Philippa Kaur

To the Editor — It is widely appreciated that the world’s wetlands provide important ecosystem services including critical biodiversity, stores of carbon and strong cultural links to people. Yet wetlands are disappearing at an alarming rate due to diversion and abstraction of water, to conversion to agricultural land and to pollution. In response, there has been a major commitment to conserve and restore wetlands worldwide, including more than 2,400 sites on the territories of 172 Contracting Parties of the Convention on Wetlands (Ramsar Sites), covering more than 2.5 million square kilometres. Some wetlands, such as Doñana in southern Spain, are also World Heritage sites to protect their natural and cultural values. The Ramsar Convention and UNESCO World Heritage Convention strongly support the rights of non-governmental organizations to appraise the status and management of designated sites and welcome reports of threats to site integrity. However, such claims should be substantiated by all the available scientific evidence. More

Bacterial community compositionIn order to study the microbial structure of the biofilm and activated sludge that were developing in the IFAS-MBSBBR reactor, a total of 15 samples were taken at intervals during an experiment lasting 573 days. The microbiome of both environments was described at the phylum and genus levels. A total of 26 bacterial phyla and 783 bacterial genera were identified. The most numerous phyla and genera in the biofim and activated sludge samples are presented in in Figs. 1 and 2. Both in the biofilm and the activated sludge, the most numerous phyla were Proteobacteria, with respective mean abundances of 39.3% ± 9.0 and 40.8% ± 8.2, and Bacteroidota, with respective mean abundances of 14.2% ± 4.9 and 26.1% ± 13.7. Additionally, the phylum Chloroflexi was rather abundant in the biofilm (with a mean abundance of 13.9 ± 8.1), while Actinobacteriota and Patescibacteria were relatively abundant in the activated sludge (with mean abundances of 9.0% ± 9.6 and 7.5% ± 8.1, respectively). STAMP analysis identified significant overrepresentations of Chloroflexi, Acidobacteriota, and Nitrospirota in biofilm and of Firmicutes in activated sludge.Figure 1Relative abundance (%) of the most prevalent phyla in the biofilm and activated sludge samples in general, as the mean values of relative abundance from all biofilm and activated sludge samples (A), and in each individual sample (B). The graph shows only phyla which contributed more than 0.5% to the total bacterial community in at least one sample. The abundance of the remaining phyla was summed and labelled as “other”.Full size imageFigure 2Relative abundance (%) of the most prevalent genera in the biofilm and activated sludge samples in general, as the mean values of relative abundance from all biofilm and activated sludge samples (A), and in each individual sample (B). The graph shows only genera which contributed more than 1.5% to the total bacterial community in at least one sample. The abundance of the remaining genera was summed and labelled as “other”.Full size imageIn both environments, the abundances of various groups of bacteria changed over time. In the biofilm, the abundance of Proteobacteria and Actinobacteria gradually decreased, while that of Chloroflexi increased. In the activated sludge, the changes in abundance were larger and more rapid, and the abundance of Bacteroidota changed to the largest extent, ranging from 12.7% after 42 days of reactor operation to 52.3% after 110 days, when it was the predominant phylum. The abundance of Patescibacteria also changed substantially: its abundance was highest on the 78th, 205th and 447th days of the process, reaching values of 20.1%, 11.0%, and 7.2%, respectively. Similar changes took place in the abundance of Armatimonadota, which reached 11.4% and 7.6% on the 547th and 573th day, but did not exceed 0.1% in the samples taken at other times.At the genus level, the less abundant genera (each  More

Field, C. B., Behrenfeld, M. J., Randerson, J. T. & Falkowski, P. Primary production of the biosphere: integrating terrestrial and oceanic components. Science 281, 237–240 (1998).CAS 
PubMed 
Article 

Google Scholar 
Goericke, R. & Welschmeyer, N. A. The marine prochlorophyte Prochlorococcus contributes significantly to phytoplankton biomass and primary production in the Sargasso Sea. Deep Res. 40, 2283–2294 (1993).Article 

Google Scholar 
Liu, H., Nolla, H. A. & Campbell, L. Prochlorococcus growth rate and contribution to primary production in the equatorial and subtropical North Pacific Ocean. Aquat. Microb. Ecol. 12, 39–47 (1997).Article 

Google Scholar 
Huang, S. et al. Novel lineages of prochlorococcus and synechococcus in the global oceans. ISME J. 6, 285–297 (2012).CAS 
PubMed 
Article 

Google Scholar 
Ting, C. S., Rocap, G., King, J. & Chisholm, S. W. Cyanobacterial photosynthesis in the oceans: the origins and significance of divergent light-harvesting strategies. Trends Microbiol. 10, 134–142 (2002).CAS 
PubMed 
Article 

Google Scholar 
Barlow, A. Photosynthetic characteristics of phycoerythrin-containing marine Synechococcus spp. Arctic 22, 63–74 (1985).
Google Scholar 
Yeh, S. W. et al. Role of phycoerythrin in marine picoplankton synechococcus spp. Science 234, 1422–1424 (1986).CAS 
PubMed 
Article 

Google Scholar 
Giovannoni, S. J. & Vergin, K. L. Seasonality in ocean microbial communities. Science 335, 671–676 (2012).CAS 
PubMed 
Article 

Google Scholar 
Carlson, D. F., Fredj, E. & Gildor, H. The annual cycle of vertical mixing and restratification in the Northern Gulf of Eilat/Aqaba (Red Sea) based on high temporal and vertical resolution observations. Deep Res. Part I Oceanogr. Res. Pap. 84, 1–17 (2014).Article 

Google Scholar 
Larkum, A. W. D. & Barrett, J. Light-harvesting processes in algae. Adv. Bot. Res. 10, 1–219 (1983).CAS 
Article 

Google Scholar 
Bibby, T. S., Mary, I., Nield, J., Partensky, F. & Barber, J. Low-light-adapted Prochlorococcus species possess specific antennae for each photosystem. Nature 424, 1051–1054 (2003).CAS 
PubMed 
Article 

Google Scholar 
Bibby, T. S., Nield, J., Chen, M., Larkum, A. W. D. & Barber, J. Structure of a photosystem II supercomplex isolated from Prochloron didemni retaining its chlorophyll a/b light-harvesting system. Proc. Natl Acad. Sci. USA 100, 9050–9054 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Palenik, B. Chromatic adaptation in marine Synechococcus strains. Appl. Environ. Microbiol. 67, 991–994 (2001).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kana, T. M. & Glibert, P. M. Effect of irradiances up to 2000 μE m-2 s-1 on marine Synechococcus WH7803-I. Growth, pigmentation, and cell composition. Deep Sea Res. Part A Oceanogr. Res. Pap. 34, 479–495 (1987).CAS 
Article 

Google Scholar 
Six, C., Ratin, M., Marie, D. & Corre, E. Marine Synechococcus picocyanobacteria: light utilization across latitudes. Proc. Natl Acad. Sci. USA 118, 1–11 (2021).Article 
CAS 

Google Scholar 
Perry, M. J., Talbot, M. C. & Alberte, R. S. Photoadaption in marine phytoplankton: response of the photosynthetic unit. Mar. Biol. 62, 91–101 (1981).Mauzerall, D. & Greenbaum, N. L. The absolute size of a photosynthetic unit. BBA Bioenerg. 974, 119–140 (1989).CAS 
Article 

Google Scholar 
Sanfilippo, J. E., Garczarek, L., Partensky, F. & Kehoe, D. M. Chromatic acclimation in cyanobacteria: a diverse and widespread process for optimizing photosynthesis. Annu. Rev. Microbiol. 73, 407–433 (2019).CAS 
PubMed 
Article 

Google Scholar 
Keren, N. & Paltiel, Y. Photosynthetic energy transfer at the quantum/classical border. Trends Plant Sci. 23, 497–506 (2018).CAS 
PubMed 
Article 

Google Scholar 
Kolodny, Y. et al. Marine cyanobacteria tune energy transfer efficiency in their light‐harvesting antennae by modifying pigment coupling. FEBS J. https://doi.org/10.1111/febs.15371 (2020).Wientjes, E., Van Amerongen, H. & Croce, R. Quantum yield of charge separation in photosystem II: functional effect of changes in the antenna size upon light acclimation the migration of LHCII from PSII to PSI has. J. Phys. Chem. B 117, 51 (2013).Article 
CAS 

Google Scholar 
Chenu, A. et al. Light adaptation in phycobilisome antennas: influence on the rod length and structural arrangement. J. Phys. Chem. B 121, 9196–9202 (2017).CAS 
PubMed 
Article 

Google Scholar 
Falkowski, P. G., Lin, H. & Gorbunov, M. Y. What limits photosynthetic energy conversion efficiency in nature? Lessons from the oceans. Philos. Trans. R. Soc. B Biol. Sci. 372, 2–8 (2017).Article 
CAS 

