleae (Diptera; Tephritidae) by Psyttalia concolor (Hymenoptera; Braconidae) in the Balearic Islands (Spain). J. Appl. Entomol. 132, 798–805 (2008).
Google Scholar 
Álvarez, H. A., Morente, M., Campos, M. & Ruano, F. L. madurez de las cubiertas vegetales aumenta la presencia de enemigos naturales y la resiliencia de la red trófica de la copa del olivo. Ecosistemas 28, 92–106 (2019).
Google Scholar 
Rusch, A., Valantin-Morison, M., Sarthou, J. P. & Roger-Estrade, J. Biological control of insect pests in agroecosystems. Effects of crop management, farming systems, and seminatural habitats at the landscape scale: A review. Adv. Agron. 109, 219–259 (2010).
Google Scholar 
Greenop, A., Cook, S. M., Wilby, A., Pywell, R. F. & Woodcock, B. A. Invertebrate community structure predicts natural pest control resilience to insecticide exposure. J. Appl. Ecol. 57, 2441–2453 (2020).CAS 

Google Scholar 
Porcel, M., Ruano, F., Cotes, B., Peña, A. & Campos, M. Agricultural management systems affect the green lacewing community (Neuroptera: Chrysopidae) in olive orchards in southern Spain. Environ. Entomol. 42, 97–106 (2013).CAS 
PubMed 

Google Scholar 
Stamou, G. P. Arthropods of Mediterranean-Type Ecosystems (Springer, 2012).Santos, J. L. et al. A farming systems approach to linking agricultural policies with biodiversity and ecosystem services. Front. Ecol. Environ. 19, 168–175 (2021).
Google Scholar 
Ribeiro, P. F. et al. An applied farming systems approach to infer conservation-relevant agricultural practices for agri-environment policy design. Land Use Policy 58, 165–172 (2016).
Google Scholar 
Herrera, J. M. et al. A food web approach reveals the vulnerability of biocontrol services by birds and bats to landscape modification at regional scale. Sci. Rep. 11, 1–10 (2021).
Google Scholar 
Solomou, A. D. & Sfougaris, A. I. Bird community characteristics as indicators of sustainable management in olive grove ecosystems of Central Greece. J. Nat. Hist. 49, 301–325 (2015).
Google Scholar 
Piñeiro, V. et al. A scoping review on incentives for adoption of sustainable agricultural practices and their outcomes. Nat Sustain. 3, 809–820 (2020).
Google Scholar  More

Sievers, M. et al. Trends Ecol. Evol. 34, 807–817 (2019).Article 

Google Scholar 
Barbier, E. B. et al. Ecol. Monogr. 81, 169–193 (2011).Article 

Google Scholar 
zu Ermgassen, P. S. E. et al. Estuar. Coast. Shelf Sci. 248, 107159 (2021).Article 

Google Scholar 
Spalding, M. & Parrett, C. L. Mar. Policy 110, 103540 (2019).Article 

Google Scholar 
Dahdouh-Guebas, F. et al. Estuar. Coast. Shelf Sci. 248, 106942 (2021).Article 

Google Scholar 
Dahdouh-Guebas, F. et al. Front. Mar. Sci. 7, 603651 (2020).Article 

Google Scholar 
Friess, D. A. & McKee, K. L. in Dynamic Sedimentary Environments of Mangrove Coasts (eds Sidik, F. & Friess, D.A.) Ch. 7 (Elsevier, 2021).Lee, S. Y. et al. Glob. Ecol. Biogeogr. 23, 726–743 (2014).Article 

Google Scholar 
Goldberg, L., Lagomasino, D., Thomas, N. & Fatoyinbo, T. Glob. Change Biol. 26, 5844–5855 (2020).Article 

Google Scholar 
Cannicci, S. et al. Proc. Natl Acad. Sci. USA 118, e2016913118 (2021).CAS 
Article 

Google Scholar 
Bouillon, S., Koedam, N., Raman, A. & Dehairs, F. Oecologia 130, 441–448 (2002).CAS 
Article 

Google Scholar 
Adame, M. F. et al. Glob. Chang. Biol. 27, 2856–2866 (2021).CAS 
Article 

Google Scholar 
Pittman, S. et al. Mar. Ecol. Prog. Ser. 663, 1–29 (2021).Article 

Google Scholar 
Nagelkerken, I., Sheaves, M. T., Baker, R. & Connolly, R. M. Fish Fish. 16, 362–371 (2015).Article 

Google Scholar 
Huxham, M., Whitlock, D., Githaiga, M. & Dencer-Brown, A. Curr. For. Rep. 4, 101–110 (2018).
Google Scholar 
Bryan-Brown, D. N. et al. Sci. Rep. 10, 7117 (2020).CAS 
Article 

Google Scholar 
Curnick, D. J. et al. Science 363, 239–239 (2019).Article 

Google Scholar 
Dahdouh-Guebas, F. & Cannicci, S. Front. Mar. Sci. 8, 799543 (2021).Article 

Google Scholar 
Bruelheide, H. et al. Nat. Ecol. Evol. 2, 1906–1917 (2018).Article 

Google Scholar 
Harvey, B. P., Marshall, K. E., Harley, C. D. G. & Russell, B. D. Trends Ecol. Evol. 37, 20–29 (2021).Article 

Google Scholar 
Rahman, M. M. et al. Nat. Commun. 12, 3875 (2021).CAS 
Article 

Google Scholar 
Yando, E. S. et al. Biol. Conserv. 263, 109355 (2021).Article 

Google Scholar 
Krauss, K. W. & Osland, M. J. Ann. Bot. 125, 213–234 (2020).PubMed 

Google Scholar 
Asbridge, E. F. et al. Estuar. Coast. Shelf Sci. 228, 106353 (2019).Article 

Google Scholar 
Sippo, J. Z., Lovelock, C. E., Santos, I. R., Sanders, C. J. & Maher, D. T. Estuar. Coast. Shelf Sci. 215, 241–249 (2018).Article 

Google Scholar 
Erftemeijer, P. L. A. & Hamerlynck, O. J. Coast. Res. 42, 228–235 (2005).
Google Scholar 
Abhik, S. et al. Sci. Rep. 11, 20411 (2021).CAS 
Article 

Google Scholar 
Osland, M. J., Day, R. H. & Michot, T. C. Divers. Distrib. 26, 1366–1382 (2020).Article 

Google Scholar 
Dahdouh-Guebas, F. et al. Curr. Biol. 15, 579–586 (2005).CAS 
Article 

Google Scholar 
Turschwell, M. P. et al. Biol. Conserv. 247, 108637 (2020).Article 

Google Scholar 
Saintilan, N. et al. Science 368, 1118–1121 (2020).CAS 
Article 

Google Scholar 
Xie, D. et al. Environ. Res. Lett. 15, 114033 (2020).Article 

Google Scholar 
Ewel, K. C., Twilley, R. R. & Ong, J. E. Glob. Ecol. Biogeogr. Lett. 7, 83–94 (1998).Article 

Google Scholar 
Dahdouh-Guebas, F. in Vers une Nouvelle Synthèse Ecologique: de L’écologie Scientifique au Développement Durable. (ed. Meerts, P.) 182–193 (Centre Paul Duvigneaud de Documentation Ecologique, 2013).Gallup, L., Sonnenfeld, D. A. & Dahdouh-Guebas, F. Ocean Coast. Manage. 185, 105001 (2020).Article 

Google Scholar 
Rist, S. & Dahdouh-Guebas, F. Environ. Dev. Sustain. 8, 467–493 (2006).Article 

Google Scholar 
Foell, J., Harrison, E. & Stirrat, R. L. Participatory Approaches to Natural Resource Management: The Case of Coastal Zone Management in the Puttalam District, Sri Lanka. Project R6977 (School of African and Asian Studies, University of Sussex, 2000).Beymer-Farris, B. A. & Bassett, T. J. Glob. Environ. Change 22, 332–341 (2012).Article 

