Ecology

Settlement stage fishesSpecimen collectionData for settlement stage tropical larval fishes includes 1372 swimming speed measurements, collected from >75 unique taxa across 35 families of fishes, most of which are coral reef associated as adults. The data are collected from five locations, including: South Caicos Island (Turks and Caicos Islands, Caribbean – TCI), Green Island or Magnetic Island (exact location not recorded for individual samples, Great Barrier Reef, Australia GI/MI), Lizard Island (Great Barrier Reef, Australia – LI), Calabash Caye (Turneffe Islands Atoll, Belize – BLZ), and Moorea, Society Islands (MOR) (Table 1).Table 1 Sampling locations for settlement stage Ucrit data. Included are the Region, year of collection, location and associated name, as well as the total number of recorded measurements (count).Full size tableData from LI and TCI were obtained almost exclusively from specimens collected using light traps, placed 100–500 m meters off the leeward side of the Island, near either the School for Field Studies facilities (TCI) or the Lizard Island Research Station (LI). An additional eight specimens of newly hatched Acanthochromis polyacanthus, a pomacentrid species which does not have a pelagic phase, were captured with nets on the Lizard Island reefs. Data from GI/MI were obtained using a combination of light traps, beach seines, fence and dip nets.For data collected in Moorea (Mor), specimens arriving over night from the open ocean and attempting to settle on the reef were captured in nets placed on the reef crest. In Belize (BLZ) specimens were collected using a variety of techniques including crest nets, channel nets, light traps and night-light lift nets, although most individuals were collected using light traps and crest nets. Crest net locations were those reported in29. Unless stated otherwise in the “notes” field of the “ucrit_sett_dat” data table (see Online-only Table 1), all U-crit measurements on individuals captured by light traps or crest nets were made on the morning of capture, usually within 6 or 12 hours (please refer to the original publications for methodological details). In a few cases, some individuals were kept in the laboratory for up to 2 days to study changes in swimming speed associated with settlement (see24,30), and here the post-settlement status of the larvae was recorded in the field “stage” in the “fish_id_dat” data table (see Online-only Table 1). Some specimens of the pomacentrid, Abudefduf saxatilis were collected with hand nets from a fish attracting device deployed over a seagrass bed from a dock and are best considered as early post-settlement individuals, although the time since settlement is unknown. All specimens of the labrid, Clepticus parrae were collected with hand-nets from deep fore-reefs. Although they had settled to the fore-reef an unknown period of time before capture, these individuals had yet to undergo complete metamorphosis. Data from such post-settlement individuals should be used with caution, as it is known that swimming performance in some species decreases markedly upon settlement11,24.Most data were collected during the summer months (May through September for TCI and BLZ, November through February for LI), but in MOR, the data came from winter (August). In Belize, data were collected in 2003, 2004 and 2005, totalling 401 U-crit measurements. Data from TCI were collected in 2003, from 109 individuals. Data from LI represented just over half of the settlement stage swimming data (556 measurements), and were collected in 2001, 2002, 2003, 2004 and 2005. A total of 144 measurement were available from GI/MI and were collected in 1992. The 152 U-crit measurements from MOR were from 2010.Captured settlement stage larvae/pelagic juveniles were held in fresh seawater for a minimum of 1–2 hours to reduce stress prior to swimming trials, either with an aeration stone in 24 L buckets (BLZ), or in flow-through seawater aquarium facilities at the Lizard Island Research Station (LI) and the Department of Environment and Coastal Resources at South Caicos (TCI).U-crit protocolAll swimming experiments were conducted at ambient seawater temperatures, which ranged between 25 °C and 30 °C, depending on location and date.Settlement-stage individuals were swum using one of several swimming flumes, including a single-lane swimming chamber11,22, a six-lane swimming chamber12 (see Fig. 1), or a three-lane swimming chamber modified from the design of12. All swimming chambers were constructed from transparent Plexiglass (internal dimensions of swimming area: 185 mm × 50 mm × 50 mm). A removable lid, sealed with an O-ring was used to introduce fish to, and remove them from the chambers. One section of flow straighteners, 45-mm long, was placed just after the inflow in order to reduce turbulence within the chamber. Fish were retained within the swimming area by two 4.0 mm mesh metal retaining fences, which were covered with a finer mesh when required for very small larvae.Fig. 1Design of the swimming channel used for settlement stage larvae, pelagic juveniles and settled juveniles. Shown are Side view (a) & Top view (b, modified from12). This swimming channel can be operated at up to 50 cm s−1. Smaller designs with three channels, or only 1 channel that could obtain higher speeds were used for swimming faster individuals. For data collected by Leis and colleagues, higher speeds were obtained by blocking some lanes using Perspex.Full size imageFlow was generated using a 2.4 Kw swimming pool pump (although the size of the pump varied across the studies), or plumbed into a laboratory seawater system. The speed was set using a protractor mounted on a gate valve and calibrated using the procedures described under the technical validation section below. Faster speeds were also calibrated using an inline blue-white F-300 series flow meter. Flumes were plumbed using union valves so they could be dismantled and easily relocated and installed in field locations. Because the pumps used to run the flumes can heat the water temperature over time, they were plumbed with a minimum reservoir volume of 70 L, with a constant flow through of fresh seawater. A mercury thermometer located in the reservoir was used to ensure temperature remained ambient during the swimming trials. Example field deployments of the swimming channels at various locations can be seen in Fig. 2.Fig. 2Examples of the ‘fast’ swimming flume setup at various field locations. Shown are the Lizard Island Research Station (Great Barrier Reef, Australia, a), the Department of Environment and Coastal Resources aquarium facilities at South Caicos (Turks and Caicos Islands, b), and the dock at the University of Belize Institute of Marine Field Studies at Calabash Caye (Belize, c).Full size imageU-crit was measured by placing specimens in the swimming flume and incrementally increasing water speed until the individual could no longer maintain position for the full-time increment interval. The exact experimental protocols differed slightly among the studies. For fish measured at Lizard Island in November 2000–January 2001, November–December 2001 and South Caicos Island, and most fish at Calabash Caye, the experimental protocol followed7, with speed increments equivalent to approximately three total standard body lengths per second (bls−1) with a time interval of 2 min. This protocol was adopted because settlement stage larvae can vary substantially in size and subsequently their swimming capacity, as swimming speeds are strongly controlled by body size4. Aligning the speed increments with the approximate size category of fishes ensured that the overall duration of the U-crit experiment was relatively similar. For fish measured at Lizard Island during December 2003, speed increments used were 1.6 cm s−1 with a time interval of 5 min. At Lizard Island in 2005, specimens of Amblyglyphidon curacao were subjected to speed increments of 4.2 cm s−1 at intervals of 5 minutes. In Moorea in 2010, all individuals were subjected to speed increments of 6.1 cm s−1 at intervals of 2 minutes. For fish measured at Green Island and Magnetic Island speed increments used were 5 cm s−1 with a time interval of 5 min. At Calabash Caye an experiment was conducted to examine the impact of time increments on U-crit measurements, and the experimental protocol was recorded in this instance.U-crit swimming speed was calculated following17:$$U mbox{-} crit=U+left(t/ti,ast ,Uiright)$$
(1)
Where:U = penultimate speed (speed increment for which the fish swam for the entire duration of the set time interval). Ui = the velocity increment (varied by the specific study).t = the time swum in the final velocity incrementti = the set time interval for each velocity increment (varied by the specific study).While the speed increments used varied across studies in this collated dataset, previous studies have found no effect of varying the length of the time interval (ti) in terms of the resulting swimming speed between fish swum at two minute intervals and those swum at 15 minute intervals for six reef fish species10.Sample handling and morphological measurementsAfter each trial specimens were anaesthetised in chilled water or using clove oil (depending on the location and according to the relevant ethics approvals) and some were photographed while still fresh to maintain body flexibility and to avoid issues with shrinkage due to dehydration associated with preserved samples. Following photographing, the samples were preserved in either 70% ethanol, 95% ethanol, or 10% buffered formalin.From digital images the ImageTool (UTHSCSA 2002) software was used for image analysis. Measurements made from digital images (where available) are shown in Fig. 3, and included: total length (from the outer edge of the caudal fin to the tip of the upper jaw), caudal fin length (from the tip of the caudal fin to the caudal peduncle), body depth (the vertical height of the fish measured at the deepest region), body area (the area of the fish in lateral view excluding the fins but including the head and gut region), propulsive area (the area of the fish including the fins but excluding the head and gut region), muscle area (the area of the fish excluding the fins and the head and gut region), caudal fin depth, caudal peduncle depth and caudal fin area. All measurements were taken to the nearest 0.1 mm. Body width (at the widest region, usually the head) was also measured to the nearest 0.1 mm using vernier callipers. In some cases total lengths (TL) were measured pre-trial using callipers (BLZ, 2003 and 2004). Body length (BL, which is equivalent to SL for postflexion stages) was measured using an ocular micrometer on a dissecting microscope in some studies23.Fig. 3Morphological measurements of settlement stage fishes. Measurements include: total length (TL; outer edge of the caudal fin to the tip of the upper jaw), caudal fin length (CFL; tip of the caudal fin to the caudal peduncle), body depth (BD; height at the deepest region), body area (BA; area in lateral view excluding the fins), propulsive area (PA; area including the fins (naturally fully extended) but excluding the head and gut region where they are inflexible or lack overlaying muscle and cannot be used for propulsion), muscle area (MA; area excluding the fins and the head and gut region), caudal fin depth (CFD; widest section when fully extended), caudal peduncle depth (CPD; height at the narrowest point between the caudal fin and the fish’s body) and caudal fin area (CFA; area with the caudal fins naturally fully extended). Callipers were used to measure head width. Adapted from8.Full size imageLarval development dataset (Australia)Rearing protocolData gathered using a combination of the ‘fast’ and ‘slow’ swimming chambers (see below) on swimming abilities throughout development are available for six species, including two damselfish – Pomacentrus amboinensis and Pomacentrus mollucensis (Pomacentridae; Pomacentrinae); two cardinalfish – Ostorhinchus (Apogon) compressus and Sphaeramia nematoptera (Apogonidae); and two anemone fish – Amphiprion percula and Amphiprion melanopus (Pomacentridae; Amphiprioninae). Note that the pomacentrids have demersal eggs, whereas the apogonids orally brood their eggs.Australian specimens for assessing swimming speeds throughout larval development were obtained mostly from larvae reared at the James Cook University Aquarium facility, from adult broodstock collected from the northern Great Barrier Reef. Adult brood stock were kept in outside aquaria ranging in size from 1000 to 3000 L. The temperature of aquaria was kept between 27 and 29.5 °C, with larvae reared in the Autumn and Winter of 1998. Brood stock were fed a diet of chopped pilchards, prawns and Ascetes twice per day. Eggs were obtained from spawning broodstock before dark on the night of hatching and transferred to a rearing tank. Once hatched, larvae were reared and maintained in 200 L (120 × 60 × 30 cm) black painted glass aquaria that were illuminated by four “daylight” fluorescent tubes. The larvae were maintained in a 14:10 light/dark photo-period at 27.5–29 °C. Cultures of the algae Nannochloropsis sp. were used to green the water during the day. This kept light at the right intensity to prevent “bashing” behaviour (young larvae have a tendency to continually butt their heads against surfaces if the water is clear and the light intensity is too bright). Larvae were fed a diet of >52 micron sieved wild caught plankton, which was occasionally supplemented by rotifers and Artemia spp. when necessary. Larvae were fed twice per day to maintain prey densities of between 2–6 individuals per ml. Examples of ontogenetic series obtained through these rearing methods, and showing pre- and post- flexions stages are show in Fig. 4.Fig. 4Examples of larval developmental series obtained for larvae reared at the James Cook University Aquarium facility. Showin are Amphiprion melanopus (a) and Sphariamia nemaptopera (b).Full size imageU-crit protocolSwimming experiments for older [i.e., postflexion] larvae (see Fig. 4(a)ii–iv and Fig. 4(b)iv–vii) were carried out using the flumes described above for settlement stage fishes. However, these flumes were unsuitable for measurement of swimming capabilities of the delicate younger [i.e., preflexion] larvae (see Fig. 4(a)i and Fig. 4(b)ii,iii). Several characteristics had to be addressed in order to design equipment suitable for the measurement of the swimming capabilities of very young larvae. These included:

The apparatus needed to produce slow flow rates while maintaining laminar flow and minimal boundary layer effects. This is because newly hatched larvae are small enough to effectively utilise the boundary layer, which is broader for slower moving water.

The apparatus had to provide an environment suitable for very young larvae as the trauma of transferring larvae between containers can be fatal. Accordingly, stress associated with sudden changes in light intensity or water quality was minimised by “greening” the water with algae and the use of dark or clear surfaces to avoid “bashing” behaviour. In addition, the apparatus had to be set up within the immediate vicinity of the rearing tanks (or possibly in a rearing tank) to minimise the distance larvae had to be moved.