Google Scholar 
Gorbunov, M. Y. & Falkowski, P. G. Using chlorophyll fluorescence to determine the fate of photons absorbed by phytoplankton in the world’s oceans. Ann. Rev. Mar. Sci. 14, 367–393 (2021).
Google Scholar 
Govindjee, Hammond, J. H. & Merkelo, H. Primary events, energy transfer, and reactions in photosynthetic events: lifetime of the excited state in vivo: II. Bacteriochlorophyll in photosynthetic bacteria at room temperature. Biophys. J. 12, 809 (1972).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Biggins, J. & Bruce, D. Regulation of excitation energy transfer in organisms containing phycobilins. Photosynth. Res. 20, 1–34 (1989).CAS 
PubMed 
Article 

Google Scholar 
Roach, T. & Krieger-Liszkay, A. Regulation of photosynthetic electron transport and photoinhibition. Curr. Protein Pept. Sci. 15, 351–362 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Govindjee, U. Non-Photochemical Quenching and Energy Dissipation in Plants, Algae, and Cyanobacteria (Springer Netherlands, 2014).
Google Scholar 
Kirilovsky, D. Photoprotection in cyanobacteria: the orange carotenoid protein (OCP)-related non-photochemical-quenching mechanism. Photosynth. Res. 93, 7–16 (2007).CAS 
PubMed 
Article 

Google Scholar 
Lin, H. et al. The fate of photons absorbed by phytoplankton in the global ocean. Science 351, 264–267 (2016).Croce, R. & Van Amerongen, H. Light-harvesting and structural organization of photosystem II: from individual complexes to thylakoid membrane. J. Photochem. Photobiol. B Biol. 104, 142–153 (2011).CAS 
Article 

Google Scholar 
Rahav, E. et al. Heterotrophic and autotrophic contribution to dinitrogen fixation in the Gulf of Aqaba. Mar. Ecol. Prog. Ser. 522, 67–77 (2015).CAS 
Article 

Google Scholar 
Reiss, Z. & Hottinger, L. The Gulf of Aqaba (Springer-Verlag, 1984).Genin, A., Lazar, B. & Brenner, S. Vertical mixing and coral death in the red sea following the eruption of Mount Pinatubo. Nature 377, 507–510 (1995).CAS 
Article 

Google Scholar 
Labiosa, R. G., Arrigo, K. R., Genin, A., Monismith, S. G. & Van Dijken, G. The interplay between upwelling and deep convective mixing in determining the seasonal phytoplankton dynamics in the Gulf of Aqaba: evidence from SeaWiFS and MODIS. Limnol. Oceanogr. 48, 2355–2368 (2003).Article 

Google Scholar 
Zarubin, M., Lindemann, Y. & Genin, A. The dispersion-confinement mechanism: phytoplankton dynamics and the spring bloom in a deeply-mixing subtropical sea. Prog. Oceanogr. 155, 13–27 (2017).Article 

Google Scholar 
Lindell, D. & Post, A. F. Ultraphytoplankton succession is triggered by deep winter mixing in the Gulf of Aqaba (Eilat), Red Sea. Limnol. Oceanogr. 40, 1130–1141 (1995).Article 

Google Scholar 
Suggett, D. J. et al. Nitrogen and phosphorus limitation of oceanic microbial growth during spring in the Gulf of Aqaba. Aquat. Microb. Ecol. 56, 227–239 (2009).Article 

Google Scholar 
Post, A. F. et al. Long term seasonal dynamics of Synechococcus population structure in the Gulf of Aqaba, Northern Red Sea. Front. Microbiol. 2, 131 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
Sherman, J., Gorbunov, M. Y., Schofield, O. & Falkowski, P. G. Photosynthetic energy conversion efficiency in the West Antarctic Peninsula. Limnol. Oceanogr. 65, 2912–2925 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Yoo, Y. D. et al. Mixotrophy in the marine red-tide cryptophyte Teleaulax amphioxeia and ingestion and grazing impact of cryptophytes on natural populations of bacteria in Korean coastal waters. Harmful Algae 68, 105–117 (2017).PubMed 
Article 

Google Scholar 
Marie, D., Partensky, F., Jacquet, S. & Vaulot, D. Enumeration and cell cycle analysis of natural populations of marine picoplankton by flow cytometry using the nucleic acid stain SYBR Green I. Appl. Environ. Microbiol. 63, 186–193 (1997).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Brody, S. S. & Rabinowitch, E. Excitation lifetime of photosynthetic pigments in vitro and in vivo. Science 125, 555 (1979).Article 

Google Scholar 
Six, C., Thomas, J. C., Brahamsha, B., Lemoine, Y. & Partensky, F. Photophysiology of the marine cyanobacterium Synechococcus sp. WH8102, a new model organism. Aquat. Microb. Ecol. 35, 17–29 (2004).Article 

Google Scholar 
Krumova, S. B. et al. Monitoring photosynthesis in individual cells of Synechocystis sp. PCC 6803 on a picosecond timescale. Biophys. J. 99, 2006–2015 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tian, L. et al. Picosecond kinetics of light harvesting and photoprotective quenching in wild-type and mutant phycobilisomes isolated from the cyanobacterium Synechocystis PCC 6803. Biophys. J. 102, 1692–1700 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bhatti, A. F., Kirilovsky, D., van Amerongen, H. & Wientjes, E. State transitions and photosystems spatially resolved in individual cells of the cyanobacterium Synechococcus elongatus. Plant Physiol. 186, 569–580 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Adir, N., Bar-Zvi, S. & Harris, D. The amazing phycobilisome. Biochim. Biophys. Acta Bioenerg. 1861, 148047 (2020).Anderson, J. M. & Andersson, B. The dynamic photosynthetic membrane and regulation of solar energy conversion. Trends Biochem. Sci. 13, 351–355 (1988).CAS 
PubMed 
Article 

Google Scholar 
Mackey, K. R. M., Post, A. F., McIlvin, M. R. & Saito, M. A. Physiological and proteomic characterization of light adaptations in marine Synechococcus. Environ. Microbiol. https://doi.org/10.1111/1462-2920.13744 (2017).Article 
PubMed 

Google Scholar 
Mendoza-Arenas, J. J. et al. Transport enhancement from incoherent coupling between one-dimensional quantum conductors. New J. Phys. 16, 053016 (2014).Campbell, D., Hurry, V., Clarke, A. K., Gustafsson, P. & Quist, G. O. Chlorophyll fluorescence analysis of cyanobacterial photosynthesis and acclimation. Microbiol. Mol. Biol. Rev. 62, 667–683 (1998).Ogawa, T., Misumi, M. & Sonoike, K. Estimation of photosynthesis in cyanobacteria by pulse-amplitude modulation chlorophyll fluorescence: problems and solutions. Photosynth. Res. 133, 63–73 (2017).CAS 
PubMed 
Article 

Google Scholar 
Kolber, Z. S., Prášil, O. & Falkowski, P. G. Measurements of variable chlorophyll fluorescence using fast repetition rate techniques: defining methodology and experimental protocols. Biochim. Biophys. Acta Bioenerg. 1367, 88–106 (1998).CAS 
Article 

Google Scholar 
Kolber, Z. & Falkowski, P. G. Use of active fluorescence to estimate phytoplankton photosynthesis in situ. Limnol. Oceanogr. 38, 1646–1665 (1993).CAS 
Article 

Google Scholar 
Siegel, D. A. et al. Regional to global assessments of phytoplankton dynamics from the SeaWiFS mission. Remote Sens. Environ. 135, 77–91 (2013).Article 

Google Scholar 
Gregg, W. W. & Rousseaux, C. S. Global ocean primary production trends in the modern ocean color satellite record (1998-2015). Environ. Res. Lett. 14, 124011 (2019).Kulk, G. et al. Primary production, an index of climate change in the ocean: satellite-based estimates over two decades. Remote Sens. 12, 826 (2020).Van De Poll, W. H. et al. Phytoplankton chlorophyll a biomass, composition, and productivity along a temperature and stratification gradient in the northeast Atlantic Ocean. Biogeosciences 10, 4227–4240 (2013).Article 
CAS 

Google Scholar 
Agusti, S., Lubián, L. M., Moreno-Ostos, E., Estrada, M. & Duarte, C. M. Projected changes in photosynthetic picoplankton in a warmer subtropical ocean. Front. Mar. Sci. 5, 1–16 (2019).Article 

Google Scholar 
Capotondi, A., Alexander, M. A., Bond, N. A., Curchitser, E. N. & Scott, J. D. Enhanced upper ocean stratification with climate change in the CMIP3 models. J. Geophys. Res. Ocean. 117, 1–23 (2012).Article 

Google Scholar 
Li, G. et al. Increasing ocean stratification over the past half-century. Nat. Clim. Chang. 10, 1116–1123 (2020).Article 

Google Scholar 
Kolodny, Y. et al. Tuning quantum dots coupling using organic linkers with different vibrational modes. J. Phys. Chem. C 124, 16159–16165 (2020).CAS 
Article 