Google Scholar 
Lovelock, C. E. & Brown, B. M. Nat. Ecol. Evol. 3, 1135 (2019).Article 

Google Scholar 
Dahdouh-Guebas, F. et al. J. Ethnobiol. Ethnomed. 2, 24 (2006).CAS 
Article 

Google Scholar  More

Thuiller, W., Lavorel, S., Araujo, M. B., Sykes, M. T. & Prentice, I. C. Climate change threats to plant diversity in Europe. Proc. Natl. Acad. Sci. USA 102, 8245–8250. https://doi.org/10.1073/pnas.0409902102 (2005).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Fagundez, J. Heathlands confronting global change: Drivers of biodiversity loss from past to future scenarios. Ann. Bot. 111, 151–172. https://doi.org/10.1093/aob/mcs257 (2013).Article 
PubMed 

Google Scholar 
Nicotra, A. B. et al. Plant phenotypic plasticity in a changing climate. Trends Plant Sci. 15, 684–692. https://doi.org/10.1016/j.tplants.2010.09.008 (2010).CAS 
Article 
PubMed 

Google Scholar 
Dubin, M. J. et al. DNA methylation in Arabidopsis has a genetic basis and shows evidence of local adaptation. Elife 4, 25. https://doi.org/10.7554/eLife.05255 (2015).Article 

Google Scholar 
Herrera, C. M., Medrano, M. & Bazaga, P. Comparative spatial genetics and epigenetics of plant populations: Heuristic value and a proof of concept. Mol. Ecol. 25, 1653–1664. https://doi.org/10.1111/mec.13576 (2016).CAS 
Article 
PubMed 

Google Scholar 
Richards, C. L. et al. Ecological plant epigenetics: Evidence from model and non-model species, and the way forward. Ecol. Lett. 20, 1576–1590. https://doi.org/10.1111/ele.12858 (2017).Article 
PubMed 

Google Scholar 
Münzbergová, Z., Latzel, V., Šurinová, M. & Hadincová, V. DNA methylation as a possible mechanism affecting ability of natural populations to adapt to changing climate. Oikos 128, 124–134. https://doi.org/10.1111/oik.05591 (2019).CAS 
Article 

Google Scholar 
Thiebaut, F., Hemerly, A. S. & Ferreira, P. C. G. A role for epigenetic regulation in the adaptation and stress responses of non-model plants. Front. Plant Sci. 10, 25. https://doi.org/10.3389/fpls.2019.00246 (2019).Article 

Google Scholar 
Verhoeven, K. J. F., Vonholdt, B. M. & Sork, V. L. Epigenetics in ecology and evolution: What we know and what we need to know. Mol. Ecol. 25, 1631–1638. https://doi.org/10.1111/mec.13617 (2016).Article 
PubMed 

Google Scholar 
Lisch, D. How important are transposons for plant evolution?. Nat. Rev. Genet. 14, 49–61. https://doi.org/10.1038/nrg3374 (2013).CAS 
Article 
PubMed 

Google Scholar 
Paszkowski, J. Controlled activation of retrotransposition for plant breeding. Curr. Opin. Biotechnol. 32, 200–206. https://doi.org/10.1016/j.copbio.2015.01.003 (2015).CAS 
Article 
PubMed 

Google Scholar 
Becker, C. et al. Spontaneous epigenetic variation in the Arabidopsis thaliana methylome. Nature 480, 245-U127. https://doi.org/10.1038/nature10555 (2011).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Schmitz, R. J. et al. Transgenerational epigenetic instability is a source of novel methylation variants. Science 334, 369–373. https://doi.org/10.1126/science.1212959 (2011).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Bossdorf, O., Richards, C. L. & Pigliucci, M. Epigenetics for ecologists. Ecol. Lett. 11, 106–115. https://doi.org/10.1111/j.1461-0248.2007.01130.x (2008).Article 
PubMed 

Google Scholar 
Walsh, M. R. et al. Local adaptation in transgenerational responses to predators. Proc. R. Soc. B Biol. Sci. https://doi.org/10.1098/rspb.2015.2271 (2016).Article 

Google Scholar 
Foust, C. M. et al. Genetic and epigenetic differences associated with environmental gradients in replicate populations of two salt marsh perennials. Mol. Ecol. 25, 1639–1652. https://doi.org/10.1111/mec.13522 (2016).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Gugger, P. F., Fitz-Gibbon, S., Pellegrini, M. & Sork, V. L. Species-wide patterns of DNA methylation variation in Quercus lobata and their association with climate gradients. Mol. Ecol. 25, 1665–1680. https://doi.org/10.1111/mec.13563 (2016).CAS 
Article 
PubMed 

Google Scholar 
Herrera, C. M. & Bazaga, P. Untangling individual variation in natural populations: Ecological, genetic and epigenetic correlates of long-term inequality in herbivory. Mol. Ecol. 20, 1675–1688. https://doi.org/10.1111/j.1365-294X.2011.05026.x (2011).CAS 
Article 
PubMed 

Google Scholar 
Medrano, M., Herrera, C. M. & Bazaga, P. Epigenetic variation predicts regional and local intraspecific functional diversity in a perennial herb. Mol. Ecol. 23, 4926–4938. https://doi.org/10.1111/mec.12911 (2014).CAS 
Article 
PubMed 

Google Scholar 
Herrera, C. M., Medrano, M. & Bazaga, P. Comparative epigenetic and genetic spatial structure of the perennial herb Helleborus foetidus: Isolation by environment, isolation by distance, and functional trait divergence. Am. J. Bot. 104, 1195–1204. https://doi.org/10.3732/ajb.1700162 (2017).Article 
PubMed 

Google Scholar 
Sheldon, E. L., Schrey, A., Andrew, S. C., Ragsdale, A. & Griffith, S. C. Epigenetic and genetic variation among three separate introductions of the house sparrow (Passer domesticus) into Australia. R. Soc. Open Sci. https://doi.org/10.1098/rsos.172185 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Gaspar, B., Bossdorf, O. & Durka, W. Structure, stability and ecological significance of natural epigenetic variation: A large-scale survey in Plantago lanceolata. New Phytol. 221, 1585–1596. https://doi.org/10.1111/nph.15487 (2019).Article 
PubMed 

Google Scholar 
Medrano, M., Alonso, C., Bazaga, P., Lopez, E. & Herrera, C. M. Comparative genetic and epigenetic diversity in pairs of sympatric, closely related plants with contrasting distribution ranges in south-eastern Iberian mount. Aob Plants https://doi.org/10.1093/aobpla/plaa013 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Wang, M. Z., Li, H. L., Li, J. M. & Yu, F. H. Correlations between genetic, epigenetic and phenotypic variation of an introduced clonal herb. Heredity 124, 146–155. https://doi.org/10.1038/s41437-019-0261-8 (2020).Article 
PubMed 

Google Scholar 
Miryeganeh, M. & Saze, H. Epigenetic inheritance and plant evolution. Popul. Ecol. 62, 17–27. https://doi.org/10.1002/1438-390x.12018 (2020).Article 

Google Scholar 
Becklin, K. M. et al. Examining plant physiological responses to climate change through an evolutionary lens. Plant Physiol. 172, 635–649. https://doi.org/10.1104/pp.16.00793 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Szymanska, R., Slesak, I., Orzechowska, A. & Kruk, J. Physiological and biochemical responses to high light and temperature stress in plants. Environ. Exp. Bot. 139, 165–177. https://doi.org/10.1016/j.envexpbot.2017.05.002 (2017).CAS 
Article 

Google Scholar 
Agrawal, A. A., Erwin, A. C. & Cook, S. C. Natural selection on and predicted responses of ecophysiological traits of swamp milkweed (Asclepias incarnata). J. Ecol. 96, 536–542. https://doi.org/10.1111/j.1365-2745.2008.01365.x (2008).Article 

