Philippa Kaur

United Nations. World Urbanization Prospects: The 2018 revision. (Department of Economic and Social Affairs, Population Division, United Nations, 2018).Grimm, N. B. et al. Global change and the ecology of cities. Science 319, 756–760 (2008).Article 

Google Scholar 
McPhearson, T. et al. Advancing urban ecology toward a science of cities. Bioscience 66, 198–212 (2016).Article 

Google Scholar 
Dodman, D. et al. Cities, settlements and key infrastructure. In Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (eds. Pörtner, H.-O. et al.) 997–1040 (Cambridge University Press, Cambridge, UK and New York, NY, USA, 2022).Díaz, S. et al. Assessing nature’s contributions to people: Recognizing culture, and diverse sources of knowledge, can improve assessments. Science 359, 270–272 (2018).Article 

Google Scholar 
Grabowski, Z. J., McPhearson, T., Matsler, A. M., Groffman, P. & Pickett, S. T. A. What is green infrastructure? A study of definitions in US city planning. Front. Ecol. Environ. 20, 152–160 (2022).Article 

Google Scholar 
Childers, D. L. et al. Urban ecological infrastructure: An inclusive concept for the non-built urban environment. Elementa 7, 1–14 (2019).
Google Scholar 
Gómez-Baggethun, E. et al. Urban ecosystem services. In Urbanization, Biodiversity and Ecosystem Services: Challenges and Opportunities (eds. Elmqvist, T. et al.) 175–251 (Springer, Netherlands, 2013).Díaz, S. & Cabido, M. Vive la différence: Plant functional diversity matters to ecosystem processes. Trends Ecol. Evol. 16, 646–655 (2001).Article 

Google Scholar 
Burkhard, B. & Maes, J. Mapping Ecosystem Services (Pensoft Publishers, Sofia, 2017).Eviner, V. T. & Chapin, F. S. Functional Matrix: A conceptual framework for predicting multiple plant effects on ecosystem processes. Annu. Rev. Ecol. Evol. Syst. 34, 455–485 (2003).Article 

Google Scholar 
Lavorel, S., McIntyre, S., Landsberg, J. & Forbes, T. D. A. Plant functional classifications: From general groups to specific groups based on response to disturbance. Trends Ecol. Evol. 12, 474–478 (1997).Article 

Google Scholar 
Cornelissen, J. H. C. et al. A handbook of protocols for standardised and easy measurement of plant functional traits worldwide. Aust. J. Bot. 51, 335–380 (2003).Article 

Google Scholar 
Suding, K. N. et al. Scaling environmental change through the community-level: A trait-based response-and-effect framework for plants. Glob. Chang. Biol. 14, 1125–1140 (2008).Article 

Google Scholar 
Lavorel, S. & Garnier, E. Predicting changes in community composition and ecosystem functioning from plant traits: Revisiting the Holy Grail. Funct. Ecol. 16, 545–556 (2002).Article 

Google Scholar 
Hevia, V. et al. Trait-based approaches to analyze links between the drivers of change and ecosystem services: Synthesizing existing evidence and future challenges. Ecol. Evol. 7, 831–844 (2017).Article 

Google Scholar 
Cadotte, M. W., Carscadden, K. & Mirotchnick, N. Beyond species: Functional diversity and the maintenance of ecological processes and services. J. Appl. Ecol. 48, 1079–1087 (2011).Article 

Google Scholar 
Lavorel, S. Plant functional effects on ecosystem services. J. Ecol. 101, 4–8 (2013).Article 

Google Scholar 
Andersson, E. et al. What are the traits of a social-ecological system: Towards a framework in support of urban sustainability. npj Urban Sustain. 1, 14 (2021).Article 

Google Scholar 
Pickett, S. T. A. et al. Urban ecological systems: Linking terrestrial ecological, physical, and socioeconomic components of metropolitan areas. Annu. Rev. Ecol. Syst. 32, 127–157 (2001).Article 

Google Scholar 
McPhearson, T., Haase, D., Kabisch, N. & Gren, Å. Advancing understanding of the complex nature of urban systems. Ecol. Indic. 70, 566–573 (2016).Article 

Google Scholar 
Zhou, W., Pickett, S. T. A. & McPhearson, T. Conceptual frameworks facilitate integration for transdisciplinary urban science. npj Urban Sustain. 1, 1 (2021).Article 

Google Scholar 
Andersson, E. et al. Scale and context dependence of ecosystem service providing units. Ecosyst. Serv. 12, 157–164 (2015).Article 

Google Scholar 
Pinho, P. et al. Research agenda on biodiversity and ecosystem functions and services in European cities. Basic Appl. Ecol. 53, 124–133 (2021).Article 

Google Scholar 
Bullock, J. M. et al. Human-mediated dispersal and the rewiring of spatial networks. Trends Ecol. Evol. 33, 958–970 (2018).Article 

Google Scholar 
Avolio, M. L., Swan, C., Pataki, D. E. & Jenerette, G. D. Incorporating human behaviors into theories of urban community assembly and species coexistence. Oikos 130, 1849–1864 (2021).Article 

Google Scholar 
Aronson, M. F. J. et al. Hierarchical filters determine community assembly of urban species pools. Ecology 97, 2952–2963 (2016).Article 

Google Scholar 
Woodward, F. I. & Diament, A. D. Functional approaches to predicting the ecological effects of global change. Funct. Ecol. 5, 212 (1991).Article 

Google Scholar 
Diaz, S., Cabido, M. & Casanoves, F. Plant functional traits and environmental filters at a regional scale. J. Veg. Sci. 9, 113–122 (1998).Article 

Google Scholar 
Boet, O., Arnan, X. & Retana, J. The role of environmental vs. biotic filtering in the structure of European ant communities: A matter of trait type and spatial scale. PLoS ONE 15, e0228625 (2020).Article 

Google Scholar 
Grimm, N. B., Grove, J. M., Pickett, S. T. A. & Redman, C. L. Integrated approaches to long-term studies of urban ecological systems. Bioscience 50, 571–584 (2000).Article 

Google Scholar 
Vandewalle, M. et al. Functional traits as indicators of biodiversity response to land use changes across ecosystems and organisms. Biodivers. Conserv. 19, 2921–2947 (2010).Article 

Google Scholar 
Williams, N. S. G. et al. A conceptual framework for predicting the effects of urban environments on floras. J. Ecol. 97, 4–9 (2009).Article 

Google Scholar 
Cavender-Bares, J. et al. Horticultural availability and homeowner preferences drive plant diversity and composition in urban yards. Ecol. Appl. 30, 1–16 (2020).Article 

Google Scholar 
Pearse, W. D. et al. Homogenization of plant diversity, composition, and structure in North American urban yards. Ecosphere 9, e02105 (2018).Article 

Google Scholar 
Cubino, J. P. et al. Drivers of plant species richness and phylogenetic composition in urban yards at the continental scale. Landsc. Ecol. 34, 63–77 (2019).Article 

Google Scholar 
Oke, T. R. The energetic basis of the urban heat island. Q. J. R. Meteorol. Soc. 108, 1–24 (1982).
Google Scholar 
Sukopp, H. Human-caused impact on preserved vegetation. Landsc. Urban Plan. 68, 347–355 (2004).Article 

Google Scholar 
Díaz, S. et al. Incorporating plant functional diversity effects in ecosystem service assessments. Proc. Natl. Acad. Sci. USA 104, 20684–20689 (2007).Article 

Google Scholar 
Williams, N. S. G., Hahs, A. K. & Vesk, P. A. Urbanisation, plant traits and the composition of urban floras. Perspect. Plant Ecol. Evol. Syst. 17, 78–86 (2015).Article 

Google Scholar 
Teskey, R. et al. Responses of tree species to heat waves and extreme heat events. Plant Cell Environ. 38, 1699–1712 (2015).Article 

Google Scholar 
Jochner, S. & Menzel, A. Urban phenological studies—past, present, future. Environ. Pollut. 203, 250–261 (2015).Article 

Google Scholar 
Cleland, E. E., Chuine, I., Menzel, A., Mooney, H. A. & Schwartz, M. D. Shifting plant phenology in response to global change. Trends Ecol. Evol. 22, 357–365 (2007).Article 

Google Scholar 
de Bello, F. et al. Towards an assessment of multiple ecosystem processes and services via functional traits. Biodivers. Conserv. 19, 2873–2893 (2010).Article 

Google Scholar 
Santangelo, J. S. et al. Global urban environmental change drives adaptation in white clover. Science 375, 1275–1281 (2022).Article 

Google Scholar 
Blois, J. L., Zarnetske, P. L., Fitzpatrick, M. C. & Finnegan, S. Climate change and the past, present, and future of biotic interactions. Science 341, 499–504 (2013).Article 

Google Scholar 
Martin, C. A., Warren, P. S. & Kinzig, A. P. Neighborhood socioeconomic status is a useful predictor of perennial landscape vegetation in residential neighborhoods and embedded small parks of Phoenix, AZ. Landsc. Urban Plan. 69, 355–368 (2004).Article 

Google Scholar 
Kinzig, A. P., Warren, P., Martin, C., Hope, D. & Katti, M. The effects of human socioeconomic status and cultural characteristics on urban patterns of biodiversity. Ecol. Soc. 10, 23 (2005).Article 

Google Scholar 
Stephenson, J. The cultural values model: An integrated approach to values in landscapes. Landsc. Urban Plan. 84, 127–139 (2008).Article 

Google Scholar 
Andersson, E., Barthel, S. & Ahrné, K. Measuring social–ecological dynamics behind the generation of ecosystem services. Ecol. Appl. 17, 1267–1278 (2007).Article 

