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    Molecular phylogeny and historical biogeography of marine palaemonid shrimps (Palaemonidae: Palaemonella–Cuapetes group)

    Phylogenetic relationships inside the family Palaemonidae remain unresolved, despite being frequently discussed in recent publications9,10. Nevertheless, the last published study5 presented the main lineages of the family as well supported. Among those, the studied Pon-I group of predominantly free-living taxa is basal-positioned to the remaining genera of the former subfamily Pontoniinae, usually more specialised and associated with a wide range of hosts. The basal separation of the symbiotic genera led some authors to consider the assemblage, following Bruce22, to be a primitive group, or descendants of such7,23. Additionally, Gan et al.8 suggested that the taxa of the Pon-I group might be direct descendants of the ancestors of the former subfamily Pontoniinae, sharing the main plesiomorphies appearing frequently in former palaemonine taxa, e.g., the genera Brachycarpus, Leptocarpus, Macrobrachium, or Palaemon. The median process on the fourth thoracic sternite can be considered a plesiomorphic feature; indeed, it is a common symplesiomorphy of all Pon-I taxa, including Ischnopontonia and Anapontonia, for which the process was formerly reported as missing24 (its presence was confirmed in present examined specimens). In addition to that, the mandibular palp occurring in the genera Exoclimenella, Eupontonia, Palaemonella, and Vir25, or the presence of two arthrobranchs on the third maxilliped in Exoclimenella26, can also be considered plesiomorphic features.The Pon-I group’s internal relations have been unclear until now due to lower generic and species coverage in previous studies4,5,8. The present analysis based on a six-marker molecular dataset allows a deeper insight into the phylogenetic relationships of the study group involving all 11 currently recognised genera, and represented by 52 species, i.e. about 60% of the overall known species diversity of the group. The results provide a strong support for the monophyly and/or taxonomic validity of the current genera Exoclimenella, Anapontonia, Ischnopontonia, and suggest the monophyly of genera Harpilius and Philarius. Moreover, the results reveal non-monophyly of the most speciose genera Palaemonella and Cuapetes, as well as the species-poor Eupontonia. The genus Palaemonella was found to be paraphyletic owing to the nested species of the genera Eupontonia and Vir, which all share a common synapomorphy, the presence of the mandibular palp (mentioned above). Such conclusion was expressed also in the study of Chow et al.5.The present phylogenetic analysis confirmed that the genus Cuapetes is not monophyletic, as found to a lesser extent, in a few previous molecular studies 4,5,23. In this study, the genus Cuapetes was recovered in four separate genetic lineages. The type species C. nilandensis is nested in the Clade 1 along with C. johnsoni and C. seychellensis. This phylogenetic finding is in line with the study of Marin and Sinelnikov27, who indicated morphological differences between two of the above-mentioned species and most of the remaining species of the genus (respective of the present Clade 5, also covering C. grandis, the type species of the ex-genus Kemponia), and questioned the validity of the two latter generic names. The further genetic lineage is shown by the position of C. americanus nested in the eastern Pacific—Atlantic branch of the genus Palaemonella (Clade 3). This result is also supported by recent phylogenetic studies suggesting the different systematic positions of this species4,5,10. Due to the lack of the mandibular palp, the species had been properly, but evidently incorrectly, assigned to the genus Cuapetes. The fourth genetic lineage is shown by a separate position of C. darwiniensis in the Clade 4 as the sister species of Madangella altirostris.The remaining majority of the Cuapetes species (Clade 5) are heterogeneous due to comprising also representatives of the genus Periclimenella. Ďuriš and Bruce26 hypothesised, based on morphological traits (mainly the unique shape of the first pereiopod chelae and the distinctly asymmetrical and specific second pereiopods), that the genera Exoclimenella and Periclimenella are closely related. Nevertheless, the present study revealed Periclimenella as a part of the genus Cuapetes. This result was previously supported in the molecular study