Two swimming channels were designed and used for younger larvae that were able to meet these requirements. These were designed to operate at “slow” and “medium” speeds. Both channels were able to produce laminar flow at much slower speeds. They consisted of a much wider swimming area so that most of the water flow occurred away from the sides, maximising the area of water not influenced by boundary layer effects. Both were able to be placed in a rearing aquarium of “greened” water. This prevented the larvae from exhibiting “bashing” behaviour, minimised the distance that larvae had to be transferred and meant that there was no change in water quality between the experimental apparatus and rearing tanks (Fig. 5).Fig. 5Design of swimming channels for younger larvae. Shown are side view (a) & Btop view (b). Dimensions are for the “slow” and “medium” flumes respectively.Full size imageFish were retained by a 0.3 mm mesh at the end of the swimming channel for the “slow” chamber and a 1 mm mesh for the “medium” chamber. The “slow” channel was powered by an Eheim 2,000 L per hour pump and the “medium” channel was powered by two such Eheim pumps. The speed for both channels was set using a protractor mounted on a gate valve as for the “fast” swimming chamber used for older larvae, and calibrated according to the description below under technical validation.Each clutch of eggs from each species was raised from hatching through to settlement and experiments were performed periodically throughout this larval period, with sampling days depending on the species. The first swimming trial was conducted on day 1, approximately 12 hours after hatching. Three clutches of each species were used for each swimming trial for the species Pomacentrus amboinensis, Sphaeramia nematoptera and Amphiprion melanopus to ensure that any clutch effects were considered4. While multiple broodstock were available for each species, no record was made at the time from which broodstock the replicate clutches were obtained. For other species only a single clutch was available. In some cases these data included light trap caught specimens to supplement the latest settlement stage. At each experimental age for each clutch 8–12 fish were used in the swimming trials.Larvae were subjected to incremental increases in flow rates equivalent to approximately 3 body lengths (BL) every two minutes until they could no longer maintain position, as for the experimental protocol described above for settlement stage fishes and U-crit calculated as per Eq. 1. Aligning the speed increments with the approximate size category of fishes ensured that the overall duration of the U-crit experiment was relatively similar throughout ontogeny.Sample handling and morphological measurementsFish that were swum, or siblings from the same batch at the same age, were anaesthetised in chilled water then fixed in 10% buffered formalin. After 12–48 hours, larvae were transferred to 70% alcohol and stored. Morphological measurements were carried out by capturing the image of each fish using a stereo dissecting microscope linked to a video recorder. These images were then saved as files on computer. As for settlement stage larvae, the image analysis program ImageTool was then used to measure lengths and areas for different regions of the fish.Measurements were made of total length (from the tip of the caudal fin to the tip of the upper jaw), body depth (the height of the fish measured at the deepest region), body area (the entire area of the fish excluding the fins) and total propulsive area (the area of the fish including the fins but excluding the head and gut region). The regions measured for both pre-flexion and post flexion larvae can be seen in Fig. 6.Fig. 6Measurements made on developmental series larvae. This includes post-flexion larvae which have developed a true caudal fin supported by a hypural plate and discrete soft rays) (a); and pre-flexion larvae that had no hypural plate or soft rays, but a continuous rayless fin-fold from anus to nape (b).Full size imageLarval development dataset (Taiwan and France)Data on development of swimming in larvae of ten species of pelagic-spawning tropical species of commercially important fishes reared by aquaculturists in Taiwan6,27,28 and two tropical species that brood their eggs that were reared for the aquarium trade in France are included26,28 (see Table 2). In addition, very limited, previously unpublished data on larvae of three species of pelagic spawning, commercial species reared in Taiwan are included. The emphasis in these studies was on postflexion-stage larvae, but for some species, swimming data on preflexion and flexion-stage larvae are included. The ‘standard’ six-lane swimming chamber was used for these measurements of U-crit. For larger larvae of some species, half of the lanes were closed off to achieve the faster speeds that these larvae can achieve. Despite this adjustment, some individuals were able to swim faster than the fastest speeds the swim chamber could achieve. In these cases, the speeds are reported in the database as greater than the maximum chamber speed.Table 2 Species whose U-crit swimming ontogeny were studied using reared larvae in Taiwan and France. Included are the location, family, species, the number of specimens assayed (N), the size range of the specimens, and the associated publication of the original data (where available).Full size tableIn Taiwan, larvae were obtained from commercial aquaculture farms (~22.4°N, 120.6°E) SE of Kaohsiung, southern Taiwan, in May 2004 and in May and June 2005. Rearing conditions varied with species, but most were reared in outdoor concrete or earth ponds. Exceptions were Epinephelus spp., which were reared in indoor concrete tanks, and Chanos chanos and some Eleutheronema tetradactylum, which were reared in outdoor concrete tanks under shade cloth. In most cases, the larvae were provided with a “natural” food source (phytoplankton and zooplankton that were resident in the pond). The aquaculturists did not maintain breeding stock, but obtained the pelagic eggs for rearing from elsewhere. The larvae obtained from the aquaculturists were placed in oxygenated plastic bags placed in insulated boxes and transported about 1 h by road to the National Museum of Marine Biology and Aquarium (NMMBA), Kenting, Taiwan (~22.1°N, 120.7°E). In the laboratory the larvae were acclimated in 40 l aquaria filled from the NMMBA seawater system. Each aquarium was fitted with an aerator and kept at ca. 25 °C. The larvae were fed twice daily with live, newly hatched brine shrimp (Artemia nauplii) and 50% of the total volume of water was exchanged with fresh seawater. The aquaria were cleaned daily by suctioning debris off the bottom. The species studied in Taiwan were all native to the western central Pacific, but the original brood stock may not have been obtained locally. The U-crit measurements were made in a shaded outdoor area where large tanks were located to hold adult fishes intended for either research purposes or for addition to the large public aquarium that forms part of the NMMBA campus. The extensive seawater system of NMMBA was used to supply seawater directly into the swimming chamber on a flow through basis. In some cases, this resulted in fluctuations in the calibration of the swimming chamber, which, as a result was calibrated more frequently than was normally the case. Water temperature in the chamber was recorded for each run, and all were within the range of temperatures in the nearby ocean, or in a few cases, the aquaculture ponds from which the larvae were obtained. The swimming chamber time increment interval was five minutes, with an increase in speed at each increment that varied with the flow from the seawater system and the number of lanes open in the swimming chamber, ranging from 1.6 to 5.3 cm s−1.The larvae studied at Lautan Production, a small company located in Meze, France (42.4°N, 3.6°E) in September 2010 were of two species reared for the aquarium trade. Gramma loreto is native to the western tropical Atlantic, and the brood stock came from Cuba. Pseudochromis fridmani is found only in the Red Sea, but the origin of the brood stock is otherwise unknown. Both species produce ‘egg balls’ that are laid in crevices or small caves and tended by an adult until hatching. The eggs typically hatch at night with little remaining yolk and with no fin-ray development, but with mouth open and eyes pigmented. Recently hatched larvae were removed from the spawning tank to rearing tanks with constant illumination and ‘green water’ at a temperature of 26 °C to 28 °C. Cohort date is for the morning when the larvae were removed from the spawning tank. For the first 5 days rotifers were supplied, and from 6 days after hatch (DAH), the larvae were fed with Artemia nauplii all by Lautan employees. Temperatures in the swimming chamber were similar to those in the rearing tank. Larvae of about 5 mm to settlement size (10–12 mm) were used to measure U-crit. The swimming chamber time increment interval was two minutes, with an increase in speed at each increment of 3.2 cm s−1.Larvae from Taiwan were either preserved in 75% ethanol or in some cases in Bouins Solution for future histology research. Larvae from Lautan were preserved in 75% ethanol. Measurements were made within 24–48 hours after preservation. Body length (BL) was measured on all larvae using a dissecting microscope ocular micrometer: this is Notochord Length (tip of snout to tip of notochord) for preflexion and flexion-stage larvae, and Standard Length for postflexion larvae (tip of snout to end of hypural plate). For some larvae from Taiwan additional measurements were made using Scion Image for Windows (Beta 4.02, Scion Corporation, Frederick, MD): Total Length (tip of snout to tip of posterior-most fin), Total Lateral Area (including fins) and Propulsive Area (Fig. 6), the last as defined by4 (see Fig. 6).Ethic declarationsData collated here are from a large array of studies collected across a range of institutions and locations, and to our knowledge in all cases complied with the required ethics procedures at the relevant institution at the time of data collection. Portions of this work were carried out under Australian Museum Animal Care and Ethics Approval 01/01 (JML) and James Cook Ethics Approvals A202, 402 (RF). In France, research was carried out under permits issued by CNRS to the USR 3278 CNRS/EPHE team to conduct research experiments in the field and laboratory at all locations (under the “Hygiène et Sécurité” section). In Moorea, the research was carried out under permits issued by le Délégué Régional à la recherche et à la technologie de la Polynesie française. More

Buxton, R. D. Forest management and the pine processionary moth. Outlook Agric. 12, 34–39 (1983).
Google Scholar 
Gatto, P. et al. Economic assessment of managing processionary moth in pine forests: A case-study in Portugal. J. Environ. Manage. 90, 683–691 (2009).PubMed 

Google Scholar 
Battisti, A., Larsson, S. & Roques, A. Processionary moths and associated urtication risk: Global change–driven effects. Annu. Rev. Entomol. 62, 323–342 (2017).CAS 
PubMed 

Google Scholar 
Moneo, I. et al. Medical and veterinary impact of the urticating processionary larvae. In Processionary Moths and Climate Change: An Update, 359–410 (Springer, 2015).Battisti, A. et al. Expansion of geographic range in the pine processionary moth caused by increased winter temperatures. Ecol. Appl. 15, 2084–2096 (2005).
Google Scholar 
Kerdelhué, C. et al. Quaternary history and contemporary patterns in a currently expanding species. BMC Evol. Biol. 9, 220 (2009).PubMed 
PubMed Central 

Google Scholar 
Robinet, C., Rousselet, J., Pineau, P., Miard, F. & Roques, A. Are heat waves susceptible to mitigate the expansion of a species progressing with global warming?. Ecol. Evol. 3, 2947–2957 (2013).PubMed 
PubMed Central 

Google Scholar 
Auger-Rozenberg, M. A. et al. Ecological responses of parasitoids, predators and associated insect communities to the climate-driven expansion of the pine processionary moth. In Processionary Moths and Climate Change: An Update, 311–357 (Springer, 2015).Garin, I. et al. Bats from different foraging guilds prey upon the pine processionary moth. PeerJ 7, e7169 (2019).PubMed 
PubMed Central 

Google Scholar 
Charbonnier, Y., Barbaro, L., Theillout, A. & Jactel, H. Numerical and functional responses of forest bats to a major insect pest in pine plantations. PLoS ONE 9, e109488 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Goiti, U., Aihartza, J. R., Almenar, D., Salsamendi, E. & Garin, I. Seasonal foraging by Rhinolophus euryale (Rhinolophidae) in an Atlantic rural landscape in northern Iberian Peninsula. Acta Chiropterol. 8, 141–155 (2006).
Google Scholar 
Russo, D. et al. Habitat selection in sympatric Rhinolophus mehelyi and R. euryale (Mammalia: Chiroptera). J. Zool. 266, 327–332 (2005).
Google Scholar 
Vincent, S., Nemoz, M. & Aulagnier, S. Activity and foraging habitats of Miniopterus schreibersii (Chiroptera: Miniopteridae) in southern France: Implications for its conservation. Hystrix Ital. J. Mammal. https://doi.org/10.4404/hystrix-22.1-4524 (2010).Article 

Google Scholar 
Rydell, J. Site fidelity in the northern bat (Eptesicus nilssoni) during pregnancy and lactation. J. Mammal. 70, 614–617 (1989).
Google Scholar 
Baroja, U. et al. Bats actively track and prey on grape pest populations. Ecol. Indic. 126, 107718 (2021).
Google Scholar 
Aldasoro, M. et al. Gaining ecological insight on dietary allocation among horseshoe bats through molecular primer combination. PLoS ONE 14, e0220081 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Baroja, U. et al. Pest consumption in a vineyard system by the lesser horseshoe bat (Rhinolophus hipposideros). PLoS ONE 14, e0219265 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Vallejo, N. et al. The diet of the notch-eared bat (Myotis emarginatus) across the Iberian Peninsula analysed by amplicon metabarcoding. Hystrix Ital. J. Mammal. 30, 59–64 (2019).
Google Scholar 
Bohmann, K. et al. Molecular diet analysis of two African free-tailed bats (Molossidae) using high throughput sequencing. PLoS ONE 6, e21441 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Pompanon, F. et al. Who is eating what: Diet assessment using next generation sequencing?. Mol. Ecol. 21, 1931–1950 (2012).CAS 
PubMed 

Google Scholar 
Elbrecht, V. & Leese, F. Validation and development of COI metabarcoding primers for freshwater macroinvertebrate bioassessment. Front. Environ. Sci. 5, 11 (2017).
Google Scholar 
Evans, N. T. et al. Quantification of mesocosm fish and amphibian species diversity via environmental DNA metabarcoding. Mol. Ecol. Resour. 16, 29–41 (2016).CAS 
PubMed 

Google Scholar 
Harper, L. R. et al. Needle in a haystack? A comparison of eDNA metabarcoding and targeted qPCR for detection of the great crested newt (Triturus cristatus). Ecol. Evol. 8, 6330–6341 (2018).PubMed 
PubMed Central 

Google Scholar 
Deagle, B. E. et al. Counting with DNA in metabarcoding studies: How should we convert sequence reads to dietary data?. Mol. Ecol. 28(2), 391–406 (2019).PubMed 

Google Scholar 
Piñol, J., Mir, G., Gomez-Polo, P. & Agustí, N. Universal and blocking primer mismatches limit the use of high-throughput DNA sequencing for the quantitative metabarcoding of arthropods. Mol. Ecol. Resour. 15, 819–830 (2015).PubMed 

Google Scholar 
Jarman, S. N., Deagle, B. E. & Gales, N. J. Group-specific polymerase chain reaction for DNA-based analysis of species diversity and identity in dietary samples. Mol. Ecol. 13, 1313–1322 (2004).CAS 
PubMed 

Google Scholar 
Piggott, M. P. Evaluating the effects of laboratory protocols on eDNA detection probability for an endangered freshwater fish. Ecol. Evol. 6, 2739–2750 (2016).PubMed 
PubMed Central 

Google Scholar 
Knudsen, S. W. et al. Species-specific detection and quantification of environmental DNA from marine fishes in the Baltic Sea. J. Exp. Mar. Biol. Ecol. 510, 31–45 (2019).CAS 

Google Scholar 
Kaňuch, P., Hájková, P., Řehák, Z. & Bryja, J. A rapid PCR-based test for species identification of two cryptic bats Pipistrellus pipistrellus and P. pygmaeus and its application on museum and dropping samples. Acta Chiropterol. 9, 277–282 (2007).
Google Scholar 
Czernik, M. et al. Fast and efficient DNA-based method for winter diet analysis from stools of three cervids: Moose, red deer, and roe deer. Acta Theriol. 58, 379–386 (2013).
Google Scholar 
Schattanek, P., Riccabona, S. A., Rennstam Rubbmark, O. & Traugott, M. Detection of prey DNA in bat feces: Effects of time since feeding, meal size, and prey identity. Environ. DNA 3, 959–969 (2021).
Google Scholar 
Martin, K. J. & Rygiewicz, P. T. Fungal-specific PCR primers developed for analysis of the ITS region of environmental DNA extracts. BMC Microbiol. 5, 28 (2005).PubMed 
PubMed Central 

Google Scholar 
Nowakowska, J. A., Malewski, T., Tereba, A. & Oszako, T. Rapid diagnosis of pathogenic Phytophthora species in soil by real-time PCR. For. Pathol. 47, e12303 (2017).
Google Scholar 
Bott, N. J. et al. Toward routine, DNA-based detection methods for marine pests. Biotechnol. Adv. 28, 706–714 (2010).CAS 
PubMed 

Google Scholar 
Schmidt, B. R., Kery, M., Ursenbacher, S., Hyman, O. J. & Collins, J. P. Site occupancy models in the analysis of environmental DNA presence/absence surveys: A case study of an emerging amphibian pathogen. Methods Ecol. Evol. 4, 646–653 (2013).
Google Scholar 
McCracken, G. F., Brown, V. A., Eldridge, M. & Westbrook, J. K. The use of fecal DNA to verify and quantify the consumption of agricultural pests. Bat Res. News 46, 195–196 (2005).
Google Scholar 
McCracken, G. F. et al. Bats track and exploit changes in insect pest populations. PLoS ONE 7, e43839 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Marshall, O. J. PerlPrimer: Cross-platform, graphical primer design for standard, bisulphite and real-time PCR. Bioinformatics 20, 2471–2472 (2004).CAS 
PubMed 

Google Scholar 
Simonato, M. et al. Host and phenology shifts in the evolution of the social moth genus Thaumetopoea. PLoS ONE 8, e57192 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Aljanabi, S. M. & Martinez, I. Universal and rapid salt-extraction of high quality genomic DNA for PCR-based techniques. Nucleic Acids Res. 25, 4692–4693 (1997).CAS 
PubMed 
PubMed Central 

Google Scholar 
Razgour, O. et al. High-throughput sequencing offers insight into mechanisms of resource partitioning in cryptic bat species. Ecol. Evol. 1, 556–570 (2011).PubMed 
PubMed Central 

