Ecology

In this picture, I’m face to face with an anaesthetized 250-kilogram male grizzly bear (Ursus arctos horribilis), which was caught near Sparwood and Elkford in Canada. With help from conservation inspector Joe Caravetta, who is sitting next to me, and my field technician Laura Smit, I’m putting a GPS-enabled collar on the bear so that we can track his movements.The first time I worked with a bear this size, it was absolutely exhilarating, a real adrenaline rush. I thought, “My whole head could fit inside this animal’s jaws.” Over time, it has become fairly routine. I learnt to trust the anaesthetic — a mix of drugs given using an air-powered dart gun — and we constantly monitor the bears’ vital signs.While I’m attaching the collar, Laura collects hair samples for genetic studies. We measure the bear’s temperature and oxygen levels, and take hair samples to get an idea of his diet. We weigh him, which is quite a challenge: we use a custom-made tarpaulin with handles to wrap him up like a bear taco. We attach the handles to a hanging scale and, with a rope over a tree branch, winch him up. This particular bear is eight years old and has 29% body fat, which is very healthy for spring.Ultimately, the collars will help us to reduce conflict between bears and the people who live in the area — I’ve seen bears rip shed doors off to get to livestock, and peel open an outdoor freezer like a can of sardines.At times, it’s chaos for both humans and bears, and people react by shooting the bear — the most common cause of death for younger ones. Tracking bears with collars will help us to find solutions.From tracking the bears, we’ve learnt that they are adapting their habits to avoid people, and they become more nocturnal as they get older. We’ve helped local communities to adapt, too: we’ve launched cost-share initiatives for electrical fencing, which is a really effective bear deterrent. More

Stanier, R. Y. & Van Niel, C. B. Arch. Mikrobiol. 42, 17–35 (1962).CAS 
Article 

Google Scholar 
Schavemaker, P. E. & Muñoz-Gómez, S. A. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-022-01833-9 (2022).Article 

Google Scholar 
Lane, N. & Martin, W. F. Nature 467, 929–934 (2010).CAS 
Article 

Google Scholar 
Lynch, M. & Marinov, G. K. PNAS 112, 15690–15695 (2015).CAS 
Article 

Google Scholar 
Cavalier-Smith, T. & Chao, E. E. Protoplasma 257, 621–753 (2020).CAS 
Article 

Google Scholar 
Zachar, I. & Szathmáry, E. Biol. Direct 12, 19 (2017).Article 

Google Scholar 
Cavalier-Smith, T. Cold Spring Harb. Perspect. Biol. 6, 1–31 (2014).Article 

Google Scholar 
de Duve, C. Nat. Rev. Genet. 8, 395–403 (2007).Article 

Google Scholar 
Shiratori, T., Suzuki, S., Kakizawa, Y. & Ishida, K. Nat. Commun. 10, 5529 (2019).Article 

Google Scholar 
Martin, W. F., Tielens, A. G. M., Mentel, M., Garg, S. G. & Gould, S. B. Microbiol. Mol. Biol. Rev. 81, 8–17 (2017).Article 

Google Scholar 
Jékely, G. Biol. Direct 2, 3 (2007).Article 

Google Scholar 
Stanier, R. Y. Some aspects of the biology of cells and their possible evolutionary significance. Organization and Control in Prokaryotic and Eukaryotic Cells. In Proc. 20th Symposium of the Society for General Microbiology (eds Charles, H. P. & Knight, B. C. J. G) 20, 1–38 (Cambridge University Press, Cambridge, 1970).Zachar, I., Szilágyi, A., Számadó, S. & Szathmáry, E. PNAS USA 115, E1504–E1510 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Burns, J. A., Pittis, A. A. & Kim, E. Nat. Ecol. Evol. 2, 697–704 (2018).Article 

Google Scholar 
Bremer, N., Tria, F. D. K., Skejo, J., Garg, S. G. & Martin, W. F. Genome Biol. Evol. 14, evac079 (2022).Article 

Google Scholar 
Imachi, H. et al. Nature 577, 519–525 (2020).CAS 
Article 

Google Scholar 
Zachar, I. & Boza, G. Cell. Mol. Life Sci. 77, 3503–3523 (2020).CAS 
Article 

Google Scholar 
Devos, D. P. Mol. Biol. Evol. 38, 3531–3542 (2021).CAS 
Article 

Google Scholar  More

Effects of grid spacing on habitat hydraulic complexity metricsThe sensitivity of the habitat hydraulic complexity metrics to Δs was examined by calculating the metrics for Δs = 0.06, 0.12, 0.18, and 0.24 m (for M4, Δs = Δx = Δy). Figure 3 shows the variation of the metrics with grid spacing for scenarios with boulders. A preliminary assessment of no-boulder scenarios (S1-L and S1-H) showed that all the metrics decreased by increasing the grid spacing. However, because the metrics are mostly used in complex rather than non-obstructed and 1-D flows, the plots only include scenarios with boulder placement to highlight the effects of grid spacing on the metrics in complex flows. All the metrics generally decreased as Δs increased. At the low flow rate, by changing the Δs from the smallest to largest, i.e., 0.06 m to 0.024, the mean decreases in the M1, M2, and M4 metrics (averaged over all the scenarios with boulders) were 45.1, 9.9, and 74.7%, respectively. At the high flow rate, these reductions were 34.8, 14.7, and 82.5% for M1, M2, and M4, respectively. Table 2 shows the p-values associated with the changes in the metrics due to increasing Δs from 0.06 to 0.24 m for all scenarios. The table indicates that changes in M1 and M4 were statistically significant while for M2 they were not (p-values  > 0.05 for all scenarios except for S2-H). This result indicated the considerable influence of grid spacing on M1 and M4 metrics in the reaches with boulder placement. Additionally, the differences in the reported average reductions due to changing the flow rate were less than 10%, indicating an insubstantial effect of flow rate on the habitat hydraulic complexity metrics’ sensitivity to the grid spacing. The significant sensitivity of the metrics M1 and M4 to the grid spacing in this study is contrary to the findings of a previous study in which an insignificant correlation was found between the habitat hydraulic complexity metrics and Δs29. This difference can be attributed to different topographic features in the studied reaches. In the previous findings, measurements were mainly taken around the bends and reaches with no significant obstruction29, in which a more uniform flow with smaller velocity gradients is expected. However, in this study, the systematic boulder placement generated more complex flow patterns with noticeable velocity gradients. Therefore, due to the variations of flow velocities in the zone studied, substantially different values for the metrics are anticipated by computing the metrics over different spatial scales.Figure 3Variation of the habitat hydraulic complexity metrics with grid spacing (Δs) for scenarios with boulder placement. (a) kinetic energy gradient metric, M1, (b) normalized kinetic energy gradient metric, M2, (c) modified recirculation metric M4.Full size imageTable 2 p-values associated with changing the grid spacing from 0.06 to 0.24 m.Full size tableStatistical distribution of habitat hydraulic complexity metricsTable 3 lists the mean, minimum, maximum, and standard deviations of the habitat hydraulic complexity metrics (Δs = 0.06 m) for all the scenarios. To complement the results from Table 3 and assess whether the influences of solely changing the boulder concentration or flow rate on the metrics were statistically significant, Table 4 shows p-values associated with changing flow rate from low to high for a given boulder concentration, and Table 5 shows p-values associated with gradually increasing the boulder concentration for a given flow rate.Table 3 The statistical parameters of the habitat hydraulic complexity metrics in the detailed measurement zone.Full size tableTable 4 p-values from a t-test associated with changes in flow rate for a given boulder concentration.Full size tableTable 5 p-values from a t-test associated with changes in boulder concertation for a given flow rate.Full size tableFor metric M1, the mean M1 values for scenarios incorporating boulders showed the same order of magnitude as values from previous studies for reaches with single and multiple boulders27 but they were about one order of magnitude larger than calculated values in the confluence of two rivers11. Using a larger grid spacing in the study in the confluence of two rivers11 can be the reason for this difference. For a scenario at the higher flow rate, the mean M1 was on average (averaged for all the scenarios) 36% greater than its counterpart at the lower flow rate and this change in M1 values was statistically significant with p  More