by Horká et al.4 and weakly supported by Kou et al.23.Fossil records of palaemonid shrimps are rare due to their aquatic habit and poorly calcified exoskeletons. Only a few palaemonid representatives are known compared to many extant taxa; the oldest fossil records contain only genera from the previous subfamily Palaemoninae from the Lower Cretaceous (middle Albian, 100 Myr)28. For this reason, we used the known mutation rate of mitochondrial gene (16S rRNA) for dating rather than fossil records.The present inferred phylogeny and ancestral analysis indicate multiple formations of primary symbioses within the clades dominated by free-living relatives, as shown by previous molecular analyses4,5. Our results revealed eight independent lineages within the Pon-I group that evolved from free-living ancestors (Fig. 3). Free-living palaemonids (Exoclimenella, Palaemonella, Cuapetes; Fig. 2) are characterised by an elongate body shape with a dentate rostrum, slender, long, a/symmetrical chelipeds and slender ambulatory pereiopods with simple dactyli. Their carapace might bear the full complement of teeth (i.e., supraorbital, antennal, hepatic, epigastric)25. Primary symbiotic forms do not fundamentally differ morphologically from free-living ancestors. Their adaptations to the host affiliation have mainly manifested by changes in body shape, colouration, and the reduction of carapace ornamentation. Their hosts belong to different invertebrate phyla, including Cnidaria (mainly Scleractinia and Antipatharia22) and Echinodermata (Crinoidea29) in ectosymbiotic forms, but also to spoon worms (Echiura), burrowing Crustacea (alpheid shrimps), and/or gobiid fishes15, in inquilinistic forms.While scleractinian corals were hypothesised as the primary hosts of palaemonid shrimp commensalism7, our results revealed the antipatharian association as possibly the earlier one among the Pon-I shrimps. That association was established via a single speciation act at approximately 43 Myr (Eocene), specifically with the ancestor of the recent Cuapetes nilandensis (Clade 1). Except a small body size, this species does not show specific morphological adaptations to antipatharian association. The possibly oldest lineage associated with the scleractinian corals forms a common multigeneric composition of Anapontonia, Ischnopontonia, Harpilius and Philarius (Clade 4), which was established at approximately 38.2 Myr (Eocene). The genera share some homoplasic adaptations with ectosymbioses, such as strongly hooked dactyli of the ambulatory pereiopods adapted to climbing on coral colonies. An extremely compressed body and similar tail fan structure of the genera Ischnopontonia (Fig. 1H) and Anapontonia (Fig. 1D) are adaptations to life in narrow spaces amongst corallites of the oculinid coral Galaxea24,30; the intercorallite channels might be temporarily fully covered by tentacles of exposed polyps. This lifestyle was thus termed ‘semi-endosymbiosis’ by Horká et al.4, as potential evolutionary precursors of the true endosymbioses. In contrast, the genera Philarius and Harpilius have depressed bodies and associate exclusively as regular ectosymbionts with scleractinian corals, mainly of the genera Acropora and Pocillopora22.A further multispecies symbiotic lineage is represented by the genus Vir (Clade 3), whose origin is dated to approximately 21.1 Myr (Miocene). All species of this genus live in associations mainly with the acroporid, pocilloporid and euphylliid genera of scleractinian corals31,32. The adaptation to their symbiotic lifestyle is expressed in the loss of the hepatic tooth, partial or full reduction of ambulatory propodal spines, and cryptic colouration, including transparency of the body and appendages31,33 (Fig. 1J). Subsequent scleractinian-associated lineages are represented by separate species that appeared in the Miocene (21.9–10.1 Myr), namely: Eupontonia oahu, Cuapetes amymone, and C. kororensis, which live in association with Pocillopora, Acropora, and Heliofungia, and show only minor adaptations to their symbiotic habits, e.g. loss of the hepatic tooth, dense distal setae on the walking propodi, or extremely slender chelae and a specific cryptic colouration, respectively22,34,35.A single crinoid-associated species, Palaemonella pottsi (Clade 3), represents the only case of the switch from a free-living lifestyle to the association with echinoderms in the present study group; it originated at approximately 10.4 Myr (Miocene). Retaining the body shape typical