Energy flows reveal declining ecosystem functions by animals across Africa

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Almost all biodiversity metrics are challenging to disaggregate into ecosystem functions, in particular animal-mediated functions such as pollination, seed and nutrient dispersal, and predation. Here, we adopt an ecosystem energetics approach2 as a physically meaningful method of translating animal species composition into a suite of ecosystem functions. We quantify historical changes to energy flows through mammal- and bird-mediated ecosystem functions across sub-Saharan Africa. In total, trophic energy flows have decreased by over one-third, with functions performed by megafauna in particular collapsing outside protected areas. The pattern of decreasing function varies by biome, driven by arboreal birds and primates in forests, terrestrial herbivores in grassy systems, and burrowing mammals in arid systems. Compared to other biodiversity metrics, an energetics approach highlights the ecological importance of smaller animals and keystone species. The results can help practitioners conserve and restore functionally diverse, energetically intact ecosystems across land uses and biomes. By relating biodiversity intactness to energy and material flows, ecosystem energetics can also advance efforts to set local, regional or planetary boundaries3 for biodiversity loss. Earth and environmental sciences/Ecology/Macroecology Earth and environmental sciences/Ecology/Biodiversity Earth and environmental sciences/Ecology/Ecosystem ecology Biological sciences/Ecology/Conservation biology Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Ecologists have devised numerous metrics to track species loss and recovery, 4–6 but alone they can be poor indicators of changes in ecological function 7 . The influence of a species on its ecosystem depends on the species’ abundance and on the specific functions it performs 8 . To assess how changing biodiversity affects ecosystem function at large scales, ecologists must develop consistent methodologies that account for species’ changing abundances and their diverse impacts on ecosystems. Doing so is central to predicting how biodiversity change affects the ability of ecosystems to provide services, such as storing carbon, supporting food production and buffering natural disasters 9 . Many ecosystem functions are moderated by animals, yet most ecosystem function literature addressing regional or larger scales focuses exclusively on vegetation functions. Animals perform functions including herbivory, seed and nutrient dispersal, and predation 10,11 , which shape ecosystems by controlling flows and patterns of carbon, nutrients and water 12,13 . Since the pre-industrial Holocene, animal populations have collapsed as intensively human-modified landscapes have expanded fivefold 5,14 . Tracking how biodiversity loss translates into changes in animal-moderated ecosystem function is challenging because species respond unevenly to land use change depending on their traits 15 . For example, agricultural conversion depletes populations of large or frugivorous animals faster than small and omnivorous ones 15,16 . These asymmetric population declines change ecosystems’ trophic structures: the partitioning of energy and biomass between plant and animal guilds. Ecosystems with simplified trophic structures provide a reduced suite of functions and services 17 . Ultimately, they become less capable of recovering from external shocks and supporting human wellbeing and livelihoods 18 . To track species abundances (a prerequisite for measuring functional changes) conservationists have developed biodiversity intactness indices (BII) 19,20 . These indices estimate how human activity has changed species populations relative to remaining highly intact landscapes such as wilderness areas, with these intact areas assumed to be representative of historic animal abundances, nominally in the pre-colonial/pre-industrial period. Local intactness scores can be aggregated to determine BII across ecoregions, countries, and taxonomic groups. BII has been proposed as a metric for biodiversity in the planetary boundaries framework, which seeks to identify safe environmental conditions for human societies 21 ; though BII was more recently abandoned because of the difficulty of relating it to ecosystem function 3 . BII alone cannot estimate changes to ecosystem function because it weights each species equally. In reality, some species disproportionately affect ecosystem function due to their population densities, body sizes, dietary preferences, rates of food consumption and behavioral features 10 . To quantify how the changing animal populations estimated by the BII alter ecosystem function, an approach is needed that accounts for species’ variable ecosystem impacts using a common unit of measurement. One option is to adopt an ecosystem energetics approach to compare how much energy species consume across land uses 2 . Species within an ecosystem can be classified into functional groups. Changes to energy flows through these groups indicate changes to the provision of associated ecosystem functions 13,22 . An ecosystem energetics approach quantifies energy flow through the trophic web, by calculating the annual food energy consumed by each species per unit area. In all terrestrial ecosystems, energy, captured as sunlight by plants, flows up the trophic web through guilds of herbivores, carnivores, scavengers, and detritivores. Because every species consumes and expends energy, it can serve as an ecologically meaningful common currency capable of comparing how ecosystem functions vary across space and time 2 . While energetics approaches have been previously used to measure how human modified landscapes alter ecosystem trophic structure 2,23 , they have not been scaled beyond a few model ecosystems, as they require extensive data measuring species abundances across different land uses. In particular, energetics approaches have never been applied at regional or continental scales. Here, we focus on Sub-Saharan Africa as a case study, a region with a striking range of ecosystems and large gradients in ecological intactness, including megafaunal abundance, and anthropogenic pressure. We take advantage of new datasets that (i) model population densities of bird and mammal species 24,25 and (ii) estimate BII, or the impacts of land use changes on species abundances, across Sub-Saharan Africa 19,26 . The BII estimates are derived from a new dataset aggregating 30,000 expert estimates of how African species abundances respond to land use change. We use these datasets to quantify how biodiversity loss has degraded a suite of ecosystem functions across sub-Saharan Africa. Approach Our approach is to quantify how human modification of land uses has changed the distribution of energy among trophic guilds and functional groups. Energy flows are calculated for African bird and mammal species under contemporary versus pre-colonial/industrial (~1700 CE) conditions, which we henceforth refer to as historical conditions. Energy flows are aggregated across biomes to compare how the dominant vegetation structures within Africa moderate the ecological impacts of land use change. We also assess the relationships between biodiversity intactness, ecosystem function, and ecological vibrancy 2 , which we define as a combination of the magnitude of energy flow through animal-mediated pathways and the diversity of contributing animal species. We focus on birds and mammals because they are important components of animal biomass 27 , as well as data-rich groups with well-understood ecological functions, while acknowledging that invertebrates play a major but data-challenged role in ecosystem energetics. While populations of some species, especially megafauna, declined substantially long before the colonial and industrial period, these declines appear to have been less severe in Africa than elsewhere, hence Africa’s contemporary association with megafauna. 28,29 Africa therefore provides the unusual opportunity to examine how human activities have changed ecosystem function within historically near-intact ecosystems 29 . To estimate energy flows through African ecosystems, we calculated historical energy consumption by each species present in each 8 x 8 km cell across sub-Saharan Africa (317,000 cells in total). We used (i) modeled potential species population densities 24,25 based on ~10,000 averaged empirical measures of species population densities and (ii) IUCN range maps 30,31 to predict historical species abundances in each cell. To calculate the average energy consumption of sub-Saharan Africa’s ~3,000 bird and mammal species, we used published allometric equations 32 and datasets on species traits, diets, and food assimilation efficiencies (Supplementary Tables 1-3; Supplementary Data 2). We quantified current energy flows according to the remaining abundance of each species in each cell estimated by the BII 19,26 . To calculate the energetic intactness of ecosystem functions, defined as the percentage of historical animal energy flows remaining in an ecosystem, we grouped species into trophic guilds and functional groups, based on their diets, lifestyles, body sizes, and, for mammals, social group sizes. Using these categories, we identified 23 unique ecosystem functions (11 for birds and 12 for mammals), which we aggregated across classes into ten major functions 11,29,33,34 (Extended Data Table 1). Changes to Ecosystem Structure and Function Changes to the total flow of energy through animal populations, and to its distribution among guilds, can alter ecosystem functionality. 17 Energy flow through food consumption by wild African birds and mammals has decreased to 64% of historical values (54 – 74%; all ranges reported are 95% confidence intervals). Energy flow decreased most in high intensity land uses, falling to 27% (19 – 36%) of historical levels in settlements, 41% (30% – 53%) in croplands, 67% (56 – 75%) in unprotected untransformed lands (comprising rangelands and near-natural lands), and 88% (80 – 96%) in strict protected areas (Figure 1). In aggregate, birds were more resilient to land use change than mammals, with the fraction of energy flowing through birds (as opposed to mammals) rising from 38% to 43%. The greater reported decline of mammals was driven entirely by the collapse of large herbivores (including grazers, browsers, and frugivores), which historically accounted for over one-quarter of mammalian energy consumption. Energy flows through large herbivores decreased by 72%, compared to a decrease of 30% for other mammals and for birds. Large herbivorous mammals have undergone substantial population declines even in protected areas (Figure 1). Therefore estimated energy flows have fallen well below historical levels even in Africa’s relatively wilder regions. Partitioning energy transfer by habitat and broad spatial niches, we find that energetic intactness has collapsed across all biomes. Total (bird and mammal) energy flows are estimated to be 64% (54 – 73%) intact in grassy systems, 66% (57 – 76%) intact in forests, and 69% (60 – 80%) intact in arid systems. Despite the similar magnitude of these declines, the trophic guilds most responsible for energy loss varied according to each biome’s dominant feeding pathways, suggesting that biome moderates changes to function (Figure 2). Arboreal species account for more energy flow in forests, where they can take advantage of greater vertical space and habitat complexity (Figure 2) 35 . Of these arboreal species, birds make up nearly half of energy flow despite their much smaller body sizes than most primates and other arboreal mammals. Overall, reduced arboreal species populations accounted for 37% of energy decline in forests, compared to 4–10% of energy decline in grassy and arid systems. Fossorial species account for more energy flow (28% of the total) in arid systems, where burrows help animals regulate temperature and conserve moisture (Figure 2) 36 . Although fossorial mammals rank among the most resilient guilds, they accounted for 23% of energy decline in arid systems, versus 9% and 3% of energy decline in grassy systems and forests. Large terrestrial herbivorous mammals were major contributors to energy decline (28–36%) in all biomes. However, the total fraction of loss attributable to large and small terrestrial herbivores was notably lower in forests (36%) than in arid and grassy systems (48–51%). When we consider the proportion of total energy flow contributed by broad trophic guilds, we find that overall patterns have changed little over time, with herbivory (including leaf-, grass-, seed-, fruit,- and nectar-eating animals) falling from 64% to 61% of total energy flow, insectivory rising from 12% to 14%, and carnivory remaining at 4%. However, energy flows through functional groups ranged from 26% intact (nutrient dispersal by large herbivores) to 75% intact (soil disturbance by fossorial mammals), when averaged across sub-Saharan Africa (Figure 1b). Megafauna-dominated functions, which include nutrient dispersal, grazing and browsing, and apex carnivory, were notably less intact (26-32%) than other ecosystem functions (61-75% intact), and were twice as depleted in non-protected untransformed lands as in strict protected areas (Figure 1c). Megafauna play a key role in controlling vegetation structure, both directly through grazing, browsing, and nutrient deposition by large herbivores, and indirectly through the control of herbivore populations by apex carnivores 13,34 . Given that 80% of Africa is unprotected untransformed land, the collapse of large herbivores and carnivores is probably altering vegetation on a continental scale 34,37 . Megafauna extinctions on other continents have been estimated to have reduced herbivory by 42% 22 and lateral nutrient flow by over 90% 13 . While some of this lost functionality is substituted by domestic herbivores, total (domestic and wild) biomass has decreased across most of Africa, as has the functional diversity of herbivore guilds 38 . Beyond megafauna decline, untransformed lands are relatively functional, with other ecosystem functions persisting above 55% intactness. However, the large absolute decreases in energy flows through arboreal guilds in forests, shared roughly equally between birds and mammals, translates to notable declines in seed dispersal by frugivores outside of protected areas (Extended Data Fig. 2). Seed dispersal is just 58% intact in untransformed lands and 14% intact in croplands. Pollination, dominated among vertebrates by birds and bats, also declines notably outside of protected areas, and is 63% intact in untransformed lands and 25% intact in croplands. Seed dispersal and pollination strongly influence plant community composition, structure and biomass. In tropical forests, primate and megafauna-dispersed tree species tend to have higher biomass than other species 39 . Reduced seed dispersal is likely to change the vegetation structure of defaunated forests and hinder ecosystem recovery. 