Pecl, G. T. et al. Biodiversity redistribution under climate change: Impacts on ecosystems and human well-being. Science (80-) https://doi.org/10.1126/science.aai9214 (2017).Article 

Google Scholar 
Walther, G. R. et al. Ecological responses to recent climate change. Nature 416, 389–395 (2002).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Thuiller, W. et al. Consequences of climate change on the tree of life in Europe. Nature 470, 531–534 (2011).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Zimmermann, N. E., Edwards, T. C. Jr., Graham, C. H., Pearman, P. B. & Svenning, J. New trends in species distribution modelling. Ecography (Cop.) 33, 985–989 (2010).Article 

Google Scholar 
Norberg, A. et al. A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels. Ecol. Monogr. 89, e01370 (2019).Article 

Google Scholar 
Smeraldo, S. et al. Generalists yet different: Distributional responses to climate change may vary in opportunistic bat species sharing similar ecological traits. Mamm. Rev. 51, 571–584 (2021).Article 

Google Scholar 
Sohlström, E. H. et al. Future climate and land-use intensification modify arthropod community structure. Agric. Ecosyst. Environ. 327, 107830 (2022).Article 
CAS 

Google Scholar 
Araújo, M. B. & New, M. Ensemble forecasting of species distributions. Trends Ecol. Evol. 22, 42–47 (2007).PubMed 
Article 

Google Scholar 
Stohlgren, T. J. et al. Ensemble habitat mapping of invasive plant species. Risk Anal. Int. J. 30, 224–235 (2010).Article 

Google Scholar 
Meller, L. et al. Ensemble distribution models in conservation prioritization: from consensus predictions to consensus reserve networks. Divers. Distrib. 20, 309–321 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Dubuis, A. et al. Improving the prediction of plant species distribution and community composition by adding edaphic to topo-climatic variables. J. Veg. Sci. 24, 593–606 (2013).Article 

Google Scholar 
Walthert, L. & Meier, E. S. Tree species distribution in temperate forests is more influenced by soil than by climate. Ecol. Evol. 7, 9473–9484 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Figueiredo, F. O. G. et al. Beyond climate control on species range: The importance of soil data to predict distribution of Amazonian plant species. J. Biogeogr. 45, 190–200 (2018).Article 

Google Scholar 
Arar, A., Nouidjem, Y., Bounar, R., Tabet, S. & Kouba, Y. Potential future changes of the geographic range size of Juniperus phoenicea in Algeria based on present and future climate change projections. Contemp. Probl. Ecol. 13, 429–441 (2020).Article 

Google Scholar 
Coudun, C., Gégout, J., Piedallu, C. & Rameau, J. Soil nutritional factors improve models of plant species distribution: An illustration with Acer campestre (L.) in France. J. Biogeogr. 33, 1750–1763 (2006).Article 

Google Scholar 
Buri, A. et al. What are the most crucial soil variables for predicting the distribution of mountain plant species? A comprehensive study in the Swiss Alps. J. Biogeogr. 47, 1143–1153 (2020).Article 

Google Scholar 
Buri, A. et al. Soil factors improve predictions of plant species distribution in a mountain environment. Prog. Phys. Geogr. 41, 703–722 (2017).Article 

Google Scholar 
Mod, H. K., Scherrer, D., Luoto, M. & Guisan, A. What we use is not what we know: environmental predictors in plant distribution models. J. Veg. Sci. 27, 1308–1322 (2016).Article 

Google Scholar 
Scherrer, D. & Guisan, A. Ecological indicator values reveal missing predictors of species distributions. Sci. Rep. 9, 1–8 (2019).ADS 
CAS 
Article 

Google Scholar 
Boulos, L. Flora of Egypt, Vol. 1. vol. 1 (Al Hadara Publishing, 1999).Farjon, A. & Filer, D. An atlas of the world’s conifers: An analysis of their distribution, biogeography, diversity and conservation status. (Brill, 2013).Allen, DJ. Juniperus phoenicea. The IUCN red list of threatened species 2017: e.T16348983A99965052. https://doi.org/10.2305/IUCN.UK.2017-2.RLTS. T16348983A99965052.en. Downloaded on 19 May 2020El-Bana, M., Shaltout, K., Khalafallah, A. & Mosallam, H. Ecological status of the Mediterranean Juniperus phoenicea L. relicts in the desert mountains of North Sinai Egypt. Flora-Morphol. Distrib. Funct. Ecol. Plants 205, 171–178 (2010).Article 

Google Scholar 
Moustafa, A. et al. Ecological Prominence of Juniperus phoenicea L. Growing in Gebel Halal, North Sinai Egypt. Catrina Int. J. Environ. Sci. 15, 11–23 (2016).
Google Scholar 
Farahat, E. A. Age structure and static life tables of the endangered Juniperus phoenicea L. in North Sinai Mountains, Egypt. J. Mt. Sci. 17, 2170–2178 (2020).Article 

Google Scholar 
El-Wahab, A. Condition assessment of plant diversity of Gebel Maghara, North Sinai, Egypt. Catrina Int. J. Environ. Sci. 3, 21–40 (2008).
Google Scholar 
Youssef, A. M., Morsy, A. A., Mosallam, H. A. & Hashim, A. M. Vegetation and soil relationships in some wadis from the North-Central part of Sinai Peninsula Egypt. Minia Sci. Bull. 25, 1–28 (2014).
Google Scholar 
Fisher, M. Decline in the juniper woodlands of Raydah Reserve in southwestern Saudi Arabia: A response to climate changes?. Glob. Ecol. Biogeogr. Lett. 6, 379–386 (1997).Article 

Google Scholar 
Salvà-Catarineu, M. et al. Past, present, and future geographic range of the relict Mediterranean and Macaronesian Juniperus phoenicea complex. Ecol. Evol. 11, 5075–5095 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Quevedo, L., Rodrigo, A. & Espelta, J. M. Post-fire resprouting ability of 15 non-dominant shrub and tree species in Mediterranean areas of NE Spain. Ann. For. Sci. 64(8), 883–890 (2007).Article 

Google Scholar 
Trabucco, A. & Zomer, R. J. Global aridity index (global-aridity) and global potential evapo-transpiration (global-PET) geospatial database. CGIAR Consort. Spat. Inf. 89, 1–2 (2009).
Google Scholar 
Hengl, T. et al. SoilGrids1km—Global soil information based on automated mapping. PLoS One 9, e105992 (2014).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kennedy, C. M., Oakleaf, J. R., Theobald, D. M., Baruch-Mordo, S. & Kiesecker, J. Documentation for the global human modification of terrestrial systems (2020).Naimi, B. & Araújo, M. B. sdm: a reproducible and extensible R platform for species distribution modelling. Ecography (Cop.) 39, 368–375 (2016).Article 

