Philippa Kaur

Rahaman, A. et al. The increasing hunger concern and current need in the development of sustainable food security in the developing countries. Trends Food Sci. Technol. 113, 423–429. https://doi.org/10.1016/j.tifs.2021.04.048 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Porter, J. R. et al. In Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change 485–533 (Cambridge University Press, 2014).
Google Scholar 
Yan, H. et al. Crop traits enabling yield gains under more frequent extreme climatic events. Sci. Total Environ. 808, 152170. https://doi.org/10.1016/j.scitotenv.2021.152170 (2022).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Lobell, D. et al. The critical role of extreme heat for maize production in the United States. Nat. Clim. Change. 3, 497–501. https://doi.org/10.1038/nclimate1832 (2013).Article 
ADS 

Google Scholar 
Vermeulen, S. J. et al. Addressing uncertainty in adaptation planning for agriculture. Proc. Natl. Acad. Sci. 110, 8357–8362. https://doi.org/10.1073/pnas.1219441110 (2013).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
FAO. Climate Change and Food Security: Risks and Responses (FAO, 2015).
Google Scholar 
Ray, D. K., Gerber, J. S., MacDonald, G. K. & West, P. C. Climate variation explains a third of global crop yield variability. Nat. Commun. 6, 5989. https://doi.org/10.1038/ncomms6989 (2015).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Ding, Z. et al. Modeling the combined impacts of deficit irrigation, rising temperature and compost application on wheat yield and water productivity. Agric. Water Manag. 244, 106626. https://doi.org/10.1016/j.agwat.2020.106626 (2021).Article 

Google Scholar 
Malhi, G. S., Kaur, M. & Kaushik, P. Impact of climate change on agriculture and its mitigation strategies: A review. Sustainability 13, 1318 (2021).Article 
CAS 

Google Scholar 
Persson, T. & Kværnø, S. Impact of projected mid-21st century climate and soil extrapolation on simulated spring wheat grain yield in Southeastern Norway. J. Agric. Sci. 155, 361–377. https://doi.org/10.1017/S0021859616000241 (2017).Article 

Google Scholar 
Zhu, X. & Troy, T. J. Agriculturally relevant climate extremes and their trends in the world’s major growing regions. Earth’s Future 6, 656–672. https://doi.org/10.1002/2017EF000687 (2018).Article 
ADS 

Google Scholar 
Fischer, T. et al. Increase in irrigated wheat yield in north-west Mexico from 1960 to 2019: Unravelling the negative relationship to minimum temperature. Field Crops Res. 275, 108331. https://doi.org/10.1016/j.fcr.2021.108331 (2022).Article 
ADS 

Google Scholar 
Lobell, D. B., Schlenker, W. & Costa-Roberts, J. Climate trends and global crop production since 1980. Science 333, 616–620. https://doi.org/10.1126/science.1204531 (2011).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Harkness, C. et al. Adverse weather conditions for UK wheat production under climate change. Agric. For. Meteorol. 282, 107862. https://doi.org/10.1016/j.agrformet.2019.107862 (2020).Article 
ADS 
PubMed 

Google Scholar 
Seehusen, T. & Uhlen, A. K. Analyses of yield gaps for the production of wheat and barley in Norway, potential to increase yields on existing farmland. Norwegian Institute for Bioeconomics, Report 5/166/2019 (2020).Hakala, K. et al. Sensitivity of barley varieties to weather in Finland. J. Agric. Sci. 150, 145–160. https://doi.org/10.1017/S0021859611000694 (2012).Article 
CAS 
PubMed 

Google Scholar 
Peltonen-Sainio, P., Jauhiainen, L., Hakala, K. & Ojanen, H. Climate change and prolongation of growing season, changes in regional potential for field crop production in Finland. Agric. Food Sci. 18, 171–190. https://doi.org/10.2137/145960609790059479 (2009).Article 

Google Scholar 
Fleisher, D. H. et al. A potato model intercomparison across varying climates and productivity levels. Glob. Change Biol. 23, 1258–1281. https://doi.org/10.1111/gcb.13411 (2017).Article 
ADS 

Google Scholar 
Moen, A. National Atlas of Norway: Vegetation (Hønefoss, 1999).
Google Scholar 
Bakkestuen, V., Erikstad, L. & Halvorsen, R. Step-less models for regional environmental variation in Norway. J. Biogeogr. 35, 1906–1922 (2008).Article 

Google Scholar 
Statistics-Norway. 2020. https://www.ssb.no/jord-skog-jakt-og-fiskeri/statistikker/stjord (Accessed 10 November 2020).Hanssen-Bauer, I. et al. Climate in Norway 2100 – a knowledge base for climate adaptation. Norwegian Centre for Climate Sciences, Report 1/2017 49 (2017).Blandford, D., Gaasland, I., Vårdal, E. & McIntosh, C. Greenhouse gas emissions, land use, and food supply under the paris climate agreement—Policy choice in Norway. Appl. Econ. Perspect. Policy 41, 249–264. https://doi.org/10.1093/aepp/ppy011 (2019).Article 

Google Scholar 
Rötter, R. P. et al. What would happen to barley production in Finland if global warming exceeded 4 °C? A model-based assessment. Eur. J. Agron. 35, 205–214. https://doi.org/10.1016/j.eja.2011.06.003 (2011).Article 

Google Scholar 
Ozturk, I., Sharif, B., Baby, S., Jabloun, M. & Olesen, J. E. The long-term effect of climate change on productivity of winter wheat in Denmark, scenario analysis using three crop models. J. Agric. Sci. 155, 733–750. https://doi.org/10.1017/S0021859616001040 (2017).Article 
CAS 

Google Scholar 
An, H. & Carew, R. Effect of climate change and use of improved varieties on barley and canola yield in Manitoba. Can. J. Plant Sci. 95, 127–139. https://doi.org/10.1139/CJPS-2014-221 (2014).Article 

Google Scholar 
Zhou, Z., Plauborg, F., Kristensen, K. & Andersen, M. Dry matter production, radiation interception and radiation use efficiency of potato in response to temperature and nitrogen application regimes. Agric. For. Meteorol. 232, 595–605. https://doi.org/10.1016/j.agrformet.2016.10.017 (2017).Article 
ADS 

Google Scholar 
Jensen, K. J. S. et al. Yield and development of winter wheat (Triticum aestivum L.) and spring barley (Hordeum vulgare L.) in field experiments with variable weather and drainage conditions. Eur. J. Agron. 122, 126075. https://doi.org/10.1016/j.eja.2020.126075 (2021).Article 
CAS 

Google Scholar 
Lobell, D. B., Cahill, K. N. & Field, C. B. Historical effects of temperature and precipitation on California crop yields. Clim. Change 81, 187–203. https://doi.org/10.1007/s10584-006-9141-3 (2007).Article 
ADS 

Google Scholar 
Skjelvag, A. O. Climatic conditions for crop production in Nordic countries. Agric. Food Sci. Finland 7(2), 149–160 (1998).Article 

Google Scholar 
Norsk-Klimaservicesenter. https://seklima.met.no/ (2020).Erikstad, L. & Bakkestuen, V. Calculating cumulative effects in GIS using a stepless multivariate model. MethodsX 8, 101407. https://doi.org/10.1016/j.mex.2021.101407 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Aune-Lundberg, L. & Strand, G.-H. The content and accuracy of the CORINE land cover dataset for Norway. Int. J. Appl. Earth Observ. Geoinform. 96, 102266. https://doi.org/10.1016/j.jag.2020.102266 (2021).Article 

Google Scholar 
QGIS Geographic Information System (QGIS Association, 2020).R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).
Google Scholar 
Lobell, D. B. & Field, C. B. Global scale climate–crop yield relationships and the impacts of recent warming. Environ. Res. Lett. 2, 014002. https://doi.org/10.1088/1748-9326/2/1/014002 (2007).Article 
ADS 

Google Scholar 
Shumway, R. H. & Stoffer, D. S. Time Series Analysis and its Applications Vol. 560 (Springer, 2016).MATH 

Google Scholar 
Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R. J. 9, 378–400 (2017).Article 

Google Scholar 
Lüdecke, D., Ben Shachar, M., Patil, I., Waggoner, P. & Makowski, D. Performance: An R Package for Assessment, Comparison and Testing of Statistical Models (2021).Hartig, F. DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models. R package version 0.3.3.0 (2020).Friedman, J. H., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33(22), 2010. https://doi.org/10.18637/jss.v033.i01 (2010).Article 

Google Scholar 
Tibshirani, R. Regression shrinkage and selection via the Lasso. J. R. Stat. Soc. Ser. B-Methodol. 58, 267–288. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x (1996).Article 
MathSciNet 
MATH 

Google Scholar 
Hastie, T., Tibshirani, R. & Friendman, J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer, 2009).Book 
MATH 

Google Scholar 
Meinshausen, N. & Bühlmann, P. Stability selection. J. Roy. Stat. Soc. B 72, 417–473. https://doi.org/10.2307/40802220 (2010).Article 
MathSciNet 
MATH 

Google Scholar 
Efron, B. & Stein, C. The jackknife estimate of variance. Ann. Stat. 9, 586–596. https://doi.org/10.1214/aos/1176345462 (1981).Article 
MathSciNet 
MATH 