40 The most stable functions are dominated by small and mid-sized herbivores, which account for a large proportion of energy in all biomes. These functions include granivory, soil disturbance, and avian grazing (mostly by water birds) (Extended Data Fig. 1). Soil disturbance is more intact in rangelands (95% intact) than in protected lands (94% intact), and is a dominant ecosystem function in arid biomes. Avian granivory is at 108% of historical levels in croplands, where birds can take advantage of seed-rich crops. These relatively stable functions, however, can generate ecosystem disservices when they harm agricultural production. The resilience of guilds and functions dominated by small animals (defined as < 3kg) 41 creates a striking pattern: African ecosystems are becoming dominated by smaller species. On average, energy consumption per unit biomass has increased by 23% across sub-Saharan Africa, indicating that smaller animals with high metabolic rates are consuming a greater proportion of energy. Our analysis highlights the ecological importance of small animals: in tropical ecosystems, rodents and passerines account for a much larger proportion of energy flow than of biomass, due to their high energy consumption per unit mass 2 . In sub-Saharan Africa, rodents accounted for 31% of total historical energy consumption but just 17% of biomass, rising to 36% of energy and 24% biomass today. Passerine birds accounted for 8% of historical energy consumption but just 2% of biomass, the same proportions as today. As large herbivores decline, these small animals are likely to exert greater relative control over the flows of nutrients, water, and material that structure ecosystems, while not compensating for the attributes (such as large seed dispersal and greater daily transport ranges) that are particular to larger animals. The species groups contributing most to total energy flow are elephants (family Elephantidae ) and fossorial rodents, although consumption by the latter is highly uncertain. Elephants historically accounted for a striking 17% of total bird and mammal biomass across the region and 8% of total energy flow across sub-Saharan Africa, with savanna elephants ( Loxodonta africana) consuming by far the most energy of any single species. Elephants perform the grazing and browsing and nutrient dispersal functions; however, the confidence intervals around functions performed by elephants are highly uncertain, due to elephants’ disproportionate energy consumption: uncertainty around energy flow values are lower for guilds and functions that have more species and more even energy consumption among species (see methods). Fossorial rodents, particularly mole rats, may also consume an outsized proportion of energy, due to their high abundance and high food consumption per unit body mass. Fossorial rodents include four of sub-Saharan Africa’s ten highest energy consuming species (Extended Data Fig. 3). However, energy flows through fossorial rodents, and through their associated soil disturbance function, are highly uncertain as confidence intervals around modeled mole rat population densities span 2.5 orders of magnitude. 42 The high energy flows through elephants and fossorial rodents suggest they are respectively key consumers of aboveground and belowground biomass in Africa. Elephants are well-known keystone species, with the potential to change ecosystem vegetation at the landscape scale and to affect continental-scale carbon sequestration 43 . The results here support emerging evidence 42 that fossorial rodents may be similarly impactful belowground, a hypothesis that bears further investigation. Biodiversity and Ecosystem Energetics Understanding the relationship between biodiversity intactness and ecosystem function can help guide conservation and restoration. Biodiversity intactness equally weights changes to species abundances, whereas energetic intactness weights changes to a given ecosystem function based on each associated species’ energy consumption. At the ecosystem scale, biodiversity intactness is a strong predictor of total energetic intactness for birds (R 2 = 0.97), and a weaker predictor for mammals (R 2 = 0.86) (Figure 3a-b). The principal difference between biodiversity intactness and total energetic intactness is driven by large herbivores, which account for 16% of historical energy consumption, but just 3% of BII. Because large herbivores are 32% more intact within protected areas than outside them, protected areas conserve a higher proportion of energy flow (and therefore ecological functionality) than of biodiversity intactness. However, energetic intactness does not predict the intactness of many individual functions. In landscapes in which BII is 50% intact, energy flows through pollinators range from 22–75% intact, flows through nutrient dispersers from 0–20% intact, and flows through aerial insectivores from 50–75% intact (Extended Data Fig.1). While BII can accurately predict total energy flow, it cannot predict which ecosystem functions remain intact and which have been depleted. This is a key knowledge gap for practitioners working to conserve and restore trophic complexity and ecosystem functionality that is uniquely addressed by our approach. Conversely, the energetic approach presented here, which relies on large continental datasets, is highly inaccurate at predicting values for individual species or landscapes. Rather, these estimates are useful for large-scale taxonomic and geographic comparisons when values are aggregated across many species and landscapes, and can elucidate how humans are impacting ecosystem function at scales relevant for policy. In addition to assessing intactness, an energetics approach can be used to estimate the historical magnitude of energy flows through African landscapes. Large absolute energy flows supported by a rich diversity of species and guilds can indicate exceptionally vibrant ecosystems. One key question for clarifying the relationship between functional resilience and ecological vibrancy is whether species richness predicts energy flow through animals, as it does other aspects of ecosystem function, including net primary productivity (NPP) 43 . We found that the species richness–energy relationship differed between birds and mammals (Figure 3c-d). Richness predicts class-wide energy consumption strongly for birds (r 2 = 0.92), but weakly for mammals (R 2 = 0.15). This discrepancy is caused by the lower richness and less even apportionment of energy consumption among mammal species. Mammals account for 62% of total historical energy flow but just 36% of species, meaning that proportionally more energy is consumed by the average mammal than bird species. In addition, species-level energy flows span a greater range of values for mammals (4.3 orders of magnitude) than for birds (2.1 orders of magnitude), due to mammal species’ wider ranges of population densities, body masses, and assimilation efficiencies. Mammalian energy consumption thus appears more driven by the presence of keystone consumer species. These include large herbivores, especially elephants; highly abundant rodents; and primates, which dominate the arboreal guilds important in forests (Extended Data Fig. 3). The uneven allocation of energy among species has implications for the biodiversity-function relationship: bird-driven functions are likely to be far more resilient to biodiversity loss, as they are supplied more evenly by a wider range of species. There are also clear biogeographical patterns in historical energetic vibrancy. Energy flows through birds tend to be highest in East Africa (Fig. 4a). These high energy landscapes broadly overlap with regions of volcanic soils and moderate to high rainfall along the Great Rift Valley. Birds and the insects many consume may benefit from nutrient-rich vegetation growing on fertile soils. Energy flows through birds tend to be lowest in Africa’s arid regions: the southwest, the Sahel, and the Horn of Africa. The map of total energy flow through mammals is more difficult to interpret, due to the high uncertainty associated with the energy consumption of dominant species (Fig. 4b). Still, mapping energy flows across sub-Saharan Africa opens up a number of interesting questions about the biogeographical factors that control total energy abundance. For example, future research might ask why birds consume a greater fraction of total energy in forests than in arid systems, or whether climatic and soil variables predict how energy flows are distributed among trophic guilds, taxonomic groups, or ecosystem functions. Application to Restoration As we enter the United Nations Decade on Ecosystem Restoration (2021-2030), energy flows can contribute to ongoing efforts 44 to meet urgent demand from governmental and corporate sectors for metrics that can set and track progress towards nature restoration targets. In particular, mapping ecosystem energy flows allows organizations to compare the intactness of an ecosystem’s contemporary versus historical suite of trophic guilds. Practitioners can then focus on restoring the most energetically depleted guilds across land uses and biomes, to restore ecosystem function and trophic complexity. For example, restoring arboreal birds and primate populations might be prioritized in forests, where seed dispersers consume a large proportion of energy. Seed dispersal impacts carbon sequestration and recovery success 40 , but is significantly depleted outside protected areas. In arid systems, restoring soil-disturbing mammals can be used to avoid over-mechanized soil maintenance techniques harmful to biodiversity 42 . In relatively intact landscapes, practitioners can increase trophic complexity by restoring megafauna, which are crucial for dispersing nutrients and maintaining open ecosystems through herbivory. Across all landscapes, practitioners should seek to restore not only energy flows but also trophic complexity: previous research has found the average ecosystem service is influenced by three trophic guilds 17 . It is also worth investigating whether high energy, ecologically vibrant landscapes are more functionally resilient to human land use change and more amenable to restoration. An energetics approach can reveal ecosystems where historically vibrant energetics coincide with depleted animal populations, which can subsequently be targeted for restoration. While human land use change has degraded biodiversity intactness and ecosystem function at the sub-continental scale, some forms of anthropogenic disturbance can amplify animal energy consumption 2 . It is not sufficient to assume that land use change degrades overall ecosystem functionality or individual ecosystem functions. Here, we find that some species and functions do better in rangelands and croplands than in protected areas. Logging has previously been found to amplify vertebrate energy consumption in some forests by increasing vegetation palatability and accessibility to herbivores 2 . Positive relationships between some functions and forms of human disturbance also seem likely in disturbance-dependent grassy ecosystems. The broad patterns revealed in regional and continental scale analyses should be tested and refined through empirical plot-based studies. Researchers can use energetics approaches to quantitatively test which types of disturbance maintain biodiversity intactness, trophic complexity, or ecosystem function. Like all biodiversity metrics, energy flows have a number of caveats in addition to their advantages (see Caveats section of Methods). Relying on energy consumption alone as a metric would not capture the intrinsic value of rare species, or the ecological impacts of species that translate energy into function exceptionally efficiently, or that perform unusual functions not easily classified based on their traits. Moreover, the large-scale approach used here does not capture many kinds of local and regional variation in historical species population densities, limiting its application at local scales. Earth System Flows and Planetary Boundaries Finally, an energetics approach to studying biodiversity loss can advance global biodiversity assessments, including the effort to set regional or planetary boundaries for biosphere integrity. The novel scale of this study, which expands previous plot-based energetics analyses to an area of over 20 million km 2 , allows it to be integrated into global assessments of biodiversity loss. These include assessments currently being undertaken by the Intergovernmental Science Policy Platform on Biodiversity and Ecosystem Services (IPBES) 18 , and under the planetary boundaries framework 3 . It is contested whether a planetary boundary for biodiversity is meaningful because of the heterogeneous, local and spatially disconnected nature of ecological functions 45 ; boundaries and thresholds may be more meaningful at local scales. Biodiversity intactness was proposed as a metric for such a planetary boundary 21 , although recent planetary boundaries literature 3 abandoned BII as a metric because of the difficulty of relating it to ecological functions, favoring Human Appropriation of Net Primary Productivity (HANPP) instead. However, it is challenging to see how HANPP relates to actual ecological function. The energetics approach we have outlined here shows a way forward that enables biodiversity and its intactness to be related to ecological vibrancy and a suite of ecosystem functions, whether at local, regional or planetary scales. Moreover, it provides a mechanism for bringing animal activity into the quantitative, mechanistic framework of biosphere modeling and Earth System Science, which to date has been dominated by the ecological functions provided by vegetation and is largely blind to the functions provided or modified by animals. Conclusion This analysis has demonstrated the potential of energetics analysis, or ecological vibrancy, to quantify the decline and recovery of ecological functions mediated by birds and mammals. It provides a tool to translate biodiversity into ecosystem function, and provides a different perspective from considering species richness and abundance alone. Our energetics analysis of sub-Saharan Africa highlights the relative importance of keystone species and small species, and the ecological importance of both protected areas and unprotected lands. Like any metric (for example, carbon stock), it should be applied with caution as only one lens on the multifaceted nature and value of biodiversity and ecological function. And energy flow needs to remain coupled with consideration of the number of species contributing to energy flow to fully capture ecological vibrancy and resilience, rather than wrongly label persistence of a few generalist species as ecological intactness. Some future steps could include integrating energy flows into global biodiversity assessments, expanding this energetic analysis to a planetary scale, incorporating domesticated animals within the same framework, extending to the much more data-challenged question of invertebrate energetics, and relating animal energy flows to vegetation structure captured through vegetation plots and through dynamic global vegetation models (DGVMs). We believe the subcontinental-scale analysis presented here presents a significant step forward in the challenge of relating biological richness and intactness to ecological and planetary function. Declarations Data Availability Statement The data on energy flows through each species, trophic guild, and functional group are available in Supplementary Data 1 and 2. Input data on species population densities are available through the TetraDENSITY dataset (https://ecaslab.com/tetradensity-database/). Input data from the biodiversity intactness index are available through the BII4Africa project (https://bii4africa.org/). Input data on species ranges are available through the IUCN red list and Birdlife International. Input data on species traits (i.e. diet, body mass, lifestyle) are available through the EltonTraits database (mammals) and through the Avonet database (birds). Code Availability Statement R Scripts as well as adapted data are available online at the project’s Mendeley Data site: https://data.mendeley.com/preview/8j4j85f82c?a=8a584c71-23c8-48ad-b025-c93e27f36de0, or from the corresponding author upon reasonable request ( [email protected] ) Author Contributions T.L. led the analysis, drafted the paper, and conceptualized the visualizations, with input and supervision from Y.M., N.S., and I.O.M., as well as input from H.S.C., L.S., S.T., and J.A.T. Y.M. conceived the analysis, developed the energetics approach, and provided feedback on the analysis, visualization, and structure of the paper. H.S.C. collected and supplied the data on biodiversity intactness and provided input on the designation of land uses. N.S. provided input on the designation of biomes, land uses, and ecosystem functions. I.O.M. guided and provided input on the uncertainty analysis. L.S. collected, modeled, and supplied the data on species population densities. 