Google Scholar  More

Explaining the heterogeneous organization of vegetation across landscapes has proved both a puzzling and an inspiring concept as patterns have formed naturally across the world. One such pattern is the existence of treelines, i.e., the demarcation zone between forestland and vegetation without trees1,2. There is a large body of work with developed and competing theories for understanding the specific limits and drivers for the non-existence of trees beyond a treeline. Yet after decades of study, there is still debate among ecologists and biologists over the mechanisms that limit the presence of trees beyond treelines. Current explanations are rooted in, but not limited to, consideration of factors such as excessive light and wind, limited CO(_2), and low temperatures1,3,4,5,6,7.
With this in mind, we ask: Is there another perspective that could provide insights complementary to and beyond what has been developed through the prevailing mechanistic approach? While the existing explanations are based on ideas of structural stability (e.g., high winds above the treeline) and limited resources pertaining to water, energy, and nutrients1,3,4,5,6,7, we instead examine the question of what determines the existence of a treeline from the perspective of thermodynamic feasibility. Our premise is that the existence or non-existence of certain vegetation first and foremost has to be ascertained through thermodynamic feasibility or infeasibility, respectively. Therefore, we approach the question of the existence of treelines by asking: If certain vegetation does not exist at a given location, is there a role that the thermodynamic perspective can play in telling us that its existence is infeasible? By approaching the topic from the thermodynamic perspective, we seek to provide important complementary insight to the broad base of scientific understanding ecologists and biologists have developed to explain the existence of treelines. Further, this work lays out additional context for the discussion around the advance of treelines (e.g., why some treelines advance and others do not).For example, several theories assert that the stature of vegetation is limited by CO(_2) balance and photosynthetic requirements under harsh winter conditions1,6. Other hypotheses argue that plant life is instead limited by the atmospheric temperature and the local environments that the plants experience7,8. Is there a perspective that could unify both of these findings? Through this work, we demonstrate how thermodynamic infeasibility inferred from model simulations pertaining to counterfactual scenarios manifests through both of these physiological limits. This means that either of these limits, individually or together, could lead to the nonexistence of trees—which limiting factor is expressed first varies by location. Thus, the commonality among locations that have different limiting mechanisms can be found in the unifying concept of thermodynamic infeasibility. While CO(_2) limitation may prevail in one location and temperature-related constraints may be limiting in another, both lead to thermodynamic infeasibility, meaning that the thermal environment results in a mechanistic infeasibility, such as net CO(_2) loss. In the examples presented in this paper, thermodynamic infeasibility manifests through negative work associated with constraints arising from temperature gradients and net CO(_2) loss, demonstrating that both limitations can be encapsulated using the thermodynamic perspective.Ecosystem thermodynamicsIt is now generally accepted that observed patterns of vegetation composition and its organization are a result of self-organization, or the spontaneous emergence of pattern without external predetermination9,10. By framing ecosystems as open thermodynamic systems, we explore further the concept of thermodynamic feasibility and its role in the self-organization of vegetation structure. Vegetation structure consists of composition (i.e., the number and type of functional groups11) and organizational patterns on the landscape12. We focus on composition rather than the spatial pattern of vegetation organization. We utilize a one-dimensional ecohydrological model that incorporates representative functional groups with no lateral transport of energy or matter under the assumption that the vegetation composition and pattern remain spatially uniform at a given site. Thus, we are able to compare the vertical thermodynamic regimes of proximal ecosystems with varying vegetation composition. We present the case that observed organization reflected in the demarcation of differing vegetation structures on either side of a treeline is established in tandem with vertical thermodynamic gradients at a given location, driven by the incoming solar energy into an ecosystem. In other words, we hypothesize that beyond a treeline, the existence of trees is prevented by conditions of thermodynamic infeasibility.The application of thermodynamic theory to ecology has been studied for the better part of the last century through the introduction of theoretical thermodynamic properties, such as entropy and exergy, into environmental systems. This work asserts that open thermodynamic systems will evolve based on the strength of applied concentration gradients on the system and will undergo irreversible processes to dissipate energy and destroy these gradients through all means available13,14. In the context of ecosystems, fluxes of mass or energy from the external environment (i.e., above the canopy) result in concentration gradients within the system itself. State variables will transition along these gradients according to the second law of thermodynamics. When the magnitude of incoming energy and consequent spatial imbalance of energy becomes great enough, dissipative structures spontaneously emerge, or self-organize, and establish temperature gradients consistent with the dissipative need of the ecosystem13,15. In this paper we conceptualize the work performed by an ecosystem as its ability to dissipate these applied concentration gradients. Consequently, work is highly dependent upon the existence and composition of self-organized vegetation.In classical thermodynamics, work is performed due to a transfer, or physical movement, of heat15. In the context of ecosystems, work performed by an ecosystem is represented by the exchange of heat with the external environment outside the ecosystem control volume12. Work performed by an ecosystem is, therefore, estimated as the vertical transport of heat in the form of latent and sensible heat, driven by the vertical gradient in temperature within the control volume structured by both the incoming downward shortwave and longwave radiation and the vegetation structure. The bottom boundary of the ecosystem control volumes studied are significantly deep such that heat exchanges due to water infiltration at this interface are insignificant in magnitude relative to latent and sensible heat flux out of the top of the control volume above the canopy. Further, we ignore the substantially slower thermodynamic processes associated with geochemistry in the soil.The vertical temperature gradient creates a directionality of dissipation of incident radiation as heat leaves out of the ecosystem from higher surface temperatures to lower air temperatures. Throughout this paper, we measure work through the net sum of heat leaving the ecosystem as latent and sensible heat—which can either be positive or negative depending on the direction of the resultant temperature gradient (see “Thermodynamic Calculations” in the “Methods” section). This temperature gradient (Eq. 1) emerges as a result of self-organization through feedback between the incoming shortwave and longwave radiation, local environmental conditions, and the heat dissipation and work performed by the vegetation. The presence of ground cover, such as snow, is impacted by aboveground vegetation structure, which provides a physical buffer between the atmosphere and the ground, further influencing the thermal environment and temperature gradient.Although significant research has been conducted by studying plant response to snowpack7,16,17, including the physiological requirements for life under prolonged snowpack and alpine climatic conditions, the thermodynamic perspective provides further insight. In addition to the physiological/mechanistic response of plants to snowpack and other environmental conditions, the thermal regime of a column of land experiencing snowpack is fundamentally different when an ecosystem does or does not have plants with stature taller than the height of snowpack (e.g., trees). Presence of trees results in shading from solar radiation and a physical buffer between the earth/snow surface and the atmosphere. Thus, the thermal profile of an ecosystem reveals valuable information about ecosystem behavior, and there is a need to explore the thermodynamic relationship between solar radiation and vegetation composition under varying environmental conditions. Thus, through this paper we define the circumstances under which multiple functional groups that include trees are no longer feasible for the available solar radiation leading to demarcated zones identifiable as treelines.Work by Körner argues that the “climate [that] plants experience” is different than the ambient temperature7. By modeling the layers within the canopy of plants with differing stand heights and leaf distributions, we are able to characterize the thermal regime and the “climate [that] plants experience” throughout the course of a given year. This characterization helps us understand the fundamental changes in behavior under varying environmental conditions with and without trees.An ecosystem’s ability to perform work manifests into four distinct cases depending on the sign of the resultant temperature gradient and the net loss or gain of heat driven by the thermal environment derived from present ground cover, such as vegetation or snow: (1) First and most common during the day when photosynthesis is occurring, the temperature of the earth surface, which receives the solar radiation, is typically warmer than the air above the canopy, and heat leaves the ecosystem upward along the negative temperature gradient, corresponding to a positive work (Fig. 1a). (2) Even when the temperature of the earth surface is warmer than the air above the canopy, there can be situations when there is a net heat gain within the ecosystem, meaning that heat moves into the ecosystem against the direction of the temperature gradient. This case is rare and counterproductive to heat dissipation, corresponding to negative work. (3) Common during the night, temperature inversion emerges. In this case, the temperature gradient from the earth surface to the atmosphere can become positive, meaning that the temperature of the air above the canopy is greater than the temperature of the earth surface. As heat enters the ecosystem to warm the surface, positive work is performed since the heat is still moving along a negative temperature gradient into the ecosystem (Fig. 1b). (4) During snowmelt conditions during the day, particularly for Arctic and alpine ecosystems, temperature inversions also emerge18,19. When this occurs and the ecosystem experiences a net heat loss through latent and sensible heat from the canopy, the heat leaving the ecosystem travels opposite of the direction dictated by the temperature gradient. Thus, in this case, ecosystems perform negative work. Our findings demonstrate how extended periods of time in this last case of work lead to thermodynamic infeasibility for the alpine/Arctic ecosystem counterfactual vegetation scenarios; i.e., ecosystems with vegetation properties from below the treelines cannot be sustained under the environmental conditions above the treelines, and, hence, they do not occur in nature.A recent study concluded that at sites where multiple functional groups exist (e.g., forests), the vegetation structure in which all groups co-exist and interact is thermodynamically more advantageous and, thus, more likely to occur than any one of the individual functional groups that the forest comprises12. Thermodynamic advantage is defined by the production of larger fluxes of entropy, more work performed, and higher work efficiency – a quantity that captures how much of the incoming energy is converted into forms useful for actively dissipating heat. It is possible to envision that under certain environmental conditions, the thermodynamic advantage offered by the existence of multiple functional groups is not tenable, indicating a thermodynamic infeasibility. Thermodynamic infeasibility occurs when a particular vegetation structure is not supported by the thermal environment at a given location. The demarcation exhibited by treelines presents an ideal case to explore this scenario, in that there is a distinct transition from multiple functional groups below the treeline to a single functional group above.Research questionIn this paper, we examine vegetation above and below Arctic and alpine treelines to determine whether the absence of trees in ecosystems above treelines are a result of thermodynamic infeasibility. Simply speaking, we seek to answer the following research question: Is the non-existence of trees beyond the transition zone demarcated as a treeline a reflection of thermodynamic infeasibility associated with the presence of trees, and if so, how is this infeasibility exhibited?Figure 1Conceptual diagram of temperature gradients. The W+ arrow indicates the positive direction of work performed through heat transport. Although in different directions, in both cases (a) and (b), the work performed is positive because heat moves from high to low temperatures. (a) Typical summertime temperature gradients from the earth surface to the air above the canopy are negative for the two real scenarios: subalpine/sub-Arctic forest (left) and alpine tundra/Arctic meadow (right). (b) A conceptual temperature inversion, or positive temperature gradient, which arise when alpine/Arctic forest are simulated as counterfactuals.Full size imageTo address this question, we use an extensively validated multi-layer 1-D physics-based ecohydrological model, MLCan12,20,21,22,23,24,25,26, consisting of 20 above-ground layers, 1 ground surface layer, and 12 below-ground layers (see Supplementary Material). This model is chosen because of its ability to capture interactions among functional groups, such as the impact of shading on understory vegetation and the resulting thermal environment within the canopy23. To balance model performance and accuracy, standing plant species are aggregated into functional groups (i.e., evergreen needleleaf trees, shrubs, grasses; see Table 1) based on literature27,28,29,30. The model output is used to compare the thermodynamic work performed at paired sites above and below the respective treelines for three different locations: the Italian Alps (IT), the United States Rocky Mountains (US), and the Western Canadian Taiga-Tundra (CA) (Fig. 2; see Site Descriptions). For each site pair, four scenarios are performed (Table 1): (1) The subalpine/sub-Arctic forest ecosystems are modeled as they exist with multiple functional groups (Fig. 1a, left). (2) The alpine/Arctic ecosystems are modeled as they exist with one functional group (i.e., shrubs or grasses; Fig. 1a, right). (3) We construct counterfactual scenarios above the treeline in which the vegetation of the subalpine or sub-Arctic forest is simulated with the environmental conditions and parameters of the alpine meadow or Arctic tundra (i.e., adding hypothetical trees where none exist; Fig. 1b). (4) As a control, a final counterfactual scenario is constructed below the treeline in which we model the understory of the subalpine/sub-Arctic forest individually (i.e., removing trees from the existing ecosystem).Table 1 Simulation scenarios with observed and hypothetical vegetation.Full size tableThe simulation of these four scenarios facilitates comparison of the existing vegetation structure of each site with the corresponding counterfactual scenarios. By varying the model inputs of vegetation present at each site while holding the environmental conditions and site-specific parameters consistent, we are able to directly compare thermodynamic outcomes as a result of varying vegetation structure and determine whether the counterfactual scenario with the simulated forest is thermodynamically feasible. Model performance was judged based on comparison to observed heat fluxes, such as latent and sensible heat (see Supplementary Material, Figs. S1–S3). As detailed below, the analysis supports the conclusion that thermodynamic feasibility is an important and complementary condition to the usual considerations of resource availability, such as water and nutrients, which determines the organizing form and function of ecosystems. More

