Philippa Kaur

Survey sites and data collectionBenthic and fish surveys were conducted at randomly stratified sites throughout the entirety of the FRT by NOAA’s National Coral Reef Monitoring Program (NCRMP). Sites were categorized into three biogeographic regions, including Dry Tortugas (DRTO, n = 228), Florida Keys (FLKs, n = 322), and Southeast Florida (SEFL, n = 173) (Fig. 1). The Florida Keys were further classified into the following four sub-regions: Lower Keys (LK, n = 103), Middle Keys (MK, n = 46), Upper Keys (UK, n = 140), Biscayne (BISC, n = 33). Within each region/sub-region (except for SEFL), reefs were categorized according to reef types. For DRTO, this included bank, forereef, and lagoon reef sites. For the LK, MK, UK, and BISC, reef types were categorized as inshore, mid-channel, and offshore. Data were collected throughout the region in 2014, 2016, and 2018.Fish and benthic surveys were conducted in accordance with NCRMP methodologies34 (Table S2). The protocol used for the fish surveys was developed from a modified Reef Visual Census (RVC) method35 and was performed using a stratified random sampling design. Divers surveyed two 15 m diameter cylinders, spaced 15 m apart. Fish species were identified to the lowest taxonomic level for a period of five minutes. This was followed by an additional five minutes dedicated to recording species abundances and sizes (10 cm bins).Surveys were used to quantify the benthic cover at each site. The protocol for these surveys followed a standard line point-intercept sampling design. At each site, a 15 m weighted transect was draped along the reef surface. Surveyors recorded benthic composition at 15 cm intervals along the transect (i.e., 100 equidistant points). The benthic composition from these 100 points was then transformed to percent cover of ecologically important functional groups (scleractinian coral [species-specific], gorgonians, hydrocoral, CCA, macroalgae, turf algae, sponges, bare/dead substrate, sand/sediment).Carbonate budget analysisPlanar benthic surveys were adjusted to account for the three-dimensional complexity (i.e., rugosity) of each site using light detection and ranging (LiDAR) data (1 m horizontal resolution; 15 cm vertical resolution) from topobathymetric mapping surveys of the South Florida eastern coastline conducted by NOAA’s National Geodetic Survey. A 15 m x 15 m region of interest (ROI) was placed around the GPS coordinates of each site using ArcGIS Pro with 3D and Spatial Analyst extensions (ESRI). The ROI was then overlaid with existing multibeam echosounder (MBES) and LiDAR bathymetry data. Within the ROI, LiDAR was extracted using the Clip Raster function from ArcPy (ArcGIS’s python coding interface), and the Surface Volume tool was used to calculate the 3D surface area. Rugosity was calculated by dividing the 3D surface area by the 2D surface area of the ROI.The methodology for standardizing reef carbonate budgets to topographic complexity (i.e., rugosity) diverged from that of the ReefBudget approach by using site-specific rugosity rather than species-specific rugosity17. This was a necessary limitation of this analysis as transect rugosity at 1 m increments was not measured using the NCRMP benthic survey protocol. To ensure that reef topographic complexity was still accounted for, however, rugosity of the entire reef site, calculated from LiDAR bathymetry data, was used in this analysis. While rugosity of the site rather than of each benthic component, specifically for corals, can lead to an under or overestimation of carbonate production rates, we note that site and species rugosity (i.e., encrusting and massive coral morphologies) was low for the vast majority of sites and species surveyed, thereby reducing the probability of an under or overestimation.Reef carbonate budget analysis was performed following a modified version of the ReefBudget approach17. Coral carbonate production was derived from species-specific linear extension rates (cm year−1), skeletal density (g cm−3), coral morphology (branching, massive, sub-massive, encrusting/plating), and percent cover. Carbonate production by CCA and other calcareous encrusters was similarly calculated as a function of surface area, literature reported linear extension rates, and skeletal density17. Gross carbonate production at each survey site was measured as the sum total of carbonate production by all calcareous organisms found at each site and was standardized to site-specific reef rugosity.Gross carbonate erosion for each survey-site was calculated as the sum total of erosion by four bioeroding groups: parrotfish, microborers, macroborers, and urchins. The calculations roughly followed the ReefBudget methodologies17 (Table 1). Parrotfish size frequency distributions from NCRMP surveys were multiplied by size and species-specific bite rates (bites min−1), volume removed per bite (cm3), and proportion of bites leaving scars to calculate total parrotfish erosion17. The substrate density (1.72 g cm−3) used in these calculations followed that of the ReefBudget protocol17. Microbioerosion was calculated from the percent cover of dead coral substrate, which was multiplied by a literature-derived rate17 of − 0.240 kg CaCO3 m−2 year−1. Macroboring was calculated as the percent cover of clionid sponges multiplied by the average erosion rate of all Caribbean/Atlantic clionid sponges17 (-6.05 kg CaCO3 m−2 year−1). External bioerosion by urchins was calculated using Diadema urchin abundance collected from the benthic surveys. Due to the lack of test size data from the NCRMP benthic surveys, urchin abundance was multiplied by the bioerosion rate of an average test sized36 (66 mm) Caribbean/Atlantic Diadema urchin (-0.003 kg CaCO3 m-2 year−1). While using an average test sized Diadema urchin for this analysis may have led to an under or overestimation of urchin erosion, the abundance of Diadema urchins measured in the surveys was minimal, as they appeared to be functionally irrelevant across the FRT.Model validationAs the survey methodologies and data sources employed in this analysis were modified from that of the standard ReefBudget approach17, we chose to validate our model through a fine scale temporal comparison of annual ReefBudget surveys conducted by NOAA at Cheeca Rocks (UK) to three nearby NCRMP sites used in our analysis. Since the NCRMP surveys were performed in 2014, 2016, and 2018, this study focused exclusively on these three survey years from the NOAA Cheeca Rocks dataset. Temporal trends related to reef growth/erosion were visually compared to see if survey types provided comparable results (SI Figure S6).Statistical analysisAll model calculations and statistical analyses were performed using R37 with the R Studio extension38. Generalized linear models (GLMs) were run on response variables involved in habitat production (i.e., net carbonate production, gross carbonate production, and gross carbonate erosion) to evaluate spatial trends related to reef development across sub-regions and reef types. Each GLM was performed with reef type being nested within sub-region. The best fit distribution for each variable was determined using the fitdistrplus R package39. Linear regression analysis was used to evaluate the relationship between net carbonate production and both live coral cover and parrotfish biomass. All plots were created using ggplot2 R package40 and edited for style with Adobe Illustrator41. More

These authors contributed equally: Yannick Garcin, Enno Schefuß, Greta C. Dargie, Simon L. LewisAix Marseille University, CNRS, IRD, INRAE, CEREGE, Aix-en-Provence, FranceYannick Garcin & Ghislain GassierInstitute of Geosciences, University of Potsdam, Potsdam, GermanyYannick GarcinMARUM—Center for Marine Environmental Sciences, University of Bremen, Bremen, GermanyEnno SchefußSchool of Geography, University of Leeds, Leeds, UKGreta C. Dargie, Bart Crezee, Dylan M. Young, Andy J. Baird, Paul J. Morris & Simon L. LewisSchool of Geography and Sustainable Development, University of St Andrews, St Andrews, UKDonna Hawthorne, Ian T. Lawson & George E. BiddulphIFP Energies Nouvelles, Earth Sciences and Environmental Technologies Division, Rueil-Malmaison, FranceDavid SebagInstitute of Earth Surface Dynamics, Geopolis, University of Lausanne, Lausanne, SwitzerlandDavid SebagFaculté des Sciences et Techniques, Université Marien Ngouabi, Brazzaville, Republic of the CongoYannick E. Bocko & Y. Emmanuel Mampouya WeninaÉcole Normale Supérieure, Université Marien Ngouabi, Brazzaville, Republic of the CongoSuspense A. IfoÉcole Normale Supérieure d’Agronomie et de Foresterie, Université Marien Ngouabi, Brazzaville, Republic of the CongoMackline MbembaFaculté de Gestion des Ressources Naturelles Renouvelables, Université de Kisangani, Kisangani, Democratic Republic of the CongoCorneille E. N. Ewango & Joseph Kanyama TabuFaculté des Sciences, Université de Kisangani, Kisangani, Democratic Republic of the CongoCorneille E. N. EwangoInstitut Supérieur Pédagogique de Mbandaka, Mbandaka, Democratic Republic of the CongoOvide Emba & Pierre BolaSchool of Geography, Geology and the Environment, University of Leicester, Leicester, UKGenevieve Tyrrell, Arnoud Boom & Susan E. PageSchool of Water, Energy and Environment, Cranfield University, Bedford, UKNicholas T. GirkinBritish Geological Survey, Centre for Environmental Geochemistry, Keyworth, UKChristopher H. VaneInstitute of Earth Sciences, University of Lausanne, Lausanne, SwitzerlandThierry AdatteNEIF Radiocarbon Laboratory, Scottish Universities Environmental Research Centre (SUERC), Glasgow, UKPauline GulliverSchool of Biosciences, University of Nottingham, Nottingham, UKSofie SjögerstenDepartment of Geography, University College London, London, UKSimon L. Lewis More