Google Scholar 
Azhar, A., Sathornkich, J., Rattanawong, R. & Kasemsap, P. Responses of chlorophyll fluorescence, stomatal conductance, and net photosynthesis rates of four rubber (Hevea brasiliensis) genotypes to drought. Adv. Rubber 844, 11–14. https://doi.org/10.4028/www.scientific.net/AMR.844.11 (2014).CAS 
Article 

Google Scholar 
Bussotti, F., Pancrazi, M., Matteucci, G. & Gerosa, G. Leaf morphology and chemistry in Fagus sylvatica (beech) trees as affected by site factors and ozone: Results from CONECOFOR permanent monitoring plots in Italy. Tree Physiol. 25, 211–219. https://doi.org/10.1093/treephys/25.2.211 (2005).CAS 
Article 
PubMed 

Google Scholar 
Carlson, J. E., Adams, C. A. & Holsinger, K. E. Intraspecific variation in stomatal traits, leaf traits and physiology reflects adaptation along aridity gradients in a South African shrub. Ann. Bot. 117, 195–207. https://doi.org/10.1093/aob/mcv146 (2016).Article 
PubMed 

Google Scholar 
De Frenne, P. et al. Temperature effects on forest herbs assessed by warming and transplant experiments along a latitudinal gradient. Glob. Change Biol. 17, 3240–3253. https://doi.org/10.1111/j.1365-2486.2011.02449.x (2011).ADS 
Article 

Google Scholar 
Reinhardt, K., Castanha, C., Germino, M. J. & Kueppers, L. M. Ecophysiological variation in two provenances of Pinus flexilis seedlings across an elevation gradient from forest to alpine. Tree Physiol. 31, 615–625. https://doi.org/10.1093/treephys/tpr055 (2011).Article 
PubMed 

Google Scholar 
Yamori, W., Hikosaka, K. & Way, D. A. Temperature response of photosynthesis in C-3, C-4, and CAM plants: Temperature acclimation and temperature adaptation. Photosynth. Res. 119, 101–117. https://doi.org/10.1007/s11120-013-9874-6 (2014).CAS 
Article 
PubMed 

Google Scholar 
Stojanova, B. et al. Adaptive differentiation of Festuca rubra along a climate gradient revealed by molecular markers and quantitative traits. PLoS One https://doi.org/10.1371/journal.pone.0194670 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Han, S. K. & Wagner, D. Role of chromatin in water stress responses in plants. J. Exp. Bot. 65, 2785–2799. https://doi.org/10.1093/jxb/ert403 (2014).CAS 
Article 
PubMed 

Google Scholar 
Han, S. K. & Torii, K. U. Lineage-specific stem cells, signals and asymmetries during stomatal development. Development 143, 1259–1270. https://doi.org/10.1242/dev.127712 (2016).CAS 
Article 
PubMed 

Google Scholar 
Torii, K. U. Stomatal differentiation: The beginning and the end. Curr. Opin. Plant Biol. 28, 16–22. https://doi.org/10.1016/j.pbi.2015.08.005 (2015).CAS 
Article 
PubMed 

Google Scholar 
Tricker, P. J., Gibbings, J. G., Lopez, C. M. R., Hadley, P. & Wilkinson, M. J. Low relative humidity triggers RNA-directed de novo DNA methylation and suppression of genes controlling stomatal development. J. Exp. Bot. 63, 3799–3813. https://doi.org/10.1093/jxb/ers076 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Vrablova, M., Hronkova, M., Vrabl, D., Kubasek, J. & Santrucek, J. Light intensity-regulated stomatal development in three generations of Lepidium sativum. Environ. Exp. Bot. 156, 316–324. https://doi.org/10.1016/j.envexpbot.2018.09.012 (2018).CAS 
Article 

Google Scholar 
Tricker, P. J., Lopez, C. M. R., Gibbings, G., Hadley, P. & Wilkinson, M. J. Transgenerational, dynamic methylation of stomata genes in response to low relative humidity. Int. J. Mol. Sci. 14, 6674–6689. https://doi.org/10.3390/ijms14046674 (2013).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Puy, J. et al. Improved demethylation in ecological epigenetic experiments: Testing a simple and harmless foliar demethylation application. Methods Ecol. Evol. 9, 744–753. https://doi.org/10.1111/2041-210x.12903 (2018).Article 

Google Scholar 
Kosová, V., Hájek, T., Hadincová, V. & Münzbergová, Z. The importance of ecophysiological traits in response of Festuca rubra to changing climate. Physiol. Plant. 174, e13608. https://doi.org/10.1111/ppl.13608 (2022).CAS 
Article 
PubMed 

Google Scholar 
Maricle, B. R. & Adler, P. B. Effects of precipitation on photosynthesis and water potential in Andropogon gerardii and Schizachyrium scoparium in a southern mixed grass prairie. Environ. Exp. Bot. 72, 223–231. https://doi.org/10.1016/j.envexpbot.2011.03.011 (2011).Article 

Google Scholar 
Münzbergová, Z. et al. Plant origin, but not phylogeny, drive species ecophysiological response to projected climate. Front. Plant Sci. 11, 400. https://doi.org/10.3389/fpls.2020.00400 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Beerling, D. J. & Chaloner, W. G. The impact of atmospheric CO2 and temperature change on stomatal density—observations from Quercus robur lammas leaves. Ann. Bot. 71, 231–235. https://doi.org/10.1006/anbo.1993.1029 (1993).CAS 
Article 

Google Scholar 
Tang, Y. L. et al. Heat stress induces an aggregation of the light-harvesting complex of photosystem II in spinach plants. Plant Physiol. 143, 629–638. https://doi.org/10.1104/pp.106.090712 (2007).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Jahns, P. & Holzwarth, A. R. The role of the xanthophyll cycle and of lutein in photoprotection of photosystem II. BBA-Bioenerget. 1817, 182–193. https://doi.org/10.1016/j.bbabio.2011.04.012 (2012).CAS 
Article 

Google Scholar 
Baker, N. R. & Rosenqvist, E. Applications of chlorophyll fluorescence can improve crop production strategies: An examination of future possibilities. J. Exp. Bot. 55, 1607–1621. https://doi.org/10.1093/jxb/erh196 (2004).CAS 
Article 
PubMed 

Google Scholar 
Baker, H. G. In The Genetics of Colonizing Species (eds Baker, H. G. & Stebbins, G. L.) 147–168 (Academic Press, 1965).
Google Scholar 
Bartlett, M. K. et al. Global analysis of plasticity in turgor loss point, a key drought tolerance trait. Ecol. Lett. 17, 1580–1590. https://doi.org/10.1111/ele.12374 (2014).Article 
PubMed 

Google Scholar 
Raven, J. A. Selection pressures on stomatal evolution. New Phytol. 153, 371–386. https://doi.org/10.1046/j.0028-646X.2001.00334.x (2002).CAS 
Article 
PubMed 

Google Scholar 
Zhang, F. F. et al. Effects of CO2 enrichment on growth and development of Impatiens hawkeri. Sci. World J. https://doi.org/10.1100/2012/601263 (2012).ADS 
Article 

Google Scholar 
Gonzalez, A. P. R. et al. Stress-induced memory alters growth of clonal off spring of white clover (Trifolium repens). Am. J. Bot. 103, 1567–1574. https://doi.org/10.3732/ajb.1500526 (2016).CAS 
Article 
PubMed 

Google Scholar 
Jones, P. A., Taylor, S. M. & Wilson, V. L. Inhibition of DNA methylation by 5-azacytidine. Recent Results Cancer Res. 84, 202–211 (1983).CAS 
PubMed 

Google Scholar 
Meineri, E., Skarpaas, O., Spindelbock, J., Bargmann, T. & Vandvik, V. Direct and size-dependent effects of climate on flowering performance in alpine and lowland herbaceous species. J. Veg. Sci. 25, 275–286. https://doi.org/10.1111/jvs.12062 (2014).Article 