Google Scholar 
Page, A. & Gomez-Curet, I. Assuring reliability of qPCR & RT-PCR results: Use of spectrophotometry on nucleic acid samples before experiment improves outcome. Genet. Eng. Biotechnol. News 31(16), 26–26 (2011).
Google Scholar 
Hall, T. A. ioEdit: A user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucleic Acids Symp. Ser. 41, 41 (1999).
Google Scholar 
Kearse, M. et al. Geneious basic: An integrated and extendable desktop software platform for the organisation and analysis of sequence data. Bioinformatics 28, 1647–1649 (2012).PubMed 
PubMed Central 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (2020). (R Foundation for Statistical Computing). https://www.R-project.org/. Accessed 10 May 2021.Pinheiro, J., Bates, D., DebRoy, S. & Sarkar, D. nlme: Linear and nonlinear mixed effects models. R Package Version 3, 1–89 (2021).
Google Scholar 
Wickham, H. Ggplot2: Elegrant Graphics for Data Analysis (Springer, 2016).MATH 

Google Scholar 
Arrizabalaga-Escudero, A. et al. Trait-based functional dietary analysis provides a better insight into the foraging ecology of bats. J. Anim. Ecol. 88, 1587–1600 (2019).PubMed 

Google Scholar 
Curtsdotter, A. et al. Ecosystem function in predator–prey food webs—Confronting dynamic models with empirical data. J. Anim. Ecol. 88, 196–210 (2019).PubMed 

Google Scholar 
Michalko, R., Pekár, S. & Entling, M. H. An updated perspective on spiders as generalist predators in biological control. Oecologia 189, 21–36 (2019).ADS 

Google Scholar 
Sun, C. et al. polymerase chain reaction assisted by metal–organic frameworks. Chem. Sci. 11, 797–802 (2020).CAS 

Google Scholar 
Roux, K. H. Optimisation and troubleshooting in PCR. Cold Spring Harbor Protoc. 4, 66 (2009).
Google Scholar 
Xia, Z. et al. Conventional versus real-time quantitative PCR for rare species detection. Ecol. Evol. 8, 11799–11807 (2018).PubMed 
PubMed Central 

Google Scholar 
Yang, T. B., Liu, J. & Chen, J. Compared with conventional PCR assay, qPCR assay greatly improves the detection efficiency of predation. Ecol. Evol. 10, 7713–7722 (2020).PubMed 
PubMed Central 

Google Scholar 
Mauvisseau, Q. et al. Influence of accuracy, repeatability and detection probability in the reliability of species-specific eDNA based approaches. Sci. Rep. 9, 1–10 (2019).
Google Scholar 
Smith, C. J. & Osborn, A. M. Advantages and limitations of quantitative PCR (Q-PCR)-based approaches in microbial ecology. FEMS Microbiol. Ecol. 67, 6–20 (2009).CAS 
PubMed 

Google Scholar 
King, R. A., Read, D. S., Traugott, M. & Symondson, W. O. C. Molecular analysis of predation: A review of best practice for DNA-based approaches. Mol. Ecol. 17, 947–963 (2008).CAS 
PubMed 

Google Scholar 
Burbank, L. P. & Ortega, B. C. Novel amplification targets for rapid detection and differentiation of Xylella fastidiosa subspecies fastidiosa and multiplex in plant and insect tissues. J. Microbiol. Methods 155, 8–18 (2018).CAS 
PubMed 

Google Scholar 
Alberdi, A. et al. Promises and pitfalls of using high-throughput sequencing for diet analysis. Mol. Ecol. Resour. 19, 327–348 (2019).PubMed 

Google Scholar 
Sow, A., Haran, J., Benoit, L., Galan, M. & Brévault, T. DNA metabarcoding as a tool for disentangling food webs in agroecosystems. Insects 11, 294 (2020).PubMed Central 

Google Scholar 
Wood, S. A. et al. A comparison of droplet digital polymerase chain reaction (PCR), quantitative PCR and metabarcoding for species-specific detection in environmental DNA. Mol. Ecol. Resour. 19, 1407–1419 (2019).CAS 
PubMed 

Google Scholar 
Maslo, B. et al. Chirosurveillance: The use of native bats to detect invasive agricultural pests. PLoS ONE 12, e0173321 (2017).PubMed 
PubMed Central 

Google Scholar 
Purcell, R. V. et al. Comparison of standard, quantitative and digital PCR in the detection of enterotoxigenic Bacteroides fragilis. Sci. Rep. 6, 1–8 (2016).MathSciNet 

Google Scholar 
Behrens-Chapuis, S., Herder, F. & Geiger, M. F. Adding DNA barcoding to stream monitoring protocols—What’s the additional value and congruence between morphological and molecular identification approaches?. PLoS ONE 16(1), e0244598 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar  More

Patch formationThe individual-based simulations produce, as an immediate result, the position of all slugs at any given time t; an example is given in Fig. 2a in the case of ‘small’ field (10times 10) m. Analysing information describing the system state when it is presented in this format can be difficult. It is a particular problem when comparing it with field data where the system state is quantified by the slug trap counts (regarded as a proxy for the local slug density) at selected spatial locations, e.g. in a rectangular grid (cf. Fig. 1a). We therefore split the spatial domain into 100 bins by dividing its linear size both in x and y into 10 equal intervals and calculate the number of slugs in each bin, i.e. the population density at the location of each bin. In order to make the results more accessible for the visual perception, we then show the binned numbers as a continuous plot of the population density. As an example, Fig. 2b shows the distribution of the population density corresponding to the simulation data shown in Fig. 2a.Figure 2Example of simulation results and their visualization obtained at (t=100) for (R=1) (meters) and the total number of slugs (N=10^4) in a (10times 10) m field. The chosen threshold density is equal to the average slug density, i.e. (d=100) (slugs/m(^{-2})). Other movement parameters are given in the text, see the lines after Eqs. (1), (3) and (5). (a) Positions of all individual slugs, (b) the corresponding density distribution reconstructed from the bin counts (see details in the text) using linear interpolation.Full size imageWe readily observe that the simulated spatial distribution of slugs is apparently heterogeneous. Two questions immediately arise here as to (i) whether this heterogeneity is different from the heterogeneity of a purely statistical origin and (ii) how the density distribution evolves in time. To answer these questions, Fig. 3b,c show the simulation results after a series of increasing time periods using the same initial condition (Fig. 3a) used for Fig. 2. It is readily seen that the distribution evolves with time and the degree of heterogeneity (e.g. as described by the difference between the smallest and the largest values of the population density, inferred from the numbers on the colour bar) tends to increase over the course of time resulting in the formation of high density patches (shown by the yellow colour). Also, the size of individual patches changes, with a tendency to increase until it reaches a certain value (see the next section for details). For comparison, Fig. 3e,f show the spatial distribution obtained at the same moments of time as in Fig. 3b,c respectively, but in case of purely random density-independent movement; no high density patches emerge in that case.Figure 3The spatial distribution of (N=10^4) slugs shown at different moments of time: (a,d) (t=0), (b,e) (t=10^3), (c,f) (t=10^4). Distributions in the upper row (a–c) are obtained using the density dependent movement model (as is described in “Methods” section). Parameter values are the same as in Fig. 2. For comparison, the lower row (d–f) shows the distributions obtained in the case of density independent movement. While patches of high density (shown by yellow colour) emerge in the course of time in the case of density dependent movement, they do not emerge in the case of purely random, density independent movement.Full size imageSimulations show that the emerging high density patches are dynamic rather than stationary (even in the large-time limit, for more details, see Appendix A.2 in online Supplement Information). No stationary distribution emerges in the course of time. However, inspection of the results shown in Figs. 2b and 3b,c (as well as results obtained in other simulation runs, not shown here for the sake of brevity) reveals that the patch dynamics is rather slow, so that some of the patches roughly preserve their size and location on the timescale of (t=10^4), i.e. about 3 weeks in dimensional units, which is in a good agreement with the field data, see Section 2.Note that, since our model is inherently stochastic, the emerging spatial distribution will differ in the precise shape and position of the patches between model runs. However, the formation of a distinct patchy structure is a generic property of the system. In this sense, the patterns shown in Fig. 3b,c are typical for the system’s dynamics. Moreover, the formation of the patchy structure appears to be robust and does not depend on the initial conditions. For example, in the case where the initial condition is chosen as a dense release (i.e., all animals are initially inside a single patch), over the course of time the initial patch eventually splits into a number of smaller patches resulting, for the same parameter values as in Fig. 3, in a spatial distribution qualitatively similar to those shown in Fig. 3; see34 for all simulation details.We now recall that, while the movement parameters in Eqs. (1–5) are determined from the field data with a sufficient accuracy, the value of threshold density d where the movement type switches is only roughly estimated. Therefore, the next step is to investigate whether the formation of a patchy spatial distribution is sensitive to the threshold density. For this purpose, simulations were run with a different value of d. The results are shown in Fig. 4. We observe that the variation in d will not eliminate the heterogeneity, a distinctly patchy spatial distribution develops for all values of d used in Fig. 4. The shape and size of the patches (as is readily seen from the visual inspection of the spatial distributions) as well as the difference between the maximum and minimum values of the population density varies slightly for different d without showing a clear tendency. Patchiness appears to be robust to the value of density threshold in a broad range of d (see also Fig. 9 below), unless the average slug density is much smaller than the density threshold; in this case, slug movement is always density independent and distinct patches never form (apart from purely stochastic fluctuations of a small magnitude, cf. Fig. 3e,f).Figure 4The spatial distribution of (N=10^4) slugs at (t=10,000) simulated for different values of the threshold density: (a) (d=80), (b) (d=100) and (c) (d=120) (slugs/m(^{-2})). Other parameters are as in Fig. 2.Full size imageA similar question arises about the effect of the perception radius, which value is only roughly estimated. Figure 5 shows the results obtained for different R. We observe that a distinct patchy structure emerges for values of R over a broad range, which includes the range estimated from the field data. However, contrary to the density threshold, the degree of spatial heterogeneity clearly depends on R. The typical size of the patches tends to increase with R while the number of the patches decreases accordingly. For a sufficiently large R, a single high density patch is formed, cf. Fig. 5c. This effect of the increase in R can be explained as follows. The perception radius is, by its definition, the distance within which slugs react to each other by slowing down their movement. It is a characteristic length of the population distribution, with the meaning similar to the correlation length. Slowing down of slug movement eventually leads to their numbers building up at the scale consistent with that characteristic length. Note that the average radius of the single patch shown in Fig. 5c is about 2–3 m, and this is consistent with the used value (R=3).Figure 5The spatial distribution of (N=10^4) slugs at (t=10^4) simulated for different values of the perception radius: (a) (R=0.5), (b) (R=1) and (c) (R=3). Other parameters are as in Fig. 2.Full size imagePatchiness quantificationWe now complement the visual inspection of the patchy pattern with a more quantitative assessment. There are several measures or indices that are used in statistical ecology for this purpose35. In particular, the Morisita index36 (I_M) provides a measure of how likely two individuals randomly selected from a given spatial domain are found within the same bin (e.g., quadrat) compared to that of a random distribution. It can be shown37 that (I_M=1) if the individuals are distributed randomly (with a constant probability density) and (I_M >1) if the individuals are aggregated for reasons other than purely statistical ones. The Morisita index has been widely used to quantify the heterogeneity of the spatial distribution38,39,40. It can be calculated as follows:$$begin{aligned} I_M = frac{Q}{N(N-1)}sum _{k=1}^{Q}n_k(n_k-1), end{aligned}$$
(8)
where (n_k) is the number of individuals in the kth bin, Q is the total number of bins (quadrats) and N is the total number of individuals.Figure 6 shows the Morisita index calculated at each time step for a few cases with a different total number of slugs. Note that, since the movement of any individual slug is a stochastic process, (I_M) is a stochastic quantity. In order to make sure that any tendency in (I_M) to change is not obscured by random fluctuations (which can be of considerable amplitude), Fig. 6 shows (I_M) averaged over ten simulation runs.Figure 6The mean Morisita Index from 10 simulation runs for different number of slugs: (a) (N=2.5cdot 10^3), (b) (N=5cdot 10^3) and (c) (N=10^4). Here (R=200) and (d=10), other parameters are as in Fig. 2. The red curves show the Morisita index obtained in the corresponding cases of a purely random individual movement, i.e., without any density dependence.Full size imageIt is readily observed that, in each case shown in Fig. 6, starting from (I_M=1) at (t=0) (which corresponds to our choice of a uniform random initial distribution), the Morisita index then shows a clear tendency to increase on average (apart from the random fluctuations) until approximately (t=8000) when it stabilizes at a certain value (I_M^* >1). We therefore conclude that (i) the spatial patterns obtained in our model are self-organized, i.e. caused by interactions between the individual slugs and not by purely random, statistical effects, and (ii) in the course of time, the system reaches a dynamical equilibrium so that the Morisita index stops growing. For comparison, the red curves in Fig. 6 show the Morisita index obtained in the corresponding cases of purely random density independent movement when no high density patches are formed (cf. Fig. 3e,f).Note that the Morisita index tends to increase slightly with an increase in the average slug density (i.e., for a fixed size of the spatial domain, with an increase in the total number of slugs N). Indeed, the value (I_M^*) at which the patchiness stabilizes after a long time period rises somewhat with an increase in N, cf. Fig. 6a–c.In order to provide an overall description of the emerging heterogeneous distribution, with a focus on the density dependence of the properties of the emerging patchy pattern, we use the Taylor’s Power Law aggregation index25. It is well known41 that, for populations of many different species, the mean (m) and the variance (v) of population numbers in a sample are not independent but related by a power law:$$begin{aligned} v = alpha m^{beta }, end{aligned}$$
(9)
where (alpha) is a coefficient and exponent (beta) is called the aggregation index. In the case where a species has a uniform spatial distribution, (beta) tends towards zero; for a purely random distribution (e.g., described by Poisson distribution), (beta =1). Values (beta >1) reflect progressively greater aggregation, i.e. formation of patches in the field resulting from self-organized, density dependent dynamics of the system.By writing Eq. (9) on the logarithmic scale:$$begin{aligned} log (v)=alpha + beta log (m), end{aligned}$$
(10)
values of (alpha) and (beta) can be established by fitting (10) to relevant data; in particular, the aggregation index (beta) is defined as the slope of the regression line.Figure 7 shows the aggregation index calculated for the patchy spatial patterns obtained in simulations. Recall that, when starting with a random uniform distribution, it takes a certain time for the patchy structure to develop. Correspondingly, in each simulation, the system was allowed to evolve over a certain time (t^*) before the population was binned and the mean and the variance of the distribution were calculated. The (a) and (b) panels in Fig. 7 are obtained for (t^*=10^3) and (t^*=10^4), respectively. We readily see that in both cases (beta >1) ((beta =1.066) and (beta =1.173), respectively) confirming the self-organised, inherent nature of the spatial patterns. Note that (beta) is larger in Fig. 7b, that is for a larger (t^*), which is consistent with an earlier observation that the patchy structure becomes fully developed by (tsim 8000).Figure 7The variance of bin populations plotted against the mean bin population on a grid of 100 bins shown on a log-log scale. Each point is calculated from a single simulation and the total population is varied between simulations from (N=300) to (N=9900) in intervals of 300. The density dependent parameters are (d=1) (equal to the average slug density) and (R=2). (a) (t=1000), the regression equation (10) is (log (v)=1.066log (m)-0.1774), (r^2=0.9686), (b) (t=10,000), (log (v)=1.173log (m)-0.4551), (r^2=0.9427).Full size imageEvaluating trap countsIn the above, we have shown that our IBM model parameterized using field data on individual slug movement produces a distinctly heterogeneous, patchy spatial distribution. The degree of aggregation, both in terms of the Morisita index and Taylor’s aggregation index is higher than it would be due to purely stochastic reasons. The emerging patchy structure is self-organized in the sense that it emerges not due to the effect of external factors but due to an inherent property of the system such as the density dependent slug movement.The simulated spatial patterns exhibit properties similar to the distribution of slugs in the field, in particular showing similar values of the aggregation index, which in the field data was found18 to be in the range (1.09 More

