Philippa Kaur

Refsnider, J. M. & Janzen, F. J. Putting eggs in one basket: Ecological and evolutionary hypotheses for variation in oviposition-site choice. Annu. Rev. Ecol. Evol. Syst. 41, 39–57 (2010).Article 

Google Scholar 
Rudolf, V. H. W. & Rodel, M. O. Oviposition site selection in a complex and variable environment: The role of habitat quality and conspecific cues. Oecologia 142, 316–325 (2005).Article 
ADS 

Google Scholar 
Blaustein, L. Oviposition site selection in response to risk of predation: Evidence from aquatic habitats and consequences for population dynamics and community structure. In Evolutionary Theory and Processes: Modern Perspectives (ed. Wasser, S. P.) 441–456 (Springer, 1999).Chapter 

Google Scholar 
Elsensohn, J. E., Schal, C. & Burrack, H. J. Plasticity in oviposition site selection behavior in drosophila suzukii (diptera: drosophilidae) in relation to adult density and host distribution and quality. J. Econ. Entomol. 114, 1517–1522 (2021).Article 

Google Scholar 
Kempraj, V., Park, S. J. & Taylor, P. W. Forewarned is forearmed: Queensland fruit flies detect olfactory cues from predators and respond with predator-specific behaviour. Sci. Rep. 10, 7297 (2020).Article 
ADS 

Google Scholar 
Damodaram, K. J. P. et al. Centuries of domestication has not impaired oviposition site-selection function in the silkmoth, Bombyx mori. Sci. Rep. 4, 1–6 (2014).
Google Scholar 
Hansson, B. S. & Stensmyr, M. C. Evolution of insect olfaction. Neuron 72, 698–711 (2011).Article 

Google Scholar 
Ghosh, E., Sasidharan, A., Ode, P. J. & Venkatesan, R. Oviposition preference and performance of a specialist herbivore is modulated by natural enemies, larval odors, and immune status. J. Chem. Ecol. 48, 670–682 (2022).Article 

Google Scholar 
Nielsen, R. A. & Brister, C. D. The greater wax moth: Adult behavior. Ann. Entomol. Soc. Am. 70, 101–103 (1977).Article 

Google Scholar 
Kwadha, C. A., Ong’Amo, G. O., Ndegwa, P. N., Raina, S. K. & Fombong, A. T. The biology and control of the greater wax moth, Galleria mellonella. Insects 8, 61 (2017).Article 

Google Scholar 
Kebede, E. Prevalence of wax moth in modern hive with colonies in Kafta Humera. Anim. Vet. Sci. 3, 132–135 (2015).Article 

Google Scholar 
Ellis, J. D., Graham, J. R. & Mortensen, A. Standard methods for wax moth research. J. Apic. Res. 52, 1–17 (2013).Article 

Google Scholar 
Hepburn, H. R. & Radloff, S. E. Honeybees of Africa 227–241 (Springer, 1998). https://doi.org/10.1007/978-3-662-03604-4.Book 

Google Scholar 
Fletcher, D. J. C. The African Bee, Apis mellifera adansonii, Africa. Annu. Rev. Entomol. 23, 151–171 (1978).Article 

Google Scholar 
Li, Y. et al. Losing the arms race: Greater wax moths sense but ignore bee alarm pheromones. Insects 10, 81 (2019).Article 
ADS 

Google Scholar 
Feng, B., Qian, K. & Du, Y. J. Floral volatiles from Vigna unguiculata are olfactory and gustatory stimulants for oviposition by the bean pod borer moth Maruca vitrata. Insects 8, 60 (2017).Article 

Google Scholar 
Janz, N. Evolutionary ecology of oviposition strategies. In Chemoecology of Insect Eggs and Egg Deposition (eds Hilker, M. & Meiners, T.) 349–376 (Willey, 2008). https://doi.org/10.1002/9780470760253.ch13.Chapter 

Google Scholar 
Renwick, J. A. A. & Chew, F. S. Oviposition behavior in lepidoptera. Annu. Rev. Entomol. 39, 377–400 (1994).Article 

Google Scholar 
Nakajima, Y. & Fujisaki, K. Fitness trade-offs associated with oviposition strategy in the winter cherry bug, Acanthocoris sordidus. Entomol. Exp. Appl. 137, 280–289 (2010).Article 

Google Scholar 
Murphy, P. J. Context-dependent reproductive site choice in a Neotropical frog. Behav. Ecol. 14, 626–633 (2003).Article 

Google Scholar 
Geoffrey, G. et al. Larviposition site selection mediated by volatile semiochemicals in Glossina palpalis gambiensis. Ecol. Entomol. 46, 301–309 (2021).Article 

Google Scholar 
Yao, F. L. et al. Oviposition preference and adult performance of the whitefly predator Serangium japonicum (Coleoptera: Coccinellidae): Effect of leaf microstructure associated with ladybeetle attachment ability. Pest Manag. Sci. 77, 113–125 (2021).Article 

Google Scholar 
Spieler, M. & Linsenmair, K. E. Choice of optimal oviposition sites by Hoplobatrachus occipitalis (Anura: Ranidae) in an unpredictable and patchy environment. Oecologia 109, 184–199 (1997).Article 
ADS 

Google Scholar 
Figiel, C. R. & Semlitsch, R. D. Experimental determination of oviposition site selection in the marbled salamander, Ambystoma opacum. J. Herpetol. 29, 452 (1995).Article 

Google Scholar 
Kotler, B. P. & Mitchell, W. A. The effect of costly information in diet choice. Evol. Ecol. 9, 18–29 (1995).Article 

Google Scholar 
Nylin, S. & Janz, N. Oviposition preference and larval performance in Polygonia c-album (Lepidoptera: Nymphalidae): the choice between bad and worse. Ecol. Entomol. 18, 394–398 (1993).Article 

Google Scholar 
Nagaya, H., Stewart, F. J. & Kinoshita, M. Swallowtail butterflies use multiple visual cues to select oviposition sites. Insects 12, 1047 (2021).Article 

Google Scholar 
Scolari, F., Valerio, F., Benelli, G., Papadopoulos, N. T. & Vaníčková, L. Tephritid fruit fly semiochemicals: Current knowledge and future perspectives. Insects 12, 408 (2021).Article 

Google Scholar 
Haverkamp, A., Hansson, B. S. & Knaden, M. Combinatorial codes and labelled lines: How insects use olfactory cues to find and judge food, mates, and oviposition sites in complex environments. Front. Physiol. 9, 49 (2018).Article 

Google Scholar 
Ichinosé, T., Honda, H. & Honda, H. Ovipositional behavior of papilio protenor demetrius Cramer and the factors involved in its host plants. Appl. Entomol. Zool. 13, 103–114 (1978).Article 

Google Scholar 
Spangler, H. G. Functional and temporal analysis of sound production in Galleria mellonella L. (Lepidoptera: Pyralidae). J. Comp. Physiol. A 159, 751–756 (1986).Article 

Google Scholar 
Spangler, H. G. & Takessian, A. Sound perception by two species of wax moths (Lepidoptera: Pyralidae). Ann. Entomol. Soc. Am. 76, 94–97 (1983).Article 

Google Scholar 
Skals, N. & Surlykke, A. Hearing and evasive behaviour in the greater wax moth, Galleria mellonella (Pyralidae). Physiol. Entomol. 25, 354–362 (2008).Article 

Google Scholar 
Kwadha, C. A. Determination of Attractant Semio-Chemicals of the Wax Moth, Galleria mellonella L., in Honeybee Colonies. M.Sc. Thesis, University of Nairobi, Kenya (2017).Pickard, S. C., Quinn, R. D. & Szczecinski, N. C. A dynamical model exploring sensory integration in the insect central complex substructures. Bioinspir. Biomim. 15, 026003. https://doi.org/10.1088/1748-3190/ab57b6 (2020).Article 
ADS 

Google Scholar 
Kamala Jayanthi, P. D., Saravan Kumar, P. & Vyas, M. Odour cues from fruit arils of artocarpus heterophyllus attract both sexes of oriental fruit flies. J. Chem. Ecol. 47, 552–563 (2021).Article 

Google Scholar 
Anfora, G., Tasin, M., de Cristofaro, A., Ioriatti, C. & Lucchi, A. Synthetic grape volatiles attract mated Lobesia botrana females in laboratory and field bioassays. J. Chem. Ecol. 35, 1054–1062 (2009).Article 

Google Scholar 
Fand, B. B. et al. Bacterial volatiles from mealybug honeydew exhibit kairomonal activity toward solitary endoparasitoid Anagyrus dactylopii. J. Pest Sci. 93, 195–206 (2020).Article 

Google Scholar 
Kovats, E. Gas chromatographic characterization of organic substances in the retention index system. Adv. Chromotogr. 1, 229–247 (1965).
Google Scholar  More

