Ecology

Hallmann, C. A. et al. More than 75 percent decline over 27 years in total flying insect biomass in protected areas. PLoS ONE 12, e0185809 (2017).PubMed 
PubMed Central 

Google Scholar 
Lister, B. C. & Garcia, A. Climate-driven declines in arthropod abundance restructure a rainforest food web. Proc. Natl. Acad. Sci. 115, E10397–E10406 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Cardoso, P. et al. Scientists’ warning to humanity on insect extinctions. Biol. Conserv. 242, 108426 (2020).
Google Scholar 
Habel, J. C., Samways, M. J. & Schmitt, T. Mitigating the precipitous decline of terrestrial European insects: Requirements for a new strategy. Biodivers. Conserv. 28, 1343–1360 (2019).
Google Scholar 
Brühl, C. A. & Zaller, J. G. Biodiversity decline as a consequence of an inappropriate environmental risk assessment of pesticides. Front. Environ. Sci. 7, 2013–2016 (2019).
Google Scholar 
Seastedt, T. R. & Crossley, D. A. The influence of arthropods on ecosystems. Bioscience 34, 157–161 (1984).
Google Scholar 
Brussaard, L. et al. Biodiversity and ecosystem functioning in soil. Ambio 26, 563–570 (1997).
Google Scholar 
Symondson, W. O. C., Sunderland, K. D. & Greenstone, M. H. Can generalist predators be effective biocontrol agents?. Annu. Rev. Entomol. 47, 561–594 (2002).CAS 
PubMed 

Google Scholar 
Goulson, D. The insect apocalypse, and why it matters. Curr. Biol. 29, R967–R971 (2019).CAS 
PubMed 

Google Scholar 
Kremen, C. et al. Pollination and other ecosystem services produced by mobile organisms: A conceptual framework for the effects of land-use change. Ecol. Lett. 10, 299–314 (2007).PubMed 

Google Scholar 
Schowalter, T. D., Noriega, J. A. & Tscharntke, T. Insect effects on ecosystem services: Introduction. Basic Appl. Ecol. 26, 1–7 (2018).
Google Scholar 
Dangles, O. & Casas, J. Ecosystem services provided by insects for achieving sustainable development goals. Ecosyst. Serv. 35, 109–115 (2019).
Google Scholar 
van der Sluijs, J. P. Insect decline, an emerging global environmental risk. Curr. Opin. Environ. Sustain. 46, 39–42 (2020).
Google Scholar 
Metcalfe, H., Hassall, K. L., Boinot, S. & Storkey, J. The contribution of spatial mass effects to plant diversity in arable fields. J. Appl. Ecol. 56, 1560–1574 (2019).PubMed 
PubMed Central 

Google Scholar 
Winter, S. et al. Effects of vegetation management intensity on biodiversity and ecosystem services in vineyards: A meta-analysis. J. Appl. Ecol. 55, 2484–2495 (2018).PubMed 
PubMed Central 

Google Scholar 
Blaise, C. et al. The key role of inter-row vegetation and ants on predation in Mediterranean organic vineyards. Agric. Ecosyst. Environ. 311, 107237 (2021).
Google Scholar 
Hoffmann, C. et al. Can flowering greencover crops promote biological control in German vineyards?. Insects 8, 121 (2017).PubMed Central 

Google Scholar 
Eckert, M., Mathulwe, L. L., Gaigher, R., der Merwe, L. J. & Pryke, J. S. Native cover crops enhance arthropod diversity in vineyards of the Cape Floristic Region. J. Insect Conserv. 24, 133–149 (2019).
Google Scholar 
Sáenz-Romo, M. G. et al. Ground cover management in a Mediterranean vineyard: Impact on insect abundance and diversity. Agric. Ecosyst. Environ. 283, 106571 (2019).
Google Scholar 
Capó-Bauçà, S., Marqués, A., Llopis-Vidal, N., Bota, J. & Baraza, E. Long-term establishment of natural green cover provides agroecosystem services by improving soil quality in a Mediterranean vineyard. Ecol. Eng. 127, 285–291 (2019).
Google Scholar 
Garcia, L. et al. Management of service crops for the provision of ecosystem services in vineyards: A review. Agric. Ecosyst. Environ. 251, 158–170 (2018).
Google Scholar 
Nicholls, C. I., Altieri, M. A. & Ponti, L. Enhancing plant diversity for improved insect pest management in Northern California organic vineyards. Acta Hortic. 785, 263–278 (2008).
Google Scholar 
Franin, K., Barić, B. & Kuštera, G. The role of ecological infrastructure on beneficial arthropods in vineyards. Spanish J. Agric. Res. 14, e303 (2016).
Google Scholar 
Shapira, I. et al. Habitat use by crop pests and natural enemies in a Mediterranean vineyard agroecosystem. Agric. Ecosyst. Environ. 267, 109–118 (2018).
Google Scholar 
Judt, C. et al. Diverging effects of landscape factors and inter-row management on the abundance of beneficial and herbivorous arthropods in andalusian vineyards (Spain). Insects 10, 320 (2019).PubMed Central 

Google Scholar 
Geldenhuys, M., Gaigher, R., Pryke, J. S. & Samways, M. J. Diverse herbaceous cover crops promote vineyard arthropod diversity across different management regimes. Agric. Ecosyst. Environ. 307, 107222 (2021).CAS 

Google Scholar 
Medail, F. & Quezel, P. Biodiversity hotspots in the Mediterranean Basin: Setting global conservation priorities. Conserv. Biol. https://doi.org/10.1046/j.1523-1739.1999.98467.x (1999).Article 

Google Scholar 
Carrère, P. La structure du vignoble du Vaucluse. Etudes Conjonct. 9, 931–949 (1957).
Google Scholar 
Nentwig, W. et al. Spiders of Europe. (2020). www.araneae.nmbe.ch.Tronquet, M. Catalogue des coléoptères de France. Rev. l’Assoc. Roussillonnaise d’Entomol. 23, 1–10 (2014).
Google Scholar 
Rosseel, Y. Lavaan: An R package for structural equation modeling. J. Stat. Softw. 48, 2 (2012).
Google Scholar 
Grace, J. B. Structural equation modeling and natural systems. Struct. Equ. Model. Nat. Syst. https://doi.org/10.1017/CBO9780511617799 (2006).Article 

Google Scholar 
Fiera, C. et al. Effects of vineyard inter-row management on the diversity and abundance of plants and surface-dwelling invertebrates in Central Romania. J. Insect Conserv. 24, 175–185 (2020).PubMed 
PubMed Central 

Google Scholar 
de Pedro, L., Perera-Fernández, L. G., López-Gallego, E., Pérez-Marcos, M. & Sanchez, J. A. The effect of cover crops on the ciodiversity and abundance of ground-dwelling arthropods in a Mediterranean pear orchard. Agrono 10, 580 (2020).
Google Scholar 
Ebeling, A. et al. Plant diversity impacts decomposition and herbivory via changes in aboveground arthropods. PLoS ONE 9, e106529 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Cobb, T. P., Langor, D. W. & Spence, J. R. Biodiversity and multiple disturbances: Boreal forest ground beetle (Coleoptera: Carabidae) responses to wildfire, harvesting, and herbicide. Can. J. For. Res. 37, 1310–1323 (2007).
Google Scholar 
Hendrickx, F. et al. How landscape structure, land-use intensity and habitat diversity affect components of total arthropod diversity in agricultural landscapes. J. Appl. Ecol. 44, 340–351 (2007).
Google Scholar 
Melbourne, B. A. Bias in the effect of habitat structure on pitfall traps: An experimental evaluation. Aust. J. Ecol. 24, 228–239 (1999).
Google Scholar 
Welti, E. A. R., Prather, R. M., Sanders, N. J., de Beurs, K. M. & Kaspari, M. Bottom-up when it is not top-down: Predators and plants control biomass of grassland arthropods. J. Anim. Ecol. 89, 1286–1294 (2020).PubMed 

Google Scholar 
Gonçalves, F. et al. Do soil management practices affect the activity density, diversity, and stability of soil arthropods in vineyards?. Agric. Ecosyst. Environ. 294, 106863 (2020).
Google Scholar 
Muscas, E. et al. Effects of vineyard floor cover crops on grapevine vigor, yield, and fruit quality, and the development of the vine mealybug under a Mediterranean climate. Agric. Ecosyst. Environ. 237, 203–212 (2017).
Google Scholar 
Nicholls, C. I., Parrella, M. P. & Altieri, M. A. Reducing the abundance of leafhoppers and thrips in a northern California organic vineyard through maintenance of full season floral diversity with summer cover crops. Agric. For. Entomol. 2, 107–113 (2000).
Google Scholar 
Vogelweith, F. & Thiéry, D. Cover crop differentially affects arthropods, but not diseases, occurring on grape leaves in vineyards. Aust. J. Grape Wine Res. 23, 426–431 (2017).
Google Scholar 
Hanna, R., Zalom, F. G. & Roltsch, W. J. Relative impact of spider predation and cover crop on population dynamics of Erythroneura variabilis in a raisin grape vineyard. Entomol. Exp. Appl. 107, 177–191 (2003).
Google Scholar 
Burgio, G. et al. Habitat management of organic vineyard in Northern Italy: the role of cover plants management on arthropod functional biodiversity. Bull. Entomol. Res. 106, 759–768 (2016).CAS 
PubMed 

Google Scholar 
Wisniewska, J. & Prokopy, R. Do spiders (Araneae) feed on rose leafhopper (Edwardsiana rosae; Auchenorrhyncha: Cicadellidae) pests of apple trees? (2013).Malumbres-Olarte, J., Vink, C. J., Ross, J. G., Cruickshank, R. H. & Paterson, A. M. The role of habitat complexity on spider communities in native alpine grasslands of New Zealand. Insect Conserv. Divers. 6, 124–134 (2013).
Google Scholar 
Wilson, H. et al. Summer flowering cover crops support wild bees in vineyards. Environ. Entomol. 47, 63–69 (2018).PubMed 

Google Scholar 
Kratschmer, S. et al. Tillage intensity or landscape features: What matters most for wild bee diversity in vineyards?. Agric. Ecosyst. Environ. 266, 142–152 (2018).
Google Scholar 
Gardarin, A., Pigot, J. & Valantin-Morison, M. The hump-shaped effect of plant functional diversity on the biological control of a multi-species pest community. Sci. Rep. 11, 1–14 (2021).
Google Scholar 
Serra, G., Lentini, A., Verdinelli, M. & Delrio, G. Effects of cover crop management on grape pests in a Mediterranean environment. IOBC/WPRS Bull. (2006).Sáenz-Romo, M. G. et al. Effects of ground cover management on insect predators and pests in a Mediterranean vineyard. Insects 10, 421 (2019).PubMed Central 

Google Scholar 
Barry, J. P., Baxter, C. H., Sagarin, R. D. & Gilman, S. E. Climate-related, long-term faunal changes in a California rocky intertidal community. Science 267, 672–675 (1995).ADS 
CAS 
PubMed 

Google Scholar 
Ewald, J. A. et al. Influences of extreme weather, climate and pesticide use on invertebrates in cereal fields over 42 years. Glob. Chang. Biol. 21, 3931–3950 (2015).ADS 
PubMed 

Google Scholar 
Celette, F., Findeling, A. & Gary, C. Competition for nitrogen in an unfertilized intercropping system: The case of an association of grapevine and grass cover in a Mediterranean climate. Eur. J. Agron. https://doi.org/10.1016/j.eja.2008.07.003 (2009).Article 

Google Scholar 
Ruiz-Colmenero, M., Bienes, R. & Marques, M. J. Soil and water conservation dilemmas associated with the use of green cover in steep vineyards. Soil Tillage Res. 117, 211–223 (2011).
Google Scholar  More