Clare, E., Barber, B., Sweeney, B., Hebert, P. & Fenton, M. Eating local: Influences of habitat on the diet of little brown bats (Myotis lucifugus). Mol. Ecol. 20, 1772–1780 (2011).CAS 
PubMed 
Article 

Google Scholar 
Larter, N. C. & Gates, C. C. Diet and habitat selection of wood bison in relation to seasonal changes in forage quantity and quality. Can. J. Zool. 69, 2677–2685 (1991).Article 

Google Scholar 
Veloso, C. & Bozinovic, F. Dietary and digestive constraints on basal energy metabolism in a small herbivorous rodent. Ecology 74, 2003–2010 (1993).Article 

Google Scholar 
Hawke, T., Bates, H., Hand, S., Archer, M. & Broome, L. Dietary analysis of an uncharacteristic population of the Mountain Pygmy-possum (Burramys parvus) in the Kosciuszko National Park, New South Wales, Australia. PeerJ 7, e6307 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Pearce-Higgins, J. W. Using diet to assess the sensitivity of northern and upland birds to climate change. Clim. Res. 45, 119–130 (2010).Article 

Google Scholar 
Eitzinger, B. et al. Assessing changes in arthropod predator-prey interactions through DNA-based gut content analysis—variable environment, stable diet. Mol. Ecol. 28, 266–280 (2019).CAS 
PubMed 
Article 

Google Scholar 
Edgar, G. J. Predator-prey interactions in seagrass beds. II. Distribution and diet of the blue manna crab Portunus pelagicus Linnaeus at Cliff Head, Western Australia. J. Exp. Mar. Biol. Ecol. 139, 23–32 (1990).Article 

Google Scholar 
Beck, J. L., Peek, J. M. & Strand, E. K. Estimates of elk summer range nutritional carrying capacity constrained by probabilities of habitat selection. J. Wildl. Manag. 70, 283–294 (2006).Article 

Google Scholar 
DeYoung, R. W., Hellgren, E. C., Fulbright, T. E., Robbins, W. F. Jr. & Humphreys, I. D. Modeling nutritional carrying capacity for translocated desert bighorn sheep in western Texas. Restor. Ecol. 8, 57–65 (2000).Article 

Google Scholar 
Hua, L. et al. Captive breeding of pangolins: current status, problems and future prospects. Zookeys 507, 99–114 (2015).Article 

Google Scholar 
Nielsen, J. M., Clare, E. L., Hayden, B., Brett, M. T. & Kratina, P. Diet tracing in ecology: Method comparison and selection. Methods Ecol. Evol. 9, 278–291 (2017).Article 

Google Scholar 
Galimberti, A. et al. DNA barcoding as a new tool for food traceability. Food Res. Int. 50, 55–63 (2013).CAS 
Article 

Google Scholar 
Soininen, E. M. et al. Shedding new light on the diet of Norwegian lemmings: DNA metabarcoding of stomach content. Polar Biol. 36, 1069–1076 (2013).Article 

Google Scholar 
Rees, G. N., Shackleton, M. E., Watson, G. O., Dwyer, G. K. & Stoffels, R. J. Metabarcoding demonstrates dietary niche partitioning in two coexisting blackfish species. Mar. Freshw. Res. 71(4), 512–517 (2019).Article 

Google Scholar 
Taberlet, P., Coissac, E., Pompanon, F., Brochmann, C. & Willerslev, E. Towards next-generation biodiversity assessment using DNA metabarcoding. Mol. Ecol. 21, 2045–2050 (2012).CAS 
PubMed 
Article 

Google Scholar 
Aylagas, E., Borja, Á., Irigoien, X. & Rodriguez-Ezpeleta, N. Benchmarking DNA metabarcoding for biodiversity-based monitoring and assessment. Front. Mar. Sci. 3, 96 (2016).
Google Scholar 
De Barba, M. et al. DNA metabarcoding multiplexing and validation of data accuracy for diet assessment: Application to omnivorous diet. Mol. Ecol. Resour. 14, 306–323 (2014).PubMed 
Article 

Google Scholar 
Kartzinel, T. R. et al. DNA metabarcoding illuminates dietary niche partitioning by African large herbivores. Proc. Natl. Acad. Sci. 112, 8019–8024 (2015).CAS 
PubMed 
PubMed Central 
ADS 
Article 

Google Scholar 
Lopes, C. et al. DNA metabarcoding diet analysis for species with parapatric vs sympatric distribution: A case study on subterranean rodents. Heredity 114, 525–536 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Guillerault, N., Bouletreau, S., Iribar, A., Valentini, A. & Santoul, F. Application of DNA metabarcoding on faeces to identify European catfish Silurus glanis diet. J. Fish Biol. 90, 2214–2219 (2017).CAS 
PubMed 
Article 

Google Scholar 
Jakubavivciute, E., Bergström, U., Eklöf, J. S., Haenel, Q. & Bourlat, S. J. DNA metabarcoding reveals diverse diet of the three-spined stickleback in a coastal ecosystem. PLoS ONE 12, e0186929 (2017).Article 

Google Scholar 
Grant, T. & Fanning, D. Platypus 4th edn. (CSIRO Publishing, 2007).Book 

Google Scholar 
Hawke, T. et al. Long-term movements and activity patterns of platypus on regulated rivers. Sci. Rep. 11, 1–11 (2021).MathSciNet 
Article 

Google Scholar 
Gregory, J., Iggo, A., McIntyre, A. & Proske, U. Receptors in the bill of the platypus. J. Physiol. 400, 349 (1988).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
McLachlan-Troup, T., Dickman, C. & Grant, T. Diet and dietary selectivity of the platypus in relation to season, sex and macroinvertebrate assemblages. J. Zool. 280, 237–246 (2010).Article 

Google Scholar 
Harrop, C. & Hume, I. Digestive tract and digestive function in monotremes and nonmacropod marsupials. Compar. Physiol. Primitive Mamm. 4, 63–77 (1980).
Google Scholar 
Klamt, M., Davis, J. A., Thompson, R. M., Marchant, R. & Grant, T. R. Trophic relationships of the platypus: Insights from stable isotope and cheek pouch dietary analyses. Mar. Freshw. Res. 67, 1196–1204 (2016).CAS 
Article 

Google Scholar 
Faragher, R., Grant, T. & Carrick, F. Food of the platypus (Ornithorhynchus anatinus) with notes on the food of brown trout (Salmo trutta) in the Shoalhaven River, NSW. Austral. J. Ecol. 4, 171–179 (1979).Article 

Google Scholar 
Grant, T. R. Food of the platypus, Ornithorhynchus anatinus (Ornithorhynchidae: Monotremata) from various water bodies in New South Wales. Aust. Mammal. 5, 135–136 (1982).
Google Scholar 
Marchant, R. & Grant, T. The productivity of the macroinvertebrate prey of the platypus in the upper Shoalhaven River, New South Wales. Mar. Freshw. Res. 66, 1128–1137 (2015).Article 

Google Scholar 
Krueger, B., Hunter, S. & Serena, M. Husbandry, diet and behaviour of platypus Ornithorhynchus anatinus at Healesville Sanctuary. Int. Zoo Yearbook 31, 64–71 (1992).Article 

Google Scholar 
Thomas, J. L., Handasyde, K. A., Temple-Smith, P. & Parrott, M. L. Seasonal changes in food selection and nutrition of captive platypuses (Ornithorhynchus anatinus). Aust. J. Zool. 65, 319–327 (2018).Article 

Google Scholar 
Hawke, T., Bino, G. & Kingsford, R. T. Damming insights: Variable impacts and implications of river regulation on platypus populations. Aquat. Conserv. Mar. Freshwat. Ecosyst. 31, 504–519 (2021).Article 

Google Scholar 
Bino, G., Kingsford, R. T., Grant, T., Taylor, M. D. & Vogelnest, L. Use of implanted acoustic tags to assess platypus movement behaviour across spatial and temporal scales. Sci. Rep. 8, 5117 (2018).PubMed 
PubMed Central 
ADS 
Article 

Google Scholar 
Chinnadurai, S. K., Strahl-Heldreth, D., Fiorello, C. V. & Harms, C. A. Best-Practice guidelines for field-based surgery and anesthesia of free-ranging wildlife. I. Anesthesia and Analgesia. J. Wildl. Dis. 52(2 Suppl), S14–27. https://doi.org/10.7589/52.2S.S14 (2016).PubMed 
Article 

Google Scholar 
Fiorello, C. V., Harms, C. A., Chinnadurai, S. K. & Strahl-Heldreth, D. Best-Practice guidelines for field-based surgery and anesthesia on free-ranging wildlife. Ii. Surgery. J. Wildl. Dis. 52(2 Suppl), S28–39. https://doi.org/10.7589/52.2S.S28 (2016).PubMed 
Article 

Google Scholar 
Vogelnest, L. & Woods, R. Medicine of Australian mammals: CSIRO Publishing (2008).Geller, J., Meyer, C., Parker, M. & Hawk, H. Redesign of PCR primers for mitochondrial cytochrome c oxidase subunit I for marine invertebrates and application in all-taxa biotic surveys. Mol. Ecol. Resour. 13, 851–861 (2013).CAS 
PubMed 
Article 

Google Scholar 
Leray, M. et al. A new versatile primer set targeting a short fragment of the mitochondrial COI region for metabarcoding metazoan diversity: Application for characterizing coral reef fish gut contents. Front. Zool. 10, 1–14 (2013).Article 

Google Scholar 
Greenfield, P. Greenfield hybrid analysis pipeline (GHAP). v1 (CSIRO, 2017).
Google Scholar 
Shackleton, M. et al. How does molecular taxonomy for deriving river health indices correlate with traditional morphological taxonomy?. Ecol. Indic. 125, 107537 (2021).Article 

Google Scholar 
Hebert, P. D., Cywinska, A., Ball, S. L. & Dewaard, J. R. Biological identifications through DNA barcodes. Proc. R. Soc. Lond. Ser. B Biol. Sci. 270, 313–321 (2003).CAS 
Article 

Google Scholar 
Ostell, J. & Sayers, E. W. Dennis A. Benson, Mark Cavanaugh, Karen Clark, Ilene Karsch-Mizrachi, David J. Lipman.Wilcoxon, F. Individual comparisons by ranking methods. Biometrics Bulletin, 1. In Breakthroughs in Statistics, 196–202 (Springer, 1992).Oksanen, J. et al. Package “vegan”. Community ecology package, version. Vol 2, No. 9, 1–295. (2013).R Development Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).
Google Scholar  More