Google Scholar 
Fraser, E. D. G. & Kenney, W. A. Cultural background and landscape history as factors affecting perceptions of the urban forest. J. Arboric. 26, 106–113 (2000).
Google Scholar 
Hope, D. et al. Socioeconomics drive urban plant diversity. Proc. Natl. Acad. Sci. USA 100, 8788–8792 (2003).Article 

Google Scholar 
Avolio, M. L. et al. Understanding preferences for tree attributes: The relative effects of socio-economic and local environmental factors. Urban Ecosyst. 18, 73–86 (2015).Article 

Google Scholar 
Körmöndi, B., Tempfli, J., Kocsis, J. B., Adams, J. & Szkordilisz, F. E. The secret ingredient—The role of governance in green infrastructure development: Through the examples of European cities. IOP Conf. Ser. Earth Environ. Sci. 323, (2019).Conway, T. M. & Vander Vecht, J. Growing a diverse urban forest: species selection decisions by practitioners planting and supplying trees. Landsc. Urban Plan. 138, 1–10 (2015).Article 

Google Scholar 
Lack, W. H. The Book of Palms (Taschen-Bibliotheca Universalis, 2015).Grilo, F. et al. Using green to cool the grey: Modelling the cooling effect of green spaces with a high spatial resolution. Sci. Total Environ. 724, 138182 (2020).Article 

Google Scholar 
Prasifka, J. R. et al. Using nectar-related traits to enhance crop–pollinator interactions. Front. Plant Sci. 9, 1–8 (2018).Article 

Google Scholar 
Veerkamp, C. J. et al. A review of studies assessing ecosystem services provided by urban green and blue infrastructure. Ecosyst. Serv. 52, 101367 (2021).Article 

Google Scholar 
Theodorou, P. et al. Urban areas as hotspots for bees and pollination but not a panacea for all insects. Nat. Commun. 11, 576 (2020).Article 

Google Scholar 
Farmer, J. Trees in Paradise: A California History (WW Norton & Company, 2013).Goodness, J., Andersson, E., Anderson, P. M. L. & Elmqvist, T. Exploring the links between functional traits and cultural ecosystem services to enhance urban ecosystem management. Ecol. Indic. 70, 597–605 (2016).Article 

Google Scholar 
Masterson, V. A. et al. The contribution of sense of place to social-ecological systems research: A review and research agenda. Ecol. Soc. 22, 49 (2017).Article 

Google Scholar 
Masterson, V. A., Enqvist, J. P., Stedman, R. C. & Tengö, M. Sense of place in social-ecological systems: From theory to empirics. Sustain. Sci. 14, 555–564 (2019).Article 

Google Scholar 
Mukherjee, A. & Agrawal, M. Use of GLM approach to assess the responses of tropical trees to urban air pollution in relation to leaf functional traits and tree characteristics. Ecotoxicol. Environ. Saf. 152, 42–54 (2018).Article 

Google Scholar 
Singh, S. K., Rao, D. N., Agrawal, M., Pandey, J. & Naryan, D. Air pollution tolerance index of plants. J. Environ. Manage. 32, 45–55 (1991).Article 

Google Scholar 
Mukherjee, A. & Agrawal, M. Pollution response score of tree species in relation to ambient air quality in an urban area. Bull. Environ. Contam. Toxicol. 96, 197–202 (2016).Article 

Google Scholar 
Barwise, Y. & Kumar, P. Designing vegetation barriers for urban air pollution abatement: A practical review for appropriate plant species selection. npj Clim. Atmos. Sci. 3, 12 (2020).Article 

Google Scholar 
Grote, R. et al. Functional traits of urban trees: Air pollution mitigation potential. Front. Ecol. Environ. 14, 543–550 (2016).Article 

Google Scholar 
Tomson, M. et al. Green infrastructure for air quality improvement in street canyons. Environ. Int. 146, 106288 (2021).Article 

Google Scholar  More

Surface morphology analysisSEM images were shown in Fig. 1. It showed that the contour of three soils were fairly clear before adsorption. But it became fuzzier and the degree of cementation was increased when phenanthrene was adsorbed on the soils. According to the surface morphology, the silty sand (A) had furrows on the surface before adsorption compared with the fairly smooth without any furrows after adsorption (B). The silts (C) were flaky and the lamellar accumulation decreased (D). The loess (E) had a smooth surface with some flaky and rod like structure, after adsorption (F), the surface of loess increased in clay-like structure.Figure 1SEM micrographs of the three soil samples. (A) Silty sand; (B) Adsorbing 5 h of Silty sand; (C) Silts; (D) Adsorbing 5 h of Silts; (E) Loess; (F) Adsorbing 5 h of Loess.Full size imageAdsorption and desorption experimentsAdsorption and desorption kineticsAdsorption kinetics is one of the most important characteristics governing solute uptake rate and represents adsorption efficiency33. The sorption and desorption kinetics of phenanthrene in three soils were shown in Fig. 2. The results showed that the adsorption processes among all soils were similar. The kinetics of phenanthrene in soils was completed in two steps: a “fast” adsorption and a “slow” adsorption. The adsorption amount increased during 0-18h. It was a rapid reaction from 0 to 200 minutes. From 200 to 600 minutes, the adsorption amount increased slightly into balance. This phenomenon was due to the adsorption of phenanthrene occurred on the surface of soil organic matter. With the increase of time, soil surface adsorption sites were gradually saturated, causing the decrease of adsorption rate until reaching the equilibrium. Phenanthrene was a hydrophobic substance. It was easy to reach the soil surface and adhere to the grain surface. The results were consistent with the study of had also found that the balance time was approximately 18h and the adsorption amount increased with the adsorption reaction time34. Under the same conditions, loess had the highest adsorption capacity, which was mainly due to the highest organic content 18. The maximum phenanthrene sorption capacities ranked as follows: loess > silty sand > silts. As shown in Fig. 2, phenanthrene desorption in soils was relatively quick and its desorption equilibrium time was 3h. To reach an adequate desorption balance while remaining consistent with the adsorption reaction time, the balance time of the adsorption–desorption experiment was set at 18h. Generally, PAHs below 4 cycles could reach the adsorption equilibrium for about 16~24h.Figure 2(a)Adsorption equilibration curves of phenanthrene sorption in soils. (b) Desorption equilibration curves of phenanthrene sorption in soils.Full size imagePseudo-second-order and Elovich models were used to study the phenanthrene adsorption mechanism (Table 3). Phenanthrene sorption kinetics were satisfactorily described by a pseudo-second-order model with coefficients of determination (R2) ranging from 0.99875 to 0.99847, compared with R2 values of 0.26508–0.73901 for the Elovich model. This well-fitting pseudo-second-order model indicated that the rate-limiting step was chemical adsorption, including electronic forces through sharing or exchange of electrons35,36. Moreover, it suggested that sorption was governed by the availability of sorption sites on the soil surfaces instead of by the phenanthrene concentration in solution.Table 3 Constants and coeffients of determination of Pseudo-second-order kinetics and Elovich models of sorption.Full size tableAdsorption and desorption isothermsThe isotherm was used for quantitative analysis of phenanthrene transport from liquid to solid phase and for understanding the nature of interactions between phenanthrene and the soil matrix. The sorption and desorption isotherms of phenanthrene in soils were shown in Fig. 3. The data showed that phenanthrene adsorption and desorption capacities of three soils varied markedly due to their different physicochemical properties. With the increase of phenanthrene concentration, the adsorbed amount increased. At the same temperature, the adsorption capacity of silty sand was minimum while loess was maximum. This is mainly related to the soil physicochemical properties. At the same initial concentration, the temperature increase from 20 °C to 40 °C showed that the adsorption and desorption capacity decreased with temperature increase. On the one hand, the rise of temperature can increase the phenanthrene solubility in the liquid phase. On the other hand, it could reduce various forces between the soil surface and phenanthrene37.Figure 3(a)20 °C adsorption isotherms for phenanthrene in soils. (b)30 °C adsorption isotherms for phenanthrene in soils. (c)40 °C adsorption isotherms for phenanthrene in soils. (d) 20 °C desorption isotherms for phenanthrene in soils. (e) 30 °C desorption isotherms for phenanthrene in soils. (f) 40 °C desorption isotherms for phenanthrene in soils.Full size imageThe Freundlich isotherm was used mainly for adsorption surfaces with nonuniform energy distribution, and the Langmuir isotherm was used for monolayer adsorption on perfectly smooth and homogeneous surfaces38. The experimental data were fitted with the Langmuir and Freundlich adsorption models, and the isotherm parameters logKF, 1/n, KL, qmax and the coefficient of determination (R2) of phenanthrene in soils were listed in Table 4.Table 4 Isotherm parameters for Phenanthrene sorption in soils.Full size tableAs shown in Table 4, according to the coefficients of determination (R2), all soils were better fitted with the Freundlich model, which assumes that phenanthrene sorption and desorption occurs on a heterogeneous surface with the possibility of sorption being multi-layered39. This phenomenon has also been observed in humic acid and nanometer clay mineral40. It showed that the soil adsorption of organic matter was not only surface adsorption but also the process of soil organic matter distribution41,42,43 reached the equilibrium isotherm fitted well with the Freundlich equation when studying the adsorption behavior of aromatic compounds by solids.Adsorption and desorption thermodynamicsTo clarify the adsorption mechanisms, the thermodynamic parameters mentioned earlier were calculated and presented in Table 5. Generally, the value of Gibbs free energy changeΔG0 indicated the spontaneity of a chemical reaction. Therefore, it could evaluate whether sorption was relate to spontaneous interaction44. Negative values of ΔG0 indicated that the feasibility and spontaneous nature. The research was under the temperature range about 293–313 K. For adsorption process, all soils ΔG0 was  0 and desorption ΔH  1, P  temperature  > phenanthrene concentration  > pH. In the interaction, the phenanthrene concentration and organic matter have a significant effect on the silt adsorption rate. The coefficient of determination of the silt complex correlation is R2 = 0.9464, indicating that the response model has a good fit, and the experimental error is within the acceptable range. Adjusting the complex correlation coefficient R2 = 0.8982 indicates that the regression relationship can explain 89.82% of the change in the dependent variable. Therefore, this The model can be used to analyze and predict the effect of different factors on the adsorption rate of phenanthrene.3D response surface analysisIn response surface optimization, the three-dimensional response surface graph reflects the influence of the interaction of the other two variables on the response value, and the slope of the response surface reflects the significance of the interaction of the two variables on the response value. The more significant the interaction effect is on the response value, when the slope is gentle, the effect is not significant. If the contour map is elliptical, it indicates that the interaction between the two variables is significant, and if the contour map is circular, it is not significant46. In addition, the slope and density of the contour line also reflect the influence of the variable on the response value. The steeper the contour line and the greater the density, the greater the influence of the variable on the response value47.