Google Scholar 
Šurinová, M., Hadincová, V., Vandvik, V. & Münzbergová, Z. Temperature and precipitation, but not geographic distance, explain genetic relatedness among populations in the perennial grass Festuca rubra. J. Plant Ecol. 12, 730–741. https://doi.org/10.1093/jpe/rtz010 (2019).Article 

Google Scholar 
Münzbergová, Z., Hadincová, V., Skálová, H. & Vandvik, V. Genetic differentiation and plasticity interact along temperature and precipitation gradients to determine plant performance under climate change. J. Ecol. 105, 1358–1373. https://doi.org/10.1111/1365-2745.12762 (2017).Article 

Google Scholar 
Klanderud, K., Vandvik, V. & Goldberg, D. The importance of biotic vs abiotic drivers of local plant community composition along regional bioclimatic gradients. PLoS One 10, e0130205. https://doi.org/10.1371/journal.pone.0130205 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Meineri, E., Skarpaas, O. & Vandvik, V. Modeling alpine plant distributions at the landscape scale: Do biotic interactions matter?. Ecol. Model. 231, 1–10. https://doi.org/10.1016/j.ecolmodel.2012.01.021 (2012).Article 

Google Scholar 
Meineri, E., Spindelbock, J. & Vandvik, V. Seedling emergence responds to both seed source and recruitment site climates: A climate change experiment combining transplant and gradient approaches. Plant Ecol. 214, 607–619. https://doi.org/10.1007/s11258-013-0193-y (2013).Article 

Google Scholar 
Vandvik, V., Klanderud, K., Meineri, E., Maren, I. E. & Topper, J. Seed banks are biodiversity reservoirs: Species-area relationships above versus below ground. Oikos 125, 218–228. https://doi.org/10.1111/oik.02022 (2016).Article 

Google Scholar 
Stojanova, B. et al. Evolutionary potential of a widespread clonal grass under changing climate. J. Evol. Biol. 32, 1057–1068. https://doi.org/10.1111/jeb.13507 (2019).Article 
PubMed 

Google Scholar 
Osorio-Montalvo, P., Saenz-Carbonell, L. & De-la-Pena, C. 5-azacytidine: A promoter of epigenetic changes in the quest to improve plant somatic embryogenesis. Int. J. Mol. Sci. 19, 20. https://doi.org/10.3390/ijms19103182 (2018).CAS 
Article 

Google Scholar 
Hurlbert, S. H. Pseudoreplication and the design of ecological field experiments. Ecol. Monogr. 54, 187–211. https://doi.org/10.2307/1942661 (1984).Article 

Google Scholar 
Münzbergová, Z. & Hadincová, V. Transgenerational plasticity as an important mechanism affecting response of clonal species to changing climate. Ecol. Evol. 7, 5236–5247. https://doi.org/10.1002/ece3.3105 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Oksanen, L. Logic of experiments in ecology: Is pseudoreplication a pseudoissue?. Oikos 94, 27–38. https://doi.org/10.1034/j.1600-0706.2001.11311.x (2001).Article 

Google Scholar 
Johnson, S. N., Gherlenda, A. N., Frew, A. & Ryalls, J. M. W. The importance of testing multiple environmental factors in legume-insect research: Replication, reviewers, and rebuttal. Front. Plant Sci. 7, 489. https://doi.org/10.3389/fpls.2016.00489 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Hurlbert, S. H. On misinterpretations of pseudoreplication and related matters: A reply to Oksanen. Oikos 104, 591–597. https://doi.org/10.1111/j.0030-1299.2004.12752.x (2004).Article 

Google Scholar 
Scheepens, J. F. & Stocklin, J. Flowering phenology and reproductive fitness along a mountain slope: Maladaptive responses to transplantation to a warmer climate in Campanula thyrsoides. Oecologia 171, 679–691. https://doi.org/10.1007/s00442-012-2582-7 (2013).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Gugger, S., Kesselring, H., Stoecklin, J. & Hamann, E. Lower plasticity exhibited by high- versus mid-elevation species in their phenological responses to manipulated temperature and drought. Ann. Bot. 116, 953–962. https://doi.org/10.1093/aob/mcv155 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Bezemer, T. M., Thompson, L. J. & Jones, T. H. Poa annua shows inter-generational differences in response to elevated CO2. Glob. Change Biol. 4, 687–691. https://doi.org/10.1046/j.1365-2486.1998.00184.x (1998).ADS 
Article 

Google Scholar 
Cavieres, L. A. & Arroyo, M. T. K. Seed germination response to cold stratification period and thermal regime in Phacelia secunda (Hydrophyllaceae)—altitudinal variation in the mediterranean Andes of central Chile. Plant Ecol. 149, 1–8. https://doi.org/10.1023/a:1009802806674 (2000).Article 

Google Scholar 
Souther, S., Lechowicz, M. J. & McGraw, J. B. Experimental test for adaptive differentiation of ginseng populations reveals complex response to temperature. Ann. Bot. 110, 829–837. https://doi.org/10.1093/aob/mcs155 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Matias, L. & Jump, A. S. Impacts of predicted climate change on recruitment at the geographical limits of Scots pine. J. Exp. Bot. 65, 299–310. https://doi.org/10.1093/jxb/ert376 (2014).CAS 
Article 
PubMed 

Google Scholar 
Zhang, H. X. et al. Germination shifts of C-3 and C-4 species under simulated global warming scenario. PLoS One 9, e105139. https://doi.org/10.1371/journal.pone.0105139 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Maxwell, K. & Johnson, G. N. Chlorophyll fluorescence—a practical guide. J. Exp. Bot. 51, 659–668 (2000).Ashraf, M. & Harris, P. J. C. Photosynthesis under stressful environments: An overview. Photosynthetica 51, 163–190. https://doi.org/10.1007/s11099-013-0021-6 (2013).CAS 
Article 

Google Scholar 
Majekova, M., Martinkova, J. & Hajek, T. Grassland plants show no relationship between leaf drought tolerance and soil moisture affinity, but rapidly adjust to changes in soil moisture. Funct. Ecol. 33, 774–785. https://doi.org/10.1111/1365-2435.13312 (2019).Article 

Google Scholar 
Volis, S., Ormanbekova, D., Yermekbayev, K., Song, M. S. & Shulgina, I. Multi-approaches analysis reveals local adaptation in the emmer wheat (Triticum dicoccoides) at macro—but not micro-geographical scale. PLoS One 10, 19. https://doi.org/10.1371/journal.pone.0121153 (2015).CAS 
Article 

Google Scholar 
Younginger, B. S., Sirova, D., Cruzan, M. B. & Ballhorn, D. J. Is biomass a reliable estimate of plant fitness?. Appl. Plant Sci. https://doi.org/10.3732/apps.1600094 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
R Development Core Team. Version 4.0.3 A language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2011).
Google Scholar 
Bossdorf, O., Arcuri, D., Richards, C. L. & Pigliucci, M. Experimental alteration of DNA methylation affects the phenotypic plasticity of ecologically relevant traits in Arabidopsis thaliana. Evol. Ecol. 24, 541–553. https://doi.org/10.1007/s10682-010-9372-7 (2010).Article 

Google Scholar 
Oksanen, J., Blanchet, F. G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., Minchin, P. R., O’Hara, R. B., Simpson, G. L., Solymos, P., Stevens, M. H. H., Szoecs, E. & Wagner, H. (2020). vegan: Community Ecology Package. R package version 2.5-7.Lande, R. & Arnold, S. J. The measurement of selection on correlated characters. Evolution 37, 1210–1226. https://doi.org/10.2307/2408842 (1983).Article 
PubMed 

Google Scholar 
Rolhauser, A. G., Nordenstahl, M., Aguiar, M. R. & Pucheta, E. Community-level natural selection modes: A quadratic framework to link multiple functional traits with competitive ability. J. Ecol. 107, 1457–1468. https://doi.org/10.1111/1365-2745.13094 (2019).Article 

