More stories

  • in

    Impacts of soil nutrition on floral traits, pollinator attraction, and fitness in cucumbers (Cucumis sativus L.)

    Fichtner, K. & Schulze, E. D. The effect of nitrogen nutrition on growth and biomass partitioning of annual plants originating from habitats of different nitrogen availability. Oecologia 92, 236–241 (1992).Article 
    ADS 
    CAS 

    Google Scholar 
    Rodger, J. G. et al. Widespread vulnerability of flowering plant seed production to pollinator declines. Sci. Adv. 7, eabd3524. https://doi.org/10.1126/sciadv.abd3524 (2021).Article 
    ADS 

    Google Scholar 
    de Groot, C. C., Marcelis, L. F. M., van den Boogaard, R., Kaiser, W. M. & Lambers, H. Interaction of nitrogen and phosphorus nutrition in determining growth. Plant Soil 248, 257–268 (2003).Article 

    Google Scholar 
    Wang, Z. & Li, S. Effects of nitrogen and phosphorus fertilization on plant growth and nitrate accumulation in vegetables. J. Plant Nutr. 27, 539–556 (2004).Article 
    CAS 

    Google Scholar 
    Razaq, M., Zhang, P. & Shen, H. L. Influence of nitrogen and phosphorous on the growth and root morphology of Acer mono. PLoS One 12, e0171321. https://doi.org/10.1371/journal.pone.0171321 (2017).Article 
    CAS 

    Google Scholar 
    Poulton, J. L., Bryla, D., Koide, R. T. & Stephenson, A. G. Mycorrhizal infection and high soil phosphorus improve vegetative growth and the female and male functions in tomato. New Phytol. 154, 255–264 (2002).Article 
    CAS 

    Google Scholar 
    Burkle, L. A. & Irwin, R. E. The effects of nutrient addition on floral characters and pollination in two subalpine plants, Ipomopsis aggregata and Linum lewisii. Plant Ecol. 203, 83–98 (2009).Article 

    Google Scholar 
    Burkle, L. A. & Irwin, R. E. Beyond biomass: measuring the effects of community-level nitrogen enrichment on floral traits, pollinator visitation and plant reproduction. J. Ecol. 98, 705–717 (2010).Article 

    Google Scholar 
    Hoover, S. E. R. et al. Warming, CO2, and nitrogen deposition interactively affect a plant-pollinator mutualism. Ecol. Lett. 15, 227–234 (2012).Article 

    Google Scholar 
    Lau, T. C. & Stephenson, A. G. Effects of soil nitrogen on pollen production, pollen grain size, and pollen performance in Cucurbita pepo (Cucurbitaceae). Am. J. Bot. 80, 763–768 (1993).Article 
    CAS 

    Google Scholar 
    Lau, T. C. & Stephenson, A. Effects of soil phosphorus on pollen production, pollen size, pollen phosphorus content, and the ability to sire seeds in Cucurbita pepo (Cucurbitaceae). Sex. Plant Reprod. 7, 215–220 (1994).Article 

    Google Scholar 
    Atasay, A., Akgül, H., Uçgun, K. & Şan, B. Nitrogen fertilization affected the pollen production and quality in apple cultivars ‘Jerseymac’ and ‘Golden Delicious’. Acta Agric. Scand. Sect. B. Soil Plant Sci. 63, 460–465 (2013).
    Google Scholar 
    Shuel, R. W. Some aspects of the relation between nectar secretion and nitrogen, phosphorus, and potassium nutrition. Can. J. Plant Sci. 37, 220–236 (1957).Article 
    CAS 

    Google Scholar 
    Robacker, D. C., Flottum, P. K., Sammataro, D. & Erickson, E. H. Effects of climatic and edaphic factors on soybean flowers and on the subsequent attractiveness of the plants to honey bees. Field Crops Res. 6, 267–278 (1983).Article 

    Google Scholar 
    Dror, I., Yaron, B. & Berkowitz, B. The human impact on all soil-forming factors during the anthropocene. ACS Environ. Au 2, 11–19 (2022).Article 
    CAS 

    Google Scholar 
    David, T. I., Storkey, J. & Stevens, C. J. Understanding how changing soil nitrogen affects plant–pollinator interactions. Arthropod. Plant Interact. 13, 671–684 (2019).Article 

    Google Scholar 
    Russo, L., Buckley, Y. M., Hamilton, H., Kavanagh, M. & Stout, J. C. Low concentrations of fertilizer and herbicide alter plant growth and interactions with flower-visiting insects. Agric. Ecosyst. Environ. 304, 107141. https://doi.org/10.1016/j.agee.2020.107141 (2020).Article 
    CAS 

    Google Scholar 
    Akter, A. & Klečka, J. Water stress and nitrogen supply affect floral traits and pollination of the white mustard, Sinapis alba (Brassicaceae). PeerJ 10, e13009. https://doi.org/10.7717/peerj.13009 (2022).Article 
    CAS 

    Google Scholar 
    Wu, Y. et al. Soil water and nutrient availability interactively modify pollinator-mediated directional and correlational selection on floral display. New Phytol. https://doi.org/10.1111/nph.18537 (2022).Article 

    Google Scholar 
    Nicolson, S. W. Sweet solutions: nectar chemistry and quality. Philos. Trans. R. Soc. Lond. B Biol. Sci. 377, 2163. https://doi.org/10.1098/rstb.2021.0163 (2022).Article 
    CAS 

    Google Scholar 
    Vaudo, A. D., Tooker, J. F., Grozinger, C. M. & Patch, H. M. Bee nutrition and floral resource restoration. Curr. Opin. Insect Sci. 10, 133–141 (2015).Article 

    Google Scholar 
    Cnaani, J., Thomson, J. D. & Papaj, D. R. Flower choice and learning in foraging bumblebees: effects of variation in nectar volume and concentration. Ethology 112, 278–285 (2006).Article 

    Google Scholar 
    Vaudo, A. D., Patch, H. M., Mortensen, D. A., Tooker, J. F. & Grozinger, C. M. Macronutrient ratios in pollen shape bumble bee (Bombus impatiens) foraging strategies and floral preferences. Proc. Natl. Acad. Sci. U. S. A. 113, E4035–E4042. https://doi.org/10.1073/pnas.1606101113 (2016).Article 
    ADS 
    CAS 

    Google Scholar 
    Vaudo, A. D. et al. Pollen protein: lipid macronutrient ratios may guide broad patterns of bee species floral preferences. Insects 11, 132. https://doi.org/10.3390/insects11020132 (2020).Article 

    Google Scholar 
    Cardoza, Y. J., Harris, G. K. & Grozinger, C. M. Effects of soil quality enhancement on pollinator-plant interactions. Psyche 2012, 581458. https://doi.org/10.1155/2012/581458 (2012).Article 

    Google Scholar 
    Ceulemans, T., Hulsmans, E., Vanden Ende, W. & Honnay, O. Nutrient enrichment is associated with altered nectar and pollen chemical composition in Succisa pratensis Moench and increased larval mortality of its pollinator Bombus terrestris L.. PLoS One 12, e0175160. https://doi.org/10.1371/journal.pone.0175160 (2017).Article 
    CAS 

    Google Scholar 
    Russo, L., Vaudo, A. D., Fisher, C. J., Grozinger, C. M. & Shea, K. Bee community preference for an invasive thistle associated with higher pollen protein content. Oecologia 190, 901–912 (2019).Article 
    ADS 

    Google Scholar 
    Russo, L., Keller, J., Vaudo, A. D., Grozinger, C. M. & Shea, K. Warming increases pollen lipid concentration in an invasive thistle, with minor effects on the associated floral-visitor community. Insects 11, 20. https://doi.org/10.3390/insects11010020 (2019).Article 

    Google Scholar 
    Awmack, C. S. & Leather, S. R. Host plant quality and fecundity in herbivorous insects. Annu. Rev. Entomol. 47, 817–844 (2002).Article 
    CAS 

    Google Scholar 
    Carisey, N. & Bauce, E. Does nutrition-related stress carry over to spruce budworm, Choristoneura fumiferana (Lepidoptera: Tortricidae) progeny?. Bull. Entomol. Res. 92, 101–108 (2002).Article 
    CAS 

    Google Scholar 
    Zhang, G. & Han, X. N: P stoichiometry in Ficus racemosa and its mutualistic pollinator. J. Plant Ecol. 3, 123–130 (2010).Article 

    Google Scholar 
    Visanuvimol, L. & Bertram, S. M. How dietary phosphorus availability during development influences condition and life history traits of the cricket Acheta domesticas. J. Insect Sci. 11, 63. https://doi.org/10.1673/031.011.6301 (2011).Article 

    Google Scholar 
    Dovrat, G., Meron, E., Shachak, M., Golodets, C. & Osem, Y. Plant size is related to biomass partitioning and stress resistance in water-limited annual plant communities. J. Arid Environ. 165, 1–9 (2019).Article 
    ADS 

    Google Scholar 
    Bobbink, R. et al. Global assessment of nitrogen deposition effects on terrestrial plant diversity: a synthesis. Ecol. Appl. 20, 30–59 (2010).Article 
    CAS 

    Google Scholar 
    Tao, L. & Hunter, M. D. Does anthropogenic nitrogen deposition induce phosphorus limitation in herbivorous insects?. Glob. Chang. Biol. 18, 1843–1853 (2012).Article 
    ADS 

    Google Scholar 
    Tognetti, P. M. et al. Negative effects of nitrogen override positive effects of phosphorus on grassland legumes worldwide. Proc. Natl. Acad. Sci. 118(28), e2023718118. https://doi.org/10.1073/pnas.2023718118 (2021).Article 
    CAS 

    Google Scholar 
    Leghari, S. J. et al. Role of nitrogen for plant growth and development: a review. Adv. Environ. Biol. 10, 209–218 (2016).
    Google Scholar 
    Carvalheiro, L. G. et al. Soil eutrophication shaped the composition of pollinator assemblages during the past century. Ecography 43, 209–221 (2020).Article 

    Google Scholar 
    Lefcheck, J. S. Piecewisesem: piecewise structural equation modelling in R for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579 (2016).Article 

    Google Scholar 
    Roulston, T. H., Cane, J. H. & Buchmann, S. L. What governs protein content of pollen: Pollinator preferences, pollen–pistil interactions, or phylogeny?. Ecol. Monogr. 70, 617–643 (2000).
    Google Scholar 
    Pacini, E. & Hesse, M. Pollenkitt—its composition, forms and functions. Flora 200, 399–415 (2005).Article 

    Google Scholar 
    Vaudo, A. D. et al. Bumble bees regulate their intake of essential protein and lipid pollen macronutrients. J. Exp. Biol. 219, 3962–3970 (2016).CAS 

    Google Scholar 
    Vaudo, A. D., Farrell, L. M., Patch, H. M., Grozinger, C. M. & Tooker, J. F. Consistent pollen nutritional intake drives bumble bee (Bombus impatiens) colony growth and reproduction across different habitats. Ecol. Evol. 8, 5765–5776 (2018).Article 

    Google Scholar 
    Treanore, E. D., Vaudo, A. D., Grozinger, C. M. & Fleischer, S. J. Examining the nutritional value and effects of different floral resources in pumpkin agroecosystems on Bombus impatiens worker physiology. Apidologie 50, 542–552 (2019).Article 

    Google Scholar 
    Baker, H. G. & Baker, I. The predictive value of nectar chemistry to the recognition of pollinator types. Israel J. Bot. 39, 157–166 (1990).CAS 

    Google Scholar 
    Thomson, J. D. Pollen transport and deposition by bumble bees in Erythronium: influences of floral nectar and bee grooming. J. Ecol. 74, 329–341 (1986).Article 

    Google Scholar 
    Gonzalez, M. V., Coque, M. & Herrero, M. Influence of pollination systems on fruit set and fruit quality in kiwifruit (Actinidia deliciosa). Ann. Appl. Biol. 132, 349–355 (1998).Article 

    Google Scholar 
    Morandin, L. A., Laverty, T. M. & Kevan, P. G. Effect of bumble bee (Hymenoptera: Apidae) pollination intensity on the quality of greenhouse tomatoes. J. Econ. Entomol. 94, 172–179 (2001).Article 
    CAS 

    Google Scholar 
    Karron, J. D., Mitchell, R. J. & Bell, J. M. Multiple pollinator visits to Mimulus ringens (Phrymaceae) flowers increase mate number and seed set within fruits. Am. J. Bot. 93, 1306–1312 (2006).Article 

    Google Scholar 
    Kiatoko, N., Raina, S. K., Muli, E. & Mueke, J. Enhancement of fruit quality in Capsicum annum through pollination by Hypotrigona gribodoi in Kakamega Western Kenya. Entomol. Sci. 17, 106–110 (2014).Article 

    Google Scholar 
    Abrol, D. P., Gorka, A. K., Ansari, M. J., Al-Ghamdi, A. & Al-Kahtani, S. Impact of insect pollinators on yield and fruit quality of strawberry. Saudi J. Biol. Sci. 26, 524–530 (2019).Article 