Google Scholar 
Naimi, B. usdm: Uncertainty analysis for species distribution models. R Packag. Version 1, 1–12 (2015).
Google Scholar 
Guisan, A., Thuiller, W. & Zimmermann, N. E. In Habitat Suitability and Distribution Models: With Applications in R. (Cambridge University Press, 2017).Dakhil, M. A. et al. Global invasion risk assessment of Prosopis juliflora at biome level : Does soil matter?. Biology 10, 203 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Iturbide, M., Bedia, J. & Gutiérrez, J. M. Background sampling and transferability of species distribution model ensembles under climate change. Glob. Planet. Change 166, 19–29 (2018).ADS 
Article 

Google Scholar 
Barbet-Massin, M., Jiguet, F., Albert, C. H. & Thuiller, W. Selecting pseudo-absences for species distribution models: How, where and how many?. Methods Ecol. Evol. 3, 327–338 (2012).Article 

Google Scholar 
Zhang, Z., Mammola, S., Xian, W. & Zhang, H. Modelling the potential impacts of climate change on the distribution of ichthyoplankton in the Yangtze Estuary, China. Divers. Distrib. 26, 126–137 (2020).Article 

Google Scholar 
Thuiller, W., Guéguen, M., Renaud, J., Karger, D. N. & Zimmermann, N. E. Uncertainty in ensembles of global biodiversity scenarios. Nat. Commun. 10, 1–9 (2019).CAS 
Article 

Google Scholar 
Breiner, F. T., Nobis, M. P., Bergamini, A. & Guisan, A. Optimizing ensembles of small models for predicting the distribution of species with few occurrences. Methods Ecol. Evol. 9, 802–808 (2018).Article 

Google Scholar 
Liu, C., Newell, G. & White, M. On the selection of thresholds for predicting species occurrence with presence-only data. Ecol. Evol. 6, 337–348 (2016).PubMed 
Article 

Google Scholar 
Haider, S. M., Benscoter, A. M., Pearlstine, L., D’Acunto, L. E. & Romañach, S. S. Landscape-scale drivers of endangered Cape Sable Seaside Sparrow (Ammospiza maritima mirabilis) presence using an ensemble modeling approach. Ecol. Modell. 461, 109774 (2021).Article 

Google Scholar 
Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43, 1223–1232 (2006).Article 

Google Scholar 
Franklin, J. Mapping Species Distributions: Spatial Inference and Prediction (Cambridge University Press, 2010).Book 

Google Scholar 
Kabiel, H. F., Hegazy, A. K., Lovett-Doust, L., Al-Rowaily, S. L. & Al Borki, A. E. N. S. Ecological assessment of populations of Juniperus phoenicea L. in the Al-Akhdar mountainous landscape of Libya. Arid L. Res. Manag. 30, 269–289 (2016).Article 

Google Scholar 
Camarero, J. J. et al. Dieback and mortality of junipers caused by drought: Dissimilar growth and wood isotope patterns preceding shrub death. Agric. For. Meteorol. 291, 108078 (2020).ADS 
Article 

Google Scholar 
Sánchez-Salguero, R. & Camarero, J. J. Greater sensitivity to hotter droughts underlies juniper dieback and mortality in Mediterranean shrublands. Sci. Total Environ. 721, 137599 (2020).ADS 
PubMed 
Article 
CAS 

Google Scholar 
Cramer, W. et al. Climate change and interconnected risks to sustainable development in the Mediterranean. Nat. Clim. Chang. 8, 972–980 (2018).ADS 
Article 

Google Scholar 
Forzieri, G. et al. Ensemble projections of future streamflow droughts in Europe. Hydrol. Earth Syst. Sci. 18, 85–108 (2014).ADS 
Article 

Google Scholar 
González-Hidalgo, J. C. et al. High-resolution spatio-temporal analyses of drought episodes in the western Mediterranean basin (Spanish mainland, Iberian Peninsula). Acta Geophys. 66, 381–392 (2018).ADS 
Article 

Google Scholar 
Stockhecke, M. et al. Millennial to orbital-scale variations of drought intensity in the Eastern Mediterranean. Quat. Sci. Rev. 133, 77–95 (2016).ADS 
Article 

Google Scholar 
Navarro Cerrillo, R. M. et al. Can habitat prediction models contribute to the restoration and conservation of the threatened tree Abies pinsapo Boiss. in Southern Spain?. New For. 52, 89–112 (2021).Article 

Google Scholar  More

Brooke, S. & Young, C. M. In situ measurement of survival and growth of Lophelia pertusa in the northern Gulf of Mexico. Mar. Ecol. Prog. Ser. 397, 153–161 (2009).ADS 

Google Scholar 
Reyes, J., Santodomingo, N. & Florez, P. Corales Escleractinios de Colombia. (Invemar Serie de Publicaciones Especiales, 2010).Alonso, D. et al. Behind the scenes for the designation of the Corales de Profundidad national natural park of Colombia. Front. Mar. Sci. 8, 1147 (2021).
Google Scholar 
Hughes, J. A., Menot, L. & Levin, L. Habitat classification and mapping on deep continental margins. Research and Consultancy Report, No 54. COMARGE Workshop (2008).Rogers, A. The biology, ecology and vulnerability of deep-water coral reefs. International Union for Conservation of Nature and Natural Resources (2004).Maier, C., Hegeman, J., Weinbauer, M. G. & Burg, D. Calcification of the cold-water coral Lophelia pertusa under ambient and reduced pH. Biogeosciences 1, 1671–1680 (2009).ADS 

Google Scholar 
DeLeo, D. M., Glazier, A., Herrera, S., Barkman, A. & Cordes, E. E. Transcriptomic responses of deep-sea corals experimentally exposed to crude oil and dispersant. Front. Mar. Sci. 8, 1–17 (2021).
Google Scholar 
Buddemeier, R., Kleypas, J. A. & Aronson, R. B. Potential contributions of climate change to stresses on coral reef ecosystems. Coral Reefs Global Clim. Change 15, 17789 (2004).
Google Scholar 
Schmidt, C. A. et al. Faster crystallization during coral skeleton formation correlates with resilience to ocean acidification. J. Am. Chem. Soc. 144, 1332–1341 (2022).CAS 
PubMed 
PubMed Central 

Google Scholar 
Bors, E. K. et al. Patterns of deep-sea genetic connectivity in the New Zealand Region: Implications for management of benthic ecosystems. PLoS One 7, 11047 (2012).
Google Scholar 
Hernández-Ávila, I. Patterns of deep-water coral diversity in the Caribbean basin and adjacent southern waters: An approach based on records from the R/V Pillsbury Expeditions. PLoS ONE 9, 11 (2014).
Google Scholar 
Alonso, D. et al. Corales de Profundidad: descripción de comunidades coralinas y fauna asociada. (Serie de Publicaciones Generales del Invemar, 2015).Frade, P. R. et al. Semi-permeable species boundaries in the coral genus Madracis: the role of introgression in a brooding coral system. Mol. Phylogenet. Evol. 57, 1072–1090 (2010).CAS 
PubMed 