Google Scholar 
Yan, W. M., Zhong, Y. Q. W. & Shangguan, Z. P. Contrasting responses of leaf stomatal characteristics to climate change: A considerable challenge to predict carbon and water cycles. Glob. Change Biol. 23, 3781–3793. https://doi.org/10.1111/gcb.13654 (2017).ADS 
Article 

Google Scholar 
González, A. P. R., Dumalasová, V., Rosenthal, J., Skuhrovec, J. & Latzel, V. The role of transgenerational effects in adaptation of clonal offspring of white clover (Trifolium repens) to drought and herbivory. Evol. Ecol. 31, 345–361. https://doi.org/10.1007/s10682-016-9844-5 (2017).Article 

Google Scholar 
Shi, W. et al. Transient stability of epigenetic population differentiation in a clonal invader. Front. Plant Sci. https://doi.org/10.3389/fpls.2018.01851 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Quan, J., Münzbergová, Z. & Latzel, V. Time dynamics of stress legacy in clonal transgenerational effects: A case study on Trifolium repens. Ecol. Evol. https://doi.org/10.1002/ece3.8959 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Harris, C. J. et al. A DNA methylation reader complex that enhances gene transcription. Science 362, 1182. https://doi.org/10.1126/science.aar7854 (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Zhang, K. R., Cheng, X. L., Shu, X., Liu, Y. & Zhang, Q. F. Linking soil bacterial and fungal communities to vegetation succession following agricultural abandonment. Plant Soil 431, 19–36. https://doi.org/10.1007/s11104-018-3743-1 (2018).ADS 
CAS 
Article 

Google Scholar 
Xiao, X. L. et al. A group of SUVH methyl-DNA binding proteins regulate expression of the DNA demethylase ROS1 in Arabidopsis. J. Integr. Plant Biol. 61, 110–119. https://doi.org/10.1111/jipb.12768 (2019).CAS 
Article 
PubMed 

Google Scholar 
Gallego-Bartolome, J. DNA methylation in plants: Mechanisms and tools for targeted manipulation. New Phytol. 227, 38–44. https://doi.org/10.1111/nph.16529 (2020).CAS 
Article 
PubMed 

Google Scholar 
Wang, Z. W., Bossdorf, O., Prati, D., Fischer, M. & van Kleunen, M. Transgenerational effects of land use on offspring performance and growth in Trifolium repens. Oecologia 180, 409–420. https://doi.org/10.1007/s00442-015-3480-6 (2016).ADS 
Article 
PubMed 

Google Scholar 
Muir, C. D., Pease, J. B. & Moyle, L. C. Quantitative genetic analysis indicates natural selection on leaf phenotypes across wild tomato species (Solanum sect. Lycopersicon; Solanaceae). Genetics 198, 1629. https://doi.org/10.1534/genetics.114.169276 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Ramirez-Valiente, J. A. et al. Natural selection and neutral evolutionary processes contribute to genetic divergence in leaf traits across a precipitation gradient in the tropical oak Quercus oleoides. Mol. Ecol. 27, 2176–2192. https://doi.org/10.1111/mec.14566 (2018).Article 
PubMed 

Google Scholar 
Jueterbock, A. et al. The seagrass methylome is associated with variation in photosynthetic performance among clonal shoots. Front. Plant Sci. 11, 19. https://doi.org/10.3389/fpls.2020.571646 (2020).Article 

Google Scholar 
Ganguly, D. R., Crisp, P. A., Eichten, S. R. & Pogson, B. J. The Arabidopsis DNA methylome is stable under transgenerational drought stress. Plant Physiol. 175, 1893–1912. https://doi.org/10.1104/pp.17.00744 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Ganguly, D. R., Crisp, P. A., Eichten, S. R. & Pogson, B. J. Maintenance of pre-existing DNA methylation states through recurring excess-light stress. Plant Cell Environ. 41, 1657–1672. https://doi.org/10.1111/pce.13324 (2018).CAS 
Article 
PubMed 

Google Scholar 
Nixon, P. J., Michoux, F., Yu, J. F., Boehm, M. & Komenda, J. Recent advances in understanding the assembly and repair of photosystem II. Ann. Bot. 106, 1–16. https://doi.org/10.1093/aob/mcq059 (2010).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Perez, T. M. & Feeley, K. J. Photosynthetic heat tolerances and extreme leaf temperatures. Funct. Ecol. 34, 2236–2245. https://doi.org/10.1111/1365-2435.13658 (2020).Article 

Google Scholar 
Kitayama, K., Pattison, R., Cordell, S., Webb, D. & MuellerDombois, D. Ecological and genetic implications of foliar polymorphism in Metrosideros polymorpha Gaud (Myrtaceae) in a habitat matrix on Mauna Loa, Hawaii. Ann. Bot. 80, 491–497. https://doi.org/10.1006/anbo.1996.0473 (1997).Article 

Google Scholar 
Konopkova, A. et al. Nucleotide polymorphisms associated with climate and physiological traits in silver fir (Abies alba Mill.) provenances. Flora 250, 37–43. https://doi.org/10.1016/j.flora.2018.11.012 (2019).Article 

Google Scholar 
Baer, A., Wheeler, J. K. & Pittermann, J. Limited hydraulic adjustments drive the acclimation response of Pteridium aquilinum to variable light. Ann. Bot. 125, 691–700. https://doi.org/10.1093/aob/mcaa006 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Hao, X. F., Jin, Z. P., Wang, Z. Q., Qin, W. S. & Pei, Y. X. Hydrogen sulfide mediates DNA methylation to enhance osmotic stress tolerance in Setaria italic L.. Plant Soil 453, 355–370. https://doi.org/10.1007/s11104-020-04590-5 (2020).CAS 
Article 

Google Scholar 
Colaneri, A. C. & Jones, A. M. Genome-wide quantitative identification of DNA differentially methylated sites in Arabidopsis seedlings growing at different water potential. PLoS One 8, 10. https://doi.org/10.1371/journal.pone.0059878 (2013).CAS 
Article 

Google Scholar 
Becker, C. & Weigel, D. Epigenetic variation: Origin and transgenerational inheritance. Curr. Opin. Plant Biol. 15, 562–567. https://doi.org/10.1016/j.pbi.2012.08.004 (2012).CAS 
Article 
PubMed 

Google Scholar 
Spens, A. E. & Douhovnikoff, V. Epigenetic variation within Phragmites australis among lineages, genotypes, and ramets. Biol. Invas. 18, 2457–2462. https://doi.org/10.1007/s10530-016-1223-1 (2016).Article 

Google Scholar 
Herrera, C. M., Pozo, M. I. & Bazaga, P. Jack of all nectars, master of most: DNA methylation and the epigenetic basis of niche width in a flower-living yeast. Mol. Ecol. 21, 2602–2616. https://doi.org/10.1111/j.1365-294X.2011.05402.x (2012).CAS 
Article 
PubMed 

Google Scholar 
Herrera, C. M. & Bazaga, P. Epigenetic correlates of plant phenotypic plasticity: DNA methylation differs between prickly and nonprickly leaves in heterophyllous Ilex aquifolium (Aquifoliaceae) trees. Bot. J. Linn. Soc. 171, 441–452. https://doi.org/10.1111/boj.12007 (2013).Article 

Google Scholar 
Keller, T. E., Lasky, J. R. & Yi, S. V. The multivariate association between genomewide DNA methylation and climate across the range of Arabidopsis thaliana. Mol. Ecol. 25, 1823–1837. https://doi.org/10.1111/mec.13573 (2016).CAS 
Article 
PubMed 

Google Scholar 
Madliger, C. L., Love, O. P., Hultine, K. R. & Cooke, S. J. The conservation physiology toolbox: Status and opportunities. Conserv. Physiol. 6, 16. https://doi.org/10.1093/conphys/coy029 (2018).CAS 
Article 