    Google Scholar 
    Osman, M. A., Raju, P. S. & Peacock, J. M. The effect of soil temperature, moisture and nitrogen on Striga asiatica (L.) Kuntze seed germination, viability and emergence on sorghum (Sorghum bicolor L. Moench) roots under field conditions. Plant Soil 131, 265–273 (1991).Article 
    CAS 

    Google Scholar 
    Rose, T. J. & Raymond, C. A. Seed phosphorus effects on rice seedling vigour in soils differing in phosphorus status. Agronomy 10(12), 1919. https://doi.org/10.3390/agronomy10121919 (2020).Article 
    CAS 

    Google Scholar 
    Cavatorta, J. et al. ‘Marketmore 97’: a monoecious slicing cucumber inbred with multiple disease and insect resistances. HortScience 42, 707–709 (2007).Article 

    Google Scholar 
    Friedman, J. The evolution of annual and perennial plant life histories: ecological correlates and genetic mechanisms. Annu. Rev. Ecol. Evol. Syst. 51, 461–481 (2020).Article 

    Google Scholar 
    Alzate-Marin, A. L. et al. Warming and elevated CO2 induces changes in the reproductive dynamics of a tropical plant species. Sci. Total Environ. 768, 144899. https://doi.org/10.1016/j.scitotenv.2020.144899 (2021).Article 
    ADS 
    CAS 

    Google Scholar 
    Mu, J. et al. Domesticated honey bees evolutionarily reduce flower nectar volume in a Tibetan lotus. Ecology 95, 3161–3172 (2014).Article 

    Google Scholar 
    Cruden, R. W. Pollen-ovule ratios: a conservative indicator of breeding systems in flowering plants. Evolution 31, 32–46 (1977).
    Google Scholar 
    Costa, C. M. & Yang, S. Counting pollen grains using readily available, free image processing and analysis software. Ann. Bot. 104, 1005–1010 (2009).Article 

    Google Scholar 
    Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).Article 
    CAS 

    Google Scholar 
    Vaudo, A. D., Patch, H. M., Mortensen, D. A., Grozinger, C. M. & Tooker, J. F. Bumble bees exhibit daily behavioral patterns in pollen foraging. Arthropod. Plant. Interact. 8, 273–283 (2014).
    Google Scholar  More

  • in

    Ant milk: The mysterious fluid that helps them thrive

    Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
    the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
    Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
    and JavaScript. More

  • in

    Grazing pressure on drylands

    Maestre and colleagues collected data using a standardized field survey at 98 sites across 25 countries and 6 continents, fitted linear mixed models to data from all sites and grazing pressure levels, and then applied a multimodel inference procedure to select the set of best-fitting models. The authors found interactions between grazing and biodiversity in almost half of the best-fitting models, where increasing grazing pressure had positive effects on ecosystem services in colder sites with high plant species richness. However, increases in grazing pressure at warmer sites with high rainfall seasonality and low plant species richness interacted with soil properties to either increase or reduce the delivery of multiple ecosystem services. The authors’ findings highlight how increasing herbivore richness could enhance ecosystem service delivery across contrasting environmental and biodiversity conditions, enhancing soil carbon storage and reducing the negative impacts of increased grazing pressure. More

  • in

    Diversity of Trichoderma species associated with soil in the Zoige alpine wetland of Southwest China