Fully terrestrial eDNA sampling approaches offer a potentially powerful addition to biodiversity monitoring efforts23,24. However, protocols for using eDNA-based methods to characterize terrestrial biodiversity, and vertebrate communities in particular, are still nascent27,28,30. In this study, we show for the first time that an eDNA metabarcoding approach can be used to broadly characterize tree-dwelling mammal communities by sampling tree trunks and surrounding soil. Our findings add to recent work (e.g., for reptiles22,24) showing that surface eDNA collection methods, which are relatively untested compared with soil-based eDNA methods, can also be effective at detecting terrestrial vertebrates. Further, we demonstrate that supplementing metabarcoding detection with qPCR-based methods can greatly improve sensitivity, a potentially important consideration for monitoring schemes focused on rare taxa (e.g., Refs.11,12). Together, our results have significant implications for global biodiversity conservation as the broader guild of arboreal vertebrates includes highly threatened5,48,49, as well as invasive alien species50, that are often cryptic, inhabit inaccessible locations, and are therefore challenging to monitor.Our methods captured over 60% of the mammalian diversity expected at the sites, and a similar fraction of the subset of arboreal species, despite sampling only 21 trees. Species accumulation curves suggest that more species would likely have been added with increased sampling effort. These results broadly agree with those of Leempoel et al.23 who found that soil eDNA metabarcoding well characterized mammal communities in California chaparral. However, in both our study and that of Leempoel et al.23, some conspicuous absences were evident. Bats comprised all of the arboreal species that we expected but failed to detect at our sites using metabarcoding (Fig. 1A). Leempoel et al.23 also noted a lack of bat detections (2 of 14 possible taxa detected), which they suggested could be due to low efficiency of either the 12S primer set or of their soil sampling methods for that order. While both reasons could also apply to the lack of bats detected in our study (discussed further below), the performance of the 12S primer set very likely contributed to our lack of American black bear detections as MiMammal-U primers are known to be ineffective at amplifying bear DNA38. These challenges highlight the reality that false negatives and varying detectability among species are common issues to all survey approaches, including eDNA metabarcoding. Our study represents a rare example among metabarcoding studies in that it uses repeated sampling and community occupancy models to quantify false negative rates. This quantitative approach, coupled with continued experimentation with different molecular techniques and survey methods (e.g., Refs.23,27), will be vital to helping researchers decide how eDNA metabarcoding methods will fit into existing biodiversity monitoring efforts moving forward.Although our results suggest that sampling for tree-roosting bats using eDNA metabarcoding still requires further research and optimization, our approach likely has application to characterizing communities in a much broader range of arboreal species globally. Geographic regions with multiple elusive arboreal mammals of management interest—for example, gliders and tree kangaroos in Australasia, or primates in the global tropics—may be particularly suited for a metabarcoding approach for community-level assessments4,8,9,49. It may be especially useful for rapid biodiversity assessments (RBAs51) in remote forested environments, where the ability to collect multiple samples relatively rapidly without regard to time of day would be a key advantage27. Existing survey methods to monitor arboreal mammals tend to be optimized for particular groups of species, often segregated by body size and behavior, with no suitable single method available to characterize all members of the guild4,8,9,16,49. Diurnal and nocturnal species, for example, often require separate survey methods or timing8. While camera traps capture both diurnal and nocturnal species, they typically miss smaller species16,23. The need for multiple methods to survey for nocturnal and diurnal, or large and small, species separately raises the cost of sampling and can result in datasets that are difficult to compare across sites because of inherent sampling biases8. Excluding bats, we found encouraging results for both diurnal and nocturnal arboreal species of a broad range of body sizes, detecting all seven expected species (Fig. 1A). While more work is needed to assemble robust genetic reference libraries before global arboreal mammal monitoring with eDNA metabarcoding will be broadly feasible, a clear advantage of the technique remains the power to detect a broad swath of species, with widely varying morphologies and behaviors, with a single method23,27,28,51.The promise of eDNA metabarcoding approaches for at least some arboreal guilds is well illustrated by our results for southern flying squirrel, Glaucomys volans. Like other flying squirrels (Tribe: Pteromyini), this species is strictly nocturnal, highly arboreal, and tends to get injured in live traps, making it difficult to directly observe and monitor48,52. Yet G. volans eDNA was readily detectable using metabarcoding in our study, occurring in 19–26% of soil samples and 47–52% of roller samples across both sites. Our similarly encouraging results for detecting other squirrels (Sciuridae) also bode well for management applications. For example, the methods would enable fine-scale mapping of habitat use in places such as the United Kingdom where native red squirrels (Sciurus vulgaris) are outcompeted by eastern gray squirrels, or the Delmarva peninsula (USA) to support the conservation efforts for the Delmarva fox squirrel (Sciurus niger cinereus)53. Further research is needed to determine the extent to which our results for squirrels generalize to other taxa with similar active tree-climbing lifestyles (e.g., gliders4, primates49).Our finding that soil samples revealed fewer species, had lower detection probability, and had lower read counts than roller samples, even for some non-arboreal species like white-tailed deer, likely reflects multiple factors. First, soil and tree bark represent markedly different biological and chemical environments that likely differ in eDNA quantity by species, eDNA persistence rates54,55, and microorganism abundance. The latter may be especially pertinent to our study as we observed a relatively large drop in the number of reads after removal of microorganism reads, especially for soil samples. This suggests that performing additional purification steps prior to sequencing could boost the ability of both methods, and especially soil eDNA, to detect target species by increasing mammalian sequencing depth. Other in-lab factors, such as method of extraction23 or choice of primers, similarly have the potential to influence the recovery and amplification of target species’ DNA and should be the focus of future research.Next, our focal trees were not chosen to occur near any special attractants or areas of multi-species use, such as saltlicks or water sources, which has proven successful in other vertebrate eDNA studies18,25,31,32,56. It is possible that adding a broader range of soil sampling sites, including some targeted towards other guilds (e.g., burrow users32,56), would have yielded a more complete inventory. Nevertheless, both soil and surface methods have advantages over the much more commonly-used metabarcoding approaches that rely on natural water bodies for assessing mammal communities16,17,27,28,29,30,38 as they are not limited to where these features occur. Our study is the first to suggest that surface eDNA metabarcoding methods can be a powerful supplement to established soil-based methods of characterizing mammal communities, especially for arboreal species.As noted, bats were especially lacking from our eDNA metabarcoding results, with only two of six likely species detected. Notably, our metabarcoding species list lacked two of the bat species that our sampling scheme was designed around (eastern red bat and northern long-eared bat) and for which we had confirmed recent presence at the sites (Table 1). The lack of northern long-eared bat detections may directly relate to recent precipitous population declines (~ 99%) caused by white-nose syndrome57. However, the lack of eastern red bat detections was especially surprising as roosting of this species was suspected based on telemetry in 17 of our 21 target trees. Reasons for this omission may relate to the fact that eastern red bats roost singly on small twigs and in leaf clusters, and therefore may not leave much DNA on tree trunks. Another possibility is low efficiency of the 12S primer set for bats, although we were unable to find information about this in the literature. It is notable that Leempoel et al.23 had a similarly poor representation of bats with comparable soil-based methods. However, our metabarcoding results did indicate that we are capable of detecting even uncommon, or at least unexpected, bat species with our methods. Eastern small-footed bat, which is typically viewed as a rock-roosting species and is considered endangered by the International Union for Conservation of Nature (IUCN)58, was detected in both soil and surface eDNA samples from Morristown National Historic Park. This species was not otherwise confirmed as present at the site until a year later, in spring 2022, when it was caught in a mist net (BM, unpublished data). Our results with respect to bat detections, along with those of others23, underscore the need for further research to adapt eDNA metabarcoding methods to this vulnerable group, which could contribute much needed demographic and distribution information. This is especially urgent as 18% of bat species are listed as “data deficient” by the IUCN, while 57% lack basic population trend information5,58.Our comparison of qPCR to metabarcoding detection methods for big brown bat represents a hopeful result for the use of eDNA to monitor rare vertebrates that are of particular conservation interest. It is well-known that qPCR-based eDNA surveys targeted towards individual species return higher detection probabilities and have greater power at low abundance, than metabarcoding approaches59. Our results agree, showing for the first time that adding a qPCR step in the analysis of surface and soil eDNA samples can be effective for detecting bats in forested environments. The addition of a qPCR step opens the door for developing species-specific assays to increase detection power for endangered or elusive bat species, or other cryptic arboreal mammals49. Emerging molecular detection approaches such as droplet digital PCR have the potential to increase this sensitivity even further59. Like other eDNA-based tools and survey tools in general, careful consideration of sampling effort, the natural history of target species, and the configuration of different field and molecular methods will be key to optimizing our approach to characterize mammal communities, or to target a particular species, in different regions.Although eDNA surveys are not inexpensive given the need for both fieldwork and molecular analyses, they can be cheaper than conventional approaches, especially if such approaches require many hours of fieldwork or expensive equipment60. Thus, the relative cost-effectiveness of surface or soil eDNA surveys will depend heavily on the mammal communities of interest, the mix of methods that must be employed to effectively sample them, and the purpose of the sampling efforts. However, even if costs are increased, eDNA surveys can reduce field time to the extent that they can improve detection rates, either by replacing or supplementing conventional sampling methods (e.g., as a supplement to visual observations). With higher detection rates, fewer visits are required to achieve the same results. This operational efficiency would be especially advantageous when field conditions present safety risks, are intrusive to sensitive habitats, or are challenging to access. For example, adding surface eDNA sampling to existing visual surveys of eastern wood rats (Neotoma floridana), a cryptic mammal that inhabits steep, rocky slopes in the eastern US, could likely increase detection power, thereby reducing the need for additional risky and costly sampling visits. More studies involving direct comparisons among methods (e.g., Refs.23,24,30,60), in a variety of ecoregions, are needed to determine the extent to which incorporating our methods into existing vertebrate monitoring workflows would increase efficiency.Finally, we detected other vertebrates, including seven birds and one salamander, in soil and surface eDNA samples, despite our use of a mammal-specific primer set. This is similar to results from Leempoel et al.23 in California using the same primer set, in which six bird species were detected. We found that surface eDNA detected more bird species than soil, perhaps for the same reasons as for mammals (above). Our results provide evidence that surface eDNA surveys, with taxon-specific primers, could be used to survey bird communities, or used to target particularly rare species in forested ecosystems (e.g., Ref.61). Our detection of a salamander, coupled with recent promising research into reptile detection using surface eDNA methods22,24 suggests a broader potential for applications with other vertebrates as well. Finally, both surface and soil eDNA metabarcoding can be expanded beyond forests, providing insight into their effectiveness in other habitats (e.g., caves17 or talus slopes). Our study and others highlight that the potential of coupling surface and soil eDNA methods for detecting and monitoring mammalian biodiversity, and terrestrial organisms generally, has yet to be fully realized. More

EU Commission. Achieve Good Environmental Status. EU Commission Web site https://ec.europa.eu/environment/marine/good-environmental-status/index_en.htm (2008).Olenin, S. et al. Marine strategy framework directive. Task Group 2 (2010).Long, R. The marine strategy framework directive: a new european approach to the regulation of the marine environment, marine natural resources and marine ecological services. Journal of Energy & Natural Resources Law 29, 1–44 (2011).Article 
ADS 

Google Scholar 
Borja, A. et al. Good environmental status of marine ecosystems: what is it and how do we know when we have attained it? Marine Pollution Bulletin 76, 16–27 (2013).Article 

Google Scholar 
Froese, R., Demirel, N., Coro, G. & Kleisner, K. M. & Winker, H. Estimating fisheries reference points from catch and resilience. Fish and Fisheries 18, 506–526 (2017).Article 

Google Scholar 
Coro, G. Open science and artificial intelligence supporting blue growth. Environmental Engineering & Management Journal (EEMJ) 19 (2020).JRC. EU data collection Web site https://datacollection.jrc.ec.europa.eu/ – Accessed June 2022 (2021).EcoScope Consortium. The EcoScope EU project Web site https://ecoscopium.eu/ – Accessed June 2022 (2021).Pikitch, E. K. et al. Ecosystem-based fishery management. Science 305, 346–347 (2004).Article 

Google Scholar 
McLeod, K. L. & Leslie, H. M. Why ecosystem-based management. Ecosystem-based management for the oceans 3–12 (2009).Coro, G., Magliozzi, C., Ellenbroek, A. & Pagano, P. Improving data quality to build a robust distribution model for architeuthis dux. Ecological modelling 305, 29–39 (2015).Article 

Google Scholar 
Coro, G., Ellenbroek, A. & Pagano, P. An open science approach to infer fishing activity pressure on stocks and biodiversity from vessel tracking data. Ecological Informatics 64, 101384 (2021).Article 

Google Scholar 
Elith, J. & Leathwick, J. R. Species distribution models: ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution and Systematics 40, 677–697 (2009).Article 

Google Scholar 
Coro, G., Bove, P. & Ellenbroek, A. Habitat distribution change of commercial species in the adriatic sea during the covid-19 pandemic. Ecological Informatics 101675 (2022).Stanton, J. C., Pearson, R. G., Horning, N., Ersts, P. & Reşit Akçakaya, H. Combining static and dynamic variables in species distribution models under climate change. Methods in Ecology and Evolution 3, 349–357 (2012).Article 

Google Scholar 
Coro, G., Magliozzi, C., Ellenbroek, A., Kaschner, K. & Pagano, P. Automatic classification of climate change effects on marine species distributions in 2050 using the aquamaps model. Environmental and ecological statistics 23, 155–180 (2016).Article 
MathSciNet 

Google Scholar 
Coro, G., Pagano, P. & Ellenbroek, A. Detecting patterns of climate change in long-term forecasts of marine environmental parameters. International Journal of Digital Earth 13, 567–585 (2020).Article 
ADS 

Google Scholar 
Wayte, S. E. Management implications of including a climate-induced recruitment shift in the stock assessment for jackass morwong (nemadactylus macropterus) in south-eastern australia. Fisheries Research 142, 47–55 (2013).Article 

Google Scholar 
Tanaka, K. R. Integrating environmental information into stock assessment models for fisheries management. Predicting Future Oceans 193–206 (2019).Szuwalski, C. S. & Hollowed, A. B. Climate change and non-stationary population processes in fisheries management. ICES Journal of Marine Science 73, 1297–1305 (2016).Article 