Epithemia isolation and cultureThe Epithemia cells were isolated from 0.5 L of seawater collected from depths of 25, 75, and 100 m in the North Pacific Subtropical Gyre (22°45′ N, 158°00′ W). Seawater was collected during the near-monthly Hawaii Ocean Time-series (HOT) expeditions to the long-term monitoring site Station ALOHA (water depth ca. 4800 m) in October 2014 (HOT cruise #266) and February–July 2019 (HOT cruises #310–313). Serial dilution (unialgal strains UHM3202, UHM3203, UHM3204) or micropipette isolation of single cells (clonal strains UHM3200, UHM3201, UHM3210, UHM3211) were used to establish the Epithemia cultures, which were grown in a seawater-based, low-nitrogen medium. Filtered (0.2 µm) and autoclaved, undiluted Station ALOHA seawater was amended with 2 μM EDTA, 50 nM ferric ammonium citrate, 7.5 μM phosphoric acid, trace metals (100 nM MnSO4, 10 nM ZnCl2, 10 nM Na2MoO4, 1 nM CoCl2, 1 nM NiCl2, 1 nM Na2SeO3), vitamins (50 μg/L inositol, 10 μg/L calcium pantothenate, 10 μg/L thiamin, 5 μg/L pyridoxine HCl, 5 μg/L nicotinic acid, 0.5 μg/L para-aminobenzoic acid, 0.1 μg/L folic acid, 0.05 μg/L biotin, 0.05 μg/L vitamin B12), and 106 μM Na2SiO3. Although not tested here, simpler formulations of diazotroph media such as PMP40 or RMP41 may also be suitable for growing Epithemia, when made with 100% seawater and adding Na2SiO3. The cultures were subsequently incubated at 24 °C on a 12:12 h light:dark cycle with 50–100 μmol quanta m−2 s−1 using cool white fluorescent bulbs. All E. pelagica and E. catenata symbioses were stable under these medium and incubation conditions. E. pelagica was successfully isolated from at least one of the three depths that were targeted during each sampling occasion.Morphological observationsEpithemia living and fixed cells were imaged by light and epifluorescence microscopy using a Nikon Eclipse 90i microscope at 40×–60× magnification. Diatom cell sizes were determined using >60 live, exponentially growing cells, imaged in either valve view (E. pelagica) or girdle view (E. catenata). Endosymbiont (spheroid body) cell sizes were averaged from DNA-stained cells for E. pelagica UHM3200 (n = 78) and E. catenata UHM3210 (n = 91), imaged by epifluorescence microscopy after preparing samples as follows: Epithemia cells were fixed in 4% glutaraldehyde for 30 min, pelleted at 1000 × g for 1 min, the supernatant was exchanged with 0.5% Triton X-100 (in autoclaved filtered seawater), samples were incubated for 10 min with gentle agitation, cells were then pelleted at 4000 × g for 1 min, supernatant was exchanged with autoclaved filtered seawater and fixed in 4% glutaraldehyde, and samples were stained with 1× final concentration of SYBR Gold nucleic acid stain (Invitrogen, cat. # S11494) for 2 h. For routine observations of endosymbionts (e.g., determining presence/absence and number per host cell), osmotic shock was used to disrupt the cell contents of diatom host cells and improve visualization of the endosymbionts. This was achieved by gently pelleting cells and exchanging the medium with either ultrapure water or 2–3 M NaCl solution, followed by immediate observation. While this is a simple technique for detecting and visualizing endosymbionts (Fig. 1c, f), it does not accurately represent the natural location of endosymbionts within the host cells, as seen when compared to fixed cell preparations for epifluorescence microscopy (Fig. 1n, o). To assess the presence of fluorescent photopigments in endosymbiont cells, live host cells were pelleted at 4000 × g for 5 min and crushed using a microcentrifuge tube pestle (SP Bel-Art, cat. # F19923-0000) to release the endosymbionts. The crushed pellet was resuspended in 75% glycerol containing live Synechococcus WH7803 cells (positive control for fluorescence), and samples were observed by epifluorescence microscopy using filter cubes appropriate for observing phycoerythrin (EX: 551/10, BS: 560, EM: 595/30) and chlorophyll (EX: 480/30, BS: 505, EM: 600LP).The loss of endosymbionts from Epithemia cultures (UHM3200 and UHM3210) was observed after propagating cells for four months in nitrogen-replete medium (K)18, where approximately 5–10% of the culture was transferred to fresh medium about every two weeks. Observations were only made at the end of the four-month period. Endosymbionts were not observed growing freely in these cultures, and the absence of endosymbionts within host cells was confirmed by the failure to observe spheroid bodies by light microscopy after osmotic shock of the diatoms, as well as a failure to amplify the endosymbiont SSU (16S rRNA) and nifH genes from cellular DNA extracts. PCR reactions were performed in parallel with DNA extracts from control cultures (grown in low-nitrogen medium), using the same template DNA amount (10 ng) and PCR conditions (see methods for Marker gene sequencing and phylogenetics).Ultrastructural observations by electron microscopy (EM) were conducted for E. pelagica UHM3200 and E. catenata UHM3210. EM preparations of diatoms typically involve the oxidative removal of organic matter to uncover the fine details of frustule ultrastructure. However, in the case of E. catenata, oxidatively cleaned cells lacked structural integrity, leading to collapsed frustules when dried and viewed by scanning EM (SEM). For this reason, both species were prepared for SEM with and without (Fig. 1a, d) the oxidative removal of organic matter, and cleaned E. catenata frustules were further analyzed by transmission EM (TEM). To remove organic matter, 100 mL of exponentially growing culture was pelleted by centrifugation at 1000 × g for 10 min and resuspended in 30% H2O2. Cells were boiled in H2O2 for 1–2 h, followed by rinsing cells six times in ultrapure water by sequential centrifugation at 1000 × g for 10 min and resuspension of cell pellets. Suspensions of the cleaned cells were dried on aluminum foil and mounted on aluminum stubs with double-sided copper tape. For some E. catenata SEM preparations, the cleaned frustules were dehydrated in an ethanol dilution series and exchanged into hexamethyldisilazane (HMDS) prior to drying on aluminum foil; this was to minimize the collapse of frustules resulting from drying. To prepare cells with organic matter intact, 25 mL of exponentially growing culture was mixed with an equal volume of fixative solution (5% glutaraldehyde, 0.2 M sodium cacodylate pH 7.2, 0.35 M sucrose, 10 mM CaCl2) and incubated overnight at 4 °C. Cells were gently filtered onto a 13 mm diameter 1.2 μm pore size polycarbonate membrane filter (Isopore, Millipore Sigma), washed with 0.1 M sodium cacodylate buffer (pH 7.4, 0.35 M sucrose), fixed with 1% osmium tetroxide in 0.1 M sodium cacodylate (pH 7.4), dehydrated in a graded ethanol series, and critical point dried. Filters were mounted on aluminum stubs with double-sided conductive carbon tape. All SEM stubs were sputter coated with Au/Pd, prior to observing on a Hitachi S-4800 field emission scanning electron microscope at the University of Hawai’i at Mānoa (UHM) Biological Electron Microscope Facility (BEMF). Cleaned E. catenata cells were prepared for TEM by drying a drop of sample on a formvar/carbon-coated grid and observing on a Hitachi HT7700 transmission electron microscope at UHM BEMF.Additional light microscopy of hydrogen-peroxide cleaned frustules was conducted for E. pelagica UHM3201 and E. catenata UHM3210. Samples were mounted in Naphrax (PhycoTech, Inc., cat. # P-Naphrax200) and observed at 100× using an Olympus BX41 Photomicroscope (Olympus America Inc., Center Valley, Pennsylvania) with differential interference contrast optics and an Olympus SC30 Digital Camera at California State University San Marcos.A key to the strains used in each micrograph is provided in Supplementary Table 2.Marker gene sequencing and phylogeneticsFor each Epithemia strain, 25–50 mL of culture was pelleted at 4000 × g for 10 min, and DNA was extracted from the pellet using the ZymoBIOMICS DNA Miniprep Kit (Zymo Research, cat. # D4300). Marker genes were amplified with the Expand High Fidelity PCR System (Roche, cat. # 4743733001), using conditions previously described for genes SSU encoding 18S rRNA (Euk328f/Euk329r)42, LSU encoding 28S rRNA (D1R/D2C)43, rbcL (rbcL66+/dp7−)44,45, psbC (psbC+/psbC−)44, and cob (Cob1f/Cob2r)21. For the endosymbionts, a partial sequence for the SSU (16S rRNA) gene was amplified using a primer set targeting unicellular cyanobacterial diazotrophs, CYA359F/Nitro821R46,47, and the nifH gene was amplified using new primers specific to the nifH of Cyanothece-like organisms, ESB-nifH-F (5′-TACGGAAAAGGCGGTATCGG-3′) and ESB-nifH-R (5′-CACCACCAAGRATACCGAAGTC-3′), with a 55 °C annealing temperature and 75 s extension time. All primers were synthesized by Integrated DNA Technologies (IDT). Amplified products were cloned and transformed into E. coli using the TOPO TA Cloning Kit for Sequencing (Invitrogen, cat. # K457501), and plated colonies were picked and grown in Circlegrow medium (MP Biomedicals, cat. # 113000132). Plasmids were extracted with the Zyppy Plasmid Miniprep kit (Zymo Research, cat. # D4019) and sequenced from the M13 vector primers using Sanger technology at GENEWIZ (South Plainfield, NJ). For the diatom SSU (18S rRNA) gene, sequencing reactions were also performed using the 502f and 1174r primers48.Phylogenetic trees (Fig. 2) were inferred using concatenated alignments for both diatom host genes (SSU encoding 18S rRNA, psbC, rbcL) and endosymbiont genes (SSU encoding 16S rRNA, nifH). For each gene, nucleotide sequences were aligned using MAFFT v7.45349 (L-INS-i method), and sites with gaps or missing data were removed. An appropriate nucleotide substitution model was selected for each gene alignment using jModelTest v2.1.1050. Bayesian majority consensus trees were inferred from the concatenated alignments using MrBayes v3.2.751 with two runs of 4–8 chains, until the average standard deviation of split frequencies dropped below 0.01. Maximum likelihood bootstrap values were generated for the Bayesian tree using RAxML v8.2.1252, implemented with 1000 iterations of rapid bootstrapping. To further analyze the phylogenetic position of the new Epithemia species in the broader context of Surirellales and Rhopalodiales diatoms, individual gene trees (SSU encoding 18S rRNA, LSU, rbcL, psbC, and cob; Supplementary Figs. 13–19) were constructed from sequences aligned using MAFFT (automatic detection method) and trimmed using trimAl v1.253 (gappyout method). rRNA gene phylogenies were also inferred using sequences aligned according to the global SILVA alignment for SSU and LSU genes using SINA54, which were either left untrimmed in the case of the LSU gene or trimmed to remove highly variable positions (SINA’s “012345” positional variability filter) and gappy positions (trimAL v1.2, gappyout method) in the case of the SSU gene. These trimming strategies were selected based on their ability to maximize the monophyly of the previously described Rhopalodiales clade and minimize the separation of known conspecific strains, such as the strains of E. pelagica described here. All gene phylogenies were inferred using the Bayesian methods described above. To investigate the level of support for constrained tree topologies placing E. catenata within or outside of the genus Epithemia and family Rhopalodiaceae, SH55 and AU56 statistical tests were performed in IQ-TREE 257 (implementing ModelFinder58) using all alignments from the individual gene trees (Supplementary Table 3).Given E. catenata’s unusual morphology, test trees were inferred with the inclusion of diatom sequences from orders Bacillariales (Nitzschia, Pseudo-nitzschia), Cymbellales (Didymosphenia), Naviculales (Amphiprora, Navicula, Pinnularia), and Thalassiophysales (Amphora, Halamphora, Thalassiophysa); however, E. catenata was consistently placed within Rhopalodiales, and these trees were not pursued further.An additional nifH phylogeny was constructed using all environmental sequences from NCBI’s non-redundant nucleotide (nt) database >300 bp and sharing >95% nucleotide sequence identity with EpSB and EcSB nifH sequences (Supplementary Fig. 23), including 51 environmental sequences from prior studies investigating marine diazotrophs34,59,60,61,62,63,64,65,66. Environmental nifH sequences were aligned to the previously generated nifH sequence alignment using MAFFT (automatic method detection and addfragments options), and the best-scoring maximum likelihood phylogeny was inferred using RAxML with 1000 iterations of rapid bootstrapping. NCBI accession numbers for all tree sequences are in the Source Data file.Analysis of Epithemia endosymbiont nifH sequences in environmental datasetsNucleotide sequences for EpSB and EcSB nifH were queried against NCBI’s non-redundant nucleotide (nt) database using webBLAST67 (megablast; https://blast.ncbi.nlm.nih.gov/) and SRA databases for nifH amplicon sequencing projects from the marine environment using the SRA Toolkit68 (dc-megablast, with database validation using vdb-validate; https://github.com/ncbi/sra-tools). Database hits with 98–100% nucleotide identity over an alignment of the entire subject sequence (BLAST alignment length = subject sequence length) were identified, and the associated sample’s latitude and longitude coordinates (where available) were mapped. Coordinates were also mapped for metagenome and metatranscriptome samples containing matches to unigene MATOU-v1_93255274 from the Marine Atlas of Tara Oceans Unigenes69, a unigene that shares 100% identity over the entire length of the EpSB UHM3202 nifH sequence and >99.4% identity with all other EpSB nifH sequences.The presence of EpSB and EcSB nifH sequences was examined in metagenomes prepared from sinking particles collected at 4000 m depth at Station ALOHA27,28. The sinking particles were collected during intervals of 12, 10, and 8 days during 2014, 2015, and 2016, respectively, using a McLane sediment trap equipped with a 21-sample bottle carousel. The presence of EpSB and EcSB nifH sequences in the metagenomes was assessed by blastn70, after first removing low quality bases from metagenomic reads using Trimmomatic v0.3971 (parameters: LEADING:20 TRAILING:20 MINLEN:100). For each sediment trap metagenome, the total number of reads matching EpSB or EcSB nifH nucleotide sequences with 100% identity were tallied and normalized to the total number of reads in the database. Only EpSB-matching reads were detected in this analysis.Quantitative PCRSpecific PCR primers were designed targeting a 102 bp region of E. pelagica’s LSU gene (Epel-LSU-F, 5′-GAAACCAGTGCAAGCCAAC-3′; Epel-LSU-R, 5′-AGGCCATTATCATCCCTTGTC-3′) and an 85 bp region EpSB’s nifH gene (EpSB-nifH-F, 5′-CACACTAAAGCACAAACTACC-3′; EpSB-nifH-R, 5′-CAAGTAGTACTTCGTCTAGCTC-3′) and were synthesized by IDT. Gene copy concentrations were quantified for Station ALOHA water samples (~2 L) collected by Niskin bottles at 5, 25, 45, 75, 100, 125, 150, and 175 m on January 16 and July 1 (except 5 m), 2014, during HOT cruises #259 and #264. Samples were filtered onto 25 mm diameter, 0.02 μm pore size aluminum oxide filters (Anotop; Whatman, cat. # WHA68092102) and stored at −80 °C until extracting DNA using the MasterPure Complete DNA and RNA Purification Kit (Epicentre, cat. # MC85200) according to Mueller et al.72. Briefly, a 3-mL syringe filled with 1 mL of tissue and cell lysis solution (MasterPure) containing 100 μg mL−1 proteinase K was attached to the outlet of the filter, and the filter inlet was sealed with a second 3-mL syringe. The lysis solution was pulled halfway through to saturate the filter membrane, and the entire assembly was incubated at 65 °C for 15 min while attached to a rotisserie in a hybridization oven rotating at ca. 16 rpm. The lysis buffer was then drawn fully into the inlet syringe, transferred to a microcentrifuge tube, and placed on ice. The remaining steps for protein precipitation and removal and nucleic acid precipitation were carried out following the manufacturer’s instructions. For each sample, DNA was resuspended in a final volume of 100 μL. Quantitative PCR (qPCR) was performed using the PowerTrack SYBR Green Master Mix system (Applied Biosystems, cat. # A46109) and run on an Eppendorf Mastercycler epgradient S realplex2 real-time PCR machine. Reactions (20 µL total volume) were prepared according to the manufacturer’s protocol, containing 500 nM of each primer. Sample reactions (four replicates) contained 2 μL of environmental DNA extract (24–76 ng DNA), while standards (three replicates) contained 2 μL of gBlocks Gene Fragments (IDT) that were prepared at 1, 2, 3, 4, 5, and 6 log gene copies/μL. The gBlocks Gene Fragments were 500 bp in length and encompassed the entire E. pelagica UHM3201 LSU sequence and positions 1–500 of the EpSB UHM3201 nifH sequence, respectively. The main cycling conditions consisted of an initial denaturation and enzyme activation step of 95 °C for 2 min, followed by 40 cycles of 95 °C for 5 s and 57 °C or 55 °C for 30 s for the LSU and nifH genes, respectively. Melting curves were analyzed to verify the specificity of the amplifications, and reactions containing Epithemia catenata DNA extract were included as negative controls. Reaction efficiencies were 104.23% and 95.15% for the LSU and nifH genes, respectively. The limit of detection for these assays was not empirically determined. gBlocks sequences, qPCR threshold cycle values, and conversion equations are provided in the Source Data file.Physiology experimentsThe daily patterns of N2 fixation were quantified for E. pelagica UHM3200 and E. catenata UHM3210 using two techniques: acetylene (C2H2) reduction to ethylene (C2H4) and argon induced dihydrogen (H2) production (AIHP). Both analyses were conducted using a gaseous flow-through system that quantified the relevant trace gas on the sample outlet line with a temporal resolution of 10 min73. To conduct the measurements, a 10-mL subsample of each Epithemia culture was placed in a 20-mL borosilicate vial and closed using gas-tight rubber stoppers and crimp seals. Separate bottles were used for H2 production and C2H2 reduction. During the experimental period, the temperature was maintained at 25 ± 0.2 °C using a benchtop incubator (Incu-Shaker; Benchmark Scientific) and light exposure was 200 μmol photons m−2 s−1 at wavelengths of 380–780 nm with a 12:12 h square light:dark cycle (Prime HD+; Aqua Illumination). To conduct the AIHP method, the sample vial containing the culture was flushed with a high purity gas mixture consisting of argon (makeup gas; 80%), oxygen (20%), and carbon dioxide (0.04%). In the absence of N2, all of the electrons that would have been used to reduce N2 to NH3 are diverted to H2 production, thereby providing a measure of Total Nitrogenase Activity (TNA). The C2H2 reduction assay also represents a measure of TNA. Our analytical set-up introduced C2H2 at a 1% addition (vol/vol) to the high purity air with a total flow rate (13 mL min−1) identical to the AIHP method. The gas emissions were analyzed using separate reductive trace gas analyzers that were optimized for the quantification of H2 and C2H4. To verify the observed daily patterns in N2 fixation, 15N2 assimilation measurements were conducted on triplicate samples of Epithemia cultures at targeted time points. Five milliliters of 15N-enriched seawater was added to the subsamples, which were subsequently crimp sealed and incubated for a 2 h period with the same light and temperature conditions as the daily gas measurements. At the end of the incubation, the contents of each vial were filtered onto a pre-combusted glass fiber filter. The concentration and isotopic composition (δ15N) of particulate nitrogen for incubated and non-incubated (i.e., natural abundance) samples was measured using an elemental analyzer/isotope ratio mass spectrometer (Carlo-Erba EA NC2500 coupled with a ThermoFinnigan Delta Plus XP). For each of the described analyses, cell-specific rates were calculated based on the average of triplicate cell concentration measurements, obtained from cell samples preserved at 4 °C with Lugol’s iodine solution and quantified within a week using a Sedgwick-Rafter counting chamber (Electron Microscopy Sciences, cat. # 68050-52). All rate measurement data is provided in the Source Data file.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