(1) Loess Fig. 5 is a three-dimensional response surface diagram of the interaction between initial phenanthrene concentration and pH to phenanthrene adsorption on loess. It can be seen from the figure that the slope of the response surface graph is steep, and the contour line is an approximate circle, indicating that the interaction between phenanthrene concentration and pH is not significant for the response value. With the increase of pH, the adsorption rate of phenanthrene on loess showed a slow decline at first to the lowest point at 6, and then gradually increased. When the soil pH was close to 6, with the increase of the initial phenanthrene concentration, the adsorption rate of loess also showed a trend of first decreasing and then increasing. According to the F value, F = 0.337, P = 0.5532  > 0.05, it can be concluded that soil pH and initial phenanthrene concentration of the solution have no significant interaction on the adsorption rate of loess.

Figure 6 shows the effects of initial phenanthrene concentration and organic matter on phenanthrene adsorption on Loess under the condition that pH value and temperature are at the central point. It can be seen from the figure that the initial phenanthrene concentration and soil organic matter contour are steep, indicating that their interaction is significant. The range of phenanthrene adsorption rate is 70 ~ 95, and the change of surface is steep. From the Loess error analysis, it can be seen that if f value is 6.05 and P value is 0.0275  More

Knowing the distribution of species is crucial for effective conservation action. However, accurate and high-resolution spatial data are only available for a limited number of species1,2. For mammals and birds, the most comprehensive and widely used global distribution dataset is the set of range maps compiled as part of the assessments for the International Union for Conservation of Nature (IUCN) Red List. These represent each species’ distributional limits and tend to minimize omission errors (i.e. false absences) at the expense of commission errors (i.e. false presences)3,4. Therefore, they often contain sizeable areas not regularly occupied by the species.Maps of the Area of Habitat (AOH; previously known as Extent of Suitable Habitat, ESH) complement range maps by indicating potential occupancy within the range, thereby reducing commission errors5. AOH is defined as ‘the habitat available to a species, that is, habitat within its range’5. These models are produced by subtracting areas unsuitable for the species within their range, using information on each species’ associations with habitat and elevation5,6,7,8. Comprehensive sets of AOH maps have been produced in the past for mammals6 and amphibians7, as well as subsets of birds8,9. The percentage of a species’ range covered by the AOH varies depending on the methodology used to associate species to their habitats, and their habitats to land-cover, the coarseness of the range map, the region in which the species is distributed, and the species’ habitat specialization and elevation limits5. For example, Rondinini et al.6 found that, when considering elevation and land cover features for terrestrial mammals, the AOH comprised, on average, 55% of the range. Ficetola et al.7 obtained a similar percentage when analyzing amphibians (55% for forest species, 42% for open habitat species and 61% for habitat generalists). Beresford et al.8 found that AOH covered a mean of 27.6% of the range maps of 157 threatened African bird species. In 2019, Brooks et al.5 proposed a formal definition and standardized methodology to produce AOH, limiting the inputs to habitat preferences, elevation limits, and geographical range.AOH production requires knowledge of which habitat types a species occurs in and their location within the range1. Information on habitat preference is documented for each species assessed in the IUCN Red List10, following the IUCN Habitats Classification Scheme11. However, the IUCN does not define habitat classes in a spatially explicit way, therefore, we used a recently published translation table that associates IUCN Habitat Classification Scheme classes with land cover classes12. Species’ elevation limits were also extracted from the IUCN Red List.We developed AOH maps for 5,481 terrestrial mammal species and 10,651 terrestrial bird species (Fig. 1). For 1,816 bird species defined by BirdLife International as migratory, we developed separate AOH maps, for the resident, breeding, and non-breeding ranges, according to the migratory distribution of the species (Fig. 2). The maps are presented in a regular latitude/longitude grid with an approximate 100 m resolution at the equator. On average, the AOH covers 66 ± 28% of the geographical range for mammals and 64 ± 27% for birds. We used the resulting AOH maps to produce four global species richness layers for: mammals, birds, globally threatened mammals and globally threatened birds13 (Fig. 3).Fig. 1Spatial distribution maps of Tangara abbas. Maps represent (a) the geographic range21, and (b) the Area of Habitat (AOH) of the species. The AOH was produced by subtracting unsuitable habitats from the geographical range. This species’ habitats are forest and terrestrial artificial habitats and has elevation range of 0 – 1600 m.Full size imageFig. 2Spatial distribution maps of Cardellina rubrifrons, divided into resident, breeding and non-breeding areas for this migratory species. Maps represent (a) the geographic range21, and (b) the Area of Habitat (AOH) of the species. The AOH was produced by subtracting unsuitable habitats from the ranges. This species is a forest species with elevation rangelimits of 1500 – 3100 m.Full size imageFig. 3Global species richness maps for (a) terrestrial mammals (considering 5,481species) and (b) terrestrial birds (considering 10,651 species). Calculated by overlaying all species’ AOH per class, resulting inon the number of species at each grid cell, latitude/longitude grid at a resolution of 1°/1008 or approximately 100 m at the equator (EPSG:4326) with the ellipsoid WGS 1984.Full size imageThe AOH maps presented in this paper are more useful for some purposes than global species distribution models, as they reduce and standardize commissions14. They are especially useful for not well-known and wide-range species. However, we note that for well-known species alternative sources may have more accurate distributions15. Moreover, AOHs are affected by the bias and errors of the underlying data, especially relevant errors associated with documentation of species’ habitats and elevations, and the translation of habitats into land cover classes, given that habitat is a complex multidimensional concept that is challenging to match to land-cover classes12, and that the current version of the IUCN Habitat Classification Scheme on IUCN’s website is described as a draft version11.The AOH maps have multiple conservation applications5,16,17, such as assessing species’ distributions and extinction risk, improving the accuracy of conservation planning, monitoring habitat loss and fragmentation, and guiding conservation actions. AOH has been proposed as an additional spatial metric to be documented in the Red List5, and is used for the identification of Key Biodiversity Areas18. More