Google Scholar 
Bevilacqua, A. H. V., Carvalho, A. R., Angelini, R. & Christensen, V. More than anecdotes: fishers’ ecological knowledge can fill gaps for ecosystem modeling. PLoS One 11, e0155655 (2016).Article 

Google Scholar 
Heymans, J. J. et al. Best practice in ecopath with ecosim food-web models for ecosystem-based management. Ecological Modelling 331, 173–184 (2016).Article 

Google Scholar 
Piroddi, C. et al. Historical changes of the mediterranean sea ecosystem: modelling the role and impact of primary productivity and fisheries changes over time. Scientific reports 7, 1–18 (2017).Article 
ADS 

Google Scholar 
Campana, E. F., Ciappi, E. & Coro, G. The role of technology and digital innovation in sustainability and decarbonization of the blue economy. Bulletin of Geophysics and Oceanography 123 (2021).Van Vuuren, D. P. et al. The representative concentration pathways: an overview. Climatic change 109, 5–31 (2011).Article 
ADS 

Google Scholar 
Intergovernmental Panel on Climate Change https://www.academia.edu/download/60673993/climate_change_emission_Special_scenarios20190922-59363-1j1i98f.pdf – Accessed October 2022. IPCC Special Report (2000).Scarcella, G. et al. The potential effects of covid-19 lockdown and the following restrictions on the status of eight target stocks in the adriatic sea. Frontiers in Marine Science 1963 (2022).Wikipedia. ESRI-GRID formats description. Wikipedia https://en.wikipedia.org/wiki/Esri_grid (2022).QGIS. Qgis software version 3.20.0. QGIS Web site https://www.qgis.org/en/site/ (2022).ESRI. Arcgis software version 10.7. ArcGIS Web site https://www.esri.com/it-it/arcgis/products/arcgis-desktop/overview (2022).American Museum of Natural History. Maxent software for modelling species distributions. AMNH Web site https://biodiversityinformatics.amnh.org/open_source/maxent/ (2022).Christensen, V. et al. Ecopath with ecosim: a user’s guide. Fisheries Centre, University of British Columbia, Vancouver 154, 31 (2005).
Google Scholar 
Coll, M., Bundy, A. & Shannon, L. J. Ecosystem modelling using the ecopath with ecosim approach. In Computers in fisheries research, 225–291 (Springer, 2009).Colléter, M. et al. Global overview of the applications of the ecopath with ecosim modeling approach using the ecobase models repository. Ecological Modelling 302, 42–53 (2015).Article 

Google Scholar 
VanDerWal, J., Falconi, L., Januchowski, S., Shoo, L. & Storlie, C. Sdmtools: Species distribution modelling tools: Tools for processing data associated with species distribution modelling exercises. R package version 1, 1 (2014).
Google Scholar 
US National Institutes of Health. ImageJ software for image analysis with Java and the Terrain Cartography plugin for reading ASC files. ImageJ Web site https://imagej.nih.gov/ij/index.html (2018).GDAL. Translator library for raster and vector geospatial data, version 3.5.0. GDAL Web site https://gdal.org/ (2022).Claus, S. et al. Marine regions: towards a global standard for georeferenced marine names and boundaries. Marine Geodesy 37, 99–125 (2014).Article 

Google Scholar 
VLIZ. World marine regions definitions and geospatial data. Marine Regions Web site www.marineregions.org (2022).Coro, G. The ASCFileManagement GitHub repository. GitHub https://github.com/cybprojects65/ASCFileManagement (2022).Ready, J. et al. Predicting the distributions of marine organisms at the global scale. Ecological Modelling 221, 467–478 (2010).Article 

Google Scholar 
Selig, E. R. et al. Global priorities for marine biodiversity conservation. PloS one 9, e82898 (2014).Article 
ADS 

Google Scholar 
O’hara, C. C., Afflerbach, J. C., Scarborough, C., Kaschner, K. & Halpern, B. S. Aligning marine species range data to better serve science and conservation. PLoS One 12, e0175739 (2017).Article 

Google Scholar 
Scarponi, P., Coro, G. & Pagano, P. A collection of aquamaps native layers in netcdf format. Data in brief 17, 292–296 (2018).Article 

Google Scholar 
CMEMS. Copernicus Marine Service ocean products data. Copernicus Marine Service Web site https://marine.copernicus.eu/ (2022).E.U. Copernicus Marine Service Information. Global Ocean 1/12° Physics Analysis and Forecast updated Daily. Copernicus Marine Service Web site https://doi.org/10.48670/moi-00016 (2021).Article 

Google Scholar 
E.U. Copernicus Marine Service Information. Global Ocean Biogeochemistry Analysis and Forecast. Copernicus Marine Service Web site https://doi.org/10.48670/moi-00015 (2021).Article 

Google Scholar 
Hijmans, R. J. et al. Terra: Spatial data analysis. R Spatial Data Science Web site https://rspatial.org/terra/ (2022).MacLeod, C. D. Habitat representativeness score (hrs): a novel concept for objectively assessing the suitability of survey coverage for modelling the distribution of marine species. Journal of the Marine Biological Association of the United Kingdom 90, 1269–1277 (2010).Article 

Google Scholar 
Abdi, H. & Williams, L. J. Principal component analysis. Wiley interdisciplinary reviews: computational statistics 2, 433–459 (2010).Article 

Google Scholar 
Coro, G., Pagano, P. & Ellenbroek, A. Combining simulated expert knowledge with neural networks to produce ecological niche models for latimeria chalumnae. Ecological modelling 268, 55–63 (2013).Article 

Google Scholar 
Coro, G., Pagano, P. & Ellenbroek, A. Automatic procedures to assist in manual review of marine species distribution maps. In International Conference on Adaptive and Natural Computing Algorithms, 346–355 (Springer, 2013).Magliozzi, C., Coro, G., Grabowski, R. C., Packman, A. I. & Krause, S. A multiscale statistical method to identify potential areas of hyporheic exchange for river restoration planning. Environmental Modelling & Software 111, 311–323 (2019).Article 

Google Scholar 
Coro, G. & Bove, P. Global-Scale Parameters for Ecological Models, FigShare, https://doi.org/10.6084/m9.figshare.c.6039275.v4 (2022).Coro, G. Means, standard deviations, geometric means, and log-normal standard deviation of the data produced for the present publication. D4Science distributed storage system https://data.d4science.net/foLS (2022).Mann, M. E., Bradley, R. S. & Hughes, M. K. Global-scale temperature patterns and climate forcing over the past six centuries. Nature 392, 779–787 (1998).Article 
ADS 

Google Scholar 
Biskaborn, B. K. et al. Permafrost is warming at a global scale. Nature communications 10, 1–11 (2019).Article 

Google Scholar 
Nemani, R. R. et al. Climate-driven increases in global terrestrial net primary production from 1982 to 1999. science 300, 1560–1563 (2003).Article 
ADS 

Google Scholar 
Huang, Y., Zhang, W., Sun, W. & Zheng, X. Net primary production of chinese croplands from 1950 to 1999. Ecological Applications 17, 692–701 (2007).Article 

Google Scholar 
Sunlu, U., Aksu, M., Buyukisik, B. & Sunlu, F. S. Spatio-temporal variations of organic carbon and chlorophyll degradation products in the surficial sediments of izmir bay (aegean sea/turkey). Environmental monitoring and assessment 146, 423–432 (2008).Article 

Google Scholar 
Kubryakov, A., Mikaelyan, A., Stanichny, S. & Kubryakova, E. Seasonal stages of chlorophyll-a vertical distribution and its relation to the light conditions in the black sea from bio-argo measurements. Journal of Geophysical Research: Oceans 125, e2020JC016790 (2020).ADS 

Google Scholar 
Gamo, T. Global warming may have slowed down the deep conveyor belt of a marginal sea of the northwestern pacific: Japan sea. Geophysical Research Letters 26, 3137–3140 (1999).Article 
ADS 

Google Scholar 
Mahaffey, C., Palmer, M., Greenwood, N. & Sharples, J. Impacts of climate change on dissolved oxygen concentration relevant to the coastal and marine environment around the uk. MCCIP Science Review 2002, 31–53 (2020).
Google Scholar 
Zhang, W., Dunne, J. P., Wu, H., Zhou, F. & Huang, D. Using timescales of deficit and residence to evaluate near-bottom dissolved oxygen variation in coastal seas. Journal of Geophysical Research: Biogeosciences 127, e2021JG006408 (2022).ADS 

Google Scholar 
Helm, K. P., Bindoff, N. L. & Church, J. A. Changes in the global hydrological-cycle inferred from ocean salinity. Geophysical Research Letters 37 (2010).Ren, L., Speer, K. & Chassignet, E. P. The mixed layer salinity budget and sea ice in the southern ocean. Journal of Geophysical Research: Oceans 116 (2011).Mahmuduzzaman, M. et al. Causes of salinity intrusion in coastal belt of bangladesh. International Journal of Plant Research 4, 8–13 (2014).
Google Scholar 
Podymov, O., Zatsepin, A. & Ocherednik, V. Increase of temperature and salinity in the active layer of the north-eastern black sea from 2010 to 2020. Physical Oceanography 28, 257–265 (2021).Article 

Google Scholar 
Mizyuk, A. & Puzina, O. Sea ice modeling in the sea of azov for a study of long-term variability. In IOP Conference Series: Earth and Environmental Science, vol. 386, 012023 (IOP Publishing, 2019).Pärn, O., Friedland, R., Rjazin, J. & Stips, A. Regime shift in sea-ice characteristics and impact on the spring bloom in the baltic sea. Oceanologia 64, 312–326 (2022).Article 

Google Scholar 
Lundesgaard, Ø., Sundfjord, A. & Renner, A. H. Drivers of interannual sea ice concentration variability in the atlantic water inflow region north of svalbard. Journal of Geophysical Research: Oceans 126, e2020JC016522 (2021).ADS 

Google Scholar 
Schwegmann, S. & Holfort, J. Regional distributed trends of sea ice volume in the baltic sea for the 30-year period 1982 to 2019. Meteorologische Zeitschrift 33–43 (2021).Simon, S. Interpretation of the correlation coefficient. PMean Web site http://www.pmean.com/definitions/correlation.htm (2020).Yacobi, Y. et al. Chlorophyll distribution throughout the southeastern mediterranean in relation to the physical structure of the water mass. Journal of Marine Systems 6, 179–190, https://doi.org/10.1016/0924-7963(94)00028-A (1995).Article 
ADS 

Google Scholar 
Kucuksezgin, F., Balci, A., Kontas, A. & Altay, O. Distribution of nutrients and chlorophyll-a in the aegean sea. Oceanologica Acta 18, 343–352 (1995).
Google Scholar 
Villate, F., Aravena, G., Iriarte, A. & Uriarte, I. Axial variability in the relationship of chlorophyll a with climatic factors and the north atlantic oscillation in a basque coast estuary, bay of biscay (1997–2006). Journal of Plankton Research 30, 1041–1049 (2008).Article 

Google Scholar 
Iriarte, A. et al. Dissolved oxygen in contrasting estuaries of the bay of biscay: effects of temperature, river discharge and chlorophyll a. Marine Ecology Progress Series 418, 57–71 (2010).Article 
ADS 

Google Scholar 
Stanev, E. V. Black sea dynamics. Oceanography 18, 56–75 (2005).Article 