Middle and Late Pleistocene climates of MSA occupationsWe first examined MSA occupations (n = 84, Fig. 2) spanning the Middle to Late Pleistocene using simulated climate data (see Methods). We extracted mean annual temperature (bio01) and total annual precipitation (bio12) values from the climate model33 within a 50 km radii, centred on the occupation’s mid-age date range rounded to the nearest 1000 year (kyr) time slice, to characterise environments across the wider logistical landscape (following Blinkhorn and Grove10,11). The climatic conditions for each occupation can be found in Supplementary Table S1 and are illustrated in Fig. 1.Figure 2Distribution of the eastern African Middle Stone Age occupations studied. This map was created in ArcGIS 10.5 using an SRTM (NASA).Full size imageWe found that average temperatures at eastern African MSA occupations varied between 9 °C and 25 °C, with 59 occupations falling within the 68% confidence interval of 14–23 °C. The warmest environments occupied were found in coastal regions, such as Abdur along the Red Sea coast of modern-day Eritrea (25 °C) and Panga Ya Saidi situated on the Kenyan coast (24 °C), as well as in the Lower Omo Valley of southwestern Ethiopia (24–23 °C). These hot environments were inhabited during MIS 5 and MIS 7. On the other hand, the coldest environments inhabited were at high altitude, at Fincha Habera in the Bale Mountains of southern Ethiopia (9–10 °C) and at Kenyan Rift Valley occupations of Marmonet Drift (10–14 °C) and Enkapune ya Muto (13 °C), most of which date to MIS 3. Average precipitation levels experienced by Middle to Late Pleistocene MSA populations in eastern Africa ranged between 396 and 1593 mm, with 59 occupations falling within the 68% confidence interval of 620-1150 mm, corresponding to the precipitation bracket of sub-humid landscapes. The wettest habitats were located on islands within and along the shore of Lake Victoria at Rusinga Nyamtia (1593 mm) and Karungu (1374-1499 mm) in MIS 3 and 5, as well as within the Ethiopian Rift Valley at Gademotta (1368 mm), the Ethiopian Highlands at Mochena Borango (1270-1297 mm), and the Kenyan Rift Valley at Marmonet Drift (1173-1368 mm) in MIS 3, 5 and 7. On the other hand, the driest occupations occurred at Laas Geel in Somaliland during MIS 3 (396 mm) as well as within the Lower Omo Valley (534-582 mm) during MIS 5 and 7.Classifying biomes and ecotones at MSA occupationsWe then used the modelled biome dataset (biome4output)33 to classify the local ecology of each MSA occupation within a 50 km radius. We found that 38% of the occupations (n = 32) had access to only tropical xerophytic shrubland within their logistical landscape (see Fig. 3. for modern examples of this biome), and a further 42% with this biome among others within a 50 km radius (n = 35). Tropical xerophytic shrubland was persistently occupied throughout the Middle to Late Pleistocene (Fig. 1), and whilst it was the most prevalent biome type available, representing 61.9% of the biomes present during occupational phases across the region (Supplementary Fig. S2 and Table S2), eastern African MSA adaptive systems were likely specialised for engagement with tropical xerophytic shrubland, and its modulation may therefore have influenced patterns of Middle to Late Pleistocene human distribution. Nonetheless, the proportion of occupations with access to tropical xerophytic shrubland was significantly higher using a 2-sample proportion test than the proportion of the biome available across the region throughout MSA occupational phases (Z-value = 3.38, p-value = 0.0007; Supplementary Table S2), suggesting preferential occupation of tropical xerophytic shrubland and emphasising it as an important ecosystem for MSA populations.Figure 3Examples of xerophytic shrubland environments in modern eastern Africa, including typical species (sp.). (A) Acacia tortilis (B) Commiphora sp. (C) Acacia sp. and Duosperma eremophilum. (D) Hyphaene compressa, Acacia sp., Salvadora persica, Cyperacea and Lawsonia inermis (E) Acacia sp. and Duosperma eremophilum, (F) Acacia tortilis (background: Commiphora sp. Capparaceae sp. Tephrosia sp. and Indigofera spinosa).Full size imageIn total, 57% of the occupations had a logistical landscape falling on the boundary between multiple biomes (n = 48; Supplementary Table S1). The majority of these ecotonal sites are situated between ‘open’ and ‘closed’ biome types, supporting the assertion of Basell9 that access to wooded ecologies was vital for MSA populations. Forest biomes made up relatively low proportions of the available environments available throughout the Middle to Late Pleistocene; however, importantly, we found the proportions of forest biomes occupied by MSA occupations to be significantly higher than would be expected based on the prevalence of these biomes, especially in MIS 3 and MIS 7 (see Supplementary Fig. S2 and Table S2), supporting the contention that MSA hominins preferred the rarer habitats that were near to woods and forests. The most common ecotone occupied during the eastern African MSA was that between tropical xerophytic shrubland and temperate conifer forest, which is seen as far north as Goda Buticha in southeastern Ethiopia, and as far south as Mumba in Tanzania. However, the region to the east of Lake Victoria shows the most intense occupation of this ecotone, the boundary of which fluctuates through time and space (Supplementary Table S1).We found that MIS 7 saw the preferential occupation of closed ecotones between temperate conifer forest and warm mixed forest, as well as tropical xerophytic shrubland and associated ecotones which are generally occupied throughout the period. MIS 5 saw a slight increase in habitat diversity, though expansions primarily involved the tracking of tropical xerophytic shrubland environments (as shown by all occupations in MIS 5 having access to this biome within 50 km) with exposure to new ecotones occurring at the peripheries. This can be seen at occupations distributed widely across the region; for example, certain occupations at Omo would have involved engagement with deserts alongside tropical xerophytic shrubland, whereas some MSA populations at Panga Ya Saidi had access to tropical deciduous forest and tropical savannah environments within their logistical landscape. MIS 3 saw the greatest variety in the ecologies occupied, where expansions can be seen into new and previously uninhabited environments, such as steppe tundra and warm mixed forest, with a distinct emphasis on temperate conifer forest rather than tropical xerophytic shrubland. Importantly, a chi-square test revealed that the relative proportions of biomes in the region do not differ significantly between the Marine Isotope Stages (χ2 = 9.07, p-value = 0.99), strongly suggesting that variation in the environments occupied through time reflects a shift in preference as opposed to fluctuation in the underlying ecology (see Supplementary Table S2).Characterising MSA environments throughout the Middle to Late PleistoceneWe used cluster analyses to group the occupations based on their climatic values to assess patterns in habitat choice. To do this, we scaled and combined the temperature and precipitation data and employed an automated clustering algorithm (the average silhouette method) to ascertain the optimal number (k) of clusters in the data. The algorithm found ten clusters to represent the best division of the data (Fig. 4, Supplementary Fig. S1).Figure 4Hierarchical clustering of the occupations according to mean annual temperature and total annual precipitation. K means clustering identified ten clusters as the optimal division of the dendrogram, which have been highlighted here as well as the range of environmental conditions occupied by each cluster and the percentage of cells within 50 km of that biome for all occupations within that cluster.Full size imageMost of the occupations (n = 45) fall within warm to temperate sub-humid clusters (2,4,5 and 7) with a broad temperature range of 13–19 °C and a precipitation range of 613-1297 mm. These clusters are dominated by tropical xerophytic shrubland and temperate conifer forest environments and their ecotones. We found that only two clusters (8,9) did not include occupations with access to tropical xerophytic shrubland, indicating that this biome was present across a large portion of the MSA climatic range, except at the coldest extreme. We found that the coldest cluster, cluster 9 (temperature range 9–10 °C), was the most ecotonal, with all occupations situated at high altitude where populations would have had access to steppe tundra, temperate conifer forest, temperate sclerophyll woodland and warm mixed forest, the complex topography allowing diverse biomes to appear closer together than is usually possible34. Extremely humid occupations from around Lake Victoria (Karungu and Rusinga Nyamita) formed cluster 10 (1374-1593 mm precipitation). These occupations have moderate temperatures (16–18 °C) and occupy an ecotone between tropical xerophytic shrubland and temperate conifer forest. Panga Ya Saidi and Laas Geel form their own respective clusters (3 and 6) due to their distinctively hot temperatures; however, at Panga Ya Saidi, this is coupled with moist sub-humid conditions and a diverse tropical environment (24 °C, 996-1153 mm), whereas Laas Geel possesses the lowest annual precipitation of all the occupations (18 °C, 396 mm), making its hot-dry environment unique for the eastern African MSA. However, the occupation at Laas Geel falls within the tropical xerophytic shrubland biome, with access to some open conifer woodland within 50 km, suggesting that whilst occupying a climatic extreme, this distinct habitat represents an extension of the types of environments that eastern African MSA populations were already well-adapted to.Phased habitability modelsWe used the precipitation and temperature data from the occupations as the parameters to produce phased ‘habitability’ models for the more abundantly populated interglacial phases of the MSA, demonstrating the extent of the landscape that experienced comparable climatic settings to occupations dated within that period. The climatic range produced by each phased subset was projected throughout every 1000-year time interval for that MIS, and then the percentage of ‘habitable’ cells (i.e., cells that remain within that climatic range) was calculated to identify areas that were persistently habitable, as well as the geographic range and temporal scope of impersistent habitable landscapes.Figure 5 demonstrates the temperature, precipitation, and combined habitability models for each phase. MIS 9 shows the most limited habitable zone out of the interglacial phases, however the lower number of occupations available to construct the distribution likely has impacted the construction of the models. MIS 7 marks a period of expansion, with the region surrounding Lake Victoria and the Eastern Rift Valley Lakes and the Ethiopian Highlands showing the most persistent habitability across the region. For temperature, large areas of the Horn and modern-day Sudan show less persistent habitability (ca. 40–50% cells falling within the temperature range of 12–23 °C seen at MIS 7 occupations), with pockets of unsuitability along the coast of the Baab el Mandeb and the border between modern-day Ethiopian and Somalia. However, arid zones of the southern Sahara are completely uninhabitable in terms of precipitation (0% of cells fall within the precipitation range of 582-1368 mm at MIS 7 occupations), as is the tip of the Horn. Precipitation is thus the limiting factor when considering habitability for MIS 7, as the area deemed habitable in terms of precipitation is more geographically restricted than that derived from temperature. MIS 5 sees the largest increase in habitable area for temperature, with all cells showing temperature values within the MIS 5 occupation range of 13–25 °C for at least 60% of the period. Precipitation habitability, that we considered here to be ranging between 554-1385 mm, is however more fragmented, with pockets of uninhabitability forming around the northeast edge of Lake Victoria, in the region to the south of Lake Tana, and within modern-day Tanzania. Like MIS 7, this means that habitability is limited by precipitation in MIS 5. However, the habitability models for MIS 3 demonstrates the opposite pattern. Temperature habitability, defined as between 9–19 °C by the sites dating to MIS 3, shows the most restricted distribution of all the models, with habitable areas concentrated to the areas around Lake Victoria and the Ethiopian highlands, which are linked towards the southeast of Lake Turkana. Yet, MIS 3 shows the most persistent and widely distributed zone of habitability for precipitation, where much of eastern Africa, except towards the Sahara and the very tip of the Horn of Africa, remains persistently within the range of precipitation values experienced by MIS3 occupations (396-1593 mm). Overall, these models propose that interglacial MSA occupations, especially in MIS 5, may have been much more spatially diverse than presently known, however we note that these distributions are based purely on climatic data and ignore the potential effects of volcanic eruptions and subsequent ashfalls that have also been argued to have conditioned habitability in this region9.Figures 5Mean annual temperature (top), total annual precipitation (middle) and combined (bottom) phased models of habitability, demonstrating the percentage of time intervals (1000 years per interval) that remain within the climatic range of the occupations dated to that Marine Isotope Stage (MIS). The palaeocoastline has been estimated based on the predicted mean sea-level for each MIS.Full size imageFigures 6Scatterplots of the Mantel test results (Table 1, Supplementary Table S4–S5) between the pairwise distance matrix of toolkit composition (top) and raw material use (bottom) and the other distances matrices excluding the two binary variables, site type and method.Full size imageExploring the relationship between climate and Middle Stone Age occupationsWe then examined the extent to which patterns of variability in stone tool assemblage composition and raw material use correlated with environmental conditions within a 50 km radius at the mid-age of occupation of each assemblage, as well as a suite of other variables recorded by Blinkhorn and Grove11 (see Methods and Supplementary Methods S1 details). Figure 6 demonstrates the relationships between these variables and toolkit composition and raw material use, revealed using simple Mantel tests (Table 1 and Supplementary Table S4–S5). We found that MSA assemblage composition was correlated with differences in both mean annual temperature (adj. p = 0.001; Table 1) and total annual precipitation (adj. p = 0.003; Table 1), and raw material use also shows statistically significant relationships with both mean annual temperature (adj. p = 0.001; Table 1) and total annual precipitation (adj. p = 0.003; Table 1). With the use of Pleistocene climate models at high temporal resolutions, these results refine the findings of Blinkhorn and Grove11, which relied on comparisons of the climatic extremes of the LGM and LIG.Table 1 Simple Mantel test results for the effects of precipitation and temperature on toolkit composition and raw material. Statistical significance highlighted at p  More

ACIA (2004) Impacts of a Warming Arctic: Arctic Climate Impact Assessment. Cambridge University Press, Cambridge, UK
Google Scholar 
Alsos IG, Ehrich D, Thuiller W, Eidesen PB, Tribsch A, Schonswetter P et al. (2012) Genetic consequences of climate change for northern plants. Proc R Soc B-Biol Sci 279:2042–2051
Google Scholar 
Andrews KR, Good JM, Miller MR, Luikart G, Hohenlohe PA (2016) Harnessing the power of RADseq for ecological and evolutionary genomics. Nat Rev Genet 17:81–92CAS 
PubMed 
PubMed Central 

Google Scholar 
Archer FI, Adams PE, Schneiders BB (2017) strataG: an R package for manipulating, summarizing and analysing population genetic data. Mol Ecol Resour 17:5–11CAS 
PubMed 

Google Scholar 
Arenas M, Ray N, Currat M, Excoffier L (2011) Consequences of range contractions and range shifts on molecular diversity. Mol Biol Evol 29:207–218PubMed 

Google Scholar 
Beaumont MA (1999) Detecting population expansion and decline using microsatellites. Genetics 153:2013–2029CAS 
PubMed 
PubMed Central 

Google Scholar 
Benjamini Y, Hochberg Y (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Royal Statistical Soc Series B 57:289–300BirdLife International (2018). Pagophila eburnea. The IUCN Red List of Threatened Species 2018: e.T22694473A132555020. https://doi.org/10.2305/IUCN.UK.2018-2.RLTS.T22694473A132555020.en. Downloaded on11 June 2020Boertmann D, Petersen IK, Nielsen HH (2020) Ivory Gull population status in Greenland 2019. Dan Orn Foren Tidsskr 114:141–150
Google Scholar 
Boitard S, Rodríguez W, Jay F, Mona S, Austerlitz F (2016) Inferring population size history from large samples of genome-wide molecular data – an approximate Bayesian computation approach. PLOS Genet 12:1–36
Google Scholar 
Box JE, Colgan WT, Christensen TR, Schmidt NM, Lund M, Parmentier F-JW et al. (2019) Key indicators of Arctic climate change: 1971–2017. Environ Res Lett 14:045010CAS 

Google Scholar 
Braune BM, Mallory ML, Gilchrist HG (2006) Elevated mercury levels in a declining population of ivory gulls in the Canadian Arctic. Mar Pollut Bull 52:978–982CAS 
PubMed 

Google Scholar 
Broquet T, Angelone S, Jaquiéry J, Joly P, Léna JP, Lengagne T et al. (2010) Genetic bottlenecks driven by population disconnection. Conserv Biol 24:1596–1605PubMed 

Google Scholar 
Chen IC, Hill JK, Ohlemüller R, Roy DB, Thomas CD (2011) Rapid range shifts of species associated with high levels of climate warming. Science 333:1024–1026CAS 
PubMed 

Google Scholar 
Chikhi L, Sousa VC, Luisi P, Goossens B, Beaumont MA (2010) The confounding effects of population structure, genetic diversity and the sampling scheme on the detection and quantification of population size changes. Genetics 186:983–995PubMed 
PubMed Central 

Google Scholar 
Collevatti RG, Nabout JC, Diniz-Filho JAF (2011) Range shift and loss of genetic diversity under climate change in Caryocar brasiliense, a Neotropical tree species. Tree Genet Genomes 7:1237–1247
Google Scholar 
Cornuet JM, Luikart G (1996) Description and power analysis of two tests for detecting recent population bottlenecks from allele frequency data. Genetics 144:2001–2014CAS 
PubMed 
PubMed Central 

Google Scholar 
Cotto O, Schmid M, Guillaume F (2020) Nemo-age: spatially explicit simulations of eco-evolutionary dynamics in stage-structured populations under changing environments. Methods Ecol Evol 11:1227–1236
Google Scholar 
Cubaynes S, Doherty PF, Schreiber EA, Gimenez O (2011) To breed or not to breed: a seabird’s response to extreme climatic events. Biol Lett 7:303–306PubMed 

Google Scholar 
Di Rienzo A, Peterson AC, Garza JC, Valdes AM, Slatkin M, Freimer NB (1994). Mutational processes of simple-sequence repeat loci in human populations. Proc Natl Acad Sci USA 91: 3166–3170Do C, Waples RS, Peel D, Macbeth GM, Tillett BJ, Ovenden JR (2014) NeEstimator V2: re-implementation of software for the estimation of contemporary effective population size (Ne) from genetic data. Mol Ecol Resour 14:209–214CAS 
PubMed 

Google Scholar 
Ellegren H (2004) Microsatellites: simple sequences with complex evolution. Nat Rev Genet 5:435–445CAS 
PubMed 

Google Scholar 
England PR, Cornuet J-M, Berthier P, Tallmon DA, Luikart G (2006) Estimating effective population size from linkage disequilibrium: severe biasin small samples. Conserv Genet 7:303
Google Scholar 
Engler JO, Secondi J, Dawson DA, Elle O, Hochkirch A (2016) Range expansion and retraction along a moving contact zone has no effect on the genetic diversity of two passerine birds. Ecography 39:884–893
Google Scholar 
Environment Canada (2014) Recovery Strategy for the Ivory Gull (Pagophila eburnea) in Canada. Species at Risk Act Recovery Strategy Series. Environment Canada, Ottawa. iv+ 21 ppEstoup A, Angers B (1998) Microsatellites and minisatellites for molecular ecology: theoretical and empirical considerations. In Carvalho GR (ed) Advances in molecular ecology, 55–85. IOS Press, Burke, Virginia, USAEwens WJ (1972) The sampling theory of selectively neutral alleles. Theor Popul Biol 3:87–112CAS 
PubMed 

Google Scholar 
Felsenstein J (1971) Inbreeding and variance effective numbers in populations with overlapping generations. Genetics 168:581–597
Google Scholar 
Fort J, Moe B, Strøm H, Grémillet D, Welcker J, Schultner J et al. (2013) Multicolony tracking reveals potential threats to little auks wintering in the North Atlantic from marine pollution and shrinking sea ice cover. Diversity Distrib 19:1322–1332
Google Scholar 
Fraïsse C, Popovic I, Mazoyer C, Spataro B, Delmotte S, Romiguier J et al. (2021). DILS: Demographic inferences with linked selection by using ABC. Mol Ecol Resourc 21:2629–2644Frankham R (1995) Effective population size/adult population size ratios in wildlife: a review. Genetical Res 66:95–107
Google Scholar 
Frankham R, Bradshaw CJA, Brook BW (2014) Genetics in conservation management: revised recommendations for the 50/500 rules, Red List criteria and population viability analyses. Biol Conserv 170:56–63
Google Scholar 
Garnier J, Lewis MA (2016) Expansion under climate change: the genetic consequences. Bull Math Biol 78:2165–2185PubMed 