Automated tracking of four social insect speciesFifty queenright colonies were used in the tracking experiments (Table 1). Honeybee colonies (subspecies A. mellifera carnica) were housed in the campus apiary of the University of Lausanne. Colonies of L. niger were raised from single mated queens collected on campus. T. nylanderi colonies were collected from the University of Lausanne campus, and L. acervorum colonies collected from Anzeindaz, Switzerland. These four species were chosen because of their abundance and easy availability in Switzerland, and because they – or closely-related species – have previously been used as model systems for the study of spatial organisation in social insects20,21,23,27,44. The colony sizes used in our experiments (Table 1) fell within the natural range of sizes experienced by these species in nature, either as recently founded colonies (L. niger colonies are founded by a single queen and progressively grow from a few workers to mature sizes of up to 40,000 workers over the course of several years; new honeybee colonies are founded by swarms counting 2400–41,000 bees61) or as mature colonies (all colonies of T. nylanderi and L. acervorum used in our experiments were mature colonies collected whole from the field).In all species, a paper tag bearing a unique two-dimensional barcode was glued to the thorax of individuals to allow automated tracking of their movements (Fig. S1). In the ants, tagging of all individuals was performed in a single session two days before the beginning of the experiment, whilst in the bees, newly-emerged workers (one-day-old or less) were tagged every 3 days over the 21 days prior to the beginning of the experiment (Supplementary Note 1).Tagged colonies were kept in glass observation nests with a single entrance (internal nest dimensions, A. mellifera: 69 × 45 × 4 cm, L. niger: 70 × 40 × 8 mm; L. acervorum: 63 × 42 × 2 mm, T. nylanderi: 63 × 42 × 1.5 mm). The honeybee observation nests also included a 64 × 44 cm wooden frame enclosing a double-sided wax comb containing honey, pollen, and developing brood20. Bees were free to move between both sides of the comb. In all species, individuals were allowed to freely exit and enter the nest. Ants were provided with ad libitum food (Drosophila, sugar solution) and water in a foraging arena, while bees foraged on natural resources outside. Both the ant and honeybee observation nests were exposed to diurnal cycles of temperature and light (Supplementary Note 1).High resolution digital video cameras operating at two frames per second were used to identify the location and orientation of each tag across successive images22. All colonies were continuously tracked for three days, which corresponded to the inter-cohort time in the honeybee colonies. The trajectories of each worker, and the physical contacts between workers (Fig. S18 and Supplementary Note 14) were extracted using an existing software pipeline62.Building bipartite site-visit networksTo quantify the spatial preferences of individual ants and bees, the interior of the nest was discretised into a regular hexagonal lattice (Fig. 1a, b). Because the worker body lengths of our four study species span an order of magnitude (from ~ 1.5 mm for T. nylanderi to ~ 15 mm for A. mellifera), the width of the hexagonal bins were defined as 1/4 of the mean worker body-length.To characterise the spatial preferences of different individuals to different parts of the nest, we counted the number of times ({n}_{i}^{s}) that each individual i visited each hexagonal site s. A visit by individual i to site s began when i crossed the border into s, and was terminated when i crossed the border out of s, regardless of the amount of time spent inside. To prevent stationary individuals located on the border between two adjacent sites from rapidly accumulating many single-frame visits to the two sites, successive visits to a same site were only counted when at least 20s elapsed between the end of the previous visit and the start of the next.The site-visit data were used to construct a bipartite network, in which individuals (layer 1) were connected by undirected edges to the sites (layer 2) they visited (Fig. 1c, d). Because individuals typically made multiple visits to the same sites, each edge i–s was weighted according to the total number of times individual i visited site s, that is, ({n}_{i}^{s}).Partitioning site-visit networks into modulesThe extent to which the site-visit networks were partitioned into discrete ‘modules’ (i.e., set of workers with similar space-use patterns and the set of sites that they exhibit strong ties to) was assessed using the DIRTLPAwb+ algorithm for partitioning weighted bipartite networks39. This algorithm searches for the partition that maximises the number and strength of the links within modules, whilst minimizing connections between modules. The number of modules was not specified a priori by the user, but was identified by the algorithm. All site-visit networks had positive modularity (Fig. S3), indicating that they could be partitioned into a set of well-separated modules (Figs. 1e–h, S2, and S4–S5). The modules in each partition were then assigned functional labels according to the following rules. First, the module whose sites were on average closest to the nest entrance was labelled ‘forager’ module. Second, the module or modules with the greatest spatial overlap with the brood pile in the ant colonies or the broodnest(s) in the honeybee colonies were labelled ‘nurse’ module(s). After defining the forager and nurse modules, the remaining modules (if any) were labelled as follows. If there was only one module remaining after identifying the nurse and forager modules, as was typically the case in honeybee colonies, it was labelled ‘peripheral’. If there were two modules remaining, as was typically the case in ant colonies, then the module whose sites were on average closer to the nest borders (i.e., to the periphery of the nest) was labelled ‘peripheral’, and the remaining module labelled ‘intermediate’. In some cases, the DIRTLPAwb+ algorithm identified five or more modules (9.0% of all iterations across all species and colonies). In these cases, the supernumerary modules never contained more than 1 or 2 individuals, and as they could not be unequivocally assigned using our labelling scheme, they were left unclassified for these iterations.Validating network modulesAs a network constructed by a purely random process could exhibit apparent modular structure by chance, we tested whether the discovered modules represent statistically significant entities. To do so, we produced 1000 null model random networks for each observed network using an established permutation method for bipartite networks63 (Supplementary Note 2). Comparisons between the maximum modularity of the observed networks with that of the corresponding random networks showed that, in all four species, the observed modularity was significantly greater than expected by chance (Fig. S3).Constructing worker task profilesA unique labour profile for each ant and each honeybee was constructed by estimating the activity of each worker in the following four tasks:1. Entrance guarding: workers were classed as guarding when they were (i) within two body lengths of the entrance, (ii) roughly facing the entrance, i.e., with a body alignment diverging from the direct heading to the entrance by no more than π/2 radians, and (iii) ‘on station’ at the entrance, as defined by trajectory coordinates with an associated first passage time (ref. 64; time taken for the individual to pass beyond a circle centred on its current location with a radius of two body-lengths) in excess of 500s.2. Patrolling: workers were classed as patrolling65 when they were (i) active, and (ii) ‘roaming’, as defined by first passage times of More

Podos, J. Correlated evolution of morphology and vocal signal structure in Darwin’s finches. Nature 409, 185–188 (2001).ADS 
CAS 
PubMed 

Google Scholar 
Bradbury, J. W. & Vehrencamp, S. Principles of Animal Communication (Sinauer Associated, 1998).
Google Scholar 
Podos, J. & Warren, P. S. The evolution of geographic variation in birdsong. Adv. Study Behav. 37, 403–458 (2007).
Google Scholar 
Charlton, B. D., Owen, M. A. & Swaisgood, R. R. Coevolution of vocal signal characteristics and hearing sensitivity in forest mammals. Nat. Commun. 10, 1–7 (2019).CAS 

Google Scholar 
Soltis, J. Vocal communication in African elephants (Loxodonta africana). Zoo Biol. 29, 192–209 (2010).PubMed 

Google Scholar 
King, S. L. & Janik, V. M. Bottlenose dolphins can use learned vocal labels to address each other. Proc. Natl. Acad. Sci. 110, 13216–13221 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ravignani, A. et al. Ontogeny of vocal rhythms in harbor seal pups: An exploratory study. Curr. Zool. 65, 107–120 (2019).PubMed 

Google Scholar 
Rauber, R. & Manser, M. B. Effect of group size and experience on the ontogeny of sentinel calling behaviour in meerkats. Anim. Behav. 171, 129–138 (2021).
Google Scholar 
Janik, V. M. & Slater, P. J. Vocal learning in mammals. Adv. Study Behav. 26, 59–100 (1997).
Google Scholar 
Fitch, W. T. Production of vocalizations in mammals. In Encyclopedia of Language and Linguistics 2nd edn, Vol. 1 115–121 (Elsevier, 2006).
Google Scholar 
Fletcher, N. H. A frequency scaling rule in mammalian vocalization. In Handbook of Behavioral Neuroscience Vol. 19 51–56 (Elsevier, 2010).
Google Scholar 
Fant, G. Acoustic Theory of Speech Production (Mouton, 1960).
Google Scholar 
Taylor, A. & Reby, D. The contribution of source–filter theory to mammal vocal communication research. J. Zool. 280, 221–236 (2010).
Google Scholar 
Fitch, W. T., Neubauer, J. & Herzel, H. Calls out of chaos: The adaptive significance of nonlinear phenomena in mammalian vocal production. Anim. Behav. 63, 407–418 (2002).
Google Scholar 
Domning, D. P. & Hayek, L. A. C. Interspecific and intraspecific morphological variation in manatees (Sirenia: Trichechus). Mar. Mamm. Sci. 2, 87–144 (1986).
Google Scholar 
Anderson, P. K. & Barclay, R. M. R. Acoustic signals of solitary Dugongs: Physical characteristics and behavioral correlates. J. Mamm. 76, 1226–1237 (1995).
Google Scholar 
Sousa-Lima, R., Paglia, A. P. & Da Fonseca, G. Signature information and individual recognition in the isolation calls of Amazonian manatees, Trichechus inunguis (Mammalia: Sirenia). Anim. Behav. 63, 301–310 (2002).
Google Scholar 
Sousa-Lima, R. S., Paglia, A. P. & Fonseca, G. A. B. Gender, age, and identity in the isolation calls of Antillean manatees (Trichechus manatus manatus). Aquat. Mamm. 34, 109–122 (2008).
Google Scholar 
O’Shea, T. J. & Poché, L. B. Aspects of underwater sound communication in Florida manatees (Trichechus manatus latirostris). J. Mamm. 87, 1061–1071 (2006).
Google Scholar 
Rosas, F. C. W. Biology, conservation and status of the Amazonian manatee Trichechus inunguis. Mamm. Rev. 24, 49–59 (1994).
Google Scholar 
Meirelles, A. C. O. & Carvalho, V. L. Peixe-boi marinho: biologia e conservação no Brasil. Aquasis, Bambu Editora e Artes Gráficas, São Paulo (2016).Alvarez-Alemán, A., Beck, C. A. & Powell, J. A. First report of a Florida manatee (Trichechus manatus latirostris) in Cuba. Aquat. Mamm. 36, 148 (2010).
Google Scholar 
Castelblanco-Martínez, D. N. et al. First documentation of long-distance travel by a Florida manatee to the Mexican Caribbean. Ethol. Ecol. Evol. 1–12 (2021).Packard, J. M. & Wetterqvist, O. F. Evaluation of manatee habitat systems on the northwestern Florida coast. Coast. Manag. 14, 279–310 (1986).
Google Scholar 
Luna, F. D. O. et al. Genetic connectivity of the West Indian manatee in the southern range and limited evidence of hybridization with Amazonian manatees. Front. Mar. Sci. 7, 1089 (2021).
Google Scholar 
Hartman, D. Ecology and behavior of the manatee (Trichechus manatus) in Florida. Spec. Publ. Am. Soc. Mammal. 5, 153 (1979).
Google Scholar 
D’AffonsecaNeto, J. A. & Vergara-Parente, J. E. Sirenia (peixe-boi-da-Amazônia, Peixe-boi-marinho). In Tratado de Animais Selvagens: medicina veterinária (eds Cubas, Z. S. et al.) 701–714 (Roca, 2006).
Google Scholar 
Deutsch, C. J., Reid, J. P., Bonde, R. K., Easton, D. E., Kochman, H. I. & O’Shea, T. J. Seasonal movements, migratory behavior, and site fidelity of West Indian manatees along the Atlantic coast of the United States. Wildl. Monogr. 1–77 (2003).Laist, D. W. & Reynolds, J. E. III. Influence of power plants and other warm-water refuges on Florida manatees. Mar. Mamm. Sci. 21, 739–764 (2005).
Google Scholar 
Reynolds, J. E. Aspects of the social behaviour and herd structure of a semi-isolated colony of West Indian manatees, Trichechus manatus. Mammalia 45, 431–452 (1981).
Google Scholar 
Dantas, G. A. Ontogenia do padrão vocal individual do peixe-boi da Amazônia Trichechus inunguis (Sirenia, trichechidae). Dissertação (Instituto Nacional de Pesquisas da Amazônia, 2009).
Google Scholar 
Brady, B., Moore, J. & Love, K. Behavior related vocalizations of the Florida manatee (Trichechus manatus latirostris). Mar. Mamm. Sci. 1–15 (2021).Nowacek, D. P., Casper, B. M., Wells, R. S., Nowacek, S. M. & Mann, D. A. Intraspecific and geographic variation of West Indian manatee (Trichechus manatus spp.) vocalizations. J. Acoust. Soc. Am. 114, 66–69 (2003).ADS 
PubMed 

