Ecology

The impacts of climate change on host–parasite dynamics are particularly complex to predict, as they involve an interplay of both physiological and behavioural factors, from both host and parasite. For example, while warming may increase parasite developmental rates and thus increase transmission, excessive heat may instead exceed thermal limits, leading to higher parasite mortality. Transmission also relates to both the distribution and abundance of host species, which may also shift under changing climates. More

Barnett, T. P. et al. Penetration of human-induced warming into the world’s oceans. Science 309, 284–287 (2005).CAS 
Article 

Google Scholar 
Levitus, S. et al. Global ocean heat content 1955–2008 in light of recently revealed instrumentation problems. Geophys. Res. Lett. 36, L07608 (2009).
Google Scholar 
Poloczanska, E. S. et al. Global imprint of climate change on marine life. Nat. Clim. Change 3, 919–925 (2013).Article 

Google Scholar 
García Molinos, J. et al. Climate velocity and the future global redistribution of marine biodiversity. Nat. Clim. Change 6, 83–88 (2016).Article 

Google Scholar 
Free, C. M. et al. Impacts of historical warming on marine fisheries production. Science 363, 979–983 (2019).CAS 
Article 

Google Scholar 
Hughes, N. F. & Grand, T. C. Physiological ecology meets the ideal-free distribution: predicting the distribution of size-structured fish populations across temperature gradients. Environ. Biol. Fishes 59, 285–298 (2000).Article 

Google Scholar 
Tittensor, D. P. et al. Global patterns and predictors of marine biodiversity across taxa. Nature 466, 1098–1101 (2010).CAS 
Article 

Google Scholar 
Sunday, J. M., Bates, A. E. & Dulvy, N. K. Global analysis of thermal tolerance and latitude in ectotherms. Proc. R. Soc. B 278, 1823–1830 (2011).Article 

Google Scholar 
Waldock, C., Stuart‐Smith, R. D., Edgar, G. J., Bird, T. J. & Bates, A. E. The shape of abundance distributions across temperature gradients in reef fishes. Ecol. Lett. 22, 685–696 (2019).Article 

Google Scholar 
Stuart-Smith, R. D., Edgar, G. J. & Bates, A. E. Thermal limits to the geographic distributions of shallow-water marine species. Nat. Ecol. Evol. 1, 1846–1852 (2017).Article 

Google Scholar 
Pinsky, M. L., Worm, B., Fogarty, M. J., Sarmiento, J. L. & Levin, S. A. Marine taxa track local climate velocities. Science 341, 1239–1242 (2013).CAS 
Article 

Google Scholar 
Beaugrand, G., Edwards, M., Raybaud, V., Goberville, E. & Kirby, R. R. Future vulnerability of marine biodiversity compared with contemporary and past changes. Nat. Clim. Change 5, 695–701 (2015).Article 

Google Scholar 
Trisos, C. H., Merow, C. & Pigot, A. L. The projected timing of abrupt ecological disruption from climate change. Nature 580, 496–501 (2020).CAS 
Article 

Google Scholar 
Levin, L. A. & Le Bris, N. The deep ocean under climate change. Science 350, 766–768 (2015).CAS 
Article 

Google Scholar 
Deutsch, C. A. et al. Impacts of climate warming on terrestrial ectotherms across latitude. Proc. Natl Acad. Sci. USA 105, 6668–6672 (2008).CAS 
Article 

Google Scholar 
Sunday, J. M., Bates, A. E. & Dulvy, N. K. Thermal tolerance and the global redistribution of animals. Nat. Clim. Change 2, 686–690 (2012).Article 

Google Scholar 
Radeloff, V. C. et al. The rise of novelty in ecosystems. Ecol. Appl. 25, 2051–2068 (2015).Article 

Google Scholar 
Lotterhos, K. E., Láruson, Á. J. & Jiang, L.-Q. Novel and disappearing climates in the global surface ocean from 1800 to 2100. Sci. Rep. 11, 15535 (2021).CAS 
Article 

Google Scholar 
Mora, C. et al. The projected timing of climate departure from recent variability. Nature 502, 183–187 (2013).CAS 
Article 

Google Scholar 
Henson, S. A. et al. Rapid emergence of climate change in environmental drivers of marine ecosystems. Nat. Commun. 8, 14682 (2017).Article 

Google Scholar 
Séférian, R. et al. Evaluation of CNRM Earth System Model, CNRM‐ESM2‐1: role of Earth system processes in present‐day and future climate. J. Adv. Model. Earth Syst. 11, 4182–4227 (2019).Article 

Google Scholar 
Gidden, M. J. et al. Global emissions pathways under different socioeconomic scenarios for use in CMIP6: a dataset of harmonized emissions trajectories through the end of the century. Geosci. Model Dev. 12, 1443–1475 (2019).CAS 
Article 

Google Scholar 
Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).Article 

Google Scholar 
Beszczynska-Möller, A., Fahrbach, E., Schauer, U. & Hansen, E. Variability in Atlantic water temperature and transport at the entrance to the Arctic Ocean, 1997–2010. ICES J. Mar. Sci. 69, 852–863 (2012).Article 

Google Scholar 
Sutton, T. T. Vertical ecology of the pelagic ocean: classical patterns and new perspectives. J. Fish. Biol. 83, 1508–1527 (2013).CAS 
Article 

Google Scholar 
Richter, I. Climate model biases in the eastern tropical oceans: causes, impacts and ways forward. WIREs Clim. Change 6, 345–358 (2015).Article 

Google Scholar 
Pozo Buil, M. et al. A dynamically downscaled ensemble of future projections for the California Current System. Front. Mar. Sci. 8, 612874 (2021).Article 

Google Scholar 
Leonard, M. et al. A compound event framework for understanding extreme impacts. WIREs Clim. Change 5, 113–128 (2014).Article 

Google Scholar 
Kwiatkowski, L. et al. Twenty-first century ocean warming, acidification, deoxygenation, and upper-ocean nutrient and primary production decline from CMIP6 model projections. Biogeosciences 17, 3439–3470 (2020).CAS 
Article 

Google Scholar 
Bopp, L. et al. Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models. Biogeosciences 10, 6225–6245 (2013).Article 

Google Scholar 
Cheng, L., Abraham, J., Hausfather, Z. & Trenberth, K. E. How fast are the oceans warming? Science 363, 128–129 (2019).CAS 
Article 

Google Scholar 
Hawkins, E. & Sutton, R. Time of emergence of climate signals. Geophys. Res. Lett. 39, L01702 (2012).Article 

Google Scholar 
Stuart-Smith, R. D., Edgar, G. J., Barrett, N. S., Kininmonth, S. J. & Bates, A. E. Thermal biases and vulnerability to warming in the world’s marine fauna. Nature 528, 88–92 (2015).CAS 
Article 