Google Scholar 
Tsimplis, M. N. & Rixen, M. Sea level in the mediterranean sea: The contribution of temperature and salinity changes. Geophysical research letters 29, 51–1 (2002).Article 

Google Scholar 
Schneider, A., Wallace, D. W. & Körtzinger, A. Alkalinity of the mediterranean sea. Geophysical Research Letters 34 (2007).Sara, G., Porporato, E. M., Mangano, M. C. & Mieszkowska, N. Multiple stressors facilitate the spread of a non-indigenous bivalve in the mediterranean sea. Journal of Biogeography 45, 1090–1103 (2018).Article 

Google Scholar 
Soto-Navarro, J. et al. Evolution of mediterranean sea water properties under climate change scenarios in the med-cordex ensemble. Climate Dynamics 54, 2135–2165 (2020).Article 
ADS 

Google Scholar 
Dietze, H. & Löptien, U. Retracing hypoxia in eckernförde bight (baltic sea). Biogeosciences 18, 4243–4264 (2021).Article 
ADS 

Google Scholar 
Ulses, C. et al. Oxygen budget of the north-western mediterranean deep-convection region. Biogeosciences 18, 937–960 (2021).Article 
ADS 

Google Scholar 
Jaskulak, M., Sotomski, M., Michalska, M., Marks, R. & Zorena, K. The effects of wastewater treatment plant failure on the gulf of gdansk (southern baltic sea). International Journal of Environmental Research and Public Health 19, 2048 (2022).Article 

Google Scholar 
Mihanović, H. et al. Observation, preconditioning and recurrence of exceptionally high salinities in the adriatic sea. Frontiers in Marine Science 8, 834 (2021).Article 

Google Scholar 
De Leo, F., Besio, G. & Mentaschi, L. Trends and variability of ocean waves under rcp8. 5 emission scenario in the mediterranean sea. Ocean Dynamics 71, 97–117 (2021).Article 
ADS 

Google Scholar 
Omar, A. M. et al. Trends of ocean acidification and pco2 in the northern north sea, 2003–2015. Journal of Geophysical Research: Biogeosciences 124, 3088–3103 (2019).Article 

Google Scholar 
Kröncke, I. et al. Comparison of biological and ecological long-term trends related to northern hemisphere climate in different marine ecosystems. Nature Conservation (2019).Bonnet, D. et al. Comparative seasonal dynamics of centropages typicus at seven coastal monitoring stations in the north sea, english channel and bay of biscay. Progress in oceanography 72, 233–248 (2007).Article 
ADS 

Google Scholar 
Borja, Á. et al. Implementation of the european marine strategy framework directive: A methodological approach for the assessment of environmental status, from the basque country (bay of biscay). Marine Pollution Bulletin 62, 889–904, https://doi.org/10.1016/j.marpolbul.2011.03.031 (2011).Article 

Google Scholar 
Coro, G. An open-source re-implementation of the habitat representativeness score. GitHub https://github.com/cybprojects65/HabitatRepresentativenessScore (2022).Coro, G. An OGC-WPS compliant interface to calculate Habitat Representativeness Score. D4Science RPrototypingLab VRE https://services.d4science.org/group/rprototypinglab/data-miner?OperatorId = org.gcube.dataanalysis.wps.statisticalmanager.synchserver.mappedclasses.transducerers.HABITAT_REPRESENTATIVENESS_SCORE (2022).Assante, M. et al. Enacting open science by d4science. Future Generation Computer Systems 101, 555–563 (2019).Article 

Google Scholar 
Assante, M. et al. The gcube system: delivering virtual research environments as-a-service. Future Generation Computer Systems 95, 445–453 (2019).Article 

Google Scholar 
Assante, M. et al. Virtual research environments co-creation: The d4science experience. Concurrency and Computation: Practice and Experience e6925 (2022).Coro, G., Candela, L., Pagano, P., Italiano, A. & Liccardo, L. Parallelizing the execution of native data mining algorithms for computational biology. Concurrency and Computation: Practice and Experience 27, 4630–4644 (2015).Article 

Google Scholar 
Coro, G., Panichi, G., Scarponi, P. & Pagano, P. Cloud computing in a distributed e-infrastructure using the web processing service standard. Concurrency and Computation: Practice and Experience 29, e4219 (2017).Article 

Google Scholar 
Gačić, M., Borzelli, G. E., Civitarese, G., Cardin, V. & Yari, S. Can internal processes sustain reversals of the ocean upper circulation? the ionian sea example. Geophysical research letters 37 (2010).Grilli, F. et al. Seasonal and interannual trends of oceanographic parameters over 40 years in the northern adriatic sea in relation to nutrient loadings using the emodnet chemistry data portal. Water 12, 2280 (2020).Article 

Google Scholar 
Cozzi, S. et al. Climatic and anthropogenic impacts on environmental conditions and phytoplankton community in the gulf of trieste (northern adriatic sea). Water 12, 2652 (2020).Article 

Google Scholar 
Ducrotoy, J.-P. & Elliott, M. The science and management of the north sea and the baltic sea: Natural history, present threats and future challenges. Marine pollution bulletin 57, 8–21 (2008).Article 

Google Scholar 
Dupont, N. & Aksnes, D. L. Centennial changes in water clarity of the baltic sea and the north sea. Estuarine, Coastal and Shelf Science 131, 282–289 (2013).Article 
ADS 

Google Scholar 
Dippner, J. W., Möller, C. & Hänninen, J. Regime shifts in north sea and baltic sea: a comparison. Journal of Marine Systems 105, 115–122 (2012).Article 
ADS 

Google Scholar 
Sisma-Ventura, G. et al. Post-eastern mediterranean transient oxygen decline in the deep waters of the southeast mediterranean sea supports weakening of ventilation rates. Frontiers in Marine Science 1202 (2021).Mavropoulou, A.-M., Vervatis, V. & Sofianos, S. Dissolved oxygen variability in the mediterranean sea. Journal of Marine Systems 208, 103348 (2020).Article 

Google Scholar 
Tyberghein, L. et al. Bio-oracle: a global environmental dataset for marine species distribution modelling. Global ecology and biogeography 21, 272–281 (2012).Article 

Google Scholar 
Assis, J. et al. Bio-oracle v2. 0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography 27, 277–284 (2018).Article 

Google Scholar 
Coro, G. & Bove, P. A high-resolution global-scale model for covid-19 infection rate. ACM Transactions on Spatial Algorithms and Systems (TSAS) 8, 1–24 (2022).Article 

Google Scholar 
Inness, A. et al. The cams reanalysis of atmospheric composition. Atmospheric Chemistry and Physics 19, 3515–3556 (2019).Article 
ADS 

Google Scholar 
Karger, D. N., Schmatz, D. R., Dettling, G. & Zimmermann, N. E. High-resolution monthly precipitation and temperature time series from 2006 to 2100. Scientific data 7, 1–10 (2020).Article 

Google Scholar 
Kesner-Reyes, K. et al. AquaMaps Environmental Dataset: Half-Degree Cells Authority File (HCAF ver. 7, 10/2019). AquaMaps Web site https://www.aquamaps.org/main/envt_data.php (2019).Kesner-Reyes, K. et al. AquaMaps Environmental Dataset: Half-Degree Cells Authority File (HCAF ver. 6, 08/2016). AquaMaps Web site https://www.aquamaps.org/main/envt_data.php (2016).NASA-NEX. NASA Earth Exchange data. NASA-NEX Web site https://www.nasa.gov/nex/data – data were publicly accessible up to 2020 (2020).Coro, G. A global-scale ecological niche model to predict sars-cov-2 coronavirus infection rate. Ecological modelling 431, 109187 (2020).Article 

Google Scholar 
CAMS. Global inversion-optimised greenhouse gas fluxes and concentrations. Copernicus Atmosphere Web site https://ads.atmosphere.copernicus.eu/cdsapp#/dataset/cams-global-greenhouse-gas-inversion?tab=doc (2020).NOAA. ETOPO2 Topography and Bathymetry. NOAA Web site https://sos.noaa.gov/catalog/datasets/etopo2-topography-and-bathymetry-natural-colors/ (2010).Coro, G. & Trumpy, E. Predicting geographical suitability of geothermal power plants. Journal of Cleaner Production 267, 121874 (2020).Article 

Google Scholar 
NOAA. World Vector Shorelines. NOAA Web site https://shoreline.noaa.gov/data/datasheets/wvs.html (2019).Tozer, B. et al. Global bathymetry and topography at 15 arc sec: Srtm15+. Earth and Space Science 6, 1847–1864, https://doi.org/10.1029/2019EA000658 (2019).Article 
ADS 

Google Scholar 
Ramesh, R. et al. Land–ocean interactions in the coastal zone: Past, present & future. Anthropocene 12, 85–98 (2015).Article 

Google Scholar 
Spalding, M. et al. World atlas of coral reefs (Univ of California Press, 2001).Laske, G. A global digital map of sediment thickness. Eos Trans. AGU 78, F483 (1997).
Google Scholar 
Davies, J. H. Global map of solid earth surface heat flow. Geochemistry, Geophysics, Geosystems 14, 4608–4622 (2013).Article 
ADS 

Google Scholar 
Rybach, L. & Muffler, L. J. P. Geothermal systems: principles and case histories. Chichester, Sussex, England and New York, Wiley-Interscience, 1981. 371 p. (1981).Glassley, W. E. Geology and hydrology of geothermal energy. Power Stations Using Locally Available Energy Sources: A Volume in the Encyclopedia of Sustainability Science and Technology Series, Second Edition 23–34 (2018).Barbier, E. Geothermal energy technology and current status: an overview. Renewable and sustainable energy reviews 6, 3–65 (2002).Article 
ADS 

Google Scholar 
Engdahl, E. R., van der Hilst, R. & Buland, R. Global teleseismic earthquake relocation with improved travel times and procedures for depth determination. Bulletin of the Seismological Society of America 88, 722–743 (1998).
Google Scholar 
Engdahl, E. R. Global seismicity: 1900–1999. International handbook of earthquake and engineering seismology 665–690 (2002).Richts, A., Struckmeier, W. F. & Zaepke, M. WHYMAP and the groundwater resources map of the world 1: 25,000,000. In Sustaining groundwater resources, 159–173 (Springer, 2011).Warszawski, L. et al. Center for international earth science information network—ciesin—columbia university.(2016). gridded population of the world, version 4 (gpwv4): Population density. palisades. ny: Nasa socioeconomic data and applications center (sedac). Atlas of Environmental Risks Facing China Under Climate Change 228, https://doi.org/10.7927/h4np22dq (2017). More

Asada, M. et al. Close relationship of Plasmodium sequences detected from South American pampas deer (Ozotoceros bezoarticus) to Plasmodium spp. in North American white-tailed deer. Int. J. Parasitol. 7, 44–47. https://doi.org/10.1016/j.ijppaw.2018.01.001 (2018).Article 

Google Scholar 
Boundenga, L. et al. Haemosporidian parasites of antelopes and other vertebrates from Gabon, Central Africa. PLoS ONE 11, e0148958. https://doi.org/10.1371/journal.pone.0148958 (2016).Article 
CAS 

Google Scholar 
Martinsen, E. S., Perkins, S. L. & Schall, J. J. A three-genome phylogeny of malaria parasites (Plasmodium and closely related genera): Evolution of life-history traits and host switches. Mol. Phylogen. Evol. 47, 261–273. https://doi.org/10.1016/j.ympev.2007.11.012 (2008).Article 
CAS 