Google Scholar 
Garza JC, Williamson EG (2001) Detection of reduction in population size using data from microsatellite loci. Mol Ecol 10:305–318CAS 
PubMed 

Google Scholar 
Gavrilo M, Martynova D (2017) Conservation of rare species of marine flora and fauna of the Russian Arctic National Park, included in the Red Data Book of the Russian Federation and in the IUCN Red List. Nat Conserv Res 2:10–42
Google Scholar 
Gienapp P, Teplitsky C, Alho JS, Mills JA, Merila J (2008) Climate change and evolution: disentangling environmental and genetic responses. Mol Ecol 17:167–178CAS 
PubMed 

Google Scholar 
Gilchrist HG, Mallory ML (2005) Declines in abundance and distribution of the ivory gull (Pagophila eburnea) in Arctic Canada. Biol Conserv 121:303–309
Google Scholar 
Gilchrist HG, Strøm H, Gavrilo MV, Mosbech A (2008). International ivory gull conservation strategy and action plan. CAFF International Secretariat,Circumpolar Seabird Group (CBird), CAFF Technical Report No. 18Gilg O, Boertmann D, Merkel F, Aebischer A, Sabard B (2009) Status of the endangered ivory gull, Pagophila eburnea, in Greenland. Polar Biol 32:1275–1286
Google Scholar 
Gilg O, Istomina L, Heygster G, Strøm H, Gavrilo M, Mallory ML et al. (2016) Living on the edge of a shrinking habitat: the ivory gull, Pagophila eburnea, an endangered sea-ice specialist. Biol Lett 12:20160277PubMed 
PubMed Central 

Google Scholar 
Gilg O, Kovacs KM, Aars J, Fort J, Gauthier G, Gremillet D et al. (2012) Climate change and the ecology and evolution of Arctic vertebrates. Ann N Y Acad Sci 1249:166–190PubMed 

Google Scholar 
Goutte A, Kriloff M, Weimerskirch H, Chastel O (2011) Why do some adult birds skip breeding? A hormonal investigation in a long-lived bird. Biol Lett 7:790–792PubMed 
PubMed Central 

Google Scholar 
Guillaume F, Rougemont J (2006) Nemo: an evolutionary and population genetics programming framework. Bioinformatics 22:2556–2557CAS 
PubMed 
PubMed Central 

Google Scholar 
Hill WG (1972) Effective size of populations with overlapping generations. Theor Popul Biol 3:278–289CAS 
PubMed 

Google Scholar 
Hoban SM, Mezzavilla M, Gaggiotti OE, Benazzo A, van Oosterhout C, Bertorelle G (2013) High variance in reproductive success generates a false signature of a genetic bottleneck in populations of constant size: a simulation study. BMC Bioinforma 14:309
Google Scholar 
Hoffmann AA, Sgrò CM (2011) Climate change and evolutionary adaptation. Nature 470:479–485CAS 
PubMed 

Google Scholar 
Hohenlohe PA, Funk WC, Rajora OP (2021) Population genomics for wildlife conservation and management. Mol Ecol 30:62–82PubMed 

Google Scholar 
Jamieson IG, Allendorf FW (2012) How does the 50/500 rule apply to MVPs? Trends Ecol Evol 27:578–584PubMed 

Google Scholar 
Kimura M, Ohta T (1978) Stepwise mutation model and distribution of allelic frequencies in a finite population. Proc Natl Acad Sci USA 75:2868–2872CAS 
PubMed 
PubMed Central 

Google Scholar 
Laporte V, Charlesworth B (2002) Effective population size and population subdivision in demographically structured populations. Genetics 162:501–519CAS 
PubMed 
PubMed Central 

Google Scholar 
Leblois R, Pudlo P, Néron J, Bertaux F, Reddy Beeravolu C, Vitalis R et al. (2014) Maximum-Likelihood Inference of Population Size Contractions from Microsatellite Data. Mol Biol Evol 31:2805–2823CAS 
PubMed 

Google Scholar 
Li H, Durbin R (2011) Inference of human population history from individual whole-genome sequences. Nature 475:493–496CAS 
PubMed 
PubMed Central 

Google Scholar 
Liu X, Fu Y-X (2015) Exploring population size changes using SNP frequency spectra. Nat Genet 47:555–559CAS 
PubMed 
PubMed Central 

Google Scholar 
Lucia M, Verboven N, Strom H, Miljeteig C, Gavrilo MV, Braune BM et al. (2015) Circumpolar contamination in eggs of the high-arctic ivory gull Pagophila eburnea. Environ Toxicol Chem 34:1552–1561CAS 
PubMed 

Google Scholar 
Luikart G, Cornuet J-M (1998) Empirical evaluation of a test for identifying recently bottlenecked populations from allele frequency data. Conserv Biol 12:228–237
Google Scholar 
Mallory ML, Allard KA, Braune BM, Gilchrist HG, Thomas VG (2012) New longevity record for ivory gulls (Pagophila eburnea) and evidence of natal philopatry. Arctic 65:98–101
Google Scholar 
Marandel F, Charrier G, Lamy J-B, Le Cam S, Lorance P, Trenkel VM (2020) Estimating effective population size using RADseq: Effects of SNP selection and sample size. Ecol Evol 10:1929–1937PubMed 
PubMed Central 

Google Scholar 
McInerny GJ, Turner JRG, Wong HY, Travis JMJ, Benton TG (2009) How range shifts induced by climate change affect neutral evolution. Proc R Soc B: Biol Sci 276:1527–1534CAS 

Google Scholar 
McRae L, Deinet S, Gill M, Collen B (2012) Arctic species trend index: tracking trends in Arctic marine populations. CAFF Assessment Series No. 7. Conservation of Arctic Flora and Fauna, Iceland
Google Scholar 
Meredith M, Sommerkorn M, Cassotta S, Derksen C, Ekaykin A, Hollowed A et al. (2020) Polar Regions. In: Pörtner HO, Roberts DC, Masson-Delmotte V, Zhai P, Tignor M, Poloczanska E, Mintenbeck K, Alegría A, Nicolai M, Okem A, Petzold J, Rama B, Weyer NM (eds.) IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. Intergovernmental Panel on Climate Change (IPCC)) Geneva, pp 203–320Miljeteig C, Strom H, Gavrilo MV, Volkov A, Jenssen BM, Gabrielsen GW (2009) High levels of contaminants in ivory gull Pagophila eburnea eggs from the Russian and Norwegian Arctic. Environ Sci Technol 43:5521–5528CAS 
PubMed 

Google Scholar 
Miller MP, Haig SM, Mullins TD, Popper KJ, Green M (2012) Evidence for population bottlenecks and subtle genetic structure in the yellow rail. Condor 114:100–112
Google Scholar 
Nadachowska-Brzyska K, Li C, Smeds L, Zhang G, Ellegren H (2015) Temporal dynamics of avian populations during Pleistocene revealed by whole-genome sequences. Curr Biol 25:1375–1380CAS 
PubMed 
PubMed Central 

Google Scholar 
Nunney L (1993) The influence of mating system and overlapping generations on effective population size. Evolution 47:1329–1341PubMed 

Google Scholar 
Nunney L, Elam DR (1994) Estimating the Effective Population Size of Conserved Populations. Conserv Biol 8:175–184
Google Scholar 
Nunziata SO, Weisrock DW (2018) Estimation of contemporary effective population size and population declines using RAD sequence data. Heredity 120:196–207CAS 
PubMed 

Google Scholar 
Nyström V, Angerbjörn A, Dalén L (2006) Genetic consequences of a demographic bottleneck in the Scandinavian arctic fox. Oikos 114:84–94
Google Scholar 
Orive ME (1993) Effective population size in organisms with complex life-histories. Theor Popul Biol 44:316–340CAS 
PubMed 

Google Scholar 
Parmesan C (2006) Ecological and evolutionary responses to recent climate change. Annu Rev Ecol Evol Syst 37:637–669
Google Scholar 
Parreira BR, Chikhi L (2015) On some genetic consequences of social structure, mating systems, dispersal, and sampling. Proc Natl Acad Sci USA 112:E3318CAS 
PubMed 
PubMed Central 

Google Scholar 
Parreira B, Quéméré E, Vanpé C, Carvalho I, Chikhi L (2020) Genetic consequences of social structure in the golden-crowned sifaka. Heredity 125:328–339PubMed 
PubMed Central 

Google Scholar 
Peart CR, Tusso S, Pophaly SD, Botero-Castro F, Wu C-C, Aurioles-Gamboa D et al. (2020) Determinants of genetic variation across eco-evolutionary scales in pinnipeds. Nat Ecol Evol 4:1095–1104PubMed 

Google Scholar 
Peery MZ, Kirby R, Reid BN, Stoelting R, Doucet-Bëer E, Robinson S et al. (2012) Reliability of genetic bottleneck tests for detecting recent population declines. Mol Ecol 21:3403–3418PubMed 

Google Scholar 
Piry S, Luikart G, Cornuet J-M (1999) Computer note. BOTTLENECK: a computer program for detecting recent reductions in the effective size using allele frequency data. J Heredity 90:502–503
Google Scholar 
Raymond M, Rousset F (1995) GENEPOP (version 1.2): population genetics software for exact tests and ecumenicism. J Heredity 86:248–249
Google Scholar 
Rousset F (1999) Genetic Differentiation in Populations with Different Classes of Individuals. Theor Popul Biol 55:297–308CAS 
PubMed 

Google Scholar 
Rousset F (2008) Genepop’007: a complete re-implementation of the Genepop software for windows and linux. Mol Ecol Notes 8:103–1006
Google Scholar 
Rousset F, Beeravolu CR, Leblois R (2018) Likelihood computation and inference of demographic and mutational parameters from population genetic data under coalescent approximations. J de la Société Française de Statistique 159:142–166
Google Scholar 
Rubidge EM, Patton JL, Lim M, Burton AC, Brashares JS, Moritz C (2012) Climate-induced range contraction drives genetic erosion in an alpine mammal. Nat Clim Change 2:285–288
Google Scholar 
Shafer ABA, Gattepaille LM, Stewart REA, Wolf JBW (2015) Demographic inferences using short-read genomic data in an approximate Bayesian computation framework: in silico evaluation of power, biases and proof of concept in Atlantic walrus. Mol Ecol 24:328–345PubMed 

Google Scholar 
Spencer NC, Gilchrist HG, Mallory ML (2014) Annual movement patterns of endangered ivory gulls: the importance of sea ice. Plos ONE 9:e115231PubMed 
PubMed Central 

Google Scholar 
Stenhouse IJ, Robertson GJ, Gilchrist HG (2004) Recoveries and survival rates of ivory gulls (Pagophila eburnea) banded in Nunavut, Canada 1971–1999. Waterbirds 27:486–492
Google Scholar 
Storz J, Ramakrishnan U, Alberts S (2002) Genetic effective size of a wild primate population: influence of current and historical demography. Evolution 56:817–29PubMed 

Google Scholar 
Strøm H, Bakken V, Skoglund, Descamps S, Fjeldheim VB, Steen H (2020) Population status and trend of the threatened ivory gull Pagophila eburnea in Svalbard. Endanger Species Res 43:435–445
Google Scholar 
Volkov AE, de Korte J (2000) Breeding ecology of the Ivory Gull (Pagophila eburnea) in Sedov Archipelago, Severnaya Zemlya. Heritage of the Russian Arctic. Research, conservation and international cooperation. Ecopros Publishers, Moscow, p 483–500
Google Scholar 
Waples RS (2016) Life-history traits and effective population size in species with overlapping generations revisited: the importance of adult mortality. Heredity 117:241–250CAS 
PubMed 
PubMed Central 

Google Scholar 
Waples RS, Antao T, Luikart G (2014) Effects of overlapping generations on linkage disequilibrium estimates of effective population size. Genetics 197:769–780PubMed 
PubMed Central 

Google Scholar 
Waples RS, Do C (2010) Linkage disequilibrium estimates of contemporary Ne using highly variable genetic markers: a largely untapped resource for applied conservation and evolution. Evolut Appl 3:244–262
Google Scholar 
Waples RS, Luikart G, Faulkner JR, Tallmon DA (2013) Simple life-history traits explain key effective population size ratios across diverse taxa. Proc R Soc B: Biol Sci 280:20131339
Google Scholar 
Weir BS, Cockerham CC (1984) Estimating F-statistics for the analysis of population structure. Evolution 38:1358–1370CAS 
PubMed 
PubMed Central 

Google Scholar 
Williamson-Natesan EG (2005) Comparison of methods for detecting bottlenecks from microsatellite loci. Conserv Genet 6:551–562
Google Scholar 
Wogan GOU, Voelker G, Oatley G, Bowie RCK (2020) Biome stability predicts population structure of a southern African aridland bird species. Ecol Evol 10:4066–4081PubMed 
PubMed Central 

Google Scholar 
Xenikoudakis G, Ersmark E, Tison J-L, Waits L, Kindberg J, Swenson JE et al. (2015) Consequences of a demographic bottleneck on genetic structure and variation in the Scandinavian brown bear. Mol Ecol 24:3441–3454CAS 
PubMed 

Google Scholar 
Yannic G, Broquet T, Strøm H, Aebischer A, Dufresnes C, Gavrilo MV et al. (2016) Genetic and morphological sex identification methods reveal a male-biased sex ratio in the Ivory Gull Pagophila eburnea. J Ornithol 157:861–873
Google Scholar 
Yannic G, Sermier R, Aebischer A, Gavrilo MV, Gilg O, Miljeteig C et al. (2011) Description of microsatellite markers and genotyping performances using feathers and buccal swabs for the ivory gull (Pagophila eburnea). Mol Ecol Resour 11:877–889PubMed 

Google Scholar 
Yannic G, Yearsley J, Sermier R, Dufresnes C, Gilg O, Aebischer A et al. (2016) High connectivity in a long-lived high-Arctic seabird, the ivory gull Pagophila eburnea. Polar Biol 39:221–236
Google Scholar 
Yurkowski DJ, Auger-Méthé M, Mallory ML, Wong SNP, Gilchrist G, Derocher AE et al. (2019) Abundance and species diversity hotspots of tracked marine predators across the North American Arctic. Diversity Distrib 25:328–345
Google Scholar  More

Witman, J. D. & Smit, F. Rapid community change at a tropical upwelling site in the Galapagos Marine Reserve. Biodivers. Conserv. 12, 25–45 (2003).
Google Scholar 
Edgar, G. J., Banks, S., Fariña, J. M., Calvopiña, M. & Martínez, C. Regional biogeography of shallow reef fish and macro-invertebrate communities in the Galapagos archipelago. J. Biogeogr. 31, 1107–1124 (2004).
Google Scholar 
Okey, T. A. et al. A trophic model of a Galápagos subtidal rocky reef for evaluating fisheries and conservation strategies. Ecol. Model. 172, 383–401 (2004).
Google Scholar 
Briggs, J. C. & Bowen, B. W. A realignment of marine biogeographic provinces with particular reference to fish distributions. J. Biogeogr. 39, 12–30 (2012).
Google Scholar 
Salinas de León, P. et al. Largest global shark biomass found in the northern Galápagos Islands of Darwin and Wolf. PeerJ 4, e1911. https://doi.org/10.7717/peerj.1911 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Humann, P. & DeLoach, N. Reef Fish Identification: Galápagos (ed. Humann, P.) (New World Publications, Inc., 2003).McCosker, J. E. & Rosenblatt, R. H. The fishes of the Galápagos archipelago: An update. Proc. Calif. Acad. Sci. 61, 167–195 (2010).
Google Scholar 
Grove, J. S. & Lavenberg, R. J. The Fishes of the Galapagos Islands (Stanford University Press, 1997).Allen, G. & Ross-Robertson, D. Fishes of Tropical Eastern Pacific (University of Hawaii Press, 1994).Ruttenberg, B. I., Haupt, A. J., Chiriboga, A. I. & Warner, R. R. Patterns, causes and consequences of regional variation in the ecology and life history of a reef fish. Oecologia 145, 394–403 (2005).ADS 
PubMed 