Google Scholar 
Filbee-Dexter, K. et al. Marine heatwaves and the collapse of marginal North Atlantic kelp forests. Sci. Rep. 10, 13388 (2020).CAS 
Article 

Google Scholar 
Román-Palacios, C. & Wiens, J. J. Recent responses to climate change reveal the drivers of species extinction and survival. Proc. Natl Acad. Sci. USA 117, 4211–4217 (2020).Article 
CAS 

Google Scholar 
Silvy, Y., Guilyardi, E., Sallée, J.-B. & Durack, P. J. Human-induced changes to the global ocean water masses and their time of emergence. Nat. Clim. Change 10, 1030–1036 (2020).CAS 
Article 

Google Scholar 
Cheng, L., Zheng, F. & Zhu, J. Distinctive ocean interior changes during the recent warming slowdown. Sci. Rep. 5, 14346 (2015).CAS 
Article 

Google Scholar 
Brito-Morales, I. et al. Climate velocity reveals increasing exposure of deep-ocean biodiversity to future warming. Nat. Clim. Change 10, 576–581 (2020).CAS 
Article 

Google Scholar 
Frölicher, T. L. & Laufkötter, C. Emerging risks from marine heat waves. Nat. Commun. 9, 650 (2018).Article 
CAS 

Google Scholar 
Oliver, E. C. J. et al. Marine Heatwaves. Ann. Rev. Mar. Sci. 13, 313–342 (2021).Article 

Google Scholar 
Perry, A. L., Low, P. J., Ellis, J. R. & Reynolds, J. D. Climate change and distribution shifts in marine fishes. Science 308, 1912–1915 (2005).CAS 
Article 

Google Scholar 
Chaudhary, C., Richardson, A. J., Schoeman, D. S. & Costello, M. J. Global warming is causing a more pronounced dip in marine species richness around the equator. Proc. Natl Acad. Sci. USA 118, e2015094118 (2021).CAS 
Article 

Google Scholar 
Burrows, M. T. et al. Ocean community warming responses explained by thermal affinities and temperature gradients. Nat. Clim. Change 9, 959–963 (2019).Article 

Google Scholar 
IPCC Climate Change 2022: Impacts, Adaptation, and Vulnerability (eds Pörtner, H.-O. et al.) (Cambridge Univ. Press, 2022).Cahill, A. E. et al. How does climate change cause extinction? Proc. R. Soc. B280, 20121890 (2013).Article 

Google Scholar 
Hastings, R. A. et al. Climate change drives poleward increases and equatorward declines in marine species. Curr. Biol. 30, 1572–1577.e2 (2020).CAS 
Article 

Google Scholar 
Jorda, G. et al. Ocean warming compresses the three-dimensional habitat of marine life. Nat. Ecol. Evol. 4, 109–114 (2020).Article 

Google Scholar 
Dulvy, N. K. et al. Climate change and deepening of the North Sea fish assemblage: a biotic indicator of warming seas. J. Appl. Ecol. 45, 1029–1039 (2008).Article 

Google Scholar 
Thatje, S. Climate warming affects the depth distribution of marine ectotherms. Mar. Ecol. Prog. Ser. 660, 233–240 (2021).Article 

Google Scholar 
Manuel, S. A., Coates, K. A., Kenworthy, W. J. & Fourqurean, J. W. Tropical species at the northern limit of their range: composition and distribution in Bermuda’s benthic habitats in relation to depth and light availability. Mar. Environ. Res. 89, 63–75 (2013).CAS 
Article 

Google Scholar 
Peck, L. S., Webb, K. E. & Bailey, D. M. Extreme sensitivity of biological function to temperature in Antarctic marine species. Funct. Ecol. 18, 625–630 (2004).Article 

Google Scholar 
Peck, L. S., Morley, S. A., Richard, J. & Clark, M. S. Acclimation and thermal tolerance in Antarctic marine ectotherms. J. Exp. Biol. 217, 16–22 (2014).Article 

Google Scholar 
Walsh, J. E. Climate of the Arctic marine environment. Ecol. Appl. 18, S3–S22 (2008).Article 

Google Scholar 
Storch, D., Menzel, L., Frickenhaus, S. & Pörtner, H.-O. Climate sensitivity across marine domains of life: limits to evolutionary adaptation shape species interactions. Glob. Change Biol. 20, 3059–3067 (2014).Article 

Google Scholar 
Araújo, M. B. et al. Heat freezes niche evolution. Ecol. Lett. 16, 1206–1219 (2013).Article 

Google Scholar 
Pörtner, H. O., Peck, L. & Somero, G. Thermal limits and adaptation in marine Antarctic ectotherms: an integrative view. Philos. Trans. R. Soc. B 362, 2233–2258 (2007).Article 
CAS 

Google Scholar 
Qu, Y.-F. & Wiens, J. J. Higher temperatures lower rates of physiological and niche evolution. Proc. R. Soc. B 287, 20200823 (2020).Article 

Google Scholar 
Cohen, D.M., Inada, T., Iwamoto, T. and Scialabba, N. FAO Species Catalogue, Vol. 10. Gadiform Fishes of the World (Order Gadiformes) (FAO, 1990).Strand, E. & Huse, G. Vertical migration in adult Atlantic cod (Gadus morhua). Can. J. Fish. Aquat. Sci. 64, 1747–1760 (2007).Article 

Google Scholar 
Frölicher, T. L., Fischer, E. M. & Gruber, N. Marine heatwaves under global warming. Nature 560, 360–364 (2018).Article 
CAS 

Google Scholar 
Wernberg, T. et al. Climate-driven regime shift of a temperate marine ecosystem. Science 353, 169–172 (2016).CAS 
Article 

Google Scholar 
Smale, D. A. et al. Marine heatwaves threaten global biodiversity and the provision of ecosystem services. Nat. Clim. Change 9, 306–312 (2019).Article 

Google Scholar 
Cheung, W. W. L. & Frölicher, T. L. Marine heatwaves exacerbate climate change impacts for fisheries in the northeast Pacific. Sci. Rep. 10, 6678 (2020).CAS 
Article 

Google Scholar 
Brierley, A. S. & Kingsford, M. J. Impacts of climate change on marine organisms and ecosystems. Curr. Biol. 19, R602–R614 (2009).CAS 
Article 

Google Scholar 
Bijma, J., Pörtner, H.-O., Yesson, C. & Rogers, A. D. Climate change and the oceans—what does the future hold? Mar. Pollut. Bull. 74, 495–505 (2013).CAS 
Article 

Google Scholar 
Jackson, J. B. C. et al. Historical overfishing and the recent collapse of coastal ecosystems. Science 293, 629–637 (2001).CAS 
Article 

Google Scholar 
Duarte, C. M. et al. The soundscape of the Anthropocene ocean. Science 371, eaba4658 (2021).CAS 
Article 

