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School of Geography, Earth and Enviornmental Sciences, University of Birmingham, Birmingham, UK
Adriane Esquivel-Muelbert & Thomas A. M. Pugh

School of Geography, University of Leeds, Leeds, UK
Adriane Esquivel-Muelbert, Oliver L. Phillips, Roel J. W. Brienen, Martin J. P. Sullivan, Timothy R. Baker, Emanuel Gloor, Aurora Levesley, Simon L. Lewis, Karina Liana Lisboa Melgaço Ladvocat, Gabriela Lopez-Gonzalez, Nadir Pallqui Camacho, Julie Peacock, Georgia Pickavance & David Galbraith

Birmingham Institute of Forest Research, University of Birmingham, Birmingham, UK
Adriane Esquivel-Muelbert & Thomas A. M. Pugh

School of Geography, Earth and Environmental Sciences, University of Plymouth, Plymouth, UK
Sophie Fauset

Department of Natural Sciences, Manchester Metropolitan University, Manchester, UK
Martin J. P. Sullivan

International Master Program of Agriculture, National Chung Hsing University, Taichung, Taiwan
Kuo-Jung Chao

Geography, College of Life and Environmental Sciences, University of Exeter, Exeter, UK
Ted R. Feldpausch

Instituto Nacional de Pesquisas da Amazônia, Manaus, Brazil
Niro Higuchi, Adriano José Nogueira Lima & Carlos Quesada

School of Mathematics, University of Leeds, Leeds, UK
Jeanne Houwing-Duistermaat & Haiyan Liu

Faculty of Natural Sciences, Department of Life, Imperial College London Sciences, London, UK
Jon Lloyd

Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford, UK
Yadvinder Malhi & Simone Matias de Almeida Reis

UNEMAT – Universidade do Estado de Mato Grosso PPG-Ecologia e Conservação, Campus de Nova Xavantina, Nova Xavantina, MT, Brazil
Beatriz Marimon, Ben Hur Marimon Junior, Paulo Morandi, Edmar Almeida de Oliveira & Simone Matias de Almeida Reis

Jardín Botánico de Missouri, Oxapampa, Peru
Abel Monteagudo-Mendoza, Victor Chama Moscoso, Luis Valenzuela Gamarra & Rodolfo Vasquez Martinez

Forest Ecology and Forest Management Group, Wageningen University and Research, Wageningen, Netherlands
Lourens Poorter, Frans Bongers, Marielos Peña-Claros & Pieter Zuidema

Centro de Ciências Biológicas e da Natureza, Universidade Federal do Acre, Rio Branco, AC, Brazil
Marcos Silveira

Instituto de Investigaciones para el Desarrollo Forestal (INDEFOR), Universidad de Los Andes, Mérida, Venezuela
Emilio Vilanova Torre & Julio Serrano

University of California, Berkeley, CA, USA
Emilio Vilanova Torre

Escuela de Ciencias Agropecuarias y Ambientales, Universidad Nacional Abierta y a Distancia, Boyacá, Colombia
Esteban Alvarez Dávila

Fundación ConVida, Medellín, Colombia
Esteban Alvarez Dávila

Instituto de Investigaciones de la Amazonia Peruana, Iquitos, Peru
Jhon del Aguila Pasquel, Gerardo A. Aymard C., Nallaret Davila Cardozo & Eurídice Honorio Coronado

Instituto de Biodiversidade e Florestas, Universidade Federal do Oeste do Pará, Santarém, Brazil
Everton Almeida

Center for Tropical Conservation, Nicholas School of the Environment, University in Durham, Durham, NC, USA
Patricia Alvarez Loayza

Projeto Dinâmica Biológica de Fragmentos, Instituto Nacional de Pesquisas da Amazônia Florestais, Manaus, AM, Brazil
Ana Andrade & José Luís Camargo

National Institute for Space Research (INPE), São José dos Campos, SP, Brazil
Luiz E. O. C. Aragão

Museo de Historia Natural Noel Kempff Mercado, Universidad Autónoma Gabriel Rene Moreno, Santa Cruz de la Sierra, Bolivia
Alejandro Araujo-Murakami & Marisol Toledo

Wageningen Environmental Research, Wageningen University and Research, Wageningen, Netherlands
Eric Arets

Dirección de la Carrera de Biología, Universidad Autónoma Gabriel René Moreno, Santa Cruz de la Sierra, Bolivia
Luzmila Arroyo

INRAE, UMR EcoFoG, CNRS, Cirad, AgroParisTech, Université des Antilles, Université de Guyane, Kourou, France
Michel Baisie, Damien Bonal, Benoit Burban, Aurélie Dourdain, Maxime Rejou-Machain & Clement Stahl

Department of Biological Sciences, International Center for Tropical Botany, Florida International University, Miami, FL, USA
Christopher Baraloto