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Energy flows were calculated independently for each 8x8 km grid cell, the scale at which biodiversity intactness data is available. The study area comprises ~317,000 cells. To assess change over time, energy flows were calculated twice for each cell: once based on estimated historical species abundances in the pre-industrial/pre-colonial Holocene (~1700 CE), and once based on contemporary abundances, given human land use, according to the population changes estimated by the biodiversity intactness index (BII) 26 . Historical Species Abundances To determine which bird and mammal species were historically present in each 8x8 km grid cell, we used historical IUCN range maps. For the 11 large mammal species for which historical range maps are not available within the IUCN database, we adapted them from other sources, following Hempson et al 47 (see Supplementary Information). A caveat to this approach is that IUCN range maps are likely to overestimate species abundances, as they do not account for fine-scale habitat heterogeneity. However, available area-of-habitat maps 48 limit species ranges based on anthropogenic land cover, and thus would not adequately predict historical species occupancy. To calculate historical species abundances, we used published median population density estimates for bird 25 and mammal 24 species. These were modeled as a function of trait, environmental, and phylogenetic predictors, using additive mixed-effect models and Bayesian inference, based on 10,484 empirical records of bird and mammal population densities 24,25 . To estimate species abundances across sub-Saharan Africa, we used mean species population densities 2 . Mean densities were calculated using log-normal distributions based on published median densities and uncertainty intervals. Because population density distributions for most species are left-skewed, mean species population densities are higher than median values for species with wide confidence intervals. Given that ~75% of the global terrestrial surface is modified by humans to some extent 49 the exclusion of non-natural population densities is not realistically possible, and is not necessarily desirable given that hominids have modified African species population densities for millions of years. Contemporary Species Abundances from the Biodiversity Intactness Index To estimate contemporary species abundances we multiplied historical abundances by the proportional intactness of each species in each 8x8 km cell under modern land use. We used the intactness values for species under various land uses that are published in the Biodiversity Intactness Index for Africa dataset (BII) 19 . The BII employs a structured expert elicitation process to estimate and validate the proportional changes to species abundances under nine land uses: strict protected areas, near-natural lands, rangelands, intensive croplands, smallholder croplands, tree croplands, timber plantations, dense settlements, and urban areas. The BII allocates each species into one of 17 bird and 76 mammal “response groups” containing species that respond similarly to land use change. The average impact of each land use class on the abundance of species in each response group was calculated from ~30,000 individual estimates produced by 200 experts on African biodiversity. To map changes in abundance, each cell was assigned a land use class and intensity according to the land use classification outlined in Clements et al in prep 26 . Cells within protected areas and timber plantations were classified categorically, and cells within croplands, rangelands, and settlements were classified and then scaled along a land use intensity gradient. In cases where land use change benefited a species, the intactness of that species was greater than 1, and its abundance increased compared to the historical baseline. Daily energy expenditure and food uptake To calculate ecosystem energy flows, we first calculated the short-term equilibrium rate of food consumption for each species following ref 2 . For each species, daily energy expenditure was calculated from body mass using multi-species allometric equations (See Supplementary Table 1 for equations). 32 Food consumption was calculated from energy expenditure based on published assimilation efficiency values for each food type and taxonomic group of birds and mammals (See Supplementary Table 2). Where available, assimilation efficiency values were assigned at the family level; otherwise they were assigned at the order or class level. Values for the body mass of each species, and for the composition of food types within each species’ diet, were derived from the Eltontraits database for mammals 50 and from the Avonet database for birds 51 . Energetic food intake was calculated in units of kJ m −2 year −1 and then averaged across cells. Allocation of species into trophic guilds and functional groups To understand how human land use has altered ecosystem structure, we allocated species into trophic (i.e. feeding) guilds. Each species was allocated into a single trophic guild, to shed light on how an ecosystem’s trophic structure, defined as the distribution of energy among guilds, varies between biomes and land uses. Species were allocated into guilds based on their taxonomic class, their diet (e.g. omnivore, carnivore, nectarivore, folivore, frugivore), and their lifestyle (e.g. arboreal, terrestrial). Data on diet and lifestyle was extracted from the Eltontraits database for mammals 50 and from the Avonet database for birds 51 . Throughout the text, herbivore is used as an umbrella term to capture species eating any kind of plant matter, including foliage, seeds and nuts, nectar, and fruit. The terms folivore, granivore, nectarivore, and frugivore are used to refer to these groups independently. In addition, large and small terrestrial herbivores were split according to a published list of African large herbivores 47 to better isolate how the distinctive vulnerability of large herbivores to human activity alters ecosystem structure. To understand how human land use has altered ecosystem function, we allocated species into 23 functional groups: 11 for birds and 12 for mammals. Species that perform multiple functions were allocated into multiple groups, so that the sum of energy flows through functional groups is greater than the total flow through the ecosystem’s birds and mammals. By contrast, the energy flows through guilds sum to the total energy flow through birds and mammals. We adapted a list of 11 bird functions from a published list of major avian ecosystem functions 11 . We added a function for aquatic carnivory and subdivided the invertivory function based on species lifestyle (e.g. insessorial, aerial, terrestrial), as invertivory is performed by over half of all bird species. We sorted birds into functional groups based on their lifestyles and diets (see Extended Data Table. 1 for sorting criteria for both birds and mammals). Unlike for birds, there is a not a single authoritative source on functions performed by mammals. After reviewing the literature we designated twelve mammal functions performed by large herbivores 10,29 , carnivores 52,53 , primates 33 , bats 54 , fossorial mammals 42 , and other small mammals 55 . We sorted mammals into functional groups based on their diet, body mass, and lifestyle. For the grazing and browsing functions performed by large terrestrial herbivores we used published data on the leaf versus grass component of large herbivore diets 47 , and included large, terrestrial, herbivorous primates ( Gorilla spp. and Theropithecus gelada ) based on the expert knowledge of the authors. We additionally used published data on herd size 47 to select herbivores that perform a nutrient dispersal function, as herd forming species have a distinctive effect on nutrient distribution within ecosystems 10 . We included in the megafauna impacts function those species that have unique ecological impacts because their large body size frees them from predation 29 . We determined the diet thresholds for each function iteratively, running the species allocation process multiple times and refining thresholds based on the authors’ expert knowledge. To increase the legibility of our results in the main text, we further aggregated our 23 preliminary functions into 10 aggregate functions, some of which are performed by both birds and mammals (See Extended Data Table 1). Comparison of energy flows across functions, biomes, and land uses To calculate energy flows through functions, we summed the energy flows through all species that contribute to each function. This approach weights the contributions of species to associated functions based on species’ average daily energy consumption. The proportionate contribution of each species to its functions therefore changes depending on whether energy flows are calculated based on historical species abundances or based on present day, human impacted species abundances. Beyond energy flow, we did not scale species-level contributions to functions based on other metrics of functional efficiency: for example, based on pollen deposition rates, seed dispersal distance, or diet proportion. These causes of efficiency vary widely between functions and species 11 and are difficult to measure consistently. To avoid biases, we therefore assumed that all species use energy equally efficiently to perform their associated ecosystem functions. For the analysis, we compared energy flow within specific functions across space and time. It is not meaningful to compare energy flows across ecosystem functions (e.g. predation vs soil disturbance) as how each function uses energy is very different. We also calculated the average energy flows through functional groups and trophic guilds across biomes and land uses. The biome is commonly proposed as the appropriate unit of analysis for assessing biodiversity trends, because biomes are biologically coherent subunits of the biosphere with structures and functions that respond to land use change in relatively consistent ways 9 . We allocated cells into biomes based on the biome map of the RESOLVE Ecoregions dataset 46 . To allow for broad comparisons between vegetation types, we further aggregated biomes into forests, grassy systems comprising savannas and grasslands, and arid systems comprising deserts and shrublands. For the biomes analysis, we excluded cells falling into the fynbos and thicket biomes, which are not easily classifiable and make up less than 2% of sub-Saharan Africa. We also excluded cells falling into mosaic biomes, as the low accuracy of available continent-scale vegetation maps makes it infeasible to subdivide mosaics into component biomes within the study scope. We calculated average energy flows through each guild and functional group across each of these three aggregated biomes under historical conditions and under modern land use conditions. We allocated cells into land uses using an adapted version of the 8x8 km resolution African land use map created for the biodiversity intactness index for Africa (Clements et al., in prep) 26 . Following Clements et al., in prep 26 , cells were allocated into four land uses: strict protected areas (IUCN categories I:III); settlements (>20% urban cover or a population density over 1000 per km 2 ); croplands (>20% crop cover); and unprotected untransformed land (remaining cells). We calculated average energy flows through each guild and functional group across each of these four land uses. Comparison of energy flows to biodiversity intactness, and species richness values To understand how well biodiversity intactness values predict functional intactness, we related the BII of each cell to the intactness of energy flows through each cell. We related the BII of birds and mammal species to the intactness of total energy flows through bird and mammal species (Fig 4a-b) and to the intactness of energy flows through species in each functional group (Extended data Fig 1). Functional groups with shallower slopes maintain high levels of energy consumption as biodiversity intactness declines, and were deemed more resilient to human impacts. We also related total energy flows to native bird and mammal species richness, to understand the extent to which high-energy keystone species versus rich communities of species drive ecosystem function (Fig 4c-d). We analyzed these relationships across all cells using linear regression. Uncertainty Calculation Following ref. 2 , we quantified uncertainty in our estimates of energetic intake by running 10,000 Monte Carlo simulations of energy flow through animal species and groups. For each simulation, we replaced the values in our original calculations with values drawn from random distributions. We assumed there was uncertainty in the following variables: species body mass, population density, daily energy expenditure equation (DEE), assimilation efficiency, and fractional diet composition of each species. Following ref. 19 , we also assumed there was uncertainty in the estimated intactness of each species in each land use. For body mass, we drew values from a truncated normal distribution (lower bound = 1g) in which the mean was published mean body mass 50,51 and standard deviation was 15% as described in ref. 2 For population densities, we drew from a log normal distribution, using mean and uncertainty values for each species published in refs. 24,25 . For DEE, we estimated the 95% confidence intervals following the methods described in ref. 32 . For assimilation efficiency, we drew from a random beta distribution using the mean and standard deviation by taxonomic group and food type in the literature (Supplementary Table 1). For diet composition, we drew from a symmetrical beta distribution with uncertainty parameters assigned following ref. 2 . For