Google Scholar 
Rochman, C. M. & Hoellein, T. The global odyssey of plastic pollution. Science 368, 1184–1185 (2020).CAS 
Article 

Google Scholar 
Gruber, N., Boyd, P. W., Frölicher, T. L. & Vogt, M. Biogeochemical extremes and compound events in the ocean. Nature 600, 395–407 (2021).CAS 
Article 

Google Scholar 
Madec, G. et al. NEMO ocean engine. Zenodo https://www.earth-prints.org/handle/2122/13309 (2017).Mathiot, P., Jenkins, A., Harris, C. & Madec, G. Explicit representation and parametrised impacts of under ice shelf seas in the z∗- coordinate ocean model NEMO 3.6. Geosci. Model Dev. 10, 2849–2874 (2017).Article 

Google Scholar 
Dai, A. & Bloecker, C. E. Impacts of internal variability on temperature and precipitation trends in large ensemble simulations by two climate models. Clim. Dyn. 52, 289–306 (2019).Article 

Google Scholar 
Deser, C., Phillips, A., Bourdette, V. & Teng, H. Uncertainty in climate change projections: the role of internal variability. Clim. Dyn. 38, 527–546 (2012).Article 

Google Scholar 
Middag, R. et al. Intercomparison of dissolved trace elements at the Bermuda Atlantic Time Series station. Mar. Chem. 177, 476–489 (2015).CAS 
Article 

Google Scholar 
Welch, B. L. The generalization of Student’s’ problem when several different population variances are involved. Biometrika 34, 28 (1947).CAS 

Google Scholar 
Lenoir, J. et al. Species better track climate warming in the oceans than on land. Nat. Ecol. Evol. 4, 1044–1059 (2020).Article 

Google Scholar 
Janzen, D. H. Why mountain passes are higher in the Tropics. Am. Nat. 101, 233–249 (1967).Article 

Google Scholar 
Seebacher, F., White, C. R. & Franklin, C. E. Physiological plasticity increases resilience of ectothermic animals to climate change. Nat. Clim. Change 5, 61–66 (2015).Article 

Google Scholar 
Hoffmann, A. A. & Sgrò, C. M. Climate change and evolutionary adaptation. Nature 470, 479–485 (2011).CAS 
Article 

Google Scholar 
Sandblom, E. et al. Physiological constraints to climate warming in fish follow principles of plastic floors and concrete ceilings. Nat. Commun. 7, 11447 (2016).CAS 
Article 

Google Scholar 
Tewksbury, J. J., Huey, R. B. & Deutsch, C. A. Putting the heat on tropical animals. Science 320, 1296–1297 (2008).CAS 
Article 

Google Scholar 
Dahlke, F. T., Wohlrab, S., Butzin, M. & Pörtner, H.-O. Thermal bottlenecks in the life cycle define climate vulnerability of fish. Science 369, 65–70 (2020).CAS 
Article 

Google Scholar  More

Agriculture soils are a source of nitrous oxide and account for 60% of total emissions. It is well established that nitrogen addition via fertilizers drives nitrous oxide emissions during crop growing season. However, little is known about the role of melting snow and thawing surface soil layers during the spring. Limited knowledge of this phenomenon reduces our ability to develop accurate nitrous oxide emissions inventories required under the UN Framework Convention on Climate Change (UNFCCC). More

Nearly two years into the United Nations Decade of Ocean Science, research, including some featured in this month’s issue, shows that there is still a wealth of scientific secrets to uncover in the ocean depths.
In many ways, considering the ocean as a single unit is overly broad. The global ocean covers 71% of the planet’s surface, reaches down to depths of over 10 kilometres, includes about 1.35 billion cubic kilometres of water and houses an approximated 2.2 million eukaryotic species. There are distinct regions, with distinct physical properties, and, in turn, there are distinct species. Yet, the world’s oceans do have a level of physical and thematic connectivity.
Credit: Daria Zaseda / DigitalVision Vectors / GettyPhysically, a large part of the connection is related to the presence of large rotating ocean currents that transfer heat across latitudes and contribute to ocean mixing (thermohaline circulation). Some of these currents are warming at alarming rates — up to three times faster than the rest of the ocean, leading to questions about the underlying mechanisms of the warming and expectations for change.Focusing on western boundary currents (WBCs) in the Southern Hemisphere, in an Article in this issue of Nature Climate Change, Li and colleagues answer a long-debated question on the mechanisms of change, showing that temperature-gradient-related instabilities, rather than flow-speed-related instabilities are behind the shifts. In another Article, focusing on the global future changes of eddies (including eddy-rich WBCs), Beech and colleagues report the development of a flexible method that maximizes local model resolution while minimizing computational costs, to reveal the long-term geographical specificities and nonlinear temperature increases expected to 2100 (see also the News and Views article by Yang on these papers).A recent paper1 has demonstrated the important role of large ocean currents in defining plankton biogeography and dynamics, and WBC warming has previously been linked to impacts such as fishery collapses. The tight link between physical processes and biological responses is an underscoring theme of climate change ecology, but is perhaps more apparent in the open ocean, where physical processes can be easily (if imperfectly) linked to primary productivity using remotely sensed phytoplankton pigment absorption, and where life is generally less impacted by geographical, political or disturbance-based boundaries compared with land and freshwater systems. These aspects may facilitate modelling of current and future communities, while also allowing broader assumptions to be made about biological movement and connectivity.Despite these benefits, understanding ocean change comes with its own difficulties. Biological sampling, while easy enough in the surface waters, becomes increasingly difficult at depth. Although future habitats for various organisms have been projected on the basis of their thermal limits in the ocean, these predictions often still rely on temperatures at the surface of the sea. Addressing this, Santana-Falcón and colleagues report in an Article the global mapping of ocean temperature changes to depths of 1,000 metres, and reveal the complex depth-dependent changes in thermal upper and lower bounds that marine organisms will soon be subjected to. In another Article, Ariza and colleagues neatly address the issue of directly monitoring deep-ocean change by compiling a large database of sound-based observations, and subsequently classifying the ocean’s ‘echobiomes’, defined as sound-scattering communities with comparable structural and functional properties (see also the accompanying News and Views article by Hazen). Sound-based methods are also increasingly being used on land2, and represent an exciting tool for monitoring change, particularly in hard-to-reach places such as deep forests, high mountaintops or underground. While the sound reflection method used in the study by Ariza and colleagues has limits in its ability to identify organisms at the individual or species levels, it does provide a community-level focus on change, which remains much needed in the field of global change ecology.At the other end of the spatial spectrum, research by Lee and colleagues reported in an Article also in this issue dives deep into the DNA of a keystone ocean organism (a copepod), to understand the mechanisms that may allow longer-term adaptation to warming and pH stress. The work reveals remarkable adaptation over just a few short generations, which is linked to epigenetic changes. As climate change impacts continue to escalate, the ability of organisms to invoke both shorter- and longer-term adaptations has become an increasingly relevant area of research. Epigenetics has previously been reported as a quick-response method to cope with environmental stress, and may be particularly relevant in defining the adaptation of short-lived animals such as insects and the resilience of the communities they uphold.The five research pieces linked to the oceans in this issue reveal just some of the diversity of topics, methods and scales relevant to understanding global change. Also increasingly relevant are works on ocean conservation3 and on the social and economic impacts of ocean change4,5. Like climate change science, the topic of ocean change is less of a field, and more of a cross-disciplinary theme. More