Google Scholar 
Milborrow, S. plotmo: Plot a Model’s Residuals, Response, and Partial Dependence Plots. R package version 3.5.7 (2020).Liu, H. Xu, X. & Li, J.J. HDCI: High Dimensional Confidence Interval Based on Lasso and Bootstrap. R package version 1.0–2 (2017).. Seehusen, T. & Uhlen, A. K. Analyses of yield gaps for the production of wheat and barley in Norway, potential to increase yields on existing farmland. Norwegian Institute for Bioeconomics, Report 5/166/2019. http://hdl.handle.net/11250/2637490 (2019).Stabbetorp, H. Dyrkingsomfang og avling i kornproduksjonen. Norsk institutt for bioøkonomi, Report 4 (1) (2017).Ebrahimi, E. et al. Assessing the impact of climate change on crop management in winter wheat—A case study for Eastern Austria. J. Agric. Sci. 154, 1153–1170. https://doi.org/10.1017/S0021859616000083 (2016).Article 

Google Scholar 
Kristensen, K., Schelde, K. & Olesen, J. Winter wheat yield response to climate variability in Denmark. J. Agric. Sci. 148, 1–15. https://doi.org/10.1017/S0021859610000675 (2010).Article 

Google Scholar 
Thaler, S., Eitzinger, J., Trnka, M. & Dubrovsky, M. Impacts of climate change and alternative adaptation options on winter wheat yield and water productivity in a dry climate in Central Europe. J. Agric. Sci. 150, 537–555. https://doi.org/10.1017/S0021859612000093 (2012).Article 
CAS 

Google Scholar 
Ortiz, R. et al. Climate change, can wheat beat the heat?. Agr. Ecosyst. Environ. 126, 46–58. https://doi.org/10.1016/j.agee.2008.01.019 (2008).Article 

Google Scholar 
Semenov, M., Stratonovitch, P., Alghabari, F. & Gooding, M. Adapting wheat in Europe for climate change. J. Cereal Sci. 59, 245–256. https://doi.org/10.1016/j.jcs.2014.01.006 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Roberts, M. J., Braun, N. O., Sinclair, T. R., Lobell, D. B. & Schlenker, W. Comparing and combining process-based crop models and statistical models with some implications for climate change. Environ. Res. Lett. 12, 095010. https://doi.org/10.1088/1748-9326/aa7f33 (2017).Article 
ADS 

Google Scholar 
Zhu, X., Troy, T. & Devineni, N. Stochastically modeling the projected impacts of climate change on rainfed and irrigated US crop yields. Environ. Res. Lett. 14, 074021. https://doi.org/10.1088/1748-9326/ab25a1 (2019).Article 
ADS 

Google Scholar 
Lobell, D. & Asseng, S. Comparing estimates of climate change impacts from process-based and statistical crop models. Environ. Res. Lett. 12, 015001. https://doi.org/10.1088/1748-9326/aa518a (2017).Article 
ADS 
CAS 

Google Scholar 
Flø, S. et al. Rom for bruk av Norsk korn. Felleskjøpet, Report 49 (2017).Lillemo, M., Reitan, L. & Bjornstad, A. Increasing impact of plant breeding on barley yields in central Norway from 1946 to 2008. Plant Breeding 129, 484–490. https://doi.org/10.1111/j.1439-0523.2009.01710.x (2010).Article 

Google Scholar 
Wonneberger, R., Ficke, A. & Lillemo, M. Mapping of quantitative trait loci associated with resistance to net form net blotch (Pyrenophora teres f. teres) in a doubled haploid Norwegian barley population. PLoS One 12, e0175773. https://doi.org/10.1371/journal.pone.0175773 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Moore, F. C. & Lobell, D. B. The fingerprint of climate trends on European crop yields. Proc. Natl. Acad. Sci. 112, 2670–2675. https://doi.org/10.1073/pnas.1409606112 (2015).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Martin, P. et al. Recent warming across the North Atlantic region may be contributing to an expansion in barley cultivation. Clim. Change 145, 351–365. https://doi.org/10.1007/s10584-017-2093-y (2017).Article 
ADS 

Google Scholar 
Martin, P., Wishart, J., Dalmannsdottir, S., Halland, H. & Thomsen, a. M. Recent warming and the thermal requirement of barley in coastal Norway. Norwegian Institute for Bioeconomics, Report T2.4.3 ii (2018).Cattivelli, L., Ceccarelli, S., Romagosa, I. & Stanca, M. Abiotic stresses in Barley: Problems and solutions. In Barley: Production, Improvement, and Uses Vol. 4 (ed. Ullrich, S.) 282–306 (Blackwell UP, 2011).
Google Scholar 
Hura, T. Wheat and barley acclimatization to abiotic and biotic stress. Int. J. Mol. Sci. 21, 7423. https://doi.org/10.3390/ijms21197423 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Kolberg, D., Persson, T., Mangerud, K. & Riley, H. Impact of projected climate change on workability, attainable yield, profitability and farm mechanization in Norwegian spring cereals. Soil Till. Res. 185, 122–138. https://doi.org/10.1016/j.still.2018.09.002 (2019).Article 

Google Scholar 
Olesen, J. E. et al. Impacts and adaptation of European crop production systems to climate change. Eur. J. Agron. 34, 96–112. https://doi.org/10.1016/j.eja.2010.11.003 (2011).Article 

Google Scholar 
Gammans, M., Mérel, P. & Ortiz-Bobea, A. Negative impacts of climate change on cereal yields: Statistical evidence from France. Environ. Res. Lett. 12, 054007. https://doi.org/10.1088/1748-9326/aa6b0c (2017).Article 
ADS 
CAS 

Google Scholar 
Ahmed, I., Harrison, M., Meinke, H. & Zhou, M. Barley phenology: physiological and molecular mechanisms for heading date and modelling of genotype-environment- management interactions. Plant Growth InTech 8, 175–202. https://doi.org/10.5772/64827 (2016).Article 
CAS 

Google Scholar 
Hossain, A., da Silva, J. A. T., Lozovskaya, M. V. & Zvolinsky, V. P. High temperature combined with drought affect rainfed spring wheat and barley in South-Eastern Russia. Saudi J. Biol. Sci. 19, 473–487. https://doi.org/10.1016/j.sjbs.2012.07.005 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Møllerhagen, P. Norsk potetproduksjon 2011. Bioforsk, Report 7(1) (2012).Hermansen, A., Lu, D. & Forbes, G. Potato production in China and Norway, similarities, differences and future challenges. Potato Res. 55, 197–203. https://doi.org/10.1007/s11540-012-9224-7 (2012).Article 

Google Scholar 
Hermansen, A., Nærstad, R., Le, V. & Nordskog, B. In Proceedings of the Eleventh EuroBlight Workshop (The Norwegian Institute for Agricultural and Environmental Research, Hamar, 2018).Raymundo, R. et al. Climate change impact on global potato production. Eur. J. Agron. 100, 87–98. https://doi.org/10.1016/j.eja.2017.11.008 (2018).Article 

Google Scholar 
Rabia, A., Yacout, D., Shahin, S., Mohamed, A. & Abdelaty, E. Towards sustainable production of potato under climate change conditions. Curr. J. Appl. Sci. Technol. 18, 200–207. https://doi.org/10.14456/cast.2018.15 (2018).Article 

Google Scholar 
Haverkort, A. J., Franke, A. C., Engelbrecht, F. A. & Steyn, J. M. Climate change and potato production in contrasting South African agro-ecosystems. Potato Res. 56, 67–84. https://doi.org/10.1007/s11540-013-9230-4 (2013).Article 

Google Scholar 
Martinelli, F. et al. Advanced methods of plant disease detection A review. Agron. Sustain. Dev. 35, 1–25. https://doi.org/10.1007/s13593-014-0246-1 (2015).Article 

Google Scholar 
Borus, D. Impacts of Climate Change on the Potato (Solanum Tuberosum L.) Productivity in Tasmania, Australia and Kenya (University of Tasmania, 2017).
Google Scholar 
Fageria, N., Baligar, V. & Jones, C. Growth and Mineral Nutrition of Field Crops Vol. 5, 586 (CRC Press, 2010).Book 

Google Scholar 
Fleisher, D. H. et al. Effects of elevated CO2 and cyclic drought on potato under varying radiation regimes. Agric. For. Meteorol. 171, 270–280. https://doi.org/10.1016/j.agrformet.2012.12.011 (2013).Article 
ADS 

Google Scholar 
Haverkort, A. J. & Struik, P. C. Yield levels of potato crops: Recent achievements and future prospects. Field Crop Res. 182, 76–85. https://doi.org/10.1016/j.fcr.2015.06.002 (2015).Article 

Google Scholar 
Van Oort, P. A. J., Timmermans, B. G. H., Meinke, H. & Van Ittersum, M. K. Key weather extremes affecting potato production in the Netherlands. Eur. J. Agron. 37, 11–22. https://doi.org/10.1016/j.eja.2011.09.002 (2012).Article 

Google Scholar 
Najafi, E., Devineni, N., Khanbilvardi, R. & Kogan, F. Understanding the changes in global crop yields through changes in climate and technology. Earth’s Future 6, 410–427. https://doi.org/10.1002/2017EF000690 (2018).Article 
ADS 
CAS 

Google Scholar 
Pulatov, B., Anna Maria, J. N., Karin, H. & Maj-Lena, L. Modeling climate change impact on potato crop phenology, and risk of frost damage and heat stress in northern Europe. Agric. For. Meteorol. 214, 281–292. https://doi.org/10.1016/j.agrformet.2015.08.266 (2015).Article 
ADS 