Google Scholar 
Rycyk, A. M. et al. First characterization of vocalizations and passive acoustic monitoring of the vulnerable African manatee (Trichechus senegalensis). J. Acoust. Soc. Am. 150, 3028–3037 (2021).ADS 
PubMed 

Google Scholar 
Landrau-Giovannetti, N., Mignucci-Giannoni, A. A. & Reidenberg, J. S. Acoustical and anatomical determination of sound production and transmission in West Indian (Trichechus manatus) and Amazonian (T. inunguis) manatees. Anat. Rec. 297, 1896–1907 (2014).
Google Scholar 
Morton, E. On the occurrence and significance of motivation-structural rules in some bird and mammal sounds. Am. Nat. 111, 855–869 (1977).
Google Scholar 
Borges, J. C. et al. Growth pattern differences of captive born Antillean manatee (Trichechus manatus) calves and those rescued in the Brazilian northeastern coast. J. Zoo Wildl. Med. 43, 494–500 (2012).PubMed 

Google Scholar 
Lima, D. S., Vergara-Parente, J. E., Young, R. J. & Paszkiewicz, E. Training of Antillean manatee Trichechus manatus manatus Linnaeus, 1758 as a management technique for individual welfare. Lat. Am. J. Mar. Mamm. 4, 61–68 (2005).
Google Scholar 
K. Lisa Yang Center for Conservation Bioacoustics at the Cornell Lab of Ornithology. Raven Pro: Interactive Sound Analysis Software (Version 1.5) [Computer software]. https://ravensoundsoftware.com/ (The Cornell Lab of Ornithology, 2022).Zollinger, S. A., Podos, J., Nemeth, E., Goller, F. & Brumm, H. On the relationship between, and measurement of, amplitude and frequency in birdsong. Anim. Behav. 84, e1–e9 (2012).
Google Scholar 
Sokal, R. R. & Rohlf, F. J. Biometry (W. H. Freeman and Co., 1995).MATH 

Google Scholar 
Charrier, I. & Harcourt, R. G. Individual vocal identity in mother and pup Australian sea lions (Neophoca cinerea). J. Mamm. 87, 929–938 (2006).
Google Scholar 
Green, S. & Salkind, N. J. Using SPSS for Windows and Macintosh: Analyzing and Understanding Data 4th edn. (Prentice Hall, 2003).
Google Scholar 
Charlton, B. D. et al. Cues to body size in the formant spacing of male koala (Phascolarctos cinereus) bellows: Honesty in an exaggerated trait. J. Exp. Biol. 214(20), 3414–3422 (2011).PubMed 

Google Scholar 
IBM Corp. Released 2020. IBM SPSS Statistics for Windows, Version 27.0.Best, R. C. The aquatic mammals and reptiles of the Amazon. In The Amazon 371–412 (Springer, 1984).
Google Scholar 
Gerhardt, H. C. The evolution of vocalization in frogs and toads. Annu. Rev. Ecol. Syst. 25, 293–324 (1994).
Google Scholar 
Mendoza, P. et al. Growth curve of Amazonian manatee (Trichechus inunguis) in captivity. Aquat. Mamm. 45 (2019).Schwarz, L. K. Methods and models to determine perinatal status of Florida manatee carcasses. Mar. Mamm. Sci. 24, 881–898 (2008).
Google Scholar 
Hauser, M. D., Chomsky, N. & Fitch, W. T. The faculty of language: What is it, who has it, and how did it evolve?. Science 298, 1569–1579 (2002).ADS 
CAS 
PubMed 

Google Scholar 
Marler, P. & Peters, S. Developmental overproduction and selective attrition: New processes in the epigenesis of birdsong. Dev. Psychol. J. Int. Soc. Dev. Psychol. 15.4, 369–378 (1982).
Google Scholar 
Casey, C., Reichmuth, C., Costa, D. P. & Le Boeuf, B. The rise and fall of dialects in northern elephant seals. Proc. R. Soc. B 285, 2018–2176 (2018).
Google Scholar 
Hunter, M. E. et al. Puerto Rico and Florida manatees represent genetically distinct groups. Conserv. Genet. 13, 1623–1635 (2012).
Google Scholar 
Castelblanco-Martínez, D. N. et al. Analysis of body condition indices reveals different ecotypes of the Antillean manatee. Sci. Rep. 11, 1–14 (2021).
Google Scholar 
McCracken, K. G. & Sheldon, F. H. Avian vocalizations and phylogenetic signal. Proc. Natl. Acad. Sci. 94, 3833–3836 (1997).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Moron, J. R. et al. Whistle variability of Guiana dolphins in South America: Latitudinal variation or acoustic adaptation? Mar. Mamm. Sci. 1–32 (2018)Luís, A. R. et al. Vocal universals and geographic variations in the acoustic repertoire of the common bottlenose dolphin. Sci. Rep. 11, 1–9 (2021).
Google Scholar 
Ey, E. & Fisher, J. The ‘Acoustic adaptations hypothesis’ a review of the evidence from birds, anurans and mammals. Bioacoustics 19, 21–48 (2009).
Google Scholar 
Miksis-Olds, J. L. & Tyack, P. L. Manatee (Trichechus manatus) vocalization usage in relation to environmental noise levels. J. Acoust. Soc. Am. 125, 1806–1815 (2009).ADS 
PubMed 

Google Scholar 
Sun, W., Wang, Z., Jamalabdollahi, M. & Reza Zekavat, S. A. Experimental study on the difference between acoustic communication channels in freshwater rivers/lakes and in oceans. In 2014 48th Asilomar Conference on Signals, Systems and Computers, 333–337 (2004)Rivera Chavarría, M., Castro, J. & Camacho, A. The relationship between acoustic habitat, hearing and tonal vocalizations in the Antillean manatee (Trichechus manatus manatus, Linnaeus, 1758). Biol. Open 4, 1237–1242 (2015).PubMed 
PubMed Central 

Google Scholar 
Gaspard, J. C. III. et al. Audiogram and auditory critical ratios of two Florida manatees (Trichechus manatus latirostris). J. Exp. Biol. 215, 1442–1447 (2012).PubMed 

Google Scholar 
Gerstein, E. R., Gerstein, L., Forsythe, S. E. & Blue, J. E. The underwater audiogram of the West Indian manatee (Trichechus manatus). J. Acoust. Soc. Am. 105, 3575–3583 (1999).ADS 
CAS 
PubMed 

Google Scholar 
Klishin, V., Pezo, R., Popov, V., Ya, A. & Supin. Some characteristics of hearing of the Brazilian manatee, Trichechus inunguis. Aquat. Mamm. 16 (1990).Johnson, M., de Soto, N. A. & Madsen, P. T. Studying the behaviour and sensory ecology of marine mammals using acoustic recording tags: A review. Mar. Ecol. Prog. Ser. 395, 55–73 (2009).ADS 