MaterialsWe collected from the literature and available databases a dataset of radiocarbon dates from Europe (West of 60°E and North of 37°N) either obtained from remains of the four analyzed species or from archaeological layers where they have been observed. However, we only considered observations dated between 7500 and 47,000 cal BP: their scarcity before this period may bias the GAMs, and after it, domesticated cattle, pigs and (later) horses arrived in Europe, making it difficult to differentiate them from their wild forms.We excluded any record fitting one or more of the following conditions: unreliable; not in accord with the expected chronology of their archaeological layer; without a reported standard error; available only as terminus ante/post quem.All dates were calibrated with OxCal5 version 4.4 using the IntCal20 curve51, and we further excluded any record for which calibration resulted in an error, resulting in the number of points presented in Table 1 as “Original dataset” (available at the link https://doi.org/10.6084/m9.figshare.20510364).Table 1 Number of observations for each species.Full size tableSDMs based on GAMs need presence/background data, not frequencies; moreover, multiple observations (i.e., presence in different archaeological layers) from the same site and time slice are likely to introduce stronger sample biases linked to chrono-geographically differential sampling efforts. For this reason, we collapsed our observations by keeping only one point per grid cell per time slice for each species, leaving the number of observations reported in Table 1 as “Collapsed datasets”, used for all the analyses presented in this work.To perform all analyses, we used the R package pastclim v. 1.042 to couple each observation from the collapsed datasets to paleoclimatic reconstructions published in8 by setting dataset = “Beyer2020”. These are based on the Hadley CM3 model, include 14 different bioclimatic variables at a spatial resolution of 0.5°, and are available for the whole world every 1000 years until 22 kya and every 2000 years before that date (referred to in the manuscript as “time slices”). Specifically, each observation was associated with the relevant bioclimatic reconstruction based on its average age and spatial coordinates.As already mentioned, the four species analyzed show different preferences regarding temperature, habitat, and altitude. Therefore, for the Species Distribution Modelling, we choose five environmental variables that should be able to capture such differences: two measures of temperature (BIO5, maximum temperature of the warmest month, and BIO6, minimum temperature of the coldest month); two variables to help capture habitat differentiation (BIO12, total annual precipitation, and Net Primary Productivity, NPP), and one measure of topography (rugosity42).High collinearity can be problematic in SDMs; we confirmed that all our variables had a correlation below 0.7, a threshold commonly adopted for this kind of analysis52,53.Whilst the GAMs predicted all time points; we visualized our results by creating an average estimate for the following periods: pre-LGM (from the beginning of the time range analyzed, i.e., 47 kya to 27 kya), LGM (from 27 to 18 kya), Late Glacial (from 18 to 11.7 kya), Holocene (from 11.7 kya to the end of the time range analyzed, i.e., 7.5 kya).MethodsWe generated 25 sets of background points for each species to adequately represent the existing climatic space in our SDMs. Each set was generated by sampling, for each observation, 50 random locations matched by time. This resulted in n = 25 datasets (“repetitions”) of background points and presences (observations) for each species, which we used to repeat our analyses to account for the stochastic sampling of the background. For each dataset, we used GAMs to fit two possible models: a “constant niche” model, which included only the environmental variables as covariates, and a “changing niche” model, that also included interactions of each environmental variable with time (fitted as tensor products).In GAMs, the effect of a given continuous predictor on the response variable (in our case, the logit transformed probability of a presence) is represented by a smooth function; this smooth function can be linear or non-linear and can become highly complex in shape depending on the number of knots selected by the GAM fitting algorithm. The interaction between two covariates is modelled by tensor products54; this approach is equivalent to an interaction term in a linear model but with the added complexity of the smooth function. In our models, we confine tensor products to the interaction between an environmental variable and time; a simple way to think about such a tensor product is that it allows the smooth representation of the relationship between the variable and the probability of a presence to change progressively over time.GAMs were fitted using the mgcv package in R54 using thin plate regression splines (TPNR; bs = “tp”, default in mgcv) for environmental variables and their tensor products with time in the “niche changing” models. The GAM algorithm automatically selects the complexity of the smooth most appropriate to the data that are being fitted; as GAM can have issues with overfitting, we added an additional penalty against overly complex smooths (gamma = 1.4) and used Restricted Maximum Likelihood (REML = TRUE), as recommended by54. It is possible that even with these settings, the complexity of the smooth is not sufficient; we used mgcv::gam.check() to check this, and increased the basis dimension of the smooth, k, to make sure that k-1 was larger than the estimated degrees of freedom (edf). We found the best maximum thresholds for k to be 16 for bio06 and 10 for all other variables.We checked for non-linear correlation among variables using the mgcv::collinearity function and checked the values of estimated concurvity. All estimates were below the threshold of 0.8 in all models, runs and variables except for a few instances for time (Supplementary Figs. 5–8). We consider this not to be worrying: this is most likely a result of sample bias, and GAM is known to be robust to correlation/concurvity55,56.We verified the model assumptions by inspecting the residuals using the R package DHARMa57. Standard tests for deviations from the expected distribution and dispersion were non-significant for all repetitions for all species, as were the tests for outliers. Furthermore, we tested for spatial autocorrelation among residuals by computing Moran’s I; all tests were either non-significant or, when significance was detected, the estimate of Moran’s I was very close to zero, revealing a trivial deviation from the assumptions which should not impact the results (Supplementary Tables 1–4).We performed model choice (Supplementary Tables 5–8) by comparing the constant- and changing-niche models for each combination of species and repetition using the Akaike Information Criterion (AIC). AIC strongly supported the changing-niche model in all species and repetitions, an inference supported by the higher Nagelkerke R2 and expected deviance for those models than for the constant-niche ones (Supplementary Tables 5–8).The model fit for each of the changing niche GAMs was evaluated with the Boyce Continuous Index25,26, designed to be used with presence-only data58,59. We set a threshold of Pearson’s correlation coefficient  >  0.8 to define acceptable models25 (Supplementary Table 9).The relative importance of each environmental variable was quantified for all the models above the BCI threshold of 0.8 in two different ways. Firstly, we computed the total deviance explained by each variable by simply fitting a GAM with only that variable. We then estimated the unique deviance explained by each variable by comparing the full model with one for which that variable was excluded (i.e., we computed the explained deviance lost by dropping that predictor). The difference between the two values represents the deviance explained by a variable which can also be accounted for by other variables (i.e., the deviance in common with other variables).To achieve more robust predictions60, we averaged in two different ensembles the repetitions for the changing niche GAMs with BCI  > 0.8: by mean and median. This step is intended to reduce the weight of models that are highly sensitive to the random sampling of the background60. Then, for each species, we selected the ensemble (either based on mean or median) with the higher BCI as the most supported and used it to perform all further analyses.The effect of different variables through time was visualized by plotting the interactions of the GAMs. For each model with a BCI  > 0.8, we used the R package gratia27 to generate a surface with time as the x-axis, the environmental variable as the y-axis, and the effect size as the z-axis (visualized as colour shades). We then plotted the mean surface for each species, which captures the signal consistent across all randomized background sets.To visualize the prediction for each species, we then transformed the predicted probabilities of occurrence from the ensemble into binary presence/absences by using the threshold needed to get a minimum predicted area encompassing 99% of our presences (function ecospat.mpa() from the ecospat R package61). The binary predictions were then visualized using the mean over the time steps within each major climatic period.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