    Trichoderma species collectionEighty strains were obtained from 100 soil samples collected from Zoige alpine wetland ecological regions in China. Details of the strains isolated from soil samples are given in Table 1. All strains were subsequently used for morphological identification, while fifty-seven were used for phylogenetic analysis.Table 1 Details of 80 Trichoderma isolates from the Zoige alpine wetland in this study.Full size tablePhylogenetic analysisThe ITS region used preliminarily as a species identification criterion was applied to TrichOKey at www.ISTH.info70. However, the ITS region has a low number of variable sites and long insertions in certain species; thus, it is unsuitable for a phylogenetic reconstruction of this group41. Our study successfully amplified most fragments of the genes tef1, rpb2, and acl1. We also designed a pair of new primers based on the full-length tef1 gene, 5′-GAGAAGTTCGAGAAGGTGAGC-3′ and 5′-ATGTCACGGACGGCGAAAC-3′, with which a 1.4-kb fragment was amplified for most isolates.All samples analyzed in our study were divided into 4 primary clades based on the gpd gene region, including 49 strains from the T. harzianum complex, 3 T. rossicum strains, 1 T. polysporum strain and one unknown species (4 Trichoderma sp. strains) (Fig. 1). Maximum parsimony analysis was conducted among 101 strains, with Protocrea farinosa (CPK 2472) and P. pallida (CBS 299.78) used as outgroup (Table 2). The dataset for the rpb2, tef1 and acl1 genes contained 3403 characteristics, among which 1152 were parsimony-informative, 988 were variable and parsimony-uninformative, and 1263 were constant. The most parsimonious trees are shown in Fig. 2 (tree length = 5054, consistency index = 0.6005, homoplasy index = 0.3995, retention index = 0.8105, rescaled consistency index = 0.4867).Figure 1Neighbor-joining tree based on partial gpd gene sequences from 57 Trichoderma isolates. Parsimony bootstrap values of more than 50% are shown at nodes.Full size imageTable 2 Trichoderma strain included in the multi-gene sequence analysis, with details of clade, strain number, location, and GenBank accessions of the sequences generated.Full size tableFigure 2Maximum parsimony tree of Trichoderma species inferred from the combined rpb2, tef1 and acl1 partial sequences. Maximum parsimony bootstrap values above 50% are shown at nodes. The tree was rooted with Protocrea farinose and P. pallida Isolates from this study are shown in red (new species in bold).Full size imageThe phylogram showed that 57 stains belonged to the following four clades: Harzianum, Polysporum, Stromaticum, and Longibrachiatum. The strains of the first three clades with neighboring named species were well supported by bootstrap values greater than 90%. The Harzianum clade contained T. alni, T. atrobrunneum, T. harzianum and T. pyramidale of the Trichoderma species complex. The Polysporum clade contained only T. polysporum, and the Stromaticum clade contained T. rossicum. The Longibrachiatum clade contained four strains of Trichoderma sp., T25, T43, T44 and T48, which were separated from any other known taxa of this clade showed a low bootstrap value (MPBP = 62%) with T. citrinoviride and T. saturnisporum. We thus regarded it as a new species and named it Trichoderma zoigense, as described in the next section.Growth ratesAs shown in Fig. 3, the genus Trichoderma from Zoige alpine wetland ecological regions was able to grow in a range from 15 to 35 °C, and the suitable growth temperature for most species ranged from 20 to 30 °C. All seven species identified had normal viability at relatively low temperature (15 °C), and they rarely grew well over 35 °C except for T. zoigense. For T. atrobrunneum, T. harzianum and T. pyramidale, the optimum growth temperature on CMD was 25 to 30 °C. T. alni and T. rossicum preferred a cool growth environment, with an optimum temperature of 25 °C, whereas T. zoigense was more partial to a hot environment, with an optimum temperature of 30 °C, and it even grew well up to 35 °C. T. polysporum was the only slow-growing species that grew with less than 6.0 mm/day between 15 and 30 °C and did not survive at 35 °C. The above results showed that all species had different growth rates but were not completely differentiated from each other on CMD. These species were roughly divided into four groups based on their optimum growth temperature.Figure 3Growth rates of 7 species of Trichoderma on CMD given as mm per day at five temperatures. The values were the means of 3–5 experiments, with 1–3 representative isolates per species.Full size imageRelationship with ecological factorsOur results revealed a substantial disparity in the number and distribution of Trichoderma species among Zoige alpine wetland ecological regions (Tables 3, 4). Table 3 showed that T. harzianum was found in all four soil types, but most isolates of this species were obtained from peat soil. T. rossicum, T. alni and T. zoigense were also present in meadow soil and subalpine meadow soil, whereas T. atrobrunneum was found in aeolian sandy soil and peat soil. T. polysporum was found only in peat soil.Table 3 Isolation frequency of Trichoderma species in different soil types (%).Full size tableTable 4 Isolation frequency of Trichoderma species in different soil layers (%) species.Full size tableIn regard to the different soil layers shown in Table 4, T. harzianum was widely distributed in the five soil layers at depths of 0–100 cm. T. rossicum, T. alni and T. zoigense were isolated mainly from the soil layers at depths of 0–50 cm. Both T. atrobrunneum and T. pyramidale were isolated from depths of 0–10 cm, and T. polysporum was found only in the soil layers at depths of 50–100 cm.Regarding isolation frequency, T. harzianum was the most common of the seven species with a 23% isolation frequency, and it was therefore the dominant species in the zone, while the rare species T. polysporum and T. pyramidale had the lowest isolation frequencies at 1%.TaxonomyNew speciesTrichoderma zoigense G.S. Gong & G.T. Tang, sp. nov. (Fig. 4).Figure 4Cultures and asexual morph of Trichoderma zoigense. (a–d). Cultures at 20 °C [(a) on CMD, 7 days; (b) on MEA, 4 days; (c) on PDA, 4 days; and (d) on SNA, 7 days]. (e) Conidiation tuft (CMD, 4 days). (f–k) Conidiophores and phialides (CMD, 5–7 days). (l) Chlamydospores (PDA, 8 days). (m) Conidia (CMD, 5 days). Scale bars: (e) = 2 mm; (f–m) = 10 μm.Full size imageMycoBank: MB 82114.Typification: CHINA. SICHUAN PROVINCE: Zoige Alpine Wetland, on soil, 29 June 2013, G.S. Gong T44 (holotype CGMCC3.20145). GenBank: ITS = KX632531; TEF = KX632588; RPB2 = KX632645; ACL1 = KX632702; GPD = KX632759.Etymology: zoigense (Latin), the specific epithet about the place where the type was found.Description: Cultures and anamorph: optimal growth at 25 °C on all four media. On CMD after 72 h, growth is 25–28 mm at 20 °C and 28–31 mm at 25 °C. Colony is dense and has a wavy to crenate margin. Surface becomes distinctly zonate and white to grayish-green but celadon to atrovirens later, and it is granular in the center and distinctly radially downy outside and shows whitish surface hyphae and reverse-diffusing croci to pale brown pigment (Fig. 4a). Aerial hyphae are numerous to punctate and long, forming radial strands, with white mycelial patches appearing in aged cultures (Fig. 4e). Autolytic excretions are rare, with no coilings observed. Conidiation was noted after 3–4 d at 25 °C, a yellow or greenish color appears after 7 days, conidiation is effuse, and in intense tufts, erect conidiophores occur around the plug and on aerial hyphae. They are mainly concentrated along the colony center, show a white color that turns green, and then finally degenerate, with conidia often adhering in chains. Conidiophores are short and simple with asymmetric branches. Branches produce phialides directly. Phialides are generally solitary along main axes and side branches and sometimes paired in the terminal position of the main axes, sometimes in whorls of 2–3. Phialides are 4.5–10.5 × 2–5 μm ((overline{x }) = 7.5 ± 1.5 × 3 ± 0.5, n = 50) and 1.5–2.5 μm ((overline{x }) = 2 ± 0.2) wide at the base, lageniform or ampulliform, mostly uncinate or slightly curved, less straight, and often distinctly widened in the middle (Fig. 4f–k). Conidia are 3–4.5 × 2.3–4 μm ((overline{x }) = 3.5 ± 0.3 × 3 ± 0.3, n = 50) and initially hyaline, and they turn green and are oblong or ellipsoidal, almost with constricted sides, and smooth, eguttulate or with minute guttules, with indistinct scars (Fig. 4m).On PDA, after 72 h, growth is 35–41 mm at 20 °C and 50–55 mm at 25 °C; and mycelium covers the plate after 5 days at 25 °C. Colonies are dense with wavy to crenate margins; and mycelia are conspicuously differentiated in width of the primary and secondary hyphae. Surface becomes distinctly zonate, yellowish-green to prasinous in color and celadon to atrovirens later, and it is farinose to granular in the center, distinctly radially downy outside, with whitish of surface hyphae and reverse-diffusing brilliant yellow to fruit-green pigment (Fig. 4c). Aerial hyphae are numerous, long and ascend several millimeters, forming radial strands, with white mycelial patches appearing in aged cultures. Autolytic excretions are rare; and no coilings are observed. Odor is indistinct or fragrant. Chlamydospores examined after 7 days at 4.5–9 × 4.5–7.5 μm ((overline{x }) = 6 ± 1.1 × 6 ± 0.7, n = 50), and they are terminal, intercalary, globose or ellipsoidal, and smooth (Fig. 4l). Conidiation is noted after 3–4 days and yellow or greenish after 7 days. Conidiophores are short and simple with asymmetric branches; conidia are greenish, ellipsoidal, and smooth.On SNA, after 72 h, growth is 13–15 mm at 20 °C and, 16–21 mm at 25 °C; and mycelium covers the plate after 12–13 days at 25 °C. Colony is similar to that on CMD, with a little wave margin, although mycelia are looser and slower on the agar surface. Aerial hyphae are relatively inconspicuous and long along the colony margin. Autolytic activity and coiling are absent or inconspicuous. No diffusing pigment or distinct odor are produced (Fig. 4d). Conidiation was noted after 3–4 days at 25 °C, and many amorphous, loose white or aqua cottony tufts occur, mostly median from the plug outwards, and they are confluent to masses up and white but then turn green. After 4–5 days, conidiation becomes dense within the tufts, which are loose at their white margins with long, straight, or slightly sinuous sterile ends in the periphery. Tufts consisting of a loose reticulum with branches often at right angles, give rise to several main axes. Main axes are regular and tree-like, with few or many paired or unpaired side branches. Branches are flexuous, and phialides are solitary along the main axes and side branches, and they are sometimes paired in the terminal position of the main axes, sometimes in whorls of 2–3 that are often cruciform or in pseudo-whorls up to 4. Phialides and conidia are similar to that on CMD.New records for ChinaTrichoderma atrobrunneum F. B. Rocha et al., Mycologia 107: 571, 2015 (Fig. 5).Figure 5Cultures and asexual morph of Trichoderma atrobrunneum. (a–d) Cultures at 25 °C [(a) on CMD, 7 days; (b) on MEA, 4 days; (c) on PDA, 15 days; and (d) on SNA, 7 days]. (e) Conidiation tuft (SNA, 7 days). (f–i,k,l) Conidiophores and phialides (CMD, 5–7 days). (j) Conidia (CMD, 6 days). (m) Chlamydospores (PDA, 7 days). Scale bars: (e) = 2 mm; (f–m) = 10 μm.Full size imageSpecimen examined: CHINA. SICHUAN PROVINCE: Zoige Alpine Wetland, on soil, 29 June 2013, G.S. Gong T42 (holotype CGMCC.20167). GenBank: ITS = KX632514; TEF = KX632571; RPB2 = KX632628; ACL1 = KX632685; GPD = KX632742.Description: Cultures and anamorph: optimal growth at 25 °C on all media. On CMD, after 72 h, growth is 35–37 mm at 20 °C and 46–53 mm at 25 °C; mycelium covers the plate after 5–6 days at 25 °C. Colonies show distinct zonation. Mycelia are loose and thin; hyphae are narrow, sinuous and often form strands on the margin (Fig. 5a). Aerial hyphae are slight, forming a thin white to green downy fluffy or floccose mat. The light brown or brown pigment is observed, with no distinct odor noted. Conidiophores are pyramidal, often with opposing and somewhat widely spaced branches, with the main axis and each branch terminating in a cruciate, sometimes verticillate, whorl of up to four phialides. Phialides are ampulliform to lageniform and 4.9–7.6 × 2.2–3.0 μm ((overline{x }) = 6 ± 0.7 × 2.5 ± 0.2, n = 50) and 1.5–2.5 μm ((overline{x }) = 1.5 ± 0.3) wide at the base (Fig. 5f–i,k,l). Conidia are 2.5–4 × 2.5–3.5 μm ((overline{x }) = 3 ± 0.3 × 3 ± 0.2, n = 50), yellow to green, smooth, and circular to ellipsoidal (Fig. 5j).On PDA, after 72 h, growth is 41–43 mm at 20 °C and 50–55 mm at 25 °C; and mycelium covers the plate after 5–6 days at 25 °C. Colonies show indistinct zonation. Mycelia are dense, opaque, and thick; hyphae are wide, sinuous and often form strands on the margin (Fig. 5c). Margin is thick and defined. Aerial hyphae are abundant and form a thick green downy mat. Conidiation forms abundantly within 4 days in broad concentric rings. Chlamydospores examined after 7 days are 5–9 × 5.5–8.5 μm ((overline{x }) = 6.5 ± 0.9 × 6.5 ± 0.9, n = 30), globose when terminal, smooth, and intercalary (Fig. 5m).On SNA, after 72 h, growth is 33–35 mm at 20 °C and 38–40 mm at 25 °C; and mycelium covers the plate after 7–8 days at 25 °C. Colonies show distinct zonation. Mycelia are thin and yellow to green; hyphae are wide and sinuous, with indistinct strands on the margin (Fig. 5d). Margin is thin and ill-defined. Aerial hyphae are slight, forming a thin green downy fluff appearing in the colony (Fig. 5e). Diffusing pigment was observed in a ring, and no distinct odor was noted. Conidiation is similar to CMD.Accepted species previously reported in ChinaTrichoderma alni Jaklitsch, Mycologia 100: 799. 2008 (Fig. 6).Figure 6Cultures and asexual morph of Trichoderma alni. (a–d). Cultures after 7 days at 25 °C [(a) on CMD; (b) on MEA; (c) on PDA; and (d) on SNA]. € Coilings of aerial hyphae (PDA, 6 days). (f–j,l). Conidiophores and phialides (CMD, 5–7 days). (k) Conidiation tuft (PDA, 7 days). (m) Conidia (CMD, 6 days). (n,o) Chlamydospores (PDA, 7 days). Scale bars: (e–j,l–o) = 10 μm; (k) = 2 mm.Full size imageDescription: Cultures and anamorph: Optimum growth at 25 °C on all media; no growth at 35 °C. On CMD, after 72 h, growth of 34–36 mm at 20 °C and 50–51 mm at 25 °C; and mycelium covers the plate after 5–6 days at 25 °C. Colonies show distinct zonation. Mycelia are loose and thin; hyphae are narrow and sinuous and often form strands on the margin (Fig. 6a). Aerial hyphae are slight and form a thin white to green downy, fluffy or floccose mat. No diffusing pigment or distinct odor is noted. Conidiophores are hyaline and thick, with side branches on several levels at the base of the elongations that are mostly paired and in right angles with phialides in whorls of 3–5. Phialides are 5.5–11.5 × 2–3.5 μm ((overline{x }) = 8 ± 1.4 × 2.5 ± 0.4, n = 50) and 1.5–2.5 μm ((overline{x }) = 2 ± 0.4) wide at the base, often short and wide, and ampulliform (Fig. 6f–j,l). Conidia are 3–4 × 2.5–3.5 μm ((overline{x }) = 3.5 ± 0.2 × 3 ± 0.2, n = 50), dark green, smooth, and ellipsoidal (Fig. 6m).On PDA, after 72 h, growth is 33–35 mm at 20 °C and 41–43 mm at 25 °C; and mycelium covers the plate after 6–7 days at 25 °C. Colonies show indistinct zonation. Mycelia are dense, opaque, and thick; hyphae are wide, sinuous and often form strands on the margin (Fig. 6c). Margin is thin and ill defined. Aerial hyphae are slight, coiled (Fig. 6e), forming a thin white to green downy, fluffy or floccose mat (Fig. 6k). Chlamydospores examined after 7 days are 6–9.5 × 5–8 μm ((overline{x }) = 7.5 ± 0.9 × 7 ± 0.9, n = 30), globose to oval when terminal, and smooth, and few are intercalary (Fig. 6n,o).On SNA, after 72 h, growth is 18–19 mm at 20 °C and 28–32 mm at 25 °C; and mycelium covers the plate after 6–7 days at 25 °C. Colonies show distinct zonation. Mycelia are thin and yellow to green; hyphae are wide and sinuous and show indistinct strands on the margin (Fig. 6d). Margin is thin and ill-defined. Aerial hyphae are slight and form a thin white downy, fluffy, or floccose mat appearing in distal parts of the colony. No diffusing pigment or distinct odor was noted. Conidiation is similar to CMD.Trichoderma harzianum Rifai, Mycol. Pap. 116: 38, 1969 (Fig. 7).Figure 7Cultures and asexual morph of Trichoderma harzianum. (a–d) Cultures after 7 days at 20 °C [(a) on CMD; (b) on MEA; (c) on PDA; and (d) on SNA]. (e) Conidiation tuft (CMD, 7 days). (f–j) Conidiophores and phialides (CMD, 5–7 days). (k) Conidia (CMD, 5 days). (l,m) Chlamydospores (PDA, 7 days). Scale bars: (e) = 2 mm; (f–m) = 10 μm.Full size imageDescription: Cultures and anamorph: optimal growth at 25 °C on all media. On CMD, after 72 h, growth is 34–38 mm at 20 °C and 46–53 mm at 25 °C; mycelium covers the plate after 5–6 days at 25 °C. Colonies show distinct zonation. Mycelia are loose and thin; hyphae are narrow, sinuous, and often form strands on the margin (Fig. 7a). Aerial hyphae are abundant and radiating and form thick green downy, fluffy, or floccose mats (Fig. 7e). No diffusing pigment, but fragrant odor noted. Conidiophores are pyramidal with opposing branches, with each branch terminating in a cruciate whorl of up to four or five phialides. Phialides are frequently solitary or in a whorl of three or four. Phialides are ampulliform to lageniform and often constricted below the tip to form a narrow neck of 4.5–8 × 2–3.5 μm ((overline{x }) = 6 ± 0.8 × 2.5 ± 0.3, n = 50) and 1–2.5 μm ((overline{x }) = 2 ± 0.3) wide at the base (Fig. 7f–j). Conidia are subglobose to ovoid, 3–4.5 × 2.5–3.3 μm ((overline{x }) = 3.5 ± 0.3 × 3 ± 0.2, n = 50), laurel-green to bright green, smooth, and ellipsoidal (Fig. 7k).On PDA, after 72 h, growth is 41–43 mm at 20 °C and 50–55 mm at 25 °C; and mycelium covers the plate after 5–6 days at 25 °C. Colonies show distinct zonation. Mycelia are dense, opaque, and thick; hyphae are wide and sinuous and often form strands on the margin (Fig. 7c). Margin is thick and ill defined. Aerial hyphae are abundant and radiating and form thick green downy, fluffy or floccose mats. Chlamydospores examined after 7 days are 5.5–9 × 5.5–9.0 μm ((overline{mathrm{x} }) = 7 ± 0.8 × 7 ± 0.8, n = 30), globose to oval when terminal and smooth, showing an almost unobserved intercalary (Fig. 7l,m).On SNA, after 72 h, growth is 33–35 mm at 20 °C and 38–40 mm at 25 °C; and mycelium covers the plate after 7–8 days at 25 °C. Colonies show distinct zonation. Mycelia are thin and green; hyphae are narrow and sinuous and show indistinct strands on the margin (Fig. 7d). Margin is thin and ill defined. Aerial hyphae are slight and form a thick downy, fluffy, or floccose mat appearing in the colony. No diffusing pigment or distinct fragrant odor was noted. Conidiation was similar to CMD.Trichoderma polysporum Rifai, Mycol. Pap. 116: 18, 1969 (Fig. 8).Figure 8Cultures and asexual morph of Trichoderma polysporum. (a–d) Cultures at 20 °C [(a) on CMD, 7 days; (b) on MEA, 15 days; (c) on PDA, 15 days; and (d) on SNA, 15 days]. (i) Conidiation tuft (PDA, 15 days). (e–h,j) Conidiophores and phialides (CMD, 5–7 days). (k) Chlamydospores (CMD, 7 days). (l) Conidia (PDA, 6 days). Scale bars: (i) = 2 mm; (e–h,j) = 10 μm.Full size imageDescription: Cultures and anamorph: optimal growth at 20 °C on all media, no growth at 35 °C. On CMD, after 72 h, growth is 14–16 mm at 20 °C and 9–12 mm at 25 °C; and mycelium covers the plate after 9–10 days at 20 °C. A colony is hyaline, thin and loose, with little mycelium on the agar surface, and it is indistinctly zonate but becomes zonate by conidiation in white tufts after 4–5 d and grass green to green after 6 days (Fig. 8a). Aerial hyphae are long and dense and forming little greenish aggregates that are granular to pulvinate. No pigment or odor. Conidiation noted after 4–5 days, and it is white to greenish, with sterile smooth to rough helical elongations in the distal zones from pustules. Conidiophores are hyaline and thick with side branches on several levels at the base of the elongations that are mostly paired and at right angles with phialides in whorls of 2–5. Phialides are 5–10.5 × 2.5–4 μm ((overline{x }) = 7 ± 1.9 × 3.5 ± 0.4, n = 50) and 2–4 μm ((overline{x }) = 3 ± 0.5) wide at the base, often short and wide and ampulliform (Fig. 8e–h,j). Conidia are 2.5–4 × 2–3 μm ((overline{x }) = 3.5 ± 0.4 × 2.5 ± 0.2, n = 50), hyaline, smooth, and ellipsoidal (Fig. 10l).On PDA, after 72 h, growth is 24–26 mm at 20 °C and 13–16 mm at 25 °C; and mycelium covers the plate after 8–9 days at 20 °C. A colony is densest, distinctly zonate, and grass green to spearmint green; mycelia are conspicuously dense; and surface hyphae form radial strands (Fig. 8c). Aerial hyphae are long and dense and form greenish aggregates that are granular to pulvinate (Fig. 8i). No diffusing pigment and odor. Chlamydospores examined after 7 days are 5.5–9 × 5–7.5 μm ((overline{x }) = 7 ± 0.9 × 6 ± 0.6, n = 30), globose to oval when terminal, and smooth, with an almost unobserved intercalary (Fig. 8k).On SNA, growth is approximately 7 mm/day at 20 °C and 5 mm/day at 25 °C; and mycelium covers the plate after 10 days at 20 °C. A colony is hyaline, thin, and loose, with little mycelium on the agar surface, not or indistinctly zonate, but becomes zonate by conidiation in white tufts after 4–5 days; and the margin is downy by long aerial hyphae, which degenerating/dissolving soon (Fig. 8d).Trichoderma pyramidale W. Jaklitsch & P. Chaverri, Mycologia 107: 581, 2015 (Fig. 9).Figure 9Cultures and asexual morph of Trichoderma pyramidale. (a–d) Cultures at 25 °C [(a) on CMD, 7 days; (b) on MEA, 4 days; (c) on PDA, 4 days; and (d) on SNA, 4 days]. (e) Conidiation tuft (PDA, 7 days). (f–j) Conidiophores and phialides (CMD, 5–7 days). (k) Conidia (CMD, 6 days). (l) Chlamydospores (PDA, 7 days). Scale bars: (e) = 2 mm; (f–l) = 10 μm.Full size imageDescription: Cultures and anamorph: optimal growth at 25 °C on all media, with little growth at 35 °C. On CMD, after 72 h, growth is 29–32 mm at 20 °C and 48–53 mm at 25 °C; and mycelium covers the plate after 5–6 days at 25 °C. Colonies show distinct zonation. Mycelium is loose and thin; hyphae are narrow, sinuous, and often form strands on the margin (Fig. 9a). Aerial hyphae are slight, forming a thin white to green downy, fluffy or floccose mat. Brown pigment is shown, but no distinct odor noted. Conidiophores are hyaline and thick with side branches on several levels at the base of the elongations that are mostly paired and at right angles with phialides in whorls of 3–5. Phialides are 5–9.5 × 2.5–3 μm ((overline{x }) = 7 ± 1.1 × 3 ± 0.3, n = 50) and 1–2.5 μm ((overline{x }) = 1.5 ± 0.3) wide at the base and often short, wide, and ampulliform (Fig. 9f–j). Conidia are 2.5–4 × 2.5–3.5 μm ((overline{x }) = 3.5 ± 0.3 × 3 ± 0.2, n = 50), green, smooth, and ellipsoidal (Fig. 9k).On PDA, after 72 h, growth is 41–43 mm at 20 °C and 50–55 mm at 25 °C; and mycelium covers the plate after 5–6 days at 25 °C. Colonies show indistinct zonation. Mycelia are dense, opaque, and thick; hyphae are wide, sinuous and often form strands on the margin (Fig. 9c). Margin is thin and ill defined. Aerial hyphae are slight and form a thin white to green downy, fluffy or floccose mat (Fig. 9e). Chlamydospores examined after 7 days are 5.5–10 × 5.5–10 μm ((overline{x }) = 7 ± 0.9 × 7 ± 0.9, n = 30), globose to oval when terminal or intercalary, and smooth (Fig. 9l).On SNA, after 72 h, growth is 33–35 mm at 20 °C and 38–40 mm at 25 °C; and mycelium covers the plate after 7–8 days at 25 °C. Colonies show distinct zonation. Mycelium is thin, yellow to green; hyphae are wide, sinuous, with indistinct strands on the margin (Fig. 9d). Margin is thin and ill defined. Aerial hyphae are slight and form a thin white downy, fluffy or floccose mat in distal parts of the colony. No diffusing pigment or distinct odor noted. Conidiation similar to CMD.Trichoderma rossicum Bissett et al., Canad. J. Bot. 81: 578, 2003 (Fig. 10).Figure 10Cultures and asexual morph of Trichoderma rossicum. (a–d) Cultures after 7 days at 25 °C [(a) on CMD; (b) on MEA; (c) on PDA; and (d) on SNA]. € Conidiation tuft (PDA, 7 days). (f–h,j,k) Conidiophores and phialides (CMD, 5–7 days). (i) Elongations (CMD, 6 days). (l,n) Conidia (CMD, 6 days). (m) Chlamydospores (PDA, 7 days). Scale bars: (e) = 2 mm; (f–n) = 10 μm.Full size imageDescription: Cultures and anamorph: optimal growth at 25 °C on all media. On CMD, growth of 10–11 mm/day at 20 °C and 15–17 mm/day at 25 °C; and mycelium covers the plate after 6–7 days at 20 °C. Colony is dense with a wavy margin, and the surface becomes distinctly zonate (Fig. 10a). Aerial hyphae are numerous, long, elongate, and villiform in the plate (Fig. 10i). No diffusing pigment or odor. Autolytic activity is variable, and coilings are scarce or inconspicuous. Conidiation noted after 3–4 days at 20 °C. Conidiation is effuse and in intense tufts that are hemispherical or irregular, and they show wide wheel grain banding that is gray green to deep green. Conidiophores radiate from the reticulum and are broad, straight, sinuous or helically twisted, show distally slightly pointed elongations, taper from the main axes to top branches, and present primary branches arranged in pairs or in whorls of 2–3, with secondary branches to solitary. Phialides are 4.5–14 × 2.5–4 μm ((overline{x }) = 7 ± 1.5 × 3.5 ± 0.3, n = 50) and 2–3.5 μm ((overline{x }) = 3 ± 0.4) wide at the base, ampulliform, and in whorls of 3–6 (Fig. 10f–h,j,k). Conidia are 3.5–5.5 × 2.5–4 μm ((overline{x }) = 4.5 ± 0.5 × 3 ± 0.2, n = 50), short cylindrical, and a gray color when single and pea green to yellow green in a group (Fig. 10l,n).On PDA, growth is 12–15 mm/day at 20 °C, 12–16 mm/day at 25 °C; and mycelium covers the plate after 4–5 days at 25 °C. Colony is denser with a wavy margin than that on CMD, and the surface is distinctly zonate (Fig. 10c). Aerial hyphae are numerous, long, and villiform to pulvinate in the plate. No diffusing pigment and odor (Fig. 10e). Autolytic activity is variable, coilings are scarce or inconspicuous. Chlamydospores examined after 7 days are 6.5–9.5 × 6–9 μm ((overline{x }) = 7 ± 1.0 × 7 ± 0.9, n = 30), terminal and intercalary, globose or ellipsoidal, and smooth (Fig. 10m).On SNA, growth is 8–13 mm/day at 20 °C and 8–12 mm/day at 25 °C; and mycelium covers the plate after 6–7 day at 25 °C. Colony is hyaline, thin and dense; and mycelium degenerate rapidly (Fig. 10d). Aerial hyphae are inconspicuous, autolytic activity is scant, and coilings are distinct. Conidiation noted after approximately 4 days and starts in white fluffy tufts spreading from the center to form concentric zones, and they compact to pustules with a white to greenish color. More