Google Scholar  More

We aimed to understand the impact of changing pCO2 (400 vs. 800 ppm, representing current and projected year 2100 concentrations) on Prochlorococcus and Synechococcus and its effects on their interactions with the co-cultured heterotrophic “helper” bacterium Alteromonas sp. EZ55. Consistent with our previous research [7], EZ55 was more strongly affected by year 2100 pCO2 than any of the photoautotrophs in our study despite the primary dependence of the latter organisms’ metabolism on CO2. Strikingly, elevated pCO2 tended to reduce or eliminate the effect of co-culture on EZ55, with far fewer genes being significantly differentially transcribed relative to axenic EZ55 at the same pCO2. Thus, pCO2 strongly impacted the metabolic conversation between cyanobacteria and EZ55. Our detailed analysis of differentially regulated metabolic pathways suggested three mutually reinforcing mechanisms underlying this dynamic interaction: (i) pCO2 impacts on the release of ‘leaky’ cyanobacteria-derived metabolites, (ii) alteration of the dynamics of competition over inorganic nutrients between the co-cultured organisms, and (iii) modulation of bacterial and phytoplankton stress states. We explore each of these mechanisms in further detail below.Carbon cycling of “leaky” metabolites in co-cultureThe media we used for coculturing phytoplankton and bacteria contained no exogenous carbon sources; therefore, EZ55 was dependent on cyanobacterial exudates to grow, and it is likely that much of its changed transcription reflected changing availability of extracellular metabolites in the medium. The significant upregulation of carbon catabolism and transport genes as well as chemotaxis genes in co-cultures relative to axenic EZ55 supports the view that bacterial remineralization of cyanobacteria-secreted organic compounds is a driving force in these simple ecosystems. Additionally, changes in transcription of carbohydrate catabolism and transport genes provide clues as to which metabolites were being secreted under different experimental conditions (Fig. 5).Fig. 5: Proposed reconstruction of Alteromonas EZ55 ecophysiology.Reconstructions are shown for four different community contexts (axenic culture, or co-culture with Prochlorococcus MIT9312, Synechococcus WH8102, or Synechococcus CC9311) at 400 or 800 ppm pCO2, reflecting possible changes in the availability of C compounds, growth limiting factors, and stress conditions consistent with differential gene transcription observations. EZ55 image was obtained by cryoelectron microscopy from the sessions reported in Hennon et al. [7]. Background colors for each partner correspond to the bar colors in Fig. 3.Full size imageLike all oxygenic phototrophs, the cyanobacteria studied here fix carbon using the enzyme rubisco, which also catalyzes the undesirable photorespiration reaction leading to the production of 2PG instead of photosynthate. Phytoplankton in the field and in culture have been observed to excrete low molecular weight carboxylic acids including glycolate [39,40,41]. Photorespiratory glycolate is one of the most abundant sources of carbon in the oceans [38] and a preferred growth substrate for some marine heterotrophic bacteria [42]. Moreover the bacterial glcD gene for converting glycolate to glyoxylate is ubiquitously transcribed in the ocean [41, 43]. Although EZ55 lacks a specific transporter for glycolate, it can be taken up by the cell using the same transporters used for acetate and lactate uptake [44, 45], both of which were upregulated in co-culture conditions at 400 ppm (Fig. 3). Our data also showed differential regulation of enzymes involved in glycolate catabolism pathways, with at least one pathway upregulated in co-culture with each cyanobacterial strain (Fig. 3). We further demonstrated that EZ55 cultures were capable of growth on glycolate as a sole source of carbon, possibly using a novel GlcDF fusion protein (Fig. S11) and/or a plant-like LOX/GOX enzyme (Fig. 4). Thus, photorespiratory byproducts are likely a source of carbon for EZ55 in these cultures, particularly in the presence of MIT9312, which has no detectable enzymes for reclaiming 2PG on its own.There was also evidence that EZ55 utilized amino acids, organic acids, and fatty acids produced by phytoplankton under certain conditions in these cultures (Fig. S9). Lactate, acetate, and propanoate transporters and catabolism pathways were upregulated in co-culture with all cyanobacteria, as was pyruvate dehydrogenase with MIT9312, but only at 400 ppm. Both valine and glycine catabolism were also upregulated at 400 ppm in co-culture with the two Synechococcus strains, and fatty acid catabolism was upregulated in co-culture with MIT9312 and CC9311 at 400 ppm pCO2. Most of these substances have been directly or indirectly observed in cyanobacterial cultures in previous studies. For example, glycolate, lactate, acetate, and pyruvate have been directly measured in Prochlorococcus spent media [39], and co-culture with Prochlorococcus can fulfill the SAR11 growth requirement for glycine and pyruvate [46]. Fatty acid catabolism genes may have targeted membrane vesicles which are abundantly released by Prochlorococcus and other marine bacteria and may be a significant source of carbon for heterotrophs in the ocean [47, 48]; if so, future studies should investigate if WH8102 produces fewer vesicles than the other two cyanobacteria, explaining the differential transcription of beta-oxidation genes observed here.Valine, fatty acid, and propanoate catabolic pathways intersect with the formation of propanoyl-coA which in bacteria is generally fed into the TCA cycle through the methylcitrate pathway [49], which was significantly downregulated at 400 ppm in co-culture with all cyanobacteria even though other genes in these pathways were upregulated. Therefore, it is not clear what the ultimate fate of carbon from these sources is, although it is possible that EZ55 may be able to convert propanoyl-coA into a TCA cycle intermediate through another alternative pathway (e.g. as has been described in Mycobacterium tuberculosis via the methylmalonyl pathway [50]).Notably, gene transcription related to the utilization of all these products declined at 800 ppm pCO2 (Figs. 3, S8, S9). This was not unexpected for enzymes in the glycolate utilization pathways, as the increased CO2/O2 ratio at 800 ppm should decrease the rate of photorespiration relative to carbon fixation and therefore the availability of photorespiratory metabolites like glycolate [51, 52]. It is not clear, however, why organic and fatty acids would be less abundant in cyanobacterial exudates at 800 ppm. One possibility is that cyanobacteria release fewer of these compounds into the medium at high pCO2 because of a change in their internal redox state under these conditions favoring full oxidation of photosynthate. If future pCO2 conditions fundamentally alter the character of phytoplankton exudates, this could have profound implications for evolution and ecosystem functioning in future oceans.Evidence for inorganic nutrient limitation and competitionAutotrophic cyanobacteria and heterotrophic EZ55 were unlikely to compete over carbon under our experimental conditions, but we observed evidence of competition over inorganic nutrients such as N, P, and Fe. EZ55 phosphate, ammonium, and iron transporters, nitrogen regulatory protein P-II, and glutamine synthetase (the primary gateway for N assimilation in bacteria) were all more highly transcribed for all co-cultures compared to axenic cultures at 400 ppm pCO2 (Fig. S6), suggesting a switch from axenic carbon limitation to nutrient limitation in the presence of a continual supply of photosynthetically derived carbon (Fig. 5). On the other hand, few nutrient transporters were upregulated compared to axenic under 800 ppm pCO2. Although gene transcription data alone is not sufficient to conclude whether Alteromonas is limited by inorganic or organic nutrients, the reduced importance of nutrient acquisition suggests that EZ55 is carbon limited under these conditions just as it is in the absence of cyanobacteria.There were comparatively few species-specific changes in EZ55 nutrient transporter gene transcription. One example was an ammonium transporter, which was strongly upregulated in co-culture with both open ocean cyanobacteria (MIT9312 and WH8102) at 400 ppm pCO2. This may reflect a response to a comparatively high affinity for N in cyanobacteria adapted to the permanently oligotrophic open ocean, making them much stronger competitors for limiting N than coastal CC9311. N competition with EZ55 has been observed to increase the relative competitive fitness of Prochlorococcus vs. Synechococcus (coastal strain WH7803) in 3-way co-cultures [53]. In contrast, WH8102 appears to have higher N demand under 800 ppm pCO2, significantly upregulating a nitrate transporter and several genes related to urea utilization (Fig. S2). This may be explained by the enhanced transcription of carbon fixation genes and faster exponential growth rates observed in WH8102 at elevated pCO2, increasing N demand, and may indicate that WH8102 was C limited at 400 ppm.It is important to note that different N sources were provided in PEv medium (in which axenic EZ55 and MIT9312 co-cultures were grown) and SEv medium (in which CC9311 and WH8102 co-cultures were grown), with NH4+ in the former and NO3- in the latter. However, we do not think this difference can explain the observed changes in gene regulation, since EZ55 is capable of growth using either N source. It is interesting to note, however, that EZ55’s ammonium transporter was upregulated in both media types (Fig. S6), suggesting it may be benefitting from ammonium excreted by Synechococcus in SEv co-cultures.Impacts of co-culture and pCO2 on stress conditionsEZ55 showed less transcription of stress-related genes at 400 than 800 ppm pCO2, and also less evidence of stress in co-culture with any cyanobacterium than in axenic culture by itself. Nearly every gene in the EZ55 genome related to protection from H2O2 was downregulated in co-culture at 400 ppm, as were a suite of other stress-related genes (Fig. 2); on the other hand, many of these genes were significantly upregulated relative to axenic conditions at 800 ppm. Additionally, at 800 ppm there was a pronounced difference in EZ55 H2O2 defense gene transcription between cyanobacterial partners. As we described previously [7], both monofunctional catalases were downregulated at 800 ppm in co-culture with MIT9312, as were 2 of 3 alkylhydroperoxide reductase genes (although the third was significantly upregulated). In contrast, the monofunctional catalase genes were significantly upregulated in co-culture with WH8102 at 800 ppm. Elevated transcription of genes involved in the biosynthesis of glycine betaine, an osmoprotectant which has also been shown to function as an antioxidant [54, 55], provides further evidence for increased oxidative stress in co-culture with Synechococcus at 800 ppm in EZ55.Some indication of the mechanism behind EZ55’s changing stress level under co-culture and elevated pCO2 can be seen in the dynamics of three stress-related RNA polymerase sigma factors. Both rpoE and rpoH, responsible for controlling envelope and heat stress regulons, respectively, were downregulated at 400 ppm in co-culture relative to axenic and 800 ppm conditions; rpoE was significantly upregulated at 800 ppm pCO2. These trends are consistent with starvation-induced oxidative stress under both axenic and 800 ppm conditions, as discussed above. In contrast, rpoS was upregulated at 400 ppm pCO2, strongly so in co-culture with MIT9312. RpoS is a specialized sigma factor that accumulates under conditions of nutrient deprivation or as cells enter the stationary phase and serves to increase general stress resistance [56, 57]. For example, in Escherichia coli RpoS was shown to play a crucial role for survival during nitrogen deprivation [58]. While the decoupling of the transcription of oxidative stress genes like catalase from rpoS transcription was unexpected, rpoS trends are consistent with EZ55 being nutrient limited at 400 ppm pCO2 (Fig. S6) and with the upregulation of catalase in co-culture with MIT9312, but not WH8102 or CC9311, at 400 ppm (Fig. 2).In contrast to EZ55, differentially transcribed genes related to stress responses were rare in cyanobacteria at 800 ppm. While both MIT9312 and WH8102 had significant growth impairments at 800 ppm (Fig. S1), there was little evidence of a stress-specific gene transcription response in either strain. DNA mismatch repair genes were enriched as a group at 800 ppm in Prochlorococcus, although the only individual stress-related protein that was differentially transcribed was a HLI protein that was strongly downregulated at 800 ppm. No stress-related genes or gene sets were enriched in WH8102, and the small number of differentially transcribed stress genes in CC9311 (e.g., heat-shock and HLI proteins) were all downregulated at 800 ppm. This could indicate a dependence of both MIT9312 and WH8102 on their co-cultured EZ55 partner for protection, as neither of these cyanobacterial genomes contains catalase or several other stress-response genes common in heterotrophic bacteria. It could also indicate that they have different stress response mechanisms than those that have been characterized in heterotrophic bacteria; for instance, several hypothetical proteins of unknown function were differentially regulated in each cyanobacterium between the pCO2 conditions. Finally, it is possible that the stresses experienced by MIT9312 and WH8102 occurred in the initial days after transfer into fresh media (i.e., the significantly extended lag period observed for both), and were alleviated by the late log phase when the cultures were sampled for RNA sequencing.Summary overview of metabolic responsesWe have shown that the response to elevated pCO2 in our algal:bacterial co-cultures was driven more by interspecies interactions than by CO2-specific responses themselves. While it is important to note that we do not have direct culture-based evidence for some of these claims, we feel that gene transcription evidence is strong for several conclusions regarding the interactions in our cultures (Fig. 5).First, increased pCO2 appears to have fundamentally altered the amount and/or types of carbon compounds secreted by all three cyanobacterial strains examined, placing EZ55 into a stationary-phase metabolic state nearly indistinguishable to being in culture media with no added carbon source at all. We suggest that this is driven directly by the higher CO2:O2 ratio, which lowered the rate of photorespiration and subsequent release of 2PG and/or glycolate and indirectly may have reduced the amount of incompletely oxidized carbon released by cyanobacteria by changing the intracellular redox state [59]. Possibly because of the changing supply of carbon, EZ55 also appeared to transition away from a state of nutrient competition with its cyanobacterial partners, exemplified by decreased transcription of nutrient transporters at elevated pCO2 (Fig. S6).Second, co-culture at 400 ppm clearly reduced stress on EZ55 relative to either axenic growth or co-culture growth at 800 ppm, possibly due to the provision of a more reliable source of C as described above by the cyanobacterial partner under these conditions. In contrast, both MIT9312 and WH8102 clearly experienced elevated stress, potentially related to the changes in EZ55’s metabolism under these conditions. One of the major conclusions from our previous work [7] was the finding that EZ55 reduced catalase transcription at 800 ppm pCO2, eliminating the “helper” effect that Prochlorococcus depends on to grow in culture [13, 14]. In this work we see that the catalase response in co-culture with MIT9312 was opposite that in co-culture with the two Synechococcus strains. One possible explanation for this lies in the fact that MIT9312, unlike the other three strains in this study, did not possess a complete 2PG catabolism pathway and therefore likely excreted this product where it was subsequently catabolized by EZ55. We confirmed by genomic analysis (Figs. S10–S13) and culture experiments (Fig. 4) that EZ55 was able to grow on glycolate as a sole carbon source, and that its intracellular H2O2 concentration was elevated compared to growth on glucose. We suggest that more 2PG was secreted by MIT9312 at 400 ppm pCO2 due to the lower CO2:O2 ratio, and that growth on this carbon source increased EZ55’s internal oxidative stress load, resulting in higher transcription of H2O2 defenses such as catalase (Fig. 2). If true, this provides one possible explanation of why the “helper” relationship broke down at elevated pCO2 – by leaking 2PG as a readily available growth substrate for EZ55 at 400 ppm, MIT9312 forced EZ55 to maintain a high degree of intracellular ROS defense, leading to the well-characterized ability of EZ55 to cross-protect Prochlorococcus strains from the relatively lower H2O2 concentrations in the bulk environment, and allowing MIT9312 to eliminate two energetically costly enzymatic pathways. When higher pCO2 reduced the rate of photorespiration, EZ55’s need to produce excess catalase decreased, resulting in lower levels of protection, and concomitant growth impairments, for MIT9312.This is an example of how leaky Black Queen functions allow organisms like Prochlorococcus to streamline their metabolism while simultaneously creating stable interdependencies within their communities. However, it also shows how Black Queen-stabilized exchanges can break down. If our hypothesized relationship between pCO2 and catalase production is correct, then this system depends on the passive release of a metabolic by-product that evolved under a set of atmospheric pCO2 conditions that have been largely stable for thousands of years – but this leaves the system particularly vulnerable to the rapid changes in pCO2 currently taking place and may leave Prochlorococcus with no protection at all in the future ocean. If Prochlorococcus is outcompeted by less-streamlined competitors, this could reduce the overall efficiency of primary production in the open ocean gyres with possible positive feedbacks on CO2 accumulation in the atmosphere. Subsequent experiments should examine whether Prochlorococcus can overcome this imbalance through adaptive evolution quickly enough to avoid serious disruptions of its current niche.In conclusion, these results provide further support for the observation that axenic cultures do not provide a good window into the behavior of natural communities. The metabolism of Alteromonas sp. EZ55, a ubiquitous consumer in the ocean, was strongly dependent on its community context, and relatively subtle shifts in the chemical environment induced by elevated pCO2 were sufficient to significantly remodel its physiology. Moreover, the transcriptional response of EZ55 to changing pCO2 was much greater than that of any of the photoautotrophs examined, suggesting that more work is needed to understand the often-ignored heterotrophic bacteria associated with marine primary producers and how they will respond to global ocean change. Thus, further research is indicated on some of our core findings and hypotheses (e.g., the role of 2PG, and the nature of the carbon exchanged between the cyanobacteria and Alteromonas) via metabolomics or direct substrate measurements. These results further highlight the importance of laboratory experiments using co-cultures as an experimentally tractable intermediate between oversimplified axenic cultures and overly complicated natural communities. They also highlight the dominant role that primary producers play in determining the metabolism and interactions of the organisms that depend on them for sustenance. More