General methodological approachTo examine if larvae utilize external cues (i.e., oriented movement) to swim in a directional manner (i.e., significant mean vector length), we develop two complementary analyses that compare the empirically observed directional precision (i.e., mean vector length) with the null distribution expected under a strict use of internal cues (i.e., unoriented movement). The empirically observed directional precision is quantified as the mean vector length (R) of larval bearings (θ) (Fig. 2a), herein ({hat{R}}_{theta }). The angular differences between consecutive bearings, herein turning angles (Fig. 2a; Δθt = θt-θt-1), are used to generate two null distributions of Rθ expected under the unoriented movement of Correlated Random Walk (CRW; ({R}_{{theta }_{0}})), based on the two analyses: Correlated Random Walk-von Mises (CRW-vm) and Correlated Random Walk- resampling (CRW-r), described below. The first is theoretical and is based on a von Mises distribution of simulated Δθ (Fig. 2b, c); the second is empirical, and is based on resampling the Δθ within each trial (Fig. 2d, e). These two analyses are complementary because the first can generate an unlimited number of trajectories but is based on a theoretical distribution rather than on observations, whereas the second is based on a finite number of observations. In addition to these two main analyses, we apply a third analysis, the Correlated Random Walk-wrapped Cauchy, herein CRW-wc, which is similar to CRW-vm, with the only difference of using wrapped Cauchy distribution instead of von Mises. The reason for applying CRW-wc is that it was shown to represent well animal movement in some cases33. Notably, we consider the simple cases of undirected movement pattern with a turning angle distribution centered at 0 (CRW), testing if the mean vector length of the trial’s sequence is higher than that expected under CRW. If true, that would be an indication for a directed movement pattern (i.e., BRW or BCRW), or an indication for more complex behaviors (discussed in Supplementary note 4).Statistics and reproducibilityQuantitative analyses are applied to directional trials, i.e., larval bearing sequences ((hat{theta })) that are significantly different from a uniform distribution based on the Rayleigh’s test8 (p  81, 162, 270). Trials with Nobs higher than the maximal Nobs were trimmed to contain the maximal Nobs per species, retaining the later-in-time data. For the scuba-following trials, the number of observations had to be Nobs  > 20 due to the sensitivity of the analysis to a low number of observations. In other words, a low number of observations limits the capacity of the quantitative analyses to distinguish between oriented and unoriented movement patterns (see Supplementary note 3, Supplementary Figure S3). Importantly, both methods were shown to be robust in terms of artifacts and biases55,56, and have been tested together demonstrating high consistency in larval orientation results16,48.Each orientation trial includes a sequence of larval swimming directions, termed bearings (θ) (Fig. 2a). For the DISC trials, θ are the cardinal directions of larval positions within the DISC’s chamber55. The angular differences between θ of consecutive time steps (t) are defined as Δθ (Δθt = θt-θt-1), such that for every θ sequence of a given length (N), there is a respective Δθ sequence of length N-1 (Fig. 2a). Directional precision with respect to external and internal cues is computed as the mean vector length of bearings (Rθ) and of turning angles (RΔθ), respectively54. Values of mean vector length (R) range from 0 to 1, with 0 indicating a uniform distribution of angles and 1 indicating that all angles are the same.We used two quantitative approaches to examine if larvae exhibit oriented movement: the Correlated Random Walk- von Mises and Correlated Random Walk- wrapped Cauchy (CRW-vm and CRW-wc) analyses and the CRW resampling (CRW-r) analysis. Both types of analyses are based on the assumption that trajectories of animals that strictly use internal cues for directional movement are characterized by a CRW pattern. Hence, their capacity for directional movement is exclusively dependent on the distribution of their turning angles (Δθ)57. In contrast, for an external-cues orienting animal, for which movement directions are correlated with an external fixed direction, the mean vector length of the observed bearings, ({hat{R}}_{theta }), is expected to exceed that of a CRW, ({R}_{{theta }_{0}})6. Both analyses compare ({hat{R}}_{theta }) against the expected ({R}_{{theta }_{0}}), but the first type computes ({R}_{{theta }_{0}^{{vm}}})and ({R}_{{theta }_{0}^{{wc}}})using theoretical von Mises and wrapped Cauchy distributions of Δθ, and the second type computes ({R}_{{theta }_{0}^{r}}) by producing 100 new θ sequences per individual trial (larva) by multiple resampling-without-replacement of the Δθ.A key principle for both analyses types stems from the fact that the mean vector length of bearings (Rθ) is inherently dependent on the mean vector length of turning angles (RΔθ)28. In other words, an animal with a high capacity for unoriented directional movement, i.e., a narrow distribution of Δθ, is likely to yield a high Rθ, even if it makes absolutely no use of external cues for oriented movement. Hence, in both analyses ({hat{R}}_{theta }) is gauged against a distribution of ({R}_{{theta }_{0}}), given its respective mean vector length of turning angles ({hat{R}}_{triangle theta }). The open-source software R58 with the package circular59 is used for all analyses in this study.Correlated Random Walk-von Mises (CRW-vm)In this analysis, we first generate the directional precision (R), expected for unoriented CRW movement using the theoretical von Mises distribution (({R}_{{theta }_{0}^{{vm}}})). The CRW bearings sequences (({theta }_{0}^{{vm}})) are generated by choosing a random initial bearing, followed by a series of Nobs-1 turning angles (({triangle theta }_{0}^{{vm}})) in bearing direction; drawn at random (with replacement) from a von Mises distribution (Nrep = 1000). The length of ({theta }_{0}^{{vm}}) sequence is according to the number of observations in our four types of experimental trials: Nobs = 21 for the scuba-following, and 90, 180 and 300 for the DISC (Table 1). The directional precision of the von Mises distribution is dependent on the concentration parameter, kappa. Kappa values ranging from 0 to 399 are applied at 1-unit increments to cover the entire range of directional precision from completely random (kappa = 0), to highly directional (kappa = 399). Next, the directional precision of the bearings (Rθ) and the turning angles (RΔθ) are computed for each simulated sequence of θ (Fig. 2a–c).These respective pairs of values (RΔθ, Rθ) provide the basis for generating the expected relationship between ({R}_{{theta }_{0}^{{vm}}}) and ({R}_{{triangle theta }_{0}^{{vm}}}). Then, for any given kappa value, the following quantiles are computed: 5th, 10th, 20th,….,90th, and 95th (grey vertical distributions in Fig. 2c). Next, smooth spline functions are fitted through all respective quantiles, generating the ({R}_{{theta }_{0}^{{vm}}})quantile contours, which represent the null expectation under CRW. This expected (RΔθ, Rθ) correspondence creates a phase diagram (Fig. 2c), based on which the observed θ patterns are gauged. The procedure is repeated four times to match the among-study differences in the number of θ observations per trial (i.e., Nobs = 21, 90, 180, and 300; see Table 1).To examine if the observed larval movement patterns differ from those expected for unoriented movement (CRW-vm), we compute RΔθ and Rθ for each individual trial (({hat{R}}_{triangle theta }) and ({hat{R}}_{theta })). We then place these values in the phase diagram and examine their positions with respect to ({R}_{{theta }_{0}^{{vm}}}) (Fig. 2c). Larvae with ({hat{R}}_{theta }) substantially higher than ({bar{R}}_{{theta }_{0}^{{vm}}}), are considered to have a higher tendency for a straighter movement than expected under CRW, suggesting oriented movement such as BRW and BCRW (Fig. 2b, c)6,28. Larvae with ({hat{R}}_{theta }) values substantially below ({bar{R}}_{{theta }_{0}^{{vm}}})indicate irregular patterns such as a one-sided drift (right or left). A larva is considered directional if the bearing sequence ((hat{theta })) is significantly different from a uniform distribution based on the Rayleigh’s test (p  More

Knight, H. et al. Impacts of the COVID-19 Pandemic and Self-Isolation on Students and Staff in Higher Education: A Qualitative Study. Int. J. Environ. Res. Public Health 18, 10675 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Higham, J. E., Ramírez, C. A., Green, M. A. & Morse, A. P. UK COVID-19 lockdown: 100 days of air pollution reduction? Air Quality. Atmosphere & Health https://doi.org/10.1007/s11869-020-00937-0 (2020).Article 

Google Scholar 
Office, P. M. s. Slides and datasets to accompany coronavirus press conference. (2020).Organization, W. H. WHO global air quality guidelines: particulate matter (PM2. 5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide: executive summary. (2021).Singh, A. et al. Impacts of emergency health protection measures upon air quality, traffic and public health: evidence from Oxford UK. Environ. Pollut. 293, 118584. https://doi.org/10.1016/j.envpol.2021.118584 (2022).Article 
CAS 
PubMed 

Google Scholar 
Shi, Z. et al. Abrupt but smaller than expected changes in surface air quality attributable to COVID-19 lockdowns. Science Advances 7, eabd6696, doi:doi:https://doi.org/10.1126/sciadv.abd6696 (2021).Lee, J. D., Drysdale, W. S., Finch, D. P., Wilde, S. E. & Palmer, P. I. UK surface NO2 levels dropped by 42% during the COVID-19 lockdown: impact on surface O3. Atmos. Chem. Phys. 20, 15743–15759. https://doi.org/10.5194/acp-20-15743-2020 (2020).Article 
CAS 

Google Scholar 
Shi, Z. et al. Abrupt but smaller than expected changes in surface air quality attributable to COVID-19 lockdowns. Science Advances 7, eabd6696, doi:https://doi.org/10.1126/sciadv.abd6696 (2021).Ropkins, K. & Tate, J. E. Early observations on the impact of the COVID-19 lockdown on air quality trends across the UK. Sci. Total Environ. 754, 142374. https://doi.org/10.1016/j.scitotenv.2020.142374 (2021).Article 
CAS 
PubMed 

Google Scholar 
Nwanaji-Enwerem, J. C., Allen, J. G. & Beamer, P. I. Another invisible enemy indoors: COVID-19, human health, the home, and United States indoor air policy. J Expo Sci Environ Epidemiol 30, 773–775. https://doi.org/10.1038/s41370-020-0247-x (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Rasha, A., Karan Jetly, J. & Shqran, S. Indoor Air Quality Monitoring Systems: A Comprehensive Review of Different IAQM Systems. International Journal of Knowledge-Based Organizations (IJKBO) 11, 1–14, doi:https://doi.org/10.4018/ijkbo.2021070101 (2021).World Health Organization. Regional Office for, E. WHO guidelines for indoor air quality: selected pollutants. xxv, 454 p. (World Health Organization. Regional Office for Europe, 2010).Stafoggia, M. et al. Long-term exposure to ambient air pollution and incidence of cerebrovascular events: Results from 11 European cohorts within the ESCAPE project. Environ. Health Perspect 122, 919–925. https://doi.org/10.1289/ehp.1307301 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Brook, R. D. et al. Particulate matter air pollution and cardiovascular disease: An update to the scientific statement from the American heart association. Circulation 121, 2331–2378. https://doi.org/10.1161/CIR.0b013e3181dbece1 (2010).Article 
CAS 
PubMed 

Google Scholar 
Raaschou-Nielsen, O. et al. Air pollution and lung cancer incidence in 17 European cohorts: prospective analyses from the European study of cohorts for air pollution effects (ESCAPE). Lancet Oncol. 14, 813–822. https://doi.org/10.1016/s1470-2045(13)70279-1 (2013).Article 
PubMed 

Google Scholar 
Guan, W. J., Zheng, X. Y., Chung, K. F. & Zhong, N. S. Impact of air pollution on the burden of chronic respiratory diseases in China: Time for urgent action. Lancet 388, 1939–1951. https://doi.org/10.1016/s0140-6736(16)31597-5 (2016).Article 
PubMed 

Google Scholar 
Atkinson, R. W. et al. Acute effects of particulate air pollution on respiratory admissions: Results from APHEA 2 project. Air pollution and health: A European approach. Am. J. Respir. Crit. Care Med. 164, 1860–1866. https://doi.org/10.1164/ajrccm.164.10.2010138 (2001).Article 
CAS 
PubMed 

Google Scholar 
Stapleton, F. et al. TFOS DEWS II epidemiology report. Ocular Surf. 15, 334–365. https://doi.org/10.1016/j.jtos.2017.05.003 (2017).Article 