A workflow for the calculation of CSI is presented in Fig. 1c. For all the analyses, we used the R v. 4.0.3 software environment20 implemented in RStudio v. 1.4.1103. The scripts used for each methodological step are available at the Figshare repository21. After data download from primary sources (PaleoClim and WorldClim), specifically for the CSI-future map set we performed an initial step aimed to obtain individual bioclimatic variables for each future time period for the four SSPs (Fig. 1b). To achieve this, the median values of nine GCMs were calculated in functions compiled in raster R package22 for each individual bioclimatic variable (see a few exceptions of number of GCMs used in Table 2).Table 2 General circulation models (GCM) used to construct the future map sets.Full size tableThe standard deviation (SD) was estimated as a measure of the amount of variation or dispersion along time series, from which the resulting output maps showed the places where climate conditions remained constant or variable across the temporal periods considered (Fig. 1a,b). The SD, as a way to identify stable/unstable climatic areas, was previously used in other climatic or evolutionary studies4,14. To compute the SD output rasters, we applied the mosaic function setting “fun = sd” from raster R package, calculating the SD for each pixel in the 12 time period rasters for CSI-past and five times for CSI-future, independently for each variable. The mosaic function was also used for the range calculation, with “fun = min” and “fun = max” to obtain the minimum and maximum values of input rasters, respectively, with a further step for subtracting maximum to minimum values.Specifically, for CSI-past, as it includes several time periods with sea-level dropping below the present level (T1, T3, T5, T6, T7, T8, T9; Fig. 1a), we applied a mask of the current land surface, i.e. taking the T12 (Anthropocene) as a template. With this additional step, we were able to remove those pixels (grid cells) currently under the sea but that were once emerged. Most of these pixels, however, were only emerged during the LGM (ca. 21 ka), thus having values for bioclimatic variables for just a single time period (instead of the 12 routinely used for the variability estimation). The inclusion of these areas would result in highly climatically stable regions (low SD values; Supplementary Fig. 1), but this would be an obviously biased result. In contrast, we did not remove those areas affected by the sea-level rising periods, as only three periods contained “NoData” values (T2, T4, T10; Fig. 1a). However, to take this fact into consideration, we created a raster file in which these areas submerged during warm periods are indicated (see Supplementary Fig. 1). Finally, for both CSI-past and CSI-future, the resulting SD values were normalized to values between 0 and 1, with 0 representing completely stable areas and 1 the most unstable ones.The next step was focused on the selection of a relatively uncorrelated set of variables for each map set. We used the removeCollinearity function from virtualspecies R package23 that estimates the correlation value among pairs of variables from a given number of random sample points (10,000 in present case) according to a given method (Pearson for the present case) and a threshold of statistic selected (r  > 0.8 as a cut-off value). The function removeCollinearity returns a list of uncorrelated variables according to the settings specified, randomly selecting just one variable from groups of correlated ones (see Table 1 for a complete list of variables used for each map set). As we compiled estimates of variability independently for each variable and map set (e.g. SD bio1 past, SD bio2 past, etc.), each user can define his own CSI, selecting the more interesting variables according to the case of study.The final CSI maps were obtained by summing the SD values of the variables selected and the subsequent outputs normalized (0 to 1) (Figs. 2–4). Histogram plots were represented with ggplot2 R package24 and maps were exported with ArcGIS v.10.2.2 (Esri, Redlands, California, USA 2014). The histograms were computed for these final CSI maps, which represent the frequency and distribution of CSI values. We presented the final CSI maps with two different colour ramp schemes with ArcGIS. The first consisted of defining equal interval breaks from 0 to 1. The second was based on defining 32 categories with different value breaks for past and future map sets according to the value frequency shown by the histogram plot, i.e. the category with the highest CSI values (no. 32) was 0.71–1 in the past map set and 0.356–1 in the future map set.Fig. 2Maps of Climate Stability Index (CSI) values for the past map set from Pliocene (3.3 Ma) to present (1979–2013), at 2.5 arc-min grid resolution. Colours range from blue for low standard deviation (SD) values, which represents areas with low climatic fluctuations (i.e, low values of CSI) during the period Pliocene–present, to red for high SD values, which shows areas where high climatic fluctuations would have taken place (i.e., high values of CSI). On the upper map, the colour ramp shows equal interval breaks. The histogram with frequency and distribution of CSI values is also shown. On the lower map, the colour ramp has been manually adjusted to a defined set of break values (see details in the text).Full size imageFig. 3Maps of Climate Stability Index (CSI) values for the future conditions (Shared Socioeconomic Pathways: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) from present (1970–2000) to future (2100), at 2.5 arc-min grid resolution. Colours range from blue for low standard deviation (SD) values, which represents areas with low climatic fluctuations (i.e, low values of CSI) from present to future, to red for high SD values, which shows areas where high climatic fluctuations would have taken place (i.e., high values of CSI). The colour ramp shows equal interval breaks. The histogram with frequency and distribution of CSI values is also shown for each future scenario.Full size imageFig. 4Maps of Climate Stability Index (CSI) values for the future conditions (Shared Socioeconomic Pathways: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) from present (1970–2000) to future (2100), at 2.5 arc-min grid resolution. Colours range from blue for low standard deviation (SD) values, which represents areas with low climatic fluctuations (i.e, low values of CSI) from present to future, to red for high SD values, which shows areas where high climatic fluctuations would have taken place (i.e., high values of CSI). The colour ramp has been manually adjusted to a defined set of break values (see details in the text).Full size image More