Google Scholar  More

Owing to drastic changes in the environment caused by human interference, wild mammals that are reservoirs of Leishmania have invaded residential areas where species of sand flies with eclectic feeding habits are found, and established a transmission cycle that eventually reaches humans23,24,25. In the study area, it was observed that the largest frequency of specimens over the years was captured in the residential environment, which are represented by residential and peridomicile areas. The lowest frequency was captured in the borders of the forest.The municipality of Paraty, located on the southern coast in the state of Rio de Janeiro, where the study was conducted, has many preserved areas of the Atlantic Forest and its climate is wet with no dry season13, which was confirmed during the three years of the present study, where the relative air humidity stayed high every month. The highest average rainfalls occur in summer and fall (autumn). The average temperature during the hottest months of the year was between approximately 25 °C and 26 °C, with a maximum of 31 °C, and in the coldest months, the temperature averaged between 20 and 21 °C, with a minimum of 16 °C, exhibiting an ideal environment for the activity of sand flies throughout the year.Barretto26 noted that atmospheric conditions, such as relative humidity, rainfall, and temperature directly influence the activity of these sand fly species. Migonemyia migonei and Ny. whitmani had lower activity at temperatures below 15 °C, Pi. fischeri below 10 °C, and Ny. intermedia at temperatures below 9.5 °C. The author also reported that heavy rains prevent sand flies from leaving their shelters; however, this can increase their density within residences, especially for species located next to residential areas. Light rain will not impede their activity, but in these conditions, they are not as frequently observed as they usually are. However, during rain periods, especially in the hot and humid summer period, the density of sand flies increases considerably.In the present study, four key vector species of Leishmania braziliensis Vianna, 1911, the etiologic agent of tegumentary leishmaniasis, were captured throughout the year. The most frequent was Ny. intermedia, followed by Pi. fischeri, Mg. migonei, and Ny. whitmani. Carvalho et al.27, in the State of Pernambuco, northeast region of Brazil, reported having found Mg. migonei infected with Leishmania infantum Nicolle, 1908, the etiologic agent of visceral leishmaniasis.According to Forattini28, there are sand fly species that are essentially resistant to climate changes throughout the seasons. Several are found, albeit in lower densities, during the cooler, dry months, while others disappear during this period. However, other factors also influence the incidence of sand flies in the same location, even under the same temperature and humidity conditions. Thus, to study the seasonality of sand fly species, it is important to perform systematized captures, for a period exceeding two years, to minimize the effects of these additional factors, for example, atypical years with a longer period of drought or humidity, more or less high temperatures, months with higher than expected rainfall or control measures applied by the municipality.In studies carried out in the Northeast region of Brazil, in a study carried out in the municipality of Codó, in the State of Maranhão, an inversely proportional correlation of the captured sandflies was observed in relation to relative air humidity, a direct correlation in relation to temperature and precipitation, a correlation directly proportional29. In the municipality of Sobral, State of Ceará, in the first year of the study, observed a negative correlation with temperature and a high positive correlation with humidity and precipitation, however, in the following year, there was no correlation between the density of captured sandflies and climatic variables30. The same occurred in this study, in the municipality of Paraty, in relation to relative air humidity and precipitation, but in relation to temperature, a strong positive correlation was obtained.In the studied area Ny. intermedia occurred in greater numbers in every month of the year, except in June and July, when it was less frequent than Pi. fischeri. The same pattern was observed for these two species, i.e., a gradual increase in abundance beginning in August, peak abundance in summer (January), followed by a decrease until winter (July). Brito et al.31, when researching the northern coast of the state of São Paulo, municipality of São Sebastião, noted the opposite, that Ny. intermedia had the highest abundance peaks during the driest and coldest period of the year, i.e., from May to August. However, the authors also emphasized the presence of this species throughout the year, mainly in the residential environment, and they stressed the importance of seasonal analyses for periods longer than a year.In the São Francisco River region, in the state of Minas Gerais, on the banks of the Rio Velhas, Saraiva et al.32, in a study over a two-year period, observed a different pattern. In the first year of study, after the rainy season from February to May, with high humidity and high temperature, Ny. intermedia was captured in greater numbers than during other months of the year. In the second year, peaks occurred in October, March, and June, with the highest peak in March, when there was elevated rainfall, high humidity, and high temperatures.In the state of Rio de Janeiro, in Serra dos Órgãos National Park, Aguiar and Soucasaux33 analyzed the monthly frequency in human bait and observed that Ny. fischeri was captured in every month except November. In the hot and humid period, from December to February, there was a gradual increase in the average abundances of this species, and then a slight decrease began in March and continued into April. During the cold and dry period of May and June, abundances started to increase, then decreased in July, and peaked in August. During August, Pi. fischeri was the dominant species of wildlife, and in September, abundances began to decline again.Mayo et al.34, studying the southeastern region of the state of São Paulo, observed that there was a seasonal trend in the abundance for species Mg. migonei, Ny. whitmani, Ny. intermedia, and Pi. fischeri, with abundance peaks recorded during the cooler, drier season (April to September) and low abundances during the warmer, wetter season (October to March). The authors revealed that the occurrence of intense fires in the study area in October, which caused severe environmental change, possibly interfered with the population dynamics of the species. In the present study, the opposite trend of seasonality was shown for the four key species, Ny. intermedia, Pi. fischeri, Mg. migonei, and Ny. whitmani, then what was observed by the above authors, the highest abundances occurred during the hottest period, increasing gradually until a maximum peak in January, and lowest abundances were seen during the coldest period, in July for the first three species, and in June for Ny. whitmani.In the neighboring municipality of this study in Angra dos Reis, in the Ilha Grande, Carvalho et al.35 reinforced the epidemiological importance of Ny. intermedia in the State of Rio de Janeiro and highlighted the role of Mg. migonei in the transmission of cutaneous leishmaniasis with its high rate of infection natural by Leishmania. Still in the same region, along the southern coast of the State of Rio de Janeiro, Aguiar et al.8 conducted systematic catches for two years, with the aim being to analyze the monthly frequency of sand flies in residential and forest environments. The authors discovered results like what occurred in this study in Paraty, that the four most important species caught, Ny. intermedia, Pi. fischeri, Mg. migonei, and Ny. whitmani, had higher average numbers during the hot and humid period of the year, i.e., between October and January, with a maximum peak in December for Ny. intermedia and Pi. fischeri, and January for Mg. migonei. The prevalence of Ny. intermedia was evident in every month, both inside the residence and around the residential area. In the colder and drier season, from May to August, there was a balance with Pi. fischeri, but from August, inside the residence, and from September, around the residence, the frequency increased until it reached its peak in December. There was a gradual increase in the frequency of this species in the warmer and wetter period (between October and January), with average temperatures ranging from 26 to 29 °C and relative air humidity between 84 and 87%.Condino et al.36, when studying the southwestern region of the state of São Paulo, observed that Ny. intermedia and Ny. whitmani had the highest frequencies during the months of May, September, and December with temperatures ranging from 21 to 25.7 °C and rainfall between 66.7 and 195.1 mm. In June, the lowest frequency of sand flies was observed, which then increased until a maximum peak in September. Temperature data and rainfall index were not correlated with the density of specimens, especially as the study was carried out over only one year. In this study, the opposite was observed for Ny. intermedia and Ny. whitmani in the month of May, one of the months with the lowest density.In the city of Petrópolis, state of Rio de Janeiro, Souza et al.24 observed a prevalence of Ny. intermedia and Ny. whitmani, with the latter species prevailing around the residence. Migonemyia migonei and Pi. fischeri were also present but to a lesser extent. In the forest, Ny. whitmani was more abundant, followed by Pi. fischeri, while Ny. intermedia was found at lower abundances. However, Ny. intermedia and Pi. fischeri were present during every month of the year. The authors also found a significant correlation between the number of sand flies and environmental changes such as temperature, relative humidity, and rainfall. The same was observed, in this study, in the forest with Ny. intermedia, however, in this environment the number of Pi. fischeri specimens was higher than that of Ny. whitmani.In the north of Espírito Santo, Virgens et al.37 observed that Ny. intermedia was present in almost every month of the study period, with peaks in the warmer and wetter months. The authors highlighted that the low numbers of this species were recorded during and after high rainfall periods, suggesting that heavy rain is unfavorable for the development of immature forms, as breeding sites in altered habitats suffered a greater impact because of extreme weather conditions.In a study carried out by Guimarães et al.38 to observe the competence of Mg. Migonei to Leishmania infantum, concluded that this species is highly susceptible to the development of this parasite and that in addition to its anthropophilia and abundance in areas with an active focus of visceral leishmaniasis, it can act as a vector of this disease in Latin America.In the studied area, Ny. intermedia, one of the main vectors of the etiological agent of tegumentary leishmaniasis in the region2, was present in significant numbers in the home environment throughout all months of the year. The species Pi. fischeri was present over the months in expressive numbers in all types and locations of capture, that is, both in the environment altered by human activity and in the natural environment where leishmaniasis occurs in its natural enzootic cycle. Migonemyia migonei, present throughout the year in the peridomestic environment, showed its association with the dog, where it was prevalent throughout the year in the kennel, being an important vector of the etiological agent of tegumentary leishmaniasis, as well as being suspected in areas of visceral leishmaniasis transmission, where the main vector of this disease is not found. And Ny. whitmani present in the peridomicile, mainly in the hottest months of the year, in addition to the forest and forest margins, it was observed that in this study region the species is emerging through a selective process of adaptation in environments that were negatively affected by the increase of human activity. Thus, despite observing a period of greater frequency of sand flies in the hottest months of the year, a period with high rainfall, the high relative humidity is observed throughout the year, as well as the presence of species of epidemiological importance Ny. intermedia, Pi. fischeri, Mg. migonei and Ny. whitmani, who are involved in the propagation of the etiological agent of tegumentary leishmaniasis to humans and animals, causing greater contact between the region’s inhabitants with these dipterans and thus, a greater risk of contracting the disease. More