Google Scholar 
Starr, C. E. et al. Dry eye disease flares: A rapid evidence assessment. Ocul. Surf. 22, 51–59. https://doi.org/10.1016/j.jtos.2021.07.001 (2021).Article 
PubMed 

Google Scholar 
Torricelli, A. A. et al. Correlation between signs and symptoms of ocular surface dysfunction and tear osmolarity with ambient levels of air pollution in a large metropolitan area. Cornea 32, e11-15. https://doi.org/10.1097/ICO.0b013e31825e845d (2013).Article 
PubMed 

Google Scholar 
Hwang, S. H. et al. Potential importance of ozone in the association between outdoor air pollution and dry eye disease in South Korea. JAMA Ophthalmol. 134, 503–510. https://doi.org/10.1001/jamaophthalmol.2016.0139 (2016).Article 
PubMed 

Google Scholar 
Wiwatanadate, P. Acute air pollution-related symptoms among residents in Chiang Mai Thailand. J. Environ. Health 76, 76–84 (2014).CAS 
PubMed 

Google Scholar 
Alves, M., Novaes, P., Morraye Mde, A., Reinach, P. S. & Rocha, E. M. Is dry eye an environmental disease? Arq. Bras. Oftalmol. 77, 193–200 https://doi.org/10.5935/0004-2749.20140050 (2014).Bourcier, T. et al. Effects of air pollution and climatic conditions on the frequency of ophthalmological emergency examinations. Br. J. Ophthalmol. 87, 809–811. https://doi.org/10.1136/bjo.87.7.809 (2003).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hao, R. et al. Impact of air pollution on the ocular surface and tear cytokine levels: A multicenter prospective cohort study. Front. Med. (Lausanne) 9, 909330. https://doi.org/10.3389/fmed.2022.909330 (2022).Article 
PubMed 

Google Scholar 
Vehof, J., Snieder, H., Jansonius, N. & Hammond, C. J. Prevalence and risk factors of dry eye in 79,866 participants of the population-based lifelines cohort study in the Netherlands. Ocul. Surf. 19, 83–93. https://doi.org/10.1016/j.jtos.2020.04.005 (2021).Article 
PubMed 

Google Scholar 
Wolffsohn, J. S. et al. Demographic and lifestyle risk factors of dry eye disease subtypes: A cross-sectional study. Ocul. Surf. 21, 58–63. https://doi.org/10.1016/j.jtos.2021.05.001 (2021).Article 
PubMed 

Google Scholar 
Núñez-Álvarez, C. & Osborne, N. N. Enhancement of corneal epithelium cell survival, proliferation and migration by red light: Relevance to corneal wound healing. Exp. Eye Res. 180, 231–241. https://doi.org/10.1016/j.exer.2019.01.003 (2019).Article 
CAS 
PubMed 

Google Scholar 
Marek, V. et al. Blue light phototoxicity toward human corneal and conjunctival epithelial cells in basal and hyperosmolar conditions. Free Radic. Biol. Med. 126, 27–40. https://doi.org/10.1016/j.freeradbiomed.2018.07.012 (2018).Article 
CAS 
PubMed 

Google Scholar 
Talens-Estarelles, C., García-Marqués, J. V., Cerviño, A. & García-Lázaro, S. Determining the best management strategy for preventing short-term effects of digital display use on dry eyes. Eye Contact Lens 48, 416–423. https://doi.org/10.1097/icl.0000000000000921 (2022).Article 
PubMed 

Google Scholar 
GOV.UK. COVID-19: guidance on protecting people defined on medical grounds as extremely vulnerable, (2020).Joy, M. et al. Reorganisation of primary care for older adults during COVID-19: A cross-sectional database study in the UK. Br. J. Gen. Pract. 70, e540–e547. https://doi.org/10.3399/bjgp20X710933 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Schiffman, R. M., Christianson, M. D., Jacobsen, G., Hirsch, J. D. & Reis, B. L. Reliability and validity of the ocular surface disease index. Arch. Ophthalmol. 118, 615–621. https://doi.org/10.1001/archopht.118.5.615 (2000).Article 
CAS 
PubMed 

Google Scholar 
Amparo, F. & Dana, R. Web-based longitudinal remote assessment of dry eye symptoms. Ocul. Surf. 16, 249–253. https://doi.org/10.1016/j.jtos.2018.01.002 (2018).Article 
PubMed 

Google Scholar 
Inomata, T. et al. Characteristics and risk factors associated with diagnosed and undiagnosed symptomatic dry eye using a smartphone application. JAMA Ophthalmol. 138, 58–68. https://doi.org/10.1001/jamaophthalmol.2019.4815 (2020).Article 
PubMed 

Google Scholar 
Toth, M. & Jokić-Begić, N. Psychological contribution to understanding the nature of dry eye disease: A cross-sectional study of anxiety sensitivity and dry eyes. Health Psychol. Behav. Med. 8, 202–219. https://doi.org/10.1080/21642850.2020.1770093 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Mehra, D. & Galor, A. Digital screen use and dry eye: A review. Asia-Pacific J. Ophthalmol. 9, 491–497. https://doi.org/10.1097/apo.0000000000000328 (2020).Article 

Google Scholar 
Galor, A., Kumar, N., Feuer, W. & Lee, D. J. Environmental factors affect the risk of dry eye syndrome in a United States veteran population. Ophthalmology 121, 972–973. https://doi.org/10.1016/j.ophtha.2013.11.036 (2014).Article 
PubMed 

Google Scholar 
Courtin, R. et al. Prevalence of dry eye disease in visual display terminal workers: A systematic review and meta-analysis. BMJ Open 6, e009675. https://doi.org/10.1136/bmjopen-2015-009675 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Torricelli, A. A. et al. Effects of ambient levels of traffic-derived air pollution on the ocular surface: Analysis of symptoms, conjunctival goblet cell count and mucin 5AC gene expression. Environ. Res. 131, 59–63. https://doi.org/10.1016/j.envres.2014.02.014 (2014).Article 
CAS 
PubMed 

Google Scholar 
Gupta, S. K., Gupta, V., Joshi, S. & Tandon, R. Subclinically dry eyes in urban Delhi: An impact of air pollution?. Ophthalmologica 216, 368–371. https://doi.org/10.1159/000066183 (2002).Article 
CAS 
PubMed 

Google Scholar 
Berg, E. J. et al. Climatic and environmental correlates of dry eye disease severity: A report from the dry eye assessment and management (DREAM) study. Trans. Vision Sci. Technol. 9, 25–25. https://doi.org/10.1167/tvst.9.5.25 (2020).Article 

Google Scholar 
Lang, S.-J., Abel, G. A., Mant, J. & Mullis, R. Impact of socioeconomic deprivation on screening for cardiovascular disease risk in a primary prevention population: A cross-sectional study. BMJ Open 6, e009984. https://doi.org/10.1136/bmjopen-2015-009984 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Denniston, A. K. et al. United Kingdom diabetic retinopathy electronic medical record (UK DR EMR) users group: Report 4, real-world data on the impact of deprivation on the presentation of diabetic eye disease at hospital services. Br. J. Ophthalmol. 103, 837–843. https://doi.org/10.1136/bjophthalmol-2018-312568 (2019).Article 
PubMed 

Google Scholar 
Nessim, M., Denniston, A. K., Nolan, W., Holder, R. & Shah, P. Research into Glaucoma and Ethnicity (ReGAE) 8: Is there a relationship between social deprivation and acute primary angle closure?. Br. J. Ophthalmol. 94, 1304–1306. https://doi.org/10.1136/bjo.2009.160721 (2010).Article 
PubMed 

Google Scholar 
Sharma, H. E. et al. The role of social deprivation in severe neovascular age-related macular degeneration. Br. J. Ophthalmol. 98, 1625–1628. https://doi.org/10.1136/bjophthalmol-2014-304959 (2014).Article 
PubMed 

Google Scholar 
Bo, M., Salizzoni, P., Clerico, M. & Buccolieri, R. Assessment of indoor-outdoor particulate matter air pollution: A review. Atmosphere 8, 136 (2017).Article 

Google Scholar 
Strøm-Tejsen, P., Zukowska, D., Fang, L., Space, D. R. & Wyon, D. P. Advantages for passengers and cabin crew of operating a gas-phase adsorption air purifier in 11-h simulated flights. Indoor Air 18, 172–181. https://doi.org/10.1111/j.1600-0668.2007.00511.x (2008).Article 
CAS 
PubMed 

Google Scholar 
Mandell, J. T., Idarraga, M., Kumar, N. & Galor, A. Impact of air pollution and weather on dry eye. J. Clin. Med. https://doi.org/10.3390/jcm9113740 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Navarro, D. Learning Statistics with R. (Daniel Joseph Navarro, 2015). More

Field, C. B., Behrenfeld, M. J., Randerson, J. T. & Falkowski, P. Primary production of the biosphere: Integrating terrestrial and oceanic components. Science 281, 237–240. https://doi.org/10.1126/science.281.5374.237 (1998).Article 
CAS 
PubMed 

Google Scholar 
Valladares, F. In Progress in Botany Vol. 64 (eds Esser, K. et al.) 439–471 (Springer, 2003).Chapter 

Google Scholar 
Anthony, K. R. N., Ridd, P. V., Orpin, A. R., Larcombe, P. & Lough, J. Temporal variation of light availability in coastal benthic habitats: Effects of clouds, turbidity, and tides. Limnol. Oceanogr. 49, 2201–2211. https://doi.org/10.4319/lo.2004.49.6.2201 (2004).Article 

Google Scholar 
Gattuso, J. P. et al. Light availability in the coastal ocean: Impact on the distribution of benthic photosynthetic organisms and their contribution to primary production. Biogeosciences 3, 489–513. https://doi.org/10.5194/bg-3-489-2006 (2006).Article 