Possingham, H. P. et al. Limits to the use of threatened species lists. Trends Ecol. Evol. 17, 503–507 (2002).Article 

Google Scholar 
Duarte, C. M. et al. Rebuilding marine life. Nature 580, 39–51 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Knowlton, N. Ocean optimism: Moving beyond the obituaries in marine conservation. Annu. Rev. Mar. Sci. 13, 13 (2021).Article 

Google Scholar 
Cinner, J. E. et al. Bright spots among the world’s coral reefs. Nature 535, 416–419 (2016).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Ripple, W. J. et al. Status and ecological effects of the world’s largest carnivores. Science 343, 1241484 (2014).PubMed 
Article 
CAS 

Google Scholar 
Estes, J. A. et al. Trophic downgrading of planet earth. Science 333, 301–306 (2011).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Hammerschlag, N. et al. Ecosystem function and services of aquatic predators in the anthropocene. Trends Ecol. Evol. 34(4), 369–383 (2019).PubMed 
Article 

Google Scholar 
Ritchie, E. G. et al. Ecosystem restoration with teeth: What role for predators?. Trends Ecol. Evol. 27, 265–271 (2012).PubMed 
Article 

Google Scholar 
Young, H. S., McCauley, D. J., Galetti, M. & Dirzo, R. Patterns, causes, and consequences of anthropocene defaunation. Annu. Rev. Ecol. Evol. Syst. 47, 333–358 (2016).Article 

Google Scholar 
Marshall, K. N., Stier, A. C., Samhouri, J. F., Kelly, R. P. & Ward, E. J. Conservation challenges of predator recovery. Conserv. Lett. 9, 70–78 (2016).Article 

Google Scholar 
Gregr, E. J. et al. Cascading social-ecological costs and benefits triggered by a recovering keystone predator. Science 368, 1243–1247 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Jones, K. R. et al. The location and protection status of earth’s diminishing marine wilderness. Curr. Biol. 28, 2506-2512.e3 (2018).CAS 
PubMed 
Article 

Google Scholar 
Dirzo, R. et al. Defaunation in the anthropocene. Science 345, 401–406 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
McCauley, D. J. et al. Marine defaunation: Animal loss in the global ocean. Science 347, 1255641 (2015).PubMed 
Article 
CAS 

Google Scholar 
Nielsen, M. R., Meilby, H., Smith-Hall, C., Pouliot, M. & Treue, T. The importance of wild meat in the global south. Ecol. Econ. 146, 696–705 (2018).Article 

Google Scholar 
Ripple, W. J. et al. Are we eating the world’s megafauna to extinction?. Conserv. Lett. 12, e12627 (2019).Article 

Google Scholar 
Pacoureau, N. et al. Half a century of global decline in oceanic sharks and rays. Nature 589, 567–571 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Carrizo, S. F. et al. Freshwater megafauna: Flagships for freshwater biodiversity under threat. Bioscience 67, 919–927 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Luskin, M. S., Albert, W. R. & Tobler, M. W. Sumatran tiger survival threatened by deforestation despite increasing densities in parks. Nat. Commun. 8, 1783 (2017).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Desforges, J.-P. et al. Predicting global killer whale population collapse from PCB pollution. Science 361, 1373–1376 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Alava, J. J., Cheung, W. W. L., Ross, P. S. & Sumaila, U. R. Climate change–contaminant interactions in marine food webs: Toward a conceptual framework. Glob. Change Biol. 23, 3984–4001 (2017).Article 

Google Scholar 
Chapron, G. et al. Recovery of large carnivores in Europe’s modern human-dominated landscapes. Science 346, 1517–1519 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
House, P. H., Clark, B. L. & Allen, L. G. The return of the king of the kelp forest: Distribution, abundance, and biomass of Giant sea bass (Stereolepis gigas) off Santa Catalina Island, California, 2014–2015. Bull. South. Calif. Acad. Sci. 115, 1–14 (2016).
Google Scholar 
Waterhouse, L. et al. Recovery of critically endangered Nassau grouper (Epinephelus striatus) in the Cayman Islands following targeted conservation actions. Proc. Natl. Acad. Sci. 117, 1587–1595 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Balmford, A. & Knowlton, N. Why Earth Optimism? (American Association for the Advancement of Science, 2017).Book 

Google Scholar 
Sutherland, W. J., Pullin, A. S., Dolman, P. M. & Knight, T. M. The need for evidence-based conservation. Trends Ecol. Evol. 19, 305–308 (2004).PubMed 
Article 

Google Scholar 
Adams, W. M. & Sandbrook, C. Conservation, evidence and policy. Oryx 47, 329–335 (2013).Article 

Google Scholar 
Faith, J. T. & Surovell, T. A. Synchronous extinction of North America’s Pleistocene mammals. Proc. Natl. Acad. Sci. 106, 20641–20645 (2009).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Davis, S. J., Peters, G. P. & Caldeira, K. The supply chain of CO2 emissions. Proc. Natl. Acad. Sci. https://doi.org/10.1073/pnas.1107409108 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
Visconti, P. et al. Projecting global biodiversity indicators under future development scenarios. Conserv. Lett. 9, 5–13 (2016).Article 

Google Scholar 
Lotze, H. K., Coll, M., Magera, A. M., Ward-Paige, C. & Airoldi, L. Recovery of marine animal populations and ecosystems. Trends Ecol. Evol. 26, 595–605 (2011).PubMed 
Article 

Google Scholar 
Queiroz, N. et al. Global spatial risk assessment of sharks under the footprint of fisheries. Nature https://doi.org/10.1038/s41586-019-1444-4 (2019).Article 
PubMed 

Google Scholar 
Pimiento, C. et al. Functional diversity of marine megafauna in the anthropocene. Sci. Adv. 6, 7650 (2020).ADS 
Article 

Google Scholar 
Estes, J. A., Heithaus, M., McCauley, D. J., Rasher, D. B. & Worm, B. Megafaunal impacts on structure and function of ocean ecosystems. Annu. Rev. Environ. Resour. 41, 83–116 (2016).Article 