Google Scholar 
Bernardi, G. et al. Darwin’s fishes: Phylogeography of Galápagos Islands reef fishes. Bull. Mar. Sci. 90, 533–549 (2014).
Google Scholar 
Banks, S., Vera, M. & Chiriboga, A. Establishing reference points to assess long-term change in zooxanthellate coral communities of the northern Galápagos coral reefs. Galapagos Res. 66, 43–64 (2009).
Google Scholar 
Palacios, D., Bograd, S., Foley, D. & Schwing, F. Oceanographic characteristics of biological hot spots in the North Pacific: A remote sensing perspective. Deep Sea Res Part II Top. Stud. Oceanogr. 53, 250–269 (2006).ADS 

Google Scholar 
Sweet, W. V. et al. Water mass seasonal variability in the Galapagos Archipelago. Deep Sea Res. Part I Oceanogr. Res. Pap. 54, 2023–2035 (2007).ADS 

Google Scholar 
Schaeffer, B. et al. Phytoplankton biomass distribution and identification of productive habitats within the Galapagos Marine Reserve by MODIS, a surface acquisition system, and in-situ measurements. Remote Sens. Environ. 112, 3044–3054 (2008).ADS 

Google Scholar 
Witman, J. D., Brandt, M. & Smith, F. Coupling between subtidal prey and consumers along a mesoscale upwelling gradient in the Galapagos Islands. Ecol. Monogr. 80, 153–177 (2010).
Google Scholar 
Moity, N. Evaluation of no-take zones in the Galápagos marine reserve, zoning plan 2000. Frontiers. 5, 244. https://doi.org/10.3389/fmars.2018.00244 (2018).Article 

Google Scholar 
Lamb, R. W., Smith, F. & Witman, J. D. Consumer mobility predicts impacts of herbivory across an environmental stress gradient. Ecology 101, e02910. https://doi.org/10.1002/ecy.2910 (2020).Article 
PubMed 

Google Scholar 
Edgar, G. J. et al. Conservation of threatened species in the Galapagos Marine Reserve through identification and protection of marine key biodiversity areas. Aquat. Conserv. 18, 955–968 (2008).
Google Scholar 
Carrión-Cortez, J. A., Zárate, P. & Seminoff, J. A. Feeding ecology of the green sea turtle (Chelonia mydas) in the Galapagos Islands. J. Mar. Biol. Assoc. U. K. 90, 1005–1013 (2010).
Google Scholar 
Moity, N., Delgado, B. & Salinas-de-León, P. Correction: Mangroves in the Galapagos islands: Distribution and dynamics. PLoS One 14, e0212440. https://doi.org/10.1371/journal.pone.0212440 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Seitz, R. D., Wennhage, H., Bergström, U., Lipcius, R. N. & Ysebaert, T. Ecological value of coastal habitats for commercially and ecologically important species. ICES J. Mar. Sci. 71, 648–665 (2014).
Google Scholar 
Aguaiza, C. The role of mangrove as nursery habitats for coral reef fish species in the Galapagos Islands. MSc Thesis (University of Queensland, 2016).Llerena-Martillo, Y., Peñaherrera-Palma, C. & Espinoza, E. Fish assemblages in three fringed mangrove bays of Santa Cruz Island, Galapagos Marine Reserve. Rev. Biol. Trop. 66, 674–687 (2018).
Google Scholar 
Fierro-Arcos, D. et al. Mangrove fish assemblages reflect the environmental diversity of the Galapagos Islands. Mar. Ecol. Prog. Ser. 664, 183–205 (2021).ADS 

Google Scholar 
Henseler, C. et al. Coastal habitats and their importance for the diversity of benthic communities: A species-and trait-based approach. Estuar. Coast. Shelf Sci. 226, 106272. https://doi.org/10.1016/j.ecss.2019.106272 (2019).Article 

Google Scholar 
Loreau, M. et al. Biodiversity and ecosystem functioning: Current knowledge and future challenges. Science 294, 804–808 (2001).ADS 
CAS 

Google Scholar 
Menezes, R. F. et al. Variation in fish community structure, richness, and diversity in 56 Danish lakes with contrasting depth, size, and trophic state: Does the method matter?. Hydrobiologia 710, 47–59 (2013).
Google Scholar 
Hu, M., Wang, C., Liu, Y., Zhang, X. & Jian, S. Fish species composition, distribution and community structure in the lower reaches of Ganjiang River, Jiangxi, China. Sci. Rep. 9, 10100. https://doi.org/10.1038/s41598-019-46600-2 (2019).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Clarke, K. R. & Warwick, R. M. Changes in Marine Communities: An Approach to Statistical Analysis and Interpretation, 2nd ed. (PRIMER-E Ltd, Plymouth Marine Laboratory, 2001).Warwick, R. M. & Clarke, K. R. New biodiversity measures reveal a decrease in taxonomic distinctness with increasing stress. Mar. Ecol. Prog. Ser. 129, 301–305 (1995).ADS 

Google Scholar 
Clarke, K. R. & Warwick, R. M. The taxonomic distinctness measure of biodiversity: Weighting of step lengths between hierarchical levels. Mar. Ecol. Prog. Ser. 184, 21–29 (1999).ADS 

Google Scholar 
Nieto-Navarro, J. T., Zetina-Rejón, M. A., Arreguín-Sánchez, F., Palacios-Salgado, D. & Jordán, F. Changes in fish bycatch during the shrimp fishing season along the eastern coast of the mouth of the Gulf of California. J. Appl. Ichthyol. 29, 610–616 (2013).
Google Scholar 
Escobar-Toledo, F., Zetina-Rejón, M. J. & Duarte, L. O. Measuring the spatial and seasonal variability of community structure and diversity of fish by-catch from tropical shrimp trawling in the Colombian Caribbean Sea. Mar. Biol. Res. 11, 528–539 (2015).
Google Scholar 
Herrera-Valdivia, E., López-Martínez, J., Castillo Vargasmachuca, S. & García-Juárez, A. R. Diversidad taxonómica y funcional en la comunidad de peces de la pesca de arrastre de camarón en el norte del Golfo de California, México. Rev. Biol. Trop. 64, 587–602 (2016).PubMed 

Google Scholar 
Heylings, P., Bensted-Smith, R. & Altamirano, M. Zonificación e historia de la Reserva Marina de Galápagos. In Reserva Marina de Galápagos. Línea Base de la Biodiversidad (eds. Danulat, E. & Edgar, G. J.) 10–21 (Fundación Charles Darwin y Servicio Parque Nacional de Galápagos, 2002).Edgar, G. J. et al. Bias in evaluating the effects of marine protected areas: The importance of baseline data for the Galapagos Marine Reserve. Environ. Conserv. 3, 212–218. https://doi.org/10.1017/S0376892904001584 (2004).Article 

Google Scholar 
Jennings, S., Brierley, A. S. & Walker, J. W. The inshore fish assemblages of the Galápagos archipelago. Biol. Conserv. 70, 49–57 (1994).
Google Scholar 
Brito, A., Pérez-Ruzafaga, A. & Bacallado, J. J. Ictiofauna costera de las islas Galápagos: composición y estructura del poblamiento de los fondos rocosos. Res. Cient. Proy. Galápagos TFCM 5, 61 (1997).
Google Scholar 
Bruneel, S. et al. Assessing the drivers behind the structure and diversity of fish assemblages associated with rocky shores in the Galapagos Archipelago. J. Mar. Sci. Eng. 9, 375. https://doi.org/10.3390/jmse9040375 (2021).Article 

Google Scholar 
Wellington, G. M., Strong, A. E. & Merlen, G. Sea surface temperature variation in the Galápagos Archipelago: A comparison between AVHRR nighttime satellite data and in-situ instrumentation (1982–1998). Bull. Mar. Res. 69, 27–42 (2001).
Google Scholar 
Snell, H., Stone, P. & Snell, H. L. A summary of geographical characteristics of the Galapagos Islands. J. Biogeogr. 23, 619–624 (1996).
Google Scholar 
Bustamante, R. H., et al. Outstanding marine features of Galápagos. In A Biodiversity Vision for the Galapagos Islands: An Exercise for Ecoregional Planning (eds. Bensted-Smith, R. & Dinnerstein, E.) 60–71 (WWF, 2002).Airoldi, L. & Beck, M. W. Loss, status and trends for coastal marine habitats of Europe. In Oceanography and Marine Biology: An Annual Review (eds. Gibson, R. N., Atkinson, R. J. A. & Gordon, J. D. M.) vol. 45, 345–405 (Taylor & Francis, 2007).Carr, M. H., Malone, D. P., Hixon, M. A., Holbrook, S. J. & Schmitt, R. J. How Scuba changed our understanding of nature: underwater breakthrough in reef fish ecology. In Research and Discoveries: The Revolution of Science Through Scuba vol. 39, 157–167 (Smithsonian Contributions to the Marine Sciences, 2013).Durkacz, S. Assessing the Oceanographic Conditions and Distribution of Reef Fish Assemblages Throughout the Galápagos Islands Using Underwater Visual Survey Methods. MSc Thesis (Texas A & M University, 2014).Fischer, W. et al. Guía FAO para la identificación de especies para los fines de pesca. Pacífico Centro-Oriental vol. II–III, 648–1652 (FAO, 1995).Clarke, K. R. & Warwick, R. M. A taxonomic distinctness index and its statistical properties. J. Appl. Ecol. 35, 523–531 (1998).
Google Scholar 
Clarke, K. R. Non-parametric multivariate analyses of changes in community structure. Aust. J. Ecol. 18, 117–143 (1993).
Google Scholar 
Rosenberg, A., Binford, T. E., Leathery, S., Hill, R. L. & Bickers, K. Ecosystem approaches to fishery management through essential fish habitat. Bull. Mar. Sci. 66, 535–542 (2000).
Google Scholar 
Aburto-Oropeza, O. & Balart, E. F. Community structure of reef fish in several habitats of a rocky reef in the Gulf of California. Mar. Ecol. 22, 283–305 (2001).ADS 

Google Scholar 
Fulton, C. J., Bellwood, D. R. & Wainwright, P. C. Wave energy and swimming performance shape coral reef fish assemblages. Proc. R. Soc. B 272, 827–832 (2005).CAS 
PubMed 

Google Scholar 
Dominici-Arosemena, A. & Wolff, M. Reef fish community structure in the Tropical Eastern Pacific (Panamá): Living on a relatively stable rocky reef environment. Helgol. Mar. Res. 60, 287–305 (2006).ADS 

Google Scholar 
Villegas-Sánchez, C. A., Abitia-Cárdenas, L. A., Gutiérrez-Sánchez, F. J. & Galván-Magaña, F. Rocky-reef fish assemblages at San José Island, Mexico. Rev. Mex. Biodivers. 80, 169–179 (2009).
Google Scholar 
Wiens, J. J. & Graham, C. H. Niche conservatism: Integrating evolution, ecology, and conservation biology. Annu. Rev. Ecol. Evol. Syst. 36, 519–539 (2005).
Google Scholar 
Glynn, P. Some physical and biological determinants of coral community structure in the eastern Pacific. Ecol. Monogr. 46, 431–456 (1976).
Google Scholar 
Ramos-Miranda, J. et al. Changes in four complementary facets of fish diversity in a tropical coastal lagoon after 18 years: A functional interpretation. Mar. Ecol. Prog. Ser. 304, 1–13 (2005).ADS 

Google Scholar 
Gristina, M., Bahri, T., Fiorentino, F. & Garofalo, G. Comparison of demersal fish assemblages in three areas of the Strait of Sicily under different trawling pressure. Fish. Res. 81, 60–71 (2006).
Google Scholar 
Pérez-Ruzafa, A. P., Marcos, C. & Bacallado, J. J. Biodiversidad marina en archipiélagos e islas: patrones de riqueza específica y afinidades faunísticas. Vieraea Folia Scientarum Biologicarum Canariensium. 33, 455–476 (2005).
Google Scholar 
Malcolm, H. A., Jordan, A. & Smith, S. D. Biogeographical and cross-shelf patterns of reef fish assemblages in a transition zone. Mar. Biodivers. 40(3), 181–193 (2010).
Google Scholar 
García-Charton, J. A. & Pérez-Ruzafa, A. P. Correlation between habitat structure and a rocky reef fish assemblage in the Southwest Mediterranean. Mar. Ecol. 19(2), 111–128 (1998).ADS 

Google Scholar 
Mumby, P. J. et al. Mangroves enhance the biomass of coral reef fish communities in the Caribbean. Nature 427, 533–536 (2004).ADS 
CAS 
PubMed 

Google Scholar 
Unsworth, R. K. F. et al. High connectivity of Indo-Pacific seagrass fish assemblages with mangrove and coral reef habitats. Mar. Ecol. Prog. Ser. 353, 213–224 (2008).ADS 

Google Scholar 
Birkeland, C. & Amesbury, S. S. Fish-transect surveys to determine the influence of neighboring habitats on fish community structure in the tropical Pacific. Co-operation for environmental protection in the Pacific. UNEP Reg. Seas Rep. Stud. 97, 195–202 (1988).
Google Scholar 
Thollot, P., Kulbicki, M., & Wantiez, L. Temporal patterns of species composition in three habitats of the St Vincent Bay area (New Caledonia): Coral reefs, soft bottoms and mangroves. In Proceedings International Soc. Reef Studies. 127–137 (1991).Kulbicki, M. Present knowledge of the structure of coral reef fish assemblages in the Pacific. UNEP Reg. Seas Rep. Stud. 147, 31–53 (1992).
Google Scholar 
Cruz-Romero, M., Chávez, E.A., Espino, E. & García, A. Assessment of a snapper complex (Lutjanus spp.) of the eastern tropical Pacific. In Biology, Fisheries and Culture of Tropical Groupers and Snappers (eds. Arreguín-Sánchez, F., Munro, J. L., Balgos, M. C. & Pauly, D.) 324–330 (ICLARM Conf. Proc. 48, 1996).Aguilar-Santana, F. Biología reproductiva de Prionurus laticlavius (Valenciennes, 1846) (Teleostei: Acanthuridae) en la Costa Sudoccidental del Golfo de California, México. PhD Thesis (Instituto Politécnico Nacional, 2020).Hall, S. The Effects of Fishing on Marine Ecosystems and Communities (Blackwell Science Ltd., 1999).Mangi, S. C. & Roberts, C. M. Quantifying the environmental impacts of artisanal fishing gear on Kenya’s coral reef ecosystems. Mar. Pollut. Bull. 52, 1646–1660 (2006).CAS 
PubMed 