Google Scholar 
Münzbergová, Z. & Haisel, D. Effects of polyploidization on the contents of photosynthetic pigments are largely population-specific. Photosynth. Res. 140, 289–299. https://doi.org/10.1007/s11120-018-0604-y (2019).CAS 
Article 
PubMed 

Google Scholar 
Balachandran, S. et al. Concepts of plant biotic stress. Some insights into the stress physiology of virus-infected plants, from the perspective of photosynthesis. Physiol. Plant. 100, 203–213. https://doi.org/10.1034/j.1399-3054.1997.1000201.x (1997).CAS 
Article 

Google Scholar 
Pavlíková, Z., Holá, D., Vlasáková, B., Procházka, T. & Münzbergová, Z. Physiological and fitness differences between cytotypes vary with stress in a grassland perennial herb. PLoS One https://doi.org/10.1371/journal.pone.0188795 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Zhang, B. B., Zhang, H., Jing, Q. & Wang, J. X. Light pollution on the growth, physiology and chlorophyll fluorescence response of landscape plant perennial ryegrass (Lolium perenne L.). Ecol. Indic. 115, 9. https://doi.org/10.1016/j.ecolind.2020.106448 (2020).CAS 
Article 

Google Scholar 
Cameron, D. D., Geniez, J. M., Seel, W. E. & Irving, L. J. Suppression of host photosynthesis by the parasitic plant Rhinanthus minor. Ann. Bot. 101, 573–578. https://doi.org/10.1093/aob/mcm324 (2008).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Molina-Montenegro, M. A., Salgado-Luarte, C., Oses, R. & Torres-Diaz, C. Is physiological performance a good predictor for fitness? Insights from an invasive plant species. PLoS One 8, 9. https://doi.org/10.1371/journal.pone.0076432 (2013).CAS 
Article 

Google Scholar 
dos Santos, V. & Ferreira, M. J. Are photosynthetic leaf traits related to the first-year growth of tropical tree seedlings? A light-induced plasticity test in a secondary forest enrichment planting. For. Ecol. Manage. 460, 9. https://doi.org/10.1016/j.foreco.2020.117900 (2020).Article 

Google Scholar 
Shi, Q. W. et al. Phosphorus-fertilisation has differential effects on leaf growth and photosynthetic capacity of Arachis hypogaea L.. Plant Soil 447, 99–116. https://doi.org/10.1007/s11104-019-04041-w (2020).CAS 
Article 

Google Scholar 
Madriaza, K., Saldana, A., Salgado-Luarte, C., Escobedo, V. M. & Gianoli, E. Chlorophyll fluorescence may predict tolerance to herbivory. Int. J. Plant Sci. 180, 81–85. https://doi.org/10.1086/700583 (2019).Article 

Google Scholar 
Franks, P. J., Drake, P. L. & Beerling, D. J. Plasticity in maximum stomatal conductance constrained by negative correlation between stomatal size and density: An analysis using Eucalyptus globulus. Plant Cell Environ. 32, 1737–1748. https://doi.org/10.1111/j.1365-3040.2009.002031.x (2009).Article 
PubMed 

Google Scholar 
Belluau, M. & Shipley, B. Linking hard and soft traits: Physiology, morphology and anatomy interact to determine habitat affinities to soil water availability in herbaceous dicots. PLoS One 13, 25. https://doi.org/10.1371/journal.pone.0193130 (2018).CAS 
Article 

Google Scholar 
Jerbi, A. et al. High biomass yield increases in a primary effluent wastewater phytofiltration are associated to altered leaf morphology and stomatal size in Salix miyabeana. Sci. Total Environ. 738, 12. https://doi.org/10.1016/j.scitotenv.2020.139728 (2020).CAS 
Article 

Google Scholar 
Sakoda, K. et al. Higher stomatal density improves photosynthetic induction and biomass production in Arabidopsis under fluctuating light. Front. Plant Sci. 11, 11. https://doi.org/10.3389/fpls.2020.589603 (2020).Article 

Google Scholar 
Liu, J. Y. et al. Effect of summer warming on growth, photosynthesis and water status in female and male Populus cathayana: Implications for sex-specific drought and heat tolerances. Tree Physiol. 40, 1178–1191. https://doi.org/10.1093/treephys/tpaa069 (2020).CAS 
Article 
PubMed 

Google Scholar 
Griffin, P. T., Niederhuth, C. E. & Schmitz, R. J. A comparative analysis of 5-azacytidine- and zebularine-induced DNA demethylation. G3 Genes Genomes Genet. 6, 2773–2780. https://doi.org/10.1534/g3.116.030262 (2016).CAS 
Article 

Google Scholar 
Zhang, Y. X. et al. Application of 5-azacytidine induces DNA hypomethylation and accelerates dormancy release in buds of tree peony. Plant Physiol. Biochem. 147, 91–100. https://doi.org/10.1016/j.plaphy.2019.12.010 (2020).CAS 
Article 
PubMed 

Google Scholar 
Sammarco, I., Muenzbergova, Z. & Latzel, V. DNA methylation can mediate local adaptation and response to climate change in the clonal plant Fragaria vesca: Evidence from a European-scale reciprocal transplant experiment. Front. Plant Sci. https://doi.org/10.3389/fpls.2022.827166 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Atighi, M. R., Verstraeten, B., De Meyer, T. & Kyndt, T. Genome-wide DNA hypomethylation shapes nematode pattern-triggered immunity in plants. New Phytol. 227, 545–558. https://doi.org/10.1111/nph.16532 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Nowicka, A. et al. Comparative analysis of epigenetic inhibitors reveals different degrees of interference with transcriptional gene silencing and induction of DNA damage. Plant J. 102, 68–84. https://doi.org/10.1111/tpj.14612 (2020).CAS 
Article 
PubMed 

Google Scholar 
Christman, J. K. 5-Azacytidine and 5-aza-2 ’-deoxycytidine as inhibitors of DNA methylation: Mechanistic studies and their implications for cancer therapy. Oncogene 21, 5483–5495. https://doi.org/10.1038/sj.onc.1205699 (2002).CAS 
Article 
PubMed 

Google Scholar 
Issa, J. P. J. & Kantarjian, H. M. Targeting DNA methylation. Clin. Cancer Res. 15, 3938–3946. https://doi.org/10.1158/1078-0432.ccr-08-2783 (2009).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Amoah, S. et al. A hypomethylated population of Brassica rapa for forward and reverse epi-genetics. BMC Plant Biol. https://doi.org/10.1186/1471-2229-12-193 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
McGuigan, K., Hoffmann, A. A. & Sgro, C. M. How is epigenetics predicted to contribute to climate change adaptation? What evidence do we need?. Philos. Trans. R. Soc. B Biol. Sci. 376, 10. https://doi.org/10.1098/rstb.2020.0119 (2021).Article 

Google Scholar 
Sano, H., Kamada, I., Youssefian, S., Katsumi, M. & Wabiko, H. A single treatment of rice seedlings with 5-azacytidine induces heritable dwarfism and undermethylation of genomic DNA. Mol. Gen. Genet. 220, 441–447. https://doi.org/10.1007/bf00391751 (1990).CAS 
Article 

Google Scholar 
Kondo, H., Ozaki, H., Itoh, K., Kato, A. & Takeno, K. Flowering induced by 5-azacytidine, a DNA demethylating reagent in a short-day plant, Perilla frutescens var. crispa. Physiol. Plant. 127, 130–137. https://doi.org/10.1111/j.1399-3054.2005.00635.x (2006).CAS 
Article 

Google Scholar 
Kumpatla, S. P. & Hall, T. C. Longevity of 5-azacytidine-mediated gene expression and re-establishment of silencing in transgenic rice. Plant Mol. Biol. 38, 1113–1122. https://doi.org/10.1023/a:1006071018039 (1998).CAS 
Article 
PubMed 