Google Scholar 
Hoffmann, M. et al. The impact of conservation on the status of the world’s vertebrates. Science 330, 1503–1509 (2010).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Tom Gelatt (National Marine Mammal Laboratory, A. F. S. C. & Sweeney, K. IUCN red list of threatened species: Eumetopias jubatus. IUCN Red List of Threatened Species. https://www.iucnredlist.org/en (2016).Taylor, M. F. J., Suckling, K. F. & Rachlinski, J. J. The effectiveness of the endangered species act: A quantitative analysis. Bioscience 55, 360–367 (2005).Article 

Google Scholar 
Hejny, J. The Trump administration and environmental policy: Reagan redux?. J. Environ. Stud. Sci. 8, 197–211 (2018).Article 

Google Scholar 
Sanderson, F. J. et al. Assessing the performance of EU nature legislation in protecting target bird species in an era of climate change. Conserv. Lett. 9, 172–180 (2016).Article 

Google Scholar 
Donald, P. F. et al. International conservation policy delivers benefits for birds in Europe. Science 317, 810–813 (2007).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Cuthbert, R. J. et al. Continuing mortality of vultures in India associated with illegal veterinary use of diclofenac and a potential threat from nimesulide. Oryx 50, 104–112 (2016).Article 

Google Scholar 
Margalida, A. & Oliva-Vidal, P. The shadow of diclofenac hangs over European vultures. Nat. Ecol. Evol. 1, 1050 (2017).PubMed 
Article 

Google Scholar 
Williams, D. R., Balmford, A. & Wilcove, D. S. The past and future role of conservation science in saving biodiversity. Conserv. Lett. 13, e12720 (2020).Article 

Google Scholar 
Barnes, M. D. et al. Wildlife population trends in protected areas predicted by national socio-economic metrics and body size. Nat. Commun. 7, 12747 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Sala, E. & Giakoumi, S. No-take marine reserves are the most effective protected areas in the ocean. ICES J. Mar. Sci. 75, 1166–1168 (2018).Article 

Google Scholar 
Watson, J. E. M., Dudley, N., Segan, D. B. & Hockings, M. The performance and potential of protected areas. Nature 515, 67–73 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Juffe-Bignoli, D. et al. Protected Planet Report 2014: Tracking Progress Towards Global Targets for Protected Areas (Springer, 2014).
Google Scholar 
Turnbull, J. W., Johnston, E. L. & Clark, G. F. Evaluating the social and ecological effectiveness of partially protected marine areas. Conserv. Biol. 35, 921–932 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Barnosky, A. D. et al. Merging paleobiology with conservation biology to guide the future of terrestrial ecosystems. Science 355, 1–10 (2017).Article 
CAS 

Google Scholar 
White, T. D. et al. Assessing the effectiveness of a large marine protected area for reef shark conservation. Biol. Conserv. 207, 64–71 (2017).Article 

Google Scholar 
Geldmann, J. et al. Effectiveness of terrestrial protected areas in reducing habitat loss and population declines. Biol. Conserv. 161, 230–238 (2013).Article 

Google Scholar 
Daskin, J. H. & Pringle, R. M. Warfare and wildlife declines in Africa’s protected areas. Nature 553, 328–332 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Pringle, R. M. Upgrading protected areas to conserve wild biodiversity. Nature 546, 91–99 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Redpath, S. M. et al. Don’t forget to look down: Collaborative approaches to predator conservation. Biol. Rev. 92, 2157–2163 (2017).PubMed 
Article 

Google Scholar 
Hazzah, L. et al. Efficacy of two lion conservation programs in Maasailand, Kenya. Conserv. Biol. 28, 851–860 (2014).PubMed 
Article 

Google Scholar 
Zarfl, C. et al. Future large hydropower dams impact global freshwater megafauna. Sci. Rep. 9, 18531 (2019).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Arthington, A. H., Dulvy, N. K., Gladstone, W. & Winfield, I. J. Fish conservation in freshwater and marine realms: Status, threats and management. Aquat. Conserv. Mar. Freshw. Ecosyst. 26, 838–857 (2016).Article 

Google Scholar 
Castello, L. & Macedo, M. N. Large-scale degradation of Amazonian freshwater ecosystems. Glob. Change Biol. 22, 990–1007 (2016).ADS 
Article 

Google Scholar 
Safford, R. et al. Vulture conservation: The case for urgent action. Bird Conserv. Int. 29, 1–9 (2019).Article 

Google Scholar 
Ogada, D. et al. Another continental vulture crisis: Africa’s vultures collapsing toward extinction. Conserv. Lett. 9, 89–97 (2016).ADS 
Article 

Google Scholar 
Buechley, E. R. & Şekercioğlu, Ç. H. The avian scavenger crisis: Looming extinctions, trophic cascades, and loss of critical ecosystem functions. Biol. Conserv. 198, 220–228 (2016).Article 

Google Scholar 
Hammerschlag, N. & Gallagher, A. J. Extinction risk and conservation of the earth’s national animal symbols. Bioscience 67, 744–749 (2017).Article 

Google Scholar 
Sutherland, W. J., Dicks, L. V., Ockendon, N. & Smith, R. K. What Works in Conservation 2015 (Open Book Publishers, 2015).Book 

Google Scholar 
Dulvy, N. K. et al. Challenges and priorities in shark and ray conservation. Curr. Biol. 27, R565–R572 (2017).CAS 
PubMed 
Article 

Google Scholar 
Finucci, B., Duffy, C. A. J., Francis, M. P., Gibson, C. & Kyne, P. M. The extinction risk of New Zealand chondrichthyans. Aquat. Conserv. Mar. Freshw. Ecosyst. 29, 783–797 (2019).Article 

Google Scholar 
Creel, S. et al. Questionable policy for large carnivore hunting. Science 350, 1473–1475 (2015).ADS 
CAS 
PubMed 
Article 

Google Scholar 
González, L. M. et al. Causes and spatio-temporal variations of non-natural mortality in the Vulnerable Spanish imperial eagle Aquila adalberti during a recovery period. Oryx 41, 495–502 (2007).Article 

Google Scholar 
Morandini, V., de Benito, E., Newton, I. & Ferrer, M. Natural expansion versus translocation in a previously human-persecuted bird of prey. Ecol. Evol. 7, 3682–3688 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Goodrich, J. M. et al. Panthera tigris, Tiger. IUCN Red List Threat. Species (2015).Wikramanayake, E. et al. A landscape-based conservation strategy to double the wild tiger population. Conserv. Lett. 4, 219–227 (2011).Article 

Google Scholar 
Bhattarai, B. R., Wright, W., Morgan, D., Cook, S. & Baral, H. S. Managing human-tiger conflict: Lessons from Bardia and Chitwan National Parks, Nepal. Eur. J. Wildl. Res. 65, 34 (2019).Article 

Google Scholar 
Pinsky, M. L. et al. Preparing ocean governance for species on the move. Science 360, 1189–1191 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Courchamp, F. et al. The paradoxical extinction of the most charismatic animals. PLoS Biol. 16, e2003997 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Nyhus, P. J. Human-wildlife conflict and coexistence. Annu. Rev. Environ. Resour. 41, 143–171 (2016).Article 

Google Scholar 
Carter, N. H. & Linnell, J. D. C. Co-adaptation is key to coexisting with large carnivores. Trends Ecol. Evol. 31, 575–578 (2016).PubMed 
Article 

Google Scholar 
Guerra, A. S. Wolves of the sea: Managing human-wildlife conflict in an increasingly tense ocean. Mar. Policy 99, 369–373 (2019).Article 

Google Scholar 
Das, C. S. Pattern and characterisation of human casualties in Sundarban by tiger attacks, India. Sustain. For. 1, 1–10 (2018).
Google Scholar 
Packer, C. et al. Conserving large carnivores: Dollars and fence. Ecol. Lett. 16, 635–641 (2013).CAS 
PubMed 
Article 

Google Scholar 
Dudley, S. F. J. A comparison of the shark control programs of New South Wales and Queensland (Australia) and KwaZulu-Natal (South Africa). Ocean Coast. Manag. 34, 1–27 (1997).Article 

Google Scholar 
O’Connell, C. P., Andreotti, S., Rutzen, M., Meӱer, M. & Matthee, C. A. Testing the exclusion capabilities and durability of the Sharksafe Barrier to determine its viability as an eco-friendly alternative to current shark culling methodologies. Aquat. Conserv. Mar. Freshw. Ecosyst. 28, 252–258 (2018).Article 

Google Scholar 
Gailey, G. et al. Effects of sea ice on growth rates of an endangered population of gray whales. Sci. Rep. 10, 1553 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hazen, E. L. et al. A dynamic ocean management tool to reduce bycatch and support sustainable fisheries. Sci. Adv. 4, 3001 (2018).ADS 
Article 

Google Scholar 
Ingeman, K. E., Samhouri, J. F. & Stier, A. C. Ocean recoveries for tomorrow’s Earth: Hitting a moving target. Science 363, 6425 (2019).Article 

Google Scholar 
Sánchez-Hernández, J. & Amundsen, P.-A. Ecosystem type shapes trophic position and omnivory in fishes. Fish Fish. 19, 1003–1015 (2018).Article 