  • in

    Global habitat suitability modeling reveals insufficient habitat protection for mangrove crabs

    Valiela, I., Bowen, J. L. & York, J. K. Mangrove Forests: One of the World’s Threatened Major Tropical Environments: At least 35% of the area of mangrove forests has been lost in the past two decades, losses that exceed those for tropical rain forests and coral reefs, two other well-known threatened environments. Bioscience 51, 807–815. https://doi.org/10.1641/0006-3568(2001)051[0807:MFOOTW]2.0.CO;2 (2001).Article 

    Google Scholar 
    Kuenzer, C., Bluemel, A., Gebhardt, S., Quoc, T. V. & Dech, S. Remote sensing of mangrove ecosystems: A review. Remote Sens. 3, 1. https://doi.org/10.3390/rs3050878 (2011).Article 

    Google Scholar 
    Turschwell, M. P. et al. Multi-scale estimation of the effects of pressures and drivers on mangrove forest loss globally. Biol. Cons. 247, 108637. https://doi.org/10.1016/j.biocon.2020.108637 (2020).Article 

    Google Scholar 
    Millennium Ecosystem Assessment. Ecosystems and Human Well-being: Synthesis. (2005).Nagelkerken, I. et al. The habitat function of mangroves for terrestrial and marine fauna: A review. Aquat. Bot. 89, 155–185. https://doi.org/10.1016/j.aquabot.2007.12.007 (2008).Article 

    Google Scholar 
    Hamilton, S. E. & Casey, D. Creation of a high spatio-temporal resolution global database of continuous mangrove forest cover for the 21st century (CGMFC-21). Glob. Ecol. Biogeogr. 25, 729–738. https://doi.org/10.1111/geb.12449 (2016).Article 

    Google Scholar 
    Friess, D. A. et al. The state of the world’s Mangrove forests: Past, present, and future. Annu. Rev. Environ. Resour. 44, 89–115. https://doi.org/10.1146/annurev-environ-101718-033302 (2019).Article 

    Google Scholar 
    Zeng, Y., Friess, D. A., Sarira, T. V., Siman, K. & Koh, L. P. Global potential and limits of mangrove blue carbon for climate change mitigation. Curr. Biol. 31, 1737-1743.e1733. https://doi.org/10.1016/j.cub.2021.01.070 (2021).Article 
    CAS 

    Google Scholar 
    zu Ermgassen, P. S. E. et al. Fishers who rely on mangroves: Modelling and mapping the global intensity of mangrove-associated fisheries. Estuar. Coast. Shelf Sci. 247, 106975. https://doi.org/10.1016/j.ecss.2020.106975 (2020).Article 

    Google Scholar 
    Walters, A. D. et al. Do hotspots fall within protected areas? A geographic approach to planning analysis of regional freshwater biodiversity. Freshw. Biol. 64, 2046–2056. https://doi.org/10.1111/fwb.13394 (2019).Article 

    Google Scholar 
    Blasco, F., Saenger, P. & Janodet, E. Mangroves as indicators of coastal change. CATENA 27, 167–178. https://doi.org/10.1016/0341-8162(96)00013-6 (1996).Article 

    Google Scholar 
    Gilman, E. L., Ellison, J., Duke, N. C. & Field, C. Threats to mangroves from climate change and adaptation options: A review. Aquat. Bot. 89, 237–250. https://doi.org/10.1016/j.aquabot.2007.12.009 (2008).Article 

    Google Scholar 
    Hamilton, S. Assessing the role of commercial aquaculture in displacing mangrove forest. Bull. Mar. Sci. 89, 585–601 (2013).Article 

    Google Scholar 
    Lovelock, C. E. et al. The vulnerability of Indo-Pacific mangrove forests to sea-level rise. Nature 526, 559–563. https://doi.org/10.1038/nature15538 (2015).Article 
    ADS 
    CAS 

    Google Scholar 
    Richards Daniel, R. & Friess Daniel, A. Rates and drivers of mangrove deforestation in Southeast Asia, 2000–2012. Proc. Natl. Acad. Sci. 113, 344–349. https://doi.org/10.1073/pnas.1510272113 (2016).Article 
    ADS 
    CAS 

    Google Scholar 
    Appeltans, W. et al. The magnitude of global marine species diversity. Curr. Biol. 22, 2189–2202. https://doi.org/10.1016/j.cub.2012.09.036 (2012).Article 
    CAS 

    Google Scholar 
    Ward, R. D., Friess, D. A., Day, R. H. & MacKenzie, R. A. Impacts of climate change on mangrove ecosystems: A region by region overview. Ecosyst. Health Sustain. 2, e01211. https://doi.org/10.1002/ehs2.1211 (2016).Article 

    Google Scholar 
    Van der Stocken, T., Vanschoenwinkel, B., Carroll, D., Cavanaugh, K. C. & Koedam, N. Mangrove dispersal disrupted by projected changes in global seawater density. Nat. Clim. Chang. 12, 685–691. https://doi.org/10.1038/s41558-022-01391-9 (2022).Article 
    ADS 

    Google Scholar 
    Alongi, D. M. The impact of climate change on Mangrove forests. Curr. Clim. Change Rep. 1, 30–39. https://doi.org/10.1007/s40641-015-0002-x (2015).Article 

    Google Scholar 
    Giri, C. et al. Status and distribution of mangrove forests of the world using earth observation satellite data. Glob. Ecol. Biogeogr. 20, 154–159. https://doi.org/10.1111/j.1466-8238.2010.00584.x (2011).Article 

    Google Scholar 
    Kristensen, E. Mangrove crabs as ecosystem engineers; with emphasis on sediment processes. J. Sea Res. 59, 30–43. https://doi.org/10.1016/j.seares.2007.05.004 (2008).Article 
    ADS 

    Google Scholar 
    Penha-Lopes, G. et al. Are fiddler crabs potentially useful ecosystem engineers in mangrove wastewater wetlands?. Mar. Pollut. Bull. 58, 1694–1703. https://doi.org/10.1016/j.marpolbul.2009.06.015 (2009).Article 
    CAS 

    Google Scholar 
    Sharifian, S., Kamrani, E. & Saeedi, H. Global biodiversity and biogeography of mangrove crabs: Temperature, the key driver of latitudinal gradients of species richness. J. Therm. Biol 92, 102692. https://doi.org/10.1016/j.jtherbio.2020.102692 (2020).Article 
    CAS 

    Google Scholar 
    Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026 (2006).Article 