RESEARCH HIGHLIGHT
03 March 2023

Human hunt at least 19% of bat species worldwide — especially flying foxes, which can have wingspans of 1.5 metres. More

Hui, C. & Richardson, D. M. Invasion Dynamics (Oxford University Press, 2017).Book 
MATH 

Google Scholar 
Clobert, J., Baguette, M., Benton, T. G. & Bullock, J. M. Dispersal Ecology and Evolution (Oxford University Press, 2012).Book 

Google Scholar 
Shigesada, N., Kawasaki, K. & Takeda, Y. Modeling stratified diffusion in biological invasions. Am. Nat. 146, 229–251 (1995).Article 

Google Scholar 
Chuang, A. & Peterson, C. R. Expanding population edges: Theories, traits, and trade-offs. Glob. Change Biol. 22, 494–512 (2016).Article 
ADS 

Google Scholar 
Cayuela, H. et al. Determinants and consequences of dispersal in vertebrates with complex life cycles: A review of pond-breeding amphibians. Q. Rev. Biol. 95, 36 (2020).Article 

Google Scholar 
Measey, G. J. et al. A global assessment of alien amphibian impacts in a formal framework. Divers. Distrib. 22, 970–981 (2016).Article 

Google Scholar 
Antonelli, A., Smith, R. J., Perrigo, A. L. & Crottini, A. Madagascar’s extraordinary biodiversity: Evolution, distribution, and use. Science 378, eabf0869 (2022).
Article 
CAS 
PubMed 

Google Scholar 
Marshall, B. M. et al. Widespread vulnerability of Malagasy predators to the toxins of an introduced toad. Curr. Biol. 28, R654–R655 (2018).Article 
CAS 
PubMed 

Google Scholar 
Licata, F. et al. Toad invasion of Malagasy forests triggers severe mortality of a predatory snake. Biol. Inv. 24, 1189–1198 (2022).Article 

Google Scholar 
Licata, F. et al. Abundance, distribution and spread of the invasive Asian toad Duttaphrynus melanostictus in eastern Madagascar. Biol. Inv. 21, 1615–1626 (2019).Article 

Google Scholar 
McClelland, P., Reardon, J. T., Kraus, F., Raxworthy, C. J. & Randrianantoandro, C. Asian toad eradication feasibility report for Madagascar (Te Anau, 2015).Smith, M. A. & Green, D. M. Dispersal and the metapopulation paradigm in amphibian ecology and conservation: Are all amphibian populations metapopulations?. Ecography 28, 110–128 (2005).Article 

Google Scholar 
Shine, R. et al. Increased rates of dispersal of free-ranging cane toads (Rhinella marina) during their global invasion. Sci. Rep. 11, 23574 (2021).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Myles-Gonzalez, E., Burness, G., Yavno, S., Rooke, A. & Fox, M. G. To boldly go where no goby has gone before: Boldness, dispersal tendency, and metabolism at the invasion front. Behav. Ecol. 26, 1083–1090 (2015).Article 

Google Scholar 
Van Petegem, K. H. P. et al. Empirically simulated spatial sorting points at fast epigenetic changes in dispersal behaviour. Evol. Ecol. 29, 299–310 (2015).Article 

Google Scholar 
Stuart, Y. E. et al. Rapid evolution of a native species following invasion by a congener. Science 346, 463–466 (2014).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Licata, F., Andreone, F., Crottini, A., Harison, R. F. & Ficetola, G. F. Does spatial sorting occur in the invasive Asian toad in Madagascar? Insights into the invasion unveiled by morphological analyses. JZSER 2021, 1–9 (2021).
Google Scholar 
Schwarzkopf, L. & Alford, R. A. Nomadic movement in tropical toads. Oikos 96, 492–506 (2002).Article 

Google Scholar 
Brown, G. P., Kelehear, C. & Shine, R. Effects of seasonal aridity on the ecology and behaviour of invasive cane toads in the Australian wet–dry tropics. Funct. Ecol. 25, 1339–1347 (2011).Article 

Google Scholar 
Duellman, W. E. & Trueb, L. Biology of Amphibians (JHU Press, 1994).Book 

Google Scholar 
Wells, K. D. The Ecology and Behavior of Amphibians (University of Chicago Press, 2010). https://doi.org/10.7208/9780226893334.Book 

Google Scholar 
Shaw, A. K., Kokko, H. & Neubert, M. G. Sex difference and Allee effects shape the dynamics of sex-structured invasions. J. Anim. Ecol. 87, 36–46 (2018).Article 
PubMed 

Google Scholar 
Schwarzkopf, L. & Alford, R. A. Desiccation and shelter-site use in a tropical amphibian: Comparing toads with physical models. Funct. Ecol. 10, 193–200 (1996).Article 

Google Scholar 
Wogan, G. O. U., Stuart, B. L., Iskandar, D. T. & McGuire, J. A. Deep genetic structure and ecological divergence in a widespread human commensal toad. Biol. Lett. 12, 20150807 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Licata, F. Exploring the invasion dynamics and impacts of the invasive Asian common toad in Madagascar (University of Porto, 2022).
Google Scholar 
Reilly, S. B. et al. Toxic toad invasion of Wallacea: A biodiversity hotspot characterized by extraordinary endemism. Glob. Change Biol. 23, 5029–5031 (2017).Article 
ADS 

Google Scholar 
Jørgensen, C. B., Shakuntala, K. & Vijayakumar, S. Body size, reproduction and growth in a tropical toad, Bufo melanostictus, with a comparison of ovarian cycles in tropical and temperate zone anurans. Oikos 46, 379 (1986).Article 

Google Scholar 
Vences, M. et al. Tracing a toad invasion: Lack of mitochondrial DNA variation, haplotype origins, and potential distribution of introduced Duttaphrynus melanostictus in Madagascar. Amphib. Reptilia 38, 197–207 (2017).Article 

Google Scholar 
Ngo, B. V. & Ngo, C. D. Reproductive activity and advertisement calls of the Asian common toad Duttaphrynus melanostictus (Amphibia, Anura, Bufonidae) from Bach Ma National Park, Vietnam. Zool. Stud. 52, 12 (2013).Article 

Google Scholar 
Licata, F. et al. The Asian toad (Duttaphrynus melanostictus) in Madagascar: A report of an ongoing invasion. In Problematic Wildlife II: New Conservation and Management Challenges in the Human-Wildlife Interactions (eds Angelici, F. M. & Rossi, L.) 617–638 (Springer, 2020). https://doi.org/10.1007/978-3-030-42335-3_21.Chapter 

Google Scholar 
Moore, M., Solofo Niaina Fidy, J. F. & Edmonds, D. The new toad in town: Distribution of the Asian toad, Duttaphrynus melanostictus, in the Toamasina area of eastern Madagascar. Trop. Conserv. Sci. 8, 440–455 (2015).Article 

Google Scholar 
Licata, F. et al. Using public surveys to rapidly profile biological invasions in hard-to-monitor areas. Anim. Conserv. https://doi.org/10.1111/acv.12835 (2023).Article 

Google Scholar 
Zhang, M. et al. Automatic high-resolution land cover production in madagascar using sentinel-2 time series, tile-based image classification and google earth engine. Remote Sensing 12, 3663 (2020).Article 
ADS 

Google Scholar 
Peel, M. C., Finlayson, B. L. & Mcmahon, T. A. Updated world map of the Köppen-Geiger climate classification. Hydrol. Earth Syst. Sci. 4, 439–473 (2007).
Google Scholar 
Merkel, A. Toamasina Climate (Madagascar). Accessed 20 July 2022. https://en.climate-data.org/africa/madagascar/toamasina/toamasina-4029/
(2021).Gordon, A. Secondary sexual characters of Bufo melanostictus schneider. Copeia 1933, 204–207 (1933).Article 

Google Scholar 
Alford, R. & Rowley, J. Techniques for tracking amphibians: The effects of tag attachment, and harmonic direction finding versus radio telemetry. Amphib. Reptilia 28, 367–376 (2007).Article 

Google Scholar 
Lassueur, T., Joost, S. & Randin, C. F. Very high resolution digital elevation models: Do they improve models of plant species distribution?. Ecol. Modell. 198, 139–153 (2006).Article 

Google Scholar 
Abrams, M., Crippen, R. & Fujisada, H. ASTER global digital elevation model (GDEM) and ASTER global water body dataset (ASTWBD). Remote Sensing 12, 1156 (2020).Article 
ADS 

Google Scholar 
Brown, G. P., Phillips, B. L., Webb, J. K. & Shine, R. Toad on the road: Use of roads as dispersal corridors by cane toads (Bufo marinus) at an invasion front in tropical Australia. Biol. Conserv. 133, 88–94 (2006).Article 

Google Scholar 
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).Article 
MATH 

Google Scholar 
Hijmans, R. J. et al. raster: Geographic data analysis and modeling. https://CRAN.R-project.org/package=raster (2021).Yagi, K. T. & Green, D. M. Performance and movement in relation to postmetamorphic body size in a pond-breeding amphibian. J. Herpetol. 51, 482–489 (2017).Article 