for Palaemonella12, the species also does not show any noticeable morphological adaptation to such a host; its affiliation with the symbiotic life is, however, clearly observed in the deep-red to black cryptic colouration36.In Palaemonella aliska (Fig. 1E) and Eupontonia nudirostris (Clade 3), a pair of sister-positioned species in the present analyses (Figs. 2, 3), the ability to co-habit with burrowing animals (e.g., alpheids, gobiid fish, or echiurids) had developed. Their type of symbiosis, inquilinism, formed at approximately 14.8 Myr (Miocene). The reduction of the rostrum length, depressed body, stout main chelae in both, and full lack of the epigastric and hepatic teeth in the latter species15,25, were evidently due to that mode of life. Inquilinism is best known in the family Alpheidae, in which multiple genera associate with a variety of burrowing animals37. In the family Palaemonidae, inquilinism developed only in the Pon-I group, including Palaemonella shirakawai (not analysed here)14.As evident from the present and previously published reports4,5,7,8,10, the life history of the Pon-I group was largely shaped by coevolution with coral reefs. The coral reefs were deeply impacted by the K–T mass extinction at the end of the Cretaceous, which was one of the most destructive events in the Phanerozoic38. However, coral reefs recovered and became increasingly abundant in the Eocene39. This also matches the time of either the origin of host associations, or a wider species radiation of the Pon-I group. The first fossil records of the main coral hosts of the present shrimps are dated after the K-T extinction during the Paleogene (e.g., Euphyllia 66.0–61.6 Myr, Acropora 59.2–56.0 Myr, Galaxea and Pocillopora 56–33.9 Myr40).The biogeographic history suggested by S-DIVA analysis points to some dispersal and vicariant events shaping the current pattern of the Pon-I group’s distribution. This reconstruction (Fig. 4) estimates the present-day IWP region within the former Paleo-Tethys Ocean as the most likely ancestral area of the present study group, which originated ~ 91.6 Myr (Late to Early Cretaceous). The present shrimp group had radiated across the entire IWP region and subsequently expanded into the Atlantic Ocean. We assume that the spread of the group took place in the following sequence of events: (1) dispersal of Palaemonella spp. from the IWP into the eastern Pacific in the Paleocene (∼ 55.2 Myr; P. asymmetrica and P. holmesi); (2) dispersal into the western Atlantic (2 spp., complex of “Cuapetes” americanus) via the eastern Pacific and vicariance event separating the IWP at Eocene (∼ 46.2 Myr). It was the time after the formation of the Eastern Pacific Barrier (EPB), which was considered the largest extension of the open ocean (ca. 5000 km), that separated the IWP area from the eastern Pacific17; (3) the another vicariance event, separating the western Atlantic populations from those of the eastern Pacific in the Oligocene (∼ 30.9 Myr), i.e., before the closure of the Isthmus of Panama, followed by a dispersion of P. atlantica into the eastern Atlantic in the Miocene (∼ 21.6 Myr). The exact time of the formation of the Isthmus of Panama, which separated the Atlantic from the eastern Pacific and remained isolated from the central Pacific by the EPB, still remains questionable. Bacon et al.18 assume that the initial land bridge formed at approximately 23 Myr, and the final closure of the Isthmus of Panama formed between 10 and 6 Myr. Montes et al.19 presupposed the earlier formation of the barrier at ∼ 14 Myr, whereas O’Dea et al.20 concluded that the potential gene flow continued between the Pacific and Atlantic subpopulations of marine organisms until at least ∼ 2.8 Myr.The eastern Pacific Cuapetes canariensis closely related to IWP Cuapetes spp., has been recently described by Fransen et al.41, from the Canary Islands. This could indicate alternative dispersal pathways into the Atlantic, as suggested by recent studies17,42. The Tethys seaway allowed natural dispersion between the Atlantic and Indian Oceans across the region of the Mediterranean Sea. The closure of this interoceanic seaway at approximately 14 Myr (18–12 Myr) was caused by intense tectonic activity in the Near East17. Since the closure of that seaway, remaining possible dispersal to the Atlantic has been limited to the warm-water corridor around the southern tip of Africa, however curtailed by the cold Benguela Current upwelling from the Late Pliocene43. More