Google Scholar 
Locke, J. M. & Coates, K. A. What are the costs of bad taxonomic practices: and what is Madracis mirabilis? Proc. 11th Int. Coral Reef Symp. 7, 1348–1351 (2008).
Google Scholar 
Palumbi, S. R. The Ecology of Marine Protected Areas. in Marine Community Ecology (eds. Bertness, M., Gaines, S. & Hay, M.) 509–530 (Sinauer Press, Inc, 2001).Jones, G. P., Srinivasan, M. & Almany, G. R. Population connecivity and conservation of marine biodiversity. Oceanography 20, 100 (2007).
Google Scholar 
Fogarty, M. J. & Botsford, L. W. Population connectivity and spatial management of marine fisheries. Oceanography 20, 112–123 (2007).
Google Scholar 
Gillis, L. G. et al. Potential for landscape-scale positive interactions among tropical marine ecosystems. Mar. Ecol. Prog. Ser. 503, 289–303 (2014).ADS 

Google Scholar 
Griffiths, S. M. et al. A Galaxy-based bioinformatics pipeline for optimised, streamlined microsatellite development from Illumina next-generation sequencing data. Conserv. Genet. Resour. 8, 481–486 (2016).PubMed 
PubMed Central 

Google Scholar 
Botsford, L. W. et al. Connectivity and resilience of coral reef metapopulations in marine protected areas: Matching empirical efforts to predictive needs. Coral Reefs 28, 327–337 (2009).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Palumbi, S. R. Population genetics, demographic connectivity, and the desing of marine reserves. Ecol. Appl. 13, 146–158 (2003).
Google Scholar 
Ridgway, T., Riginos, C., Davis, J. & Hoegh-Guldberg, O. Genetic connectivity patterns of Pocillopora verrucosa in southern African Marine Protected Areas. Mar. Ecol. Prog. Ser. 354, 161–168 (2008).ADS 

Google Scholar 
Hemond, E. M. & Vollmer, S. V. Genetic diversity and connectivity in the threatened staghorn coral (Acropora cervicornis) in Florida. PLoS One 5, 1140 (2010).
Google Scholar 
Goodbody-Gringley, G., Woollacott, R. M. & Giribet, G. Population structure and connectivity in the Atlantic scleractinian coral Montastraea cavernosa (Linnaeus, 1767). Mar. Ecol. 33, 32–48 (2012).ADS 
CAS 

Google Scholar 
Montoya-Maya, P. H., Macdonald, A. H. H. & Schleyer, M. H. Cross-amplification and characterization of microsatellite loci in Acropora austera from the south-western Indian Ocean. Genet. Mol. Res. 13, 1244–1250 (2014).CAS 
PubMed 

Google Scholar 
Le Goff-Vitry, M., Pybus, O. G. & Roger, N. Genetic structure of the deep-sea coral. Mol. Ecol. 13, 537–549 (2004).CAS 
PubMed 

Google Scholar 
Zeng, C., Rowden, A. A., Clark, M. R. & Gardner, J. P. A. Population genetic structure and connectivity of deep-sea stony corals (Order Scleractinia) in the New Zealand region: Implications for the conservation and management of vulnerable marine ecosystems. Evol. Appl. 10, 1040–1054 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Addamo, A. M., García-Jiménez, R., Taviani, M. & Machordom, A. Development of microsatellite markers in the deep-sea cup coral desmophyllum dianthus by 454 sequencing and cross-species amplifications in scleractinia order. J. Hered. 106, 322–330 (2015).CAS 
PubMed 

Google Scholar 
Morrison, C. L., Springmann, M. J., Shroades, K. M. & Stone, R. P. Development of twelve microsatellite loci in the red tree corals Primnoa resedaeformis and Primnoa pacifica. Conserv. Genet. Resour. 7, 763–765 (2015).
Google Scholar 
Baranets, V., Forsman, Z. H. & Karl, S. A. Microsatellite loci for the plate-and-pillar coral, Porities rus. Conserv. Genet. Resour. 3, 519–521 (2011).
Google Scholar 
Gang, H. et al. Evaluating the reliability of microsatellite genotyping from low-quality DNA templates with a polynomial distribution model. Chin. Sci. Bull. 56, 2523–2530 (2011).
Google Scholar 
Taberlet, P. et al. Reliable genotyping of samples with very low DNA quantities using PCR. Nucleic Acids Res. 24, 3189–3194 (1996).CAS 
PubMed 
PubMed Central 

Google Scholar 
Casado-Amezúa, P. et al. Development of microsatellite markers as a molecular tool for conservation studies of the Mediterranean reef builder coral cladocora caespitosa (Anthozoa, Scleractinia). J. Hered. 102, 622–626 (2011).PubMed 

Google Scholar 
Nakajima, Y. et al. Microsatellite markers for multiple Pocillopora genetic lineages offer new insights about coral populations. Sci. Rep. 7, 1–8 (2017).ADS 

Google Scholar 
Jenkins, T. L. & Stevens, J. R. Assessing connectivity between MPAs: Selecting taxa and translating genetic data to inform policy. Mar. Policy 94, 165–173 (2018).
Google Scholar 
Flot, J. F., Magalon, H., Cruaud, C., Couloux, A. & Tillier, S. Patterns of genetic structure among Hawaiian corals of the genus Pocillopora yield clusters of individuals that are compatible with morphology. Comptes Rendus Biol. 331, 239–247 (2008).
Google Scholar 
Benzoni, F. et al. Morphological and genetic divergence between Mediterranean and Caribbean populations of Madracis pharensis (Heller 1868) (Scleractinia, Pocilloporidae): Too much for one species? Zootaxa 4471, 473–492 (2018).PubMed 

Google Scholar 
Filatov, M. V., Frade, P. R., Bak, R. P. M., Vermeij, M. J. A. & Kaandorp, J. A. Comparison between colony morphology and molecular phylogeny in the Caribbean Scleractinian Coral Genus Madracis. PLoS One 8, 1104 (2013).
Google Scholar 
Althaus, F. et al. Impacts of bottom trawling on deep-coral ecosystems of seamounts are long-lasting. Mar. Ecol. Prog. Ser. 397, 279–294 (2009).ADS 

Google Scholar 
Alonso, D. et al. Caracterización de las comunidades coralinas del Parque Nacional Natural Corales de Profundidad en el Caribe colombiano: una aproximación a la conservación de su biodiversidad. (2014).Cairns, S. D., Jaap, W. C. & Lang, J. Scleractinia (Cnidaria) of the Gulf of Mexico. (2009).Werding, B. & Erhardt, H. Un encuentro de Madracis Myriaster (Milne-Edwards & Haime) (Scleractinia) en la Bahia de Santa Marta. Colombia. Bull. Mar. Coast. Res. 9, 415 (1977).
Google Scholar 
Blacket, M. J., Robin, C., Good, R. T., Lee, S. F. & Miller, A. D. Universal primers for fluorescent labelling of PCR fragments-an efficient and cost-effective approach to genotyping by fluorescence. Mol. Ecol. Resour. 12, 456–463 (2012).CAS 
PubMed 

Google Scholar 
Culley, T. M. et al. An efficient technique for primer development and application that integrates fluorescent labeling and multiplex PCR. Appl. Plant Sci. 1, 1300027 (2013).
Google Scholar 
Holleley, C. E. & Geerts, P. G. Multiplex Manager 1.0: A cross-platform computer program that plans and optimizes multiplex PCR. Biotechniques 46, 511–517 (2009).CAS 
PubMed 