Google Scholar 
Labocha, M. K., Schutz, H. & Hayes, J. P. Which body condition index is best?. Oikos 123, 111–119 (2014).Article 

Google Scholar 
Tingley, R. & Shine, R. Desiccation risk drives the spatial ecology of an invasive anuran (Rhinella marina) in the australian semi-desert. PLoS ONE 6, e25979 (2011).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Richards, S. J., Sinsch, U. & Alford, R. A. Radio Tracking. In Measuring and Monitoring Biological Diversity: Standard Methods for Amphibians (eds Heyer, R. et al.) 155–158 (Smithsonian Institution, 1994).
Google Scholar 
Altobelli, J. T., Dickinson, K. J. M., Godfrey, S. S. & Bishop, P. J. Methods in amphibian biotelemetry: Two decades in review. Austral. Ecol. 47, 1382–1395 (2022).Article 

Google Scholar 
Dormann, C. F. et al. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46 (2013).Article 

Google Scholar 
Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (Springer, 2002). https://doi.org/10.1007/978-1-4757-2917-7_3.Book 
MATH 

Google Scholar 
Richards, S. A., Whittingham, M. J. & Stephens, P. A. Model selection and model averaging in behavioural ecology: The utility of the IT-AIC framework. Behav. Ecol. Sociobiol. 65, 77–89 (2011).Article 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. (2021).Bates, D. et al. lme4: Linear Mixed-Effects Models using ‘Eigen’ and S4. (2020).Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest package: Tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).Article 

Google Scholar 
Barton, K. MuMIn: Multi-Model Inference. (2022).Hodges, C. W., Marshall, B. M., Hill, J. G. & Strine, C. T. Malayan kraits (Bungarus candidus) show affinity to anthropogenic structures in a human dominated landscape. bioRxiv https://doi.org/10.1101/2021.09.08.459477 (2021).Article 

Google Scholar 
Muller, B. J., Cade, B. S. & Schwarzkopf, L. Effects of environmental variables on invasive amphibian activity: Using model selection on quantiles for counts. Ecosphere 9, e02067 (2018).Article 

Google Scholar 
Linsenmair, K. E. & Spieler, M. Migration patterns and diurnal use of shelter in a ranid frog of a West African savannah: A telemetric study. Amphib. Reptilia 19, 43–64 (1998).Article 

Google Scholar 
Clobert, J., Le Galliard, J.-F., Cote, J., Meylan, S. & Massot, M. Informed dispersal, heterogeneity in animal dispersal syndromes and the dynamics of spatially structured populations. Ecol. Lett. 12, 197–209 (2009).Article 
PubMed 

Google Scholar 
Ward-Fear, G., Greenlees, M. J. & Shine, R. Toads on lava: spatial ecology and habitat use of invasive cane yoads (Rhinella marina) in Hawai’i. PLoS ONE 11, e0151700 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Huang, W.-S., Lin, J.-Y. & Yu, J.Y.-L. Male reproductive cycle of the toad Bufo melanostictus in Taiwan. Zool. Sci. 14, 497–503 (1997).Article 

Google Scholar 
Brown, G. P., Phillips, B. L. & Shine, R. The straight and narrow path: the evolution of straight-line dispersal at a cane toad invasion front. Proc. R. Soc. B 281, 20141385 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Perkins, T. A., Phillips, B. L., Baskett, M. L. & Hastings, A. Evolution of dispersal and life history interact to drive accelerating spread of an invasive species. Ecol. Lett. 16, 1079–1087 (2013).Article 
PubMed 

Google Scholar 
Ochocki, B. M. & Miller, T. E. X. Rapid evolution of dispersal ability makes biological invasions faster and more variable. Nat. Commun. 8, 14315 (2017).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Phillips, B. L., Brown, G. P., Travis, J. M. J. & Shine, R. Reid’s paradox revisited: The evolution of dispersal kernels during range expansion. Am. Nat. 172, S34–S48 (2008).Article 
PubMed 

Google Scholar 
Kot, M., Lewis, M. A. & van den Driessche, P. Dispersal data and the spread of invading organisms. Ecology 77, 2027–2042 (1996).Article 

Google Scholar 
Deguise, I. & Richardson, J. S. Movement behaviour of adult western toads in a fragmented, forest landscape. Can. J. Zool. 87, 1184–1194 (2009).Article 

Google Scholar 
Mitrovich, M. J., Gallegos, E. A., Lyren, L. M., Lovich, R. E. & Fisher, R. N. Habitat use and movement of the endangered Arroyo toad (Anaxyrus californicus) in coastal southern California. J. Herpetol. 45, 319–328 (2011).Article 

Google Scholar 
Urban, M. C., Phillips, B. L., Skelly, D. K. & Shine, R. A toad more traveled: The heterogeneous invasion dynamics of cane toads in Australia. Am. Nat. 171, E134–E148 (2008).Article 
PubMed 

Google Scholar 
Enriquez-Urzelai, U., Montori, A., Llorente, G. A. & Kaliontzopoulou, A. Locomotor mode and the evolution of the hindlimb in western mediterranean anurans. Evol. Biol. 42, 199–209 (2015).Article 

Google Scholar 
Junior, B. T. & Gomes, F. R. Relation between water balance and climatic variables associated with the geographical distribution of anurans. PLoS ONE 10, e0140761 (2015).Article 

Google Scholar 
Klockmann, M., Günter, F. & Fischer, K. Heat resistance throughout ontogeny: Body size constrains thermal tolerance. Glob. Change Biol. 23, 686–696 (2017).Article 
ADS 

Google Scholar 
Petrovskii, S., Mashanova, A. & Jansen, V. A. A. Variation in individual walking behavior creates the impression of a Lévy flight. PNAS 108, 8704–8707 (2011).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lindström, T., Brown, G. P., Sisson, S. A., Phillips, B. L. & Shine, R. Rapid shifts in dispersal behavior on an expanding range edge. PNAS 110, 13452–13456 (2013).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Tingley, R. et al. New weapons in the toad toolkit: A review of methods to control and mitigate the biodiversity impacts of invasive Cane toads (Rhinella marina). Q. Rev. Biol. 92, 123–149 (2017).Article 
PubMed 

Google Scholar 
Novoa, A. et al. Invasion syndromes: A systematic approach for predicting biological invasions and facilitating effective management. Biol. Invasions 22, 1801–1820 (2020).Article 

Google Scholar 
DeVore, J. L., Crossland, M. R., Shine, R. & Ducatez, S. The evolution of targeted cannibalism and cannibal-induced defenses in invasive populations of cane toads. Proc. Natl. Acad. Sci. 118, e2100765118 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Muller, B. J. & Schwarzkopf, L. Relative effectiveness of trapping and hand-capture for controlling invasive cane toads (Rhinella marina). Int. J. Pest Manag. 64, 185–192 (2018).Article 
CAS 

Google Scholar  More

Dillon, M. E., Wang, G. & Huey, R. B. Nature 467, 704–706 (2010).Article 
CAS 

Google Scholar 
Gillooly, J. F., Brown, J. H., West, G. B., Savage, V. M. & Charnoy, E. L. Science 293, 2248–2251 (2001).Article 
CAS 

Google Scholar 
Alton, L. A. & Kellermann, V. Nat. Clim. Change https://doi.org/10.1038/s41558-023-01607-6 (2023).Article 

Google Scholar 
Lighton, J. R. B. Measuring Metabolic Rates: A Manual for Scientists (Oxford Univ. Press, 2019).Careau, V., Killen, S. S. & Metcalfe, N. B. in Integrative Organismal Biology (eds Martin, L. B. et al.) Ch. 14, 219–233 (John Wiley & Sons, 2014).Speakman, J. R., Selman, C., Mclaren, J. S. & Harper, E. J. J. Nutr. 132, 1583S–1597S (2002).Article 
CAS 

Google Scholar 
Janča, M. & Gvoždík, L. Sci. Rep. 7, 5177 (2017).Article 

Google Scholar 
Seebacher, F., White, C. R. & Franklin, C. E. Nat. Clim. Change 5, 61–66 (2016).Article 

Google Scholar  More

A hippo swims in Colombia’s Magdalena River, near where Pablo Escobar’s compound was located.Credit: Fernando Vergara/AP/Shutterstock

Colombian environment minister Susana Muhamad has triggered fear among researchers that she will protect, rather than reduce, a growing population of invasive hippos that threaten the country’s natural ecosystems and biodiversity. Although she did not directly mention the hippos — a contentious issue in Colombia — Muhamad said during a speech in late January that her ministry would create policies that prioritize animal well-being, including the creation of a new division of animal protection.
Landmark Colombian bird study repeated to right colonial-era wrongs
The hippos escaped from drug-cartel leader Pablo Escobar’s estate after he died in 1993. Left alone, the male and three females that Escobar had illegally imported from a US zoo established themselves in Colombia’s Magdalena River and some small lakes nearby — part of the country’s main watershed. After years of breeding, the ‘cocaine hippos’ have multiplied to about 150 individuals, scientists estimate.Given that the hippos (Hippopotamus amphibius) — considered the largest invasive animal in the world — have no natural predators in Colombia and have been mating at a steady rate, their population could reach 1,500 in 16 years, according to a modelling study published in 20211. “I do not understand what the government is waiting for to act,” says Nataly Castelblanco Martínez, a Colombian conservation biologist at the Autonomous University of Quintana Roo in Chetumal, Mexico, and co-author of the study. “If we don’t do anything, 20 years from now the problem will have no solution.”Researchers have called for a strict management plan that would eventually reduce the wild population to zero, through a combination of culling some animals and capturing others, then relocating them to facilities such as zoos. But the subject of what to do with the hippos has polarized the country, with some enamoured by the animals’ charisma and value as a tourist attraction and others concerned about the threat they pose to the environment and local fishing communities.‘A bit surreal’Several studies and observations suggest how destructive it could be to allow the Colombian hippo population to explode. A 2019 paper2, for example, showed that, compared with lakes without hippos, those where the animals have taken up residence contain more nutrients and organic matter that favour the growth of cyanobacteria — aquatic microbes associated with toxic algal blooms. These blooms can reduce water quality and cause mass fish deaths, affecting local fishing communities.