Google Scholar 
Lira-Medeiros, C. F. et al. Epigenetic variation in mangrove plants occurring in contrasting natural environment. PLoS One https://doi.org/10.1371/journal.pone.0010326 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
Raj, S. et al. Clone history shapes Populus drought responses. Proc. Natl. Acad. Sci. USA 108, 12521–12526. https://doi.org/10.1073/pnas.1103341108 (2011).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Richards, C. L., Schrey, A. W. & Pigliucci, M. Invasion of diverse habitats by few Japanese knotweed genotypes is correlated with epigenetic differentiation. Ecol. Lett. 15, 1016–1025. https://doi.org/10.1111/j.1461-0248.2012.01824.x (2012).Article 
PubMed 

Google Scholar 
Platt, A., Gugger, P. F., Pellegrini, M. & Sork, V. L. Genome-wide signature of local adaptation linked to variable CpG methylation in oak populations. Mol. Ecol. 24, 3823–3830. https://doi.org/10.1111/mec.13230 (2015).CAS 
Article 
PubMed 

Google Scholar 
Pfeifer, G. P. Mutagenesis at methylated CpG sequences. DNA Methyl. Basic Mech. 301, 259–281 (2006).CAS 
Article 

Google Scholar 
Walsh, C. P. & Xu, G. L. Cytosine methylation and DNA repair. DNA Methyl. Basic Mech. 301, 283–315 (2006).CAS 
Article 

Google Scholar  More

Angélil, O. et al. An independent assessment of anthropogenic attribution statements for recent extreme temperature and rainfall events. J. Clim. 30(1), 5–16 (2017).ADS 

Google Scholar 
Rosenzweig, C. et al. Coordinating AgMIP data and models across global and regional scales for 1.5°C and 2.0°C assessments. Philos. Trans. R. Soc. A. 376, 20160455 (2018).ADS 

Google Scholar 
Mitchell, D. et al. Half a degree additional warming, prognosis and projected impacts (HAPPI): Background and experimental design. Geosci. Model Dev. 10, 571–583 (2017).ADS 
CAS 

Google Scholar 
Coumou, D. & Rahmstorf, S. A decade of weather extremes. Nat. Clim. Change 2, 491–496 (2012).ADS 

Google Scholar 
IPCC: Summary for Policymakers. In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change 4–6 (Cambridge University Press, 2013).Diffenbaugh, N. S. et al. Quantifying the influence of global warming on unprecedented extreme climate events. PNAS 114(19), 4881–4886 (2016).ADS 

Google Scholar 
Tai, A. P. K., Martin, M. V. & Heald, C. L. Threat to future global food security from climate change and ozone air pollution. Nat. Clim. Change 4, 817–821 (2014).ADS 
CAS 

Google Scholar 
Román-Palacios, C. & Wiens, J. J. Recent responses to climate change reveal the drivers of species extinction and survival. PNAS 117(8), 4211–4217 (2020).ADS 
PubMed 
PubMed Central 

Google Scholar 
Dong, W. H., Liu, Z., Liao, H., Tang, Q. H. & Li, X. E. New climate and socio-economic scenarios for assessing global human health challenges due to heat risk. Clim. Change 130(4), 505–518 (2015).ADS 

Google Scholar 
Brown, S. C., Wigley, T. M. L., Otto-Bliesner, B. L., Rahbek, C. & Fordham, D. A. Persistent Quaternary climate refugia are hospices for biodiversity in the Anthropocene. Nat. Clim. Change 10, 244–248 (2020).ADS 

Google Scholar 
Fischer, H., Amelung, D. & Said, N. The accuracy of German citizens’ confidence in their climate change knowledge. Nat. Clim. Change 9, 776–780 (2020).ADS 

Google Scholar 
Hasegawa, T. et al. Risk of increased food insecurity under stringent global climate change mitigation policy. Nat. Clim. Change 8, 699–703 (2018).ADS 

Google Scholar 
Lobell, D. B., Schlenker, W. & Costa-Roberts, J. Climate trends and global crop production since 1980. Science 333, 616–620 (2011).ADS 
CAS 
PubMed 

Google Scholar 
UNFCCC. The Paris Agreement. 2015, https://unfccc.int/process-and-meetings/the-paris-agreement/the-paris-agreement.Roche, K. R., Müller-Itten, M., Dralle, D. N., Bolster, D. & Müller, M. F. Climate change and the opportunity cost of conflict. PNAS 117(4), 1935–1940 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Challinor, A. J. et al. A meta-analysis of crop yield under climate change and adaptation. Nat. Clim. Change 4, 287–291 (2014).ADS 

Google Scholar 
Lobell, D. B. et al. Prioritizing climate change adaptation needs for food security in 2030. Science 319, 607–610 (2017).
Google Scholar 
Lv, S. et al. Yield gap simulations using ten maize cultivars commonly planted in Northeast China during the past five decades. Agric. For. Meteorol. 205, 1–10 (2015).ADS 

Google Scholar 
Chao, W., Kehui, C. & Shah, F. Heat stress decreases rice grain weight: Evidence and physiological mechanisms of heat effects prior to flowering. Int. J. Mol. Sci. 23(18), 10922 (2022).
Google Scholar 
Chao, W. et al. Estimating the yield stability of heat-tolerant rice genotypes under various heat conditions across reproductive stages: A 5-year case study. Sci. Rep. 11, 13604 (2021).ADS 

Google Scholar 
IPCC. Food security and food production systems. In Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel of Climate Change 485–533 (Cambridge University Press, 2014).Tigchelaar, M., Battisti, D. S., Naylor, R. L. & Ray, D. K. Future warming increases probability of globally synchronized maize production shocks. PNAS 115(26), 6644–6649 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Zhao, C. et al. Temperature increase reduces global yields of major crops in four independent estimates. PNAS 114, 9326–9331 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Diffenbaugh, N. S., Hertel, T. W., Scherer, M. & Verma, M. Response of corn markets to climate volatility under alternative energy futures. Nat. Clim. Change 2, 514–518 (2012).ADS 

Google Scholar 
Jensen, H. G. & Anderson, K. Grain price spikes and beggar-thy-neighbor policy responses: A global economywide analysis. World Bank Econ. Rev. 31, 158–175 (2017).
Google Scholar 
Fraser, E. D. G., Simelton, E., Termansen, M., Gosling, S. N. & South, A. “Vulnerability hotspots”: Integrating socio-economic and hydrological models to identify where cereal production may decline in the future due to climate change induced drought. Agric. For. Meteorol. 170, 195–205 (2013).ADS 

Google Scholar 
Puma, M. J., Bose, S., Chon, S. Y. & Cook, B. I. Assessing the evolving fragility of the global food system. Environ. Res. Lett. 10, 024007 (2015).ADS 

Google Scholar 
Wheeler, T. & Braun, J. V. Climate change impacts on global food security. Science 341(6145), 508–513 (2013).ADS 
CAS 
PubMed 

Google Scholar 
Lunt, T., Jones, A. W., Mulhern, W. S., Lezaks, D. P. M. & Jahn, M. M. Vulnerabilities to agricultural production shocks: An extreme, plausible scenario for assessment of risk for the insurance sector. Clim. Risk Manag. 13, 1–9 (2016).
Google Scholar 
Jägermeyr, J. & Frieler, K. Spatial variations in crop growing seasons pivotal to reproduce global fluctuations in maize and wheat yields. Sci. Adv. 4(11), eaat4517 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Elliott, J. et al. Characterizing agricultural impacts of recent large-scale US droughts and changing technology and management. Agric. Syst. 159, 275–281 (2017).
Google Scholar 
Tack, J., Barkley, A. & Nalley, L. L. Effect of warming temperatures on US wheat yields. Proc. Natl. Acad. Sci. 112, 6931–6936 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Tao, F., Zhang, Z., Liu, J. & Yokozawa, M. Modelling the impacts of weather and climate variability on crop productivity over a large area: A new super-ensemblebased probabilistic projection. Agric. For. Meteorol. 149, 1266–1278 (2009).ADS 