European Commission Directorate General for Research and Innovation. A sustainable Bioeconomy for Europe: Strengthening the Connection Between Economy, Society and the Environment: Updated Bioeconomy Strategy (Directorate General for Research and Innovation, 2018).
Google Scholar 
Teitelbaum, L., Boldt, C. & Patermann, C. Global Bioeconomy Policy Report (IV): A Decade of Bioeconomy policy (International Advisory Council on Global Bioeconomy, 2020).
Google Scholar 
European Parliament; European Council. Directive (EU) 2018/2001 of the European Parliament and of the Council of 11 December 2018 on the promotion of the use of energy from renewable sources (2018). (Online). http://data.europa.eu/eli/dir/2018/2001/oj.European Parliament; European Council. Directive 2009/28/EC on the Promotion of the Use of Energy from Renewable Sources (2009). (Online). http://data.europa.eu/eli/dir/2009/28/oj.Glasenapp, S., & McCusker, A. Wood energy data: the joint wood, in Wood Energy in the ECE Region: Data, Trends and Outlook in Europe, the Commonwealth of Independent States and North America, Geneva, United Nations’ Economic Commission for Europe: ECE/TIM/SP/42, 12–29 (2018).Eurostat. Wood Products—Production and Trade (2021). (Online). https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Wood_products_-_production_and_trade#Wood-based_industries. Accessed 10 9 2021.Food and Agriculture Organization of the United Nations. FAOSTAT: Forestry Production and Trade (2021). (Online). http://www.fao.org/faostat/en/#data. Accessed 13 September 2021.The Intergovernmental Panel on Climate Change. Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (PCC Task Force on National Greenhouse Gas Inventories, 2019).
Google Scholar 
European Parliament; European Council. Commission Delegated Regulation (EU) 2019/807 of 13 March 2019 Supplementing Directive (EU) 2018/2001 of the European Parliament and of the Council as Regards the Determination of High Indirect Land-Use Change-Risk (2018) (Online). fttps://eur-lex.europa.eu/eli/reg_del/2019/807/oj.de Oliveira Garcia, W., Amann, T. & Hartmann, J. Increasing biomass demand enlarges negative forest nutrient budget areas in wood export regions. Sci. Rep. 8, 5280 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Searchinger, T. et al. Europe’s renewable energy directive poised to harm global forests. Nat. Commun. 9, 3741 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Galik, C. S. & Abt, R. C. Sustainability guidelines and forest market response: An assessment of European Union pellet demand in the southeastern United States. GCB Bioenergy 8, 658–669 (2016).
Google Scholar 
Favero, A. D. & Sohngen, B. Forests: Carbon sequestration, biomass energy, or both?. Sci. Adv. 6(13), eaay6792 (2020).ADS 
PubMed 
PubMed Central 

Google Scholar 
Cowie, A. et al. Applying a science-based systems perspective to dispel misconceptions about climate effects of forest bioenergy. GCB-Bioenergy 13, 1210–1231 (2021).
Google Scholar 
Camia, A, Jonsson, G. J. R., Robert, N., Cazzaniga, N., Jasinevičius, G., Avitabile, V., Grassi, G., Barredo, J., & Mubareka, S. The Use of Woody Biomass for Energy Production in the EU (European Commission, Joint Research Center, 2021).Aguilar, F. X., Mirzaee, A., McGarvey, R., Shifley, S. & Burtraw, D. Expansion of US wood pellet industry points to positive trends but the need for continued monitoring. Sci. Rep. 10, 18607 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Dale, V., Parish, E., Kline, K. & Tobin, E. How is wood-based pellet production affecting forest conditions in the southeastern United States?. For Ecol Manag 396, 143–14 (2017).
Google Scholar 
Ceccherini, G. et al. Abrupt increase in harvested forest area over Europe after 2015. Nature 583, 72–77 (2020).ADS 
CAS 
PubMed 

Google Scholar 
FORISK Consulting. U.S. Wood Bioenergy Database (2020). (Online). https://forisk.com/. Accessed 2020.Domke, G. et al. Toward inventory-based estimates of soil organic carbon in forests of the United States. Ecol. Appl. 27(4), 1223–1235 (2017).CAS 
PubMed 

Google Scholar 
Python Org. Python Programming Language (2022) (Online). https://www.python.org/. Accessed 1 January 2018.STATA. Stata: statistical software for data science (2022) (Online). https://www.stata.com/. Accessed 1 January 2018.QGIS. Free and Open Source Geographic Information System (2021). (Online). https://qgis.org/en/site/.US Department of Agriculture, Forest Service. Forest Inventory and Analysis National Program (2020). (Online). https://www.fia.fs.fed.us/.Burrill, E. A., Wilson, A. M., Turner, J. A., Pugh, S. A., Menlove, J., Christiansen, G., Conkling, B., & David, W. The Forest Inventory and Analysis Database: Database Description and User Guide Version 8.0 for Phase 2 (US Department of Agriculture, US Forest Service, 2018).Ahmed, M. et al. Spatially-explicit modeling of multi-scale drivers of aboveground forest biomass and water yield in watersheds of the Southeastern United States. J. Environ. Manag. 199, 158–171 (2017).
Google Scholar 
Timilsina, N. et al. A framework for identifying carbon hotspots and forest management drivers. J. Environ. Manag. 114, 293–302 (2012).
Google Scholar 
Coulston, J., Ritters, K., McRoberts, R., Reams, G. & Smith, W. True versus perturbed forest inventory plot locations for modeling: A simulation study. Can. J. For. Res. 36, 801–807 (2006).
Google Scholar 
Anselin, L. Spatial effects in econometric practice in environmental and resource economics. Am. J. Agric. Econ. 83(3), 705–710 (2001).MathSciNet 