Google Scholar 
Wright, D. H. Species-energy theory: An extension of species-area theory. Oikos 41, 496–506 (1983).Article 

Google Scholar 
Cusens, J., Wright, S. D., McBride, P. D. & Gillman, L. N. What is the form of the productivity–animal-species-richness relationship? A critical review and meta-analysis. Ecology 93, 2241–2252. https://doi.org/10.1890/11-1861.1 (2012).Article 
PubMed 

Google Scholar 
Rosenzweig, M. L. & Abramsky, Z. in Species Diversity in Ecological Communities. Historical and Geographical Perspectives (eds Ricklefs, R. E. & Schluter, D.) Ch. 5, 52–65 (The University of Chicago Press, 1993).Abrams, P. A. Monotonic or unimodal diversity-productivity gradients: What does competition theory predict?. Ecology 76, 2019–2027 (1995).Article 

Google Scholar 
Huston, M. A. Disturbance, productivity, and species diversity: Empiricism vs. logic in ecological theory. Ecology 95, 2382–2396 (2014).Article 

Google Scholar 
Roberts, T. E. et al. Testing biodiversity theory using species richness of reef-building corals across a depth gradient. Biol. Lett. 15, 20190493. https://doi.org/10.1098/rsbl.2019.0493 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Frankowiak, K. et al. Photosymbiosis and the expansion of shallow-water corals. Sci. Adv. 2, e1601122. https://doi.org/10.1126/sciadv.1601122 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Goreau, T. F. & Goreau, N. I. The physiology of skeleton formation in corals. II. Calcium deposition by hermatypic corals under various conditions in the reef. Biol. Bull. 117, 239–250. https://doi.org/10.2307/1538903 (1959).Article 
CAS 

Google Scholar 
Kirk, J. T. O. Light and Photosynthesis in Aquatic Ecosystems 3rd edn. (Cambridge University Press, 2011).
Google Scholar 
Stoddart, D. R. Ecology and morphology of recent coral reefs. Biol. Rev. 44, 433–498. https://doi.org/10.1111/j.1469-185X.1969.tb00609.x (1969).Article 

Google Scholar 
Lesser, M. P., Slattery, M. & Leichter, J. J. Ecology of mesophotic coral reefs. J. Exp. Mar. Biol. Ecol. 375, 1–8 (2009).Article 

Google Scholar 
Ackleson, S. G. Light in shallow waters: A brief research review. Limnol. Oceanogr. 48, 323–328. https://doi.org/10.4319/lo.2003.48.1_part_2.0323 (2003).Article 

Google Scholar 
Connell, J. H. Diversity in tropical rain forests and coral reefs. High diversity of trees and corals is maintained only in a nonequilibrium state. Science 199, 1302–1310. https://doi.org/10.1126/science.199.4335.1302 (1978).Article 
CAS 
PubMed 

Google Scholar 
Dollar, S. J. Wave stress and coral community structure in Hawaii. Coral Reefs 1, 71–81. https://doi.org/10.1007/BF00301688 (1982).Article 

Google Scholar 
Hughes, T. P. Community structure and diversity of coral reefs: The role of history. Ecology 70, 275–279. https://doi.org/10.2307/1938434 (1989).Article 

Google Scholar 
Fraser, R. H. & Currie, D. J. The species richness-energy hypothesis in a system where historical factors are thought to prevail: Coral reefs. Am. Nat. 148, 138–159 (1996).Article 

Google Scholar 
Cornell, H. V. & Karlson, R. H. Coral species richness: Ecological versus biogeographical influences. Coral Reefs 19, 37–49 (2000).Article 

Google Scholar 
Bellwood, D. R., Hughes, T., Connolly, S. & Tanner, J. Environmental and geometric constraints on Indo-Pacific coral reef biodiversity. Ecol. Lett. 8, 643–651. https://doi.org/10.1111/j.1461-0248.2005.00763.x (2005).Article 

Google Scholar 
Brown, B. E. et al. Diurnal changes in photochemical efficiency and xanthophyll concentrations in shallow water reef corals: Evidence for photoinhibition and photoprotection. Coral Reefs 18, 99–105 (1999).Article 

Google Scholar 
Hoegh-Guldberg, O. & Jones, R. J. Photoinhibition and photoprotection in symbiotic dinoflagellates from reef-building corals. Mar. Ecol. Prog. Ser. 183, 73–86. https://doi.org/10.3354/meps183073 (1999).Article 

Google Scholar 
Lesser, M. P. & Gorbunov, M. Y. Diurnal and bathymetric changes in chlorophyll fluorescence yields of reef corals measured in situ with a fast repetition rate fluorometer. Mar. Ecol. Prog. Ser. 212, 69–77. https://doi.org/10.3354/meps212069 (2001).Article 
CAS 

Google Scholar 
Hoogenboom, M. O., Anthony, K. R. N. & Connolly, S. R. Energetic cost of photoinhibition in corals. Mar. Ecol. Prog. Ser. 313, 1–12. https://doi.org/10.3354/meps313001 (2006).Article 
CAS 

Google Scholar 
Huot, Y. & Babin, M. Chlorophyll a Fluorescence in Aquatic Sciences: Methods and Applications 31–74 (Springer, 2010).Book 

Google Scholar 
Warner, M. E., Lesser, M. P. & Ralph, P. J. Chlorophyll a Fluorescence in Aquatic Sciences: Methods and Applications Ch. Chapter 10, 209–222 (Springer Science+Business Media B.V., 2010).Skirving, W. et al. Remote sensing of coral bleaching using temperature and light: Progress towards an operational algorithm. Remote Sens. 10, 18 (2018).Article 

Google Scholar 
Enríquez, S., Merino, M. & Iglesias-Prieto, R. Variations in the photosynthetic performance along the leaves of the tropical seagrass Thalassia testudinum. Mar. Biol. 140, 891–900. https://doi.org/10.1007/s00227-001-0760-y (2002).Article 
CAS 

Google Scholar 
Sundby, C., McCaffery, S. & Anderson, J. M. Turnover of the photosystem II D1 protein in higher plants under photoinhibitory and nonphotoinhibitory irradiance. J. Biol. Chem. 268, 25476–25482 (1993).Article 
CAS 
PubMed 

Google Scholar 
Tyystjärvi, E. & Aro, E. M. The rate constant of photoinhibition, measured in lincomycin-treated leaves, is directly proportional to light intensity. Proc. Natl. Acad. Sci. U. S. A. 93, 2213–2218. https://doi.org/10.1073/pnas.93.5.2213 (1996).Article 
PubMed 
PubMed Central 

Google Scholar 
Iglesias-Prieto, R., Beltrán, V. H., LaJeunesse, T. C., Reyes-Bonilla, H. & Thomé, P. E. Different algal symbionts explain the vertical distribution of dominant reef corals in the eastern Pacific. Proc. R. Soc. Lond. B 271, 1757–1763. https://doi.org/10.1098/rspb.2004.2757 (2004).Article 
CAS 

Google Scholar 
Jassby, A. D. & Platt, T. Mathematical formulation of the relationship between photosynthesis and light for phytoplankton. Limnol. Oceanogr. 21, 540–547 (1976).Article 
CAS 

Google Scholar 
Long, S. P., Humphries, S. & Falkowski, P. G. Photoinhibition of photosynthesis in nature. Annu. Rev. Plant Physiol. Plant Mol. Biol. 45, 633–662. https://doi.org/10.1146/annurev.pp.45.060194.003221 (1994).Article 
CAS 

Google Scholar 
Huner, N. P. A., Öuist, G. & Sarhan, F. Energy balance and acclimation to light and cold. Trends Plant Sci. 3, 224–230 (1998).Article 

Google Scholar 
Sheppard, C. R. C. Coral cover, zonation and diversity on reef slopes of Chagos Atolls, and population structures of the major species. Mar. Ecol. Prog. Ser. 2, 193–205 (1980).Article 

Google Scholar 
Huston, M. A. Patterns of species diversity in relation to depth at Discovery Bay, Jamaica. Bull. Mar. Sci. 37, 928–935 (1985).
Google Scholar 
Loya, Y. Community structure and species diversity of hermatypic corals at Eilat, Red Sea. Mar. Biol. 13, 100–123. https://doi.org/10.1007/BF00366561 (1972).Article 

Google Scholar 
Chow, G. S. E., Chan, Y. K. S., Jain, S. S. & Huang, D. Light limitation selects for depth generalists in urbanised reef coral communities. Mar. Environ. Res. 147, 101–112. https://doi.org/10.1016/j.marenvres.2019.04.010 (2019).Article 
CAS 
PubMed 

Google Scholar 
Kahng, S. E. et al. Community ecology of mesophotic coral reef ecosystems. Coral Reefs 29, 255–275. https://doi.org/10.1007/s00338-010-0593-6 (2010).Article 

Google Scholar 
Iglesias-Prieto, R. Temperature-dependent inactivation of Photosystem II in symbiotic dinoflagellates. in Proc. 8th Int. Coral Reef Sym, 1313–1318 (1997).Jones, R. J., Hoegh-Guldberg, O., Larkum, A. W. D. & Schreiber, U. Temperature-induced bleaching of corals begins with impairment of the CO2 fixation mechanism in zooxanthellae. Plant Cell Environ. 21, 1219–1230. https://doi.org/10.1046/j.1365-3040.1998.00345.x (1998).Article 
CAS 

Google Scholar 
Hennige, S. J., Suggett, D. J., Warner, M. E., McDougall, K. E. & Smith, D. J. Photobiology of Symbiodinium revisited: Bio-physical and bio-optical signatures. Coral Reefs 28, 179–195. https://doi.org/10.1007/s00338-008-0444-x (2008).Article 