Google Scholar 
Rees, M. J., Jordan, A., Price, O. F., Coleman, M. A. & Davis, A. R. Abiotic surrogates for temperate rocky reef biodiversity: Implications for marine protected areas. Divers. Distrib. 20(3), 284–296 (2014).
Google Scholar 
Ferrari, R. et al. Habitat structural complexity metrics improve predictions of fish abundance and distribution. Ecography 41(7), 1077–1091 (2018).
Google Scholar 
Pihl, L. & Wennhage, H. Structure and diversity of fish assemblages on rocky and soft bottom shores on the Swedish west coast. J. Fish Biol. 61, 148–166 (2002).
Google Scholar 
La Mesa, G., Molinari, A., Gambaccini, S. & Tunesi, L. Spatial pattern of coastal fish assemblages in different habitats in North-western Mediterranean. Mar. Ecol. 32, 104–114 (2011).ADS 

Google Scholar 
Kristensen, L. D. et al. Establishment of blue mussel beds to enhance fish habitats. Appl. Ecol. Environ. Res. 13, 783–798 (2015).
Google Scholar 
Bergström, L., Karlsson, M., Bergström, U., Pihl, L. & Kraufvelin, P. Distribution of mesopredatory fish determined by habitat variables in a predator-depleted coastal system. Mar. Biol. 163, 201. https://doi.org/10.1007/s00227-016-2977-9 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Galván-Villa, C. M., Arreola-Robles, J. L., Ríos-Jara, E. & Rodríguez-Zaragoza, F. A. Ensamblajes de peces arrecifales y su relación con el hábitat bentónico de la Isla Isabel, Nayarit, México. Rev. Biol. Mar. Oceanogr. 45, 311–324 (2010).
Google Scholar 
Lunt, J. & Smee, D. L. Turbidity alters estuarine biodiversity and species composition. ICES J. Mar. Sci. 77, 379–387 (2019).
Google Scholar 
Anthony, K. R., Ridd, P. V., Orpin, A. R., Larcombe, P. & Lough, J. Temporal variation of light availability in coastal benthic habitats: Effects of clouds, turbidity, and tides. Limnol. Oceanogr. 49, 2201–2211 (2004).ADS 

Google Scholar 
Helfman, G. S. Patterns of community structure in fishes: Summary and overview’. Environ. Biol. Fishes 3, 129–148 (1978).
Google Scholar 
Helfman, G. S. Fish behaviour by day, night and twilight. In The Behaviour of Teleost Fishes (ed. Pitcher T.J.) (Springer, 1986).Warwick, R. M. & Clarke, K. R. Taxonomic distinctness and environmental assessment. J. Appl. Ecol. 35, 532–543 (1998).
Google Scholar 
Rogers, S. I., Clarke, K. R. & Reynolds, J. D. The taxonomic distinctness of coastal bottom-dwelling fish communities of the North-east Atlantic. J. Anim. Ecol. 68, 769–782 (1999).
Google Scholar 
Robertson, A. I., & Blaber, S. J. M. Plankton, epibenthos and fish communities. In Tropical Mangrove Ecosystems (eds. Robertson, A. I. & Alongi, D. M.) Coastal and Estuarine Studies No. 41, 173–224 (American Geophysical Union, 1992).Koranteng, K. A. Diversity and stability of demersal species assemblages in the Gulf of Guinea. West Afr. J. Appl. Ecol. 2, 49–63 (2001).
Google Scholar 
McCormick, M. I. Comparison of field methods for measuring surface topography and their associations with a tropical reef fish assemblage. Mar. Ecol. Prog. Ser. 112, 87–96 (1994).ADS 

Google Scholar 
Moraes, L. E., Paes, E., Garcia, A., Möller, O. Jr. & Vieira, J. Delayed response of fish abundance to environmental changes: A novel multivariate time-lag approach. Mar. Ecol. Prog. Ser. 456, 159–168 (2012).ADS 

Google Scholar 
Edgar, G. J. et al. El Niño, grazers and fisheries interact to greatly elevate extinction risk for Galapagos marine species. Glob. Change. Biol. 16, 2876–2890 (2010).ADS 

Google Scholar 
Glynn, P. W., Enochs, I. C., Afflerbach, J. A., Brandtneris, V. W. & Serafy, J. E. Eastern Pacific reef fish responses to coral recovery following El Niño disturbances. Mar. Ecol. Prog. Ser. 495, 233–247 (2014).ADS 

Google Scholar  More

Field experiments: collective structuresWe found that self-assembled Eciton hamatum bridges adaptively adjust in response to shifts in the terrain on which they are built. Detailed methods are included in Methods: Field experiments. Briefly, we moved foraging trails onto an apparatus where we could introduce a terrain gap. We repeatedly changed the size of this gap by first incrementally increasing it to 30 mm, by 1 mm every 30 s, and then incrementally contracting it at the same rate (See Fig. 1, Methods: Field experiments, and Supplementary Movie 1). As the size of the gap was expanded (the period before the dotted line in Fig. 2a, b) both the volume and number of ants increased to mean maximum values of 1080 mm3 (standard error, s.e. 84) and 18.9 ants (s.e. 1.6), respectively. Ants typically began forming a bridge when the gap was ~5 mm. As the gap size was decreased (period after dotted line), volume and the number of ants decreased back to zero as ants left the bridge. These broad dynamics across the ten complete trials were similar (Fig. 2a, b, inset panels and Supplementary Figs. 2, 3). Additionally, bridge volume (Fig. 2a) strongly correlated with the number of ants in the bridge (Fig. 2b), indicating that the density of ants per unit volume in these structures is relatively consistent (Pearson correlation coefficients range from 0.88 to 0.98 across the ten trials, see also Supplementary Fig. 4). Bridges broke and quickly reformed in eight of the ten trials; breaks occurred in both experimental phases, and these broken periods were excluded from analyses. Overall, these results show that bridges adjust dynamically to changing terrain geometry, as stretching the bridges caused them to become larger, with more ants, and contracting bridges caused them to become smaller, with fewer ants.Fig. 1: Experimental procedure and data extraction summary.Experiments were conducted on robust E. hamatum foraging trails, which were moved onto the experimental apparatus while it was closed. a Experimental procedure: The size of the gap was increased by 1 mm every 30 s until the gap reached 30 mm (expansion phase), then decreased at the same rate till no gap remained (the contraction phase). b Field setup: Experiments were recorded from both the side and the top, examples of bridges during each phase of the same trial are shown. c Data extraction: Example images and silhouettes from the maximum size bridge (30 mm) of the same trial as the images of 20 mm bridges shown in panel a. The envelopes of the bridges were extracted at a temporal resolution of 1 s; for each focal second, image frames were averaged over 10 s to remove ants walking on the bridge from the extracted envelopes. Envelopes were automatically extracted using hue-saturation-value (HSV) thresholding, with thresholds checked independently for each trial due to lighting differences. Locations of fixed points on the platform were used to re-scale and combine data from the side and top views into a single coordinate system in which 100 pixels = 1 cm. Estimates of bridge volume, mean cross-sectional area, and relative height of the center of mass were recorded from the extracted envelopes as shown. See Methods: Data extraction and Supplementary Note 1 for additional details of the data extraction process, including additional bridge metrics.Full size imageFig. 2: Changes in collective structures in experiments.a, b Volume and group size of self-assembled bridges: a Estimated volume of collective bridge structures over time for one focal trial (main figure) and three other examples (inset). The dotted vertical line indicates the time when the experiment shifted from the expansion phase (increasing gap size) to the contraction phase (decreasing gap size). Gray shading indicates that the bridge was broken or recovering from a break; result metrics may be inaccurate during these periods and they were, therefore, excluded from analyses. b The number of ants in the bridge structure over time for the same focal trial (main figure) and three other examples (inset). c–f Hysteresis: Trials consistently show hysteresis, with bridge status at a particular gap size differing during the expansion and contraction phases, for volume (c), number of ants (d), mean cross-sectional area (e), and tautness, or the height of the center of mass of the bridge from the side view (f; lower values indicate bridge is hanging lower). c–f Panels show result metrics over gap size for the same focal trial as in panels a and b, as well as for three other examples (inset). Points show individual measurements, taken every second, lines are smoothed LOESS (local regression) for the expansion (orange points, dashed orange line) and contraction (green points, solid green line) phases. The area between the smoothed lines (shaded gray) shows the extent of hysteresis. c, e, f) Points are jittered to improve clarity. a–f See Supplementary Figs. 2, 3, 5–8 for all complete trials.Full size imageHowever, these changes were not symmetric—adjustments in the contraction phase were not the inverse of adjustments in the expansion phase. We found consistent hysteresis in several metrics; for a given gap size, bridges were larger and made up of more individuals during the contraction of the gap than the expansion (Fig. 2c, d; t-test for volume: mean extent of hysteresis = 0.43, 95% CI = 0.29 to 0.58, t = 6.7, df = 9, p  More