Google Scholar 
Templeton, T. J. et al. Ungulate malaria parasites. Sci. Rep. 6, 23230. https://doi.org/10.1038/srep23230 (2016).Article 
ADS 
CAS 

Google Scholar 
Templeton, T. J., Martinsen, E., Kaewthamasorn, M. & Kaneko, O. The rediscovery of malaria parasites of ungulates. Parasitology 143, 1501–1508. https://doi.org/10.1017/s0031182016001141 (2016).Article 

Google Scholar 
Bruce, D., Harvey, D., Hamerton, A. E. & Bruce, L. Plasmodium cephalophi, sp. nov. Proc. R. Soc. B. 87, 45–47 (1913).ADS 

Google Scholar 
Sheather, A. L. A malarial parasite in the blood of a buffalo. J. Comp. Pathol. 32, 223–229 (1919).Article 

Google Scholar 
Kandel, R. C. et al. First report of malaria parasites in water buffalo in Nepal. Vet. Parasitol. Reg. Stud. Rep. 18, 100348. https://doi.org/10.1016/j.vprsr.2019.100348 (2019).Article 

Google Scholar 
de Mello, F. & Paes, S. Sur une plasmodiae du sang des chèvres. C. R. Séanc. Soc. Biol 88, 829–830 (1923).
Google Scholar 
Kaewthamasorn, M. et al. Genetic homogeneity of goat malaria parasites in Asia and Africa suggests their expansion with domestic goat host. Sci. Rep. 8, 5827. https://doi.org/10.1038/s41598-018-24048-0 (2018).Article 
ADS 
CAS 

Google Scholar 
Garnham, P. C. & Edeson, J. F. Two new malaria parasites of the Malayan mousedeer. Riv. Malariol. 41, 1–8 (1962).CAS 

Google Scholar 
Garnham, P. C. & Kuttler, K. L. A malaria parasite of the white-tailed deer (Odocoileus virginianus) and its relation with known species of Plasmodium in other ungulates. Proc. R. Soc. Lond. B 206, 395–402. https://doi.org/10.1098/rspb.1980.0003 (1980).Article 
ADS 
CAS 

Google Scholar 
Martinsen, E. et al. Hidden in plain sight: Cryptic and endemic malaria parasites in North American white-tailed deer (Odocoileus virginianus). Sci. Adv. 2, e1501486. https://doi.org/10.1126/sciadv.1501486 (2016).Article 
ADS 
CAS 

Google Scholar 
Rattanarithikul, R. et al. Illustrated keys to the mosquitoes of Thailand. IV. Anopheles. Southeast Asian. Trop. Med. Public Health 37, 1–128 (2006).
Google Scholar 
Walter Reed Biosystematics Unit. Systematic catalogue of Culicidae. http://mosquitocatalog.org (2021).Manguin, S., Garros, C., Dusfour, I., Harbach, R. E. & Coosemans, M. Bionomics, taxonomy, and distribution of the major malaria vector taxa of Anopheles subgenus Cellia in Southeast Asia: An updated review. Infect. Genet. Evol. 8, 489–503. https://doi.org/10.1016/j.meegid.2007.11.004 (2008).Article 
CAS 

Google Scholar 
Brosseau, L. et al. A multiplex PCR assay for the identification of five species of the Anopheles barbirostris complex in Thailand. Parasit. Vectors 12, 223. https://doi.org/10.1186/s13071-019-3494-8 (2019).Article 

Google Scholar 
Paredes-Esquivel, C., Donnelly, M. J., Harbach, R. E. & Townson, H. A molecular phylogeny of mosquitoes in the Anopheles barbirostris Subgroup reveals cryptic species: implications for identification of disease vectors. Mol. Phylogen. Evol. 50, 141–151. https://doi.org/10.1016/j.ympev.2008.10.011 (2009).Article 
CAS 

Google Scholar 
Taai, K. & Harbach, R. E. Systematics of the Anopheles barbirostris species complex (Diptera: Culicidae: Anophelinae) in Thailand. Zool. J. Linn. Soc. 174, 244–264. https://doi.org/10.1111/zoj.12236 (2015).Article 

Google Scholar 
Garros, C., Van Bortel, W., Trung, H. D., Coosemans, M. & Manguin, S. Review of the Minimus Complex of Anopheles, main malaria vector in Southeast Asia: From taxonomic issues to vector control strategies. Trop. Med. Int. Health 11, 102–114. https://doi.org/10.1111/j.1365-3156.2005.01536.x (2006).Article 
CAS 

Google Scholar 
Dahan-Moss, Y. et al. Member species of the Anopheles gambiae complex can be misidentified as Anopheles leesoni. Malar. J. 19, 89. https://doi.org/10.1186/s12936-020-03168-x (2020).Article 
CAS 

Google Scholar 
Van Bortel, W. et al. Confirmation of Anopheles varuna in Vietnam, previously misidentified and mistargeted as the malaria vector Anopheles minimus. Am. J. Trop. Med. Hyg. 65, 729–732. https://doi.org/10.4269/ajtmh.2001.65.729 (2001).Article 

Google Scholar 
Wharton, R. H., Eyles, D. E., Warren, M., Moorhouse, D. E. & Sandosham, A. A. Investigations leading to the identification of members of the Anopheles umbrosus group as the probable vectors of mouse deer malaria. Bull. 29, 357–374 (1963).CAS 

Google Scholar 
Nugraheni, Y. R. et al. Myzorhynchus series of Anopheles mosquitoes as potential vectors of Plasmodium bubalis in Thailand. Sci. Rep. 12, 5747. https://doi.org/10.1038/s41598-022-09686-9 (2022).Article 
ADS 
CAS 

Google Scholar 
Tu, H. L. C. et al. Development of a novel multiplex PCR assay for the detection and differentiation of Plasmodium caprae from Theileria luwenshuni and Babesia spp. in goats. Acta Trop. 220, 105957. https://doi.org/10.1016/j.actatropica.2021.105957 (2021).Article 
CAS 

Google Scholar 
Cywinska, A., Hunter, F. F. & Hebert, P. D. Identifying Canadian mosquito species through DNA barcodes. Med. Vet. Entomol. 20, 413–424. https://doi.org/10.1111/j.1365-2915.2006.00653.x (2006).Article 
CAS 

Google Scholar 
Hebert, P. D., Cywinska, A., Ball, S. L. & de Waard, J. R. Biological identifications through DNA barcodes. Proc. R. Soc. Lond. B 270, 313–321. https://doi.org/10.1098/rspb.2002.2218 (2003).Article 
CAS 

Google Scholar 
Ogola, E. O., Chepkorir, E., Sang, R. & Tchouassi, D. P. A previously unreported potential malaria vector in a dry ecology of Kenya. Parasit. Vectors 12, 80. https://doi.org/10.1186/s13071-019-3332-z (2019).Article 

Google Scholar 
Maquart, P. O., Fontenille, D., Rahola, N., Yean, S. & Boyer, S. Checklist of the mosquito fauna (Diptera, Culicidae) of Cambodia. Parasite 28, 60. https://doi.org/10.1051/parasite/2021056 (2021).Article 

Google Scholar 
Tainchum, K. et al. Diversity of Anopheles species and trophic behavior of putative malaria vectors in two malaria endemic areas of northwestern Thailand. J. Vector. Ecol. 39, 424–436. https://doi.org/10.1111/jvec.12118 (2014).Article 

Google Scholar 
Vantaux, A. et al. Anopheles ecology, genetics and malaria transmission in northern Cambodia. Sci. Rep. 11, 6458. https://doi.org/10.1038/s41598-021-85628-1 (2021).Article 
ADS 
CAS 

Google Scholar 
Chookaew, S. et al. Anopheles species composition in malaria high-risk areas in Ranong Province. Dis. Control J. 46, 483–493. https://doi.org/10.14456/dcj.2020.45 (2020).Article 

Google Scholar 
Makanga, B. et al. Ape malaria transmission and potential for ape-to-human transfers in Africa. Proc. Natl. Acad. Sci. USA. 113, 5329–5334. https://doi.org/10.1073/pnas.1603008113 (2016).Article 
ADS 
CAS 

Google Scholar 
Ariey, F., Gay, F. & Ménard, R. Malaria Control and Elimination Vol. 254 (Springer, 2020).
Google Scholar 
Williams, J. & Pinto, J. Training Manual on Malaria Entomology (Springer, 2012).
Google Scholar 
Rigg, C. A., Hurtado, L. A., Calzada, J. E. & Chaves, L. F. Malaria infection rates in Anopheles albimanus (Diptera: Culicidae) at Ipetí-Guna, a village within a region targeted for malaria elimination in Panamá. Infect. Genet. Evol. 69, 216–223. https://doi.org/10.1016/j.meegid.2019.02.003 (2019).Article 

Google Scholar 
Torres-Cosme, R. et al. Natural malaria infection in anophelines vectors and their incrimination in local malaria transmission in Darién Panama. PLoS ONE 16, e0250059. https://doi.org/10.1371/journal.pone.0250059 (2021).Article 
CAS 

Google Scholar 
Beebe, N. W. & Saul, A. Discrimination of all members of the Anopheles punctulatus complex by polymerase chain reaction-restriction fragment length polymorphism analysis. Am. J. Trop. Med. Hyg. 53, 478–481. https://doi.org/10.4269/ajtmh.1995.53.478 (1995).Article 
CAS 

Google Scholar 
Perkins, S. L. & Schall, J. J. A molecular phylogeny of malarial parasites recovered from cytochrome b gene sequences. J. Parasitol. 88, 972–978. https://doi.org/10.1645/0022-3395(2002)088[0972:AMPOMP]2.0.CO;2 (2002).Article 
CAS 

Google Scholar 
Snounou, G. et al. High sensitivity of detection of human malaria parasites by the use of nested polymerase chain reaction. Mol. Biochem. Parasitol. 61, 315–320. https://doi.org/10.1016/0166-6851(93)90077-B (1993).Article 
CAS 

Google Scholar 
Hall, T. A. BioEdit: A user-friendly biological sequence alignment editor and analysis program for windows 95/98/NT. Nucleic. Acids. Symp. Ser. 41, 95–98 (1999).CAS 

Google Scholar 
Huelsenbeck, J. P. & Ronquist, F. MRBAYES: Bayesian inference of phylogenetic trees. Bioinformatics 17, 754–755 (2001).Article 
CAS 

Google Scholar 
Rambaut, A., Drummond, A. J., Xie, D., Baele, G. & Suchard, M. A. Posterior summarization in Bayesian phylogenetics using tracer 1.7. Syst. Biol. 67, 901–904. https://doi.org/10.1093/sysbio/syy032 (2018).Article 
CAS 

Google Scholar 
Nguyen, L. T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: A fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274. https://doi.org/10.1093/molbev/msu300 (2015).Article 
CAS 

Google Scholar 
Ventim, R. et al. Avian malaria infections in western European mosquitoes. Parasitol. Res. 111, 637–645. https://doi.org/10.1007/s00436-012-2880-3 (2012).Article 

Google Scholar  More

Martinez-Garcia, L. B., De Deyn, G. B., Pugnaire, F. I., Kothamasi, D. & van der Heijden, M. G. A. Symbiotic soil fungi enhance ecosystem resilience to climate change. Glob. Chang. Biol. 23, 5228–5236 (2017).Article 
ADS 

Google Scholar 
Averill, C. & Hawkes, C. V. Ectomycorrhizal fungi slow soil carbon cycling. Ecol. Lett. 19, 937–947 (2016).Article 