the proportional intactness of species abundances in each land use, we drew from a random beta distribution using the mean intactness values and standard deviations published in ref. 19 . Intactness values were previously validated in ref. 19 through a structured expert elicitation process. The uncertainty in each of these variables captures the natural variability occurring within species among individuals and groups, as well as ecologists’ uncertainty about mean values. For example, the population density of a given species will naturally vary geographically based on habitat suitability, resource availability, and competition. But there is also absolute uncertainty about the mean species population density of each species based on limitations on empirical data and model accuracy. This division of uncertainty into geographic and absolute components is true of the other variables as well. The uncertainty derived from natural variability decreases as there is an increasing number of analyzed landscapes in which the species occurs. We assumed that half the uncertainty in species energy flow in a given landscape is from natural variability and that half is from absolute uncertainty about mean values, which does not decline as geographic area increases. To account for the effects of area in our uncertainty estimates, we grouped species-level energy flows into 1˚ grid squares (~12,000 km 2 at the equator) following ref. 47 . We treated uncertainty about natural variability as independent in each 1˚ square in which a given species occurs and drew from independent distributions in each square. For each species, we calculated range-wide spatial means of energy flow for each of the 10,000 Monte Carlo simulations, and then propagated this area-scaled uncertainty into the absolute uncertainty about mean energy flow values generated from the area-independent Monte Carlo simulation estimates. We estimated total uncertainty by assuming uncertainty in all variables simultaneously, and calculated the 2.5 th and 97.5 th confidence intervals to derive 95 th confidence intervals for our estimates. Caveats There are a number of caveats in our analysis. The approach uses range-wide average species population densities. However, due to the large number (~3000) of species included we were unable to model geographic variability in population densities within species. For the vast majority of species, there is insufficient data to predict how population densities respond to environmental gradients. In addition, population densities vary inconsistently along environmental gradients across species. Because the density estimates used here are average densities, they do not account for intra-specific competition. It is expected that species reach higher densities when competitors are missing. The approach is thus likely to overestimate energy flows through species-rich guilds in species-rich cells. Because the analysis relies on coarse-scale IUCN range maps to predict historical species ranges, it is also likely to overestimate species abundances for species restricted to specialized habitat. Energy flows through colonial species including many fossorial rodents and water birds may be overstated. While these caveats may cause the analysis to overestimate absolute energy flows, they are less likely to create biases when comparing variation within functional groups across biomes and land uses, the core aim of the study. There is also insufficient data to model whether biome or land use causes intra-specific variation in species trait data such as diet and body mass, although we accounted for the possibility of such variability in the uncertainty analysis. There are additional caveats about the BII input data. The BII estimates species responses to land use change as a function of land use, averaging responses from experts in different countries and regions. Consequently, the BII does not account for how national political factors impact species abundances. These factors include war, protected area management capacity, wildlife legislation, and cultural differences about hunting. This study analyzes continent-wide average energy flows through guilds in different land use classes and biomes, which are less likely to be affected by national factors. However, an effort to use this approach to analyze energy flows over smaller areas (e.g. within a country or protected area) would need to account for regional and national variables affecting species abundances. Methods References 46. Dinerstein, E. et al. An Ecoregion-Based Approach to Protecting Half the Terrestrial Realm. BioScience 67 , 534–545 (2017). https://doi.org/10.1093/biosci/bix014 47. Hempson, G. P., Archibald, S. & Bond, W. J. A continent-wide assessment of the form and intensity of large mammal herbivory in Africa. Science 350 , 1056–1061 (2015). https://doi.org/10.1126/science.aac7978 48. Lumbierres, M. et al. Area of Habitat maps for the world’s terrestrial birds and mammals. Sci Data 9 , 749 (2022). https://doi.org/10.1038/s41597-022-01838-w 49. Venter, O. et al. Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. Nat Commun 7 , 12558 (2016). https://doi.org/10.1038/ncomms12558 50. Wilman, H. et al. EltonTraits 1.0: Species-level foraging attributes of the world’s birds and mammals. Ecology 95 , 2027–2027 (2014). https://doi.org/10.1890/13-1917.1 51. Tobias, J. A. et al. AVONET: morphological, ecological and geographical data for all birds. Ecology Letters 25 , 581–597 (2022). https://doi.org/10.1111/ele.13898 52. Ripple, W. J. et al. Status and Ecological Effects of the World’s Largest Carnivores. Science 343 , 1241484 (2014). 53. Dalerum, F., Cameron, E. z, Kunkel, K. & Somers, M. j. Diversity and depletions in continental carnivore guilds: implications for prioritizing global carnivore conservation. Biology Letters 5 , 35–38 (2008). https://doi.org/10.1098%2Frsbl.2008.0520 54. Kunz, T. H., Braun de Torrez, E., Bauer, D., Lobova, T. & Fleming, T. H. Ecosystem services provided by bats. Annals of the New York Academy of Sciences 1223 , 1–38 (2011). https://doi.org/10.1111/j.1749-6632.2011.06004.x 55. Bogoni, J. A., Peres, C. A. & Ferraz, K. M. P. M. B. Effects of mammal defaunation on natural ecosystem services and human well being throughout the entire Neotropical realm. Ecosystem Services 45 , 101173 (2020). https://doi.org/10.1016/j.ecoser.2020.101173 Additional Declarations There is NO Competing Interest. Supplementary Files ExtendedDataFigures.docx LoftetalSupplementaryInformation.docx Supplementary Information SupplementaryData1.xlsx Supplementary Data 1 SupplementaryData2.xlsx Supplementary Data 2 Cite Share Download PDF Status: Published Journal Publication published 29 Oct, 2025 Read the published version in Nature → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3844832","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Biological Sciences - Article","associatedPublications":[],"authors":[{"id":271100190,"identity":"e89a0ee0-1d1b-4913-b6fd-c3d88e06ade6","order_by":0,"name":"Ty Loft","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYHACxgMMNgxyDBIMbAihBjzqeYD4AEOagTHpWhIbiNZiL5HAcOBDwp/0/tnNzx4wVBxm4G8/wCY5A58tQC0HZyQY5M64c8zcgOFMGoPEmQQ2yQ0EtBzm/WGQu0Eih02Csc2GgeEGA5vkA0JaeBIM0g0gWiQY5InVkmAAs8UApAWvw848bAD6xdhwxo00M4mEM2k8hmcSmy3xeZ+9Pfnggw8JcvL8M5KfSXyoOCwnd/zwwZs9eLQwCCQ2IDgJ4IjCGytAwH8Av/woGAWjYBSMAgYA8eJGYD6E79gAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0008-0591-3948","institution":"University of Oxford","correspondingAuthor":true,"prefix":"","firstName":"Ty","middleName":"","lastName":"Loft","suffix":""},{"id":271100191,"identity":"a0164f24-1da6-443e-8c45-04348126e740","order_by":1,"name":"Imma Oliveras Menor","email":"","orcid":"https://orcid.org/0000-0001-5345-2236","institution":"Institut de Recherche pour le Developpement","correspondingAuthor":false,"prefix":"","firstName":"Imma","middleName":"Oliveras","lastName":"Menor","suffix":""},{"id":271100192,"identity":"5d01a51e-e7ec-4654-87e7-610c8a715aa4","order_by":2,"name":"Nicola Stevens","email":"","orcid":"https://orcid.org/0000-0002-0693-8409","institution":"University of Oxford","correspondingAuthor":false,"prefix":"","firstName":"Nicola","middleName":"","lastName":"Stevens","suffix":""},{"id":271100193,"identity":"d3d9abc3-4b03-4a2f-adb3-3fd071b873d9","order_by":3,"name":"Hayley Clements","email":"","orcid":"","institution":"Centre for Sustainability Transitions, Stellenbosch University","correspondingAuthor":false,"prefix":"","firstName":"Hayley","middleName":"","lastName":"Clements","suffix":""},{"id":271100194,"identity":"89a6454e-b137-4c10-a161-a467955701ce","order_by":4,"name":"Luca Santini","email":"","orcid":"https://orcid.org/0000-0002-5418-3688","institution":"Sapienza Università di Roma","correspondingAuthor":false,"prefix":"","firstName":"Luca","middleName":"","lastName":"Santini","suffix":""},{"id":271100195,"identity":"181448bf-882f-4526-8ffb-551284375ba4","order_by":5,"name":"Seth Thomas","email":"","orcid":"https://orcid.org/0000-0001-9610-539X","institution":"Department of Biology, University of Oxford","correspondingAuthor":false,"prefix":"","firstName":"Seth","middleName":"","lastName":"Thomas","suffix":""},{"id":271100196,"identity":"9a296b81-2b64-4d3f-be57-33373d40b4e8","order_by":6,"name":"Joseph Tobias","email":"","orcid":"https://orcid.org/0000-0003-2429-6179","institution":"Imperial College London","correspondingAuthor":false,"prefix":"","firstName":"Joseph","middleName":"","lastName":"Tobias","suffix":""},{"id":271100197,"identity":"1de80983-63dd-4eed-a41e-e54656f29fd9","order_by":7,"name":"Yadvinder Malhi","email":"","orcid":"https://orcid.org/0000-0002-3503-4783","institution":"University of Oxford","correspondingAuthor":false,"prefix":"","firstName":"Yadvinder","middleName":"","lastName":"Malhi","suffix":""}],"badges":[],"createdAt":"2024-01-08 07:52:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3844832/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3844832/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41586-025-09660-1","type":"published","date":"2025-10-29T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":50718260,"identity":"110d143c-771a-487d-85df-c4004ae3b085","added_by":"auto","created_at":"2024-02-06 09:20:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":232316,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntactness of energy flows through animal-mediated ecosystem functions. \u003c/strong\u003eSpecies were allocated into functional groups based on diet, lifestyle, body size, and group size.\u003cstrong\u003e a\u003c/strong\u003e, Total \u0026nbsp;historical (white) and modern (black) mean energy flow through ten bird and mammal functional cohorts across sub-Saharan African. Error bars denote 95% confidence intervals derived from 10,000 Monte-Carlo simulation estimates incorporating uncertainty in body mass, population density, the daily energy expenditure equation, assimilation efficiency of different food types, composition of the diet of each species, and the biodiversity intactness of each species within each land use. \u003cstrong\u003eb\u003c/strong\u003e, Average energy flow through modern sub-Saharan Africa (black) as a proportion of historical energy flow (white). Error bars are 95% confidence intervals derived from 10,000 Monte-Carlo simulation estimates of the biodiversity intactness of each species within each land use. \u003cstrong\u003ec\u003c/strong\u003e, Average remaining proportion of pre-industrial energy flows through birds, mammals, and each functional group, across sub-Saharan Africa and within its predominant land uses. Bluer cells indicate more intact functional groups, and redder cells indicate less intact groups.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-3844832/v1/d36e258abfa4f67864be7fd1.png"},{"id":50719959,"identity":"5fbaf0dc-e648-4fda-a96c-60316d7ecbdc","added_by":"auto","created_at":"2024-02-06 09:36:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":462566,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMean magnitude of energy flows through bird and mammal trophic guilds across African biomes\u003c/strong\u003e. Values represent the mean energy flow through all cells given historical species abundances (light shading) and current species abundances accounting for human land use (dark shading). The size of the circle indicates the magnitude of food energy consumption (kJ m\u003csup\u003e-2 \u003c/sup\u003eyear\u003csup\u003e-1\u003c/sup\u003e) by species in animal guilds. Flows through present day ecosystems were calculated using the mean intactness of bird and mammal groups according to the Biodiversity Intactness Index (BII). For clarity, guilds with small energetic flows are not shown, and complete results as well as uncertainty are available in Supplementary Data 1.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-3844832/v1/e5c708438f0364942d31d3e4.png"},{"id":50718266,"identity":"92ce8d2e-20cb-4c34-bf03-7ed50b738b92","added_by":"auto","created_at":"2024-02-06 09:20:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":294138,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationships between biodiversity intactness, energetic intactness, total energy flow, and species richness. (a-d)\u003c/strong\u003e Relationships were calculated for each of the ~317,000 cells that compose sub-Saharan Africa, and color indicates the number of cells with a given relationship. R\u003csup\u003e2\u003c/sup\u003e values were calculated using linear regression. (\u003cstrong\u003ea-b\u003c/strong\u003e) Relationships between energetic intactness and biodiversity intactness were calculated over each cell for (\u003cstrong\u003ea)\u003c/strong\u003e birds and (\u003cstrong\u003eb\u003c/strong\u003e) mammals. Energetic intactness indicates the proportion of historical pre-modern energy flow remaining under modern landscapes, with species weighted based on their contribution to energy flow. Biodiversity intactness indicates the changes in species population abundances between pre-modern and modern land uses, with each species weighted equally (\u003cstrong\u003ec-d\u003c/strong\u003e) Relationships between species richness and total energy flow were calculated over each cell for (\u003cstrong\u003ed\u003c/strong\u003e) birds and (\u003cstrong\u003ed\u003c/strong\u003e) mammals. Species richness for each cell was calculated by summing species occurrences based on IUCN range maps.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-3844832/v1/93b40c61749d5d86f1dae8b4.png"},{"id":50718265,"identity":"d22745bb-7938-4b07-8481-1147c3b51bc1","added_by":"auto","created_at":"2024-02-06 09:20:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":581726,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAggregate energy flow through birds and mammals mapped across sub-Saharan Africa.\u003c/strong\u003e (\u003cstrong\u003ea-b\u003c/strong\u003e) To map historical energy flows, energy flows were summed for each (\u003cstrong\u003ea\u003c/strong\u003e) bird and (\u003cstrong\u003eb\u003c/strong\u003e) mammal species occurring in each 8x8 km\u003csup\u003e2 \u003c/sup\u003ecell, assuming they existed in pre-industrial population densities. (\u003cstrong\u003ec-d\u003c/strong\u003e) To map energy flows through modern, transformed landscapes for (\u003cstrong\u003ea\u003c/strong\u003e) birds and (\u003cstrong\u003eb\u003c/strong\u003e) mammals, historical energy flows for each species were multiplied by the biodiversity intactness index (BII) value of each species, which is calculated independently for each species in each landscape based on the species response group and the cell’s modern land use class and intensity. (\u003cstrong\u003ea-d\u003c/strong\u003e) The color gradients indicating aggregate energy flows were scaled independently for birds and for mammals.