Model descriptionThe model involves a discrete time, discrete valued stochastic Markov process. Model variables and parameters are given in Tables 1 and 2 respectively. Both time and the number of individuals of each type are constrained to be integer valued. Death and reproductive mutation are stochastic processes derived from sampling from binomial distributions given by the relevant probabilities. All ensemble results give the 100-replicate average for the parameter choices in question.Growth of individuals from species ({S}_{1}) and ({S}_{2}) is proportional to the bio-available level of environmental substances ({R}_{1}) and ({R}_{2}) respectively. At time (t) (where time is in units of biological generations) the change in the number ({N}_{q,j}) of individuals of genotype (j) (non-producer, producer, plastic) within species (q) (({S}_{1}) or ({S}_{2})) can be written as a function of the state of the variables at the previous time-step:$${N}_{q,j}left(t+1right)=left(left({N}_{q,j}left(tright)-{rho }_{q,j}left(tright)right)cdot {G}_{q,j}left(tright)-{{{{{{rm{{Upsilon }}}}}}}}_{q,j}left(tright)-{delta }_{q,j}left(tright)+{{{{{{rm{{Upsilon }}}}}}}}_{q,xne j}left(tright)right)cdot left(1-frac{{S}_{q}left(tright)}{K}right)$$
(1)
The leftmost bracket on the right-hand side represents the number of individuals escaping starvation (death due to insufficient environmental substance) at the previous time-step and ({G}_{q,j}left(tright)) is the per capita reproductive growth rate. ({{{{{{rm{{Upsilon }}}}}}}}_{q,j}left(tright)) gives the number of mutant offspring individuals produced during reproduction from parent individuals of genotype (j). ({{{{{{rm{{Upsilon }}}}}}}}_{q,xne j}left(tright)) represents the number of (j) genotype individuals derived from mutation in parent individuals of other genotypes. ({delta }_{q,j}left(tright)) is the number of individuals of genotype (j) lost to random death, and the rightmost bracket relates the total number ({S}_{q}left(tright)) of individuals of species (q) to carrying capacity (K), which represents limitation of growth by any factor other than the relevant environmental substance, e.g. space. (The steady state population size in all simulations shown is below (K) and limited by the environmental substance influx. The carrying capacity is included in the model for computational reasons and as a “crash preventer” but has no qualitative effect on the results).The total number of individuals ({S}_{q}left(tright)) in species (q) is the sum of the number of individuals of each genotype (producer, non-producer and plastic, as discussed in the main text):$${S}_{q}left(tright)=mathop{sum }limits_{j=1}^{{j}_{{total}}}{N}_{q,j}left(tright)={N}_{q,{prod}}left(tright)+{N}_{q,{non}-{prod}}left(tright)+{N}_{q,{plast}}left(tright)$$
(2)
The genotype-specific reproductive growth rate ({G}_{q,j}left(tright)) (again for genotype (j) within species (q), time (t)), gives the number of offspring individuals produced per parent individual, per time-step. Growth rate is an increasing function of the bio-available level of environmental substance ({R}_{q,{BIOAVAIL}{ABLE}}) (the subscript (q) being identical because species ({S}_{1}) and ({S}_{2}) assimilate substances ({R}_{1}) and ({R}_{2}) respectively). Growth rate also includes a substance-to-biomass conversion efficiency parameter ({f}_{{conv}}) and a genotype-specific per capita term ({G}_{q,j,{PR}}) (number of offspring per parent, per unit environmental substance assimilated, per unit time). In the absence of growth-limitation by environmental substance levels, growth rate is capped at a genotype-specific maximum ({G}_{q,{jMAX}}):$${G}_{q,j}left(tright)={MIN}[{G}_{q,j,{PR}}cdot {R}_{q,{BIOAVAILABLE}}(t)cdot {f}_{{conv}},{G}_{q,{jMAX}}]$$
(3)
$${G}_{q,{non}-{prod},{PR}}={G}_{0}$$
(4)
$${G}_{q,{non}-{prod},{MAX}}={G}_{0}cdot {R}_{{assimMAX}}$$
(5)
({G}_{0}) is the baseline number of offspring, per parent, per unit substance assimilated. ({R}_{{assimMAX}}) is a universal maximum potential number of units of environmental substance that can be assimilated by a single individual per time-step (i.e. representing basic physiological constraints on growth). The producer genotype incurs a per capita reproductive growth rate cost ({kappa }_{{prod}}) relative to the non-producer:$${G}_{q,{prod},{PR}}={G}_{0}cdot (1-{kappa }_{{prod}})$$
(6)
$${G}_{q,{prod},{MAX}}={G}_{0}cdot (1-{kappa }_{{prod}})cdot {R}_{{assimMAX}}$$
(7)
This growth rate formulation is therefore a highly simplified linearization of the Michaelis-Menten kinetics normally used in models of resource and nutrient assimilation.The plastic genotype switches phenotype depending upon the level of environmental substance relative to a fixed threshold ({{R}_{q,{BIOAVAILABLE}}}_{{crit}}), in effect becoming a second non-producer genotype below this threshold and a second producer genotype above it:$${IF}[{R}_{q,{BIOAVAILABLE}}(t)ge {{R}_{q,{BIOAVAILABLE}}}_{{crit}}],{G}_{q,{plast}}left(tright)={G}_{q,{prod}}left(tright)$$$${ELSEIF}[{R}_{q,{BIOAVAILA}{BLE}}left(tright) , < , {{R}_{q,{BIOAVAILABLE}}}_{{crit}}],{G}_{q,{plast}}left(tright)={G}_{q,{non}-{prod}}left(tright)$$ (8) There is no spatial structure whatsoever, thus access to environmental substance is uniform across individuals. The bioavailable quantity of each environmental substance is simply the total amount ({R}_{q,{NET}}(t)) divided by the total number of individuals assimilating it:$${R}_{q,{BIOAVAILABLE}}left(tright)=frac{{R}_{q,{NET}}(t)}{{S}_{q}left(tright)}$$ (9) We allow the per capita reproductive growth rate to fall below ({G}_{q,j}left(tright)=1), which, if interpreted deterministically at the individual level would correspond to an individual failing to sustain its biomass to the next time-step and thus dying. However, a population-level average ({G}_{q,j}left(tright) , < , 1) is interpretable in terms of a thinning factor that maps between discretized individuals and continuously distributed environmental substance. Thus, a thinning factor of (left(1-{G}_{q,j}left(tright)right)) is used to calculate the total number of individuals dying of starvation ({rho }_{q,j}) (again genotype (j), species (q)). This represents pre-reproduction deaths, corresponding to the difference between the actual population size and the population size that the environmental substance pool is capable of supporting. ({rho }_{q,j}left(tright)) is constrained to be an integer and is zero for ({G}_{q,j}left(tright) , > , 1):$${rho }_{q,j}left(tright)={N}_{q,j}left(tright)cdot {MAX}left[0,left(1-{G}_{q,j}left(tright)right)right]$$
(10)
A subset of offspring are a different genotype from their parent via mutation. For parent genotype (j), the number ({{{{{{rm{{Upsilon }}}}}}}}_{q,j}left(tright)) of mutant offspring with genotype (ne j) is calculated using baseline mutation probability per reproductive event ({mu }_{0}), with the total number of new individuals produced by the parent individuals surviving starvation ({G}_{q,j}left(tright)cdot left({N}_{q,j}left(tright)-{rho }_{q,j}left(tright)right)). The total number of mutant offspring ({{{{{{rm{{Upsilon }}}}}}}}_{q,j}left(tright)) is thus a binomially distributed random variable with success probability ({mu }_{0}) and number of trials ({G}_{q,j}left(tright)cdot left({N}_{q,j}left(tright)-{rho }_{q,j}left(tright)right)). The expected value (Eleft[{{{{{{rm{{Upsilon }}}}}}}}_{q,j}left(tright)right]) is the product of these two numbers:$$ {{{{{{rm{{Upsilon }}}}}}}}_{q,j}left(tright) sim Bleft({G}_{q,j}left(tright)cdot left({N}_{q,j}left(tright)-{rho }_{q,j}left(tright)right),{mu }_{0}right), \ Eleft[{{{{{{rm{{Upsilon }}}}}}}}_{q,j}left(tright)right]={G}_{q,j}left(tright)cdot left({N}_{q,j}left(tright)-{rho }_{q,j}left(tright)right)cdot {mu }_{0}$$
(11)
Mutation to genotype (j) from the other genotypes is calculated in exactly the same way using the number and reproductive growth rates of the relevant (other) genotypes. Any particular mutant offspring is randomly allocated to one of the other genotypes with equal probability ({p}_{kto j}=frac{1}{{j}_{{total}}-1}=0.5) (where ({j}_{{total}}=3) is the total number of genotypes per species). The expected number of offspring with genotype (j) produced by mutation within parent offspring of other genotypes (kne j) is therefore:$$E[{{{{{{rm{{Upsilon }}}}}}}}_{q,x , ne , j}left(tright)]={left(mathop{sum }limits_{k=1}^{{k}_{{to}{tal}}}{{{{{{rm{{Upsilon }}}}}}}}_{q,k}left(tright)right)}_{kne j}cdot frac{1}{{j}_{{total}}-1}$$
(12)
Independently of reproduction and assimilation of environmental substance, any given individual has a probability ({delta }_{0}) at each time point of death due to stochastic factors. The genotype/species specific number of such deaths is again a random sample from a binomial distribution, with success probability ({delta }_{0}):$${delta }_{q,j}left(tright) sim Bleft({N}_{q,j}left(tright),{delta }_{0}right),Eleft[{delta }_{q,j}left(tright)right]={N}_{q,j}left(tright)cdot {delta }_{0}$$
(13)
The net quantity of growth-limiting environmental substance at each time-step is given by the difference between total biotic assimilation ({A}_{{R}_{q}}) and the production ({P}_{{R}_{q}}) and abiotic input ({varphi }_{{R}_{q}}) fluxes:$${R}_{q,{NET}}(t+1)={varphi }_{{R}_{q}}(t)+{P}_{{R}_{q}}(t)-{A}_{{R}_{q}}(t)$$
(14)
The abiotic net influx is the sum of two fluxes. First, an input term that is the product of a baseline scaling factor ({{varphi }_{0}}_{{R}_{q}}) and a model forcing (frac{partial {t}_{{geo}}}{partial {t}_{{bio}}}) representing the mapping between abiotic-geological and biotic-evolutionary timescales. In practice (frac{partial {t}_{{geo}}}{partial {t}_{{bio}}}(t)) was set to either (1) or (0) or (in fluctuation runs) a time-dependent switching between the two. (More sophisticated implementations of (frac{partial {t}_{{geo}}}{partial {t}_{{bio}}}(t)), e.g. sinusoidal oscillations and stochastic time dependence, were attempted but made little qualitative difference to the results). Second, an abiotic removal term that scales linearly with the quantity of environmental substance:$${varphi }_{{R}_{q}}(t)={{varphi }_{0}}_{{R}_{q}}cdot frac{partial {t}_{{geo}}}{partial {t}_{{bio}}}left(tright)-frac{{R}_{q,{NET}(t)}}{{R}_{q,{NET}0}}$$
(15)
where ({R}_{q,{NET}0}) is a normalization factor representing the sensitivity of the abiotic efflux to the influx. In the absence of any biota and for (frac{partial {t}_{{geo}}}{partial {t}_{{bio}}}=1) the steady state environmental substance level is immediately given by (15) as ({R}_{q,{NET}(t)}={R}_{q,{NET}0}cdot {{varphi }_{0}}_{{R}_{q}}), thus the numerical value of ({R}_{q,{NET}0}) corresponds to the abiotic steady state residence time.Total biotic assimilation ({A}_{R}) of each environmental substance is given by:$${A}_{{R}_{q}}left(tright)=mathop{sum }limits_{k=1}^{{j}_{{total}}}frac{{G}_{q,k}left(tright)cdot left({N}_{q,k}left(tright)-{rho }_{q,k}left(tright)right)}{{G}_{q,{kPR}}}$$
(16)
The numerator gives the total number of individuals produced as a result of biological assimilation of environmental substance ({R}_{q}) and the denominator is the genotype specific number of individuals produced per unit substance assimilated, dividing through by which therefore converts to total units of substance assimilated by the population as a whole.Net biotic ({P}_{{R}_{q}{NET}}) production of substance ({R}_{q}) by the producer genotype in the other species (p) is calculated equivalently, via the product of the per capita production rate ({P}_{q,{prod}}) and the total number of reproducing individuals:$${P}_{q,{prod}}left(tright)=frac{{MIN}[{G}_{q,{prod}}left(tright)cdot {f}_{{convprod}},{G}_{q,{prodMAX}}]}{{G}_{p,{PRODUCER;PR}}}$$
(17)
$${P}_{{R}_{q}{NET}}left(tright)={P}_{q,{prod}}left(tright)cdot left({N}_{p,{PRODUCER}}left(tright)-{rho }_{p,P{RODUCER}}left(tright)right)$$
(18)
Where ({f}_{{conv},{PROD}}) is the per capita efficiency by which producers convert the environmental substance that they assimilate into the by-product they produce (note that the equivalent conversion efficiency for assimilation ({f}_{{conv}}) already appears in the growth functions of each genotype, therefore does not appear in Eq. (17)).The residence time ({T}_{{R}_{q}})of each environmental substance is given by the net quantity of this substance divided by the influx, to give units of the average number of biological generations a unit of environmental substance spends in the relevant pool before being removed. In those simulations in which the abiotic influx ({varphi }_{{R}_{q}}(t)) was set to zero (i.e. during the shut-off intervals) production ({P}_{{R}_{q}{NE}T}left(tright)) was used as an alternative denominator:$${IF}left[{varphi }_{{R}_{q}}left(tright) , > , 0,{T}_{{R}_{q}}left(tright)=frac{{R}_{q,{NET}}left(tright)}{{varphi }_{{R}_{q}}left(tright)}right]!,{IF}left[left(left({varphi }_{{R}_{q}}left(tright)=0right){& }left({P}_{{R}_{q}{NET}}left(tright) , > ,0right)right)!,{T}_{{R}_{q}}left(tright)=frac{{R}_{q,{NET}}left(tright)}{{P}_{{R}_{q}{NET}}left(tright)}right]{ELSE}[{T}_{{R}_{q}}left(tright)=0]$$
(19)
The cycling ratio ({{CR}}_{{R}_{q}}) of each substance is given by the ratio between net biotic assimilation of that substance ({A}_{{R}_{q}}left(tright)) and the abiotic influx of that substance ({varphi }_{{R}_{q}}left(tright)). As with the residence time, when the abiotic influx was zero, the input from biological production was used as an alternative denominator:$${IF}left[{varphi }_{{R}_{q}}left(tright) , > , 0,{{CR}}_{{R}_{q}}left(tright)=frac{{A}_{{R}_{q}}left(tright)}{{varphi }_{{R}_{q}}left(tright)}right]{IF}left[left(left({varphi }_{{R}_{q}}left(tright)=0right){{& }}left({P}_{{R}_{q}{NET}}left(tright) , > ,0right)right),{{CR}}_{{R}_{q}}left(tright)=frac{{A}_{{R}_{q}}left(tright)}{{P}_{{R}_{q}{NET}}left(tright)}right]{ELSE}[{{CR}}_{{R}_{q}}left(tright)=0]$$
(20)
Deterministic approximation to steady stateAssume that at steady state substance assimilation will reach a maximal state such that the level of environmental substance is limiting to population size. Assume that such a state is below the level ({{R}_{q,{BIOAVAILABLE}}}_{{crit}}) at which the plastic genotype effectively becomes a second non-producer genotype and can thus be subsumed into non-producer frequency, such that (2) becomes ({S}_{q}left(tright)=mathop{sum }nolimits_{j=1}^{{j}_{{total}}}{N}_{q,j}left(tright)={N}_{q,{prod}}left(tright)+{N}_{q,{non}-{prod}}left(tright)). Assume that there are non-zero starvations at each time-step for all genotypes, which implies growth rate ({G}_{q,j}left(tright) , < , 1,ll {G}_{q,{jMAX}},forall j,q), which gives by (3)({G}_{q,j}left(tright)={G}_{q,j,{PR}}cdot {R}_{q,{BIOAVAILABLE}}left(tright)cdot {f}_{{conv}}). Substituting this into (10), then the first bracketed term in (1), then labeling the post-starvation number of individuals as ({({N}_{q,j}left(tright))}_{{NET}}):$${({N}_{q,j}left(tright))}_{{NET}}=left({N}_{q,j}left(tright)-{rho }_{q,j}left(tright)right)={N}_{q,j}left(tright)cdot left(1-left(1-{G}_{q,j}left(tright)right)right)={N}_{q,j}left(tright)cdot {G}_{q,j}left(tright)$$ (21) Approximate (14) deterministically by a fixed fractional parameter corresponding to the baseline random death rate:$${delta }_{q,j}left(tright)approx {N}_{q,j}left(tright)cdot {delta }_{0}$$ (22) Doing the same for mutation:$${{{{{{rm{{Upsilon }}}}}}}}_{q,j}left(tright)={G}_{q,j}left(tright)cdot {left({N}_{q,j}left(tright)right)}_{{NET}}cdot {mu }_{0}={N}_{q,j}left(tright)cdot {{G}_{q,j}left(tright)}^{2}cdot {mu }_{0}$$ (23) The term in (12) for mutation to (j) from other genotypes simplifies to$${{{{{{rm{{Upsilon }}}}}}}}_{q,x ,ne , j}left(tright)={sum }_{k,=,1}^{{j}_{{total}}}frac{{G}_{q,k}left(tright)cdot {left({N}_{q,k}left(tright)right)}_{{NET}}cdot {mu }_{0}}{{j}_{{total}}-1}={N}_{q,k}left(tright)cdot {{G}_{q,k}left(tright)}^{2}cdot {mu }_{0}$$Substituting Eqs. (21–23) into (1):$${N}_{q,j}left(tright)cdot {{G}_{q,j}left(tright)}^{2}cdot (1-{mu }_{0})-{N}_{q,j}left(tright)cdot {delta }_{0}+{N}_{q,k}left(tright)cdot {{G}_{q,k}left(tright)}^{2}cdot {mu }_{0}=0$$ (24) Noting that by Eqs. (3)–(5) combined with the above assumptions, the growth rate of the producer can be written as:$${G}_{q,{prod}}left(tright)={G}_{q,{cheat}}left(tright)cdot (1-{kappa }_{{prod},q})$$ (25) Writing (24) explicitly for each genotype:$${N}_{q,{non}-{prod}}left(tright)cdot {{G}_{q,{non}-{prod}}left(tright)}^{2}cdot (1-{mu }_{0})-{N}_{q,{non}-{prod}}left(tright)cdot {delta }_{0}+{N}_{q,{prod}}left(tright)cdot {{G}_{q,{prod}}left(tright)}^{2}cdot {mu }_{0}=0$$$${N}_{q,{prod}}left(tright)cdot {{G}_{q,{prod}}left(tright)}^{2}cdot (1-{mu }_{0})-{N}_{q,{prod}}left(tright)cdot {delta }_{0}+{N}_{q,{non}-{prod}}left(tright)cdot {{G}_{q,{non}-{prod}}left(tright)}^{2}cdot {mu }_{0}=0$$Adding:$${N}_{q,{non}-{prod}}left(tright)cdot {{G}_{q,{non}-{prod}}left(tright)}^{2}cdot left(1-{mu }_{0}right)-{N}_{q,{non}-{prod}}left(tright)cdot {delta }_{0}$$$$+{N}_{q,{prod}}left(tright)cdot {left({G}_{q,{non}-{prod}}left(tright)cdot left(1-{kappa }_{{prod},q}right)right)}^{2}cdot {mu }_{0}+{N}_{q,{prod}}left(tright)cdot {left({G}_{q,{non}-{prod}}left(tright)cdot left(1-{kappa }_{{prod},q}right)right)}^{2}cdot left(1-{mu }_{0}right)$$$$-{N}_{q,{prod}}left(tright)cdot {delta }_{0}+{N}_{q,{non}-{prod}}left(tright)cdot {{G}_{q,{non}-{prod}}left(tright)}^{2}cdot {mu }_{0}=0$$Because the mutation terms cancel:$$left({N}_{q,{non}-{prod}}left(tright)+{N}_{q,{prod}}left(tright)cdot {left(1-{kappa }_{{prod},q}right)}^{2}right)cdot left({{G}_{q,{non}-{prod}}left(tright)}^{2}-{delta }_{0}right)=0$$ (26) Substituting in for the growth rate terms (3–7) gives:$$left({N}_{q,{non}-{prod}}(t)+{N}_{q,{prod}}(t)cdot {left(1-{kappa }_{{prod},q}right)}^{2}right)cdot left({left({G}_{0}cdot {R}_{q,{BIOAVAILABLE}}(t)cdot {f}_{{conv}}right)}^{2}-{delta }_{0}right)=0$$ (27) By (16), (4), (6) and the above, total steady state assimilation of the growth limiting environmental substance by species (q) is:$${A}_{{R}_{q}}left(tright)=mathop{sum }limits_{k=1}^{{j}_{{total}}}frac{{G}_{q,k}left(tright)cdot left({N}_{q,k}left(tright)-{rho }_{q,k}left(tright)right)}{{G}_{q,{kPR}}}$$$$kern2.4pc=frac{{N}_{q,{non}-{prod}}left(tright)cdot {left({G}_{q,{non}-{prod}}left(tright)right)}^{2}}{{G}_{0}}+frac{left({N}_{q,{prod}}left(tright)cdot {left(1-{kappa }_{{prod},q}right)}^{2}right)cdot {left({G}_{q,{non}-{prod}}left(tright)right)}^{2}}{{G}_{0}cdot left(1-{kappa }_{{prod}}right)}$$$$=left({N}_{q,{non}-{prod}}left(tright)+{N}_{q,{prod}}left(tright)cdot left(1-{kappa }_{{prod},q}right)right)cdot {{G}_{0}cdot ({R}_{q,{BIOAVAILABLE}}left(tright)cdot {f}_{{conv}})}^{2}$$ (28) By (16–18), production of this substance by the producer allele in the other species (p , ne , q), assuming the various arguments above simultaneously apply to this species, is:$${P}_{{R}_{q}}left(tright)= frac{{G}_{p,{prod}}left(tright)cdot left({N}_{p,{prod}}left(tright)-{rho }_{p,{PRODUCER}}left(tright)right)}{{G}_{p,{prodPR}}}cdot {f}_{{conv},{PROD}}\ = {N}_{p,{prod}}left(tright)cdot left(1-{kappa }_{{prod},p}right) cdot {G}_{0}cdot {({R}_{p,{BIOAVAILABLE}}left(tright)cdot {f}_{{conv}})}^{2}cdot {f}_{{conv},{PROD}}$$ (29) Balance between input and output fluxes of each environmental substance requires ({varphi }_{{R}_{q}}left(tright)+{P}_{{R}_{q}}left(tright)={A}_{{R}_{q}}left(tright)), meaning that by substituting in ({A}_{{R}_{q}}left(tright)) from (28) it is possible to solve for bioavailable substance level, then substitute in the production flux of this substance derived from the producer allele in the other species (p,ne, q):$${R}_{q,{BIO}{AVAILABLE}}left(tright) =frac{1}{{f}!_{{conv}}}sqrt{frac{{varphi }_{{R}_{q}}left(tright)+{P}_{{R}_{q}}left(tright)}{left({N}_{q,{non}-{prod}}left(tright)+{N}_{q,{prod}}left(tright)cdot left(1-{kappa }_{{prod},q}right)right)cdot {G}_{0}}}\ =frac{1}{{f}!_{{conv}}}sqrt{frac{{varphi }_{{R}_{q}}left(tright)+{N}_{p,{prod}}left(tright)cdot left(1-{kappa }_{{prod},p}right)cdot {G}_{0}cdot {({R}_{p,{BIOAVAILABLE}}left(tright)cdot {f}_{{conv}})}^{2}cdot {f}_{{conv},{PROD}}}{left({N}_{q,{non}-{prod}}left(tright)+{N}_{q,{prod}}left(tright)cdot left(1-{kappa }_{{prod},q}right)right)cdot {G}_{0}}}$$ (30) Substituting this into (27) gives a symmetrical condition for steady state genotype frequencies and substance levels across the system:$$ left({N}_{q,{non}-{prod}}left(tright)+{N}_{q,{prod}}left(tright)cdot {left(1-{kappa }_{{prod},q}right)}^{2}right)cdot\ left(frac{{varphi }_{{R}_{q}}left(tright)+{N}_{p,{prod}}left(tright)cdot left(1-{kappa }_{{prod},p}right)cdot {G}_{0}cdot {({R}_{p,{BIOAVAILABLE}}left(tright)cdot {f}_{{conv}})}^{2}cdot {f}_{{conv},{PROD}}}{left({N}_{q,{non}-{prod}}left(tright)+{N}_{q,{prod}}left(tright)cdot left(1-{kappa }_{{prod},q}right)right)cdot {G}_{0}}-{delta }_{0}right)=0$$ (31) This solution illustrates the intuitive ideas that growth and reproduction balance random death at steady state and that the associated producer frequency is lower than that of the non-producer by a factor of the cost. (This factor is of second order because the growth rate is used both directly and (by (10)) in the calculation of starvations). Because our model is a discrete stochastic process, (31) can be viewed as an approximation to a steady state condition, subject to the above assumptions combined with the continuous generation of producers by mutation at a sufficient rate to preclude their extinction. The key point is that over long timescales in the finite populations with which we deal, organism-level selection unavoidably favors the non-producer, with no possibility for multi-level fecundity selection. The producer’s stable presence is thus attributable to the combination of mutation and cycle-level selection. More