    Google Scholar 
    Guisan, A. & Zimmermann, N. E. Predictive habitat distribution models in ecology. Ecol. Model. 135, 147–186. https://doi.org/10.1016/S0304-3800(00)00354-9 (2000).Article 

    Google Scholar 
    Guisan, A. et al. Predicting species distributions for conservation decisions. Ecol. Lett. 16, 1424–1435. https://doi.org/10.1111/ele.12189 (2013).Article 

    Google Scholar 
    Guisan, A., Thuiller, W. & Zimmermann, N. E. Habitat Suitability and Distribution Models: With Applications in R. (Cambridge University Press, 2017).Luan, J., Zhang, C., Xu, B., Xue, Y. & Ren, Y. Modelling the spatial distribution of three Portunidae crabs in Haizhou Bay, China. PLoS ONE 13, e0207457. https://doi.org/10.1371/journal.pone.0207457 (2018).Article 
    CAS 

    Google Scholar 
    Kafash, A. et al. The Gray Toad-headed Agama, Phrynocephalus scutellatus, on the Iranian Plateau: The degree of niche overlap depends on the phylogenetic distance. Zool. Middle East 64, 47–54. https://doi.org/10.1080/09397140.2017.1401309 (2018).Article 

    Google Scholar 
    Yousefi, M., Shabani, A. A. & Azarnivand, H. Reconstructing distribution of the Eastern Rock Nuthatch during the Last Glacial Maximum and Last Interglacial. Avian Biol. Res. 13, 3–9. https://doi.org/10.1177/1758155919874537 (2019).Article 

    Google Scholar 
    De Rock, P. et al. Predicting large-scale habitat suitability for cetaceans off Namibia using MinxEnt. Mar. Ecol. Prog. Ser. 619, 149–167 (2019).Article 
    ADS 

    Google Scholar 
    Saeedi, H., Basher, Z. & Costello, M. J. Modelling present and future global distributions of razor clams (Bivalvia: Solenidae). Helgol. Mar. Res. 70, 23. https://doi.org/10.1186/s10152-016-0477-4 (2016).Article 

    Google Scholar 
    Bosso, L. et al. The rise and fall of an alien: why the successful colonizer Littorina saxatilis failed to invade the Mediterranean Sea. Biol. Invas. 24, 3169–3187. https://doi.org/10.1007/s10530-022-02838-y (2022).Article 

    Google Scholar 
    Moradmand, M. & Yousefi, M. Ecological niche modelling and climate change in two species groups of huntsman spider genus Eusparassus in the Western Palearctic. Sci. Rep. 12, 4138. https://doi.org/10.1038/s41598-022-08145-9 (2022).Article 
    ADS 
    CAS 

    Google Scholar 
    Compton, T. J., Leathwick, J. R. & Inglis, G. J. Thermogeography predicts the potential global range of the invasive European green crab (Carcinus maenas). Divers. Distrib. 16, 243–255. https://doi.org/10.1111/j.1472-4642.2010.00644.x (2010).Article 

    Google Scholar 
    Kafash, A., Ashrafi, S. & Yousefi, M. Modeling habitat suitability of bats to identify high priority areas for field monitoring and conservation. Environ. Sci. Pollut. Res. 29, 25881–25891. https://doi.org/10.1007/s11356-021-17412-7 (2022).Article 

    Google Scholar 
    Leathwick, J. et al. Novel methods for the design and evaluation of marine protected areas in offshore waters. Conserv. Lett. 1, 91–102. https://doi.org/10.1111/j.1755-263X.2008.00012.x (2008).Article 

    Google Scholar 
    Charrua, A. B., Bandeira, S. O., Catarino, S., Cabral, P. & Romeiras, M. M. Assessment of the vulnerability of coastal mangrove ecosystems in Mozambique. Ocean Coast. Manag. 189, 105145. https://doi.org/10.1016/j.ocecoaman.2020.105145 (2020).Article 

    Google Scholar 
    Khajoei Nasab, F., Mehrabian, A. & Mostafavi, H. Mapping the current and future distributions of Onosma species endemic to Iran. J. Arid Land 12, 1031–1045. https://doi.org/10.1007/s40333-020-0080-z (2020).Article 

    Google Scholar 
    Allyn, A. J. et al. Comparing and synthesizing quantitative distribution models and qualitative vulnerability assessments to project marine species distributions under climate change. PLoS ONE 15, e0231595. https://doi.org/10.1371/journal.pone.0231595 (2020).Article 
    CAS 

    Google Scholar 
    Makki, T., Mostafavi, H., Matkan, A. & Aghighi, H. Modelling Climate-Change Impact on the Spatial Distribution of Garra Rufa (Heckel, 1843) (Teleostei: Cyprinidae). Iran. J. Sci. Technol. Trans. A: Sci. 45, 795–804. https://doi.org/10.1007/s40995-021-01088-2 (2021).Article 

    Google Scholar 
    Bolon, I. et al. What is the impact of snakebite envenoming on domestic animals? A nation-wide community-based study in Nepal and Cameroon. Toxicon: X 9–10, 100068. https://doi.org/10.1016/j.toxcx.2021.100068 (2021).Sharma, A., Dubey, V. K., Johnson, J. A., Rawal, Y. K. & Sivakumar, K. Is there always space at the top? Ensemble modeling reveals climate-driven high-altitude squeeze for the vulnerable snow trout Schizothorax richardsonii in Himalaya. Ecol. Ind. 120, 106900. https://doi.org/10.1016/j.ecolind.2020.106900 (2021).Article 

    Google Scholar 
    Yousefi, M., Naderloo, R. & Keikhosravi, A. Freshwater crabs of the Near East: Increased extinction risk from climate change and underrepresented within protected areas. Glob. Ecol. Conserv. 38, e02266. https://doi.org/10.1016/j.gecco.2022.e02266 (2022).Article 

    Google Scholar 
    Sheykhi Ilanloo, S. et al. Applying opportunistic observations to model current and future suitability of the Kopet Dagh Mountains for a Near Threatened avian scavenger. Avian Biol. Res. 14, 18–26. https://doi.org/10.1177/1758155920962750 (2020).Article 

    Google Scholar 
    Naderloo, R. Grapsoid crabs (Decapoda: Brachyura: Thoracotremata) of the Persian Gulf and the Gulf of Oman. Zootaxa 3048(1), 1. https://doi.org/10.11646/zootaxa.3048.1.1 (2011).Article 

    Google Scholar 
    Naderloo, R. Atlas of crabs of the Persian Gulf. (2017).Innocenti, G., Schubart, C. D. & Fratini, S. Description of Metopograpsus cannicci, new species, a pseudocryptic crab species from East Africa and the Western Indian Ocean (Decapoda: Brachyura: Grapsidae). Raffles Bull. Zool. (RBZ) 68, 619–628 (2020).
    Google Scholar 
    Hemmati, M. R., Shojaei, M. G., Taheri Mirghaed, A., Mashhadi Farahani, M. & Weigt, M. Food sources for camptandriid crabs in an arid mangrove ecosystem of the Persian Gulf: a stable isotope approach. Isotop. Environ. Health Stud. 57, 457–469. https://doi.org/10.1080/10256016.2021.1925665 (2021).Article 
    CAS 

    Google Scholar 
    Tittensor, D. P. et al. Global patterns and predictors of marine biodiversity across taxa. Nature 466, 1098–1101. https://doi.org/10.1038/nature09329 (2010).Article 
    ADS 
    CAS 

    Google Scholar 
    Kordas, R. L., Harley, C. D. G. & O’Connor, M. I. Community ecology in a warming world: The influence of temperature on interspecific interactions in marine systems. J. Exp. Mar. Biol. Ecol. 400, 218–226. https://doi.org/10.1016/j.jembe.2011.02.029 (2011).Article 

    Google Scholar 
    Hall, S. & Thatje, S. Temperature-driven biogeography of the deep-sea family Lithodidae (Crustacea: Decapoda: Anomura) in the Southern Ocean. Polar Biol. 34, 363–370. https://doi.org/10.1007/s00300-010-0890-0 (2011).Article 

    Google Scholar 
    Hannah, L. Climate Change Biology. Academic Press (2015).Ali, H. et al. Expanding or shrinking? range shifts in wild ungulates under climate change in Pamir-Karakoram mountains, Pakistan. PLoS ONE 16, e0260031. https://doi.org/10.1371/journal.pone.0260031 (2022).Article 
    CAS 

    Google Scholar 
    Yousefi, M. et al. Climate change is a major problem for biodiversity conservation: A systematic review of recent studies in Iran. Contemp. Probl. Ecol. 12, 394–403. https://doi.org/10.1134/S1995425519040127 (2019).Article 

    Google Scholar 
    Doney, S. C. et al. Climate Change Impacts on Marine Ecosystems. Ann. Rev. Mar. Sci. 4, 11–37. https://doi.org/10.1146/annurev-marine-041911-111611 (2011).Article 

    Google Scholar 
    Worm, B. & Lotze, H. K. in Climate Change (Second Edition) (ed Trevor M. Letcher) 195–212 (Elsevier, 2016).Ramírez, F., Afán, I., Davis, L. S. & Chiaradia, A. Climate impacts on global hot spots of marine biodiversity. Sci. Adv. 3, e1601198. https://doi.org/10.1126/sciadv.1601198 (2017).Article 
    ADS 

    Google Scholar 
    Worm, B. et al. Impacts of Biodiversity Loss on Ocean Ecosystem Services. Science 314, 787–790. https://doi.org/10.1126/science.1132294 (2006).Article 
    ADS 
    CAS 

    Google Scholar 
    Lester, S. E. et al. Biological effects within no-take marine reserves: a global synthesis. Mar. Ecol. Prog. Ser. 384, 33–46 (2009).Article 
    ADS 

    Google Scholar 
    Daru, B. H. & le Roux, P. C. Marine protected areas are insufficient to conserve global marine plant diversity. Glob. Ecol. Biogeogr. 25, 324–334. https://doi.org/10.1111/geb.12412 (2016).Article 

    Google Scholar 
    Sala, E. et al. Protecting the global ocean for biodiversity, food and climate. Nature https://doi.org/10.1038/s41586-021-03371-z (2021).Article 

    Google Scholar 
    Embling, C. B. et al. Using habitat models to identify suitable sites for marine protected areas for harbour porpoises (Phocoena phocoena). Biol. Cons. 143, 267–279. https://doi.org/10.1016/j.biocon.2009.09.005 (2010).Article 

    Google Scholar 
    Magris, R. A. & Déstro, G. F. G. Predictive modeling of suitable habitats for threatened marine invertebrates and implications for conservation assessment in Brazil. Braz. J. Oceanogr. 58, 57–68 (2010).Article 

    Google Scholar 
    Welch, H., Pressey, R. L. & Reside, A. E. Using temporally explicit habitat suitability models to assess threats to mobile species and evaluate the effectiveness of marine protected areas. J. Nat. Conserv. 41, 106–115. https://doi.org/10.1016/j.jnc.2017.12.003 (2018).Article 

    Google Scholar 
    Rhoden, C. M., Peterman, W. E. & Taylor, C. A. Maxent-directed field surveys identify new populations of narrowly endemic habitat specialists. PeerJ 5, e3632–e3632. https://doi.org/10.7717/peerj.3632 (2017).Article 

    Google Scholar 
    Ancillotto, L., Mori, E., Bosso, L., Agnelli, P. & Russo, D. The Balkan long-eared bat (Plecotus kolombatovici) occurs in Italy—First confirmed record and potential distribution. Mamm. Biol. 96, 61–67. https://doi.org/10.1016/j.mambio.2019.03.014 (2019).
    Article 

    Google Scholar 
    Imtiyaz, B. B., Sweta, P. D., Prakash, K. K. Threats to marine biodiversity. Mar. Biodivers.: Present Status Prospects (2011).Robinson, N. M., Nelson, W. A., Costello, M. J., Sutherland, J. E. & Lundquist, C. J. A systematic review of marine-based species distribution models (SDMs) with recommendations for best practice. Front. Mar. Sci. 4, 421 (2017).Article 

    Google Scholar 
    Fabri-Ruiz, S., Danis, B., David, B. & Saucède, T. Can we generate robust species distribution models at the scale of the Southern Ocean?. Divers. Distrib. 25, 21–37. https://doi.org/10.1111/ddi.12835 (2019).Article 

    Google Scholar 
    Maxwell, D. L., Stelzenmüller, V., Eastwood, P. D. & Rogers, S. I. Modelling the spatial distribution of plaice (Pleuronectes platessa), sole (Solea solea) and thornback ray (Raja clavata) in UK waters for marine management and planning. J. Sea Res. 61, 258–267. https://doi.org/10.1016/j.seares.2008.11.008 (2009).Article 
    ADS 

    Google Scholar 
    Marshall, C. E., Glegg, G. A. & Howell, K. L. Species distribution modelling to support marine conservation planning: The next steps. Mar. Policy 45, 330–332. https://doi.org/10.1016/j.marpol.2013.09.003 (2014).Article 