Google Scholar 
Covarrubias-pazaran, A. G., Diaz-Garcia, L., Schlautman, B., Salazar, W. & Zalapa, J. Fragman: An R package for fragment analysis. BMC Genet. 17, 1–8 (2016).
Google Scholar 
Alberto, F. MsatAllele_1.0: An R package to visualize the binning of microsatellite alleles. J. Hered. 100, 394–397 (2013).
Google Scholar 
Kamvar, Z. N., Tabima, J. F. & Grunwald, N. J. Poppr: An R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. PeerJ 2014, 1–14 (2014).
Google Scholar  More

Analysis of changes in ecosystem services value in the YRBThe results showed that from 1990 to 2020, the total ecosystem services value in the YRB showed a dynamic trend of decrease-increase–decrease, with overall increasing trend, and a total increase of 31.85 × 1010 USD, with an average annual increase of 1.14 × 1010 USD (Table 2). This changing trend is consistent with land use cover change in the area. In 30a, YRB cultivated land decreased by 8663 km2, due to rapid urbanization. In addition, after year 2000, China began to implement the policy of returning farmland to forest and grassland on a large scale, which accelerated the reduction of cultivated land. Results again showed that the forest area increased by 30,933,093 km2, indicating that the implementation of “returning farmland to forest and grassland”policy achieved great results, thus increased the value of ecosystem services generated by forest land by 167.66 × 1010 USD. Grassland increased by 738 km2, as corresponding ESV increased by 28.73 × 1010 USD, while unused land decreased by 8131 km2, with 9.52 × 1010 USD ESV decrease. In general, the ecological protection and management measures in the YRB have achieved remarkable results, and ecosystem service values has been significantly improved due to forest and grassland increase.Table 2 The value of ecosystem services in the YRB from 1990 to 2020.Full size tableIn terms of ecosystem service structure in the YRB (Table 3), the relative proportions of various ESVs did not change significantly, resulting in relatively stable ESV structure. Soil conservation and waste disposal are the most important among them, accounting for about 37% of ESV’s total value. The YRB ecosystem, as can be seen, emphasises the importance of soil conservation and waste disposal in the basin, with Climate regulation, Biodiversity conservation, and Entertainment accounting for only 11.99 percent of the total. Various services have changed to varying degrees during the study period. Waste disposal and climate regulation, for example, have suffered losses of 22.23 × 1010 USD and 20.29 × 1010 USD, respectively.The rest of the services showed an upward trend, among which the value of the Food production service increased the most, which was 19.03 × 1010 USD, owing to the obvious increase of the forest land and grassland area in the YRB.Table 3 The value of individual ecosystem functions in the YRB from 1990 to 2020.Full size tableSpatial distribution and variation characteristics of ecosystem services in the YRBThe total ESV value of the study area and changes in the value of each service could not reflect their spatial differences. To describe the temporal and spatial distribution pattern of ESV in the study area, the natural breakpoint method was used. This method was further used to classify ESV with reference to existing studies, and divided the area into four levels: low-value, lower-value, higher-value, and high-value areas. Takin the three-level watershed of the YRB as the statistical unit for analysis, the result showed that the higher the level, the higher the ESV. As shown in Fig. 2, from 1990 to 2020, the spatial characteristics of ESV were relatively stable. The YRB’s upper reaches, from Shizuishan to the north bank of Hekou Town, the Fenhe River Basin, from Hekou Town to Longmen, and the Jinghe River Basin are all rich in high-values. The forest and grassland are relatively concentrated in the above-mentioned areas, the ESV coefficient is high, and the watershed area is large, resulting in a high total ESV. The higher value areas are mainly distributed in the areas from Longyang Gorge to Lanzhou main stream basin, the Daxia River and Tao River basin, and the Wei River basin. The area above Baoji Gorge and the inflow area fall in the transition zone between the high-value area and the lower-value area. For example, the transition area between the Loess and Qinghai-Tibet Plateaus is a higher-value area. The lower-value area mainly includes the Huangshui River Basin, the Datong River Basin, the basin below Lanzhou, and the Guanzhong Plain area. Thus, the unused land in this area is widely distributed. Due to the large area of construction land in the Guanzhong Plain, the ecosystem service value has shrunk. The low-value area is found in the YRB’s lower reaches, which contains the most extensive and large area of construction land in the basin, has a poor ecosystem service function, and is also the YRB’s most economically developed area. In terms of changes in the value of watershed ecosystem services, the number of watersheds at the ESV level did not change significantly between 1990 and 2020. The average ESV of the watershed is 40.52 × 1010 USD. There were 7 high-value, 5 higher-value, 12 lower-value, and 5 low-value watersheds respectively. Figure 2Spatial distribution of ESV changes in YRB from 1990 to 2020. (a) 1990, (b) 2000, (c) 2010, (d) 2020.Full size imageThe hotspot analysis revealed the spatial agglomeration characteristics and ESV evolution law in the YRB from 1990 to 2020 (Fig. 3). In most of the YRB, the ESV accumulation characteristics were not significant in space, and significant areas were dominated with high and low ESV accumulation. The Maqu-Longyangxia River Basin, Daxia River and Tao River Basin, the Datong River Basin, and Fen River Basin were the five core areas where ESV had the highest value. The Inner River, YRB’s northern and eastern margins, and the lower reaches are primarily low-value agglomeration areas. The high-value agglomeration area and low-value agglomeration area did not change significantly in space from 1990 to 2020, but the number of grids in each decreased from 647 to 627 and 699 to 681, respectively. In general, the YRB’s high-value agglomeration areas are strewn about, whereas the low-value agglomeration areas are scattered.Figure 3Spatial agglomeration characteristics of ESV in the YRB from 1990 to 2020. (a) 1990, (b) 2000, (c) 2010, (d) 2020.Full size imageFrom 1990 to 2020, the barycenter coordinates of the ESV in the YRB remained stable between 106.78°–106.94° E and 36.40°–36.65° N (Fig. 4). During the study period, the ESV barycenter coordinates showed a transfer trajectory of first to southwest, then to northeast, and then to southwest. From the perspective of overall transfer direction, ESV barycenter shifted from northeast of Huanxian County to southwest from 1990 to 2020. The ESV in the northeast decreased, while that in southeast increased. From 1995 to 2000 and from 2000 to 2005, the migration distance of ESV barycenter in the YRB was longer by 16.33 km and 15.75 km, respectively, while the barycenter migration distance of ESV from 2005 to 2020 was shorter.Figure 4Barycenter coordinates of ecosystem services in the YRB from 1990 to 2020.Full size imageResponse of ecosystem services to land-use changeThe area of land use type change in the YRB increased by 64,356 km2 between 1990 and 2020. Each land type’s area has changed to varying degrees. Cultivated land and construction land are the two land types that have seen the most changes. The area of cultivated land has shrunk by 8663 km2, while the area of construction land has grown by 13,109 km2. In comparison to water, forest, and grassland, unused land has undergone significant transformations. However, in comparison to 1990, it shrunk by 8131 km2 in 2020. The forest increased by 3093 km2 while grassland increased by 738 km2. Ecosystem services are significantly impacted by changes in land use types. Using the spatial analysis method, the researcher introduces a resilience index to reflect ESV’s response to land-use change in this paper. During 1990–2000 and 2000–2010, average elasticity of ESV change in the YRB relative to land use change was 0.27 and 0.44, respectively, but dropped to 0.04 during 2010–2020. This indicates that the disturbance capacity of land-use change on ecosystem services was low between 1990 and 2000, but increased between 2000 and 2010. Land-use change has had less of an impact on ecosystem services since 2010. The range of changes in land land-use types was wide during this time, but the average elasticity index was low because there were so many different types of land land-use changes, such as the conversion of forest land and cultivated land to construction land, and the conversion of forest land and water area to cultivated land. The decrease in ESV caused by the change in land use per unit area was minor. Furthermore, the forest land and grassland in the river basin have been effectively increased, as ESV has increased. Overall, the value of ecosystem services has remained relatively constant.Accurate spatial statistics on the elasticity index from 1990 to 2020 was carried out (Fig. 5). The elastic index of the upper YRB and Loess Plateau is higher, and the impact of land use change on ecosystem services is more apparent in this region, according to the findings. This is mainly due to the implementation of large-scale ecological engineering measures in response to vegetation degradation in the upper reaches of the YRB and soil erosion in the middle reaches (Loess Plateau), by the Chinese government. In addition, Lanzhou New District, Guanzhong Plain, and the lower Yellow River region also showed higher elasticity index. The above-mentioned regional development and construction, as well as human activities, have resulted in a rapid increase in construction land, resulting in a significant decline in ecosystem services and a higher resilience index as a result of rapid urbanisation.Figure 5Spatial distribution of elastic coefficients in the YRB from 1990 to 2020.Full size imageIn general, the land use types in the YRB have changed dramatically, and land type conversion is very common. The conversion of ecological land to urban construction land, as well as the conversion of unused and cultivated land to ecological land, has resulted in significant changes in ecosystem service value. This demonstrates that the basin’s ecological construction projects have yielded positive environmental results. More