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    Variations in limited resources allocation towards friends and strangers in children and adolescents from seven economically and culturally diverse societies

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    Register animal-tracking tags to boost conservation

    In early 2020, my colleagues and I realized that animal-tracking data collected before, during and after the pandemic lockdowns could provide invaluable insights into human–wildlife interactions and conservation benefits on a global scale. We launched a research consortium — the COVID-19 Bio-Logging Initiative — to investigate how animals behaved while much of the world’s human population sheltered at home.But we had no way to establish how many, and which, animals were wearing tags. Miniature tracking devices are routinely attached to a vast range of species — from songbirds to whales — to collect detailed data on their movements, behaviour and physiology. Yet, of the thousands of ‘bio-loggers’ deployed every year, many generate data sets that remain effectively undiscoverable — they are saved on personal hard drives or institutional servers, inaccessible to the wider community. This problem can be solved by setting up a global registry for all tags on wild animals.Although individual tracking studies make important contributions to our understanding of the ecological needs of animal species, pooling data (across taxa, longer time periods or multiple locations) can reveal general patterns, aiding the design of particularly effective conservation strategies. For example, integrating the tracks of 4,060 animals across 17 marine species (including albatrosses, penguins, seals and whales) has helped to identify conservation priority areas in the Southern Ocean (M. A. Hindell et al. Nature 580, 87–92; 2020).In an ideal world, all animal-tracking data would be archived — with either open or restricted access — in public repositories, such as Movebank. Excellent progress has been made towards this goal, but universal uptake is hindered by time constraints, governmental or institutional restrictions and concerns over inappropriate data use.To encourage as many data owners as possible to join the COVID-19 Bio-Logging Initiative, we launched a recruitment campaign through Movebank, social media, mailing lists, newsletters, personal contacts and a published call to action (C. Rutz et al. Nature Ecol. Evol. 4, 1156–1159; 2020). Our consortium has grown to more than 600 international collaborators, accumulating a staggering one billion location records for some 200 animal species. Despite this impressive community response, we know that this is only the tip of the iceberg.The global tag registry that I suggest would contain metadata for tags (including tag type and settings, information on the animal, and date and location of deployment), as well as researchers’ contact details — but not the actual tracking data. This decoupling of information would unlock the field’s full conservation potential in the short term and would build the trust required to allow raw data to be archived routinely in public repositories in the longer term. Over time, the tag registry is likely to evolve naturally into a ‘meta-repository’, linking to raw data sets hosted across a multitude of repositories.The registry would enable researchers to check data availability at the push of a button — for example, for a particular taxonomic group, such as terrestrial carnivores, or a specific region, such as the Pacific Ocean — and to get in touch with the relevant data owners. Registry management must comply with international best practices, so robust processes would need to be set up to vet queries, pass on collaboration proposals to data owners and minimize overlap between studies.For the registry to fulfil its intended purpose, it must be used by the entire animal-tracking community. How can this be achieved? I see an opportunity to integrate tag registration into existing ethical-review processes. Governmental authorities, research institutions, funders, publishers and fieldworkers agree that permits must be in place before animals can be tagged. Building on this international consensus, ethical review boards could make tag registration a condition of study approval.To complement this bottom-up approach, well established initiatives — such as those associated with the United Nations Environment Programme or the International Union for Conservation of Nature — could help to build an international policy mandate and provide independent oversight. The International Bio-Logging Society, which has been working to unite animal-tracking efforts on land and at sea, could provide crucial support.This vision is no doubt ambitious, but it is achievable. Every civil aircraft on the planet must be registered — so I am convinced that, with effective coordination, we can accomplish the same for tagged animals. Furthermore, the basic principle of hosting metadata, but not raw data, is being used productively by other databases, such as AviSample — a registry for biological samples collected from wild birds.Many researchers, myself included, feel a moral obligation to the animals carrying our tags. A global tag registry would help to realize the full conservation potential of all tracking data, minimize duplication of tagging efforts and facilitate sharing of welfare-related expertise. The conservation cost of missing data in large-scale collaborative projects cannot be easily measured, but is probably substantial. We simply cannot afford this, and must ensure that all animal-tracking data are immediately discoverable.