Barrientos, R. et al. A review of searcher efficiency and carcass persistence in infrastructure-driven mortality assessment studies. Biol. Conserv. 222, 146–153 (2018).
Google Scholar 
Stevens, B. S., Reese, K. P. & Connelly, J. W. Survival and detectability bias of avian fence collision surveys in sagebrush steppe. J. Wildl. Manag. 75, 437–449 (2011).
Google Scholar 
Hunting, K. A Roadmap for PIER Research on Avian Collisions with Power Lines in California. (2002).Barrientos, R. et al. Wire marking results in a small but significant reduction in avian mortality at power lines: A baci designed study. PLoS ONE 7, e32569 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Costantini, D., Gustin, M., Ferrarini, A. & Dell’Omo, G. Estimates of avian collision with power lines and carcass disappearance across differing environments. Anim. Conserv. 20, 173–181 (2017).
Google Scholar 
Jenkins, A. R. et al. Estimating the impacts of power line collisions on Ludwig’s Bustards Neotis ludwigii. Bird Conserv. Int. 21, 303–310 (2011).
Google Scholar 
Shaw, J. M., Reid, T. A., Schutgens, M., Jenkins, A. R. & Ryan, P. G. High power line collision mortality of threatened bustards at a regional scale in the Karoo, South Africa. Ibis (Lond. 1859) 1859(160), 431–446 (2018).
Google Scholar 
Gómez-Catasús, J. et al. Factors affecting differential underestimates of bird collision fatalities at electric lines: a case study in the Canary Islands. Ardeola 68, 71–94 (2021).
Google Scholar 
Ponce, C., Alonso, J. C., Argandoña, G., García Fernández, A. & Carrasco, M. Carcass removal by scavengers and search accuracy affect bird mortality estimates at power lines. Anim. Conserv. 13, 603–612 (2010).
Google Scholar 
Bernardino, J. et al. Bird collisions with power lines: State of the art and priority areas for research. Biol. Conserv. 222, 1–13 (2018).
Google Scholar 
Brooks, J. W. & Sutton, L. in Veterinary Forensic Pathology (ed. Brooks, J. W.) 43–63 (2018). https://doi.org/10.1007/978-3-319-67172-7_4Brooks, J. W. Postmortem changes in animal carcasses and estimation of the postmortem interval. Vet. Pathol. 53, 929–940 (2016).CAS 
PubMed 

Google Scholar 
Ascensão, F. et al. Beware that the lack of wildlife mortality records can mask a serious impact of linear infrastructures. Glob. Ecol. Conserv. 19, e00661 (2019).
Google Scholar 
Hau, T. C., Hamzah, N. H., Lian, H. H. & Amir Hamzah, S. P. A. Decomposition process and post mortem changes: Review. Sains Malaysiana 43, 1873–1882 (2014).
Google Scholar 
Cooper, J. E. in Wildlife Forensic Investigation: Principles and Practice (eds. Cooper, J. & Cooper, M.) 237–324 (CRC Press, 2013). https://doi.org/10.1201/b14553Sutherland, A., Myburgh, J., Steyn, M. & Becker, P. J. The effect of body size on the rate of decomposition in a temperate region of South Africa. Forensic Sci. Int. 231, 257–262 (2013).CAS 
PubMed 

Google Scholar 
Valverde, I., Espín, S., María-Mojica, P. & García-Fernández, A. J. Protocol to classify the stages of carcass decomposition and estimate the time of death in small-size raptors. Eur. J. Wildl. Res. 66, 1–13 (2020).
Google Scholar 
Goff, M. L. in Current Concepts in Forensic Entomology (eds. Amendt, J., Goff, M., Campobasso, C. & Grassberger, M.) 1–24 (Springer, 2010). https://doi.org/10.1007/978-1-4020-9684-6_1Pittner, S. et al. A field study to evaluate PMI estimation methods for advanced decomposition stages. Int. J. Legal Med. 134, 1361–1373 (2020).PubMed 
PubMed Central 

Google Scholar 
Probst, C. et al. Estimating the postmortem interval of wild boar carcasses. Vet. Sci. 7, 6 (2020).PubMed Central 

Google Scholar 
Cambra-Moo, Ó., Delgado-Buscalioni, Á. & Delgado-Buscalioni, R. An approach to the study of variations in early stages of Gallus gallus decomposition. J. Taphon. 6, 21–40 (2008).
Google Scholar 
Oates, D., Coggin, J., Hartman, F. & Hoilien, G. Guide to Time of Death in Selected Wildlife Species. (Nebraska Technical Series No. 14. Lincoln, N.E., Nebraska Game and Parks Commission, 1984).Hewadikaram, K. A. & Goff, M. L. Effect of carcass size on rate of decomposition and arthropod succession patterns. Am. J. Forensic Med. Pathol. 12, 240–265 (1991).
Google Scholar 
Zhou, C. & Byard, R. W. Factors and processes causing accelerated decomposition in human cadavers—An overview. J. Forensic Leg. Med. 18, 6–9 (2011).PubMed 

Google Scholar 
Cockle, D. L. & Bell, L. S. Human decomposition and the reliability of a ‘Universal’ model for post mortem interval estimations. Forensic Sci. Int. 253(136), e1-136.e9 (2015).
Google Scholar 
Azevedo, R. R. & Krüger, R. F. The influence of temperature and humidity on abundance and richness of Calliphoridae (Diptera). Iheringia. Série Zool. 103, 145–152 (2013).
Google Scholar 
Barnes, K. M. in Wildlife Forensic Investigation: Principles and Practice (eds. Cooper, J. & Cooper, M.) 149–160 (CRC Press, 2013).Mann, R. W., Bass, W. M. & Meadows, L. Time since death and decomposition of the human body: Variables and observations in case and experimental field studies. J. Forensic Sci. 35, 103–111 (1990).CAS 
PubMed 