A sign near Doradal, Colombia, warns passersby of the danger of invasive hippos.Credit: Juancho Torres/Anadolu Agency via Getty

Other scientists have predicted that the hippos could displace endangered species that are native to the Magdalena River, such as the Antillean manatee (Trichechus manatus manatus), by outcompeting them for food and space. They caution that traffic accidents and attacks on people caused by the hippos will become more common. And they warn that wildlife traffickers are already taking advantage of the situation by illegally selling baby hippos — a trend that could intensify.“It’s a bit surreal,” says Jorge Moreno Bernal, a vertebrate palaeontologist at the University of the North in Barranquilla, Colombia. “This is just a taste of what may come.”When Colombian authorities first recognized the speed at which the hippo population was growing, during the 2000s, they acted to reduce their numbers. But in 2009, when photos appeared online after soldiers gunned down Pepe, Escobar’s fugitive male hippo, the outcry from animal-rights activists and others plunged the environment ministry into an “institutional paralysis”, says Sebastián Restrepo Calle, an ecologist at Javeriana University in Bogotá.Researchers say that the hippos don’t belong in Colombia — they are native to sub-Saharan Africa. Simulations run by Castelblanco Martínez and her colleagues suggest that to reduce the population to zero by 2033, about 30 hippos would need to be removed from the wild population per year1. No other course of action, including sterilization or castration, would eradicate them, according to the modelling of various management scenarios, says Castelblanco Martínez.The cost of inactionThe worry now is that, instead of basing decisions on evidence and expertise in conservation, the government is listening to popular opinion, says Restrepo Calle. Neither Muhamad nor representatives of the environment ministry replied to Nature’s requests for comment.
Ancient stone tools suggest early humans dined on hippo
“Why prioritize one species over our own ecosystems?” — especially a species that isn’t native, asks Alejandra Echeverri, a Colombian conservation scientist at Stanford University in California. Along with her colleagues, Echeverri published a study last month showing that Colombia has few policies governing invasive species compared with its overall number of biodiversity policies3.Animals-rights advocates, meanwhile, argue that they aren’t ignoring environmental concerns. Luis Domingo Gómez Maldonado, an animal-rights activist and specialist in animal law at Saint Thomas University in Bogotá, says “It’s not about saving the hippos on a whim,” but rather about solving the issue while also giving the hippos justice. “My indisputable position is: let’s save as many individuals as possible, let’s do it ethically.”Researchers, too, say they have the animals’ best interests at heart. “Even if [advocates] don’t see it, we care about the hippos,” Castelblanco Martínez says. “The more time that passes, the more hippos will either have to be culled, castrated or captured.”The question is whether environmental authorities will act swiftly to draft and enforce a management plan that is both ethical and effective. Should they sit on the issue for too long, Castelblanco Martínez warns, rural communities that are most affected by the hippos might take matters into their own hands.If the government doesn’t cull them, she says, people will use shotguns to do it. More

Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).Article 

Google Scholar 
Friedlingstein, P. et al. Global carbon budget 2022. Earth Syst. Sci. Data 14, 4811–4900 (2022).Article 

Google Scholar 
Peng, S.-S. et al. Afforestation in China cools local land surface temperature. Proc. Natl Acad. Sci. USA 111, 2915–2919 (2014).Article 

Google Scholar 
Li, Y. et al. Local cooling and warming effects of forests based on satellite observations. Nat. Commun. 6, 6603 (2015).Article 

Google Scholar 
Houghton, R. A. & Nassikas, A. A. Global and regional fluxes of carbon from land use and land cover change 1850-2015. Glob. Biogeochem. Cycles 31, 456–472 (2017).Article 

Google Scholar 
Alkama, R. & Cescatti, A. Biophysical climate impacts of recent changes in global forest cover. Science 351, 600–604 (2016).Article 

Google Scholar 
Longo, M. et al. Aboveground biomass variability across intact and degraded forests in the Brazilian Amazon. Glob. Biogeochem. Cycles 30, 1639–1660 (2016).Article 

Google Scholar 
Qie, L. et al. Long-term carbon sink in Borneo’s forests halted by drought and vulnerable to edge effects. Nat. Commun. 8, 1966 (2017).Article 

Google Scholar 
Smith, I. A., Hutyra, L. R., Reinmann, A. B., Marrs, J. K. & Thompson, J. R. Piecing together the fragments: elucidating edge effects on forest carbon dynamics. Front. Ecol. Environ. 16, 213–221 (2018).Article 

Google Scholar 
Franklin, C. M. A., Harper, K. A. & Clarke, M. J. Trends in studies of edge influence on vegetation at human-created and natural forest edges across time and space. Can. J. For. Res. 51, 274–282 (2020).Article 

Google Scholar 
Hansen, M. C. et al. The fate of tropical forest fragments. Sci. Adv. 6, eaax8574 (2020).Article 

Google Scholar 
Matricardi, E. A. T. et al. Long-term forest degradation surpasses deforestation in the Brazilian Amazon. Science 369, 1378–1382 (2020).Article 

Google Scholar 
Baccini, A. et al. Tropical forests are a net carbon source based on aboveground measurements of gain and loss. Science 358, 230–234 (2017).Article 

Google Scholar 
Qin, Y. et al. Carbon loss from forest degradation exceeds that from deforestation in the Brazilian Amazon. Nat. Clim. Change 11, 442–448 (2021).Article 

Google Scholar 
Vancutsem, C. et al. Long-term (1990–2019) monitoring of forest cover changes in the humid tropics. Sci. Adv. 7, eabe1603 (2021).Article 

Google Scholar 
Schoene, D., Killmann, W., Lüpke, H. V. & LoycheWilkie, M. Forests and Climate Change Working Paper 5: Definitional Issues Related to Reducing Emissions from Deforestation in Developing Countries (FAO, 2007).Goetz, S. J. et al. Measurement and monitoring needs, capabilities and potential for addressing reduced emissions from deforestation and forest degradation under REDD+. Environ. Res. Lett. 10, 123001 (2015).Article 

Google Scholar 
Pearson, T. R. H., Brown, S., Murray, L. & Sidman, G. Greenhouse gas emissions from tropical forest degradation: an underestimated source. Carbon Balance Manag. 12, 3 (2017).Article 

Google Scholar 
Cadenasso, M. L., Traynor, M. M. & Pickett, S. T. Functional location of forest edges: gradients of multiple physical factors. Can. J. For. Res. 27, 774–782 (1997).Article 

Google Scholar 
Schmidt, M., Jochheim, H., Kersebaum, K.-C., Lischeid, G. & Nendel, C. Gradients of microclimate, carbon and nitrogen in transition zones of fragmented landscapes – a review. Agric. For. Meteorol. 232, 659–671 (2017).Article 

Google Scholar 
Bonan, G. B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449 (2008).Article 

Google Scholar 
Silva Junior, C. H. L. et al. Amazonian forest degradation must be incorporated into the COP26 agenda. Nat. Geosci. 14, 634–635 (2021).Article 

Google Scholar 
Bala, G. et al. Combined climate and carbon-cycle effects of large-scale deforestation. Proc. Natl Acad. Sci. USA 104, 6550–6555 (2007).Article 

Google Scholar 
Windisch, M. G., Davin, E. L. & Seneviratne, S. I. Prioritizing forestation based on biogeochemical and local biogeophysical impacts. Nat. Clim. Change 11, 867–871 (2021).Article 

Google Scholar 
Santoro, M. et al. The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations. Earth Syst. Sci. Data 13, 3927–3950 (2021).Article 

Google Scholar 
Chuvieco, E. et al. Generation and analysis of a new global burned area product based on MODIS 250 m reflectance bands and thermal anomalies. Earth Syst. Sci. Data 10, 2015–2031 (2018).Article 

Google Scholar 
Zhao, Z. et al. Fire enhances forest degradation within forest edge zones in Africa. Nat. Geosci. https://doi.org/10.1038/s41561-021-00763-8 (2021).Cook, M., Schott, J. R., Mandel, J. & Raqueno, N. Development of an operational calibration methodology for the Landsat thermal data archive and initial testing of the atmospheric compensation component of a land surface temperature (LST) product from the archive. Remote Sens. https://doi.org/10.3390/rs61111244 (2014).Wan, Z. New refinements and validation of the collection-6 MODIS land-surface temperature/emissivity product. Remote Sens. Environ. 140, 36–45 (2014).Article 

Google Scholar 
Broadbent, E. N. et al. Forest fragmentation and edge effects from deforestation and selective logging in the Brazilian Amazon. Biol. Conserv. 141, 1745–1757 (2008).Article 

Google Scholar 
Chaplin-Kramer, R. et al. Degradation in carbon stocks near tropical forest edges. Nat. Commun. 6, 10158 (2015).Article 