Centro de Energia Nuclear na Agricultura, Universidade de São Paulo, Piracicaba, Brazil
Plínio Barbosa Camargo

Universidade Federal do Acre, Campus Floresta, Cruzeiro do Sul, Brazil
Jorcely Barroso

UR Forest & Societies, CIRAD, Montpellier, France
Lilian Blanc

Department of Biology, Utrecht, Netherlands
René Boot

Woods Hole Research Center, Falmouth, MA, USA
Foster Brown

Laboratório de Botânica e Ecologia Vegetal, Universidade Federal do Acre, Rio Branco, AC, Brazil
Wendeson Castro

Laboratoire Evolution et Diversite Biologique, CNRS, Toulouse, France
Jerome Chave

Inventory and Monitoring Program, National Park Service, Fort Collins, CO, USA
James Comiskey

Proyecto Castaña, Madre de Dios, Peru
Fernando Cornejo Valverde

Instituto de Geociências, Faculdade de Meteorologia, Universidade Federal do Para, Belém, Brazil
Antonio Lola da Costa

Department of Anthropology and Primate Molecular Ecology and Evolution Laboratory, University of Texas, Austin, TX, USA
Anthony Di Fiore

National Museum of Natural History, Smithsonian Institute, Washington, DC, USA
Terry Erwin

Universidad Nacional Jorge Basadre de Grohmann, Tacna, Peru
Gerardo Flores Llampazo

Museu Paraense Emílio Goeldi, Belém, Brazil
Ima Célia Guimarães Vieira & Rafael Salomão

Instituto Venezolano de Investigaciones Científicas (IVIC), Caracas, Venezuela
Rafael Herrera

IIAMA, Universitat Politécnica de València, València, Spain
Rafael Herrera

Universidad Nacional de San Antonio Abad del Cusco, Cusco, Peru
Isau Huamantupa-Chuquimaco

Instituto Amazónico de Investigaciones Imani, Universidad Nacional de Colombia Sede Amazonia, Leticia, Colombia
Eliana Jimenez-Rojas

Agteca, Santa Cruz, Bolivia
Timothy Killeen

College of Science and Engineering, James Cook University, Cairns, QLD, Australia
Susan Laurance & William Laurance

Department of Geography, University College London, London, UK
Simon L. Lewis

Environmental Science and Policy, George Mason University, Fairfax, VA, USA
Thomas Lovejoy

Research School of Biology, Australian National University, Canberra, ACT, Australia
Patrick Meir

School of Geosciences, University of Edinburgh, Edinburgh, UK
Patrick Meir

Escuela de Ciencias Forestales, Unidad Académica del Trópico, Universidad Mayor de San Simón, Cochabamba, Bolivia
Casimiro Mendoza

Facultad de Ingeniería Ambiental, Universidad Estatal Amazónica, Puyo, Ecuador
David Neill

Universidad Nacional de San Antonio Abad del Cusco, Cusco, Perú
Percy Nuñez Vargas, Nadir Pallqui Camacho & Javier Silva Espejo

Universidad Autónoma del Beni José Ballivián, Trinidad, Bolivia
Guido Pardo & Vincent Vos

Universidad Regional Amazónica Ikiam, Ikiam, Ecuador
Maria Cristina Peñuela-Mora

Broward County Parks Recreation, Oakland Park, FL, USA
John Pipoly

Keller Science Action Center, Field Museum, Chicago, IL, USA
Nigel Pitman

Instituto de Ciencias Naturales, Universidad Nacional de Colombia, Bogotá, Colombia
Adriana Prieto & Agustín Rudas

Institute of Research for Forestry Development (INDEFOR), Universidad de los Andes, Mérida, Venezuela
Hirma Ramirez-Angulo

Socioecosistemas y Cambio Climatico, Fundacion Con Vida, Medellín, Colombia
Zorayda Restrepo Correa

Centro de Conservacion, Investigacion y Manejo de Areas Naturales, CIMA Cordillera Azul, Lima, Peru
Lily Rodriguez Bayona

Universidade Federal Rural da Amazônia, Belém, Brazil
Rafael Salomão & Natalino Silva

Departamento de Biología, Universidad de La Serena, La Serena, Chile
Javier Silva Espejo

Guyana Forestry Commission, Georgetown, Guyana
James Singh

Federal University of Alagoas, Maceió, Brazil
Juliana Stropp

Institute for Conservation Research, Escondido, CA, USA
Varun Swamy

Institute for Transport Studies, University of Leeds, Leeds, UK
Joey Talbot

Biodiversity Dynamics, Naturalis Biodiversity Center, Leiden, The Netherlands
Hans ter Steege

Systems Ecology, Free University, De Boelelaan 1087, Amsterdam, Netherlands
Hans ter Steege

Department of Biology, University of Florida, Gainesville, FL, USA
John Terborgh

Iwokrama International Centre for Rainforest Conservation and Development, Georgetown, Guyana
Raquel Thomas