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-3844832/v1/77bd11bdba3100e523c4687e.png"},{"id":94733274,"identity":"3e76e0f8-f7d4-4f3b-91f1-d76eacb5c680","added_by":"auto","created_at":"2025-10-30 07:11:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2531623,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3844832/v1/b6d097ab-f30a-4bd2-b7e9-07000c1779f1.pdf"},{"id":50718261,"identity":"f53e4d6d-e841-46ef-817b-cdcc1441fb08","added_by":"auto","created_at":"2024-02-06 09:20:33","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1367314,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"ExtendedDataFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-3844832/v1/020fcf87081cc6155caba09b.docx"},{"id":50718837,"identity":"156f856f-8c98-4e91-b4cd-74186f1c24fe","added_by":"auto","created_at":"2024-02-06 09:28:33","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":38317,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Information\u003c/p\u003e","description":"","filename":"LoftetalSupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-3844832/v1/8cb545716ed4bd79e7185188.docx"},{"id":50718264,"identity":"2bd60a72-c2f2-4c98-b8e9-94184a41eca0","added_by":"auto","created_at":"2024-02-06 09:20:33","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":59770,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Data 1\u003c/p\u003e","description":"","filename":"SupplementaryData1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3844832/v1/09e95dadaf3ca84f77edd13e.xlsx"},{"id":50718267,"identity":"7357ef2d-82d2-419d-9893-2a820571cb3e","added_by":"auto","created_at":"2024-02-06 09:20:33","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":1165499,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Data 2\u003c/p\u003e","description":"","filename":"SupplementaryData2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3844832/v1/6b2a5ef5a368bb1da3867250.xlsx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Energy flows reveal declining ecosystem functions by animals across Africa","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEcologists have devised numerous metrics to track species loss and recovery,\u003csup\u003e4\u0026ndash;6\u003c/sup\u003e but alone they can be poor indicators of changes in ecological function\u003csup\u003e7\u003c/sup\u003e. The influence of a species on its ecosystem depends on the species\u0026rsquo; abundance and on the specific functions it performs\u003csup\u003e8\u003c/sup\u003e. To assess how changing biodiversity affects ecosystem function at large scales, ecologists must develop consistent methodologies that account for species\u0026rsquo; changing abundances and their diverse impacts on ecosystems. Doing so is central to predicting how biodiversity change affects the ability of ecosystems to provide services, such as storing carbon, supporting food production and buffering natural disasters\u003csup\u003e9\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eMany ecosystem functions are moderated by animals, yet most ecosystem function literature addressing regional or larger scales focuses exclusively on vegetation functions. Animals perform functions including herbivory, seed and nutrient dispersal, and predation\u003csup\u003e10,11\u003c/sup\u003e, which shape ecosystems by controlling flows and patterns of carbon, nutrients and water\u003csup\u003e12,13\u003c/sup\u003e. Since the pre-industrial Holocene, animal populations have collapsed as intensively human-modified landscapes have expanded fivefold\u003csup\u003e5,14\u003c/sup\u003e. Tracking how biodiversity loss translates into changes in animal-moderated ecosystem function is challenging because species respond unevenly to land use change depending on their traits\u003csup\u003e15\u003c/sup\u003e. For example, agricultural conversion depletes populations of large or frugivorous animals faster than small and omnivorous ones\u003csup\u003e15,16\u003c/sup\u003e. These asymmetric population declines change ecosystems\u0026rsquo; trophic structures: the partitioning of energy and biomass between plant and animal guilds. Ecosystems with simplified trophic structures provide a reduced suite of functions and services\u003csup\u003e17\u003c/sup\u003e. Ultimately, they become less capable of recovering from external shocks and supporting human wellbeing and livelihoods\u003csup\u003e18\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eTo track species abundances (a prerequisite for measuring functional changes) conservationists have developed biodiversity intactness indices (BII)\u003csup\u003e19,20\u003c/sup\u003e. These indices estimate how human activity has changed species populations relative to remaining highly intact landscapes such as wilderness areas, with these intact areas assumed to be representative of historic animal abundances, nominally in the pre-colonial/pre-industrial period. Local intactness scores can be aggregated to determine BII across ecoregions, countries, and taxonomic groups. BII has been proposed as a metric for biodiversity in the planetary boundaries framework, which seeks to identify safe environmental conditions for human societies\u003csup\u003e21\u003c/sup\u003e; though BII was more recently abandoned because of the difficulty of relating it to ecosystem function\u003csup\u003e3\u003c/sup\u003e. BII alone cannot estimate changes to ecosystem function because it weights each species equally. In reality, some species disproportionately affect ecosystem function due to their population densities, body sizes, dietary preferences, rates of food consumption and behavioral features\u003csup\u003e10\u003c/sup\u003e. To quantify how the changing animal populations estimated by the BII alter ecosystem function, an approach is needed that accounts for species\u0026rsquo; variable ecosystem impacts using a common unit of measurement.\u003c/p\u003e\n\u003cp\u003eOne option is to adopt an ecosystem energetics approach to compare how much energy species consume across land uses\u003csup\u003e2\u003c/sup\u003e. Species within an ecosystem can be classified into functional groups. Changes to energy flows through these groups indicate changes to the provision of associated ecosystem functions\u003csup\u003e13,22\u003c/sup\u003e. An ecosystem energetics approach quantifies energy flow through the trophic web, by calculating the annual food energy consumed by each species per unit area. In all terrestrial ecosystems, energy, captured as sunlight by plants, flows up the trophic web through guilds of herbivores, carnivores, scavengers, and detritivores. Because every species consumes and expends energy, it can serve as an ecologically meaningful common currency capable of comparing how ecosystem functions vary across space and time\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eWhile energetics approaches have been previously used to measure how human modified landscapes alter ecosystem trophic structure \u003csup\u003e2,23\u003c/sup\u003e, they have not been scaled beyond a few model ecosystems, as they require extensive data measuring species abundances across different land uses. In particular, energetics approaches have never been applied at regional or continental scales. Here, we focus on Sub-Saharan Africa as a case study, a region with a striking range of ecosystems and large gradients in ecological intactness, including megafaunal abundance, and anthropogenic pressure. We take advantage of new datasets that (i) model population densities of bird and mammal species\u003csup\u003e24,25\u003c/sup\u003e and (ii) estimate BII, or the impacts of land use changes on species abundances, across Sub-Saharan Africa\u003csup\u003e19,26\u003c/sup\u003e. The BII estimates are derived from a new dataset aggregating 30,000 expert estimates of how African species abundances respond to land use change. We use these datasets to quantify how biodiversity loss has degraded a suite of ecosystem functions across sub-Saharan Africa.\u003c/p\u003e"},{"header":"Approach","content":"\u003cp\u003eOur approach is to quantify how human modification of land uses has changed the distribution of energy among trophic guilds and functional groups. Energy flows are calculated for African bird and mammal species under contemporary versus pre-colonial/industrial (~1700 CE) conditions, which we henceforth refer to as historical conditions. Energy flows are aggregated across biomes to compare how the dominant vegetation structures within Africa moderate the ecological impacts of land use change. We also assess the relationships between biodiversity intactness, ecosystem function, and ecological vibrancy\u003csup\u003e2\u003c/sup\u003e, which we define as a combination of the magnitude of energy flow through animal-mediated pathways and the diversity of contributing animal species. We focus on birds and mammals because they are important components of animal biomass\u003csup\u003e27\u003c/sup\u003e, as well as data-rich groups with well-understood ecological functions, while acknowledging that invertebrates play a major but data-challenged role in ecosystem energetics. While populations of some species, especially megafauna, declined substantially long before the colonial and industrial period, these declines appear to have been less severe in Africa than elsewhere, hence Africa\u0026rsquo;s contemporary association with megafauna.\u003csup\u003e28,29\u003c/sup\u003e\u0026nbsp; Africa therefore provides the unusual opportunity to examine how human activities have changed ecosystem function within historically near-intact ecosystems\u003csup\u003e29\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eTo estimate energy flows through African ecosystems, we calculated historical energy consumption by each species present in each 8 x 8 km cell across sub-Saharan Africa (317,000 cells in total). We used (i) modeled potential species population densities\u003csup\u003e24,25\u003c/sup\u003e based on ~10,000 averaged empirical measures of species population densities and (ii) IUCN range maps\u003csup\u003e30,31\u003c/sup\u003e to predict historical species abundances in each cell. To calculate the average energy consumption of sub-Saharan Africa\u0026rsquo;s ~3,000 bird and mammal species, we used published allometric equations\u003csup\u003e32\u003c/sup\u003e and datasets on species traits, diets, and food assimilation efficiencies (Supplementary Tables 1-3; Supplementary Data 2). We quantified current energy flows according to the remaining abundance of each species in each cell estimated by the BII\u003csup\u003e19,26\u003c/sup\u003e. To calculate the energetic intactness of ecosystem functions, defined as the percentage of historical animal energy flows remaining in an ecosystem, we grouped species into trophic guilds and functional groups, based on their diets, lifestyles, body sizes, and, for mammals, social group sizes. Using these categories, we identified 23 unique ecosystem functions (11 for birds and 12 for mammals), which we aggregated across classes into ten major functions\u003csup\u003e11,29,33,34\u003c/sup\u003e (Extended Data Table 1).\u003c/p\u003e"},{"header":"Changes to Ecosystem Structure and Function","content":"\u003cp\u003eChanges to the total flow of energy through animal populations, and to its distribution among guilds, can alter ecosystem functionality.\u003csup\u003e17\u003c/sup\u003e Energy flow through food consumption by wild African birds and mammals has decreased to 64% of historical values (54 \u0026ndash; 74%; all ranges reported are 95% confidence intervals). Energy flow decreased most in high intensity land uses, falling to 27% (19 \u0026ndash; 36%) of historical levels in settlements, 41% (30% \u0026ndash; 53%) in croplands, 67% (56 \u0026ndash; 75%) in unprotected untransformed lands (comprising rangelands and near-natural lands), and 88% (80 \u0026ndash; 96%) in strict protected areas (Figure 1). In aggregate, birds were more resilient to land use change than mammals, with the fraction of energy flowing through birds (as opposed to mammals) rising from 38% to 43%. The greater reported decline of mammals was driven entirely by the collapse of large herbivores (including grazers, browsers, and frugivores), which historically accounted for over one-quarter of mammalian energy consumption. Energy flows through large herbivores decreased by 72%, compared to a decrease of 30% for other mammals and for birds. Large herbivorous mammals have undergone substantial population declines even in protected areas (Figure 1). Therefore estimated energy flows have fallen well below historical levels even in Africa\u0026rsquo;s relatively wilder regions.\u003c/p\u003e\n\u003cp\u003ePartitioning energy transfer by habitat and broad spatial niches, we find that energetic intactness has collapsed across all biomes. Total (bird and mammal) energy flows are estimated to be 64% (54 \u0026ndash; 73%) intact in grassy systems, 66% (57 \u0026ndash; 76%) intact in forests, and 69% (60 \u0026ndash; 80%) intact in arid systems. Despite the similar magnitude of these declines, the trophic guilds most responsible for energy loss varied according to each biome\u0026rsquo;s dominant feeding pathways, suggesting that biome moderates changes to function (Figure 2). Arboreal species account for more energy flow in forests, where they can take advantage of greater vertical space and habitat complexity (Figure 2)\u003csup\u003e35\u003c/sup\u003e.\u0026nbsp;Of these arboreal species, birds make up nearly half of energy flow despite their much smaller body sizes than most primates and other arboreal mammals.\u0026nbsp;Overall, reduced arboreal species populations accounted for 37% of energy decline in forests, compared to 4\u0026ndash;10% of energy decline in grassy and arid systems. Fossorial species account for more energy flow (28% of the total) in arid systems, where burrows help animals regulate temperature and conserve moisture (Figure 2)\u003csup\u003e36\u003c/sup\u003e. Although fossorial mammals rank among the most resilient guilds, they accounted for 23% of energy decline in arid systems, versus 9% and 3% of energy decline in grassy systems and forests. Large terrestrial herbivorous mammals were major contributors to energy decline (28\u0026ndash;36%) in all biomes. However, the total fraction of loss attributable to large and small terrestrial herbivores was notably lower in forests (36%) than in arid and grassy systems (48\u0026ndash;51%).