Google Scholar 
Gliksman, D. et al. Biotic degradation at night, abiotic degradation at day: Positive feedbacks on litter decomposition in drylands. Glob. Change Biol. 23, 1564–1574 (2017).ADS 

Google Scholar 
Araujo, P. I., Grasso, A. A., González-Arzac, A., Méndez, M. S. & Austin, A. T. Sunlight and soil biota accelerate decomposition of crop residues in the Argentine Pampas. Agric. Ecosyst. Environ. 330, 107908 (2022).
Google Scholar 
Fernández-Palacios, J. M. & Martín-Esquivel, J. L. Naturaleza de las Islas Canarias: Ecología y Conservación. (Turquesa, 2001).Kenward, M. G. & Roger, J. H. An improved approximation to the precision of fixed effects from restricted maximum likelihood. Comput. Stat. Data Anal. 53, 2583–2595 (2009).MathSciNet 
MATH 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. http://www.r-project.org (2020).Bates, D., Mächler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Google Scholar 
Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest package: Tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).
Google Scholar 
Zeileis, A. & Hothorn, T. Diagnostic checking in regression relationships. R News 2, 7–10 (2002).
Google Scholar 
Halekoh, U. & Højsgaard, S. A Kenward–Roger approximation and parametric bootstrap methods for tests in linear mixed models-the R package pbkrtest. J. Stat. Softw. 59, 1–30 (2014).
Google Scholar 
Fox, J. & Weisberg, S. An {R} Companion to Applied Regression, Second Edition. (Sage, 2011).Bartoń, K. MuMIn: Multi-Model Inference. (R Package Version 1.43.6, 2019).De Rosario-Martinez, H., Fox, J. & R Core Team. Package ‘phia’ Title Post-Hoc Interaction Analysis. (R Package Version 0.2–1, 2015).Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol. Evol. 4, 133–142 (2013).
Google Scholar 
Vass, A. Beyond the grave—Understanding human decomposition. Microbiol. Today 28, 190–192 (2001).
Google Scholar 
Gill-King, H. in Forensic Taphonomy: The Postmortem Fate of Human Remains (eds. Haglund, W. D. & Sorg, M. H.) 93–104 (CRC Press, 1996). https://doi.org/10.1201/9781439821923.sec2Campobasso, C. P., Di Vella, G. & Introna, F. Factors affecting decomposition and Diptera colonization. Forensic Sci. Int. 12, 18–27 (2001).
Google Scholar 
Austin, A. T., Araujo, P. I. & Leva, P. E. Interaction of position, litter type, and water pulses on decomposition of grasses from the semiarid Patagonian steppe. Ecology 90, 2642–2647 (2009).PubMed 

Google Scholar 
Brandt, L. A., Bonnet, C. & King, J. Y. Photochemically induced carbon dioxide production as a mechanism for carbon loss from plant litter in arid ecosystems. J. Geophys. Res. Biogeosci. 114, G02004 (2009).ADS 

Google Scholar 
Lee, H., Rahn, T. & Throop, H. An accounting of C-based trace gas release during abiotic plant litter degradation. Glob. Chang. Biol. 18, 1185–1195 (2012).ADS 

Google Scholar 
Zepp, R. G., Erickson, D. J., Paul, N. D. & Sulzberger, B. Interactive effects of solar UV radiation and climate change on biogeochemical cycling. Photochem. Photobiol. Sci. 6, 286–300 (2007).CAS 
PubMed 

Google Scholar 
Archer, M. S. Rainfall and temperature effects on the decomposition rate of exposed neonatal remains. Sci. Justice J. Forensic Sci. Soc. 44, 35–41 (2004).Simmons, T., Adlam, R. E. & Moffatt, C. Debugging decomposition data—Comparative taphonomic studies and the influence of insects and carcass size on decomposition rate. J. Forensic Sci. 55, 8–13 (2010).PubMed 

Google Scholar 
Spicka, A., Johnson, R., Bushing, J., Higley, L. G. & Carter, D. O. Carcass mass can influence rate of decomposition and release of ninhydrin-reactive nitrogen into gravesoil. Forensic Sci. Int. 209, 80–85 (2011).CAS 
PubMed 

Google Scholar 
Tracqui. in Encyclopaedia of Forensic Sciences (eds. Siegel, J. A., Saukko, P. J. & Max, M. H.) 1357–1363 (Academic Press, 2000).Riding, C. S. & Loss, S. R. Factors influencing experimental estimation of scavenger removal and observer detection in bird–window collision surveys. Ecol. Appl. 28, 2119–2129 (2018).PubMed 