Global fire activity is changing in many areas as temperatures increase and land use intensifies1,2,3,4,5. This is sparking an increase in attention given to fire activity and fire ecology. However, the availability of data for spatially delineated fire events is limited or non-existent in many countries6, with most global fire data coming from satellite-based active fire detections7,8 and gridded burned area products9,10. The lack of products containing delineated events has led to many global studies about fire ecology that are computationally-intensive, coarse-scale trend analyses1,4.A key advantage of datasets like Monitoring Trends in Burn Severity (MTBS)11 or the Fire Occurrence Dataset12 lies in their ease of use. Since its inception in 2007 MTBS has been cited 947 times in peer-reviewed studies according to a Google Scholar search at the time of this writing, despite documented limitations for scientific use of some facets of the product13. The MTBS dataset is regularly updated, easy to find on the internet, and it is free, fast and easy to download and use. Many environmental scientists and resource managers do not have the computational budget or expertise in big data or remote sensing to deal with the challenges one must overcome to process large fire datasets. This is especially true for cases when all that is needed is a shapefile of fire perimeters that can be used to map fire history. Other global fire perimeter datasets have been produced from satellite-derived burned area products14,15, but these are only available in yearly or monthly global shapefiles. Often field-based studies of fire effects require an entire time series over study areas that are only a few hundred km in diameter16 or a single ecoregion17. The end user who wants to understand the fire history for their region would have to download yearly shapefiles with a global extent, clip all of those shapefiles to their area of interest, and then combine them into one shapefile, just to get started. We suspect that the lack of accessible fire perimeter datasets that are easy to download and use contributes to a disparity in research, where fire ecology studies are conducted mostly in developed countries that have either research infrastructure capable of handling big data or longer-term government records, or temperate forested regions that have substantial tree-ring records18.There are two existing global perimeter products, the Global Fire Atlas (GFA) (Andela et al.14) and the Global Wildfire Information System (GWIS) (Artes et al.15). Both were created by applying spatiotemporal flooding algorithms to the MODIS MCD64 Burned Area Product. These algorithms assign burned pixels from the MCD64 products using a moving window whose size is defined by spatial and temporal parameters. They are created as monthly or yearly slices of the entire globe, and they can be subsetted. These products are extremely valuable for global scale studies. But when we look at how those products delineate known fire events we see a consistent problem in that they both seem to over-segment events in ways that appear unrealistic. This inconsistent event delineation is not problematic for coarse-scale or regional estimates of burned area or fire seasonality, but can lead to unrealistic estimates for number of fire events and event-level characteristics like fire size and spread rate. In Fig. 1 we illustrate this with an example of the 2013 Rim Fire in California, United States, which was unmistakably a single event that burned about 90,000 ha over the course of three months. Figure 2 illustrates how the day-to-day progression of the Rim Fire was a steady progression from a single ignition in late August. Table 1 shows how the differences in event delineation propagate to calculations of burned area and number of events. In the GFA, the Rim Fire is delineated as one large event of 804.5 km2, and 13 additional events totaling 88.7 km2. in GWIS it is delineated as one event of 878 km2 and 47 additional events totalling 20 km2. With FIRED, there is one event of 892 km2 and 2 single pixel events totalling less than one km2. One cause for potential differences is how one defines a “fire event”. Large fires often have multiple ignition sources. The Global Fire Atlas algorithm and others19, for example, search for local minima to identify various ignition locations that may begin as small patches, only to later form a large complex and in the end described with a single fire perimeter. The choice of outside sources for optimizing the spatial-temporal parameters, the method of optimization, and the intent of the final product’s meaning (defining events as single ignition patches vs contiguous burned area) all lead to different outcomes in the final events that are delineated. Another likely source of this discrepancy is that GWIS and GFA are calibrated to create a single global product. Because different geographical areas have different types of fire regimes, they have fires that grow at different rates and to different sizes, and occur in greater or fewer frequencies, and so the spatial and temporal parameters that work well for defining a fire event in one area may result in over- or under-segmentation in other areas. Here, we decided upon an approach of creating many regional products across the globe, rather than one product for everywhere on earth.Fig. 1Comparison of global fire event products performance for the 2013 Rim Fire (a). In the FIRED product (b), the Rim fire was classified as one very large event with two single pixel events. The Global Fire Atlas (GFA, c) and Global Wildfire Information System (GWIS, d) each delineated a very large event, with 13 and 47 smaller events, respectively.Full size imageFig. 2The two primary outputs FIREDpy provides are a daily- and event-level product. Panel a shows the default single event polygon. In b, each day has a separate polygon, with associated statistics generated, within each event. Panel c shows the daily perimeters derived from the airborne infrared by the incident management team for comparison.Full size imageTable 1 Rim fire comparison.Full size tableBesides the ease of access and use, the advantage of the FIRED product lies in the user’s ability to use the open-source software, FIREDpy, to tailor the spatial and temporal parameters of the moving window algorithm in order to realistically delineate events for their region of interest. In Fig. 3, we illustrate this by comparing the three products for a pair of small fires in Florida. In this case, the FIRED product that was created with a larger moving window (5 pixels and 11 days) over-aggregated the events, but it only required one line of code at command line to recreate the product with a smaller moving window (1 pixel and 5 days) to get more realistic results.Fig. 3Product comparison for two small events in Florida, the Moonshine Bay and Sour Orange fires (outlined) that both ignited in February of 2007 and were delineated by MTBS. In b the firedpy product that was optimized for the entire United States with a moving window of 5 pixels, 11 days resulted in aggregation of the two fires delineated by MTBS, but also several smaller fires nearby. In b, it was re-ran with a window of one pixel and five days, for a more realistic result. Delineations by the Global Fire Atlas (c) and the Global Wildfire Information System (d) are shown for comparison.Full size imageHere, we present a collection of regionally-tailored fire perimeter datasets for every country in the world with significant fire activity20, which we created with the open source algorithm, FIREDpy21. Each dataset is either a single country or a broader region, depending on the data volume. These datasets differ from other similar efforts14,15 in that each dataset created by FIREDpy is a single file containing a collection of polygons that is generated for the entire time series, rather than monthly or yearly aggregations with a global extent. Furthermore, we have generated the data products at a spatial extent land managers and ecologists would typically use to do regional-scale research, and we adjusted the spatial and temporal parameters for each country to yield realistic event delineations. We also made every effort to ensure that download sizes are reasonable (  More