Google Scholar 
Gainsbury, A. M., Tallowin, O. J. S. & Meiri, S. An updated global data set for diet preferences in terrestrial mammals: testing the validity of extrapolation. Mammal Rev. 48, 160–167 (2018).Article 

Google Scholar 
Faurby, S. et al. PHYLACINE 1.2: The phylogenetic atlas of mammal macroecology. Ecology 99, 2626–2626 (2018).PubMed 
Article 

Google Scholar 
Costello, M. J. et al. Marine biogeographic realms and species endemicity. Nat. Commun. 8, 1057 (2017).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Olson, D. M. et al. Terrestrial ecoregions of the world: A new map of life on earth: A new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. Bioscience 51, 933–938 (2001).Article 

Google Scholar 
Rodrigues, A. S. L., Pilgrim, J. D., Lamoreux, J. F., Hoffmann, M. & Brooks, T. M. The value of the IUCN red list for conservation. Trends Ecol. Evol. 21, 71–76 (2006).PubMed 
Article 

Google Scholar  More

Snyder-Mackler, N. et al. Science 368, eaax9553 (2020).CAS 
Article 

Google Scholar 
Wrzus, C., Hänel, M., Wagner, J. & Neyer, F. J. Psychol. Bull. 139, 53–80 (2013).Article 

Google Scholar 
Steptoe, A., Shankar, A., Demakakos, P. & Wardle, J. Proc. Natl Acad. Sci. USA 110, 5797–5801 (2013).CAS 
Article 

Google Scholar 
Almeling, L., Hammerschmidt, K., Sennhenn-Reulen, H., Freund, A. M. & Fischer, J. Curr. Biol. 26, 1744–1749 (2016).CAS 
Article 

Google Scholar 
Rosati, A. G. et al. Science 370, 473–476 (2020).CAS 
Article 

Google Scholar 
Schino, G. & Pinzaglia, M. Am. J. Primatol. 80, e22746–e22747 (2018).Article 

Google Scholar 
Machanda, Z. P. & Rosati, A. G. Phil. Trans. R. Soc. Lond. B 375, 20190620 (2020).Article 

Google Scholar 
Kroeger, S. B., Blumstein, D. T. & Martin, J. G. A. Phil. Trans. R. Soc. Lond. B 376, 20190745 (2021).Article 

Google Scholar 
Weiss, M. N. et al. Proc. R. Soc. Lond. B 288, 20210617 (2021).
Google Scholar 
Albery, G. F. et al. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-022-01817-9 (2022).Article 
PubMed 

Google Scholar 
Siracusa, E. R., Higham, J. P., Snyder-Mackler, N. & Brent, L. J. N. Biol. Lett. 18, 20210643 (2022).Article 

Google Scholar 
Nussey, D. H., Coulson, T., Festa-Bianchet, M. & Gaillard, J. M. Funct. Ecol. 22, 393–406 (2008).Article 

Google Scholar 
Nussey, D. H., Froy, H., Lemaître, J.-F., Gaillard, J.-M. & Austad, S. N. Ageing Res. Rev. 12, 214–225 (2013).Article 

Google Scholar  More

Bengtsson, J. Biological control as an ecosystem service: partitioning contributions of nature and human inputs to yield. Ecol. Entomol. 40, 45–44 (2015).Article 

Google Scholar 
Zalucki, M., Furlong, M. J., Schellhorn, N. A., Macfadyen, S. & Davies, A. P. Assessing the impact of natural enemies in agroecosystems: toward “real” IPM or in quest of Holy Grail? Insect. Sci. 22, 1–5 (2015).PubMed 
Article 

Google Scholar 
Van Lenteren, J. C., Bolckmans, K., Kohl, J., Ravensberg, W. J. & Urabaneja, A. Biological control using invertebrates and microorganisms: plenty of new opportunities. BioControl 63, 39–59 (2018).Article 

Google Scholar 
Symondson, W. O. C., Sunderland, K. D. & Greenstone, M. H. Can generalist predators be effective biological control agents. Annu. Rev. Entomol. 47, 561–594 (2002).CAS 
PubMed 
Article 

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 
Article 

Google Scholar 
Van Nouhuys, S., Niemikapee, S. & Hanski, I. Variation in a host-parasitoid interaction across independent populations. Insects 3, 1236–1256 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
Hedlund, K., Vet, L. E. M. & Dicke, M. Generalist and specialist parasitoid strategies of using odours of adult drosophilid flies when searching for larval hosts. Oikos 77, 390–398 (1996).Article 

Google Scholar 
Evans, E. W., Stevenson, A. T. & Richards, D. R. Essential versus alternative foods of insect predators: benefits of a mixed diet. Oelcologia 121, 107–112 (1999).Article 

Google Scholar 
Lovei, G. L. & Sunderland, K. M. Ecology and behavior of ground beetles (Coleoptera: Carabidae). Annu. Rev. Entomol. 41, 231–256 (1996).CAS 
PubMed 
Article 

Google Scholar 
Kromp, B. Carabid beetles in sustainable agriculture: a review on pest control efficacy, cultivation impacts and enhancement. Agric. Ecosyt. Environ. 74, 187–228 (1999).Article 

Google Scholar 
Tuf, H., Dedek, P. & Vesley, M. Does the diurnal activity pattern of carabid beetles depend on season, ground temperature, or habitat? Arch. Biol. Sci. 64, 721–732 (2012).Article 

Google Scholar 
Firlej, A., Doyon, J., Harwood, J. D. & Brodeur, J. A multi-approach study to delineate interaction between carabid beetles and soybean aphids. Environ. Entomol. 42, 89–96 (2013).PubMed 
Article 

Google Scholar 
Clark, M. S., Luna, J. M., Stone, N. D. & Youngman, R. R. Generalist predator consumption of armyworm (Lepidoptera: Noctuidae) and effect of predator removal and damage in no-till corn. Environ. Entomol. 23, 617–622 (1994).Article 

Google Scholar 
Floate, K. D., Doane, J. F. & Gillot, C. Carabid predators of the wheat midge (Diptera: Cecidomyiidae) in Saskatchewan. Environ. Entomol. 19, 1503–1511 (1990).Article 

Google Scholar 
Barsics, F., Haubruge, E. & Verheggen, F. J. Wireworms’ management: an overview of the existing methods, with particular regards to Agriotis spp. (Coleoptera: Elateridae). Insects 4, 117–152 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Oberholzer, F., Escher, N. & Frank, T. The potential of carabid beetles (Coleoptera) to reduce slug damage to oilseed rape in the laboratory. Eur. J. Entomol. 100, 81–85 (2003).Article 

Google Scholar 
Honek, A., Martinkova, Z. & Jarosik, V. Ground beetles Carabidae as seed predators. Eur. J. Entomol. 100, 531–544 (2003).Article 

Google Scholar 
Lundgren, J. G. Relationship of Natural Enemies and Non-prey Foods 1–460 (Springer, 2009).Carbonne, B. et al. The resilience of weed seedbank regulation by carabid beetles, at continental scales, to alternative prey. Sci. Rep. 10, 1935 (2020).Article 
CAS 

Google Scholar 
Wilder, S. M., Norris, M., Lee, R. W., Raubenheimer, D. & Simpson, S. J. Arthropod food webs become increasingly lipid-limited at higher trophic levels. Ecol. Lett. 16, 895–902 (2013).PubMed 
Article 

Google Scholar 
Denno, R. F. & Fagan, W. F. Might nitrogen limitation promote omnivory among carnivorous arthropods? Ecology 84, 2522–2531 (2003).Article 

Google Scholar 
Saska, P. & Jarosik, V. Laboratory study of larval food requirements in nine species of Amara (Coleoptera: Carabidae). Plant Prot. 37, 103–110 (2001).
Google Scholar 
Saska, P., Van der Werf, W. & Westerman, P. Spatial and temporal patterns of carabid activity-density in cereals do not explain levels of weed seed predation. Bull. Entomological Res. 98, 169–181 (2008).CAS 
Article 

Google Scholar 
Talarico, F., Giglio, A., Pizzolotto, R. & Brandmayr, P. P. A synthesis of the feeding habits and reproductive rhythms in Italian seed feeding ground beetles (Coleoptera: Carabidae). Eur. J. Entomol. 113, 325–336 (2016).Article 

Google Scholar 
Fawki, S., Bak, S. S. & Toft, S. Food preference and food value for the carabid beetles Pterostichus melanarius, P. versicolor, and Carabus nemoralis. Eur. Carabidol. 114, 99–109 (2003).
Google Scholar 
Frei, B., Guenay, Y., Bohan, B. A., Traugett, M. & Wallinger, C. Molecular analysis indicates high levels of carabid weed seed consumption in cereal fields across central Europe. J. Plant Sci. 92, 935–942 (2019).
Google Scholar 
Kulkarni, S. S., Dosdall, L. M., Spence, J. R. & Willenborg, C. J. Brassicaceous weed seed predation by ground beetles (Coleoptera: Carabidae). Weed. Sci. 64, 294–302 (2016).Article 

Google Scholar 
Saska, P., Honek, A., Foffova, H. & Martinkova, Z. Burial-induced changes in the seed preferences of carabid beetles (Coleoptera: Carabidae). Eur. J. Entomol. 116, 113–140 (2019).Article 