Google Scholar 
Strange-Olesen, A., Bager, S., Kittler, B., Price, W., & Aguilar, F. Environmental Implications of Increased Reliance of the EU on Biomass from the South East US (European Commission Report ENV.B.1/ETU/2014/0043, 2015).Spelter, H., & Toth, D. North America’s Wood Pellet Sector (U.S. Department of Agriculture, Forest Service, Forest Products Laboratory, 2009).Goerndt, M., Aguilar, F. & Skog, K. Drivers of biomass co-firing in US coal-fired power plants. Biomass Bioenerg. 58, 158–167 (2013).
Google Scholar 
US Department of Agriculture, Forest Service. Forest Inventory and Analysis National Program: Timber Products Output Studies (2022). (Online). https://www.fia.fs.fed.us/program-features/tpo/. Accessed 2022.Sonter, L. et al. Mining drives extensive deforestation in the Brazilian Amazon. Nat. Commun. 8(1013), 66. https://doi.org/10.1038/s41467-017-00557-w (2017).CAS 

Google Scholar 
Mirzaee, A., McGarvey, R., Aguilar, F. & Schliep, E. Impact of biopower generation on eastern US forests. Environ. Dev. Sustain. https://doi.org/10.1007/s10668-022-02235-4 (2022).
Google Scholar 
Brandeis, C., Taylor, M., Abt, K., & Alderman, D. Status and Trends for the U.S. Forest Products Sector: A Technical Document Supporting the Forest Service 2020 RPA Assessment (US Department of Agriculture, Forest Service Southern Research Station, Forest Inventory and Analysis, 2021).US Environmental Protection Agency. Emissions & Generation Resource Integrated Database (eGRID) (2021) (Online). https://www.epa.gov/egrid.US Department of Transportation. Ports: ArcGIS Online (2021) (Online). https://data-usdot.opendata.arcgis.com/datasets/usdot::ports/about.US Census Bureau. TIGER/Line Shapefiles (2021) (Online). https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html.US Census Bureau. Population and Housing Units Estimates Datasets (2021) (Online). https://www.census.gov/programs-surveys/popest/data/data-sets.html.McCann, P. The Economics of Industrial Location: A Logistics-Costs Approach (Springer, 1998).Singh, D., Cubbage, F., Gonzalez, R. & Abt, R. Locational determinants for wood pellet plants: A review and case study of North and South America. BioResources 11(3), 7928–7952 (2016).
Google Scholar 
Boukherroub, T., LeBel, L. & Lemieux, S. An integrated wood pellet supply chain development: Selecting among feedstock sources and a range of operating scales. Appl. Energy 198, 385–400 (2017).
Google Scholar 
Heckman, J., Ichimura, H. & Todd, P. Matching as an econometric evaluation estimator: Evidence from evaluating a JobTraining Programme. Rev. Econ. Stud. 64(4), 605–654 (1997).MATH 

Google Scholar 
Caliendo, M. & Kopeinig, S. Some practical guidance for the implementation of propensity score matching. J. Econ. Surv. 22(1), 31–72 (2008).
Google Scholar 
Woo, H., Eskelson, B. & Monleon, V. Matching methods to quantify wildfire effects on forest carbon mass in the U.S. Pacific Northwest. Ecol. Appl. 31(3), e02283 (2021).PubMed 

Google Scholar 
Morreale, L., Thompson, J., Tang, X., Reinmann, A. & Hutyra, L. Elevated growth and biomass along temperate forest edges. Nat. Commun. 12(7181), 66 (2021).
Google Scholar 
Isard, W. The general theory of location and space-economy. Q. J. Econ. 63(4), 476–506 (1949).
Google Scholar 
Aguilar, F. X. Spatial econometric analysis of location drivers in a renewable resource-based industry: The U.S. South Lumber Industry. For. Policy Econ. 11(3), 184–193 (2009).
Google Scholar 
Aguilar, F. X. Conjoint analysis of industry location preferences: evidence from the softwood lumber industry in the US. Appl. Econ. 66, 3265–3274 (2010).
Google Scholar 
Aguilar, F. X., Goerndt, M., Song, N. & Shifley, S. Internal, external and location factors influencing cofiring of biomass with coal in the US northern region. Energy Econ. 34, 1790–1798 (2012).
Google Scholar 
Ferraro, P. J. et al. Estimating the impacts of conservation on ecosystem services and poverty by integrating modeling and evaluation. Proc. Natl. Acad. Sci. 112(24), 7420–7425 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhang, D. & Pearse, P. Forest Economics 412 (UBC Press, 2011).
Google Scholar 
Villalobos, L., Coria, J. & Nordén, L. Has forest certification reduced forest degradation in Sweden?. Land Econ. 94, 220–238 (2018).
Google Scholar 
Wooldridge, J. Econometric Analysis of Cross Section and Panel Data (MIT Press, 2010).Blackman, A., Corral, L., Lima, E. & Asner, G. Titling indigenous communities protects forests in the Peruvian Amazon. PNAS 114(16), 4123–4128 (2016).ADS 

Google Scholar 
Abt, K. L., Abt, R. C., Galik, C. S., & Skog, K. E. Effect of Policies on Pellet Production and Forests in the U.S. South: A Technical Document Supporting the Forest Service Update of the 2010 RPA Assessment USDA (Forest Service GTR Srs-202, 2014).Hardie, P. Parks, P. Gottleib and D. Wear, “Responsiveness of rural and urban land uses to land rent determinants in the U.S. South,” Land Economics, vol. 76, no. 4, pp. 659–673, 2000.Parish, E., Herzberger, A., Phifer, C. & Dale, V. Transatlantic wood pellet trade demonstrates telecoupled benefits. Ecol. Soc. 23(1), 28 (2018).
Google Scholar 
Titus, B. et al. Sustainable forest biomass: A review of current residue harvesting guidelines. Energy Sustain. Soc. 11, 66. https://doi.org/10.1186/s13705-021-00281-w (2021).
Google Scholar 
Jandl, R. et al. How strongly can forest management influence soil carbon sequestration?. Geoderma 137(3), 253–268 (2007).ADS 
CAS 

Google Scholar 
Nave, L., Vance, E., Swanston, C. & Cepas, P. S. Harvest impacts on soil carbon storage in temperate forests. For. Ecol. Manag. 259, 857–866 (2010).
Google Scholar 
Mayer, M. et al. Tamm review: Influence of forest management activities on soil organic carbon stocks: A knowledge synthesis. For. Ecol. Manag. 466, 118127 (2020).
Google Scholar 
Berryman, E., Hatten, J., Page-Dumroese, D. S., Heckman, K. A., D’Amore, D. V., Puttere, J., & Domke, G. M. Soil carbon in Forest and Rangeland Soils of the United States Under Changing Conditions 9–31 (Springer, 2020).Nave, L. E. et al. Land use and management effects on soil carbon in US Lake States, with emphasis on forestry, fire, and reforestation. Ecol. Appl. 66, 2356 (2021).
Google Scholar 
Cao, B., Domke, G. M., Russell, M. B. & Walters, B. Spatial modeling of litter and soil carbon stocks on forest land in the conterminous United States. Sci. Total Environ. 654, 94–106 (2019).ADS 
CAS 
PubMed 

Google Scholar 
Coulston, J. & Wear, D. From sink to source: Regional variation in U.S. forest carbon futures. Sci. Rep. 5, 66. https://doi.org/10.1038/srep16518 (2015).
Google Scholar 
Röder, M., Whittaker, C. & Thornley, P. How certain are greenhouse gas reductions from bioenergy? Life cycle assessment and uncertainty analysis of wood pellet-to-electricity supply chains from forest residues. Biomass Bioenerg. 79, 50–63 (2015).
Google Scholar 
Hanssen, S., Duden, A., Junginger, M., Dale, D. & D. vander Hilst,. Wood pellets, what else? Greenhouse gas parity times of European electricity from wood pellets produced in the south-eastern United States using different softwood feedstocks. GC-Bioenergy 9(9), 1406–1422 (2017).CAS 

Google Scholar 
Picciano, P., Aguilar, F., Burtraw, D. & Mirzaee, A. Environmental and socio-economic implications of woody biomass co-firing at coal-fired power plants. Resour. Energy Econ. 6, 66 (2022).
Google Scholar 
Hetchner, S., Schelhas, J., & Brosius, J. Forests as Fuel: Energy, Landscape, Climate, and Race in the U.S. South (Lexington Books, 2022).Coulston, J., Wear, D. & Vose, J. Complex forest dynamics indicate potential for slowing carbon accumulation in the southeastern United States. Sci. Rep. 5, 8002 (2015).ADS 
PubMed 
PubMed Central 

Google Scholar 
Palahí, M. et al. Concerns about reported harvests in European forests. Nature 592, E15–E17 (2021).PubMed 

Google Scholar  More

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

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

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

Hooper, D. U. et al. Effects of biodiversity on ecosystem functioning: a consensus of current knowledge. Ecol. Monogr. 75, 3–35 (2005).Article 

Google Scholar 
Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).Article 
CAS 
PubMed 

Google Scholar 
Tilman, D., Isbell, F. & Cowles, J. M. Biodiversity and ecosystem functioning. Annu. Rev. Ecol. Evol. Syst. 45, 471–493 (2014).Article 

Google Scholar 
Hector, A. et al. Plant diversity and productivity experiments in European grasslands. Science 286, 1123–1127 (1999).Article 
CAS 
PubMed 

Google Scholar 
Soliveres, S. et al. Biodiversity at multiple trophic levels is needed for ecosystem multifunctionality. Nature 536, 456–459 (2016).Article 
CAS 
PubMed 

Google Scholar 
Gross, N. et al. Functional trait diversity maximizes ecosystem multifunctionality. Nat. Ecol. Evol. 1, 0132 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
van der Plas, F. et al. Towards the development of general rules describing landscape heterogeneity–multifunctionality relationships. J. Appl. Ecol. 56, 168–179 (2019).Article 

Google Scholar 
Jochum, M. et al. The results of biodiversity–ecosystem functioning experiments are realistic. Nat. Ecol. Evol. 4, 1485–1494 (2020).Article 
PubMed 

Google Scholar 
Duffy, J. E., Godwin, C. M. & Cardinale, B. J. Biodiversity effects in the wild are common and as strong as key drivers of productivity. Nature 549, 261–264 (2017).Article 
CAS 
PubMed 