    Competing Interests
    This article is a contribution of the COVID-19 Bio-Logging Initiative, which is funded in part by the Gordon and Betty Moore Foundation (GBMF9881) and the National Geographic Society (NGS-82515R-20) (both grants to C.R.), and endorsed by the United Nations Decade of Ocean Science for Sustainable Development. More

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    Reply to: The risks of overstating the climate benefits of ecosystem restoration

    Rio Conservation and Sustainability Science Centre, Department of Geography and the Environment, Pontifical Catholic University, Rio de Janeiro, BrazilBernardo B. N. Strassburg, Alvaro Iribarrem, Carlos Leandro Cordeiro, Renato Crouzeilles, Catarina Jakovac, André Braga Junqueira, Eduardo Lacerda & Agnieszka E. LatawiecInternational Institute for Sustainability, Rio de Janeiro, BrazilBernardo B. N. Strassburg, Alvaro Iribarrem, Carlos Leandro Cordeiro, Renato Crouzeilles, Catarina Jakovac, André Braga Junqueira, Eduardo Lacerda, Agnieszka E. Latawiec, Robin L. Chazdon & Carlos Alberto de M. ScaramuzzaPrograma de Pós Graduacão em Ecologia, Universidade Federal do Rio de Janeiro, Rio de Janeiro, BrazilBernardo B. N. Strassburg, Renato Crouzeilles & Fabio R. ScaranoBotanical Garden Research Institute of Rio de Janeiro, Rio de Janeiro, BrazilBernardo B. N. StrassburgSchool of Biological Sciences, University of Queensland, St Lucia, Queensland, AustraliaHawthorne L. BeyerAgricultural Science Center, Federal University of Santa Catarina, Florianópolis, BrazilCatarina JakovacInstitut de Ciència i Tecnologia Ambientals, Universitat Autònoma de Barcelona, Barcelona, SpainAndré Braga JunqueiraDepartment of Geography, Fluminense Federal University, Niterói, BrazilEduardo LacerdaDepartment of Production Engineering, Logistics and Applied Computer Science, Faculty of Production and Power Engineering, University of Agriculture in Kraków, Kraków, PolandAgnieszka E. LatawiecSchool of Environmental Sciences, University of East Anglia, Norwich, UKAgnieszka E. LatawiecDepartment of Zoology, University of Cambridge, Cambridge, UKAndrew Balmford, Stuart H. M. Butchart & Paul F. DonaldInternational Union for Conservation of Nature (IUCN), Gland, SwitzerlandThomas M. BrooksWorld Agroforestry Center (ICRAF), University of The Philippines, Los Baños, The PhilippinesThomas M. BrooksInstitute for Marine & Antarctic Studies, University of Tasmania, Hobart, Tasmania, AustraliaThomas M. BrooksBirdLife International, Cambridge, UKStuart H. M. Butchart & Paul F. DonaldDepartment of Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT, USARobin L. ChazdonWorld Resources Institute, Global Restoration Initiative, Washington, DC, USARobin L. ChazdonTropical Forests and People Research Centre, University of the Sunshine Coast, Sippy Downs, Queensland, AustraliaRobin L. ChazdonInstitute of Social Ecology, University of Natural Resources and Life Sciences Vienna, Vienna, AustriaKarl-Heinz Erb & Christoph PlutzarDepartment of Forest Sciences, ‘Luiz de Queiroz’ College of Agriculture, University of São Paulo, Piracicaba, BrazilPedro BrancalionRSPB Centre for Conservation Science, Royal Society for the Protection of Birds, Edinburgh, UKGraeme Buchanan & Paul F. DonaldSecretariat of the Convention on Biological Diversity (SCBD), Montreal, Quebec, CanadaDavid CooperInstituto Multidisciplinario de Biología Vegetal, CONICET and Universidad Nacional de Córdoba, Córdoba, ArgentinaSandra DíazUnited Nations Environment Programme World Conservation Monitoring Centre, Cambridge, UKValerie Kapos & Lera MilesBiodiversity and Natural Resources (BNR) program, International Institute for Applied Systems Analysis (IIASA), Laxenburg, AustriaDavid Leclère, Michael Obersteiner & Piero ViscontiDivision of Conservation Biology, Vegetation Ecology and Landscape Ecology, University of Vienna, Vienna, AustriaChristoph PlutzarB.B.N.S. wrote the first version of the paper. All authors provided input on subsequent versions of the Reply. More

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    Lost trees, booster benefits — the week in infographics

    Treasure our treesNearly one-third of tree species are threatened with extinction. This is more than twice the number of threatened mammals, birds, amphibians and reptiles combined.The loss of tree species is often overlooked, as our News Feature reports. In 2021, after a huge tree-hunting exercise called the Global Tree Assessment, plant conservationists announced that they had found 58,497 tree species, of which 17,510 were threatened. Since then, almost 2,800 of those have been labelled critically endangered. Some 142 species are thought to be extinct in the wild.