\u003c/p\u003e\n\u003cp\u003eWhen we consider the proportion of total energy flow contributed by broad trophic guilds, we find that overall patterns have changed little over time, with herbivory (including leaf-, grass-, seed-, fruit,- and nectar-eating animals) falling from 64% to 61% of total energy flow, insectivory rising from 12% to 14%, and carnivory remaining at 4%. However, energy flows through functional groups ranged from 26% intact (nutrient dispersal by large herbivores) to 75% intact (soil disturbance by fossorial mammals), when averaged across sub-Saharan Africa (Figure 1b). Megafauna-dominated functions, which include nutrient dispersal, grazing and browsing, and apex carnivory, were notably less intact (26-32%) than other ecosystem functions (61-75% intact), and were twice as depleted in non-protected untransformed lands as in strict protected areas (Figure 1c). Megafauna play a key role in controlling vegetation structure, both directly through grazing, browsing, and nutrient deposition by large herbivores, and indirectly through the control of herbivore populations by apex carnivores\u003csup\u003e13,34\u003c/sup\u003e. \u0026nbsp;Given that 80% of Africa is unprotected untransformed land, the collapse of large herbivores and carnivores is probably altering vegetation on a continental scale\u003csup\u003e34,37\u003c/sup\u003e. Megafauna extinctions on other continents have been estimated to have reduced herbivory by 42%\u003csup\u003e22\u003c/sup\u003e and lateral nutrient flow by over 90%\u003csup\u003e13\u003c/sup\u003e. While some of this lost functionality is substituted by domestic herbivores, total (domestic and wild) biomass has decreased across most of Africa, as has the functional diversity of herbivore guilds\u003csup\u003e38\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eBeyond megafauna decline, untransformed lands are relatively functional, with other ecosystem functions persisting above 55% intactness. However, the large absolute decreases in energy flows through arboreal guilds in forests, shared roughly equally between birds and mammals, translates to notable declines in seed dispersal by frugivores outside of protected areas (Extended Data Fig. 2). Seed dispersal is just 58% intact in untransformed lands and 14% intact in croplands. Pollination, dominated among vertebrates by birds and bats, also declines notably outside of protected areas, and is 63% intact in untransformed lands and 25% intact in croplands. Seed dispersal and pollination strongly influence plant community composition, structure and biomass. In tropical forests, primate and megafauna-dispersed tree species tend to have higher biomass than other species\u003csup\u003e39\u003c/sup\u003e. Reduced seed dispersal is likely to change the vegetation structure of defaunated forests and hinder ecosystem recovery.\u003csup\u003e40\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThe most stable functions are dominated by small and mid-sized herbivores, which account for a large proportion of energy in all biomes. These functions include granivory, soil disturbance, and avian grazing (mostly by water birds) (Extended Data Fig. 1). Soil disturbance is more intact in rangelands (95% intact) than in protected lands (94% intact), and is a dominant ecosystem function in arid biomes. Avian granivory is at 108% of historical levels in croplands, where birds can take advantage of seed-rich crops. These relatively stable functions, however, can generate ecosystem disservices when they harm agricultural production.\u003c/p\u003e\n\u003cp\u003eThe resilience of guilds and functions dominated by small animals (defined as \u0026lt; 3kg)\u003csup\u003e41\u003c/sup\u003e creates a striking pattern: African ecosystems are becoming dominated by smaller species. On average, energy consumption per unit biomass has increased by 23% across sub-Saharan Africa, indicating that smaller animals with high metabolic rates are consuming a greater proportion of energy. Our analysis highlights the ecological importance of small animals: in tropical ecosystems, rodents and passerines account for a much larger proportion of energy flow than of biomass, due to their high energy consumption per unit mass\u003csup\u003e2\u003c/sup\u003e. In sub-Saharan Africa, rodents accounted for 31% of total historical energy consumption but just 17% of biomass, rising to 36% of energy and 24% biomass today. Passerine birds accounted for 8% of historical energy consumption but just 2% of biomass, the same proportions as today. As large herbivores decline, these small animals are likely to exert greater relative control over the flows of nutrients, water, and material that structure ecosystems, while not compensating for the attributes (such as large seed dispersal and greater daily transport ranges) that are particular to larger animals.\u003c/p\u003e\n\u003cp\u003eThe species groups contributing most to total energy flow are elephants (family\u003cem\u003e\u0026nbsp;Elephantidae\u003c/em\u003e) and fossorial rodents, although consumption by the latter is highly uncertain. Elephants historically accounted for a striking 17% of total bird and mammal biomass across the region and 8% of total energy flow across sub-Saharan Africa, with savanna elephants (\u003cem\u003eLoxodonta africana)\u003c/em\u003e consuming by far the most energy of any single species. Elephants perform the grazing and browsing and nutrient dispersal functions; however, the confidence intervals around functions performed by elephants are highly uncertain, due to elephants\u0026rsquo; disproportionate energy consumption: uncertainty around energy flow values are lower for guilds and functions that have more species and more even energy consumption among species (see methods). Fossorial rodents, particularly mole rats, may also consume an outsized proportion of energy, due to their high abundance and high food consumption per unit body mass. Fossorial rodents include four of sub-Saharan Africa\u0026rsquo;s ten highest energy consuming species (Extended Data Fig. 3). However, energy flows through fossorial rodents, and through their associated soil disturbance function, are highly uncertain as confidence intervals around modeled mole rat population densities span 2.5 orders of magnitude.\u003csup\u003e42\u003c/sup\u003e The high energy flows through elephants and fossorial rodents suggest they are respectively key consumers of aboveground and belowground biomass in Africa. Elephants are well-known keystone species, with the potential to change ecosystem vegetation at the landscape scale and to affect continental-scale carbon sequestration\u003csup\u003e43\u003c/sup\u003e. The results here support emerging evidence\u003csup\u003e42\u003c/sup\u003e that fossorial rodents may be similarly impactful belowground, a hypothesis that bears further investigation.\u003c/p\u003e"},{"header":"Biodiversity and Ecosystem Energetics ","content":"\u003cp\u003eUnderstanding the relationship between biodiversity intactness and ecosystem function can help guide conservation and restoration. Biodiversity intactness equally weights changes to species abundances, whereas energetic intactness weights changes to a given ecosystem function based on each associated species\u0026rsquo; energy consumption. At the ecosystem scale, biodiversity intactness is a strong predictor of total energetic intactness for birds (R\u003csup\u003e2\u003c/sup\u003e = 0.97), and a weaker predictor for mammals (R\u003csup\u003e2\u003c/sup\u003e = 0.86) (Figure 3a-b). The principal difference between biodiversity intactness and total energetic intactness is driven by large herbivores, which account for 16% of historical energy consumption, but just 3% of BII. Because large herbivores are 32% more intact within protected areas than outside them, protected areas conserve a higher proportion of energy flow (and therefore ecological functionality) than of biodiversity intactness.\u003c/p\u003e\n\u003cp\u003eHowever, energetic intactness does not predict the intactness of many individual functions. In landscapes in which BII is 50% intact, energy flows through pollinators range from 22\u0026ndash;75% intact, flows through nutrient dispersers from 0\u0026ndash;20% intact, and flows through aerial insectivores from 50\u0026ndash;75% intact (Extended Data Fig.1). While BII can accurately predict total energy flow, it cannot predict which ecosystem functions remain intact and which have been depleted. This is a key knowledge gap for practitioners working to conserve and restore trophic complexity and ecosystem functionality that is uniquely addressed by our approach. Conversely, the energetic approach presented here, which relies on large continental datasets, is highly inaccurate at predicting values for individual species or landscapes. Rather, these estimates are useful for large-scale taxonomic and geographic comparisons when values are aggregated across many species and landscapes, and can elucidate how humans are impacting ecosystem function at scales relevant for policy.\u003c/p\u003e\n\u003cp\u003eIn addition to assessing intactness, an energetics approach can be used to estimate the historical magnitude of energy flows through African landscapes. Large absolute energy flows supported by a rich diversity of species and guilds can indicate exceptionally vibrant ecosystems. One key question for clarifying the relationship between functional resilience and ecological vibrancy is whether species richness predicts energy flow through animals, as it does other aspects of ecosystem function, including net primary productivity (NPP)\u003csup\u003e43\u003c/sup\u003e. We found that the species richness\u0026ndash;energy relationship differed between birds and mammals (Figure 3c-d). Richness predicts class-wide energy consumption strongly for birds (r\u003csup\u003e2\u003c/sup\u003e = 0.92), but weakly for mammals (R\u003csup\u003e2\u003c/sup\u003e = 0.15). This discrepancy is caused by the lower richness and less even apportionment of energy consumption among mammal species. Mammals account for 62% of total historical energy flow but just 36% of species, meaning that proportionally more energy is consumed by the average mammal than bird species. In addition, species-level energy flows span a greater range of values for mammals (4.3 orders of magnitude) than for birds (2.1 orders of magnitude), due to mammal species\u0026rsquo; wider ranges of population densities, body masses, and assimilation efficiencies. Mammalian energy consumption thus appears more driven by the presence of keystone consumer species. These include large herbivores, especially elephants; highly abundant rodents; and primates, which dominate the arboreal guilds important in forests (Extended Data Fig. 3). The uneven allocation of energy among species has implications for the biodiversity-function relationship: bird-driven functions are likely to be far more resilient to biodiversity loss, as they are supplied more evenly by a wider range of species.\u003c/p\u003e\n\u003cp\u003eThere are also clear biogeographical patterns in historical energetic vibrancy. Energy flows through birds tend to be highest in East Africa (Fig. 4a). These high energy landscapes broadly overlap with regions of volcanic soils and moderate to high rainfall along the Great Rift Valley. Birds and the insects many consume may benefit from nutrient-rich vegetation growing on fertile soils. Energy flows through birds tend to be lowest in Africa\u0026rsquo;s arid regions: the southwest, the Sahel, and the Horn of Africa. The map of total energy flow through mammals is more difficult to interpret, due to the high uncertainty associated with the energy consumption of dominant species (Fig. 4b). Still, mapping energy flows across sub-Saharan Africa opens up a number of interesting questions about the biogeographical factors that control total energy abundance. For example, future research might ask why birds consume a greater fraction of total energy in forests than in arid systems, or whether climatic and soil variables predict how energy flows are distributed among trophic guilds, taxonomic groups, or ecosystem functions.\u003c/p\u003e"},{"header":"Application to Restoration","content":"\u003cp\u003eAs we enter the United Nations Decade on Ecosystem Restoration (2021-2030), energy flows can contribute to ongoing efforts\u003csup\u003e44\u003c/sup\u003e to meet urgent demand from governmental and corporate sectors for metrics that can set and track progress towards nature restoration targets. In particular, mapping ecosystem energy flows allows organizations to compare the intactness of an ecosystem\u0026rsquo;s contemporary versus historical suite of trophic guilds. Practitioners can then focus on restoring the most energetically depleted guilds across land uses and biomes, to restore ecosystem function and trophic complexity. For example, restoring arboreal birds and primate populations might be prioritized in forests, where seed dispersers consume a large proportion of energy. Seed dispersal impacts carbon sequestration and recovery success\u003csup\u003e40\u003c/sup\u003e, but is significantly depleted outside protected areas. In arid systems, restoring soil-disturbing mammals can be used to avoid over-mechanized soil maintenance techniques harmful to biodiversity\u003csup\u003e42\u003c/sup\u003e. In relatively intact landscapes, practitioners can increase trophic complexity by restoring megafauna, which are crucial for dispersing nutrients and maintaining open ecosystems through herbivory. Across all landscapes, practitioners should seek to restore not only energy flows but also trophic complexity: previous research has found the average ecosystem service is influenced by three trophic guilds\u003csup\u003e17\u003c/sup\u003e. It is also worth investigating whether high energy, ecologically vibrant landscapes are more functionally resilient to human land use change and more amenable to restoration. An energetics approach can reveal ecosystems where historically vibrant energetics coincide with depleted animal populations, which can subsequently be targeted for restoration.\u003c/p\u003e\n\u003cp\u003eWhile human land use change has degraded biodiversity intactness and ecosystem function at the sub-continental scale, some forms of anthropogenic disturbance can amplify animal energy consumption\u003csup\u003e2\u003c/sup\u003e. It is not sufficient to assume that land use change degrades overall ecosystem functionality or individual ecosystem functions. Here, we find that some species and functions do better in rangelands and croplands than in protected areas. Logging has previously been found to amplify vertebrate energy consumption in some forests by increasing vegetation palatability and accessibility to herbivores\u003csup\u003e2\u003c/sup\u003e. Positive relationships between some functions and forms of human disturbance also seem likely in disturbance-dependent grassy ecosystems. The broad patterns revealed in regional and continental scale analyses should be tested and refined through empirical plot-based studies. Researchers can use energetics approaches to quantitatively test which types of disturbance maintain biodiversity intactness, trophic complexity, or ecosystem function.\u003c/p\u003e\n\u003cp\u003eLike all biodiversity metrics, energy flows have a number of caveats in addition to their advantages (see Caveats section of Methods). Relying on energy consumption alone as a metric would not capture the intrinsic value of rare species, or the ecological impacts of species that translate energy into function exceptionally efficiently, or that perform unusual functions not easily classified based on their traits. Moreover, the large-scale approach used here does not capture many kinds of local and regional variation in historical species population densities, limiting its application at local scales.