Google Scholar 
van der Plas, F. et al. Biotic homogenization can decrease landscape-scale forest multifunctionality. Proc. Natl Acad. Sci. USA 113, E2549–E2549 (2016).
Google Scholar 
Isbell, F. et al. High plant diversity is needed to maintain ecosystem services. Nature 477, 199–202 (2011).Article 
CAS 
PubMed 

Google Scholar 
Hautier, Y. et al. Local loss and spatial homogenization of plant diversity reduce ecosystem multifunctionality. Nat. Ecol. Evol. 2, 50–56 (2018).Article 
PubMed 

Google Scholar 
Srivastava, D. S. & Vellend, M. Biodiversity–ecosystem function research: is it relevant to conservation? Annu. Rev. Ecol. Evol. Syst. 36, 267–294 (2005).Article 

Google Scholar 
Isbell, F. et al. Linking the influence and dependence of people on biodiversity across scales. Nature 546, 65–72 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Mori, A. S., Isbell, F. & Seidl, R. β-Diversity, community assembly, and ecosystem functioning. Trends Ecol. Evol. 33, 549–564 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Chase, J. M. & Knight, T. M. Scale-dependent effect sizes of ecological drivers on biodiversity: why standardised sampling is not enough. Ecol. Lett. 16, 17–26 (2013).Article 
PubMed 

Google Scholar 
Chase, J. M. et al. Embracing scale-dependence to achieve a deeper understanding of biodiversity and its change across communities. Ecol. Lett. 21, 1737–1751 (2018).Article 
PubMed 

Google Scholar 
Barry, K. E. et al. The future of complementarity: disentangling causes from consequences. Trends Ecol. Evol. 34, 167–180 (2019).Article 
PubMed 

Google Scholar 
Loreau, M. & Hector, A. Partitioning selection and complementarity in biodiversity experiments. Nature 412, 72–76 (2001).Article 
CAS 
PubMed 

Google Scholar 
Hagan, J. G., Vanschoenwinkel, B. & Gamfeldt, L. We should not necessarily expect positive relationships between biodiversity and ecosystem functioning in observational field data. Ecol. Lett. 24, 2537–2548 (2021).Article 
PubMed 

Google Scholar 
Brose, U. & Hillebrand, H. Biodiversity and ecosystem functioning in dynamic landscapes. Philos. Trans. R. Soc. B 371, 20150267 (2016).Article 

Google Scholar 
Isbell, F. et al. Benefits of increasing plant diversity in sustainable agroecosystems. J. Ecol. 105, 871–879 (2017).Article 

Google Scholar 
Tscharntke, T. et al. Landscape moderation of biodiversity patterns and processes-eight hypotheses. Biol. Rev. 87, 661–685 (2012).Article 
PubMed 

Google Scholar 
Ricotta, C. On beta diversity decomposition: trouble shared is not trouble halved. Ecology 91, 1981–1983 (2010).Article 
PubMed 

Google Scholar 
Kraft, N. J. B. et al. Disentangling the drivers of β diversity along latitudinal and elevational gradients. Science 333, 1755–1758 (2011).Article 
CAS 
PubMed 

Google Scholar 
Gonthier, D. J. et al. Biodiversity conservation in agriculture requires a multi-scale approach. Proc. R. Soc. Lond. B 281, 20141358 (2014).
Google Scholar 
Flynn, D. F. et al. Loss of functional diversity under land use intensification across multiple taxa. Ecol. Lett. 12, 22–33 (2009).Article 
PubMed 

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

Google Scholar 
Foley, J. A. et al. Solutions for a cultivated planet. Nature 478, 337–342 (2011).Article 
CAS 
PubMed 

Google Scholar 
Allan, E. et al. Land use intensification alters ecosystem multifunctionality via loss of biodiversity and changes to functional composition. Ecol. Lett. 18, 834–843 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Le Provost, G. et al. Land-use history impacts functional diversity across multiple trophic groups. Proc. Natl Acad. Sci. USA 117, 1573–1579 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Adl, S. M., Coleman, D. C. & Read, F. Slow recovery of soil biodiversity in sandy loam soils of Georgia after 25 years of no-tillage management. Agric. Ecosyst. Environ. 114, 323–334 (2006).Article 

Google Scholar 
Le Provost, G. et al. Contrasting responses of above- and belowground diversity to multiple components of land-use intensity. Nat. Commun. 12, 3918 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
James, L. A. Legacy effects. Oxford Bibliographies in Environmental Science https://doi.org/10.1093/OBO/9780199363445-0019 (2015).Lamy, T., Liss, K. N., Gonzalez, A. & Bennett, E. M. Landscape structure affects the provision of multiple ecosystem services. Environ. Res. Lett. 11, 124017 (2016).Article 

Google Scholar 
Alsterberg, C. et al. Habitat diversity and ecosystem multifunctionality—the importance of direct and indirect effects. Sci. Adv. 3, e1601475 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Tscharntke, T., Klein, A. M., Kruess, A., Steffan-Dewenter, I. & Thies, C. Landscape perspectives on agricultural intensification and biodiversity—ecosystem service management. Ecol. Lett. 8, 857–874 (2005).Article 

Google Scholar 
Gámez-Virués, S. et al. Landscape simplification filters species traits and drives biotic homogenization. Nat. Commun. 6, 8568 (2015).Article 
PubMed 

Google Scholar 
Benton, T. G., Vickery, J. A. & Wilson, J. D. Farmland biodiversity: is habitat heterogeneity the key? Trends Ecol. Evol. 18, 182–188 (2003).Article 

Google Scholar 
Bullock, J. M., Aronson, J., Newton, A. C., Pywell, R. F. & Rey-Benayas, J. M. Restoration of ecosystem services and biodiversity: conflicts and opportunities. Trends Ecol. Evol. 26, 541–549 (2011).Article 
PubMed 

Google Scholar 
Dainese, M. et al. A global synthesis reveals biodiversity-mediated benefits for crop production. Sci. Adv. 5, eaax0121 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Mitchell, M. G. E., Bennett, E. M. & Gonzalez, A. Linking landscape connectivity and ecosystem service provision: current knowledge and research gaps. Ecosystems 16, 894–908 (2013).Article 

Google Scholar 
Fischer, M. et al. Implementing large-scale and long-term functional biodiversity research: The Biodiversity Exploratories. Basic Appl. Ecol. 11, 473–485 (2010).Article 

Google Scholar 
Blüthgen, N. et al. A quantitative index of land-use intensity in grasslands: Integrating mowing, grazing and fertilization. Basic Appl. Ecol. 13, 207–220 (2012).Article 

Google Scholar 
Vogt, J. et al. Eleven years’ data of grassland management in Germany. Biodivers. Data J. 7, e36387 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Manning, P. et al. Redefining ecosystem multifunctionality. Nat. Ecol. Evol. 2, 427–436 (2018).Article 
PubMed 

Google Scholar 
Linders, T. E. W. et al. Stakeholder priorities determine the impact of an alien tree invasion on ecosystem multifunctionality. People Nat. 3, 658–672 (2021).Article 

Google Scholar 
Nathan, R. Long-distance dispersal of plants. Science 313, 786–788 (2006).Article 
CAS 
PubMed 

Google Scholar 
Manning, P. et al. Grassland management intensification weakens the associations among the diversities of multiple plant and animal taxa. Ecology 96, 1492–1501 (2015).Article 

Google Scholar 
Clough, Y. et al. Density of insect-pollinated grassland plants decreases with increasing surrounding land-use intensity. Ecol. Lett. 17, 1168–1177 (2014).Article 
PubMed 

Google Scholar 
Vickery, J. A. et al. The management of lowland neutral grasslands in Britain: effects of agricultural practices on birds and their food resources. J. Appl. Ecol. 38, 647–664 (2001).Article 

Google Scholar 
López-Jamar, J., Casas, F., Díaz, M. & Morales, M. B. Local differences in habitat selection by Great Bustards Otis tarda in changing agricultural landscapes: implications for farmland bird conservation. Bird. Conserv. Int. 21, 328–341 (2011).Article 

Google Scholar 
Wells, K., Böhm, S. M., Boch, S., Fischer, M. & Kalko, E. K. Local and landscape-scale forest attributes differ in their impact on bird assemblages across years in forest production landscapes. Basic Appl. Ecol. 12, 97–106 (2011).Article 

Google Scholar 
Bommarco, R., Lindborg, R., Marini, L. & Öckinger, E. Extinction debt for plants and flower-visiting insects in landscapes with contrasting land use history. Divers. Distrib. 20, 591–599 (2014).Article 

Google Scholar 
Kuussaari, M. et al. Extinction debt: a challenge for biodiversity conservation. Trends Ecol. Evol. 24, 564–571 (2009).Article 
PubMed 

Google Scholar 
Lee, M., Manning, P., Rist, J., Power, S. A. & Marsh, C. A global comparison of grassland biomass responses to CO2 and nitrogen enrichment. Philos. Trans. R. Soc. B 365, 2047–2056 (2010).Article 
CAS 

Google Scholar 
Smith, P. Do grasslands act as a perpetual sink for carbon? Glob. Change Biol. 20, 2708–2711 (2014).Article 

Google Scholar 
Wagg, C., Bender, S. F., Widmer, F. & van der Heijden, M. G. A. Soil biodiversity and soil community composition determine ecosystem multifunctionality. Proc. Natl Acad. Sci. USA 111, 5266–5270 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bradford, M. A. et al. Discontinuity in the responses of ecosystem processes and multifunctionality to altered soil community composition. Proc. Natl Acad. Sci. USA 111, 14478–14483 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Schaub, S. et al. Plant diversity effects on forage quality, yield and revenues of semi-natural grasslands. Nat. Commun. 11, 768 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Mace, G. M., Norris, K. & Fitter, A. H. Biodiversity and ecosystem services: a multilayered relationship. Trends Ecol. Evol. 27, 19–26 (2012).Article 
PubMed 

Google Scholar 
Peter, S., Le Provost, G., Mehring, M., Müller, T. & Manning, P. Cultural worldviews consistently explain bundles of ecosystem service prioritisation across rural Germany. People Nat. 4, 218–230 (2022).Article 

Google Scholar 
Emmerson, M. et al. How agricultural intensification affects biodiversity and ecosystem services. Adv. Ecol. Res. 55, 43–97 (2016).Article 