    Killer cancersThis chart shows some of the results from the largest study yet of the link between cancer burden and risk factors. Researchers used extensive data on death and disability from more than 200 countries to estimate that potentially avoidable risk factors were responsible for more than 44% of global cancer deaths in 2019. Of these, tumours of the lung, trachea and bronchus were the leading cause of death.Smoking, alcohol use and a high body-mass index were the risk factors with the biggest contribution to cancer. The findings emphasize familiar health advice not to smoke, drink too much or become overweight.

    New breed of vaccinesIt was hoped that a new breed of COVID-19 vaccine — based on Omicron variants of the virus SARS-CoV-2 — would offer substantially greater protection than older vaccines that are based on the strain of the virus that emerged in 2019. But an analysis of data from several studies suggests that updated boosters offer much the same level of protection as does an extra dose of the older vaccines. The study is a preprint that has not yet been peer reviewed.The team’s modelling showed that, in a population where half of people are already protected against a symptomatic SARS-CoV-2 infection through previous vaccination or infection, an updated vaccine booster bumped protection up to 90%, compared with 86% protection provided by an extra dose of the original vaccine. For protection against severe disease, however, the difference was less than 1%. But the relative benefits of variant-based boosters could grow stronger if a new variant appears, as our News story explains. More

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    Marine predators aggregate in anticyclonic ocean eddies

    RESEARCH BRIEFINGS
    07 September 2022

    A diverse range of marine predators — including tunas, billfishes and sharks — in the North Pacific Ocean cluster together in clockwise-rotating eddies, seemingly to hunt deep-ocean prey, which are unusually abundant there. This suggests that there is a relationship between the foraging opportunities of predators and the energetics of this marine biome. More

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    The conditional defector strategies can violate the most crucial supporting mechanisms of cooperation

    We used two agent-based simulation models to investigate the concepts of “cooperate for the spread” and “pay for the escape,” both were net logo models created by Dr. Susan Hanisch.Afterward, we modified the first model to represent the concept of sharing the dispersal costs. We used the second model without modifications. Instead, we assigned definite values of some parameters that highlight the pay for the escape strategy.First modelThe original model was entitled “Evolution and patchy resource”18. She first developed it for educational purposes. It illustrates the concepts of cooperator-cheater competition, natural selection, spatial structure mechanisms, multilevel selection, and founder effects.Changeable variables

    Distance-resource-areas: the distance between the centers of the resource areas.

    Size-resource areas: the size of resource areas as a radius in the number of patches.

    Living costs: the costs that each agent has to deduct from energy per iteration for basic survival.

    Mutation rate: The probability that offspring agents have different traits than their parents.

    Evolution: the ability of agents to produce offspring.

    Constant variables

    The number of patches is 112 × 112 patches.

    Carrying capacity per patch: Resource = 10, Agents = 1

    The growth rate of the resource = 0.2

    The resources on a patch regrow by a logistic growth function up to the carrying capacity: New resource level = current resource level + (Growth-Rate × current resource level) × (1 – (Current resource level/carrying capacity)).

    The cost for producing offspring is ten subtracted units of energy.

    The initial level of energy of agents is set at living costs.

    Role of randomness

    Agents are distributed randomly in resource areas at the beginning of a simulation.

    Sustainable behavior is distributed randomly with a probability of percent sustainables among the initial agent population.

    The order in which agents move and harvest within one iteration is random.

    Agents move to a randomly selected patch if several patches fulfill the objectives.

    The order in which agents produce offspring within one iteration is random.

    Agents reproduce offspring with a probability of (0.0005 × Energy).