Continental, regional, and local spatiotemporal patterns of carbon lossFor Africa’s primary tropical humid forest, carbon losses due to forest disturbances reached 42.2 ± 5.1 MtC yr−1 (mean ± standard deviation, where MtC yr−1 is one million metric tons of carbon loss per year) in 2019 and 53.4 ± 6.5 MtC yr−1 in 2020. Just 9 countries out of the 23 analyzed accounted for 95.0% of total gross losses in 2019 and 94.3% in 2020. These countries contain about 95.7% of all primary tropical humid forests of Africa, with the DRC accounting for 52.8%, Gabon 11.8%, the Republic of the Congo 11.0%, and Cameroon 9.8%. Of these, DRC and Cameroon were responsible for 49.3% and 19.1% of losses in 2019 and 44.7% and 20.6% in 2020. DRC and Cameroon had an annual increase of 15.0% and 36.5% respectively, between 2019 and 2020. From countries with at least 1 MtC emitted in the two years analyzed, Madagascar had the highest annual increase in carbon loss (+153.9%), while Equatorial Guinea is the only country with a decrease in carbon loss (−20.1%). Extending the carbon loss analysis for both past and future will help to better understand these variations and whether the COVID-19 global pandemic had any influence on the general increase between 2019 and 202019. While the absolute numbers for carbon loss estimates should be treated carefully and a sample-based approach should be preferred for an unbiased estimate of absolute numbers20, we focused our analysis on the trends of carbon loss at the continental, country, and local scale (Fig. 1 and Supplementary Fig. 1).Fig. 1: Carbon loss across Africa’s rainforests.We analyzed 23 countries containing primary moist forest. The aboveground carbon stock (green palette) underlies the carbon loss estimations (red palette). Several hotspots can be seen across these regions. The uncertainties of the carbon loss estimations are expressed as standard deviations and shown in Supplementary Fig. 1.Full size imageThe high temporal detail of the analysis revealed various monthly patterns of carbon losses for countries, highly related to local rainfall patterns18 (Fig. 2). Countries like Cameroon, Liberia, Nigeria, Central African Republic (CAR), and Madagascar showed a clear dry-wet seasonal variation in carbon loss per year, while the Republic of the Congo and the DRC, due to their latitudinal extent, exhibited two dry-wet season variations per year with varying intensities (Fig. 2). The seasonal variation can be explained by higher accessibility to forests during the dry months when activities related to smallholder agriculture and logging are more feasible than in the wet season when many roads become inaccessible.Fig. 2: Temporal patterns of carbon loss for the top 10 countries.We show monthly statistics for 2019 and 2020 and the associated uncertainty (black lines). We separate between high (red bars) and low (yellow bars) confidence alerts, the latter showing up for the last 3 months of 2020.Full size imageOne of the highest differences between the months with the most and the least carbon losses was found for Madagascar (72 times more carbon loss in March compared to November 2019). In CAR, the three consecutive months with the highest cumulative carbon loss (January to March 2020) contributed to 75.7% of the total annual loss (between February and April 2020), in Nigeria 73.9% (January to March 2020), Liberia 73.1% (February to April 2020), Madagascar 70.7% (September to November 2020), and Cameroon 62.2% (January to March 2020). Lower percentages were found for countries with mixed seasonality and patterns, like DRC 36.7% (January to March 2020), and the Republic of the Congo 32.8% (January to March 2020) (Fig. 2). For the latter two countries, we expect better-defined peaks of carbon loss at local scales, where climatic conditions are not mixed. The annual cumulative carbon loss (%) per country (Fig. 3) showed that Liberia, Nigeria, CAR, and Cameroon reached between 70-90% of their annual carbon loss in April, while Madagascar reached 60% in October. The DRC, Gabon, Republic of the Congo, Equatorial Guinea, and Ghana have a more gradual monthly increase of cumulative carbon loss with less contrasting seasonality effects. Monthly patterns of carbon losses between the two years analyzed resulted in a correlation coefficient of 0.94 for the CAR, 0.92 for the DRC, 0.91 for Madagascar, 0.90 for Gabon, and 0.83 for Cameroon (Supplementary Fig. 2). For the Republic of the Congo, the two years correlated 0.51. Knowing the peak months of carbon loss for each country and that these patterns are repeatable from one year to another can contribute to better target and prioritize enforcement activities, as well as predicting future patterns and early reporting of annual forest carbon losses.Fig. 3: Annual cumulative carbon loss (%) for both years analyzed, 2019 and 2020.Africa’s total cumulative carbon loss is shown with a black line. The 10 topmost emitting countries out of 23 countries analyzed are shown and represented by distinct colored lines.Full size imageSeveral hotspots of carbon losses can be seen in Fig. 1. The high spatial and temporal details of our analysis are shown in Fig. 4, where several local examples with different drivers of forest disturbances are shown, like logging roads, selective logging, mining, oil palm plantations, urban expansion, and small-holder agriculture. This kind of information, coupled with auxiliary datasets (e.g., legal concessions, protected areas) can identify the legality of forest disturbance21.Fig. 4: Local examples of approx. 10 × 10 km in extent showing different spatiotemporal patterns and drivers of carbon loss.The first column shows the carbon loss, the second column the associated uncertainty, the third column the day-of-the-year when the loss occurred, and the last column shows the monthly distribution of carbon loss and associated uncertainty for each local example. The center coordinates of each location are shown in the third column as latitude and longitude. Exact locations are shown in Supplementary Fig. 3. a Logging roads and selective logging in the Central African Republic, b mining of gold and titanium in the Republic of the Congo, c development of an oil palm plantation in Cameroon, d forest disturbance related to building a new capital city in Equatorial Guinea, and e small-scale agriculture expansion at the edge of the forest in the DRC.Full size imageImplications of rapid monitoring of local carbon lossNear-real time alerts combined with biomass maps result in spatially explicit forest carbon loss, unlike global tabular statistics of national data22,23. We provide new insights into the spatiotemporal dynamics of carbon loss with consistent assessment of accuracy that could enable transparency and completeness for countries reporting on their REDD + progress to the UNFCCC24. We provide monthly carbon loss estimates that could play a key role in local, national, and international forest initiatives for global carbon policy goals25. Such a system can be implemented with minimal costs and is based on open-source datasets and Google Earth Engine cloud computing platform26, thus enabling cost-effective national monitoring of forest carbon loss7. Providing rapid reporting on the location, time, and amount of carbon lost across Africa’s primary humid forest will help undertake immediate action to protect and conserve carbon-rich threatened forests. Furthermore, countries will be able to predict and estimate their annual carbon loss before a reporting period ends, thus having the opportunity to adjust their practices to meet their country-specific commitments for climate change mitigation initiatives.Limitations and future improvementsWe used the RADD alerts (Radar for Detecting Deforestation)18 with a minimum mapping unit (MMU) of 0.2 ha as accuracy estimates were available for this MMU. Events smaller than 0.2 ha would add to the total carbon loss but are by nature associated with higher uncertainties18. The implications of the RADD alerts using a global humid tropical forest product as a forest baseline for 201816,27,28 are twofold. First, the global nature of this product might result in inconsistencies at the local level18. Second, because the forest cover loss information used to generate the forest baseline is based on optical Landsat data, persistent cloud cover in the second half of 2018 in some areas led to missed reporting of forest disturbances, thus being detected at the beginning of 2019 by the RADD alerts. This possible overestimation of carbon loss at the start of 2019 is not an issue for a near-real-time alerting system since later months are not affected. Furthermore, the alerts do not distinguish between human-induced disturbances and natural forest disturbances18. When a new forest disturbance alert is detected, it will be confirmed or rejected within 90 days by subsequent Sentinel-1 images18. That is why our carbon loss reporting separates between high and low confidence alerts for the last three months of 2020, which is common for most forest disturbance alerting products18,29. We separated all the alerts into core and boundary pixels. Core alerts represent complete tree cover removal and we assumed complete carbon loss within a pixel. For boundary alerts, we assumed a 50% carbon loss since these mainly represent forest disturbances with partial tree cover removal. Detecting and quantifying the level of degradation remains challenging and future developments will minimize this uncertainty by providing variable percentages of degraded forest30. The timeliness and spatial details of future forest disturbance alerting products will improve with the availability of open access long-wavelength radar data from near-future satellite missions (e.g., NISAR L-band SAR in 202331), by using a combination of optical and radar forest disturbance alert products, and integration with high-resolution satellite products.We relied on an aboveground biomass baseline map from 201832, prior to RADD alerts starting from 2019. Biomass estimation for the tropical moist forests is based on ALOS-2 PALSAR-2 L-band satellite and its usage needs to account for the local biases, especially underestimating AGB values higher than 250 Mg ha−1 (ref. 32). Although we reduced this underestimation by adjusting the AGB map based on ground field data, more research is needed on providing up-to-date high-resolution aboveground carbon estimates33 that could further increase the accuracy of local carbon loss estimation. Radar-based estimation of forest carbon stocks is challenging over mountainous terrain and is less accurate in complex canopies3 and future integration of radar and optical satellite data will provide more robust estimates33. Nevertheless, new spaceborne missions (e.g., GEDI34, BIOMASS35) will provide an unprecedented amount of forest structure samples that will improve the algorithms and thus the final accuracy of aboveground biomass estimates.We focused on exploring and analyzing local carbon losses and showing high temporal and spatial patterns of carbon losses. We showed the country statistics to emphasize the temporal dynamics of carbon losses and compare the temporal profiles across our study region. Our approach was not to provide stratified area estimations36 associated with forest disturbances but we used this concept in the sense that we had a stratified sample of higher quality reference data18 to estimate the omission and commission errors and consider those in our uncertainty estimation on the pixel level. The analysis showed that omission and commission errors are small and rather balanced, and thus do not result in a major area bias for the forest disturbances. The uncertainties of the aboveground biomass product32 were adjusted for known regional biases using regional forest biomass plot data sources. With this approach, the original aboveground biomass map bias was partly corrected using a model-based approach deemed to be an alternative to a sample-based approach whenever country data are unavailable37. Our uncertainty analysis and error reduction showed that we expect only minor bias in the forest disturbance and the biomass data and the remaining uncertainties are propagated in our pixel-based uncertainty layer. More

Kraaijeveld, A. R. & Godfray, H. C. J. Trade-off between parasitoid resistance and larval competitive ability in Drosophila melanogaster. Nature 389(6648), 278–280 (1997).ADS 
CAS 
Article 

Google Scholar 
Lochmiller, R. L. & Deerenberg, C. Trade-offs in evolutionary immunology: Just what is the cost of immunity?. Oikos 88(1), 87–98 (2000).Article 

Google Scholar 
Zuk, M. & Stoehr, A. M. Immune defense and host life history. Am. Nat. 160(4), S9–S22 (2002).Article 

Google Scholar 
McKean, K. A. & Nunney, L. Increased sexual activity reduces male immune function in Drosophila melanogaster. Proc. Natl. Acad. Sci. 98(14), 7904–7909 (2001).ADS 
CAS 
Article 

Google Scholar 
Schwenke, R., Lazzaro, B. P. & Wolfner, M. F. Reproduction–immunity trade-offs in insects. Annu. Rev. Entomol. 61(1), 239–256. https://doi.org/10.1146/annurev-ento-010715-023924 (2016).CAS 
Article 
PubMed 

Google Scholar 
McNamara, K. B., Wedell, N. & Simmons, L. W. Experimental evolution reveals trade-offs between mating and immunity. Biol. Lett. 9(4), 20130262. https://doi.org/10.1098/rsbl.2013.0262 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
Nystrand, M. & Dowling, D. K. Effects of immune challenge on expression of life-history and immune trait expression in sexually reproducing metazoans—a meta-analysis. BMC Biol. 18(1), 135. https://doi.org/10.1186/s12915-020-00856-7 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Lawniczak, M. K. N. et al. Mating and immunity in invertebrates. Trends Ecol. Evol. 22(1), 48–55 (2007).Article 

Google Scholar 
Ahtiainen, J. J., Alatalo, R. V., Kortet, R. & Rantala, M. J. A trade-off between sexual signalling and immune function in a natural population of the drumming wolf spider Hygrolycosa rubrofasciata. J. Evol. Biol. 18(4), 985–991. https://doi.org/10.1111/j.1420-9101.2005.00907.x (2005).CAS 
Article 
PubMed 

Google Scholar 
Simmons, L. W., Zuk, M. & Rotenberry, J. T. Immune function reflected in calling song characteristics in a natural population of the cricket Teleogryllus commodus. Anim. Behav. 69, 1235–1241. https://doi.org/10.1016/j.anbehav.2004.09.011 (2005).Article 