Klein, A. M. et al. Importance of pollinators in changing landscapes for world crops. Proc. R. Soc. B. 274, 303–313 (2007).PubMed 
Article 

Google Scholar 
IPBES. The assessment report of the intergovernmental science-policy platform on biodiversity and ecosystem services on pollinators, pollination and food production. in Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, Bonn, Germany (Potts, S.G., Imperatriz-Fonseca, V.L., Ngo, H.T. eds.). 1–552. https://ipbes.net/sites/default/files/downloads/pdf/individual_chapters_pollination_20170305.pdf (2016).Ollerton, J. et al. How many flowering plants are pollinated by animals?. Oikos 120, 321–326 (2011).Article 

Google Scholar 
Linder, H. P. Morphology and the Evolution of Wind Pollination. Reproductive Biology 123–135 (Royal Botanic Gardens, 1998).
Google Scholar 
Friedman, J. & Barrett, S. C. H. Wind of change: New insights on the ecology and evolution of pollination and mating in wind-pollinated plants. Ann. Bot. 103, 1515–1527 (2009).PubMed 
PubMed Central 
Article 

Google Scholar 
Gallai, N. et al. Economic valuation of the vulnerability of world agriculture confronted with pollinator decline. Ecol. Econ. 68, 810–821 (2009).Article 

Google Scholar 
Potts, S. G. et al. Safeguarding pollinators and their values to human well-being. Nature 540, 220–229 (2016).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Srinivasan, M. R. et al. Impact of Pesticides on Honey Bees and Pollinators. Pesticide Application in Agro Ecosystem-Its Dynamics and Implications 243–248 (TNAU Publications, 2015).
Google Scholar 
Sanchez-Bayo, F. & Goka, K. Impacts of Pesticides on Honey Bees. Beekeeping and Bee Conservation—Advances in Research. https://doi.org/10.5772/62487. (InTech, 2016).Berry, I. Dead bees don’t pollinate. Orchardist. New Zealand, 60, 287 (1987). Rev. Appl. Entomol. Ser. A 76, 1087 (1998).
Google Scholar 
Chandrasekaran, S. et al. Disposed paper cups and declining bees. Curr. Sci. 101(10), 1262 (2011).
Google Scholar 
Sandilyan, S. Decline in honey bee population in Southern India: Role of disposable paper cups. J. Zool. Biosci. Res. 1, 6–9 (2014).
Google Scholar 
Allen-Wardell, G. et al. The potential consequences of pollinator declines on the conservation of biodiversity and stability of food crop yields. Conserv. Biol. 12, 8–17 (1998).Article 

Google Scholar 
FAO. Declining Bee Populations Pose Threat to Global Food Security and Nutrition. UN World Bee Day, 20 May, Rome. https://www.fao.org/news/story/en/item/1194910/icode/. (2019).Najaran, Z.T. et al. Dill (Anethum graveolens L.) Essential Oils in Food Preservation, Flavor and Safety. https://doi.org/10.1016/C2012-0-06581-7 (Academic Press, 2016). Khare, C.P. Indian Herbal Remedies: Rational Western Therapy, Ayurvedic, and Other Traditional Usage, Botany. 1st edn. 326–327. (Springer, 2004).Jana, S. & Shekhawat, G. S. Anethum graveolens: An indian traditional medicinal herb and spice. Pharmacogn. Rev. 4, 179–184 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Biesiada, A. et al. Nutritional value of garden dill (Anethum graveolens L.), depending on genotype. Notulae Bot. Horti Agrobot. Cluj-Napoca 47, 784–791 (2019).CAS 

Google Scholar 
Pulliah, T. Medicinal Plants in India. Vol. 1. 55–56. (Regency Publications New Delhi, 2002).Hornok, L. Cultivation and Processing of Medicinal Plants. 338. (Academic Publications, 1992).Nair, R. & Chanda, S. Antibacterial activities of some medicinal plants of the western region of India. Turk. J. Biol. 31, 231–236 (2007).
Google Scholar 
DASD. State Agriculture/Horticulture Departments/DASD Kozhikode, Kerala. https://www.dasd.gov.in/index.php/content/index/statistics (2020).Nemeth, E. & Szekely, G. Floral biology of medicinal plants I. Apiaceae species. Int. J. Horticult. Sci. 6, 133–136 (2000).
Google Scholar 
Weiss, E. A. Spice Crops. 268–283 (CAB International, 2002).Book 

Google Scholar 
Peter, K.V. Dill in Handbook of Herbs and Spices (Gupta, R., Answer, M.M., Sharma, Y.K. Eds.). 275–285. (Woodhead Publishing Limited, 2012).Meena, N. K. et al. Role of insect pollinators in pollination of seed spices—A review. Int. J. Seed Spices 5, 1–17 (2015).
Google Scholar 
Faegri, K. & van der Pijl, L. The Principles of Pollination Ecology 3rd edn. (Pergamon, 1980).
Google Scholar 
Ali, M., Saeed, S., Sajjad, A. & Whittington, A. In search of the best pollinators for canola (Brassica napus L.) production in Pakistan. Appl. Entomol. Zool. 46, 353–361 (2011).Article 

Google Scholar 
Singh, H., Swaminathan, R. & Hussain, T. Influence of certain plant products on the insect pollinators of coriander. J. Biopest. 3, 208–211 (2010).
Google Scholar 
Kant, K. et al. Relative abundance and foraging behavior of honey bee species on minor seed spice crops. Int. J. Seed Spices 3, 51–54 (2013).
Google Scholar 
Willmer, P. G. et al. The superiority of bumblebees to honeybees as pollinators: Insect visits to raspberry flowers. Ecol. Entomol. 19, 271–284 (1994).Article 

Google Scholar 
Stone, J. L. Components of pollination effectiveness in Psychotria suerrensis, a tropical distylous shrub. Oecologia 107, 504–512 (1996).ADS 
PubMed 
Article 

Google Scholar 
Olsen, K. M. Pollination effectiveness and pollinator importance in a population of Heterotheca subaxillaris (Asteraceae). Oecologia 109, 114–121 (1997).ADS 

Google Scholar 
Ivey, C. T. et al. Variation in pollinator effectiveness in swamp milkweed, Asclepias incarnate (Apocynaceae). Am. J. Bot. 90, 214–225 (2003).PubMed 
Article 

Google Scholar 
Korpela, S. The influence of honeybee pollination on turnip rape (Brassica campestris) yield and yield components. Ann. Agric. Fenniae 27, 295–303 (1988).
Google Scholar 
Sabbahi, R. et al. Influence of honey bee (Hymenoptera: Apidae) density on the production of canola (Cruciferae: Brassicacae). J. Econ. Entomol. 98, 267–372 (2005).Article 