Google Scholar 
Silva Junior, C. et al. Persistent collapse of biomass in Amazonian forest edges following deforestation leads to unaccounted carbon losses. Sci. Adv. 6, eaaz8360 (2020).Article 

Google Scholar 
Laurance, W. F. et al. Biomass collapse in Amazonian forest fragments. Science 278, 1117–1118 (1997).Article 

Google Scholar 
Mu, Q., Zhao, M. & Running, S. W. Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sens. Environ. 115, 1781–1800 (2011).Article 

Google Scholar 
Zheng, C., Jia, L. & Hu, G. Global land surface evapotranspiration monitoring by ETMonitor model driven by multi-source satellite Earth observations. J. Hydrol. 613, 128444 (2022).Article 

Google Scholar 
Brinck, K. et al. High resolution analysis of tropical forest fragmentation and its impact on the global carbon cycle. Nat. Commun. 8, 14855 (2017).Article 

Google Scholar 
Laurance, W. F. et al. The fate of Amazonian forest fragments: a 32-year investigation. Biol. Conserv. 144, 56–67 (2011).Article 

Google Scholar 
de Paula, M. D., Costa, C. P. A. & Tabarelli, M. Carbon storage in a fragmented landscape of Atlantic forest: the role played by edge-affected habitats and emergent trees. Trop. Conserv. Sci. 4, 349–358 (2011).Article 

Google Scholar 
van der Werf, G. R. et al. Global fire emissions estimates during 1997–2016. Earth Syst. Sci. Data 9, 697–720 (2017).Article 

Google Scholar 
Gillett, N. P., Arora, V. K., Matthews, D. & Allen, M. R. Constraining the ratio of global warming to cumulative CO2 emissions using CMIP5 simulations. J. Clim. 26, 6844–6858 (2013).Article 

Google Scholar 
Bowman, D. M. J. S. et al. Vegetation fires in the Anthropocene. Nat. Rev. Earth Environ. 1, 500–515 (2020).Article 

Google Scholar 
Kozlowski, T. T. Responses of woody plants to flooding and salinity. Tree Physiol. 17, 490–490 (1997).Article 

Google Scholar 
Garnett, S. T. et al. A spatial overview of the global importance of Indigenous lands for conservation. Nat. Sustain. 1, 369–374 (2018).Article 

Google Scholar 
Sze, J. S., Carrasco, L. R., Childs, D. & Edwards, D. P. Reduced deforestation and degradation in Indigenous lands pan-tropically. Nat. Sustain. 5, 123–130 (2022).Article 

Google Scholar 
Masson-Delmotte, V. et al. IPCC: Summary for Policymakers. In Climate Change 2021: The Physical Science Basis (eds) (Cambridge Univ. Press, 2021).Santoro, M. & Cartus, O. ESA Biomass Climate Change Initiative (Biomass_cci): Global Datasets of Forest Above-Ground Biomass for the Years 2010, 2017 and 2018, v3 (NERC EDS Centre for Environmental Data Analysis, 2021); https://doi.org/10.5285/5f331c418e9f4935b8eb1b836f8a91b8Gerland, P. et al. World population stabilization unlikely this century. Science 346, 234–237 (2014).Article 

Google Scholar 
Alkama, R. et al. Vegetation-based climate mitigation in a warmer and greener world. Nat. Commun. 13, 606 (2022).Article 

Google Scholar 
Duveiller, G., Hooker, J. & Cescatti, A. The mark of vegetation change on Earth’s surface energy balance. Nat. Commun. 9, 679 (2018).Article 

Google Scholar 
Matthews, H. D., Gillett, N. P., Stott, P. A. & Zickfeld, K. The proportionality of global warming to cumulative carbon emissions. Nature 459, 829–832 (2009).Article 

Google Scholar 
Li, W. et al. Land-use and land-cover change carbon emissions between 1901 and 2012 constrained by biomass observations. Biogeosciences 14, 5053–5067 (2017).Article 