Google Scholar 
Spencer, K. A., Buchanan, K. L., Leitner, S., Goldsmith, A. R. & Catchpole, C. K. Parasites affect song complexity and neural development in a songbird. Proc. R. Soc. B 272(1576), 2037–2043. https://doi.org/10.1098/rspb.2005.3188 (2005).Article 
PubMed 
PubMed Central 

Google Scholar 
Rantala, M. J., Koskimaki, J., Taskinen, J., Tynkkynen, K. & Suhonen, J. Immunocompetence, developmental stability and wingspot size in the damselfly Calopteryx splendens L. Proc R Soc B 267(1460), 2453–2457 (2000).CAS 
Article 

Google Scholar 
Clotfelter, E. D., Ardia, D. R. & McGraw, K. J. Red fish, blue fish: Trade-offs between pigmentation and immunity in Betta splendens. Behav. Ecol. 18(6), 1139–1145. https://doi.org/10.1093/beheco/arm090 (2007).Article 

Google Scholar 
Rantala, M., Jokinen, I., Kortet, R., Vainikka, A. & Suhonen, J. Do pheromones reveal male immunocompetence?. Proc. R. Soc. B 269, 1681–1685 (2002).Article 

Google Scholar 
Worden, B., Parker, P. & Pappas, P. Parasites reduce attractiveness and reproductive success in male grain beetles. Anim. Behav. 59, 543–550 (2000).CAS 
Article 

Google Scholar 
Barthel, A., Staudacher, H., Schmaltz, A., Heckel, D. G. & Groot, A. T. Sex-specific consequences of an induced immune response on reproduction in a moth. BMC Evol. Biol. 15(1), 282. https://doi.org/10.1186/s12862-015-0562-3 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Sadd, B. et al. Modulation of sexual signalling by immune challenged male mealworm beetles (Tenebrio molitor L.): Evidence for terminal investment and dishonesty. J. Evol. Biol. 19(2), 321–325. https://doi.org/10.1111/j.1420-9101.2005.01062.x (2006).CAS 
Article 
PubMed 

Google Scholar 
Chemnitz, J., Bagrii, N., Ayasse, M. & Steiger, S. Variation in sex pheromone emission does not reflect immunocompetence but affects attractiveness of male burying beetles—a combination of laboratory and field experiments. Sci. Nat. 104(7), 53. https://doi.org/10.1007/s00114-017-1473-5 (2017).CAS 
Article 

Google Scholar 
Johansson, B. G. & Jones, T. M. The role of chemical communication in mate choice. Biol. Rev. 82(2), 265–289. https://doi.org/10.1111/j.1469-185X.2007.00009.x (2007).Article 
PubMed 

Google Scholar 
Rantala, M. J., Kortet, R., Kotiaho, J. S., Vainikka, A. & Suhonen, J. Condition dependence of pheromones and immune function in the grain beetle Tenebrio molitor. Funct. Ecol. 17(4), 534–540 (2003).Article 

Google Scholar 
Niven, J. E. & Laughlin, S. B. Energy limitation as a selective pressure on the evolution of sensory systems. J. Exp. Biol. 211(11), 1792. https://doi.org/10.1242/jeb.017574 (2008).CAS 
Article 
PubMed 

Google Scholar 
Stöckl, A. et al. Differential investment in visual and olfactory brain areas reflects behavioural choices in hawk moths. Sci. Rep. 6(1), 26041. https://doi.org/10.1038/srep26041 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Elgar, M. A. et al. Insect antennal morphology: The evolution of diverse solutions to odorant perception. Yale J. Biol. Med. 91(4), 457–469 (2018).PubMed 
PubMed Central 

Google Scholar 
Symonds, M. R. E., Johnson, T. L. & Elgar, M. A. Pheromone production, male abundance, body size, and the evolution of elaborate antennae in moths. Ecol. Evol. 2(1), 227–246. https://doi.org/10.1002/ece3.81 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Chapman, R. F. Chemoreception: The significance of receptor numbers. In Advances in Insect Physiology (eds Berridge, M. J. et al.) 247–356 (Academic Press, Cambridge, 1982).
Google Scholar 
Symonds, M. R. E. & Elgar, M. A. The evolution of pheromone diversity. Trends Ecol. Evol. 23(4), 220–228. https://doi.org/10.1016/j.tree.2007.11.009 (2008).Article 
PubMed 

Google Scholar 
Wyatt, T. Pheromones and Animal Behaviour: Communication by Smell and Taste (Cambridge University Press, Cambridge, 2003).Book 

Google Scholar 
Elgar, M. A., Johnson, T. L. & Symonds, M. R. E. Sexual selection and organs of sense: Darwin’s neglected insight. Anim. Biol. 69(1), 63–82. https://doi.org/10.1163/15707563-00001046 (2019).Article 

Google Scholar 
Wang, Q. et al. 2018 Antennal scales improve signal detection efficiency in moths. Proc. R. Soc. B 285, 20172832. https://doi.org/10.1098/rspb.2017.2832 (1874).CAS 
Article 

Google Scholar 
Johnson, T. L., Symonds, M. & Elgar, M. Sexual selection on receptor organ traits: Younger females attract males with longer antennae. Sci. Nat. 104, 1–6 (2017).CAS 
Article 

Google Scholar 
Xu, J. & Wang, Q. Male moths undertake both pre- and in-copulation mate choice based on female age and weight. Behav. Ecol. Sociobiol. 63(6), 801–808. https://doi.org/10.1007/s00265-009-0713-x (2009).MathSciNet 
Article 

Google Scholar 
Fricke, C., Adler, M. I., Brooks, R. C. & Bonduriansky, R. The complexity of male reproductive success: Effects of nutrition, morphology, and experience. Behav. Ecol. 26(2), 617–624. https://doi.org/10.1093/beheco/aru240 (2015).Article 

Google Scholar 
Bernays, E. A. & Chapman, R. F. Phenotypic plasticity in numbers of antennal chemoreceptors in a grasshopper: Effects of food. J. Comp. Physiol. 183(1), 69–76. https://doi.org/10.1007/s003590050235 (1998).CAS 
Article 

Google Scholar 
Johnson, T. L., Symonds, M. R. E. & Elgar, M. A. 2017 Anticipatory flexibility: Larval population density in moths determines male investment in antennae, wings and testes. Proc. R. Soc. B 284(1866), 2017–2087. https://doi.org/10.1098/rspb.2017.2087 (1866).Article 

Google Scholar 
Pomiankowski, A. & Møller, A. P. A resolution of the lek paradox. Proc. R. Soc. Lond. B 260(1357), 21–29. https://doi.org/10.1098/rspb.1995.0054 (1995).ADS 
Article 

Google Scholar 
Cardé, R. & Baker, T. Sexual communication with pheromones. In Chemical Ecology of Insects (eds Bell, W. & Cardé, R.) (Chapman and Hall, London, 1984).
Google Scholar 
Kokko, H. & Wong, B. B. M. What determines sex roles in mate searcing?. Evolution 61(5), 1162–1175. https://doi.org/10.1111/j.1558-5646.2007.00090.x (2007).Article 
PubMed 

Google Scholar 
Alberts, A. Constraints on the design of chemical communication systems in terrestrial vertebrates. Am. Nat. 139, S62–S89 (1992).Article 

Google Scholar 
van Dongen, S., Matthysen, E., Sprengers, E. & Dhondt, A. A. Mate selection by male winter moths Operophtera brumata (Lepidoptera, Geometridae): Adaptive male choice or female control?. Behaviour 135, 29–42 (1998).Article 

Google Scholar 
Henneken, J., Goodger, J. Q. D., Jones, T. M. & Elgar, M. A. Diet-mediated pheromones and signature mixtures can enforce signal reliability. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2016.00145 (2017).Article 

Google Scholar 
Harari, A. R., Zahavi, T. & Thiéry, D. Fitness cost of pheromone production in signaling female moths. Evolution 65(6), 1572–1582. https://doi.org/10.1111/j.1558-5646.2011.01252.x (2011).Article 
PubMed 

Google Scholar 
Pham, H. T., McNamara, K. B. & Elgar, M. A. Socially cued anticipatory adjustment of female signalling effort in a moth. Biol. Lett. 16(12), 20200614. https://doi.org/10.1098/rsbl.2020.0614 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Morgan, F. D. & Cobbinah, J. R. Oviposition and establishment of Uraba lugens (Walker), the gum leaf skeletoniser. Aust. For. 40(1), 44–55. https://doi.org/10.1080/00049158.1977.10675665 (1977).Article 

Google Scholar 
Pham, H. T., McNamara, K. B. & Elgar, M. A. Age-dependent chemical signalling and its consequences for mate attraction in the gumleaf skeletonizer moth, Uraba lugens. Anim. Behav. 173, 207–213. https://doi.org/10.1016/j.anbehav.2020.12.010 (2021).Article 

Google Scholar 
McNamara, K. B., van Lieshout, E., Jones, T. M. & Simmons, L. W. Age-dependent trade-offs between immunity and male, but not female, reproduction. J. Anim. Ecol. 82(1), 235–244. https://doi.org/10.1111/j.1365-2656.2012.02018.x (2012).Article 
PubMed 

Google Scholar 
Simmons, L. W. Resource allocation trade-off between sperm quality and immunity in the field cricket, Teleogryllus oceanicus. Behav. Ecol. 23(1), 168–173. https://doi.org/10.1093/beheco/arr170 (2012).Article 

Google Scholar 
Triseleva, T. A. & Safonkin, A. F. Variation in antennal sensory system in different phenotypes of large fruit-tree tortrix Archips podana Scop (Lepidoptera: Tortricidae). Biol Bull 33(6), 568–572. https://doi.org/10.1134/s1062359006060069 (2006).Article 

Google Scholar 
Rasband, W. S. ImageJ (National Institutes of Health, Maryland USA, 2009).
Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Austria, 2013).
Google Scholar 
Sanes, J. R. & Hildebrand, J. G. Origin and morphogenesis of sensory neurons in an insect antenna. Dev. Biol. 51(2), 300–319. https://doi.org/10.1016/0012-1606(76)90145-7 (1976).CAS 
Article 
PubMed 

Google Scholar 
Gill, K. P., Wilgenburg, E. V., Macmillan, D. L. & Elgar, M. A. Density of antennal sensilla influences efficacy of communication in a social insect. Am. Nat. 182(6), 834–840. https://doi.org/10.1086/673712 (2013).Article 
PubMed 

Google Scholar 
Jayaweera, A. & Barry, K. L. Male antenna morphology and its effect on scramble competition in false garden mantids. Sci. Nat. 104(9), 75. https://doi.org/10.1007/s00114-017-1494-0 (2017).CAS 
Article 

Google Scholar 
Greenfield, M. D. Moth sex pheromones: An evolutionary perspective. Fla Entomol. 64(1), 4–17. https://doi.org/10.2307/3494597 (1981).Article 

Google Scholar 
McNamara, K. B., van Lieshout, E. & Simmons, L. W. The effect of maternal and paternal immune challenge on offspring immunity and reproduction in a cricket. J. Evol. Biol. 27(6), 1020–1028. https://doi.org/10.1111/jeb.12376 (2014).CAS 
Article 
PubMed 

Google Scholar 
Foster, S. P. & Anderson, K. G. 2020 Sex pheromone biosynthesis, storage and release in a female moth: Making a little go a long way. Proc. R. Soc. B 287, 20202775. https://doi.org/10.1098/rspb.2020.2775 (1941).CAS 
Article 

Google Scholar 
Gibb, A. R. et al. Major sex pheromone components of the Australian gum leaf skeletonizer Uraba lugens: (10E,12Z)-hexadecadien-1-yl acetate and (10E,12Z)-hexadecadien-1-ol. J. Chem. Ecol. 34(9), 1125–1133. https://doi.org/10.1007/s10886-008-9523-2 (2008).CAS 
Article 
PubMed 

Google Scholar 
Kerr, A. M., Gershman, S. N. & Sakaluk, S. K. Experimentally induced spermatophore production and immune responses reveal a trade-off in crickets. Behav. Ecol. 21(3), 647–654. https://doi.org/10.1093/beheco/arg035 (2010).Article 

Google Scholar 
Ahmed, A. M., Baggott, S. L., Maingon, R. & Hurd, H. The costs of mounting an immune response are reflected in the reproductive fitness of the mosquito Anopheles gambiae. Oikos 97(3), 371–377 (2002).Article 

Google Scholar 
Hurd, H. Host fecundity reduction: A strategy for damage limitation?. Trends Parasitol. 17(8), 363–368. https://doi.org/10.1016/S1471-4922(01)01927-4 (2001).CAS 
Article 
PubMed 

Google Scholar 
Adamo, S. A. Evidence for adaptive changes in egg laying in crickets exposed to bacteria and parasites. Anim. Behav. 57(1), 117–124. https://doi.org/10.1006/anbe.1998.0999 (1999).CAS 
Article 
PubMed 

Google Scholar 
Arnqvist, G. & Nilsson, T. The evolution of polyandry: Multiple mating and female fitness in insects. Anim. Behav. 60, 145–164 (2000).CAS 
Article 

Google Scholar 
Parker, G. A., Lessells, C. M. & Simmons, L. W. Sperm competition games: A general model for precopulatory male-male competition. Evolution 67(1), 95–109. https://doi.org/10.1111/j.1558-5646.2012.01741.x (2013).Article 
PubMed 

Google Scholar 
Simmons, L. W., Lüpold, S. & Fitzpatrick, J. L. Evolutionary trade-off between secondary sexual traits and ejaculates. Trends Ecol. Evol. 32(12), 964–976. https://doi.org/10.1016/j.tree.2017.09.011 (2017).Article 
PubMed 

Google Scholar 
Parker, G. A. & Pizzari, T. Sperm competition and ejaculate economics. Biol. Rev. 85(4), 897–934. https://doi.org/10.1111/j.1469-185X.2010.00140.x (2010).Article 
PubMed 

Google Scholar 
Katsuki, M. & Lewis, Z. A trade-off between pre- and post-copulatory sexual selection in a bean beetle. Behav. Ecol. Sociobiol. 69(10), 1597–1602. https://doi.org/10.1007/s00265-015-1971-4 (2015).Article 

Google Scholar 
Gage, M. J. G. Continuous variation in reproductive strategy as an adaptive response to population-density in the moth Plodia interpunctella. Proc. R. Soc. B 261(1360), 25–30 (1995).ADS 
Article 

Google Scholar 
Shiel, B. P., Sherman, C. D. H., Elgar, M. A., Johnson, T. L. & Symonds, M. R. E. Investment in sensory structures, testis size, and wing coloration in males of a diurnal moth species: Trade-offs or correlated growth?. Ecol. Evol. 5(8), 1601–1608. https://doi.org/10.1002/ece3.1459 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Rolff, J. Bateman’s principle and immunity. Proc. R. Soc. B 269(1493), 867–872. https://doi.org/10.1098/rspb.2002.1959 (2002).Article 
PubMed 
PubMed Central 

Google Scholar 
Calabrese, E. J. & Baldwin, L. A. Hormesis: A generalizable and unifying hypothesis. Crit. Rev. Toxicol. 31(4–5), 353–424. https://doi.org/10.1080/20014091111730 (2001).CAS 
Article 
PubMed 

Google Scholar 
Calabrese, E. J. & Mattson, M. P. How does hormesis impact biology, toxicology, and medicine?. NPJ Aging Mech. Dis. 3(1), 13. https://doi.org/10.1038/s41514-017-0013-z (2017).Article 
PubMed 
PubMed Central 

Google Scholar  More