    Google Scholar 
    GBIF. GBIF Occurrence Download https://doi.org/10.15468/dl.khpu28. GBIF (2021).Spalding, M. D. et al. Marine ecoregions of the world: A bioregionalization of coastal and shelf areas. Bioscience 57, 573–583. https://doi.org/10.1641/B570707 (2007).Article 

    Google Scholar 
    Basher, Z., Bowden, D. A. & Costello, M. J. Global marine environment datasets (GMED). World Wide Web Electron. Publ. 14, 1 (2018).
    Google Scholar 
    Barnes, D. Ecology of subtropical hermit crabs in SW Madagascar: short-range migrations. Mar. Biol. 142, 549–557. https://doi.org/10.1007/s00227-002-0968-5 (2003).Article 

    Google Scholar 
    Naimullah, M. et al. Association of environmental factors in the Taiwan Strait with distributions and habitat characteristics of three swimming crabs. Remote Sens. 12, 1. https://doi.org/10.3390/rs12142231 (2020).Article 

    Google Scholar 
    Malvé, M. E., Rivadeneira, M. M. & Gordillo, S. Northward range expansion of the European green crab Carcinus maenas in the SW Atlantic: a synthesis after ~20 years of invasion history. bioRxiv, 2020.2011.2004.368761, doi:https://doi.org/10.1101/2020.11.04.368761 (2020).Merow, C., Smith, M. J. & Silander, J. A. Jr. A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography 36, 1058–1069. https://doi.org/10.1111/j.1600-0587.2013.07872.x (2013).Article 

    Google Scholar 
    Naimi, B. & Araújo, M. B. sdm: a reproducible and extensible R platform for species distribution modelling. Ecography 39, 368–375. https://doi.org/10.1111/ecog.01881 (2016).Article 

    Google Scholar 
    Team, R. C. R: A Language and Environment for Statistical Computing (2020).Fielding, A. H. & Bell, J. F. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ. Conserv. 24, 38–49. https://doi.org/10.1017/S0376892997000088 (1997).Article 

    Google Scholar 
    Swets John, A. Measuring the Accuracy of Diagnostic Systems. Science 240, 1285–1293. https://doi.org/10.1126/science.3287615 (1988).Article 
    ADS 
    MathSciNet 
    MATH 

    Google Scholar 
    Phillips, S. J., Anderson, R. P., Dudík, M., Schapire, R. E. & Blair, M. E. Opening the black box: an open-source release of Maxent. Ecography 40, 887–893. https://doi.org/10.1111/ecog.03049 (2017).Article 

    Google Scholar 
    Hijmans, R. J. raster: Geographic Data Analysis and Modeling. R package version 3.3–7 (2020).UNEP-WCMC and IUCN. Protected Planet: The World Database on Protected Areas (WDPA) and World Database on Other Effective Area-based Conservation Measures (WD-OECM). UNEP-WCMC and IUCN (2021). More

  • in

    Improving access to aquatic foods

    Bennett, A. et al. Nat. Food https://doi.org/10.1038/s43016-022-00642-4 (2022).Article 

    Google Scholar 
    Simmance, F. A. et al. Nat. Commun. 3, 174 (2022).
    Google Scholar 
    Kolding, J., van Zwieten, P., Martin, F., Funge-Smith, S. & Poulain, F. Freshwater Small Pelagic Fish and Their Fisheries in the Major African Lakes and Reservoirs in Relation to Food Security and Nutrition (Food and Agriculture Organization of the United Nations, 2019).Pradhan, S. K., Nayak, P. K. & Armitage, D. Curr. Res. Environ. Sustain. 4, 100128 (2022).Article 

    Google Scholar 
    Byrd, K. A., Pincus, L., Pasqualino, M. M., Muzofa, F. & Cole, S. M. Matern. Child Nutr. 17, e13192 (2021).Article 

    Google Scholar 
    Chiwaula, L. S., Chirwa, G. C., Binauli, L. S., Banda, J. & Nagoli, J. Agric. Food Econ. 6, 1–15 (2018).Article 

    Google Scholar 
    Cole, S. M. et al. Ecol. Soc. 23, 18 (2018).Article 

    Google Scholar 
    Manyungwa, C. L., Hara, M. M. & Chimatiro, S. K. Marit. Stud. 18, 275–285 (2019).Article 

    Google Scholar 
    Coates, J. et al. Food Policy 81, 82–94 (2018).Article 

    Google Scholar 
    Stevens, G. A. et al. Lancet Glob. Health 10, e1590–e1599 (2022).Article 

    Google Scholar 
    Hicks, C. C. et al. Nat. Food 3, 851–861 (2022).Article 

    Google Scholar  More

  • in

    A signal-like role for floral humidity in a nocturnal pollination system

    Kulahci, I. G., Dornhaus, A. & Papaj, D. R. Multimodal signals enhance decision making in foraging bumble-bees. Proc. Biol. Sci. 275, 797–802 (2008).
    Google Scholar 
    Goldshtein, A. et al. Reinforcement learning enables resource partitioning in foraging bats. Curr. Biol. 30, 4096–4102.e4096 (2020).CAS 

    Google Scholar 
    Skogen, K. A., Overson, R. P., Hilpman, E. T. & Fant, J. B. Hawkmoth pollination facilitates long-distance pollen dispersal and reduces isolation across a gradient of land-use change. Ann. Mo. Bot. Gard. 104, 495–511 (2019). 417.
    Google Scholar 
    Deng, J.-Y., van Noort, S., Compton, S. G., Chen, Y. & Greeff, J. M. Conservation implications of fine scale population genetic structure of Ficus species in South African forests. Ecol. Manag. 474, 118387 (2020).
    Google Scholar 
    Galizia, C. G. et al. Relationship of visual and olfactory signal parameters in a food-deceptive flower mimicry system. Behav. Ecol. 16, 159–168 (2004).
    Google Scholar 
    Gibernau, M., HossaertMcKey, M., Frey, J. & Kjellberg, F. Are olfactory signals sufficient to attract fig pollinators. Ecoscience 5, 306–311 (1998).
    Google Scholar 
    Kapustjansky, A., Chittka, L. & Spaethe, J. Bees use three-dimensional information to improve target detection. Naturwissenschaften 97, 229–233 (2010).ADS 
    CAS 

    Google Scholar 
    Hempel de Ibarra, N., Langridge, K. V. & Vorobyev, M. More than colour attraction: behavioural functions of flower patterns. Curr. Opin. Insect Sci. 12, 64–70 (2015).
    Google Scholar 
    Boff, S., Henrique, J. A., Friedel, A. & Raizer, J. Disentangling the path of pollinator attraction in temporarily colored flowers. Int. J. Trop. Insect Sci. 41, 1305–1311 (2021).
    Google Scholar 
    Leonard, A. S. & Papaj, D. R. ‘X’ marks the spot: the possible benefits of nectar guides to bees and plants. Funct. Ecol. 25, 1293–1301 (2011).
    Google Scholar 
    Dobson, H. E. M. & Bergström, G. The ecology and evolution of pollen odors. Plant Syst. Evol. 222, 63–87 (2000).CAS 

    Google Scholar 
    Raguso, R. A. Why are some floral nectars scented? Ecology 85, 1486–1494 (2004).
    Google Scholar 
    Corbet, S. A., Kerslake, C. J. C., Brown, D. & Morland, N. E. Can bees select nectar-rich flowers in a patch. J. Apic. Res. 23, 234–242 (1984).
    Google Scholar 
    Policha, T. et al. Disentangling visual and olfactory signals in mushroom-mimicking Dracula orchids using realistic three-dimensional printed flowers. N. Phytol. 210, 1058–1071 (2016).CAS 

    Google Scholar 
    Stout, J. C., Goulson, D. & Allen, J. A. Repellent scent-marking of flowers by a guild of foraging bumblebees (Bombus spp.). Behav. Ecol. Sociobiol. 43, 317–326 (1998).
    Google Scholar 
    Howell, A. D. & Alarcón, R. Osmia bees (Hymenoptera: Megachilidae) can detect nectar-rewarding flowers using olfactory cues. Anim. Behav. 74, 199–205 (2007).von Arx, M. Floral humidity and other indicators of energy rewards in pollination biology. Commun. Integr. Biol. 6, e22750–e22750 (2013).
    Google Scholar 
    Goyret, J. The breath of a flower: CO2 adds another channel-and then some-to plant-pollinator interactions. Commun. Integr. Biol. 1, 66–68 (2008).CAS 

    Google Scholar 
    Bradbury, J. W. & Vehrencamp, S. L. Principles of Animal Communication 2nd edn (Sinauer Associates, 2011).McMeniman, C. J., Corfas, R. A., Matthews, B. J., Ritchie, S. A. & Vosshall, L. B. Multimodal integration of carbon dioxide and other sensory cues drives mosquito attraction to humans. Cell 156, 1060–1071 (2014).CAS 

    Google Scholar 
    Smith, J. M. & Harper, D. Animal Signals (Oxford Univ. Press, 2003).Smith, M. J. & Harper, D. G. C. Animal signals: models and terminology. J. Theor. Biol. 177, 305–311 (1995).ADS 

    Google Scholar 
    Laidre, M. E. & Johnstone, R. A. Animal signals. Curr. Biol. 23, R829–R833 (2013).CAS 

    Google Scholar 
    Smith, J. M. Must reliable signals always be costly? Anim. Behav. 47, 1115–1120 (1994).
    Google Scholar 
    Guerenstein, P. G., A.Yepez, E., van Haren, J., Williams, D. G. & Hildebrand, J. G. Floral CO2 emission may indicate food abundance to nectar-feeding moths. Naturwissenschaften 91, 329–333 (2004).ADS 
    CAS 

    Google Scholar 
    Goyret, J., Markwell, P. M. & Raguso, R. A. Context- and scale-dependent effects of floral CO2 on nectar foraging by Manduca sexta. Proc. Natl Acad. Sci. USA 105, 4565–4570 (2008).ADS 
    CAS 

    Google Scholar 
    Thom, C., Guerenstein, P. G., Mechaber, W. L. & Hildebrand, J. G. Floral CO2 reveals flower profitability to moths. J. Chem. Ecol. 30, 1285–1288 (2004).CAS 

    Google Scholar 
    Gilbert, F. S., Haines, N. & Dickson, K. Empty flowers. Funct. Ecol. 5, 29–39 (1991).
    Google Scholar 
    von Arx, M., Goyret, J., Davidowitz, G. & Raguso, R. A. Floral humidity as a reliable sensory cue for profitability assessment by nectar-foraging hawkmoths. Proc. Natl Acad. Sci. USA 109, 9471–9476 (2012).ADS 

    Google Scholar 
    Harrap, M. J. M., Hempel de Ibarra, N., Knowles, H. D., Whitney, H. M. & Rands, S. A. Floral humidity in flowering plants: A preliminary survey. Front. Plant Sci. https://doi.org/10.3389/fpls.2020.00249 (2020).Harrap, M. J. M. & Rands, S. A. The role of petal transpiration in floral humidity generation. Planta 255, 78 (2022).CAS 

    Google Scholar 
    Harrap, M. J. M., Hempel de Ibarra, N., Knowles, H. D., Whitney, H. M. & Rands, S. A. Bumblebees can detect floral humidity. J. Exp. Biol. https://doi.org/10.1242/jeb.240861 (2021).Hebets, E. A. & Papaj, D. R. Complex signal function: developing a framework of testable hypotheses. Behav. Ecol. Sociobiol. 57, 197–214 (2005).
    Google Scholar 
    Bronstein, J. L., Huxman, T., Horvath, B., Farabee, M. & Davidowitz, G. Reproductive biology of Datura wrightii: the benefits of a herbivorous pollinator. Ann. Bot. 103, 1435–1443 (2009).
    Google Scholar 
    Johnson, C. A. et al. Coevolutionary transitions from antagonism to mutualism explained by the co-opted antagonist hypothesis. Nat. Commun. https://doi.org/10.1038/s41467-021-23177-x (2021).Clark, C. J. The role of power versus energy in courtship: what is the ‘energetic cost’ of a courtship display? Anim. Behav. 84, 269–277 (2012).
    Google Scholar 
    Willmott, A. P. & Ellington, C. P. The mechanics of flight in the hawkmoth Manduca sexta. I. Kinematics of hovering and forward flight. J. Exp. Biol. 200, 2705–2722 (1997).CAS 

    Google Scholar 
    Shields, V. D. C. & Hildebrand, J. G. Fine structure of antennal sensilla of the female sphinx moth, Manduca sexta (Lepidoptera: Sphingidae). II. Auriculate, coeloconic, and styliform complex sensilla. Can. J. Zool. 77, 302–313 (1999).
    Google Scholar 
    Lee, J. K. & Strausfeld, N. J. Structure, distribution and number of surface sensilla and their receptor cells on the olfactory appendage of the male moth Manduca sexta. J. Neurocytol. 19, 519–538 (1990).CAS 

    Google Scholar 
    Shields, V. D. & Hildebrand, J. G. Recent advances in insect olfaction, specifically regarding the morphology and sensory physiology of antennal sensilla of the female sphinx moth Manduca sexta. Microsc. Res. Tech. 55, 307–329 (2001).CAS 