    Agents place offspring on a randomly selected unoccupied neighboring patch.

    Offspring mutate with a potential mutation rate.

    Model processesIn each iteration, each agent moves around in random order. There are three likelihoods:

    If there are no unoccupied patches in a two-patch radius, they stay on the current patch.

    If there are unoccupied patches with resources amounting to more than living costs, the agents move to them.

    If the resource amount is less than the living costs, the agents move randomly to other unoccupied patches.

    The agents harvest the resources from separated patches to gain energy for metabolism and proliferation. If the energy level of any agent falls to zero, it dies. The cooperator type harvests half of the resource, while the greedy type consumes 99%.The living costs are deducted from the energy amount of the agent constantly everywhere all the time. This process occurs whether an agent moves within the patch, between the patches, or even not. Therefore, the model does not consider dispersal cost explicitly.If there is an unoccupied neighbor patch, the agent can reproduce with a probability of 0.0005 of his energy, place the offspring on the unoccupied neighbor patch, and then transfer ten units of the energy to his offspring.Resources regrow only on resource patches. When the resource amount is more than or equal to 0.1, then it regrows. When the resource is less than 0.1, its value is set to 0.1.Output diagrams and monitors

    The average energy of agents: average energy levels of sustainable and greedy agents, resulting from resource harvest minus living costs and reproduction.

    Trait frequencies: the relative frequencies of sustainable and greedy agents in the total population, resulting from mutations, different reproduction rates, and death.

    Agent population: the absolute number of the total population size resulting from reproduction and death.

    ModificationsIn the first modification, we added a different type of cost that agents only incur when they disperse from one patch to another (in-between the patches). It is the slider entitled “dispersal costs”.In the second modification, we added another sharing dispersal costs tool to reduce them by dividing their value by the number of included agents (flock-mates) in the identified range from the same type. It is the slider entitled “group-dispersal-range.” which is the flock mate’s areas as a radius in the number of patches. Therefore, changing the value of the group dispersal range will change the area around every agent. Accordingly, the number of its flock mates who share the dispersal costs also adjusts.The group dispersal range is not confined to greedy agents but applies to all agents. Therefore, it represents the case of the wild-type cooperators who can also cooperate for the spread. The group dispersal range also does not only target the agents in between patches. However, it counts the agents inside and outside the patches. For example, once an agent starts its dispersion with a determined range containing ten agents, four from another type, three non-dispersal agents from the same type that existed inside a patch, and three dispersal agents from the same type outside the patches. The dispersal costs for this agent will be divided by 6.Our assumption that non-dispersal agents at the pre-departure stage share dispersion costs with dispersal agents; seems justified because they reap mutual benefits by reducing kin competition inside patches if they promote the migrators. However, can agents remotely pay the dispersion costs? Yes. For instance, some bacterial species can trigger the migration of other species if located in their vicinity, even if the two bacterial colonies are separated by a barrier19,20 or if they are non-motile21. On the other hand, dispersion is an extended process with many factors, including escape from predators, suppression of host defense mechanisms, and production of biosurfactants to reduce surface tension to facilitate motility. Therefore, the agent’s contribution (inside/outside the patches) to support such factors is considered a shared dispersal cost.Finally, cheaters can arise within cooperator patches by mutation or immigration. Therefore, to investigate the efficacy of migration, the mutation rate value should be 0 to cancel its effect in the meta-population dynamics.Second modelThe model is entitled “Evolution, resources, monitoring, and punishment.”22 is a simulation of a population with four types of agents competing for the same resource. It demonstrates many concepts, such as kin selection, cooperation, selfishness, public good, monitoring, punishment, sharing the costs, positive/negative frequency-dependent selection, and multilevel selection. The four agent colors and types: (1) Red: greedy, non-punishing. (2) Orange: greedy, punishing. (3) Turquoise: sustainable, non-punishing. (4) Green: sustainable, punishing.Punishing agents can perceive other agents in their environment to some degree (perception accuracy) and react to their behavior. There are three kinds of punishment: Punishers can kill agents with greedy harvesting behavior, stop them from harvesting in the next iteration, or have them pay a penalty fee to their neighbors.Agents have a cost (energy) to pay for, both detection and punishment, so this behavior is altruistic. Punisher agents of one type share punishment costs equally.Changeable variables

    Death rate: the probability that agents die independent of their energy level.