Google Scholar  More

Dataset 1: Analysis of truffle growers archivesWe selected eleven T. melanosporum orchards located across the South-West France, from Montpellier (43°44′01.4″N 3°42′13.2″E) to Jonzac (45°27′17.7″N, 0°25′26.9″W; Fig. 2). These sites were selected for (1) the quality of the records of fruitbody production and practices by truffle growers (Table S1), including the detail of inoculations since plantation (amount and frequency of added crushed sporocarps), (2) the use of truffle traps by the owners and the quality of the record from these devices, and (3) the presence of oaks (Quercus ilex, Q. pubescens and Q. suber) as the only hosts tree species. Based on the archives of truffle growers, including a systematic recording of truffle production within and outside traps, we reported at each study site the contribution of truffle traps to the annual fruitbody production of the entire truffle grounds, by using number and/or weight of collected fruitbodies within (Pin) and outside (Pout) truffle traps.Dataset 2: In situ experiment tracing the inoculation effectThree orchards located near Angoulème (45°74′35.5″N, − 0°63′78.4″W), Jonzac (45°44′09.8″N, 0°43′96.7″W), and Arles-sur-Tech (42°45′44.9″N, 2°62′89.4″W), hereafter referred to Site 1 to 3 (Fig. 2) were selected for testing both disturbance effect and inoculum effect on fruitbody production in truffle traps. These sites presented a high fruitbody production and a high Pin/Pout ratio, thus optimum conditions to test mechanisms underlying how truffle traps influence fruitbody production. Host trees were between 5 and 18 years old at the beginning of the experiment (Fig. 2). At each site, we selected three non-adjacent trees (four on Site 3) that displayed a continuous fruitbody production over the three previous years. Under each selected tree, we excavated, at two-thirds of the distance between the tree trunk and the limit of brûlé (a vegetation-poor zone that shows the extension mycelia in the soil40, eight equidistant truffle traps [20 × 20 cm large × 20 cm deep] as shown in Fig. 3a. Under each tree, two traps were filled with only a mixture of peat and vermiculite (hereafter referred as non-inoculated controls) to test for disturbance effect. The used mixture was identical to that which is currently applied in commercial orchards. In three other traps, 5 g of crushed material from a single black truffle fruitbody (including its gleba and spores) were added to the previous mixture (hereafter referred as one mating-type inoculum). In the three last traps, 5 g of crushed material from two ascocarps with gleba of opposite mating types (hereafter referred as two mating-type inoculum) were added to the previous mixture. We added the two mating-type condition to accurately test a potential contribution of the gleba (haploid and thus with a single mating type) on future production. As quoted in Introduction, maternal individuals with opposite mating types tend to exclude each other locally (spatial segregation of clusters of individuals of same mating types26. Thus, the two mating-type inoculum allows us to detect in each trap a maternal contribution by the introduced gleba, despite potential exclusion by pre-installed individuals of the locally dominant mating type in the surrounding. Moreover, it allows us to detect a paternal contribution by the introduced gleba of the mating type opposite to the locally dominant. The eight truffle traps were randomly arranged, so that two repetitions of same modality were always separated by a repetition of another modality (Fig. 3a).In March 2013, six freshly collected truffles (weighting  > 60 g) were molecularly analyzed for the mating type of their gleba as in18. On Site 1 and Site 2, the inoculum was made of fruitbodies collected at Site 1. On Site 3, fruitbodies used as inoculum originated from truffle grounds in Sarrion (Spain). In April 2013, truffles traps were installed as explained above (in all, 8 traps × 3 (or 4) trees × 3 sites) and monitored for two years by truffle growers. Harvesting was performed by trained dogs (one different dog per site) checking truffle traps and the surrounding brûlés at each visit of the orchard by truffle growers. When dogs detected truffles, a small hole was excavated to collect ascocarps without disturbing the trap further. At the end of January, 2015, all truffle traps were completely excavated, remnant truffles overlooked by dogs were systematically collected (Fig. 3b). Three soil aliquots were collected within all traps and pooled. All truffles and soil aliquots were frozen for subsequent DNA analysis.Molecular and genetic analysesDNA extractions, mating typing and genotyping were done as in18. Briefly, DNA was extracted from the gleba and from spores of each fruitbody to get access to the maternal and zygotic DNA, respectively. Simple sequence repeat (SSRs) genotyping was performed using 12 polymorphic markers and the mating-type locus as in18. Gleba extracts displaying apparent heterozygous genotypes, likely due to contamination by spore DNA were systematically discarded from further analyses. For each fruitbody, the haploid paternal genotype was then deduced by subtracting the haploid maternal genotype from the zygotic diploid genotype. This data set was used for relatedness estimations. We discarded from all further analysis the marker me11, which displayed more than 39% missing data, as well as all samples with missing data for at any locus.Multilocus genotypes comparisonsBased on the 11 remaining SSRs and the mating-type (Table S5 and Figure S2), MLGs were identified on all maternal and paternal haploid genomes using GenClone v.2.041, and the probability that MLGs represented more than once resulted from independent events of sexual reproduction was calculated (PSex41,42). On each site, clonal diversity was measured as R = (G − 1)/(N − 1) according to43, where N is the number of fruitbodies and G the number of MLGs. For testing whether the gleba of the inoculated fruitbody contributed, either paternally (H1) or maternally (H2) to the harvested fruitbodies (Fig. 1c), the inoculated maternal MLG was compared to the paternal and maternal MLG of the harvested fruitbodies.Relatedness estimationFor testing whether the spores of the inoculum, which carry many distinct haploid MLGs due to meiosis, had paternal or maternal contribution(s) to the harvested fruitbodies (H3; Fig. 1c), we used relatedness estimation.For testing whether spores of the inoculum had a paternal contribution, an individual relatedness estimate to the spore inoculum was computed for each paternal genome detected in truffle traps. Relatedness r here describes the expected frequency E[p_offpat] of each allele in a given genome, E[p_offpat] = p_pop + r * (p_inoc − p_pop), where p_pop is the allele frequency in the local population (here estimated from the glebas of other truffles collected under the focal tree), and p_inoc is the frequency of the allele in the inoculum. Thus, p_offpat takes values 0 or 1, and p_inoc takes values 0, 0.5 or 1, except when two fruitbodies were used as inoculum (two gleba mating types traps). Thus r = (p_offpat − p_pop)/(p_inoc − p_pop). An individual relatedness estimate for each genome is then obtained by summing over alleles and loci the observed values of the numerator and denominator in this expression. A population-level estimate is further obtained by summing numerators and denominators over the paternity events in each population.To test whether such estimates are compatible with the hypothesis that the paternal individuals are not from the inocula, we obtained the distribution of population-level relatedness estimates by simulating samples under this hypothesis: paternal genotypes were randomly simulated according to alleles frequencies in the local population. For each population, 10,000 samples were simulated, and p-values were estimated as the proportion of simulations with higher population-level relatedness with inocula than the observed one. Confidence intervals for these p-values were computed from the binomial distribution for 10,000 draws, and Bonferroni-corrected over the three populations.For testing whether spores of the inoculum had a maternal contribution (H4, Fig. 1c), we estimated the relatedness of the locally used spore inoculum to each maternal genome detected in truffle traps (deduced from the gleba), and we confronted it to simulated samples as previously but with one modification: if the focal fruitbody was harvested in a trap inoculated with the inoculum A1, all genomes of truffles from traps inoculated with the same inoculum (A1 or A1 + A2 + A3, see Fig. 3c.) were discarded from the estimation of p_pop.Assessment of T. melanosporum mycelium concentration in truffle trapsOn Sites 1, 2 and 3, soil samples were collected in all traps and in the surrounding brûlés at harvesting date (January, 2015). In collected soils, total DNA was extracted and quantified as in19. Briefly, after sieving and homogenizing soil collected in each trap and from out of the brûlés, aliquots (10 g) were analyzed as follows. After extraction with the kit Power Soil (MoBio Laboratories, Carlsbad, CA, USA), the extra-radical mycelium of T. melanosporum was quantified using quantitative Taqman™ PCR (qPCR) with the primers and probe described in44. Triplicate real-time PCR were performed on each sample using the same concentration of primer and the same thermocycling program as in19. Standards were prepared using fresh immature T. melanosporum ascocarp, and a standard curve was generated for each site by plotting serial tenfold dilutions against corresponding initial amount of ascocarp. Absolute quantification of mycelium biomass of T. melanosporum was expressed in mg of mycelium per g of soil.Statistical analysesStatistics were done using R version 4.0.445.Effect of truffle traps on fruitbody production—The contribution of truffle traps to the overall production of orchards was assessed by (1) data mining of truffle growers’ archives (Dataset 1) and (2) comparing the density of truffles harvested in traps (expressed in number of truffles per m2 per orchard; for each sampled tree, traps correspond to an investigated soil surface of s = 8 × 0.2 x 0.2 = 0.32 m2) with the density measured within surrounding brûlés (Dataset 1). On Dataset2, at each site, the area occupied by brûlés was evaluated by measuring in the field the surface of soil devoid of vegetation consecutively to spontaneous T. melanosporum brûlé.Fruitbody production under different conditions (i.e. non-inoculated controls versus one gleba mating type traps versus two gleba mating type traps) were compared using generalized linear mixed models with negative binominal family and log link (R, spam package46). The full model included the logarithm of the sampled area as offset to account for variations in this sampled area, interactions of trap-modality effects with site effect. Formal likelihood ratio tests are based on one-step deletions from this full model, applied to subsets of the data relevant for each hypothesis tested. Additional bootstrap tests (1000 iterations) were run to correct any bias in small sample likelihood ratio tests.Concentrations of T. melanosporum mycelium in soil—Similarly as above, the inoculum effect on mycelium concentrations was compared using generalized linear mixed models with Gamma log family.Plant materialThe use of plants in the present study complies with international, national and/or institutional guidelines. All permissions to collect T. melanosporum fruitbodies in truffle orchards were obtained. The formal identification of biological material used in the study (T. melanosporum fruitbodies) was undertaken by F. Richard and E. Taschen. Voucher specimens of all collected fruitbodies have been deposited in the Centre d’Ecologie Fonctionnelle et Evolutive herbarium in Montpellier (France).Ethical approvalAll co-authors approve the ethical statement regarding the submitted manuscript.Consent to participateAll co-authors consent to participate to the research and agree with the content of the submitted manuscript. All authors reviewed and submitted manuscript. More