Google Scholar 
Quigg, A. & Beardall, J. Protein turnover in relation to maintenance metabolism at low photon flux in two marine microalgae. Plant Cell Environ. 26, 693–703. https://doi.org/10.1046/j.1365-3040.2003.01004.x (2003).Article 
CAS 

Google Scholar 
Järvi, S., Suorsa, M. & Aro, E. M. Photosystem II repair in plant chloroplasts—Regulation, assisting proteins and shared components with photosystem II biogenesis. Biochim. Biophys. Acta Bioenerg. 900–909, 2015. https://doi.org/10.1016/j.bbabio.2015.01.006 (1847).Article 
CAS 

Google Scholar 
Jokiel, P. L. Solar ultraviolet radiation and coral reef epifauna. Science 207, 1069–1071 (1980).Article 
CAS 
PubMed 

Google Scholar 
López-Londoño, T. et al. Physiological and ecological consequences of the water optical properties degradation on reef corals. Coral Reefs 40, 1243–1256. https://doi.org/10.1007/s00338-021-02133-7 (2021).Article 

Google Scholar 
Vermeij, M. J. A. & Bak, R. P. M. How are coral populations structured by light? Marine light regimes and the distribution of Madracis. Mar. Ecol. Prog. Ser. 233, 105–116. https://doi.org/10.3354/meps233105 (2002).Article 

Google Scholar 
Hoogenboom, M. O., Connolly, S. R. & Anthony, K. R. N. Interactions between morphological and physiological plasticity optimize energy acquisition in corals. Ecology 89, 1144–1154. https://doi.org/10.1890/07-1272.1 (2008).Article 
PubMed 

Google Scholar 
Kaniewska, P., Anthony, K., Sampayo, E., Campbell, P. & Hoegh-Guldberg, O. Implications of geometric plasticity for maximizing photosynthesis in branching corals. Mar. Biol. 161, 313–328 (2014).Article 
CAS 

Google Scholar 
Kramer, N., Tamir, R., Eyal, G. & Loya, Y. Coral morphology portrays the spatial distribution and population size-structure along a 5–100 m depth gradient. Front. Mar. Sci. https://doi.org/10.3389/fmars.2020.00615 (2020).Article 

Google Scholar 
Lesser, M. P., Mobley, C. D., Hedley, J. D. & Slattery, M. Incident light on mesophotic corals is constrained by reef topography and colony morphology. Mar. Ecol. Prog. Ser. 670, 49–60. https://doi.org/10.3354/meps13756 (2021).Article 

Google Scholar 
Prada, C. et al. Linking photoacclimation responses and microbiome shifts between depth-segregated sibling species of reef corals. R. Soc. Open Sci. 9, 211591. https://doi.org/10.1098/rsos.211591 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Rowan, R., Knowlton, N., Baker, A. & Jara, J. Landscape ecology of algal symbionts creates variation in episodes of coral bleaching. Nature 388, 265–269. https://doi.org/10.1038/40843 (1997).Article 
CAS 
PubMed 

Google Scholar 
Warner, M. E., LaJeunesse, T. C., Robison, J. D. & Thur, R. M. The ecological distribution and comparative photobiology of symbiotic dinoflagellates from reef corals in Belize: Potential implications for coral bleaching. Limnol. Oceanogr. 51, 1887–1897. https://doi.org/10.4319/lo.2006.51.4.1887 (2006).Article 

Google Scholar 
Anthony, K. R. N. & Fabricius, K. E. Shifting roles of heterotrophy and autotrophy in coral energetics under varying turbidity. J. Exp. Mar. Biol. Ecol. 252, 221–253 (2000).Article 
CAS 
PubMed 

Google Scholar 
Hoogenboom, M., Rodolfo-Metalpa, R. & Ferrier-Pagès, C. Co-variation between autotrophy and heterotrophy in the Mediterranean coral Cladocora caespitosa. J. Exp. Biol. 213, 2399–2409 (2010).Article 
PubMed 

Google Scholar 
Carlson, R. R., Foo, S. A. & Asner, G. P. Land use impacts on coral reef health: A ridge-to-reef perspective. Front. Mar. Sci 6, 562. https://doi.org/10.3389/fmars.2019.00562 (2019).Article 

Google Scholar 
Wang, M. et al. The great Atlantic Sargassum belt. Science 365, 83–87. https://doi.org/10.1126/science.aaw7912 (2019).Article 
CAS 
PubMed 

Google Scholar 
Alvarez-Filip, L., González-Barrios, F. J., Pérez-Cervantes, E., Molina-Hernández, A. & Estrada-Saldívar, N. Stony coral tissue loss disease decimated Caribbean coral populations and reshaped reef functionality. Commun. Biol. 5, 440. https://doi.org/10.1038/s42003-022-03398-6 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Muscatine, L., McCloskey, L. R. & Marian, R. E. Estimating the daily contribution of carbon from zooxanthellae to coral animal respiration. Limnol. Oceanogr. 26, 601–611. https://doi.org/10.4319/lo.1981.26.4.0601 (1981).Article 
CAS 

Google Scholar 
Jørgensen, S. E. & Bendoricchio, G. Fundamentals of Ecological Modelling 3rd edn, Vol. 21 (Elsevier Sceince B. V., 2001).
Google Scholar 
Hennige, S. J. et al. Acclimation and adaptation of scleractinian coral communities along environmental gradients within an Indonesian reef system. J. Exp. Mar. Biol. Ecol. 391, 143–152. https://doi.org/10.1016/j.jembe.2010.06.019 (2010).Article 

Google Scholar 
Scheufen, T., Iglesias-Prieto, R. & Enríquez, S. Changes in the number of symbionts and Symbiodinium cell pigmentation modulate differentially coral light absorption and photosynthetic performance. Front. Mar. Sci 4, 309. https://doi.org/10.3389/fmars.2017.00309 (2017).Article 

Google Scholar 
Veron, J. E. N. Corals in Space and Time. The Biogeography and Evolution of the Scleractinia 321 (Cornell University Press, 1995).
Google Scholar 
Nelder, J. A. & Mead, R. A simplex method for function minimization. J. Comput. 7, 308–313. https://doi.org/10.1093/comjnl/7.4.308 (1965).Article 
MathSciNet 
MATH 

Google Scholar 
R: A languate and environment for statistical computing. Retrieved from http://www.R-project.org (R Foundation for Statistical Computing, Vienna, Austria, 2010). More

Scientists with visible and invisible disabilities take on adversity, helping themselves and others.Shigehiro Namiki always wanted to study insects. After his PhD research at the University of Tsukuba, he was a postdoctoral fellow, then a staff scientist at Janelia Research Campus. Among his projects, Namiki worked with others on a method to analyze how the few so-called descending neurons in fruit flies control a wide range of movements and behavior. These neurons run from the brain to the ventral nerve cord and branch out to circuits that control the insect’s neck, legs and wings. More