Google Scholar 
Cairney, J. W. G. Extramatrical mycelia of ectomycorrhizal fungi as moderators of carbon dynamics in forest soil. Soil Biol. Biochem. 47, 198–208 (2012).Article 
CAS 

Google Scholar 
Milovic, M., Kebert, M. & Orlovic, S. How mycorrhizas can help forests to cope with ongoing climate change? Sumar. List 145, 279–286 (2021).Article 

Google Scholar 
Hawksworth, D. L. & Luecking, R. Fungal diversity revisited: 2.2 to 3.8 million species. Microbiol. Spectr. 5, 5.4.10 (2017).Article 

Google Scholar 
Stork, N. E. How many species of insects and other terrestrial arthropods are there on Earth? Annu. Rev. Entomol. 63, 31–45 (2018).Article 
CAS 

Google Scholar 
Christenhusz, M. J. M. & Byng, J. W. The number of known plants species in the world and its annual increase. Phytotaxa 261, 201–217 (2016).Article 

Google Scholar 
Terrer, C., Vicca, S., Hungate, B. A., Phillips, R. P. & Prentice, I. C. Mycorrhizal association as a primary control of the CO2 fertilization effect. Science 353, 72–74 (2016).Article 
ADS 
CAS 

Google Scholar 
van der Heijden, M. G. A., Martin, F. M., Selosse, M. A. & Sanders, I. R. Mycorrhizal ecology and evolution: the past, the present, and the future. New Phytol. 205, 1406–1423 (2015).Article 

Google Scholar 
Braghiere, R. K. et al. Modeling global carbon costs of plant nitrogen and phosphorus acquisition. J. Adv. Model. Earth Syst. 14, e2022MS003204 (2022).Article 
ADS 
CAS 

Google Scholar 
Jaouen, G. et al. Fungi of French Guiana gathered in a taxonomic, environmental and molecular dataset. Sci. Data 6, 206 (2019).Article 

Google Scholar 
Beninde, J. et al. CaliPopGen: A genetic and life history database for the fauna and flora of California. Sci. Data 9, 380 (2022).Article 

Google Scholar 
Gyeltshen, C. & Prasad, K. Biodiversity checklists for Bhutan. Biodivers. Data J. 10, e83798 (2022).Article 

Google Scholar 
Steidinger, B. S. et al. Climatic controls of decomposition drive the global biogeography of forest-tree symbioses. Nature 569, 404–408 (2019).Article 
ADS 
CAS 

Google Scholar 
Soudzilovskaia, N. A. et al. Global mycorrhizal plant distribution linked to terrestrial carbon stocks. Nat. Commun. 10, 5077 (2019).Article 
ADS 

Google Scholar 
Melo, C. D., Walker, C., Freitas, H., Machado, A. C. & Borges, P. A. V. Distribution of arbuscular mycorrhizal fungi (AMF) in Terceira and Sao Miguel Islands (Azores). Biodivers. Data J. 8, e49759 (2020).Article 

Google Scholar 
Ordynets, A. et al. Aphyllophoroid fungi in insular woodlands of eastern Ukraine. Biodivers. Data J. 5, e22426 (2017).Article 

Google Scholar 
Monteiro, M. et al. A database of the global distribution of alien macrofungi. Biodivers. Data J. 8, e51459 (2020).Article 

Google Scholar 
Filippova, N. et al. Yugra State University Biological Collection (Khanty-Mansiysk, Russia): general and digitisation overview. Biodivers. Data J. 10, e77669 (2022).Article 

Google Scholar 
Wu, B. et al. Current insights into fungal species diversity and perspective on naming the environmental DNA sequences of fungi. Mycology 10, 127–140 (2019).Article 

Google Scholar 
Nilsson, R. H. et al. The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications. Nucleic Acids Res. 47, D259–D264 (2019).Article 
CAS 

Google Scholar 
Gorczak, M. et al. 18th Congress of European Mycologists Bioblitz 2019 – naturalists contribute to the knowledge of mycobiota and lichenobiota of Białowieża Primeval Forest. Acta Mycol. 55, 1–26 (2020).
Google Scholar 
Goncalves, S. C., Haelewaters, D., Furci, G. & Mueller, G. M. Include all fungi in biodiversity goals. Science 373, 403–403 (2021).Article 
ADS 

Google Scholar 
Hochkirch, A. et al. A strategy for the next decade to address data deficiency in neglected biodiversity. Conserv. Biol. 35, 502–509 (2021).Article 

Google Scholar 
Allen, E. B. et al. Patterns and regulation of mycorrhizal plant and fungal diversity. Plant Soil 170, 47–62 (1995).Article 
CAS 

Google Scholar 
Mueller, G. M. & Schmit, J. P. Fungal biodiversity: what do we know? What can we predict? Biodivers. Conserv. 16, 1–5 (2007).Article 

Google Scholar 
Waters, D. P. & Lendemer, J. C. The lichens and allied fungi of Mercer County, New Jersey. Opusc. Philolichenum 18, 17–51 (2019).
Google Scholar 
Waters, D. P. & Lendemer, J. C. A revised checklist of the lichenized, lichenicolous and allied fungi of New Jersey. Bartonia, 1–62 (2019).Schwarze, C. A. The parasitic fungi of New Jersey. (New Jersey Agricultural Experiment Stations, 1917).Moose, R. A., Schigel, D., Kirby, L. J. & Shumskaya, M. Dead wood fungi in North America: an insight into research and conservation potential. Nat. Conserv. 32, 1–17 (2019).Article 

Google Scholar 
Hibbett, D. S. et al. A higher-level phylogenetic classification of the Fungi. Mycol. Res. 111, 509–547 (2007).Article 

Google Scholar 
Hibbett, D. The invisible dimension of fungal diversity. Science 351, 1150–1151 (2016).Article 
ADS 
CAS 

Google Scholar 
James, T. Y., Stajich, J. E., Hittinger, C. T. & Rokas, A. Toward a Fully Resolved Fungal Tree of Life. Annu. Rev. Microbiol. 74, 291–313 (2020).Article 
CAS 

Google Scholar 
Braghiere, R. K. et al. Mycorrhizal distributions impact global patterns of carbon and nutrient cycling. Geophys. Res. Lett. 48, e2021GL094514 (2021).Article 
ADS 
CAS 

Google Scholar 
Bonney, R. et al. Citizen science: A developing tool for expanding science knowledge and scientific literacy. Bioscience 59, 977–984 (2009).Article 

Google Scholar 
Van Vliet, K. & Moore, C. Citizen science initiatives: engaging the public and demystifying science. J. Microbiol. Biol. Educ. 17, 13–16 (2016).Article 

Google Scholar 
Feldman, M. J. et al. Trends and gaps in the use of citizen science derived data as input for species distribution models: A quantitative review. PLoS One 16, e0234587 (2021).Article 
CAS 

Google Scholar 
Shumskaya, M. et al. Fungi of parks, forests and reserves of New Jersey (2007–2019). Version 1.4. Sampling event dataset. Kean University https://doi.org/10.15468/7scek4 (2022).Heilmann-Clausen, J. et al. How citizen science boosted primary knowledge on fungal biodiversity in Denmark. Biol. Conserv. 237, 366–372 (2019).Article 

Google Scholar 
GBIF.Org User. NJMA dataset. GBIF Occurrence Download. GBIF https://doi.org/10.15468/dl.93232n (2022).GBIF.Org User. New Jersey Agaricomycetes. GBIF Occurrence Download. Dataset. GBIF https://doi.org/10.15468/dl.6j6382 (2022).GBIF.Org User. USA Agaricomycetes. GBIF Occurrence Download. GBIF https://doi.org/10.15468/dl.ncukzy (2022).GBIF.Org User. Global records Agaricomycetes. GBIF Occurrence Download. GBIF https://doi.org/10.15468/dl.nk54e7 (2022).Meyke, E. When data management meets project management. Biodivers. Inf. Sci. Stand. 3, e37224 (2019).
Google Scholar 
Wieczorek, J. et al. Darwin Core: an evolving community-developed biodiversity data standard. PLoS One 7, e29715 (2012).Article 
ADS 
CAS 

Google Scholar 
Pagad, S., Genovesi, P., Carnevali, L., Schigel, D. & McGeoch, M. A. Data Descriptor: introducing the global register of introduced and invasive species. Sci. Data 5, 170102 (2018).Article 

Google Scholar 
Registry-Migration.Gbif.Org.GBIF Backbone Taxonomy. GBIF Secretariat. https://doi.org/10.15468/39omei (2021).Mesibov, R. Archived websites: A Data Cleaner’s Cookbook (version 3) and all BASHing data blog posts 1–200. Zenodo https://doi.org/10.5281/zenodo.6423347 (2022).Chamberlain, S. A. & Boettiger, C. R Python, and Ruby clients for GBIF species occurrence data. PeerJ Preprints 5, e3304v3301 (2017).
Google Scholar 
Chamberlain, S. et al. rgbif: Interface to the Global Biodiversity Information Facility API. R package version 3.7.1. Available from https://cran.rproject.org/package=rgbif (2022).Nguyen, N. H. et al. FUNGuild: An open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol. 20, 241–248 (2016).Article 

Google Scholar 
Sousa, D. et al. Tree canopies reflect mycorrhizal composition. Geophys. Res. Lett. 48, e2021GL092764 (2021).Article 
ADS 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. https://www.R-project.org/ (2020).Wickham, H. ggplot2: Elegant Graphics for Data Analysis. https://ggplot2.tidyverse.org (2016).Bederson, B. B., Shneiderman, B. & Wattenberg, M. Ordered and quantum treemaps: Making effective use of 2D space to display hierarchies. ACM Trans. Graph. 21, 833–854 (2002).Article 

Google Scholar 
Simpson, H. J. & Schilling, J. S. Using aggregated field collection data and the novel r package fungarium to investigate fungal fire association. Mycologia 113, 842–855 (2021).Article 

Google Scholar 
Robertson, T. et al. The GBIF Integrated Publishing Toolkit: Facilitating the efficient publishing of biodiversity data on the Internet. PLoS One 9, e102623 (2014).Article 
ADS 

Google Scholar  More

A map of the Amazon River and its tributaries, as published in Alfred Russel Wallace’s 1853 book.Credit: Mary Evans/Natural History Museum