Study siteAn open field small-scale release of a GM strain of Anopheles mosquitoes was carried-out in July 2019 in the village of Bana in Western Burkina Faso (see Supplementary Fig. 5). The study was granted regulatory authorisation from the National Biosafety Agency (NBA) (order No. 2018-453/MESRSI/SG/ANB of 10 August 2018 authorising the controlled release of genetically modified sterile male mosquitoes) and institutional ethical permission from the Institutional Ethics Committee for Research in Health Sciences: CEIRES (No. A-003/2019-CEIRES granted on January 9th 2019) and a programme of engagement established community acceptance. Details of the extensive stakeholder and communication processes and activities that were conducted in preparation of this release will be published elsewhere. The village of Bana is located in Western Burkina Faso (12°36′00″N, 3°28′59″W), 23 km west of the city of Bobo-Dioulasso.Bana has two main inhabited agglomerations of similar size: Bana Centre (administrative area) and Bana Market (economic area), separated by a 1.5 km unpopulated land band, crossed by a small semi-permanent river and a forest (see Supplementary Fig. 5). In its entirety, the village comprises about 130 compounds for about 759 inhabitants (local census, IRSS 2014). This region is characterised by two seasons: a wet season from June to September and a dry season from November to April. The mean annual rainfall in the village is about 800 mm and the mean temperature is about 27 °C (22–32 °C)52.Study designThe study design followed the format of an MRR experiment with an intensive period of recaptures followed by several months of monitoring to confirm the disappearance of the transgene. Both the period (July) and design (MRR-like experiment) were informed by previous baseline entomological studies and MRR experiments conducted in the same village41,52. Given the low population size expected in July and to avoid over-sampling, a lower recapture effort (reduction of daily swarm sampling number) was implemented than in previous MRR studies performed in the same area.41 The month of July corresponds to the start of the rainy season, when regular rains and cooler weather promote mosquito survival, and the target population of A. coluzzii is at a much lower level than later in the rainy season41,52. In July, plant coverage is still sparse and males tend to seek refuge inside houses and can be captured in good numbers via indoor sampling52.GM sterile strain maintenanceThe mosquito strain used in the experiment was the genetically modified mosquito Anopheles coluzzii sterile male strain referred to as Ac(DSM)2 (for Anopheles coluzzii Dominant Sterile male strain 2). This strain is the product of local introgression (series of backcrosses) of the original Ag(DSM)2 (dominant sterile male on Anopheles gambiae G3 mosquitoes strain 2) with a local A. coluzzii wild-type (WT) colony (female DSM-carrier crossed with male WT)34. The importation of Ag(DSM)2 in Burkina Faso, introgression with local wild type background and maintenance were conducted under regulatory authorisation from the National Biosafety Agency (N°000002/MRSI/SG/ANB of October 21th 2016). The wild-type A. coluzzii strain used for introgression and maintenance of Ac(DSM)2 was colonised in July 2014 from gravid female adults collected in village 7 of the Kou valley (VK7) in western Burkina Faso. Both colonies were maintained in a dedicated ACL2 (Arthropod Containment Level 2) insectary located within the IRSS main campus at Bobo-Dioulasso, Burkina Faso.For general stock-keeping purposes, Ac(DSM)2 was reared in a dedicated and highly secured climate-controlled room at a temperature fixed at 27.4 °C (±0.2, 95% Confidence intervals) and a relative humidity of 76.3% (±3.2, 95% CIs). Rearing rooms have natural light via windows and were supplemented with an artificial lighting regime of LD 12/12 h photoperiod, including dusk (1 h) and dawn (1 h). Larvae were reared in plastic trays (20 × 30 cm) with 1 l of deionized water and fed with an optimised larvae diet regime53. When mosquito larvae reached their level 3 instar (L3) larvae stage they were sorted manually between transgenic and non-transgenic mosquito larvae using a fluorescent stereomicroscope (Olympus SZX7, 2-8 Honduras street, London, United Kingdom) and put in separated trays to continue their development till pupation. At the pupal stage a second round of sorting occurred to separate male and female (sexing) from both strains. The sexing was done under a basic stereomicroscope (Olympus SZX7 basic, 2–8 Honduras street, London, United Kingdom) using a thin soft brush. Pupae from each strain and sex were placed in small plastic cups inside separate fresh adult cages to emerge. Adults were kept in 30 × 30 × 30 cm insect cages (produced locally) and continuously supplied with 10% (w/v) glucose solution (made with deionized water). Each generation, adult female transgenic mosquitoes were mated with male mosquitoes from the wild-type colony and blood-fed with fresh rabbit’s blood, using a membrane feeder (Hemotek® feeder, Hemotek Ltd, Blackburn United Kingdom). Gravid females were allowed to oviposit in plastic Petri dishes containing a wet sponge covered with filter paper. Eggs were collected and hatched in plastic trays. First instar larvae (L1) were then redistributed into several trays to keep similar larvae abundance (about 250 L1 larvae per tray).In accordance with Mendelian inheritance, stock-maintenance crosses between Ac(DSM)2 females and wild type colony males are expected to generate ~50% hemizygous transgenic male and female progeny referred to as Ac(DSM)2 and 50% non-transgenic sibling with a wild-type phenotype referred to as WT-Ac(DSM)2. That the actual phenotypic proportions matched the expected ratio was checked at each generation a part of standard procedures of colony maintenance.Production, sexing, marking and transport of release mosquitoesReleased males were derived from the 41st backcross generation from strain importation. Assuming Mendelian inheritance, the proportion of residual non-local genetic background after so many generations would be negligible (= 0.541).In rearing the release mosquito cohort, some changes were made in the stock-keeping procedure to maximise the fitness of male mosquitoes to be released. These changes aimed to minimise male mosquito handling during the entire process (rearing, sorting, marking and transport). Crucially, no transgenic versus non-transgenic sorting was done at larval stage resulting in a mix of transgenic and non-transgenic sibling males in the release generation. Additionally, to minimise the number of transgenic female mosquitoes released during the study, male versus female sexing was done at both pupae (initial) and adult (complementary) stages leading to a very high sorting accuracy (over 99.5%). Pupae sexing followed the procedure described for stock maintenance. Next, adult sexing focused on removing the few females resulting from errors in pupal sexing. It consisted of removing those rare females from male mosquito cages through inspection by eye of cages and in using a heat source to attract females. Once spotted, these were removed from male release cages using a mouth aspirator.After pupae sexing, male pupae were placed in 25 × 25 × 25 cm emergence cages (made locally and specially designed to fit dimensions of the secured coolboxes used for secure transportation) at a density of ~1400 pupae per cage. Following adult emergence, and over the following days, the cages were inspected by eye daily to check for and remove any females that had not been detected during the pupae sexing process. This procedure led to a total of 15,384 male mosquitoes aged 3–7 days have emerged in 15 cages and ready for marking and release purposes. Screening of ~50 males randomly picked from each emergence cage was conducted in the ACL2 insectary and revealed a slight bias in favour of WT-Ac(DSM)2 sibling males while Ac(DSM)2 male represented 43.3% (39.7–46.9, 95% CIs) of all emerged males. Based on this genotypic ratio, it was estimated that the male release cohort was equivalent to about 6659 transgenic male mosquitoes Ac(DSM)2 and 8725 non-transgenic sibling mosquitoes called WT-Ac(DSM)2 sibling. All males were kept untouched and in the same cages throughout the whole process until being released.The marking process was performed inside the ACL2 insectary facility, and was carried out the day before field release to allow enough time for mosquito recovery, rest and feeding. The environmental conditions were similar to those used during mosquito production. The mosquitoes were marked directly in their cages by using a cloud dye dusting technique. Aside from being fast, this highly efficient marking procedure (100% of mosquitoes successfully marked) was developed to allow the dust-marking of males in their original emergence cages, thereby avoiding male handling and damage during the marking process. This marking technique consisted of injecting pressurised red fluorescent colour powder (Bioquip® Gladwick Rancho Dominguez, CA 90220, USA; Ref: 1162R) into the cages by using a 5 ml syringe and needle to create a cloud of powder. The cages were wrapped with aluminium foil on all sides to prevent the dust from escaping through the meshed walls. Forceful injection of small amounts of powder from different sides of the cages through the aluminium cover and side netting created a dense cloud of fluorescent powder inside the cages to mark all the mosquitoes. Following marking, sugar-water was available ad-libitum to all marked mosquitoes until field release.About 2 h before the release time, the marked mosquitoes within the mosquito cages were transferred from the IRSS insectary to the release site in Bana village. Before leaving the IRSS insectary, the mosquito cages were covered by a second layer of mosquito net for security purposes. The cages were then wrapped with damp towels and placed in lockable cool boxes dedicated to their transport into the field. After having been secured, the cool boxes containing marked mosquitoes were transported to the release site. The entire process complied carefully with all regulatory requirements related to the permissions received for maintenance, handling and the release of these genetically modified organisms in Burkina Faso.Release phaseAll marked mosquitoes were released on the same day at around 5 pm (about one hour before swarming) in the centre of Bana village by opening the travel cages and allowing free exodus. Mosquitoes that did not leave were counted and subtracted from the released total (n = 534, 3.5%). Taking into account mortality and based on the ratio of Ac(DSM)2 and their siblings previously established, a total of 14,850 male mosquitoes were effectively released, with estimated numbers of 6428 hemizygous transgenic male A. coluzzii mosquitoes Ac(DSM)2 and 8422 non-transgenic WT-Ac(DSM)2 siblings.Recapture phaseMosquito recapture activities started the same day of release (about 2 h after mosquito release) and took place daily for a period of 20 days after release. Two different recapture methods were used: swarm collections using sweep nets (SWN) and pesticides spray catches (PSC) inside houses.Swarm sampling started on the evening of the release day using a well-established sweep net collection method47,54. Previous surveys in the same village41 had allowed mapping of swarm location or natural markers where swarming repeatedly occurs. To ensure sampling across the whole study area, a stratified randomised sampling procedure was used to select and sample 15 mosquito swarms daily at dusk using the sweep net collection method. The area of Bana village and Bana Marché were divided in six and four zones, respectively. Zone 1 and 2 in Bana Village are areas of high swarm abundance and the design ensured that these were not over-represented in swarm collections. Each evening, the teams of capturers set-out to collect up to five swarms per zones depending on swarm availability (swarms are fewer and smaller in early July than later in the month). All mosquitoes captured in the swarms were transported in their sweep nets to the field laboratory and frozen until the next morning for processing. At this stage, a random sample of 15 swarms each day was picked for dust screening and genetic analyses.Pyrethroid spray catches started the morning following the release and continued for 19 days. A set of 20 compounds were sampled each day. The sampling design followed that established in baseline studies leading to the release and in previous MRR studies41. Ten of the compounds were selected completely randomly and the other ten are a fixed set of compounds distributed regularly across the whole village. For each compound selected, a single room (1 sleeping room) within one of the house of compound was chosen for sampling. Although some compounds were selected more than once during the recapture period days, a different room (from a different house inside the same compound when applicable) was selected and no room was sampled twice during the survey period.Pyrethroid spray catches started the morning following the release and continued for 19 days. A set of 20 compounds were selected randomly each day. For each compound selected, a single room (sleeping room) was chosen for sampling. Although some compounds were selected more than once during the seven days, a different room (from a different house inside the same compound when applicable) was selected and no room was sampled twice during the survey period.Captured mosquitoes were identified morphologically in the field using adult anopheline morphological identification keys developed by Holstein55 and a field stereomicroscope (Perfex Sciences® Zoom Pro, Reference: S0852Z5 Toulouse, France). All An. gambiae s.l. mosquitoes were counted, checked for fluorescent dust marking using a Biofinder portable ultraviolet illuminator (Vansky, Shenzhen, China) and preserved in 80% ethanol. The identification of each marked mosquito was confirmed independently by two well-trained members of the staff before conservation in individual 1.5 ml storage microtubes for further analysis. The non-dusted wild Anopheles mosquitoes were pooled (10 individuals per tube) and stored in similar conditions. The location of each collection was recorded and mapped using a GPS (Garmin GPS) device, series GPSMAP®62.2.3. For all recaptured mosquitoes, we calculated the straight line distance from the release point to the recapture location using a Euclidean dispersal distance56. In the present case, the space was assimilated to a two-dimensional orthogonal axis system where xl and yl represent the coordinates of the release point and xr and yr represent the coordinates of the recapture point56. Calculation of the estimated flight distance of the mosquitoes then used the following formula:$${EFD}=sqrt{{left({x}_{r}-{x}_{l}right)}^{2}+{left({y}_{r}-{y}_{l}right)}^{2}}$$
(1)
Ac(DSM)2 male identificationMolecular analysis of recaptured marked mosquitoes was performed by PCR, to identify the Ac(DSM)2 strain and distinguish them from their non-transgenic WT-Ac(DSM)2 siblings. This PCR analysis consisted of detecting the integration of the eGFP::I-PpoI of the DSM transgene which characterised the transgenic mosquito strain Ac(DSM)2. In addition, a molecular species-diagnostic was performed concomitantly using the PCR technique based on the detection of SINE 200× locus57 and this PCR served as a control for DNA integrity. Each mosquito was split into two parts (abdomen and thorax) using forceps. The abdomen was used for the PCR and processed for DNA extraction using ‘squish’ buffer (PCR reaction buffer). The thorax was stored in 80% ethanol at −20 °C. For each mosquito analysed, the same DNA extract was used for both eGFP::I-PpoI transgene detection (identification of Ac(DSM)2 transgenic mosquito) and SINE 200X locus detection (for specie identification and DNA quality control). The Ac(DSM)2 construct was detected using the primers: pBacR-fwd [ATCGGTCTGTATATCGAGGTTTATT] and pBacR-Rev [CTCTAATATTTTGCCAAATGAAGTGCC] targeting the piggyBacR region required for insertion of the transgene. PCR reactions used the Gotaq® PCR kit (GoTaq® G2 Flexi DNA Polymerase, reference: M829B, Promega Corporation, 2800 Woods Hollow Road·Madison, WI 53711-5399, USA).Monitoring of Ac(DSM)2 non-persistenceMonthly mosquito collections were carried out using PSC and swarm sampling to confirm the disappearance of the Ac(DSM)2 transgene from the release site. Monitoring collections were conducted monthly for seven months. This period of monitoring was justified by the regulatory requirement of describing the Ac(DSM)2 disappearance through failure to detect the Ac(DSM)2 transgene for a minimum period of three consecutive months and with high statistical power. During each month of survey, a randomised selection of 20 houses (one room per house) and 20 swarms was sampled. All collected mosquitoes were identified morphologically using identification keys and a field stereomicroscope. Mosquitoes from A. gambiae complex were counted and preserved in 80% (v/v) ethanol for subsequent molecular identification. Each month, a representative sample of collected mosquitoes (up to 300 when available, from both PSC and swarm sampling) was analysed using the Ac(DSM)2-specific and species-specific PCR diagnostics described above to detect whether any A. gambiae s.l. mosquitoes were carrying the DSM transgene.Bayesian inference of mosquito survival, movement and population sizeWe fitted the recapture data to a diffusion model to further investigate dispersal and survival of the marked Ac(DSM)2 and their sibling males, and also to estimate the number of mosquitoes in the background population. This model assumes that the released mosquitoes tend to move in a random manner, meaning they repeatedly take short randomly directed flights that are independent of one another and of the environment. As described below, however, our estimation procedure does also allow for small additional movements where mosquitoes are attracted into nearby swarms at swarming time (dusk), or nearby houses for resting behaviour.We write the diffusion equation as$${partial }_{t}u=D{partial }_{x}^{2}u,$$
(2)
where (u(x,t)) is the probability density of the location of a single marked mosquito at location (x) and time (t), conditional on the individual being alive, and (D) is the diffusion coefficient. Assuming a point release at time (t=0), the above equation has solution$$uleft(r,tright)=frac{{e}^{-frac{{r}^{2}}{4Dt}}}{4,pi D,t}$$
(3)
where (r) is the distance from the release point. We next assume that the released mosquitoes have a constant survival probability of (s) per day, so that the expected number of extant released mosquitoes on day (d) is (R{s}^{d}) where (R) is the number that were released. The expected number of released mosquitoes in a small area ({dA}) is then given by$$qleft(r,dright)=R{s}^{d}frac{{e}^{-frac{{r}^{2}}{4Dd}}}{4,pi D,d}{dA}.$$
(4)
We take three further steps to convert this equation for (q(r,t)) into a likelihood function for the spatio-temporal distribution of recaptures of either Ac(DSM)2 or their sibling males. First, we pool the recaptures on a given day, and made by a given method (either swarm sampling or PSC), by partitioning the study area into annuli centred on the release location. These annuli are the recapture regions, and the expected number of extant marked mosquitoes in a given annulus is the integral of (q(r,d)) over that annulus. This step, therefore, averages out the expected number of marked mosquitoes from the inner to the outer radius of each annulus, and the annulus widths set the scale at which small movements towards swarms or houses, where mosquitoes may be recaptured, are assumed to occur in addition to random movements that underpin the diffusion model. We set the width of each annulus to 50 m, based on our judgement that this distance balances the capacity to separate recaptures at different distances (this capacity reduces with width), with the confidence that movements towards swarms or houses will largely remain within annuli (this confidence increases with width).Second, we assume the observation probability of mosquitoes in a given sample (representing an annulus, capture method, and day), is the number of unmarked mosquitoes in the sample divided by the (unknown) unmarked population size in that annulus. The unmarked population is assumed to have a uniform density, that we will infer alongside the mobility and survival parameters. Finally, we assume the number of marked mosquitoes in a given sample is Poisson-distributed around the expected number.For the data from each recapture method, we used the likelihood function to sample a posterior distribution for the diffusion coefficients and survival rates of the two types of released male mosquitoes, and the density of the unmarked population. We assumed uniform priors with respect to all five parameters and used a Markov chain Monte Carlo algorithm based on Metropolis-Hastings sampling to sample the posterior distribution directly from the log-likelihood. For each analysis (swarm or PSC), we sampled for 100,000 iterations, of which we discarded the initial 20,000 as a transient and thinned the remainder by 100, giving 800 samples in total.Statistical analysisData were analysed using the software JMP 14 (SAS Institute, Inc.). All data were checked for deviations from normality and heterogeneity, and analyses were conducted using parametric and non-parametric methods as appropriate. General linear modelling with Poisson distribution was used to describe male recaptures as a function of genotype and time. Kruskall-Wallis and Mann-Whitney test was used to describe respectively male participation in swarm and Euclidian dispersal distances. General linear modelling with Poisson distribution was used to describe male recaptures as a function of genotype and time. Estimates of population size, survival, and mobility were calculated using a Bayesian approach as described above.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