Google Scholar 
Saska, P., Honek, A. & Martinkova, Z. Preference of carabid beetles (Coleoptera: Carabidae) for herbaceous seeds. Acta Zool. Acad. Sci. Hung. 65, 57–76 (2019).Article 

Google Scholar 
Sih, A. & Christensen, B. Optimal diet theory: when does it work, and when and why does it fail? Anim. Behav. 61, 379–390 (2001).Article 

Google Scholar 
Barron, A. B., Gurney, K. N., Meah, L. F. S., Vasilaki, E. & Marshall, J. A. R. Decision-making and action selection in insects: inspiration from vertebrate-based theories. Front. Behav. Neurosci. 9, 216 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Kulkarni, S. S., Dosdall, L. M., Spence, J. R. & Willenborg, C. J. C. J. The role of ground beetles (Coleoptera: Carabidae) in weed seed consumption: a review. Weed. Sci. 63, 355–376 (2015).Article 

Google Scholar 
Kulkarni, S. S., Dosdall, L. M., Spence, J. R. & Willenborg, C. J. Seed detection and discrimination by ground beetles (Coleoptera: Carabidae) are associated with olfactory cues. PLoS One 12, e0170593 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Law, J. J. & Gallagher, R. S. The role of imbibition on seed selection by Harpalus pensylvanicus. Appl. Soil. Ecol. 87, 118–124 (2015).Article 

Google Scholar 
Davis, A. S., Schutte, B. J., Iannuzzi, J. & Renner, K. A. Chemical and physical defenses of weed seeds in relation to soil seedbank persistence. Weed Sci. 56, 676–684 (2008).CAS 
Article 

Google Scholar 
Ali, K. A. & Willneborg., C. J. C. J. The biology of seed discrimination and its role in shaping the foraging ecology of carabids: a review. Ecol. Evol. 11, 13702–13722 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Wheater, C. P. Prey detection by some predatory Coleoptera (Carabidae and Staphylinidae). J. Zool. 215, 171–185 (1989).Article 

Google Scholar 
Mundy, C. A., Aleen-Williams, L. J., Underwood, N. & Warrington, S. Prey selection and foraging behavior by Pterostichus cupreus L. (Col., Carabidae) under laboratory conditions. J. Appl. Entomol. 124, 349–358 (2000).Article 

Google Scholar 
Kielty, J. P., Allen-Williams, L. J., Underwood, N. & Eastwood, E. A. Behavioral responses of three species of ground beetles (Carabidae: Coloeptera) to olfactory cues associated with prey and habitat. J. Insect. Behav. 9, 237–249 (1996).Article 

Google Scholar 
Tréfás, H., Canning, H., McKinlay, R. G., Armstrong, G. & Bujaki, G. Preliminary experiments on the olfactory responses of Pterostichus melanarius Illiger (Coleoptera:Carabidae) to intact plants. Agric. Entomol. 3, 71–76 (2001).Article 

Google Scholar 
McKemey, A. R., Symondson, W. O. C. & Glen, D. M. Predation and prey size choice by the carabid Pterostichus melanarius (Coleoptera: Carabidae): the dangers of extrapolating from laboratory to field. Bull. Entomol. Res. 93, 227–234 (2003).CAS 
PubMed 
Article 

Google Scholar 
Thomas, R. S., Glen, D. M. & Symondson, W. O. C. Prey detection through olfaction by the soil-dwelling larvae of the carabid predator Pterostichus melanarius. Soil Biol. Biochem. 40, 207–216 (2008).CAS 
Article 

Google Scholar 
Talarico, F. et al. Electrophysiological and behavioral analyses on prey selecting in the myrmecophagous carabid beetle Siagona europaea Dejean 1826 (Coleoptera: Carabidae). Etho. Ecol. Evol. 22, 375–384 (2010).Article 

Google Scholar 
Dessaint, F., Chadoeuf, R. & Barrales, G. Spatial pattern analysis of weed seeds in the cultivated soil seed bank. J. Appl. Ecol. 28, 721–730 (1991).Article 

Google Scholar 
Oster, M., Smith, L., Beck, J. J., Howard, A. & Field, C. B. Orientational behavior of predaceous ground beetle species in response to volatile emissions identified from yellow starthistle damaged by an invasive slug. Arthropod-Plant. Inte. 8, 429–437 (2014).Article 

Google Scholar 
Srinivasan, M. V., Poteser, M. & Karl, K. Motion detection in insect orientation and navigation. Vis. Res. 39, 2749–2766 (1999).CAS 
PubMed 
Article 

Google Scholar 
Sato, K. & Touhara, K. Insect olfaction: receptors, signal transduction, and behavior. Cell 47, 121–138 (2009).CAS 

Google Scholar 
Leal, W. S. Odorant reception in insects: roles of receptors, binding proteins, and degrading enzymes. Ann. Rev. Entomol. 58, 373–391 (2013).CAS 
Article 

Google Scholar 
Schmidt, H. R. & Benton, R. Molecular mechanisms of olfactory detection in insects: beyond receptors. Open Biol. 10, 200252 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Prokopy, R. J. & Owens, E. D. Visual detection of plants by herbivorous insects. Ann. Rev. Entomol. 28, 337–364 (1983).Article 

Google Scholar 
Ploomi, A. et al. Antennal sensilla in ground beetles (Coleoptera: Carabidae). Agron. Res. 1, 221–228 (2003).
Google Scholar 
Merivee, E. et al. Electrophysiological responses from neurons of antennal taste sensilla in the polyphagous predatory ground beetle Pterostichus oblongopunctatus (Fabricius 1787) to plant sugars and amin acids. J. Insect. Physiol. 54, 1213–1219 (2008).CAS 
PubMed 
Article 

Google Scholar 
Merivee, E., Ploomi, A., Luik, A., Rahi, M. & Smmelselg, V. Antennal sensilla of the ground beetle Platynus dorsalis (Pontoppidan, 1763) (Coleoptera: Carabidae). Micros. Res. Tech. 55, 339–349 (2001).CAS 
Article 

Google Scholar 
Merivee, E. et al. Antennal sensilla of the ground beetle Bembidion properans Steph. (Coleoptera: Carabidae). Micron 33, 429–440 (2002).PubMed 
Article 

Google Scholar 
Giglio, A., Perotta, E., Talarico, F., Brandmayr, T. E. & Ferrera, E. A. Sensilla on the maxillary and labial palps in a helicophagous ground beetle larva (Coleoptera: Carabidae). Acta Zool. 200, 1463–6393 (2013).
Google Scholar 
Van Naters, W. V. D. G. & Carlson, J. R. J. R. Receptors and neurons for fly odors in Drosophila. Curr. Biol. 17, 606–612 (2007).Article 
CAS 

Google Scholar 
Amrein, H. & Throne, N. Gustatory perception and behavior in Dropsophila melanogaster. Curr. Biol. 15, R673–R684 (2005).CAS 
PubMed 
Article 

Google Scholar 
Su, C. Y., Menuz, K. & Carlson, J. R. Olfactory perception: receptors, cells, and circuits. Cell 139, 45–59 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Krieger, J. & Breer, H. Olfactory receptors in invertebrates. Science 286, 720–723 (1999).CAS 
PubMed 
Article 

Google Scholar 
Chapman, R. F. The Insects: Structure and Function 4th edn, 1–584 (Cambridge University Press, 1998).Bhandari, S. R., Jo, J. S. & Lee, J. G. Comparisons of glucosinolate profiles in different tissues of nine Brassica crops. Molecules 20, 15827–15841 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Reifenrath, K., Riederer, M. & Muller, M. Leaf surface wax layers of Brassicaceae lack feeding stimulants for Phaedon cochleariae. Entomol. Exp. Appl. 115, 41–50 (2005).CAS 
Article 

Google Scholar 
Stadler, E. & Reifenrath, K. Glucosinolates on the leaf surface perceived by insect herbivores: review of ambiguous results and new investigations. Phytoch. Rev. 8, 207–225 (2009).Article 
CAS 

Google Scholar 
Sharma, A., Sandhi, R. K. & Reddy, G. V. P. A review of interactions between insect biological control agents and semiochemicals. Insects 10, 439 (2019).PubMed Central 
Article 

Google Scholar 
Warwick, S. I., Francis, A. & Susko, D. J. The biology of Canadian weeds. 9. Thlaspi arvense L. (updated). Can. J. Plant. Sci. 82, 803–823 (2002).Article 

Google Scholar 
Moyna, P. & Garcia, M. Chemical composition of oat seed epicuticular lipids. J. Sci. Food Agric. 34, 209–211 (1983).CAS 
PubMed 
Article 

Google Scholar 
Kunst, L. & Samuels, A. L. Biosynthesis and secretion of plant cuticular wax. Prog. Lipid Res. 42, 51–80 (2003).CAS 
PubMed 
Article 

Google Scholar 
Eigenbrode, S. D. & Espelie, K. E. Effects of plants epicuticular lipids on insect herbivores. Annu. Rev. Entomol. 40, 171–194 (1995).Article 