Study systemOur focal ecosystem is in Selvíria, state of Mato Grosso do Sul, Brazil ((hbox {20}^{circ }) (22′) (41.86”) S, (hbox {51}^{circ }) (24′) (58.90”) W), on a property owned by the São Paulo State University (UNESP). The location covers 350 ha of pasture composed of liverseed grass (Urochloa decumbens). The native vegetation was removed, pasture areas were implemented, and livestock was introduced in the 1970s, maintaining this configuration during the following 50 years. The climate of this area is categorized as equatorial savanna, with dry periods concentrated mostly during the winter, from April to August. During our sampling period (from November 23th, 1989, to November 19th, 2015), no vermifuges and insecticides that could affect negatively the community of dung beetles associated with cow pads were used1.The native dung beetle community at this site is composed of dwellers and tunnelers. Dwellers comprise the Aphodiinae subfamily, whereas all the tunnelers belong to the Scarabaeinae subfamily31. In total, there were eight species classified as dwellers (Ataenius crenulatus, A. picinus and Atanius aequalis-platensis grouped as one species, Blackburneus furcatus, Genieridium bidens, Labarrus pseudolividus, Nialaphodius nigrita and Trichillum externepunctatum) and ten native tunnelers (Ateuchus nr. puncticollis, A. vividus, Canthidium nr. pinotoides, Dichotomius bos, D. semiaeneus, D. sexdentatus, Ontherus appendiculatus, O. dentatus, O. sulcator). These species were chosen for our study because, as the invasive tunneler D. gazella (also from the Scarabaeinae subfamily), they all co-occur in pasture and exploit the same resource (cow pad)32. The initial establishment of D. gazella caused the loss of most of the native tunnelers from the community, with the invader becoming the overwhelming representative of the functional group, and an initial decrease of abundance for dwellers. Differently from native tunnelers, however, dwellers were able to recover their number a few years after invasion (Fig. 1a, Fig. S1).As reported in1, the abundance of dung beetles was significantly affected by both local minimum temperature and relative humidity. The influence of these two factors is expected, as they determine egg and larval survival and development of dung beetles. For example, because dung beetles are poikilotherms, environmental temperature is key to their development and fecundity33. One of the main dweller species, Labarrus pseudolividus, is widely found in locations with temperature averages ranging between (hbox {12},^{circ }hbox {C}) and (hbox {18},^{circ }hbox {C})34, making it tolerant to colder local temperatures. On the other hand, for D. gazella the lower developmental threshold is (hbox {15.5},^{circ }hbox {C}) (individuals cannot survive below this temperature), and the optimum temperature for population growth is (hbox {28},^{circ }hbox {C})35. For both groups, physiological growth and reproduction rates are maintained even when outside temperatures are close to the lower developmental threshold; dwellers, for example, live inside the dung pile, where temperature is higher and less variable than outside36,37. However, while tunnelers oviposit deep in the soil to protect the eggs, warmer and drier conditions reduce dweller egg viability on dung piles since they are exposed38. Low humidity conditions lead to drier dung and can cause egg and insect dessication. In addition, dwellers from our focal system have Palearctic evolutionary origins39; D. gazella’s natural distribution ranges from central to southern Africa40, presenting high physiological plasticity that allows it to tolerate high temperatures and low relative humidity better than other tunneler species41.Functional-group data collection and community structure characterizationDung beetles were collected once a week in a black-light flight intercept trap42, which guarantees the collection of coprophagic beetles. During all collection periods, climate variables were also collected from a meteorological station located within 2 km of our collecting site. See1 for the complete description of the collection process and database. For our purposes, we retained the species, number of individuals per species, and climate variables for each week sampled (Supplementary Information, SI, Figs. S1–S2).We focused first on the weekly abundance data, which we needed to process in order to avoid spurious results in our analyses stemming from the measurement protocol. Specifically, we filtered out seasonal low values associated with sampling in the coldest periods, when few beetles are captured because the reduced activity in all functional groups restricts their spatio-temporal distribution43. Including such samples would not be representative of the community and could bias the analysis since we are investigating community composition (i.e. proportions, very sensitive to low sampling). Thus, we considered only samples with a total number of beetles (that is, summing up all groups together) higher than the value of the median of all data, a conservative threshold that retains observations that allow for as much representation of the community as possible. As will become evident in the Results section and Supplementary Information, less conservative choices for the threshold did not alter our main conclusions.Following Mesquita -Filho et al.1, we categorized all sampled species into either dwellers or tunnelers. D. gazella is a tunneler and, as explained above, the native tunneler species experienced massive declines in abundance after its establishment, leaving D. gazella as almost the single representative in the tunneler functional group during the period of observation1. Thus, given the sharp contrast in community composition, we also separated the data into before and after invasion using to that end the 200th week, when D. gazella was first observed at the study site (September 11th, 1993, starting date for what we will call “after invasion”, our focal period henceforth).To describe community functional composition (i.e. system state) through time, we derived a normalized functional group ratio. First, because the abundance of each functional group spanned up to four orders of magnitude, we performed a logarithmic transformation of the number of captured insects from each group i, (log _{10}(N_{i}+K)), following  Yamamura44. Here, we chose (K=1), but the value of K did not alter our results qualitatively. In addition, the original data showed random mismatches in the phenology of each group, which gave the wrong impression of extreme short-term shifts in functional group dominance within the community. To avoid such artifacts, we used nonparametric local regression (LOESS)45 to smooth the dynamics of each group46. For this smoothing, we employed the loess function in the R software 3.6.147 with a smooth parameter equal to 0.25, but other moderate values (or an optimal value calculated with Bayesian inference by the R function optimal_span) did not alter our conclusions. Finally, we extracted back from the smoothed curve the number of beetles within each functional group to calculate the fraction (f_{dwell}) that measures the relative abundance of dwellers:$$begin{aligned} f_{dwell} = frac{N_D}{N_D+N_T} end{aligned}$$
(1)
where (N_D) corresponds to the number of dwellers per week and (N_T) corresponds to the number of native tunnelers (for the period before invasion), or only the number of D. gazella observed per week (after invasion), using their corresponding smoothed curves. Including also native tunnelers after invasion did not alter our conclusions.Climate driverWe devised a single climatic driver variable that merges the weekly measurement of temperature and relative humidity over the years, abiotic factors key to the survival and reproduction of both groups (see above). We first converted minimum temperatures and relative humidity to normalized climate variables using a min-max normalization (a feature scaling that uses the total range of temperatures or relative humidity, respectively, as normalization factor):$$begin{aligned} T = frac{T_{week} – T_{min}}{T_{max}-T_{min}};;,~ ~ ~ ~ ~ ~ RH = frac{RH_{week} – RH_{min}}{RH_{max}-RH_{min}};;, end{aligned}$$
(2)
where T corresponds to the normalized temperature, (T_{week}) is the weekly temperature, and (T_{max}) and (T_{min}) are the absolute maximum and minimum temperatures observed during the whole sampling period, respectively. We used a similar notation for relative humidity, RH. Based on the information above regarding beetle response to climate, the merged climate factor c was defined as the relationship:$$begin{aligned} c = frac{T}{RH};;, end{aligned}$$
(3)
for (RHne 0). That is, higher temperatures and/or drier conditions (expected to favor D. gazella) lead to higher values for c. On the other hand, lower temperatures and/or more humid conditions (expected to favor dwellers) imply lower values for c. Intermediate values of c can represent either moderate or extreme values for both T and RH.Identifying ecological states and quantifying resilienceWith our (f_{dwell}) data as an index of community composition (i.e. system state), we calculated kernel density functions to interpolate a continuous probability distribution of the relative fraction of dwellers in the community, (p_{n}(f_{dwell})) (function density, R software 3.1.647) for a given range of climatic driver c values. We grouped the (f_{dwell}) data using ranges for c of size 0.4, to ensure a significant amount of weekly samples that allowed for the reconstruction of these probability distributions (see Table S1, first column). Note that bins with extreme values showed few data points (see first and last rows in Table S1), and thus were rejected to prevent misleading results due to reduced sampling. Also note that, for the density function, we used the default Gaussian kernel with a smoothing bandwidth adjusted to be (50%) larger than the default value (“adjust” argument set to 1.5). This conservative choice aims to reduce the effect of the different sampling across c bins and to ensure that differences among distributions across c values are not the result of spurious sampling noise.Further, we transformed the kernel density function:$$begin{aligned} V(f_{dwell}) = -ln (p_{n}(f_{dwell})) end{aligned}$$
(4)
This (V(f_{dwell})) function, called potential (e.g.48), shows by design well-defined minima for the most frequently observed values of (f_{dwell}) (i.e. configurations most frequently observed for the community, which conform the modes of the probability distribution) in a given group of data. At these points, the potential exhibits a change of trend from decreasing to increasing, and therefore its derivative shows a change of sign. Eq. (4), thus, provides a simple criterion to identify possible system states, which is a reason why potentials have been used extensively across disciplines49,50,51. Nonetheless, because the position of extrema is invariant under the transformation, using probability distributions instead would not alter our conclusions.Representing the potential obtained from all the (f_{dwell}) system states associated with a same range of climatic driver c values allowed us to identify stable community configurations associated with a specific climate. The comparison of the potentials obtained for different c ranges enabled the description of how the community changed in response to climatic variation. The location of the minima revealed which states were stable for a given value of the climatic driver; the presence of two minima, then, flagged the existence of bistability (i.e. two different community compositions possible for the same c value).These minima are materialized as wells in the potential’s landscape, which provides an easy way to understand the concept of stability: the dynamics of the system for the given value of the driver will eventually “fall” into a well (either a state dominated by dwellers or a state dominated by tunnelers), with the shape of the well (e.g. its depth) determining how difficult it is for the system to “escape” that state. Therefore, the area inside a well provides quantification of the tendency of a system to stay in that specific state, i.e. the resilience of the associated ecological state or how strong a perturbation has to be to move the system from such an ecological state to another2,3,50,51,52,53. Thus, in addition to number and location of wells, measuring their associated area allowed us to further characterize the resilience of the community. To this end, we first set a visualization window common to all potentials. Specifically, we plotted the potentials within a range for the vertical variable (the potential, V) given by ([-1.5,1.5]); the horizontal variable (fraction of dwellers, (f_{dwell})) is by definition bounded between 0 and 1. For potentials that showed one single well, the area of the well was measured as the area above the potential curve within this visualization window. For potentials that showed two wells (bistability), we measured the value of the potential at the local maximum separating the two wells, and established that value as the upper (horizontal) line closing the area of each well. To ensure all cases were comparable and eliminate any arbitrariness of the choices above, we expressed resilience as a relative area; in other words, we further normalized the well area by the total area across wells for that potential, which means that any single-well case will show a resilience (or relative area) of 1, and the resilience of the two wells when there is bistability adds up to 1.Figure 1Left: Community composition by functional group for all weeks of observation1. Green represents dwellers, blue represents tunnelers, and orange represents the invader D. gazella. Right: Sketch of responses of the community composition to the climatic driver (i.e. phase diagram) expected from the physiological and behavioral characteristics of the functional groups in the community as described in text: linear (red), or non-linear but monotonic without (blue) or with (brown) hysteresis.Full size imageIdentifying ecological transitionsMeasuring a state variable, (f_{dwell}), and a driver, c (order and control parameter, respectively, in the jargon of regime shift theory), allowed us to study how their observed behavior over time materializes in a driver-state relationship (the so-called phase diagram) defining the possible shifts in dominance (i.e. regime shifts) that the community may undergo as climate changes12. The non-monotonic temporal behavior of the components of the order parameter (i.e. dwellers and tunneler availability) and the components of the control parameter (i.e. temperature and relative humidity) makes it difficult to predict the shape of the phase diagram, and therefore whether we can expect alternative stable states in the focal example. For such cases, the dominance of the dung beetle community could (1) shift in a linear fashion toward the functional group favored by climatic conditions; (2) shift between functional groups in non-linear threshold response to climatic conditions without hysteresis; or (3) shift between functional groups in non-linear threshold response to climatic conditions with hysteresis –and thus showing bistability (see Fig. 1b, or12). Other possibilities, e.g. a non-linear shift between functional groups where one group is favored at intermediate climatic conditions12 are discarded as the invader is better suited for warmer and drier conditions. To evaluate which of these possibilities occurred, we represented (f_{dwell}) as a function of c, as well as the location of the minima shown by the potentials above. In addition to the emerging shape of this relationship, this plot can reveal the presence of alternative stable states if two or more different points occur for the same value of the control parameter, c. More