    Google Scholar 
    Tichy, H. & Loftus, R. Hygroreceptors in insects and a spider: Humidity transduction models. Naturwissenschaften 83, 255–263 (1996).ADS 
    CAS 

    Google Scholar 
    Ahrens, M., Huang, K.-H., Narayan, S., Mensh, B. & Engert, F. Two-photon calcium imaging during fictive navigation in virtual environments. Front. Neural Circuits https://doi.org/10.3389/fncir.2013.00104 (2013).Lacher, V. Elektrophysiologische untersuchungen an einzelnen rezeptoren für geruch, kohlendioxyd, luftfeuchtigkeit und tempratur auf den antennen der arbeitsbiene und der drohne (Apis mellifica L.). Z. f.ür. Vgl. Physiologie 48, 587–623 (1964).
    Google Scholar 
    Waldow, U. Elektrophysiologische untersuchungen an feuchte-, trocken- und kälterezeptoren auf der antenne der wanderheuschrecke Locusta. Z. f.ür. Vgl. Physiologie 69, 249–283 (1970).
    Google Scholar 
    Yokohari, F. & Tateda, H. Moist and dry hygroreceptors for relative humidity of the cockroach, Periplaneta americana L. J. Comp. Physiol. 106, 137–152 (1976).
    Google Scholar 
    Tichy, H. Low rates of change enhance effect of humidity on the activity of insect hygroreceptors. J. Comp. Physiol. A Neuroethol. Sens Neural Behav. Physiol. 189, 175–179 (2003).CAS 

    Google Scholar 
    Tichy, H., Hellwig, M. & Kallina, W. Revisiting theories of humidity transduction: a focus on electrophysiological data. Front. Physiol. 8, 650 (2017).
    Google Scholar 
    Tichy, H. & Kallina, W. Insect hygroreceptor responses to continuous changes in humidity and air pressure. J. Neurophysiol. 103, 3274–3286 (2010).CAS 

    Google Scholar 
    Wolfin, M. S., Raguso, R. A., Davidowitz, G. & Goyret, J. Context dependency of in-flight responses by Manduca sexta moths to ambient differences in relative humidity. J. Exp. Biol. https://doi.org/10.1242/jeb.177774 (2018).Smith, G., Kim, C. & Raguso, R. A. Pollen accumulation on hawkmoths varies substantially among moth-pollinated flowers. Preprint at bioRxiv https://doi.org/10.1101/2022.07.15.500245 (2022).Haverkamp, A., Bing, J., Badeke, E., Hansson, B. S. & Knaden, M. Innate olfactory preferences for flowers matching proboscis length ensure optimal energy gain in a hawkmoth. Nat. Commun. 7, 11644 (2016).ADS 
    CAS 

    Google Scholar 
    Harrison, A. S. & Rands, S. A. The ability of bumblebees Bombus terrestris (hymenoptera: Apidae) to detect floral humidity is dependent upon environmental humidity. Environ. Entomol. 51, 1010–1019 (2022).
    Google Scholar 
    Kelber, A. What a hawkmoth remembers after hibernation depends on innate preferences and conditioning situation. Behav. Ecol. 21, 1093–1097 (2010).
    Google Scholar 
    Riffell, J. A. et al. Flower discrimination by pollinators in a dynamic chemical environment. Science 344, 1515–1518 (2014).ADS 
    CAS 

    Google Scholar 
    Schellenberg, R. The trouble with humidity: the hidden challenge of RH calibration. Cal. Lab. 9, 40–42 (2002).
    Google Scholar 
    Roddy, A. B., Brodersen, C. R. & Dawson, T. E. Hydraulic conductance and the maintenance of water balance in flowers. Plant Cell Environ. 39, 2123–2132 (2016).CAS 

    Google Scholar 
    Sane, S. P. & Jacobson, N. P. Induced airflow in flying insects. II. Measurement of induced flow. J. Exp. Biol. 209, 43–56 (2006).
    Google Scholar 
    Daly, K. C., Kalwar, F., Hatfield, M., Staudacher, E. & Bradley, S. P. Odor detection in Manduca sexta is optimized when odor stimuli are pulsed at a frequency matching the wing beat during flight. PLoS ONE 8, e81863 (2013).ADS 

    Google Scholar 
    Yokohari, F. Hygroreceptor mechanism in the antenna of the cockroach. Periplaneta. J. Comp. Physiol. 124, 153 (1978).
    Google Scholar 
    Loftus, R. Temperature-dependent dry receptor on antenna of Periplaneta. Tonic response. J. Comp. Physiol. 111, 153–170 (1976).
    Google Scholar 
    Tichy, H. & Kallina, W. Sensitivity of honeybee hygroreceptors to slow humidity changes and temporal humidity variation detected in high resolution by mobile measurements. PLoS ONE 9, e99032 (2014).ADS 

    Google Scholar 
    Galen, C., Sherry, R. A. & Carroll, A. B. Are flowers physiological sinks or faucets? Costs and correlates of water use by flowers of Polemonium viscosum. Oecologia 118, 461–470 (1999).ADS 

    Google Scholar 
    Elle, E., van Dam, N. M. & Hare, J. D. Cost of glandular trichomes, a “resistance” character in Datura wrightii regel (solanaceae). Evolution 53, 22–35 (1999).
    Google Scholar 
    Elle, E. & Hare, J. D. Environmentally induced variation in floral traits affects the mating system in Datura wrightii. Funct. Ecol. 16, 79–88 (2002).
    Google Scholar 
    Marler, C. A. & Ryan, M. J. Energetic constraints and steroid hormone correlates of male calling behaviour in the túngara frog. J. Zool. 240, 397–409 (1996).
    Google Scholar 
    Bernal, X. E., Rand, A. S. & Ryan, M. J. Acoustic preferences and localization performance of blood-sucking flies (Corethrella Coquillett) to túngara frog calls. Behav. Ecol. 17, 709–715 (2006).
    Google Scholar 
    Raguso, R. A. Flowers as sensory billboards: progress towards an integrated understanding of floral advertisement. Curr. Opin. Plant Biol. 7, 434–440 (2004).
    Google Scholar 
    Peach, D. A. H., Gries, R., Zhai, H., Young, N. & Gries, G. Multimodal floral cues guide mosquitoes to tansy inflorescences. Sci. Rep. 9, 3908 (2019).ADS 

    Google Scholar 
    Riffell, J. A. & Alarcón, R. Multimodal floral signals and moth foraging decisions. PLoS ONE 8, e72809 (2013).ADS 
    CAS 

    Google Scholar 
    van der Kooi, C. J., Kevan, P. G. & Koski, M. H. The thermal ecology of flowers. Ann. Bot. 124, 343–353 (2019).
    Google Scholar 
    Terry, L. I., Roemer, R. B., Walter, G. H., Booth, D. & Lee, K. P. Thrips’ responses to thermogenic associated signals in a cycad pollination system: the interplay of temperature, light, humidity and cone volatiles. Funct. Ecol. 28, 857–867 (2014).
    Google Scholar 
    Bronstein, J. L., Alarcón, R. & Geber, M. The evolution of plant–insect mutualisms. N. Phytol. 172, 412–428 (2006).
    Google Scholar 
    Schaefer, H. M. & Ruxton, G. D. Deception in plants: mimicry or perceptual exploitation. Trends Ecol. Evol. 24, 676–685 (2009).
    Google Scholar 
    Franchi, G. G., Nepi, M. & Pacini, E. Is flower/corolla closure linked to decrease in viability of desiccation-sensitive pollen? Facts and hypotheses: a review of current literature with the support of some new experimental data. Plant Syst. Evol. 300, 577–584 (2014).
    Google Scholar 
    Safavian, D. et al. High humidity partially rescues the Arabidopsis thaliana exo70A1 stigmatic defect for accepting compatible pollen. Plant Reprod. 27, 121–127 (2014).CAS 

    Google Scholar 
    Shivanna, K. R. & Cresti, M. Effects of high humidity and temperature stress on pollen membrane integrity and pollen vigour in Nicotiana tabacum. Sex. Plant Reprod. 2, 137–141 (1989).
    Google Scholar 
    Richman, S. K. et al. The sensory and cognitive ecology of nectar robbing. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2021.698137 (2021).Raguso, R. A. et al. Trumpet flowers of the Sonoran Desert: floral biology of Peniocereus Cacti and Sacred Datura. Int. J. Plant Sci. 164, 877–892 (2003).CAS 

    Google Scholar 
    Carazo, P. & Font, E. ‘Communication breakdown’: the evolution of signal unreliability and deception. Anim. Behav. 87, 17–22 (2014).
    Google Scholar 
    Schemske, D. W. Evolution of floral display in the orchid Brassavola nodosa. Evolution 34, 489–493 (1980).
    Google Scholar 
    Haber, W. A. Pollination by deceit in a mass-flowering tropical tree Plumeria rubra L. (apocynaceae). Biotropica 16, 269–275 (1984).
    Google Scholar 
    Brandenburg, A., Kuhlemeier, C. & Bshary, R. Hawkmoth pollinators decrease seed set of a low-nectar Petunia axillaris line through reduced probing time. Curr. Biol. 22, 1635–1639 (2012).CAS 

    Google Scholar 
    Bye, R. & Sosa, V. Molecular phylogeny of the jimsonweed genus Datura (solanaceae). Syst. Bot. 38, 818–829 (2013).
    Google Scholar 
    Kariñho-Betancourt, E., Agrawal, A. A., Halitschke, R. & Núñez-Farfán, J. Phylogenetic correlations among chemical and physical plant defenses change with ontogeny. N. Phytol. 206, 796–806 (2015).
    Google Scholar 
    Kawahara, A. Y. et al. Evolution of Manduca sexta hornworms and relatives: biogeographical analysis reveals an ancestral diversification in Central America. Mol. Phylogenet. Evol. 68, 381–386 (2013).
    Google Scholar 
    Contreras, H. L. et al. The effect of ambient humidity on the foraging behavior of the hawkmoth Manduca sexta. J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol. 199, 1053–1063 (2013).
    Google Scholar 
    Cardoso, J. C. F., Gonzaga, M. O., Cavalleri, A., Maruyama, P. K. & Alves-Silva, E. The role of floral structure and biotic factors in determining the occurrence of florivorous thrips in a dystilous shrub. Arthropod-Plant Interact. 10, 477–484 (2016).
    Google Scholar 
    Nicolson, S. W. Sweet solutions: nectar chemistry and quality. Philos. Trans. R. Soc. Lond. B Biol. Sci. 377, 20210163 (2022).CAS 

    Google Scholar 
    Pellmyr, O. & Thien, L. B. Insect reproduction and floral fragrances: keys to the evolution of the Angiosperms. Taxon 35, 76–85 (1986).
    Google Scholar 
    Enjin, A. et al. Humidity sensing in Drosophila. Curr. Biol. 26, 1352–1358 (2016).CAS 

    Google Scholar 
    Knecht, Z. A. et al. Distinct combinations of variant ionotropic glutamate receptors mediate thermosensation and hygrosensation in Drosophila. eLife 5, e17879 (2016).
    Google Scholar 
    Knecht, Z. A. et al. Ionotropic receptor-dependent moist and dry cells control hygrosensation in Drosophila. eLife 6, e26654 (2017).
    Google Scholar 
    Croset, V. et al. Ancient protostome origin of chemosensory ionotropic glutamate receptors and the evolution of insect taste and olfaction. PLoS Genet. 6, e1001064–e1001064 (2010).
    Google Scholar 
    Dahake, A. et al. MATLAB codes: a signal-like role for floral humidity in a nocturnal pollination system. Zenodo https://doi.org/10.5281/zenodo.7320037 (2022).Pereira, T. D. et al. Fast animal pose estimation using deep neural networks. Nat. Methods 16, 117–125 (2019).CAS 

    Google Scholar 
    Nilsson, S. R. et al. Simple behavioral analysis (SimBA) – an open source toolkit for computer classification of complex social behaviors in experimental animals. Preprint at bioRxiv https://doi.org/10.1101/2020.04.19.049452 (2020).Casey, T. M. Flight energetics of sphinx moths: power input during hovering flight. J. Exp. Biol. 64, 529–543 (1976).CAS 

    Google Scholar 
    Riffell, J. A. et al. Behavioral consequences of innate preferences and olfactory learning in hawkmoth-flower interactions. Proc. Natl Acad. Sci. USA 105, 3404–3409 (2008).ADS 
    CAS 

    Google Scholar 
    Lott, G. K., Johnson, B. R., Bonow, R. H., Land, B. R. & Hoy, R. R. g-PRIME: a free, windows based data acquisition and event analysis software package for physiology in classrooms and research labs. J. Undergrad. Neurosci. Educ. 8, A50–A54 (2009).
    Google Scholar 
    Chaure, F. J., Rey, H. G. & Quiroga, R. Q. A novel and fully automatic spike-sorting implementation with variable number of features. J. Neurophysiol. 120, 1859–1871 (2018).CAS 