Dzoodzo Baniwa, a member of an Indigenous community in Brazil’s Amazonas state, has been collecting data on the region’s biodiversity for around 15 years. He lives in a remote village called Canadá on the Ayari River, a tributary of the Içana, which in turn feeds the Rio Negro, one of the main branches of the Amazon. The nearest city, São Gabriel da Cachoeira, is a three-day trip by motor boat.Dzoodzo (who goes by his Indigenous name but is also known as Juvêncio Cardoso) takes inspiration for his work from many cross-cultural sources. A perhaps unexpected one is a 170-year-old book by the British naturalist Alfred Russel Wallace, who visited the Amazon and Negro rivers on his expeditions in 1848–52. A Narrative of Travels on the Amazon and Rio Negro gives detailed accounts of the wildlife and people Wallace encountered near Dzoodzo’s home, including the Guianan cock-of-the-rock (Rupicola rupicola), a bright orange bird that Wallace describes as “magnificent … sitting amidst the gloom, shining out like a mass of brilliant flame”1.Dzoodzo’s passion for local biodiversity is reflected in his work at Baniwa Eeno Hiepole School, an internationally praised education centre for Indigenous people. He dreams of one day turning it into a research institute and university that might increase scientific understanding of the region’s species, including R. rupicola.
Alfred Russel Wallace’s first expedition ended in flames
Wallace, who was born 200 years ago, on 8 January 1823, is best known for spurring Charles Darwin into finally publishing On the Origin of Species, after Wallace sent Darwin his own independent discovery of evolution by natural selection in 1858. Most of Wallace’s subsequent work drew on observations from his 1854–62 expeditions in southeast Asia; his earlier work in Amazonia is much less well known.Yet there are lessons from Wallace’s time in Brazil that are especially relevant for conservationists and other scientists today — notably, what can come from paying attention to what local people say about their own territory.Barriers and boundariesWallace made two key contributions that still shape thinking about Amazonia, the world’s most biodiverse region, which covers parts of Bolivia, Brazil, Colombia, Ecuador, Peru, Venezuela, Guyana, Suriname and French Guiana.On 14 December 1852, Wallace read out his manuscript ‘On the monkeys of the Amazon’ at a meeting of the Zoological Society of London. In this study, which was later published2, Wallace relays observations that form the basis of the most debated hypothesis for how Amazonian organisms diversified: the riverine barrier hypothesis.His paper refers to the large Amazonian rivers as spatial boundaries to the ranges of several primate species. “I soon found that the Amazon, the Rio Negro and the Madeira formed the limits beyond which certain species never passed,” he writes. Since 1852, Wallace’s observations that large rivers could act as geographical barriers that shape the distribution of species have been corroborated, criticized and debated by many. The phenomenon he described clearly holds for some groups, such as monkeys and birds3,4, but not for other groups, such as plants and insects5.Subsequent researchers have explored whether the distribution patterns of species, such as those observed by Wallace, indicate that the evolution of the Amazonian drainage system has itself driven the diversification of species6. Work in the past few years by geologists and biologists show that this drainage system, which includes some of the largest rivers in the world, is dynamic7, and that its rearrangements lead to changes in the distribution ranges of species8. Current species ranges thus hold information about how the Amazonian landscape has changed over time.

The Guianan cock-of-the-rock (Rupicola rupicola), which Wallace likened to a “brilliant flame”.Credit: Hein Nouwens/Getty

The second crucial observation made by Wallace, also in his 1852 paper, was that the composition of species varies in different regions. He describes how “several Guiana species come up to the Rio Negro and Amazon, but do not pass them; Brazilian species on the contrary reach but do not pass the Amazon to the north. Several Ecuador species from the east of the Andes reach down into the tongue of land between the Rio Negro and Upper Amazon, but pass neither of those rivers, and others from Peru are bounded on the north by the Upper Amazon, and on the east by the Madeira.” From these observations, he concluded that “there are four districts, the Guiana, the Ecuador, the Peru and the Brazil districts, whose boundaries on one side are determined by the rivers I have mentioned.”
Evolution’s red-hot radical
Even though Amazonia is presented as a single, large, green ellipse in most world maps, it is actually a heterogeneous place, with each region and habitat type holding a distinct set of species9,10. The four districts proposed by Wallace are bounded by the region’s largest rivers: the Amazon, Negro and Madeira. But further studies of species ranges since then have revealed more districts, now called areas of endemism, some of which are also bounded by these and other large Amazonian rivers, such as the Tapajós, Xingu and Tocantins9,11.This recognition of spatial heterogeneity in Amazonian species distributions — first accomplished by Wallace — is essential for today’s research, conservation and planning10. Each area of endemism includes species that occur only in that area. And different areas of endemism are affected differently by anthropogenic impacts, such as deforestation, fires and development10. More than half of Amazonia is now within federal or state reserves or Indigenous lands — territories that are recognized by current governments as belonging to Indigenous people. But nearly half of the region’s areas of endemism are located in the south of the region, close to the agricultural frontier, and the species they contain are severely threatened by habitat loss10 (see also www.raisg.org/en).Local knowledgeAlthough Wallace’s writings indicate that in many ways he admired most of the Indigenous people he met, especially those from the upper Rio Negro basin, he still viewed Indigenous people through the European colonial lens of his time. In A Narrative of Travels on the Amazon and Rio Negro1, Wallace describes the Indigenous communities he encountered as “in an equally low state of civilization” — albeit seemingly “capable of being formed, by education and good government, into a peaceable and civilized community”.Yet he did better than many of his contemporaries when it came to respecting local knowledge. In his 1852 paper, for example, Wallace notes that his fellow European naturalists often give vague information about the locality of their collected specimens, and fail to specify such localities in relation to river margins. By contrast, he writes, the “native hunters are perfectly acquainted” with the impact of rivers on the distribution of species, “and always cross over the river when they want to procure particular animals, which are found even on the river’s bank on one side, but never by any chance on the other.” Likewise, in his 1853 book1, Wallace frequently corroborates his findings with information he has obtained from Indigenous people — for example, about the habitat preferences of umbrellabirds (Cephalopterus ornatus) or of “cow-fish” (manatees; Trichechus inunguis).Considering the vastness and complexity of Amazonia, it is hard to see how Wallace could have gained the insights he did after working in the region for only four years, had he not paid close attention to local knowledge.
The other beetle-hunter
Amazonian Indigenous peoples have had to endure invasion of their lands, enslavement, violence from invaders and the imposition of other languages and cultures. Despite this, numerous Indigenous researchers wish to expand their knowledge about Amazonia by combining Indigenous and European world views. Meanwhile, a better understanding of how the Amazonian socio-ecological system is organized, and how it is being affected by climate change and local and regional impacts12, hinges on the ability of researchers worldwide to learn from and to be led by Indigenous scientists.The 98 Indigenous lands in the Rio Negro basin cover more than 33 million hectares (see go.nature.com/3wkkftu). If the hopes of Dzoodzo and others to build a research institute and university for the region are met, school students will no longer have to leave their homeland to pursue higher education. The community would have a way to document its own knowledge and that of its ancestors in a more systematic way. And the legitimization of Indigenous people’s research efforts in the legal and academic frameworks recognized by non-Indigenous scientists — such as through the awarding of degrees — would make it easier for Indigenous researchers to partner with other organizations, both nationally and internationally.Indigenous people in the Rio Negro basin today are no longer objects of observation — they have taken charge of their own research using tools from different cultures. Indeed, Dzoodzo is turning to Wallace’s writings, in part, to learn more about how his own ancestors lived.Perhaps the thread between Wallace and Dzoodzo, spanning so many years and such disparate cultures, could seed new kinds of partnership in which learning is reciprocal and for the benefit of all. More

Naturalist Alfred Russel Wallace went on an expedition to Amazonas state in Brazil in 1848–52.Credit: Mondadori Portfolio via Getty

Best known for formulating the theory of evolution by natural selection, independently of Charles Darwin, Alfred Russel Wallace is an appealing if enigmatic figure. The appeal stems in part from his underdog status: poor and self-educated, Wallace had none of Darwin’s social and financial advantages. The enigma comes from his keen embrace of a range of eccentric non-scientific causes, including spiritualism, phrenology and anti-vaccination (for smallpox).Scientists do not like their scientific heroes to bear the taint of irrational thinking. Wallace’s enthusiasms have therefore contributed to him becoming marginalized in the history of evolutionary thought. Most people know about Darwin and the HMS Beagle. But what about Wallace and the Helen?The Helen story is worth revisiting because it shows Wallace at his resolute best. Despite numerous disastrous career setbacks — of which the Helen episode was the most severe — he persevered and eventually succeeded as a scientist.More than 150 years after Wallace’s experience on the Helen, doing science continues to be hard and can be disappointing. Wallace’s misadventure provides both perspective and an object lesson in how to navigate setbacks. His response to problems showcases his most inspiring traits: his commitment to science, his almost superhuman resilience and his refusal to mire himself in self-pity.Tropical explorationsIn his first job as a land surveyor, Wallace developed an interest in the plants he encountered as he tramped across the countryside. Then, in 1844 at the age of 21, he met Henry Walter Bates, who would later discover ‘Batesian mimicry’ (whereby members of a palatable prey species gain protection by mimicking an unpalatable one).Bates, two years Wallace’s junior, had a fixation with beetles, and he catalysed Wallace’s transformation from hobbyist naturalist to serious collector. Wallace’s new-found focus on beetles transcended mere entomological stamp-collecting; he developed an interest in some of the great scientific questions of the time. He was particularly inspired by the anonymously published Vestiges of the Natural History of Creation (1844) by Robert Chambers, which put forward a vision of a transmutational process, with life progressing from simple to complex.Without money or connections, Wallace and Bates aspired to careers in science at a time when the field was the preserve of the moneyed elite. They would have to fund their scientific explorations by collecting and selling specimens. After a hasty choice of destination — tropical South America — and a crash course in collecting methods, Wallace, aged 25, and Bates, aged 23, arrived in Belém, Brazil, in May 1848 (see ‘Doggedly determined’).
Doggedly determined

Alfred Russel Wallace tends to be unjustly relegated to a footnote in the Charles Darwin story. He was, in fact, a pioneering biologist who refused to let disadvantage or disaster prevent him from pursuing his scientific dreams.
January 1823: Alfred Russel Wallace is born in Usk in Wales.
May 1848: Wallace and Henry Walter Bates arrive in Belém, Brazil.
July 1852: Wallace boards the Helen, which catches fire three weeks later while at sea.
October 1852: Wallace reaches Deal, England, aboard the Jordeson.
March 1854: Wallace leaves Southampton for southeast Asia.
September 1855: Wallace’s first evolutionary paper describing his ‘Sarawak Law’ is published.
May 1856: Citing the Sarawak Law paper, geologist Charles Lyell alerts Darwin to the possibility that Wallace is developing ideas similar to Darwin’s.
February 1858: Wallace sends his paper on natural selection to Darwin from Ternate in the Maluku islands (Moluccas), Indonesia.
July 1858: The joint Darwin–Wallace paper is presented at the Linnean Society in London.
November 1859: Darwin’s On the Origin of Species is published.
March 1862: Wallace returns from southeast Asia.
November 1913: Wallace dies in Broadstone, England.

The two split up early on, with Wallace concentrating on the Amazon River’s northern tributary, the Rio Negro, and Bates on the southern fork, the Solimões.Collecting was challenging. The Amazon’s ubiquitous ants often deprived science of hard-won specimens. Crucial collecting materials also disappeared: Wallace once recovered from a bout of fever to discover that local people had drunk the cachaça (a Brazilian rum) he’d been using to pickle specimens. Transport was a constant headache, with travel upstream past rapids requiring unwieldy portages of canoes and cargo. And thanks to his collecting, the cargo became ever more voluminous and unwieldy.Wallace and Bates sporadically sent back shipments of material to their agent in London, Samuel Stevens, who publicized their adventures in scientific journals and sold their specimens, taking a 20% commission.
Escaping Darwin’s shadow: how Alfred Russel Wallace inspires Indigenous researchers
Wallace’s journeys on the Rio Negro and its tributaries took him into areas that had not yet been visited by Europeans. He saw (and collected) an extraordinary array of species, many of them new to science. He had a chance to observe and collect artefacts from several Indigenous groups with little or no previous contact with Europeans. As he travelled, Wallace capitalized on his surveying skills to map the terrain. But the remoteness took its toll. He made an “inward vow never to travel again in such wild, unpeopled districts without some civilised companion or attendant”1.Wallace was frequently ill, on one occasion nearly lethally so. His younger brother came out to join him as an assistant in 1849 but died of yellow fever two years later in Belém, on his way back to England. Wallace learnt that his brother was sick but had to wait many anxious months before news of his death made it upriver.In 1852, after four years of exploring and collecting, it was time for Wallace himself to head home. He envisaged a triumphant return. He would complement his collections of preserved organisms with a menagerie of living ones. Mr Wallace’s biological wonders would surely be the toast of scientific London.On 12 July in Belém, Wallace boarded the Helen, a freighter ship bound for London. The trip across the continent to Belém had not gone smoothly. The authorities in Manaus, Brazil, had had to be persuaded to release some of his earlier shipments meant for London, which they had impounded, making the final haul aboard the Helen even larger. But now all that remained was the long voyage back across the Atlantic. Wallace, who shared Captain Turner’s cabin, was the only passenger.Disaster strikesThree weeks into the voyage, Captain Turner interrupted Wallace’s morning routine to tell him that the ship was on fire.Friction caused by the rocking of the ship had ignited poorly stowed cargo. Attempts to intervene were counterproductive — removing the hold covers merely oxygenated the fire — and soon the ship became what Wallace later called “a most magnificent conflagration”1.Captain Turner gave the order to abandon ship, and the scramble to prepare two small wooden boats began. Having been stored on deck in the tropical sunshine, both boats leaked badly. The cook had to find corks to plug their hulls.Before he left the ship, Wallace “went down into the cabin, now suffocatingly hot and full of smoke, to see what was worth saving”1. He retrieved his “watch and a small tin box containing some shirts and a couple of old note-books, with some drawings of plants and animals, and scrambled up with them on deck”1. He tried to lower himself on a rope into one of the small boats, but fever-weakened, he ended up sliding down the rope, stripping the skin off his hands.