\u003c/p\u003e"},{"header":"Earth System Flows and Planetary Boundaries","content":"\u003cp\u003eFinally, an energetics approach to studying biodiversity loss can advance global biodiversity assessments, including the effort to set regional or planetary boundaries for biosphere integrity. The novel scale of this study, which expands previous plot-based energetics analyses to an area of over 20 million km\u003csup\u003e2\u003c/sup\u003e, allows it to be integrated into global assessments of biodiversity loss. These include assessments currently being undertaken by the Intergovernmental Science Policy Platform on Biodiversity and Ecosystem Services (IPBES)\u003csup\u003e18\u003c/sup\u003e, and under the planetary boundaries framework\u003csup\u003e3\u003c/sup\u003e.\u0026nbsp;It is contested whether a planetary boundary for biodiversity is meaningful because of the heterogeneous, local and spatially disconnected nature of ecological functions\u003csup\u003e45\u003c/sup\u003e; boundaries and thresholds may be more meaningful at local scales. Biodiversity intactness was proposed as a metric for such a planetary boundary\u003csup\u003e21\u003c/sup\u003e, although recent planetary boundaries literature\u003csup\u003e3\u003c/sup\u003e abandoned BII as a metric because of the difficulty of relating it to ecological functions, favoring Human Appropriation of Net Primary Productivity (HANPP) instead. However, it is challenging to see how HANPP relates to actual ecological function. The energetics approach we have outlined here shows a way forward that enables biodiversity and its intactness to be related to ecological vibrancy and a suite of ecosystem functions, whether at local, regional or planetary scales. Moreover, it provides a mechanism for bringing animal activity into the quantitative, mechanistic framework of biosphere modeling and Earth System Science, which to date has been dominated by the ecological functions provided by vegetation and is largely blind to the functions provided or modified by animals.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis analysis has demonstrated the potential of energetics analysis, or ecological vibrancy, to quantify the decline and recovery of ecological functions mediated by birds and mammals. It provides a tool to translate biodiversity into ecosystem function, and provides a different perspective from considering species richness and abundance alone. Our energetics analysis of sub-Saharan Africa highlights the relative importance of keystone species and small species, and the ecological importance of both protected areas and unprotected lands. Like any metric (for example, carbon stock), it should be applied with caution as only one lens on the multifaceted nature and value of biodiversity and ecological function. And energy flow needs to remain coupled with consideration of the number of species contributing to energy flow to fully capture ecological vibrancy and resilience, rather than wrongly label persistence of a few generalist species as ecological intactness. Some future steps could include integrating energy flows into global biodiversity assessments, expanding this energetic analysis to a planetary scale, incorporating domesticated animals within the same framework, extending to the much more data-challenged question of invertebrate energetics, and relating animal energy flows to vegetation structure captured through vegetation plots and through dynamic global vegetation models (DGVMs). We believe the subcontinental-scale analysis presented here presents a significant step forward in the challenge of relating biological richness and intactness to ecological and planetary function.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data on energy flows through each species, trophic guild, and functional group are available in Supplementary Data 1 and 2. Input data on species population densities are available through the TetraDENSITY dataset (https://ecaslab.com/tetradensity-database/). Input data from the biodiversity intactness index are available through the BII4Africa project (https://bii4africa.org/). Input data on species ranges are available through the IUCN red list and Birdlife International. Input data on species traits (i.e. diet, body mass, lifestyle) are available through the EltonTraits database (mammals) and through the Avonet database (birds).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eR Scripts as well as adapted data are available online at the project\u0026rsquo;s Mendeley Data site: https://data.mendeley.com/preview/8j4j85f82c?a=8a584c71-23c8-48ad-b025-c93e27f36de0, or from the corresponding author upon reasonable request ([email protected])\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eT.L. led the analysis, drafted the paper, and conceptualized the visualizations, with input and supervision from Y.M., N.S., and I.O.M., as well as input from H.S.C., L.S., S.T., and J.A.T. Y.M. conceived the analysis, developed the energetics approach, and provided feedback on the analysis, visualization, and structure of the paper. H.S.C. collected and supplied the data on biodiversity intactness and provided input on the designation of land uses. N.S. provided input on the designation of biomes, land uses, and ecosystem functions. I.O.M. guided and provided input on the uncertainty analysis. L.S. collected, modeled, and supplied the data on species population densities. S.T. aggregated data on bird and mammal ecosystem functions and worked on the ecosystem function classification. J.A.T. provided input on the ecosystem function classification as well as on the discussion of bird trophic guilds and bird-performed ecosystem functions. All authors commented on the draft.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References ","content":"\u003col\u003e\n\u003cli\u003eD\u0026iacute;az, S. \u0026amp; Malhi, Y. 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Does the terrestrial biosphere have planetary tipping points? \u003cem\u003eTrends in Ecology \u0026amp; Evolution\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e, 396\u0026ndash;401 (2013). https://doi.org/10.1016/j.tree.2013.01.016 \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Scope\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe geographical scope of the study was sub-Saharan Africa, defined as comprising ecoregions\u003csup\u003e46\u003c/sup\u003e in the Afrotropic realm within continental Africa. We calculated energy flows for the 1088 mammal and 1955 bird species for which data was available, composing 98% of total African species excluding seabirds. Energy flows were calculated independently for each 8x8 km grid cell, the scale at which biodiversity intactness data is available. The study area comprises ~317,000 cells. To assess change over time, energy flows were calculated twice for each cell: once based on estimated historical species abundances in the pre-industrial/pre-colonial Holocene (~1700 CE), and once based on contemporary abundances, given human land use, according to the population changes estimated by the biodiversity intactness index (BII)\u003csup\u003e26\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHistorical Species Abundances\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo determine which bird and mammal species were historically present in each 8x8 km grid cell, we used historical IUCN range maps. For the 11 large mammal species for which historical range maps are not available within the IUCN database, we adapted them from other sources, following Hempson et al\u003csup\u003e47\u003c/sup\u003e (see Supplementary Information). A caveat to this approach is that IUCN range maps are likely to overestimate species abundances, as they do not account for fine-scale habitat heterogeneity. However, available area-of-habitat maps\u003csup\u003e48\u003c/sup\u003e limit species ranges based on anthropogenic land cover, and thus would not adequately predict historical species occupancy.\u003c/p\u003e\n\u003cp\u003eTo calculate historical species abundances, we used published median population density estimates for bird\u003csup\u003e25\u003c/sup\u003e and mammal\u003csup\u003e24\u003c/sup\u003e species. These were modeled as a function of trait, environmental, and phylogenetic predictors, using additive mixed-effect models and Bayesian inference, based on 10,484 empirical records of bird and mammal population densities\u003csup\u003e24,25\u003c/sup\u003e. To estimate species abundances across sub-Saharan Africa, we used mean species population densities\u003csup\u003e2\u003c/sup\u003e. Mean densities were calculated using log-normal distributions based on published median densities and uncertainty intervals. Because population density distributions for most species are left-skewed, mean species population densities are higher than median values for species with wide confidence intervals. Given that ~75% of the global terrestrial surface is modified by humans to some extent\u003csup\u003e49\u003c/sup\u003e the exclusion of non-natural population densities is not realistically possible, and is not necessarily desirable given that hominids have modified African species population densities for millions of years.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContemporary Species Abundances from the Biodiversity Intactness Index\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo estimate contemporary species abundances we multiplied historical abundances by the proportional intactness of each species in each 8x8 km cell under modern land use. We used the intactness values for species under various land uses that are published in the Biodiversity Intactness Index for Africa dataset (BII)\u003csup\u003e19\u003c/sup\u003e. The BII employs a structured expert elicitation process to estimate and validate the proportional changes to species abundances under nine land uses: strict protected areas, near-natural lands, rangelands, intensive croplands, smallholder croplands, tree croplands, timber plantations, dense settlements, and urban areas. The BII allocates each species into one of 17 bird and 76 mammal \u0026ldquo;response groups\u0026rdquo; containing species that respond similarly to land use change. The average impact of each land use class on the abundance of species in each response group was calculated from ~30,000 individual estimates produced by 200 experts on African biodiversity. To map changes in abundance, each cell was assigned a land use class and intensity according to the land use classification outlined in Clements et al in prep\u003csup\u003e26\u003c/sup\u003e. Cells within protected areas and timber plantations were classified categorically, and cells within croplands, rangelands, and settlements were classified and then scaled along a land use intensity gradient. In cases where land use change benefited a species, the intactness of that species was greater than 1, and its abundance increased compared to the historical baseline.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDaily energy expenditure and food uptake\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo calculate ecosystem energy flows, we first calculated the short-term equilibrium rate of food consumption for each species following ref\u003csup\u003e2\u003c/sup\u003e. For each species, daily energy expenditure was calculated from body mass using multi-species allometric equations (See Supplementary Table 1 for equations).\u003csup\u003e32\u003c/sup\u003e Food consumption was calculated from energy expenditure based on published assimilation efficiency values for each food type and taxonomic group of birds and mammals (See Supplementary Table 2). Where available, assimilation efficiency values were assigned at the family level; otherwise they were assigned at the order or class level. Values for the body mass of each species, and for the composition of food types within each species\u0026rsquo; diet, were derived from the Eltontraits database for mammals\u003csup\u003e50\u003c/sup\u003e and from the Avonet database for birds\u003csup\u003e51\u003c/sup\u003e. Energetic food intake was calculated in units of kJ m\u003csup\u003e\u0026minus;2\u003c/sup\u003e year\u003csup\u003e\u0026minus;1\u003c/sup\u003e and then averaged across cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAllocation of species into trophic guilds and functional groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo understand how human land use has altered ecosystem structure, we allocated species into trophic (i.e. feeding) guilds. Each species was allocated into a single trophic guild, to shed light on how an ecosystem\u0026rsquo;s trophic structure, defined as the distribution of energy among guilds, varies between biomes and land uses. Species were allocated into guilds based on their taxonomic class, their diet (e.g. omnivore, carnivore, nectarivore, folivore, frugivore), and their lifestyle (e.g. arboreal, terrestrial). Data on diet and lifestyle was extracted from the Eltontraits database for mammals\u003csup\u003e50\u003c/sup\u003e and from the Avonet database for birds\u003csup\u003e51\u003c/sup\u003e. Throughout the text, herbivore is used as an umbrella term to capture species eating any kind of plant matter, including foliage, seeds and nuts, nectar, and fruit. The terms folivore, granivore, nectarivore, and frugivore are used to refer to these groups independently. In addition, large and small terrestrial herbivores were split according to a published list of African large herbivores\u003csup\u003e47\u003c/sup\u003e to better isolate how the distinctive vulnerability of large herbivores to human activity alters ecosystem structure.\u003c/p\u003e\n\u003cp\u003eTo understand how human land use has altered ecosystem function, we allocated species into 23 functional groups: 11 for birds and 12 for mammals. Species that perform multiple functions were allocated into multiple groups, so that the sum of energy flows through functional groups is greater than the total flow through the ecosystem\u0026rsquo;s birds and mammals. By contrast, the energy flows through guilds sum to the total energy flow through birds and mammals. We adapted a list of 11 bird functions from a published list of major avian ecosystem functions\u003csup\u003e11\u003c/sup\u003e. We added a function for aquatic carnivory and subdivided the invertivory function based on species lifestyle (e.g. insessorial, aerial, terrestrial), as invertivory is performed by over half of all bird species. We sorted birds into functional groups based on their lifestyles and diets (see Extended Data Table. 