    Google Scholar 
    Tichy, H. Humidity-dependent cold cells on the antenna of the stick insect. J. Neurophysiol. 97, 3851–3858 (2007).
    Google Scholar 
    Campbell, R. raacampbell/shadedErrorBar. https://github.com/raacampbell/shadedErrorBar (2022).Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. https://doi.org/10.18637/jss.v067.i01 (2015).Broadhead, G. T. & Raguso, R. A. Associative learning of non-sugar nectar components: amino acids modify nectar preference in a hawkmoth. J. Exp. Biol. https://doi.org/10.1242/jeb.234633 (2021). More

  • in

    Anthrax hotspot mapping in Kenya support establishing a sustainable two-phase elimination program targeting less than 6% of the country landmass

    Data sourcesThis study builds on two datasets; 666 livestock anthrax outbreaks collected over 60 years (1957–2017) by the Kenya Directorate of Veterinary Services (KDVS), and 13 reported anthrax outbreaks we investigated between 2017 and 201811,13. These datasets were combined with data from targeted active anthrax surveillance we conducted in 2019–2020 (see below) to define anthrax suitable areas in Kenya, including hotspots, and subsequently assessed effectiveness of livestock vaccination as a control strategy.Targeted active surveillance-collected anthrax data, 2019–2020Active anthrax surveillance was conducted for 12 months between 2019 and 2020 in randomly selected areas to ensure representation of all AEZs of the country. AEZs are land units defined based on the patterns of soil, landforms and climatic characteristics. Kenya has seven AEZs that include agro-alpine, high potential, medium potential, semi-arid, arid, very-arid and desert. In 2013, Kenya devolved governance into 47 semi-autonomous counties that are subdivided into 290 subcounties which are in turn divided into 1450 administrative wards, the smallest administrative units in the country. Using a geographic map that condensed Kenya into five AEZs; agro-alpine, high potential, medium potential, semi-arid, and arid/very arid zones, we randomly selected 4 administrative sub-counties from each AEZ (N = 20). To increase geographic spread of the study and enhance detection of anthrax outbreaks, we surveilled the larger administrative county (consisting of 20 to 45 administrative wards) where the randomly selected sub-counties were located. As shown in Fig. S1, we ultimately carried out the active anthrax surveillance in 18 counties, containing 523 administrative wards, the latter being used for measuring spatial association (see below).We conducted the surveillance between April 2019 and June 2020, through 523 animal health practitioners (AHPs), one in each ward, after intensive training to identify anthrax using a standard case definition, and to collect and electronically transmit the data weekly using telephone-based short messaging system (SMS) to a central server hosted by KDVS. Regarding case definition, any livestock death classified as anthrax through clinical or laboratory diagnosis was considered an anthrax event. Using standard guidelines issued by the KDVS, a clinical diagnosis was made by the AHPs across the country as an acute cattle, sheep or goat disease characterized by sudden death with or without bleeding from natural orifices, accompanied by absence of rigor mortis. Further, if the carcass was accidentally opened, failure of blood to clot and/or the presence of splenomegaly were included. In pigs, symptoms included swelling of the face and neck with oedema. A laboratory confirmed anthrax was diagnosed using Gram and methylene blue stains followed by identification of the capsule and typical rod-shaped B. anthracis in clinical specimens that the AHPs submitted to the central or regional veterinary investigation laboratories in Kenya. One case of anthrax in either species was considered an outbreak.During the surveillance, the programmed server sent prompting texts directly to the AHPs’ cell phones every Friday of each week for the 52 weeks. The AHPs interacted with the platform by responding to prompting questions sent via SMS to their telephones. Data were securely stored in an online encrypted platform which was subsequently downloaded into Ms Excel for analysis. This surveillance detected 119 anthrax outbreaks, whose partial data were used to model effects of climate change on future anthrax distribution in Kenya14. Here, we integrated these active surveillance data with other datasets to conduct detailed ENM and kernel-smoothed density mapping with a goal of refining suitable anthrax areas including crystalizing hotspots in the country.Anthrax outbreak incidence per livestock population by countyWe knew the total number of livestock per county and wards by species for the active surveillance period. The counties represented the level of disease management including vaccine distribution while the wards within counties represented the modeling unit for targeting control. Therefore, we estimated the outbreak incidence as the total number of outbreaks per livestock species per 100,000 head of that species.Ecological niche modeling and validationWe used boosted regression tree (BRT) algorithm as previously published13. In those studies, we estimated the geographic distribution of anthrax in southern Kenya using 69 spatially unique outbreak points (thinned from the 86 outbreaks in the records) and 18 environmental variables resampled to 250 m resolution. In this study, the final experiments were run with a learning rate (lr) = 0.001, bagging fraction (br) = 5, and maximum tree = 2500. We then mapped anthrax suitability as the mean output of the 100 experiments and the lower 2.5% and upper 97.5% mapped as confidence intervals. We determined variable contribution and derived partial dependence as previously described13. As BRTs are a random walk and each experiment randomly resamples training and test data, it was necessary to repeat those outputs along with the map predictions.Here, our goal was to evaluate the BRT models built with records data from 2011 to 2017 data and use the predict function to calculate model accuracy metrics using the 2017–2020 outbreaks as presence points and the sub-counties reporting zero outbreaks during the 2019–2020 active surveillance period as absence points. The model of southern Kenya was projected onto all of Kenya using climate variables clipped to the whole of Kenya. We tested the BRT models in two ways; first, evaluating 2011–2017 data models with holdout data using a random resampling and multi-modeling approach. Here, we report the area under curve (AUC) for each of the original training/testing split into the 69 historical points and the 2017–2020 data serving as independent data, the latter representing true model validation. Second, to determine the total percentage of surveillance data predicted and map areas of anthrax suitability to compare with kernel density estimates (see below), we produced a dichotomized map using the Youden index cutoff17 following Otieno et al.14.Outbreak concentrations from kernel density estimation (KDE)To describe the spatial concentration of reported outbreaks, we calculated descriptive spatial statistics, including the spatial mean, standard distance, and standard deviational ellipse of outbreak locations from the prospective surveillance dataset following Blackburn et al.18 These spatial statistics help to differentiate the geographic focus (spatial mean) and dispersion of outbreak reports from year to year and across the sampling period. We then conducted kernel density estimation (KDE) to visualize the concentration of anthrax outbreaks per square kilometer per year and across the study period18. We used the spatstat package for all KDE analyses using the quadratic kernel function19:$$fleft( x right) = frac{1}{{nh^{2} }} mathop sum limits_{i = 1}^{n} Kleft( {frac{{x – X_{i} }}{h}} right)$$where h is the bandwidth, x-Xi is the distance to each anthrax outbreak i. Finally, K is the quadratic kernel function, defined as:$$Kleft( x right) = frac{3}{4}left( {1 – x^{2} } right), left| x right| le 1$$$$Kleft( x right) = 0,x > 1$$This function was employed to estimate anthrax outbreak concentration across space using each outbreak weighted as one. We calculated the bandwidth (kernel) using hopt that uses the sample size (number of outbreaks) and the standard distance to estimate bandwidth. Finally, we estimated bandwidth for each year and then averaged them to apply the same fixed bandwidth for each year under study in Q-GIS version 3.1.8. The resulting outputs were map surfaces representing the spatial concentrations of outbreaks across the country per 1 km2 for each study year and all study years combined. For this study, we used the cutoff criteria of Nelson and Boots19 to identify outbreak hotspots as areas with density values in the upper 25%, 10%, and 5% of outbreak concentrations. The analyses identified these areas by year (2017–2020) and for all surveillance years combined.Local spatial clustering at the ward levelAnthrax outbreak incidence per livestock speciesThe ENM and KDE-derived maps provide a first estimate of potential risk and outbreak concentration, respectively. We were also interested in estimating anthrax outbreak intensity relative to livestock populations at a local level. For the active surveillance period, we knew the total number of outbreaks per ward (the smallest administrative spatial unit) by livestock species. For this two-year period, we estimated the ward-level outbreak incidence as the total number of outbreaks per livestock species per 10,000 head of that species. To estimate livestock population per ward, we extracted the values in the raster file of the areal weighted gridded livestock of the world data using the zonal statistic routine in Q-GIS version 3.1.8, into the polygon consisting of all pixels per ward as the total population19,20. We calculated outbreak incidence as the number of outbreaks per ward cattle population per 10,000 cattle for each administrative ward. We limited this analysis to those 18 counties participating in the active surveillance study (Fig. S1), as we could appropriately assume any ward with no reports was a ‘true zero’ for the estimation. Given that most reported outbreaks were in domestic cattle (see results below), we here report those results involving cattle alone. Given the overall high number of wards and the high number of wards without outbreaks, we performed the empirical Bayes smoothing and spatial Bayes smoothing routines in GeoDa version 1.12.1.161 to reduce the variance in anthrax incidence estimates20,21. To evaluate smoothing routine performance, we box plotted rates per ward and selected the method with the greatest reduction in outliers21. Smoothed rates were mapped as choropleth map in Q-GIS version 3.1.8 using the four equal area bins.Spatial cluster analysisWe used Local Moran’s I16 to test for spatial cluster of livestock anthrax in cattle using the smoothed outbreak incidence estimates. The Local Moran’s I statistic tests whether individual wards are part of spatial cluster, like incidence estimates surrounded by similar estimate (high-high or low-low) or spatial outliers where wards with significantly high or low estimates are surrounded by dissimilar values (high-low or low–high). The local Moran’s I is written as16:$$I_{i} = Z_{i} sum W_{ij} Z_{j}$$where Ii is the statistic for a ward i, Zi is the difference between the incidence at i and the mean anthrax incidence rate for all of wards in the study, Zj is the difference between anthrax risk at ward j and the mean for all wards. Wij is the weights matrix. In this study, the 1st order queen contiguity was employed. Here, Wij equals 1/n if a ward shared a boundary or vertex and 0 if not. For this study, Local Moran’s I was performed on the wards using 999 permutations and p = 0.05 using GeoDa version 1.12.1.161.Assessing effectiveness of cattle vaccination in burden hotspotsAs a first estimate of how we might scale up livestock anthrax vaccination efforts in Kenya, we slightly adjusted a simple published anthrax outbreak simulation model in a cattle population. For this study we applied an early mathematical approach of Funiss and Hahn22 to simulate anthrax at the ward level. While other recent models are available23,24, these are difficult to parameterize or require time series data we could not derive with the surveillance approach in this study. Like the more recent models, Funiss and Hahn22 assumed anthrax transmission was driven by cattle accessing spore-contaminated environments. Here the proportion of infected cattle each day depended on the population of susceptible animals in the population and probability of getting infected. This probability depends on environmental contamination (“a”), and a fraction of anthrax carcasses in the environment on a day (“f,”). Each day, the newly infected cattle are transferred to an incubation period vector, “d,” waiting to die following a probability “p”. In this model, all infected animals, “n,” die following the incubation periods given by the vector, “p”, in which pi is the probability of a cow dying i days after the infection. Following death, the cattle are transferred to a carcass state, providing a direct infection source to the susceptible cattle via environmental contamination. Environmental contamination “a,” is therefore defined as the number of spores ingested by an animal in a day. This environmental contamination depends on spores from carcasses and an assumed spore decay rate γ22.The complete set of difference equations with a daily time step is given by:$${text{S}}_{(t + 1)} = {text{S}}_{(t)} – {text{ S}}_{(t)} *left( {{1} – {text{e}}^{{ – left( {{text{a}}_{t} + gamma {text{f}}_{{{text{t}} + 1}} } right)}} } right)$$$${text{I}}_{(t + 1)} = {text{I}}_{(t)} + {text{ S}}_{(t)} *left( {{1} – {text{e}}^{{ – left( {{text{a}}_{{text{t}}} + gamma {text{f}}_{{{text{t}} + {1}}} } right)}} } right)$$where the expression (left( {{1} – {text{e}}^{{ – left( {{text{a}}_{t} + gamma {text{f}}_{{{text{t}} + 1}} } right)}} } right)) denotes the probability of an animal becoming infected and at + γft+1 is the mean number of spores ingested by a cow in a day. The equation for environmental contamination, a, is given by:$${text{a}}_{t + 1} {-}{text{a}}_{{text{t}}} = alpha {text{a}}_{{text{t}}} + beta {text{c}}_{{{text{t}} + {1}}}$$The newly infected animals die after a certain number of days. The distribution of incubation periods is given by the vector, p. On each day, the new cases are placed in a due-to-die vector, d, and when they die, they are subsequently moved down one step to fresh carcasses, ft. The fresh carcasses provide a direct source of infection to the susceptible cattle via the ‘fresh carcass term’, γ. These carcasses decay or are scavenged or disposed by man. The equation expressing the disseminating carcasses, c, is:$${text{C}}_{t + 1} – {text{c}}_{t} = {text{f}}_{t + 1} – delta {text{c}}_{t}$$The model parameters variables are provided in Table 1 and are similar to those used by Funiss and Hahn22 to generate a standard run. We ran the model for one year and extrapolated to cattle population in the identified hotspot wards.Table 1 Model parameters and variables.Full size table More