Google Scholar 
Warakomska, Z. et al. Biology of the bloom and pollination of the umbelliferous vegetables. Part 1: garden dill (Anethum graveolens L.). Acta Agrobot. 35, 69–78 (1982).Article 

Google Scholar 
Meena, N. K. et al. Pollinator’s diversity and abundance on cumin (Cuminum cyminum L.) and their impact on yield enhancement at semi-arid region. J. Entomol. Zool. Stud. 6, 1017–1021 (2018).
Google Scholar 
Malhotra, S.K. & Vashishtha, B.B. Package of practices for production of seed spices. in Book Published by the Director, ICAR-National Research Centre on Seed Spices, Ajmer. 71–79. (2008).Chaudhary, O. P. diversity, foraging behaviour of floral visitors and pollination ecology of fennel (Foeniculum vulgare Mill). J. Spices Aromatic Crops 15, 34–41 (2006).
Google Scholar 
Rianti, P. et al. Diversity and effectiveness of insect pollinators of Jatropha curcas L. (Euphorbiaceae). HAYATI J. Biosci. 17, 38–42 (2010).Article 

Google Scholar 
Choi, S. W. & Jung, C. Diversity of insect pollinators in different agricultural crops and wild flowering plants in korea: Literature review. J. Apicult. 30, 191–201 (2015).MathSciNet 
Article 

Google Scholar 
Siregar, E. F. et al. Diversity and abundance of insect pollinators in different agricultural lands in Jambi, Sumatera. HAYATI J. Biosci. 23, 13–17 (2016).Article 

Google Scholar 
Devi, M. et al. Diversity of insect pollinators in reference to seed set of mustard (Brassica juncea L.). Int. J. Curr. Microbiol. Appl. Sci. 6, 2131–2144 (2017).Article 

Google Scholar 
Martin, P. & Bateson, P. Measuring Behaviour: An Introductory Guide. 2nd edn. (Cambridge University Press, 1993).Dafni, A. Pollination Ecology: A Practical Approach (Oxford University Press, 1992).
Google Scholar 
Chaudhary, O. P. & Singh, J. Diversity, temporal abundance, foraging behaviour of floral visitors and effect of different modes of pollination on coriander (Coriandrum sativum L.). J. Spices Aromatic Crops 16, 8–14 (2007).
Google Scholar 
Kulkarni, S. R., Gurve, S. S. & Chormule, A. J. Effect of different indigenous bee attractants in onion (Allium cepa L.) crop. Ann. Plant Protect. Sci. 25, 78–82 (2017).
Google Scholar 
Manhare, J. S. & Painkra, G. P. Impact of bee attractants on bee visitation on buckwheat (Fagopyrum esculentum L.) crop. J. Entomol. Zool. Stud. 6, 28–31 (2018).
Google Scholar 
Kapas, A. et al. The kinetic of essential oil separation from fennel by microwave assisted hydro-distillation (MWHD). UPB Sci. Bull. Ser. B 73, 113–120 (2011).CAS 

Google Scholar 
Warrier, P. K. et al. Indian Medicinal Plants. Vol. 1. 153–157. (Orient Longman Limited, 1994).Baswana, K. S. Role of insect pollination on seed production in coriander and fennel. South Indian Horticult. 32, 117–118 (1984).
Google Scholar 
Koul, A. K. Pollination mechanism in Coriandrum sativum L. (Apiaceae). Proc. Indian Acad. Sci. Plant Sci. 99, 509–515 (1989).Article 

Google Scholar 
Narayana, E. S., Sharma, P. L. & Phadke, K. G. Insect pollinators of saunf (Foenicuum vulgare) with particular reference to the honeybees at Pusa (Bihar). Indian Bee J. 22, 7–13 (1960).
Google Scholar 
Mukherjee, S. et al. Pollination events in Nigella sativa L. black cumin. Int. J. Res. Ayurveda Pharm. 4, 342–344 (2013).Article 

Google Scholar 
Abrar, M. et al. Insect pollinators and their relative abundance on black cumin Nigella sativa L. at Dera Ismail Khan. J. Entomol. Zool. Stud. 5, 1252–1258 (2017).
Google Scholar 
Ollerton, J. & Louise, C. Latitudinal trends in plant-pollinator interactions: Are tropical plants more specialized?. Oikos 98, 340–350 (2002).Article 

Google Scholar 
Meena, N. K. et al. Diversity of floral visitors and foraging behavior and abundance of major pollinators on fennel under semi-arid conditions of Rajasthan. Int. J. Trop. Agric. 34, 1891–1897 (2016).
Google Scholar 
Sikdar, S. et al. Diurnal foraging activity of flower visiting insects on some seed spices under terai agro-climatic zone of West Bengal. J. Entomol. Zool. Stud. 7, 299–303 (2019).
Google Scholar 
Kapil, R. P. et al. Integration of bee behaviour with aphid control for seed production of Brassica campestris var. toria. Indian J. Entomol. 33, 221–223 (1971).
Google Scholar 
Bhalla, O. P. et al. Insect visitors of mustard bloom Brassica campestris var sarson, their number and foraging behavior under mid-hill conditions. J. Entomol. Res. 1, 15–17 (1983).
Google Scholar 
Rao, G. M. & Suryanarayana, M. C. Studies on the foraging behaviour of honeybees and its effect in seed yield of niger. Indian Bee J. 52, 31–33 (1990).
Google Scholar 
Abrol, D. P. Foraging behavior of Apis mellifera L. and A. cerana F. as determined by the energetics of nectar production in different cultivars of Brassica campestris var toria. J. Apicult. Sci. 51, 19–24 (2007).
Google Scholar 
Inouye, D. W. The effect of proboscis and corolla tube lengths on patterns and rates of flower visitation by bumble bees. Oecologia 45, 197–201 (1980).ADS 
PubMed 
Article 

Google Scholar 
Vicens, N. & Bosch, J. Pollination efficacy of Osmia cornuta and Apis mellifera (Hymenoptera: Megachilidae, Apidae) on ‘Red Delicious’ apple. Environ. Entomol. 29, 235–240 (2000).Article 

Google Scholar 
Singh, J. et al. Foraging rates of different Apis species visiting parental lines of Brassica napus L. Zoos’ Print J. 21, 2226–2227 (2006).Article 

Google Scholar 
Engel, E. C. & Irwin, R. E. Linking pollinator visitation and rate of pollen receipt. Am. J. Bot. 90, 1612–1618 (2003).Article 

Google Scholar 
Sihag, R. C. Insect pollination increase seed production in cruciferous and umbelliferous crops. J. Apic. Res. 25, 121–126 (1986).Article 

Google Scholar 
Verma, S. & Dwivedi, S. N. Floral biology of Trachyspermum ammi (Linn.) Spr. Inventi rapid. Planta Activa 2, 1–6 (2018).
Google Scholar 
Singh, B. Effectiveness of different pollinators on yield and quality of greenhouse grown tomatoes and melons: A review. Haryana J. Horticult. Sci. 31, 245–250 (2002).ADS 

Google Scholar 
Biswanath, B. et al. Role of insect pollinators in seed yield of coriander (Coriandrum sativum L.) and their electroantennogram response to crop volatiles. Agric. Res. J. 54, 227–235 (2017).Article 

Google Scholar 
Giannini, T. C. et al. The dependence of crops for pollinators and the economic value of pollination in Brazil. J. Econ. Entomol. 108, 849–857 (2015).CAS 
PubMed 
Article 

Google Scholar  More