Some of Alfred Russel Wallace’s sketches were salvaged from the fire aboard the Helen on his return journey from South America in 1852.Credit: The Natural History Museum/Alamy

With fine weather, the best hope of rescue lay in other ships seeing the fire. The two boats duly circled the burning wreck for the next 24 hours, meaning that Wallace got to witness every moment of the tragedy. The animals he had brought with him on the long river journey across the continent, now free from their cages, sought refuge on the one part of the ship still untouched by the flames, the bowsprit. Wallace watched as the monkeys, parrots and more — his pets as well as his best hope of impressing London’s scientific elite — were incinerated.The hoped-for rescue did not immediately materialize, and Captain Turner turned the two open boats towards Bermuda, 1,100 kilometres away to the northwest.As the days ticked by, the situation became increasingly desperate. Water ran low and the tropical sun left Wallace’s “hands and face very much blistered”1. Wallace nevertheless remained upbeat, later recalling that during one night, he “saw several meteors, and in fact could not be in a better position for observing them, than lying on [his] back in a small boat in the middle of the Atlantic”1.Finally, ten days into the ordeal, salvation appeared on the horizon in the form of the Jordeson, a creaking and already overladen cargo ship bound for London.With the immediate crisis past, the magnitude of what had happened started to sink in. In a letter2 written aboard the Jordeson to botanist Richard Spruce (see go.nature.com/3prhbdk), Wallace tallied his catastrophic losses — “almost all the reward of my four years of privation & danger was lost” — and concluded with characteristic understatement, “I have some need of philosophic resignation to bear my fate with patience and equanimity.”
Evolution’s red-hot radical
The Jordeson finally limped into Deal, England, on 1 October 1852. Wallace had been at sea for 80 days. His outward voyage with Bates had taken only 29 days.Wallace added a PS to his letter to Spruce. First there was immediate exhilaration about the return — “Such a dinner! Oh! beef steaks & damson tart”. But then came thoughts about the future: “Fifty times since I left Pará [Belém] have I vowed if I once reached England never to trust myself more on the ocean.” Even then, he noted that “good resolutions soon fade”.Stevens had thoughtfully taken out insurance. So Wallace had £200 (US$980 at the time) — a fraction of his collections’ actual value — to cover his costs for a year in London while he tried to salvage what he could from the disaster and make future plans.He rushed out two books, one a travelogue, the other a more technical account of the palm trees of the Amazon. Neither did well — 250 copies remained unsold a decade later from the travel book’s print run of 750. But he was getting his name out there. Stevens, too, had a done a good job of publicizing Wallace’s discoveries while Wallace had been away.Perhaps most crucially, the positive response of the UK Royal Geographical Society to his mapping work of the Rio Negro yielded a free steamship ticket to Singapore.In March 1854, less than 18 months since the Jordeson’s bedraggled arrival at Deal, Wallace departed from Southampton in England for what he would call the “central and controlling incident”2 of his life.Eight more years of perilous travel awaited. So, too, did the discoveries of what came to be known as Wallace’s Line (a boundary between the Asian and Australasian biogeographic regions) and of the theory of evolution by natural selection3,4.The scientific acclaim that greeted Wallace’s return from southeast Asia in 1862 was a just reward both for his contributions and for that phenomenal doggedness — his determination, despite everything, to be a scientist. More

Material characterization resultsTo investigate the structural composition of NMC-2, XRD analysis plots were performed. Figure 1a shows the XRD pattern of the NMC-2 composite before adsorption. The XRD pattern shows the corresponding strong and narrow peaks, from which it can be seen that the peaks of broad diffraction NMC-2 can correspond to the standard cards of Fe, C, Fe7C3, Fe2C, and FeC, indicating that the synthesized adsorbent is an iron-carbon composite. It can be indicated that mesoporous nitrogen-doped composites were formed during the carbonization process. During the experiments, it was found that the materials are magnetic, probably because of the presence of Fe, FeC, Fe7C3, Fe2C. Due to the magnetic properties of this type of material, rapid separation and recovery can be obtained under the conditions of an applied magnetic field, which allows easy separation of the adsorbent and metal ions from the wastewater15.Figure 1XRD and nitrogen adsorption and desorption tests on materials: (a) XRD pattern of NMC-2 adsorbent before adsorption, (b) pore size distribution of NMC-2, (c) nitrogen adsorption–desorption curve of NMC-2 adsorbent.Full size imageFrom the adsorption–desorption curves of adsorbent N2 in Fig. 1b, it can be seen that the NMC-2 isotherm belongs to the class IV curve, and the appearance of H3-type hysteresis loops is observed at the medium pressure end, and H3 is commonly found in aggregates with laminar structure, producing slit mesoporous or macroporous materials, which indicates that N2 condenses and accumulates in the pore channels, and these phenomena prove that NMC-2 is a porous material16. Figure 1c shows the pore size distribution of the adsorbent NMC-2 obtained according to the BJH calculation method, from which it can be seen that the pore size distribution is not uniform in the range, and most of them are concentrated below 20 nm, while according to Table 1, the specific surface area of the original sample of Fenton sludge and fly ash is 124.08 m2/g and 3.79 m2/g, respectively, and the specific surface area of NMC-2 is 228.65 m2/g. The Fenton The pore volume of the original samples of Fenton sludge and fly ash were 0.18 cm3/g and 0.006 cm3/g respectively, while the pore volume of NMC-2 was 0.24 cm3/g. The pore diameters of the original sample of Fenton sludge and fly ash were 5.72 nm and 6.70 nm respectively, while the pore diameter of NMC-2 was 4.22 nm. The above data indicated that the synthetic materials have increased the specific surface area and pore volume compared with the original samples, indicating that the doping of nitrogen can increase the specific surface area of the material. Since the pore size of mesoporous materials is 2–50 nm, NMC-2 is a porous material with main mesopores. Thanks to the large specific surface area provided by the mesopores, the material has a large number of active sites, and in addition, the mesopores can store more Cr(VI)16, which contributes to efficient removal.Table 1 Total pore-specific surface area, pore volume, and pore size of BJH adsorption and accumulation of Fenton sludge, fly ash and NMC-2.Full size tableThe morphological analysis of the material surface using SEM can see the surface structure and the pore structure of NMC-2. And Fig. 2a–d shows the swept electron microscope image of NMC-2. Figure 2a shows that the surface of the material is not smooth, and there are more lint-like fiber structures. The fibers in Fig. 2b are loosely and irregularly arranged, which may be due to the irregular morphology caused by the small particles of the NMC-2 sample. As shown in Fig. 2c and Fig. 2a there are more pores generated on the surface of NMC-2, which may be due to the addition of K2CO3 to urea and, Fenton sludge solution to generate CO217.Figure 2SEM, TEM and EDS testing of materials: (a–d) SEM image of NMC-2 adsorbent, (e) TEM image of NMC-2; (g–i) TEM-EDS spectrum of NMC-2, (j) TEM-EDS spectra of NMC-2 obtained from.Full size imageThese pores can provide many active sites, which is consistent with the results derived in Fig. 1, where NMC-2 is a mesoporous-dominated porous material, and also demonstrates that the addition of urea can provide a nitrogen source for the material, providing abundant active sites. Figure 2j depicts the TEM of NMC-2. the TEM images show that the synthesized NMC-2 has a folded structure with a surface covered by a carbon film, and the HRTEM (Fig. 2e) also confirms this result with a lattice spacing of 0.13, 0.15, 0.20, 0.23, 0.24, and 0.25 nm, corresponding to the (4 5 2) and (1 0 2) of C, the (2 0 1) of FeC) surface, the (2 1 0) surface of Fe7C3, the (5 3 1) surface of Fe2C, and the (2 0 1) surface of FeC, which also confirms the synthesis of the above substances. The corresponding EDS spectra of the dark field diagram NMC-2 were obtained from Fig. 2j, and the EDS spectra proved the presence of various elements: carbon (C) (Fig. 2f) from fly ash, iron (Fe) (Fig. 2g) from Fenton sludge, nitrogen (N) (Fig. 2h) from urea, and the presence of (O) (Fig. 2i), further confirming the successful preparation of NMC-2.The type of functional groups and chemical bonding on the surface of the material can be analyzed by IR spectrogram analysis. Figure 3b shows the FTIR image of NMC-2 adsorbent 3440 cm−1 wide and strong absorption peak is due to the stretching vibration of –OH, there is a large amount of –OH present on the surface of the material; the peak appearing at 1640 cm−1 is –COOH. Characterization reveals that the –OH absorption peak is wider18,19. In addition, the absorptions at 1390 cm−1 and 1000 cm−1 were attributed to the bending of –OH vibrations of alcohols and phenol and the stretching vibration of C–O20. The above results indicate that the surface of NMC-2 contains a large number of oxygen-containing functional groups, and these functional groups can provide many active sites for the removal of Cr(VI). It was also found that the weak peaks corresponding to 573 cm−1 and 550 cm−1 were attributed to Fe–O groups21. The stretching of Fe–O may be due to the oxidation of loaded Fe0 and Fe2+ to Fe3+22. Figure 3a shows the Fenton sludge and fly ash FTIR images. It can be seen from the figure that the surfaces of Fenton sludge and fly ash contain a large number of oxygen-containing functional groups, the surface functional groups of the two raw materials are more abundant, and the functional groups of NMC-2 around 3441 cm−1, 1632 cm−1, and 1400 cm−1 are not significantly different from those of the raw materials, and the C–H stretching vibration peaks of NMC-2 around 873 cm−1 and 698 cm−1 is not obvious, which may be because the material the C–H bond on the surface of the raw material was oxidized to C–O in the process of synthesis.Figure 3FTIR testing of materials: (a) FTIR image of Fenton sludge, fly ash, (b) Ftir image of NMC-2 adsorbent.Full size imageCr(VI) adsorption experimentSelection of adsorbentTo select the best adsorbent, Cr(VI) adsorption tests were performed on four adsorbents. Figure 4a shows the effect of Fenton sludge and the urea addition on the adsorption efficiency. The Cr(VI) removal rates of the four adsorbents were ranked from low to high: MC-1  More