Google Scholar 
Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).Article 

Google Scholar  More

Test organisms and exposuresIn this study, we used test organisms and reagents according to the Acute Toxicity Test Method of Daphnia magna Straus(Cladocera, Crustacea); ES 04704.1b29. Daphnia magna were fostered at the National Institute of Environmental Research and were adopted. During the test, adult female Daphnia magna over two weeks of age, cultured over several generations, were transferred to a freshly prepared container the day before the test. Daphnia magna are neonates for less than 24 h after birth29. To maintain the sensitivity of the organism, young individuals less than 24 h old that reproduced the following day were used. Individuals of a similar size were selected for the test. Daphnia magna was fed YCT, which is a mixture of green algae in Chlorella sp., yeast, Cerophy II(R), and trout chow. Sufficient amounts of prey were supplied 2 h before the test to minimize the effects of prey during the test. The test medium was prepared by dissolving KCl (8 mg/L), (text {MgSO}_4) (120 mg/L), (text {CaSO}_4 cdot 2 text {H}_2 text {O} ) (120 mg/L), and (text {NaHCO}_3) (192 mg/L) in deionized water.Automatic high-throughput Daphnia magna tracking systemTo build an automatic high-throughput Daphnia magna tracking system, we equipped the system with a video analysis algorithm as well as flow cells (Fig. 1). In the tracking system, six flow cells filled with culture medium were installed in the device. Each flow cell contained 10 Daphnia magna. Subsequently, to automatically measure the state of Daphnia magna, the six flow cells were photographed at 15 frames per second using a camera (Industrial Development Systems imaging) equipped with a CMOSIS sensor capable of infrared imaging. A red light close to the infrared spectrum was placed at the back of the flow cells for uniform illumination and to minimize stress on Daphnia magna. To capture the size and movement of the Daphnia magna as accurately as possible, the camera was set to a frame rate of 15 fps and a resolution of 2048 (times ) 1088 (2.23 MB), using a 12 mm lens. The distance between the flow cell and the camera was set to 16 cm. To measure the number of mobile Daphnia magna, their lethality, and swimming inhibition automatically and simultaneously, one camera for every two cells was used to collect the status data of Daphnia magna. For assessing ecotoxicity, the video analysis system used images obtained from the six flow cells to track each Daphnia magna and estimate key statistics such as the number of mobile individuals, average distance, and radius of activity.Figure 1New automatic high-throughput video tracking system for behavioral analysis using Daphnia magna as a model organismFull size imageThe automatic high-throughput video tracking system in the ecotoxicity measuring device was designed to continuously measure the ecotoxicity of Daphnia magna (Fig. 2). Daphnia magna moves faster at high temperatures and is less active at low temperatures. Thus, a constant temperature module that can be set to an appropriate Daphnia magna habitat temperature (20 ± 2 (^{circ })C) was added to create a suitable culture environment for Daphnia magna29. Natural pseudo-light ((lambda >590) nm, 3000 k) was installed on the upper part of the detector for proper habitat light intensity (500 Lux–1000 Lux). The size of the flow cell was set as small as possible while observing the movement of the Daphnia magna. An automatic feeding system was installed so that food could be injected during the replacement cycle. The six independent multi-flow cells were designed with an automatic dilution injection module; therefore, these flow cells were diluted to six different concentrations (100%, 50%, 25%, 12.5%, 6.25%, and 0%).Figure 2Schematic representation of the automatic high-throughput video tracking systemFull size imageAutomatic tracking algorithmThe CPU used for Daphnia magna tracking was Intel i5-9300H @ 2.40 GHz, with 8 GB of memory and Windows 10 Pro 64-bit operating system. In this experiment, the algorithms were trained using 12 Daphnia magna videos and tested using an additional four Daphnia magna videos. Subsequently, the detection and tracking methods were compared. The videos, each of which had a duration of 30 s, were captured at a rate of 15 frames per second. Generally, for long-time or real-time videos, the following factors must be considered in tracking Daphnia magna: automatic binarization between the object and background, effective classification of Daphnia magna or noise, and the speed of the algorithm. Therefore, to develop an efficient tracking algorithm, we propose the following tracking process (Fig. 3A). In this process, each frame is initially converted into an image and the background is identified from the obtained video (Fig. 3B). The background is the average of the frames over the previous 20 s, and the tracking system takes 20 s to capture the first background image. The background is subtracted from the image for object detection (Fig. 3C). The objects include Daphnia magna and noise such as droplets and sediment. The difference between the background and frame images is binarized, and each area of the binarized values is regarded as an object. Conventionally, the binarized values are manually generated using specific thresholds. In this study, the images are automatically binarized using k-means clustering to select the threshold value. After binarization, several machine learning methods are used to classify the objects as Daphnia magna or noise (Fig. 3D). For a faster tracking algorithm, we use simple machine learning methods such as random forest (RF) and support vector machine (SVM). The predicted Daphnia magna are tracked using SORT24, which is a fast and highly accurate tracking algorithm (Fig. 3E). Finally, based on the tracked results, statistics for assessing ecotoxicity, such as the number of mobile individuals, average distance, and radius of activity, are estimated to evaluate the toxicity of the aquatic environment.Figure 3Automatic Daphnia magna tracking algorithm process. (A) Overview of automatic tracking algorithm process. (B) Image extraction step. (C) Background subtraction step. (D) Daphnia magna detection step. (E) Daphnia magna tracking step.Full size imagek-means clustering for automatic background subtractionMany tracking algorithms assume that the background is fixed. With fixed backgrounds, the difference between the frame and background can be used to identify objects. However, automatically selecting the precise threshold value for image pixel binarization becomes one of the key problems in identifying objects. The proposed method applies k-means clustering to the pixel values of the subtracted image30, and the center value of each calculated cluster mean is selected as the threshold value (Fig. 4). In the k-means clustering method, grouping is repeatedly performed using the distance between data points31. For binarization, two groups are formed. Let (mu _1 (t)) be the mean of pixels less than the threshold and (mu _2(t)) be the mean of pixels greater than the threshold. At first, (mu _1(t), mu _2(t)) are randomly initialized. Subsequently, each pixel is grouped into a closer mean of each group. The above steps are repeated several times until the group experiences a few changes. Finally, the threshold is calculated as an average of the two means.Figure 4Example of automatic threshold value setting for binarization between objects and background using k-means clusteringFull size imageClassification methodsObject detection based solely on the subtraction between the background and frame images may have low accuracy. As the background in the proposed process is the average value of the frame images, noise may occur. Although this noise is removed by threshold selection in binarization, using only the threshold selection is not efficient for long or real-time videos. Therefore, additional noise must be classified and removed using machine learning models, requiring the construction of a database. In the database, the obtained objects are manually labeled as noise or Daphnia magna and are called ground truth. For classification, the resized 8 (times ) 8 image of each object is stored in the database. The resized image is transformed into a feature using the Sobel edge detection algorithm32 and entered as inputs to the classification models. In this study, classification models such as RF33 SVM34 were used.RF is a model that integrates several decision tree models35. All training data are sampled with a replacement for training each decision tree model. The decision tree model is trained to split intervals of each independent variable by minimizing the gini index (Eq. 1) or entropy index (Eq. 2). The gini index and entropy index denote the impurity within the intervals.$$begin{aligned} G= & {} 1- sum _{i=1}^{c} p_i ^2 end{aligned}$$
(1)
$$begin{aligned} E= & {} – sum _{i=1}^{c} p_i log_2 p_i end{aligned}$$
(2)
where (p_i) is a probability within i-th interval, and c is the number of intervals. For better performance, the RF selects independent variables of training data randomly. This step serves to reduce the correlation of each model. If predictions of each decision tree are uncorrelated, then the variance of an integrated prediction of models is smaller than the variance of each model. RF integrates several model predictions using the voting method. An advantage of the RF method is that it avoids overfitting because the model uses the average of many predictions.SVM is a model designed to search for a hyperplane to maximize the distance, or margin, between support vectors. The hyperplane refers to the plane that divides two different groups, and the support vector represents the closest vector to the hyperplane. Let (D=({textbf{x}}_i, y_i), i=1, ldots , n, {textbf{x}}_i in {mathbb {R}}^p, y_n in { -1,1 }) be training data. Suppose that the training data are completely separated linearly by a hyperplane; then, the hyperplane is expressed as Eq. 3.$$begin{aligned} {textbf{w}}^T {textbf{x}} + b = 0, end{aligned}$$
(3)
where ({textbf{w}}) is a weight vector of the hyperplane, and b is a bias. The weight vector is updated by minimizing Eq. 4.$$begin{aligned} L = {1 over 2} {textbf{w}}^T {textbf{w}} text { subject to } y_i ({textbf{w}}^T {textbf{x}} + b) ge 1 end{aligned}$$
(4)
We can transform Eqs. 4 to  5 by using the Lagrange multiplier method.$$begin{aligned} L^* = {1 over 2} {textbf{w}}^T {textbf{w}} – sum _{i=1}^n a_i { y_i ({textbf{w}}^T x_i + {-}) – 1 }, end{aligned}$$
(5)
where (a_i) is the Lagrange multiplier. We can efficiently solve Eq. 5 using a dual form. Furthermore, Eq. 5 can be solved in a case where it is not completely separated using a slack variable and a kernel trick can be used to estimate the nonlinear hyperplane.SORT trackerSORT, one of the frameworks for solving the multiple object tracking (MOT) problem, aims to achieve efficient real-time tracking24. The SORT method framework is created by combining the estimation step and the association step. The estimation step forecasts the next position of each predicted Daphnia magna. The association step matches the forecasting position and next true position of each predicted Daphnia magna. In the estimation step, the SORT framework uses the Kalman filter to forecast the position of the predicted Daphnia magna in the next frame. The position of each predicted Daphnia magna is expressed as Eq. 6.$$begin{aligned} {textbf{x}} = [u,v,s,r,{dot{u}}, {dot{v}}, {dot{s}}]^T end{aligned}$$
(6)
where u and v are the center positions of each predicted Daphnia magna, s is the scale size of the bounding box, and r is the aspect ratio of the bounding box. ({dot{u}}), ({dot{v}}), and ({dot{s}}) are the amounts of change in each variable. In the association step, to associate the forecasting position and true position, the framework adopts the intersection-over-union (IOU)36 as the association metric. The Hungarian algorithm is loaded into the SORT framework to perform fast and efficient Daphnia magna association prediction. In this study, a mixed metric of IOU36 and Euclidean distance37 was used instead of only the IOU that is used in SORT (Eq. 7) for more efficient association.$$begin{aligned} C_{ij} = (1-lambda ) {max_d – d_{ij} over max_d} + lambda cdot IOU_{ij} end{aligned}$$
(7)
where (d_{ij}) is the Euclidean distance between the i-th predicted Daphnia magna in the before frame and the j-th predicted Daphnia magna in the next frame, and (lambda ) is the weight of (IOU_{ij}). (IOU_{ij}) is the IOU between the i-th predicted Daphnia magna in the before-frame and the j-th predicted Daphnia magna in the next frame.MetricsThe binary confusion matrix consists of true positive (TP), true negative (TN), false positive (FP), and false negative (FN)38. TP is the number of cases where the predicted Daphnia magna matches the actual Daphnia magna, TN is the number of cases where the objects predicted as noise are actual noise, FP is the number of cases where the predicted Daphnia magna differs from the actual Daphnia magna, and FN is the number of cases where the objects predicted as noise are not actual noise. In this study, accuracy, recall, precision, and F1 scores (Eq. 8) were used as the metrics for comparing the machine learning methods.$$begin{aligned} begin{aligned} Accuracy&= {TP + FP over TP + TN + FP + FN} \ Recall&= {TP over TP + TN} \ Precision&= {TP over TP + FP} \ F1 score&= 2 times {Precision times Recall over Precision + Recall} end{aligned} end{aligned}$$
(8)
Standard MOT metrics to evaluate tracking performance include multi-object tracking accuracy (MOTA) and multi-object tracking precision (MOTP). An important task of MOT is to identify and track the same object across two frames. Identification (ID) precision (IDP), ID recall (IDR), ID F1 measure (IDF1), and ID switches (IDs) may be used as measures for evaluating the identification and tracking of the same objects39,40.Data analysisThe toxicity test using Daphnia magna was performed following the Korean official Acute Toxicity Test Method29. The test medium was prepared by dissolving KCl (8 mg/L), (text {MgSO}_4) (120 mg/L), (text {CaSO}_4 cdot 2 text {H}_2 text {O} ) (120 mg/L), and (text {NaHCO}_3) (192 mg/L) in deionized water. Considering that Daphnia magna are neonates for less than 24 h after birth29, five neonates were exposed to 50 mL of different concentrations of heavy metals such as Potassium dichromate, Copper(II) sulfate pentahydrate, and Lead(II) sulfate (6.25, 12.5, 25, 50, and 100%) and 50 mL of culture media. Potassium dichromate is a common inorganic reagent used as an oxidizing agent in chemical industries. Copper(II) sulfate pentahydrate is a trace material widely used in industrial processes and agriculture. A significant amount of copper is emitted in semiconductor manufacturing processes, which adversely impacts the aquatic ecosystem. When present as an ion in water, copper can be acutely toxic to aquatic organisms such as Daphnia magna. Lead(II) sulfate is another nonessential and nonbiodegradable heavy metal. It is highly toxic to numerous organisms even at low concentrations and can accumulate in aquatic ecosystems41. Twenty Daphnia magna (four replicates of five each) were exposed to each test solution for 24 h. The term “immobility” means that the Daphnia magna remains stationary after exposure to chemicals such as Potassium dichromate, Copper(II) sulfate pentahydrate, and Lead(II) sulfate. In this study, immobility was used as an endpoint identifier, and the number of mobile Daphnia magna were counted to evaluate the EC50 values for the samples using the ToxCalc 5.0 program (Tidepoll Software, USA).The locomotory responses of Daphnia magna were tested after 0, 12, 18, and 24 h of exposure at different concentrations. Potassium dichromate ((text {K}_2text {Cr}_2text {O}_7)) at 2 mg/L was connected to the Daphnia magna tracking system, and standard toxic substances were automatically diluted to 100%, 50%, 25%, 12.5%, and 6.25%. The automatic high-throughput Daphnia magna tracking system automatically measured the tracking results of a 1-minute-long video at hourly intervals. The average moving distance for 20 s of each Daphnia magna in each chamber was analyzed using a repeated measures ANOVA (RMANOVA). RMANOVA was used for the analysis of data obtained by repeatedly measuring the same Daphnia magna42. It analyzes the concentration effect excluding the time effect at each hour. The time effect means the change in average distance per 20 s. RMANOVA was implemented using the agricolae package of the R 4.0.4 program43. To remove the noise affecting RMANOVA, the Daphnia magna that remained stationary for 20 s or more were removed from the observations. In this study, we used the significance level at 5%. More