1 for sorting criteria for both birds and mammals). Unlike for birds, there is a not a single authoritative source on functions performed by mammals. After reviewing the literature we designated twelve mammal functions performed by large herbivores\u003csup\u003e10,29\u003c/sup\u003e, carnivores\u003csup\u003e52,53\u003c/sup\u003e, primates\u003csup\u003e33\u003c/sup\u003e, bats\u003csup\u003e54\u003c/sup\u003e, fossorial mammals\u003csup\u003e42\u003c/sup\u003e, and other small mammals\u003csup\u003e55\u003c/sup\u003e. We sorted mammals into functional groups based on their diet, body mass, and lifestyle. For the grazing and browsing functions performed by large terrestrial herbivores we used published data on the leaf versus grass component of large herbivore diets\u003csup\u003e47\u003c/sup\u003e, and included large, terrestrial, herbivorous primates (\u003cem\u003eGorilla\u003c/em\u003e spp. and \u003cem\u003eTheropithecus gelada\u003c/em\u003e) based on the expert knowledge of the authors. We additionally used published data on herd size\u003csup\u003e47\u003c/sup\u003e to select herbivores that perform a nutrient dispersal function, as herd forming species have a distinctive effect on nutrient distribution within ecosystems\u003csup\u003e10\u003c/sup\u003e. We included in the megafauna impacts function those species that have unique ecological impacts because their large body size frees them from predation\u003csup\u003e29\u003c/sup\u003e. We determined the diet thresholds for each function iteratively, running the species allocation process multiple times and refining thresholds based on the authors\u0026rsquo; expert knowledge. To increase the legibility of our results in the main text, we further aggregated our 23 preliminary functions into 10 aggregate functions, some of which are performed by both birds and mammals (See Extended Data Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparison of energy flows across functions, biomes, and land uses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo calculate energy flows through functions, we summed the energy flows through all species that contribute to each function. This approach weights the contributions of species to associated functions based on species\u0026rsquo; average daily energy consumption. The proportionate contribution of each species to its functions therefore changes depending on whether energy flows are calculated based on historical species abundances or based on present day, human impacted species abundances. Beyond energy flow, we did not scale species-level contributions to functions based on other metrics of functional efficiency: for example, based on pollen deposition rates, seed dispersal distance, or diet proportion. These causes of efficiency vary widely between functions and species\u003csup\u003e11\u003c/sup\u003e and are difficult to measure consistently. To avoid biases, we therefore assumed that all species use energy equally efficiently to perform their associated ecosystem functions. For the analysis, we compared energy flow within specific functions across space and time. It is not meaningful to compare energy flows across ecosystem functions (e.g. predation vs soil disturbance) as how each function uses energy is very different. \u003c/p\u003e\n\u003cp\u003eWe also calculated the average energy flows through functional groups and trophic guilds across biomes and land uses. The biome is commonly proposed as the appropriate unit of analysis for assessing biodiversity trends, because biomes are biologically coherent subunits of the biosphere with structures and functions that respond to land use change in relatively consistent ways\u003csup\u003e9\u003c/sup\u003e. We allocated cells into biomes based on the biome map of the RESOLVE Ecoregions dataset\u003csup\u003e46\u003c/sup\u003e. To allow for broad comparisons between vegetation types, we further aggregated biomes into forests, grassy systems comprising savannas and grasslands, and arid systems comprising deserts and shrublands. For the biomes analysis, we excluded cells falling into the fynbos and thicket biomes, which are not easily classifiable and make up less than 2% of sub-Saharan Africa. We also excluded cells falling into mosaic biomes, as the low accuracy of available continent-scale vegetation maps makes it infeasible to subdivide mosaics into component biomes within the study scope. We calculated average energy flows through each guild and functional group across each of these three aggregated biomes under historical conditions and under modern land use conditions.\u003c/p\u003e\n\u003cp\u003eWe allocated cells into land uses using an adapted version of the 8x8 km resolution African land use map created for the biodiversity intactness index for Africa (Clements et al., in prep)\u003csup\u003e26\u003c/sup\u003e. Following Clements et al., in prep\u003csup\u003e26\u003c/sup\u003e, cells were allocated into four land uses: strict protected areas (IUCN categories I:III); settlements (\u0026gt;20% urban cover or a population density over 1000 per km\u003csup\u003e2\u003c/sup\u003e); croplands (\u0026gt;20% crop cover); and unprotected untransformed land (remaining cells). We calculated average energy flows through each guild and functional group across each of these four land uses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparison of energy flows to biodiversity intactness, and species richness values\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo understand how well biodiversity intactness values predict functional intactness, we related the BII of each cell to the intactness of energy flows through each cell. We related the BII of birds and mammal species to the intactness of total energy flows through bird and mammal species (Fig 4a-b) and to the intactness of energy flows through species in each functional group (Extended data Fig 1). Functional groups with shallower slopes maintain high levels of energy consumption as biodiversity intactness declines, and were deemed more resilient to human impacts. We also related total energy flows to native bird and mammal species richness, to understand the extent to which high-energy keystone species versus rich communities of species drive ecosystem function (Fig 4c-d). We analyzed these relationships across all cells using linear regression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUncertainty Calculation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFollowing ref.\u003csup\u003e2\u003c/sup\u003e, we quantified uncertainty in our estimates of energetic intake by running 10,000 Monte Carlo simulations of energy flow through animal species and groups. For each simulation, we replaced the values in our original calculations with values drawn from random distributions. We assumed there was uncertainty in the following variables: species body mass, population density, daily energy expenditure equation (DEE), assimilation efficiency, and fractional diet composition of each species. Following ref.\u003csup\u003e19\u003c/sup\u003e, we also assumed there was uncertainty in the estimated intactness of each species in each land use.\u003c/p\u003e\n\u003cp\u003eFor body mass, we drew values from a truncated normal distribution (lower bound = 1g) in which the mean was published mean body mass\u003csup\u003e50,51\u003c/sup\u003e and standard deviation was 15% as described in ref.\u003csup\u003e2\u003c/sup\u003e For population densities, we drew from a log normal distribution, using mean and uncertainty values for each species published in refs.\u003csup\u003e24,25\u003c/sup\u003e. For DEE, we estimated the 95% confidence intervals following the methods described in ref.\u003csup\u003e32\u003c/sup\u003e. For assimilation efficiency, we drew from a random beta distribution using the mean and standard deviation by taxonomic group and food type in the literature (Supplementary Table 1). For diet composition, we drew from a symmetrical beta distribution with uncertainty parameters assigned following ref.\u003csup\u003e2\u003c/sup\u003e. For the proportional intactness of species abundances in each land use, we drew from a random beta distribution using the mean intactness values and standard deviations published in ref.\u003csup\u003e19\u003c/sup\u003e. Intactness values were previously validated in ref.\u003csup\u003e19\u003c/sup\u003e through a structured expert elicitation process.\u003c/p\u003e\n\u003cp\u003eThe uncertainty in each of these variables captures the natural variability occurring within species among individuals and groups, as well as ecologists\u0026rsquo; uncertainty about mean values. For example, the population density of a given species will naturally vary geographically based on habitat suitability, resource availability, and competition. But there is also absolute uncertainty about the mean species population density of each species based on limitations on empirical data and model accuracy. This division of uncertainty into geographic and absolute components is true of the other variables as well. The uncertainty derived from natural variability decreases as there is an increasing number of analyzed landscapes in which the species occurs. We assumed that half the uncertainty in species energy flow in a given landscape is from natural variability and that half is from absolute uncertainty about mean values, which does not decline as geographic area increases.\u003c/p\u003e\n\u003cp\u003eTo account for the effects of area in our uncertainty estimates, we grouped species-level energy flows into 1˚ grid squares (~12,000 km\u003csup\u003e2\u003c/sup\u003e at the equator) following ref.\u003csup\u003e47\u003c/sup\u003e. We treated uncertainty about natural variability as independent in each 1˚ square in which a given species occurs and drew from independent distributions in each square. For each species, we calculated range-wide spatial means of energy flow for each of the 10,000 Monte Carlo simulations, and then propagated this area-scaled uncertainty into the absolute uncertainty about mean energy flow values generated from the area-independent Monte Carlo simulation estimates. We estimated total uncertainty by assuming uncertainty in all variables simultaneously, and calculated the 2.5\u003csup\u003eth\u003c/sup\u003e and 97.5\u003csup\u003eth\u003c/sup\u003e confidence intervals to derive 95\u003csup\u003eth\u003c/sup\u003e confidence intervals for our estimates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCaveats\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere are a number of caveats in our analysis. The approach uses range-wide average species population densities. However, due to the large number (~3000) of species included we were unable to model geographic variability in population densities within species. For the vast majority of species, there is insufficient data to predict how population densities respond to environmental gradients. In addition, population densities vary inconsistently along environmental gradients across species. Because the density estimates used here are average densities, they do not account for intra-specific competition. It is expected that species reach higher densities when competitors are missing. The approach is thus likely to overestimate energy flows through species-rich guilds in species-rich cells. Because the analysis relies on coarse-scale IUCN range maps to predict historical species ranges, it is also likely to overestimate species abundances for species restricted to specialized habitat. Energy flows through colonial species including many fossorial rodents and water birds may be overstated. While these caveats may cause the analysis to overestimate absolute energy flows, they are less likely to create biases when comparing variation within functional groups across biomes and land uses, the core aim of the study. There is also insufficient data to model whether biome or land use causes intra-specific variation in species trait data such as diet and body mass, although we accounted for the possibility of such variability in the uncertainty analysis. There are additional caveats about the BII input data. The BII estimates species responses to land use change as a function of land use, averaging responses from experts in different countries and regions. Consequently, the BII does not account for how national political factors impact species abundances. These factors include war, protected area management capacity, wildlife legislation, and cultural differences about hunting. 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Effects of mammal defaunation on natural ecosystem services and human well being throughout the entire Neotropical realm. \u003cem\u003eEcosystem Services\u003c/em\u003e \u003cstrong\u003e45\u003c/strong\u003e, 101173 (2020). https://doi.org/10.1016/j.ecoser.2020.101173 \u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3844832/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3844832/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"A key challenge for ecological science is to understand how biodiversity loss is changing ecosystem structure and function at scales relevant for policy1. Almost all biodiversity metrics are challenging to disaggregate into ecosystem functions, in particular animal-mediated functions such as pollination, seed and nutrient dispersal, and predation. Here, we adopt an ecosystem energetics approach2 as a physically meaningful method of translating animal species composition into a suite of ecosystem functions. We quantify historical changes to energy flows through mammal- and bird-mediated ecosystem functions across sub-Saharan Africa. In total, trophic energy flows have decreased by over one-third, with functions performed by megafauna in particular collapsing outside protected areas. The pattern of decreasing function varies by biome, driven by arboreal birds and primates in forests, terrestrial herbivores in grassy systems, and burrowing mammals in arid systems. Compared to other biodiversity metrics, an energetics approach highlights the ecological importance of smaller animals and keystone species. The results can help practitioners conserve and restore functionally diverse, energetically intact ecosystems across land uses and biomes. By relating biodiversity intactness to energy and material flows, ecosystem energetics can also advance efforts to set local, regional or planetary boundaries3 for biodiversity loss.","manuscriptTitle":"Energy flows reveal declining ecosystem functions by animals across Africa","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-06 09:20:29","doi":"10.21203/rs.3.rs-3844832/v1","editorialEvents":[],"status":"published","journal":{"display":false,"email":"[email protected]","identity":"nature","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"nature","sideBox":"Learn more about [Nature](http://www.nature.com/nature/)","snPcode":"","submissionUrl":"","title":"Nature","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"6dadcdb2-c0a3-4257-b807-c9a3eba09abb","owner":[],"postedDate":"February 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":28570225,"name":"Earth and environmental sciences/Ecology/Macroecology"},{"id":28570226,"name":"Earth and environmental sciences/Ecology/Biodiversity"},{"id":28570227,"name":"Earth and environmental sciences/Ecology/Ecosystem ecology"},{"id":28570228,"name":"Biological sciences/Ecology/Conservation biology"}],"tags":[],"updatedAt":"2025-10-30T07:11:20+00:00","versionOfRecord":{"articleIdentity":"rs-3844832","link":"https://doi.org/10.1038/s41586-025-09660-1","journal":{"identity":"nature","isVorOnly":false,"title":"Nature"},"publishedOn":"2025-10-29 04:00:00","publishedOnDateReadable":"October 29th, 2025"},"versionCreatedAt":"2024-02-06 09:20:29","video":"","vorDoi":"10.1038/s41586-025-09660-1","vorDoiUrl":"https://doi.org/10.1038/s41586-025-09660-1","workflowStages":[]},"version":"v1","identity":"rs-3844832","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3844832","identity":"rs-3844832","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}