Changes in temperature and extreme environmental conditions can affect the dynamics of vector-borne pathogens. These include leishmaniasis, transmitted by phlebotomine sandflies, as well as mosquitoes that spread arboviruses like dengue, encephalitis, yellow fever, West Nile fever, and lymphatic filariasis19,20,21.The CCA analysis showed that maximum temperature significantly influenced the abundance of mosquito populations in the study area. In addition, the NMDS showed two different groupings that consisted of samples collected during the rainy and dry seasons. Accordingly, Refs.22,23 report that changes in temperature and relative humidity determine the abundance of mosquitoes, which can disappear entirely during the dry season. Moreover, Refs.22,24,25 note that certain species of mosquitoes increase proportionally with the regional rainfall regime. This is consistent with Ref.10, who find alternating patterns in tropical and temperate climates in some Brazilian regions.As shown by the geometric regression, there is a positive correlation between cumulative rainfall in the days before collection and the number of species found in the study period. Likewise, Ref.26 reported that under the conditions observed in the Serra do Mar State Park, climate variables directly influenced the abundance of Cq. chrysonotum and Cq. venezuelensis, favoring the occurrence of culicids during the more warm, wet, and rainy months.The current climate scenario and future projections about climate, environmental, demographic, and meteorological factors directly influence the distribution and abundance of mosquito vectors and/or diseases27,28,29,30. Environmental temperature alters mosquito population dynamics, thereby affecting the development of immature stages as well as reproduction31. While temperature has an important effect on population dynamics, rainfall and drought also affect the density and dispersal of mosquitoes in temperate and tropical regions32.To be sure, environmental changes other than climate can modify the behavior of vector insects and, subsequently, the mechanism of transmission of parasites20. Specifically, human impacts on the environment can result in drastically different disease transmission cycles in and around inhabited areas33.A previous study34 reported that changes in land use influence the mosquito communities with potential implications for the emergence of arboviruses. Another study35 noted that environmental changes negatively affect natural ecosystems with accelerated biodiversity loss. This is due to the modification and loss of natural habitat and unsustainable land use, which leads to the spread of pathogens and disease vectors.Hence, understanding the relationship between humans and the environment becomes increasingly critical, given the way in which climate changes can lead to alterations in the epidemiology of diseases such as dengue in areas considered free of the disease, as well as in endemic areas36.We found that the abundance and diversity of Mansoniini were directly influenced by the effect of the rainy season and other climatic factors. The rainfall regime has been shown to affect the development of immature forms12,37; explaining the greater frequency of these specimens in the warmer and wetter months38,39,40. According to Ref.41, stable ecosystems such as forests contain great species diversity. On the other hand, diversity tends to be reduced in biotic communities suffering from stress.Studies of insect populations in natural areas are important because they allow a direct analysis of how environmental factors influence phenomena such as the choice of breeding sites by females for oviposition, hematophagous behavior, and the distribution of species along a vegetation gradient12,26,42,43.Throughout the experimental period of the present study, we observed that Shannon light traps are an effective method for catching mosquitoes from the Mansoniini tribe. Interestingly, Ref.44 reported a species richness pattern strongly influenced by Coquillettidia fasciolata (Lynch Arribálzaga, 1891) on mosquito samples from different capture points by using CDC and Shannon light traps as sampling methods. In contrast to the results of Ref.44, where the highest population density of mosquitoes was captured with CDC traps, we observed that these traps were not effective at capturing specimens of Mansoniini in spite of being used in large numbers in the present study. Moreover, Ref.45 conducted another study on faunal diversity in an Atlantic Forest remnant of the state of Rio de Janeiro and observed the highest abundance of Cq. chrysonotum (Peryassú, 1922) and Cq. venezuelensis by using Shannon light traps, while the numbers of captures of Ma. titillans were very similar using CDC and Shannon traps.The results of this study indicate that the makeup of culicid fauna remains quite similar throughout the year, despite seasonal variations in abundance, though there was a lower variability of fauna in the dry season. Therefore, although the seasonality did not affect the temporal variation of the faunal composition in a generalized way, it was possible to detect a partial effect of the seasonality on fauna abundance.
Reference46 report that the incidence peaks of mosquitoes in the warmer and wetter months, as well as mosquito populations remaining between tolerance limits for most of the year, indicate the sensitivity of some species to the local climate.The elevated abundance and diversity of species of Mansoniini in the study area were influenced by the favorable maintenance of breeding sites, including specific water accumulations with emerging vegetation that remain present throughout the year and the well-defined rainy season in the region. In addition, the representatives of Mansoniini, which prefer breeding sites containing macrophytes, made up nearly all of the species collected7.Besides providing a greater awareness of mosquito populations’ ecological and biological aspects, research carried out in wild areas also provides information on the relationship between species diversity and the area in which they are found. Considering that wild insects may become potential vectors of diseases, research in wild areas also provides helpful information for understanding relevant epidemiological aspects. These studies facilitate the identification, monitoring, and control of mosquito populations following environmental changes caused by direct human action, which can lead to major epidemics26.We observed considerable heterogeneity among Mansoniini fauna, and the months with the highest rainfall directly influence the structure of the communities and contribute to the increase in mosquito diversity and abundance, possibly due to variations in the availability of habitat for their immature forms. More