Van Marle, M. J. E. et al. Historic global biomass burning emissions for CMIP6 (BB4CMIP) based on merging satellite observations with proxies and fire models (1750–2015). Geosci. Model Dev. 10, 3329–3357 (2017).
Google Scholar 
Erb, K. H. et al. Unexpectedly large impact of forest management and grazing on global vegetation biomass. Nature 553, 73–76 (2018).
Google Scholar 
Cook-Patton, S. C. et al. Mapping carbon accumulation potential from global natural forest regrowth. Nature 585, 545–550 (2020).
Google Scholar 
Bastin, J. F. et al. The global tree restoration potential. Science 365, 76–79 (2019).
Google Scholar 
Bowman, D. M. J. S. et al. Fire in the Earth system. Science 324, 481–485 (2009).
Google Scholar 
Archibald, S. et al. Biological and geophysical feedbacks with fire in the Earth system. Environ. Res. Lett. 13, 033003 (2018).
Google Scholar 
Mills, B. J. W., Belcher, C. M., Lenton, T. M. & Newton, R. J. A modeling case for high atmospheric oxygen concentrations during the Mesozoic and Cenozoic. Geology 44, 1023–1026 (2016).
Google Scholar 
Lenton, T. M. in Fire Phenomena and the Earth System: An Interdisciplinary Guide to Fire Science (ed. Belcher, C. M.) 298–308 (Wiley, 2013).Pechony, O. & Shindell, D. T. Driving forces of global wildfires over the past millennium and the forthcoming century. Proc. Natl Acad. Sci. USA 107, 19167–19170 (2010).
Google Scholar 
Marlon, J. R. et al. Reconstructions of biomass burning from sediment-charcoal records to improve data-model comparisons. Biogeosciences 13, 3225–3244 (2016).
Google Scholar 
Archibald, S., Staver, A. C. & Levin, S. A. Evolution of human-driven fire regimes in Africa.Proc. Natl Acad. Sci. USA 109, 847–852 (2012).
Google Scholar 
Santín, C. et al. Towards a global assessment of pyrogenic carbon from vegetation fires. Global Change Biol. 22, 76–91 (2016).
Google Scholar 
Jones, M. W., Santín, C., van der Werf, G. R. & Doerr, S. H. Global fire emissions buffered by the production of pyrogenic carbon. Nat. Geosci. 12, 742–747 (2019).
Google Scholar 
Bird, M. I., Wynn, J. G., Saiz, G., Wurster, C. M. & McBeath, A. The pyrogenic carbon cycle. Annu. Rev. Earth Planet. Sci. 43, 273–298 (2015).
Google Scholar 
Hammes, K. & Abiven, S. in Fire Phenomena and the Earth System: An Interdisciplinary Guide to Fire Science (ed. Belcher, C. M.) 157–176 (Wiley, 2013).Schmidt, M. W. I. et al. Persistence of soil organic matter as an ecosystem property. Nature 478, 49–56 (2011).
Google Scholar 
Lavallee, J. M. et al. Selective preservation of pyrogenic carbon across soil organic matter fractions and its influence on calculations of carbon mean residence times. Geoderma 354, 113866 (2019).
Google Scholar 
Coppola, A. I. et al. Global-scale evidence for the refractory nature of riverine black carbon. Nat. Geosci. 11, 584–588 (2018).
Google Scholar 
Kuzyakov, Y., Bogomolova, I. & Glaser, B. Biochar stability in soil: decomposition during eight years and transformation as assessed by compound-specific 14C analysis. Soil Biol. Biochem. 70, 229–236 (2014).
Google Scholar 
Singh, B. P., Cowie, A. L. & Smernik, R. J. Biochar carbon stability in a clayey soil as a function of feedstock and pyrolysis temperature. Environ. Sci. Technol. 46, 11770–11778 (2012).
Google Scholar 
Masiello, C. A. & Druffel, E. R. M. Black carbon in deep-sea sediments. Science 280, 1911–1913 (1998).
Google Scholar 
Santos, F., Torn, M. S. & Bird, J. A. Biological degradation of pyrogenic organic matter in temperate forest soils. Soil Biol. Biochem. https://doi.org/10.1016/j.soilbio.2012.04.005 (2012).Zimmermann, M. et al. Rapid degradation of pyrogenic carbon. Glob. Change Biol. 18, 3306–3316 (2012).
Google Scholar 
Jones, M. W. et al. Fires prime terrestrial organic carbon for riverine export to the global oceans. Nat. Commun. 11, 2791 (2020).
Google Scholar 
Qi, Y. et al. Dissolved black carbon is not likely a significant refractory organic carbon pool in rivers and oceans. Nat. Commun. 11, 5051 (2020).
Google Scholar 
Pausas, J. G. & Paula, S. Fuel shapes the fire-climate relationship: evidence from Mediterranean ecosystems. Glob. Ecol. Biogeogr. 21, 1074–1082 (2012).
Google Scholar 
Archibald, S., Lehmann, C. E. R., Gómez-Dans, J. L. & Bradstock, R. A. Defining pyromes and global syndromes of fire regimes. Proc. Natl Acad. Sci. USA 110, 6442–6447 (2013).
Google Scholar 
Abatzoglou, J. T., Williams, A. P., Boschetti, L., Zubkova, M. & Kolden, C. A. Global patterns of interannual climate-fire relationships. Glob. Change Biol. 24, 5164–5175 (2018).
Google Scholar 
Brando, P. M. et al. Prolonged tropical forest degradation due to compounding disturbances: implications for CO2 and H2O fluxes. Glob. Change Biol. 25, 2855–2868 (2019).
Google Scholar 
Silva, C. V. J. et al. Drought-induced Amazonian wildfires instigate a decadal-scale disruption of forest carbon dynamics. Phil. Trans. R. Soc. B 373, 20180043 (2018).
Google Scholar 
Withey, K. et al. Quantifying immediate carbon emissions from El Niño-mediated wildfires in humid tropical forests. Phil. Trans. R. Soc. B 373, 20170312 (2018).
Google Scholar 
Pellegrini, A. F. A. et al. Fire frequency drives decadal changes in soil carbon and nitrogen and ecosystem productivity. Nature 553, 194–198 (2018).
Google Scholar 
Reisser, M., Purves, R. S., Schmidt, M. W. I. & Abiven, S. Pyrogenic carbon in soils: a literature-based inventory and a global estimation of its content in soil organic carbon and stocks.Front. Earth Sci. 4, 80 (2016).
Google Scholar 
Wei, X., Hayes, D. J., Fraver, S. & Chen, G. Global pyrogenic carbon production during recent decades has created the potential for a large, long-term sink of atmospheric CO2. J. Geophys. Res. Biogeosci. 123, 3682–3696 (2018).
Google Scholar 
Guimberteau, M. et al. ORCHIDEE-MICT (v8.4.1), a land surface model for the high latitudes: model description and validation. Geosci. Model Dev. 11, 121–163 (2018).
Google Scholar 
Thonicke, K. et al. The influence of vegetation, fire spread and fire behaviour on biomass burning and trace gas emissions: results from a process-based model. Biogeosciences 7, 1991–2011 (2010).
Google Scholar 
Yue, C. et al. Modelling the role of fires in the terrestrial carbon balance by incorporating SPITFIRE into the global vegetation model ORCHIDEE—Part 1: simulating historical global burned area and fire regimes. Geosci. Model Dev. 7, 2747–2767 (2014).
Google Scholar 
Abiven, S. & Santín, C. Editorial: From fires to oceans: dynamics of fire-derived organic matter in terrestrial and aquatic ecosystems. Front. Earth Sci 7, 31 (2019).
Google Scholar 
Santín, C., Doerr, S. H., Preston, C. M. & González-Rodríguez, G. Pyrogenic organic matter production from wildfires: a missing sink in the global carbon cycle. Glob. Change Biol. 21, 1621–1633 (2015).
Google Scholar 
Santín, C. et al. Carbon sequestration potential and physicochemical properties differ between wildfire charcoals and slow-pyrolysis biochars. Sci. Rep. 7, 11233 (2017).
Google Scholar 
Andela, N. et al. A human-driven decline in global burned area. Science 356, 1356–1362 (2017).
Google Scholar 
Arora, V. K. & Melton, J. R. Reduction in global area burned and wildfire emissions since 1930s enhances carbon uptake by land. Nat. Commun. 9, 1326 (2018).
Google Scholar 
Mouillot, F. & Field, C. B. Fire history and the global carbon budget: a 1° × 1° fire history reconstruction for the 20th century. Global Change Biol. 11, 398–420 (2005).
Google Scholar 
Gibson, D. Grasses and Grassland Ecology. Annals of Botany (Oxford Univ. Press, 2009).Dixon, A. P., Faber-Langendoen, D., Josse, C., Morrison, J. & Loucks, C. J. Distribution mapping of world grassland types. J. Biogeogr. 41, 2003–2019 (2014).
Google Scholar 
Bond, W. J. Ancient grasslands at risk. Science 351, 120–122 (2016).
Google Scholar 
Retallack, G. J. Global cooling by grassland soils of the geological past and near future. Annu. Rev. Earth Planet. Sci. 41, 69–86 (2013).
Google Scholar 
Leys, B. A., Marlon, J. R., Umbanhowar, C. & Vannière, B. Global fire history of grassland biomes. Ecol. Evol. 8, 8831–8852 (2018).
Google Scholar 
Alvarado, S. T., Andela, N., Silva, T. S. F. & Archibald, S. Thresholds of fire response to moisture and fuel load differ between tropical savannas and grasslands across continents. Glob. Ecol. Biogeogr. 29, 331–344 (2020).
Google Scholar 
Buisson, E. et al. Resilience and restoration of tropical and subtropical grasslands, savannas and grassy woodlands. Biol. Rev. 94, 590–609 (2019).
Google Scholar 
Rodionov, A. et al. Black carbon in grassland ecosystems of the world. Glob. Biogeochem. Cycles 24, GB3013 (2010).
Google Scholar 
Haberl, H., Erb, K. H. & Krausmann, F. Human appropriation of net primary production: patterns, trends and planetary boundaries. Annu. Rev. Environ. Resources 39, 363–391 (2014).
Google Scholar 
Medan, D., Torretta, J. P., Hodara, K., de la Fuente, E. B. & Montaldo, N. H. Effects of agriculture expansion and intensification on the vertebrate and invertebrate diversity in the Pampas of Argentina. Biodivers. Conserv. 20, 3077–3100 (2011).
Google Scholar 
González-Roglich, M., Swenson, J. J., Villarreal, D., Jobbágy, E. G. & Jackson, R. B. Woody plant-cover dynamics in Argentine savannas from the 1880s to 2000s: the interplay of encroachment and agriculture conversion at varying scales. Ecosystems 18, 481–492 (2015).
Google Scholar 
Satir, O. & Erdogan, M. A. Monitoring the land use/cover changes and habitat quality using Landsat dataset and landscape metrics under the immigration effect in subalpine eastern Turkey. Environ. Earth Sci. 75, 1118 (2016).
Google Scholar 
Şekercioĝlu, Ç. H. et al. Turkey’s globally important biodiversity in crisis. Biol. Conserv. 144, 2752–2769 (2011).
Google Scholar 
Schierhorn, F. et al. Post-Soviet cropland abandonment and carbon sequestration in European Russia, Ukraine and Belarus. Glob. Biogeochem. Cycles 27, 1175–1185 (2013).
Google Scholar 
Jaglan, M. S. & Qureshi, M. H. Irrigation development and its environmental consequences in arid regions of India. Environ. Manage. 20, 323–336 (1996).
Google Scholar 
Joshi, A. A., Sankaran, M. & Ratnam, J. ‘Foresting’ the grassland: historical management legacies in forest-grassland mosaics in southern India, and lessons for the conservation of tropical grassy biomes. Biol. Conserv. 224, 144–152 (2018).
Google Scholar 
Huang, F., Wang, P. & Zhang, J. Grasslands changes in the Northern Songnen Plain, China during 1954–2000. Environ. Monit. Assess. 184, 2161–2175 (2012).
Google Scholar 
Zhou, Y., Hartemink, A. E., Shi, Z., Liang, Z. & Lu, Y. Land use and climate change effects on soil organic carbon in north and northeast China. Sci. Total Environ. 647, 1230–1238 (2019).
Google Scholar 
Williams, N. S. G. Environmental, landscape and social predictors of native grassland loss in western Victoria, Australia. Biol. Conserv. 137, 308–318 (2007).
Google Scholar 
Dowling, P. M. et al. Effect of continuous and time-control grazing on grassland components in south-eastern Australia. Aust. J. Exp. Agric. 45, 369–382 (2005).
Google Scholar 
DeLuca, T. H. & Zabinski, C. A. Prairie ecosystems and the carbon problem. Front. Ecol. Environ. 9, 407–413 (2011).
Google Scholar 
Ceballos, G. et al. Rapid decline of a grassland system and its ecological and conservation implications. PLoS ONE 5, e8562 (2010).
Google Scholar 
Haugo, R. et al. A new approach to evaluate forest structure restoration needs across Oregon and Washington, USA. For. Ecol. Manage. https://doi.org/10.1016/j.foreco.2014.09.014 (2015).DeLuca, T. H. & Aplet, G. H. Charcoal and carbon storage in forest soils of the Rocky Mountain West. Front. Ecol. Environ. 6, 18–24 (2008).
Google Scholar 
Walker, X. J. et al. Increasing wildfires threaten historic carbon sink of boreal forest soils. Nature 572, 520–523 (2019).
Google Scholar 
Bellè, S. L. et al. Key drivers of pyrogenic carbon redistribution during a simulated rainfall event. Biogeosciences 18, 1105–1126 (2021).
Google Scholar 
Abney, R. B., Jin, L. & Berhe, A. A. Soil properties and combustion temperature: controls on the decomposition rate of pyrogenic organic matter. Catena 182, 104127 (2019).
Google Scholar 
Bradstock, R. A., Hammill, K. A., Collins, L. & Price, O. Effects of weather, fuel and terrain on fire severity in topographically diverse landscapes of south-eastern Australia. Landsc. Ecol. 25, 607–619 (2010).
Google Scholar 
Rogers, B. M., Soja, A. J., Goulden, M. L. & Randerson, J. T. Influence of tree species on continental differences in boreal fires and climate feedbacks. Nat. Geosci. 8, 228–234 (2015).
Google Scholar 
Coppola, A. I. & Druffel, E. R. M. Cycling of black carbon in the ocean. Geophys. Res. Lett. 43, 4477–4482 (2016).
Google Scholar 
Stenzel, J. E. et al. Fixing a snag in carbon emissions estimates from wildfires. Glob. Change Biol. 25, 3985–3994 (2019).
Google Scholar 
Murphy, B. P., Prior, L. D., Cochrane, M. A., Williamson, G. J. & Bowman, D. M. J. S. Biomass consumption by surface fires across Earth’s most fire prone continent. Glob. Change Biol. 25, 254–268 (2019).
Google Scholar 
Brando, P. M. et al. Droughts, wildfires and forest carbon cycling: a pantropical synthesis. Annu. Rev. Earth Planet. Sci. 47, 555–581 (2019).
Google Scholar 
Appezzato-da-Glória, B., Cury, G., Soares, M. K. M., Rocha, R. & Hayashi, A. H. Underground systems of Asteraceae species from the Brazilian Cerrado. J. Torrey Bot. Soc. 135, 103–113 (2008).
Google Scholar 
Belcher, C. M. et al. The rise of angiosperms strengthened fire feedbacks and improved the regulation of atmospheric oxygen. Nat. Commun. 12, 503 (2021).
Google Scholar 
Barbero, R., Abatzoglou, J. T., Larkin, N. K., Kolden, C. A. & Stocks, B. Climate change presents increased potential for very large fires in the contiguous United States. Int. J. Wildl. Fire 24, 892–899 (2015).
Google Scholar 
Stephens, S. L. et al. Managing forests and fire in changing climates. Science 342, 41–42 (2013).
Google Scholar 
Trenberth, K. E. Changes in precipitation with climate change. Clim. Res. 47, 123–138 (2011).
Google Scholar 
Prein, A. F. et al. The future intensification of hourly precipitation extremes. Nat. Clim. Change 7, 48–52 (2017).
Google Scholar 
Abatzoglou, J. T., Williams, A. P. & Barbero, R. Global emergence of anthropogenic climate change in fire weather indices. Geophys. Res. Lett. 46, 326–336 (2019).
Google Scholar 
Silveira, F. A. O. et al. Myth-busting tropical grassy biome restoration. Restor. Ecol. 28, 1067–1073 (2020).
Google Scholar 
Strassburg, B. B. N. et al. Global priority areas for ecosystem restoration. Nature 586, 724–729 (2020).
Google Scholar 
Schmidt, H. P. et al. Pyrogenic carbon capture and storage. GCB Bioenergy 11, 573–591 (2019).
Google Scholar 
Fu, Z. et al. Recovery time and state change of terrestrial carbon cycle after disturbance. Environ. Res. Lett. 12, 104004 (2017).
Google Scholar 
Zhu, D. et al. Improving the dynamics of Northern Hemisphere high-latitude vegetation in the ORCHIDEE ecosystem model. Geosci. Model Dev. 8, 2263–2283 (2015).
Google Scholar 
Zhu, D. et al. Simulating soil organic carbon in Yedoma deposits during the Last Glacial Maximum in a land surface model. Geophys. Res. Lett. 43, 5133–5142 (2016).
Google Scholar 
Krinner, G. et al. A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system. Glob. Biogeochem. Cycles 19, GB1015 (2005).
Google Scholar 
Yue, C., Ciais, P., Cadule, P., Thonicke, K. & Van Leeuwen, T. T. Modelling the role of fires in the terrestrial carbon balance by incorporating SPITFIRE into the global vegetation model ORCHIDEE—Part 2: carbon emissions and the role of fires in the global carbon balance. Geosci. Model Dev. 8, 1321–1338 (2015).
Google Scholar 
Hantson, S. et al. The status and challenge of global fire modelling. Biogeosciences 13, 3359–3375 (2016).
Google Scholar 
Hantson, S. et al. Quantitative assessment of fire and vegetation properties in simulations with fire-enabled vegetation models from the Fire Model Intercomparison Project. Geosci. Model Dev. 13, 3299–3318 (2020).
Google Scholar 
Li, F. et al. Historical (1700–2012) global multi-model estimates of the fire emissions from the Fire Modeling Intercomparison Project (FireMIP). Atmos. Chem. Phys. 19, 12545–12567 (2019).
Google Scholar 
Forkel, M. et al. Emergent relationships with respect to burned area in global satellite observations and fire-enabled vegetation models. Biogeosciences 16, 57–76 (2019).
Google Scholar 
Parton, W. J., Stewart, J. W. B. & Cole, C. V. Dynamics of C, N, P and S in grassland soils: a model. Biogeochemistry 5, 109–131 (1988).
Google Scholar 
Singh, N. et al. Transformation and stabilization of pyrogenic organic matter in a temperate forest field experiment. Glob. Change Biol. 20, 1629–1642 (2014).
Google Scholar 
Viovy, N. CRUNCEP Version 7—Atmospheric Forcing Data for the Community Land Model (Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, 2018); https://doi.org/10.5065/PZ8F-F017Mckee, T. B. T. et al. The relationship of drought frequency and duration to time scales. In Proc. Eighth Conference on Applied Climatology 179–184 (American Meteorological Society, 1993).The NCAR Command Language, Version 6.6.2 (UCAR/NCAR/CISL/TDD, 2019).Freeborn, P. H., Wooster, M. J., Roy, D. P. & Cochrane, M. A. Quantification of MODIS fire radiative power (FRP) measurement uncertainty for use in satellite-based active fire characterization and biomass burning estimation. Geophys. Res. Lett. 41, 1988–1994 (2014).
Google Scholar 
Giglio, L. MODIS Collection 5 Active Fire Product User’s Guide Version 2.5 (Science Systems and Applications, 2013).Huang, N. et al. Spatial and temporal variations in global soil respiration and their relationships with climate and land cover. Sci. Adv. 6, eabb8508 (2020).
Google Scholar 
Warner, D. L., Bond-Lamberty, B., Jian, J., Stell, E. & Vargas, R. Spatial predictions and associated uncertainty of annual soil respiration at the global scale. Glob. Biogeochem. Cycles 33, 1733–1745 (2019).
Google Scholar  More