    Carrying capacity: the maximum amount of resource units on a patch from 1 to 100.

    Growth rate: the rate at which resources on patches regrow. The maximum sustainable yield is calculated based on the carrying capacity and growth rate.

    Harvest-sustainable: the number of resource units harvested by sustainable agents.

    Harvest-greedy: the number of resource units harvested by sustainable agents.

    Perception accuracy: the probability with which punishing agents notice greedy agents.

    Costs-perception: the costs in units of energy, punishing agents have to pay for perceiving other agents.

    Costs-punishment: the costs as units of energy that punishing agents have to pay in each iteration to punish other agents. All punishing agents of an agent divide the costs of punishment.

    Punishment: the kinds of punishing behavior that punishing agents perform.

    Fine: if the kind of punishment is “pay fine”, the fine in energy units that punished agents have to pay (shared between all their neighbors).

    Living costs and mutation rate: see the first model.

    Constant variables

    The number of patches: There are 60 × 60 patches in the world.

    The initial energy level of agents is set at living costs + 1.

    The initial number of resource units on a patch is set to the carrying capacity.

    The resources on a patch regrow: see the first model.

    Role of randomness* In addition to items in the first model.

    Agents take on their traits (harvest preference and ability to notice and punish) randomly based on the probability of percent-sustainable and percent-punishers.

    The order in which punishing agents notice greedy agents within one iteration is random.

    Greedy agents are noticed by punishing agents with a probability of perception accuracy.

    The order in which detected greedy agents are punished within one iteration is random.

    Agents produce offspring with a probability of (0.001 × Energy).

    Agents die with a probability of (death-rate).

    Model processesIn each iteration, each agent attempts to harvest resources from the patches it is on and the eight neighboring patches until the harvest preference level is reached, except for the punished agent with the sanction (suspend harvest once), its harvest amount = 0 in the current iteration. If the amount of resources available is lower than the amount that the unpunished agent attempts to harvest. Then, the agent moves to a neighboring unoccupied patch with the most resources after losing one energy unit as a move cost.Punishers pay the costs of perceiving the greedy agents. The greedy neighbors have been noticed with the probability of perception accuracy. The agent lost an amount of energy as living costs. The agent dies with the likelihood of death rate or if the energy level falls to zero.If there is an unoccupied neighbor patch, the agent can reproduce with a probability of 0.001 of its energy, place the offspring on the unoccupied neighbor patch, and then transfer half of its energy to its offspring that mutate according to the probability of the mutation rate.Resources regrow on all patches. When the resource amount is more than or equal to 0.1, then it regrows. When the resource is less than 0.1, its value is set to 0.1.Output diagrams and monitors

    Populations (% of carrying capacity): the state of the resource and the agent population in the world as a percentage of total carrying capacity resulting from resource harvesting behavior and resource regrowth, agent reproduction, and death.

    Average harvest per iteration: the average harvested amounts of agents per iteration by trait, resulting from harvested resource units, minus costs for monitoring and punishing (for punishing agents), minus fines (for punished agents in case of punishment “Pay fine”)

    The average energy of agents and trait frequencies: see the first model.

    How does the model represent a conditional defector strategy?The model aims to highlight the role of kin selection and punishment mechanisms in supporting cooperation evolution against cheats. We did not need to modify the model but just thought about what the conditional defector should do to upside down the game. The answer was to pay for the escape.For instance, if the standard Harvest-greedy of a cheater (greedy, non-punishing) was 13 and the Perception-accuracy of its actual punishers was 75%. Now suppose this cheater faces troubles, and it cannot dominate. However, if it gives up some of its profit to become 12, to escape punishment, and to reduce the perception accuracy to 60%, it could dominate and take over the population.The conditional cheater can pay something and reduce its profit to escape punishment by reducing perception accuracy if there is a positive correlation between these two variables. Therefore, this model is appropriate if it can support/deny such a correlation. More

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    The micronutrient content in underutilized crops: the Lupinus mutabilis sweet case

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