Google Scholar 
Gonzalez, A. et al. Scaling-up biodiversity–ecosystem functioning research. Ecol. Lett. 23, 757–776 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Loreau, M., Mouquet, N. & Gonzalez, A. Biodiversity as spatial insurance in heterogeneous landscapes. Proc. Natl Acad. Sci. USA 100, 12765–12770 (2003).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Anderson, B. J. et al. Spatial covariance between biodiversity and other ecosystem service priorities. J. Appl. Ecol. 46, 888–896 (2009).Article 

Google Scholar 
Maes, J. et al. Mapping ecosystem services for policy support and decision making in the European Union. Ecosyst. Serv. 1, 31–39 (2012).Article 

Google Scholar 
Metzger, J. P. et al. Considering landscape-level processes in ecosystem service assessments. Sci. Total Environ. 796, 149028 (2021).Article 
CAS 
PubMed 

Google Scholar 
Costanza, R. et al. Twenty years of ecosystem services: how far have we come and how far do we still need to go? Ecosyst. Serv. 28, 1–16 (2017).Article 

Google Scholar 
DeFries, R. & Nagendra, H. Ecosystem management as a wicked problem. Science 356, 265–270 (2017).Article 
CAS 
PubMed 

Google Scholar 
Díaz, S. et al. Assessing nature’s contributions to people. Science 359, 270–272 (2018).Article 
PubMed 

Google Scholar 
Schenk, N. et al. Assembled ecosystem measures from grassland EPs (2008–2018) for multifunctionality synthesis—June 2020. Version 40. Biodiversity Exploratories Information System https://www.bexis.uni-jena.de/ddm/data/Showdata/27087 (2022).Michael Scherer-Lorenzen, M. & Mueller, S. Acoustic diversity index based on environmental sound recordings on all forest EPs, HAI, 2016. Version 2. Biodiversity Exploratories Information System https://www.bexis.uni-jena.de/ddm/data/Showdata/27568 (2020).Michael Scherer-Lorenzen, M. & Mueller, S. Acoustic diversity index based on environmental sound recordings on all forest EPs, Alb, 2016. Version 2. Biodiversity Exploratories Information System https://www.bexis.uni-jena.de/ddm/data/Showdata/27569 (2020).Michael Scherer-Lorenzen, M. & Mueller, S. Acoustic diversity index based on environmental sound recordings on all forest EPs, SCH, 2016. Version 2. Biodiversity Exploratories Information System https://www.bexis.uni-jena.de/ddm/data/Showdata/27570 (2020).Penone, C. et al. Assembled RAW diversity from grassland EPs (2008–2020) for multidiversity synthesis—November 2020. Version 2. Biodiversity Exploratories Information System https://www.bexis.uni-jena.de/ddm/data/Showdata/27707 (2021).Penone, C. et al. Assembled species information from grassland EPs (2008–2020) for multidiversity synthesis—November 2020. Version 3. Biodiversity Exploratories Information System https://www.bexis.uni-jena.de/ddm/data/Showdata/27706 (2021).Junge, X., Schüpbach, B., Walter, T., Schmid, B. & Lindemann-Matthies, P. Aesthetic quality of agricultural landscape elements in different seasonal stages in Switzerland. Landsc. Urban Plan. 133, 67–77 (2015).Article 

Google Scholar 
Lindemann-Matthies, P., Junge, X. & Matthies, D. The influence of plant diversity on people’s perception and aesthetic appreciation of grassland vegetation. Biol. Conserv. 143, 195–202 (2010).Article 

Google Scholar 
Haines-Young, R. & Potschin, M. B. Common International Classification of Ecosystem Services (CICES) V5.1 and Guidance on the Application of the Revised Structure. https://cices.eu/content/uploads/sites/8/2018/01/Guidance-V51-01012018.pdf (2018)Byrnes, J. E. et al. Investigating the relationship between biodiversity and ecosystem multifunctionality: challenges and solutions. Methods Ecol. Evol. 5, 111–124 (2014).Article 

Google Scholar 
Neyret, M. et al. Assessing the impact of grassland management on landscape multifunctionality. Ecosyst. Serv. 52, 101366 (2021).Article 

Google Scholar 
Ferraro, D. M. et al. The phantom chorus: birdsong boosts human well-being in protected areas. Proc. R. Soc. B 287, 20201811 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Graves, R. A., Pearson, S. M. & Turner, M. G. Species richness alone does not predict cultural ecosystem service value. Proc. Natl Acad. Sci. USA 114, 3774–3779 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Chan, K. M. A., Satterfield, T. & Goldstein, J. Rethinking ecosystem services to better address and navigate cultural values. Ecol. Econ. 74, 8–18 (2012).Article 

Google Scholar 
Villamagna, A. M., Angermeier, P. L. & Bennett, E. M. Capacity, pressure, demand, and flow: a conceptual framework for analyzing ecosystem service provision and delivery. Ecol. Complex. 15, 114–121 (2013).Article 

Google Scholar 
Bolliger, R., Prati, D., Fischer, M., Hoelzel, N. & Busch, V. Vegetation Records for Grassland EPs, 2008–2018. Version 2. Biodiversity Exploratories Information System https://www.bexis.uni-jena.de/ddm/data/Showdata/24247 (2020).Le Provost, G. & Manning, P. Cover of all vascular plant species in representative 2×2 quadrats of the major surrounding homogeneous vegetation zones in a 75-m radius of the 150 grassland EPs, 2017–2018. Version 4. Biodiversity Exploratories Information System https://www.bexis.uni-jena.de/ddm/data/Showdata/27846 (2021).Koleff, P., Gaston, K. J. & Lennon, J. J. Measuring beta diversity for presence–absence data. J. Anim. Ecol. 72, 367–382 (2003).Article 

Google Scholar 
Baselga, A. Partitioning the turnover and nestedness components of beta diversity. Glob. Ecol. Biogeogr. 19, 134–143 (2010).Article 

Google Scholar 
Ostrowski, A., Lorenzen, K., Petzold, E. & Schindler, S. Land use intensity index (LUI) calculation tool of the Biodiversity Exploratories project for grassland survey data from three different regions in Germany since 2006, BEXIS 2 module. Zenodo https://doi.org/10.5281/zenodo.3865579 (2020).Thiele, J., Weisser, W. & Scherreiks, P. Historical land use and landscape metrics of grassland EP. Version 2. Biodiversity Exploratories Information System https://www.bexis.uni-jena.de/ddm/data/Showdata/25747 (2020).Steckel, J. et al. Landscape composition and configuration differently affect trap-nesting bees, wasps and their antagonists. Biol. Conserv. 172, 56–64 (2014).Article 

Google Scholar 
Westphal, C., Steckel, J. & Rothenwöhrer, C. InsectScale / LANDSCAPES – Landscape heterogeneity metrics (grassland EPs, radii 500 m–2000 m, 2009) – shape files. Version 2. Biodiversity Exploratories Information System https://www.bexis.uni-jena.de/ddm/data/Showdata/24046 (2019).Fahrig, L. et al. Functional landscape heterogeneity and animal biodiversity in agricultural landscapes. Ecol. Lett. 14, 101–112 (2011).Article 
PubMed 

Google Scholar 
Sirami, C. et al. Increasing crop heterogeneity enhances multitrophic diversity across agricultural regions. Proc. Natl Acad. Sci. USA 116, 16442–16447 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Gessler, P. E., Moore, I. D., Mckenzie, N. J. & Ryan, P. J. Soil–landscape modelling and spatial prediction of soil attributes. Int. J. Geogr. Inf. Syst. 9, 421–432 (1995).Article 

Google Scholar 
Zinko, U., Seibert, J., Dynesius, M. & Nilsson, C. Plant species numbers predicted by a topography-based groundwater flow index. Ecosystems 8, 430–441 (2005).Article 
CAS 

Google Scholar 
Moeslund, J. E. et al. Topographically controlled soil moisture drives plant diversity patterns within grasslands. Biodivers. Conserv. 22, 2151–2166 (2013).Article 

Google Scholar 
Keddy, P. A. Assembly and response rules: two goals for predictive community ecology. J. Veg. Sci. 3, 157–164 (1992).Article 

Google Scholar 
Myers, M. C., Mason, J. T., Hoksch, B. J., Cambardella, C. A. & Pfrimmer, J. D. Birds and butterflies respond to soil-induced habitat heterogeneity in experimental plantings of tallgrass prairie species managed as agroenergy crops in Iowa, USA. J. Appl. Ecol. 52, 1176–1187 (2015).Article 

Google Scholar 
Carvalheiro, L. G. et al. Soil eutrophication shaped the composition of pollinator assemblages during the past century. Ecography 43, 209–221 (2020).Article 

Google Scholar 
Schöning, I., Klötzing, T., Schrumpf, M., Solly, E. & Trumbore, S. Mineral soil pH values of all experimental plots (EP) of the Biodiversity Exploratories project from 2011, Soil (core project). Version 8. Biodiversity Exploratories Information System https://www.bexis.uni-jena.de/ddm/data/Showdata/14447 (2021).Sørensen, R., Zinko, U. & Seibert, J. On the calculation of the topographic wetness index: evaluation of different methods based on field observations. Hydrol. Earth Syst. Sci. 10, 101–112 (2006).Article 

Google Scholar 
Le Provost, G. et al. Aggregated environmental and land-use covariates of the 150 grassland EPs used in ‘Contrasting responses of above- and belowground diversity to multiple components of land-use intensity’. Version 5. Biodiversity Exploratories Information System https://www.bexis.uni-jena.de/ddm/data/Showdata/31018 (2021).R: a language and environment for statistical computing (R Foundation for Statistical Computing, 2020).Grace, J. B. Structural equation modeling for observational studies. J. Wildl. Manag. 72, 14–22 (2008).Article 

Google Scholar 
Grace, J. B. Structural Equation Modeling and Natural Systems (Cambridge University Press, 2006).Rosseel, Y. Lavaan: an R package for structural equation modeling and more. Version 0.5–12 (BETA). J. Stat. Softw. 48, 1–36 (2012).Article 

Google Scholar 
Le Bagousse-Pinguet, Y. et al. Phylogenetic, functional, and taxonomic richness have both positive and negative effects on ecosystem multifunctionality. Proc. Natl Acad. Sci. USA 116, 8419–8424 (2019).Article 
PubMed 
PubMed Central 

Google Scholar  More