Google Scholar 
Parent, B. et al. Maize yields over Europe may increase in spite of climate change, with an appropriate use of the genetic variability of flowering time. PNAS 115(42), 10642–10647 (2018).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Yang, C. Y., Fraga, H., Ieperen, W. V. & Santos, J. A. Assessment of irrigated maize yield response to climate change scenarios in Portugal. Agric. Water Manag. 184, 178–190 (2017).
Google Scholar 
Miller, S. A. & Moore, F. C. Climate and health damages from global concrete production. Nat. Clim. Change https://doi.org/10.1038/s41558-020-0733-0 (2020).Article 

Google Scholar 
Kassie, B. T. et al. Exploring climate change impacts and adaptation options for maize production in the Central Rift Valley of Ethiopia using different climate change scenarios and crop models. Clim. Change 129, 145–158 (2015).ADS 

Google Scholar 
Tao, F. & Zhang, Z. Climate change, high-temperature stress, rice productivity, and water use in Eastern China: A new superensemble-based probabilistic projection. J. Appl. Meteorol. Climatol. 52, 531–551 (2013).ADS 

Google Scholar 
Glotter, M. & Elliott, J. Simulating US agriculture in a modern Dust Bowl drought. Nat. Plants 3, 16193 (2016).PubMed 

Google Scholar 
Challinor, A. J., Koehler, A. K., Ramirez-Villegas, J., Whitfield, S. & Das, B. Current warming will reduce yields unless maize breeding and seed systems adapt immediately. Nat. Clim. Change 6, 954–958 (2016).ADS 

Google Scholar 
Cammarano, D. et al. Using historical climate observations to understand future climate change crop yield impacts in the Southeastern US. Clim. Change 134, 311–326 (2016).ADS 

Google Scholar 
Etten, J. V. et al. Crop variety management for climate adaptation supported by citizen science. PNAS 116(10), 4194–4199 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
Urban, D. W., Sheffield, J. & Lobell, D. B. The impacts of future climate and carbon dioxide changes on the average and variability of US maize yields under two emission scenarios. Environ. Res. Lett. 10, 045003 (2015).ADS 

Google Scholar 
IPCC. Summary for policymakers. In Global Warming of 1.5°C. An IPCC Special Report on the Impacts of Global Warming of 1.5°C Above Pre-industrial Levels and Related Global Greenhouse Gas Emission Pathways, in the Context of Strengthening the Global Response to the Threat of Climate Change, Sustainable Development, and Efforts to Eradicate Poverty 32 (World Meteorological Organization, 2018).Ruane, A. C., Goldberg, R. & Chryssanthacopoulos, J. Climate forcing datasets for agricultural modeling: Merged products for gap-filling and historical climate series estimation. Agr. For. Meteorol. 200, 233–248 (2015).
Google Scholar 
Hempel, S., Frieler, K., Warszawski, L., Schewe, J. & Piontek, F. A trendpreserving bias correction-the ISI-MIP approach. Earth Syst. Dyn. 4, 219–236 (2013).ADS 

Google Scholar 
Monfreda, C., Ramankutty, N. & Foley, J. A. Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000. Glob. Biogeochem. Cycles 22, 1022 (2008).ADS 

Google Scholar 
You, L.Z., et al. Spatial Production Allocation Model (SPAM) 2000 Version 3.2. http://mapspam.info (2015).Hoogenboom, G., et al. Decision Support System for Agrotechnology Transfer (DSSAT) Version 4.6 (DSSAT Foundation, 2015). http://dssat.net (2015).Sacks, W. J., Deryng, D., Foley, J. A. & Ramankutty, N. Crop planting dates: An analysis of global patterns. Glob. Ecol. Biogeogr. 19, 607–620 (2010).
Google Scholar 
Batjes, H.N. A Homogenized Soil Data File for Global Environmental Research: A Subset of FAO. ISRIC and NRCS Profiles (Version 1.0). Working Paper and Preprint 95/10b (International Soil Reference and Information Centre, 1995).Xiong, W. et al. Can climate-smart agriculture reverse the recent slowing of rice yield growth in China?. Agric. Ecosyst. Environ. 196, 125–136 (2014).
Google Scholar 
Hertel, T. W. Global Trade Analysis: Modeling and Applications 5–30 (Cambridge University Press, 1997).
Google Scholar 
Corong, E. L., Hertel, T. W., McDougall, R., Tsigas, M. E. & Mensbrugghe, D. V. The standard GTAP model, version 7. J. Glob. Econ. Anal. 2(1), 1–119 (2017).
Google Scholar 
Ciscar, J. C. et al. Physical and economic consequences of climate change in Europe. PNAS 108, 2678–2683 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hsiang, S. et al. Estimating economic damage from climate change in the United States. Science 356(6345), 1362–1369 (2017).ADS 
CAS 
PubMed 

Google Scholar 
Taheripour, F., Hertel, T. W. & Liu, J. The role of irrigation in determining the global land use impacts of biofuels. Energy Sustain. Soc. 3(1), 4 (2013).
Google Scholar 
Ali, T., Huang, J. K. & Yang, J. Impact assessment of global and national biofuels developments on agriculture in Pakistan. Appl. Energy 104, 466–474 (2013).
Google Scholar 
Yang, J., Huang, J. K., Qiu, H. G., Rozelle, S. & Sombilla, M. A. Biofuels and the greater Mekong Subregion: Assessing the impact on prices, production and trade. Appl. Energy 86, S37–S46 (2009).
Google Scholar 
Horridge, M. SplitCom, programs to disaggregate a GTAP sector (Centre of Policy Studies, Vitorial University). https://www.copsmodels.com/splitcom.htm (2005).Taylor, K. E., Stouffer, B. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).ADS 

Google Scholar 
Zhou, B. T., Wen, H. Q. Z., Xu, Y., Song, L. C. & Zhang, X. B. Projected changes in temperature and precipitation extremes in China by the CMIP5 multimodel ensembles. J. Clim. 27, 6591–6611 (2014).ADS 

Google Scholar 
Knutti, R., Rogelj, J., Sedláček, J. & Ficher, E. M. A scientific critique of the two-degree climate change target. Nat. Geosci. 9(1), 1–6 (2015).
Google Scholar 
Rogelj, J. et al. Energy system transformations for limiting end-of-century warming to below 1.5°C. Nat. Clim. Change 5(6), 519–527 (2015).ADS 

Google Scholar 
Friedlingstein, P. et al. Persistent growth of CO2 emissions and implications for reaching climate targets. Nat. Geosci. 7(10), 709–715 (2014).ADS 
CAS 

Google Scholar 
Azar, C., Johansson, D. J. A. & Mattsson, N. Meeting global temperature targets the role of bioenergy with carbon capture and storage. Environ. Res. Lett. 8(3), 1345–1346 (2013).
Google Scholar 
Liu, B. et al. Testing the responses of four wheat crop models to heat stress at anthesis and grain filling. Glob. Change Biol. 22, 1890–1903 (2016).ADS 

Google Scholar 
Elad, Y. & Pertot, I. Climate change impacts on plant pathogens and plant diseases. J. Crop Improv. 28, 99–139 (2014).CAS 

Google Scholar 
Challinora, A. J. et al. Improving the use of crop models for risk assessment and climate change adaptation. Agric. Syst. 159, 296–306 (2018).
Google Scholar 
Bassu, S. et al. How do various maize crop models vary in their responses to climate change factors?. Glob. Change Biol. 20, 2301–2320 (2014).ADS 

Google Scholar 
Wang, N. et al. Increased uncertainty in simulated maize phenology with more frequent supra-optimal temperature under climate warming. Eur. J. Agron. 71, 19–33 (2015).
Google Scholar 
Rosenzweig, C. et al. Assessing agricultural risks of climate change in the twenty-first century in a global gridded crop model intercomparison. PNAS 111, 3268–3273 (2014).ADS 
CAS 
PubMed 

Google Scholar  More