Google Scholar 
Finch, S. Volatile plant chemicals and their effect on host plant by the cabbage root fly (Delia brassicae). Entomol. Exp. Appl. 24, 350–359 (1978).CAS 
Article 

Google Scholar 
Udayagiri, S. & Mason, C. E. Epicuticular wax chemicals in Zea mays influence oviposition in Ostrinia nubilalis. J. Chem. Ecol. 23, 1675–1687 (1997).CAS 
Article 

Google Scholar 
Adati, T. & Matsuda, K. The effect of leaf surface wax on feeding of the strawberry leaf beetle, Galerucella vittaticollis, with reference to host plant preference. Tohoku. J. Agric. Res. 50, 57–61 (2000).
Google Scholar 
Damon, S. J., Groves, R. L. & Harvey, M. J. Variation for epicuticular waxes on onion foliage and impacts on numbers of onion thrips. J. Am. Soc. Hortic. Sci. 139, 495–501 (2014).CAS 
Article 

Google Scholar 
Braccini, C. L., Vega, A. S., Chludil, H. D., Leicach, S. R. & Fernandez, P. C. Host selection, oviposition behavior and leaf traits in a specialist willow sawfly on species of Salix (Salicaceae). Ecol. Entomol. 38, 617–626 (2013).Article 

Google Scholar 
Wojcicka, A. Effects of epicuticular waxes from triticale on the feeding behaviour and mortality of the grain aphid, Sitobion avenae (Fabricius) (Hemiptera: Aphididae). J. Plant. Prot. Res. 56, 39–44 (2016).CAS 
Article 

Google Scholar 
Medina, E. et al. Taxonomic significance of the epicuticular wax composition in species of genus Clusia from Panama. Biochem. Syst. Ecol. 34, 319–326 (2006).CAS 
Article 

Google Scholar 
Schulz-Bohm, K., Martin-Sanchez, L. & Garbeva, P. Microbial volatiles: small molecules with an inter-kingdom interactions. Front. Microbiol. 8, 2484 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Ali, K. A. Mechanisms of Seed Discrimination and Selective Seed Foraging in Carabid Weed Seed Predators. https://harvest.usask.ca/bitstream/handle/10388/13815/ALI-DISSERTATION-2022.pdf?sequence=1&isAllowed=y (2022).Webster, B., Qvarfordt, E., Olsson, U. & Glinwood, R. Different roles for innate and learnt behavioral responses to odors in insect host location. Behav. Ecol. 24, 366–372 (2013).Article 

Google Scholar 
Luff, M. L. Adult and larval feeding habits of Pterostichus madidus (F.) (Carabidae: Coleoptera). J. Nat. Hist. 8, 403–409 (1974).Article 

Google Scholar 
Blubaugh, C. K. & Kaplan, I. Invertebrate seed predators reduce weed emergence following seed rain. Weed Sci. 64, 80–86 (2016).Article 

Google Scholar 
Blubaugh, C. K., Hagler, J. R., Machtley, S. A. & Kaplan, I. Cover crops increase foraging activity of omnivorous predators in seed patches and facilitate weed biological control. Agric. Ecosyst. Environ. 231, 264–270 (2016).Article 

Google Scholar 
Foffova, H. et al. Which seed properties determine the preferences of carabid beetles seed predators? Insects 11, 757 (2020).Petit, S., Boursault, A. & Bohan, D. A. Weed seed choice by carabid beetles (Coleoptera: Carabidae): linking field measurements and laboratory diet assessments. Eur. J. Entomol. 111, 615–620 (2014).Article 

Google Scholar 
Carbonne, B. et al. Direct and indirect effects of landscape and field management intensity on carabids through trophic resources and weeds. J. Appl. Ecol. 59, 176–187 (2022).Article 

Google Scholar 
Foffova, H., Bohan, D. A. & Saska, P. Do properties and species of weed seeds affect their consumption by carabid beetles? Acta Zool. Acad. Sci. Hung. 66, 37–48 (2020b).Article 

Google Scholar 
De Heij, S. E. & Willenborg, C. J. Connected carabids: network interactions and their impact on biocontrol by carabid beetles. Bioscience 70, 90–500 (2020).Article 

Google Scholar 
Honek, A., Martinkova, Z., Saska, P. & Pekar, S. Size and taxonomic constraints determine seed preference of Carabidae (Coleoptera). Basic Appl. Ecol. 8, 343–353 (2007).Article 

Google Scholar 
Spence, J. R. & Niemela, J. K. Sampling carabid assemblages with pitfall traps: the madness and the method. Can. Entomol. 126, 881–884 (1994).Article 

Google Scholar 
Lindroth, C. H. The Ground Beetles (Carabidae, excluding Cicindelinae) of Canada and Alaska. Supplement 20, 24, 29, 33, 34, 35. Part I, pages I–XLVIII, 1969. Part II, pages 1–200, 1961. Part III, pages 201–408, 1963. Part IV, pages 409–648, 1966. Part V, pages 649–944, 1968. Part VI, pages 945–1192 (Opusca Entomology, 1961–1969).White, S. S., Renner, K. A., Menalled, F. D. & Landis, D. A. Feeding preferences of weed seed predators and effect on weed emergence. Weed. Sci. 55, 606–612 (2007).CAS 
Article 

Google Scholar 
Glinwood, R., Ahmed, E., Ovarfordt, E. & Ninkovic, V. Olfactory learning of plant genotypes by a polyphagous predator. Oecologia 166, 637–647 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
Sablon, L., Dickens, J. C., Haubruge, E. H. & Verhggen., F. J. Chemical ecology of the Colorado potato beetle, Leptinotarsa decemlineata (Say) (Coleoptera: Chrysomelidae), and potential for alternative control methods. Insects 4, 31–54 (2013).Article 

Google Scholar 
Zhang, L., Li, H. & Zhang, L. Two olfactory pathways to detect aldehydes on locust mouthpart. Int. J. Biol. Sci. 13, 759–771 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pekar, S. & Hruskova, M. M. How granivorous Coreus marginatus (Hemiptera: Cereidae) recognizes its food. Acta Ethol. 9, 26–30 (2006).Article 

Google Scholar 
Ardenghi, N., Mulch, A., Pross, J. & Niedermeyer, E. M. Leaf wax n-alkane extraction: an optimized procedure. Org. Geochem. 113, 283–292 (2017).CAS 
Article 

Google Scholar 
Takahashi, S. & Gassa, A. Roles of cuticular hydrocarbons in intra- and interspecific recognition behavior of two Rhinotermitidae species. J. Chem. Ecol. 21, 1837–1845 (1995).CAS 
PubMed 
Article 

Google Scholar 
Bates, D., Machler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 
CAS 

Google Scholar 
Nobre, J. S. & Singer, J. D. M. Residual analysis for linear mixed models. Biom. J. 49, 863–875 (2007).PubMed 
Article 

Google Scholar 
Schielzeth, H. et al. Robustness of linear mixed-effects models to violations of distributional assumptions. Methods Ecol. Evol. 11, 1141–1152 (2020).Article 

Google Scholar  More

Field-data collectionFieldwork was conducted in DRC between January 2018 and March 2020. Ten transects (4–11 km long) were installed, identical to the approach in ref. 9, in locations that were highly likely to be peatland. These were selected to help test hypotheses about the role of vegetation, surface wetness, nutrient status and topography in peat accumulation (Fig. 1a and Supplementary Table 1). A further eight transects (0.5–3 km long) were installed to assess our peat mapping capabilities (Fig. 1a and Supplementary Table 1).Every 250 m along each transect, land cover was classified as one of six classes: water, savannah, terra firme forest, non-peat-forming seasonally inundated forest, hardwood-dominated peat swamp forest or palm-dominated peat swamp forest. Peat swamp forest was classified as palm dominated when >50% of the canopy, estimated by eye, was palms (commonly Raphia laurentii or Raphia sese). In addition, several ground-truth points were collected at locations in the vicinity of each transect from the clearly identifiable land-cover classes water, savannah and terra firme forest.Peat presence/absence was recorded every 250 m along all transects, and peat thickness (if present) was measured by inserting metal poles into the ground until the poles were prevented from going any further by the underlying mineral layer, identical to the pole method of ref. 9. In addition, a core of the full peat profile was extracted every kilometre along the ten hypothesis-testing transects, if peat was present, with a Russian-type corer (52 mm stainless steel Eijkelkamp model); these 63 cores were sealed in plastic for laboratory analysis.Peat-thickness laboratory measurementsPeat was defined as having an organic matter (OM) content of ≥65% and a thickness of ≥0.3 m (sensu ref. 9). Therefore, down-core OM content of all 63 cores was analysed to measure peat thickness. The organic matter content of each 0.1-m-thick peat sample was estimated via loss on ignition (LOI), whereby samples were heated at 550 °C for 4 h. The mass fraction lost after heating was used as an estimate of total OM content (% of mass). Peat thickness was defined as the deepest 0.1 m with OM ≥ 65%, after which there is a transition to mineral soil. Samples below this depth were excluded from further analysis. Rare mineral intrusions into the peat layer above this depth, where OM 4× the mean Cook’s distance were excluded as influential outliers. Mean pole-method offset was significantly higher along the DRC transects (0.94 m) than along those in ROC (0.48 m; P  More