Universidad de los Andes, Mérida, Venezuela
Armando Torres-Lezama

School of Geography, University of Nottingham, Nottingham, UK
Geertje van der Heijden

Van Hall Larenstein University of Applied Sciences, Leeuwarden, Netherlands
Peter van der Meer

Van der Hoult Forestry Consulting, Leeuwarden, The Netherlands
Peter van der Hout

Núcleo de Estudos e Pesquisas Ambientais – Universidade Estadual de Campinas, Campinas, Brazil
Simone Aparecida Vieira

Herbario del Sur de Bolivia, Universidad de San Francisco Xavier de Chuquisaca, Sucre, Bolivia
Jeanneth Villalobos Cayo

Tropenbos International, Wageningen, Netherlands
Roderick Zagt

A.E.-M. and D.G. designed the study with contributions from O.L.P., R.J.W.B., S.F. and M.J.P.S. A.E.-M. carried out the analyses with inputs from D.G., O.L.P., R.J.W.B., S.F., M.J.P.S., J.H.-.D. and H.L. A.E.-M. wrote a first draft with contributions from D.G., M.J.P.S., T.A.M.P., S.F. and O.L.P. O.L.P., R.J.W.B., S.F., M.J.P.S., T.R.B., K.-J.C., T.R.F., N.H., Y.M., B.M., B.H.M.J., A.M.-M., L.P., M.S., E.V.T., E.A.D., J.d.A.P., E.A., P.A.L., A.A., L.E.O.CA., A.A.-M., E.Arets, L.A., G.A.A.C., M.B., C.B., P.B.C., J.B., L.B., D.B., F.B., R.J.W.B., F.Brown, B.B., J.L.C., W.C., V.C.M., J.C., J.Comiskey, F.C.V., A.L.d.C., N.D.C., A.D.F., A.D., T.E., G.F.L., I.C.G.V., R.H., E.H.C., I.H.-C., E.J.-R., T.K., S.L., W.L., S.L.L., T.L., P.M., C.M., P.Morandi, D.N., A.J.N.L., P.N.V., E.A.d.O., N.P.C., G.Prado, J.Pipoly, M.P.-C., M.C.P.-M., N.P., A.P., C.Q., H.R.-A., S.M.d.A.R., M.R.-M., Z.R.C., L.R.B., A.R., R.S., J.S., J.S.E., N.S., J.Singh, C.S., J.Stroop, V.S., J.T., H.t.S., J.T., R.T., M.T., A.T.-L., L.V.G., G.v.d.H., P.v.d.M., P.v.d.H., R.V.M., S.A.V., J.V.C., V.V., R.Z. and P.Z. led field expeditions for data collection. O.L.P., J.L. and Y.M. conceived the RAINFOR forest plot network; D.G., E.G. and T.R.B. contributed to its development. O.L.P., R.J.W.B., T.R.F., T.R.B., A.M.‐M., L.E.O.C.A., E.A.D., B.M., B.H.M.J., N.H., E.V.T., J.C., E.G. and Y.M. coordinated data collection with the help of many co‐authors. O.L.P., T.R.B., S.L.L. and G.L.-G. conceived ForestPlots.net, and M.J.P.S., A.L., J.Peacock, G.P., K.L.L.M.L., D.G. and E.G. helped to develop it. All authors read and approved the manuscript (with important insights provided by O.L.P., L.P., H.t.S., T.E., W.C., S.M.d.A.R., E.G., E.A.d.O., P.M., M.J.P.S., D.B., G.v.d.H. and P.Z.). More

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Summary statistics
To date, The Connecticut Agricultural Experiment Station (CAES) has collected and tested 4,602,240 female mosquitoes comprised of 47 species in 8 genera. Approximately 98% of these collections were obtained from 92 trapping sites in 73 towns throughout the state, while the remainder of collections were from an additional 365 supplemental sites sampled between 1996 and 2007. Eighty-eight percent of collections come from CDC Light Traps, CDC Gravid Traps and Biogents BG Sentinel Traps (beginning in 2012). There have been several other collection methods used throughout the years that account for 11.6% of the mosquitoes collected (S. Table 1). Overall, there was considerable variation in mosquito abundance, surveillance effort, species richness/evenness, and the proportion of single species detections across CT (Fig. 1). One clear trend was that surveillance effort was greatest in CT’s human population centers (predominately CT’s southwestern and central counties) where WNV is commonly detected and along the CT-Rhode Island border where EEEV is most commonly detected (Fig. 1A). Another noticeable visual trend was that species evenness tends to be higher in the eastern portion of CT (Fig. 1B).
Figure 1

Maps of total mosquito abundance (log10 transformed) (A), total number of trap nights (A), average annual mosquito species richness (B), average annual mosquito species evenness (B), and average annual prevalence of single species detections (C) across 87 mosquito surveillance sites throughout Connecticut, U.S. sampled with ground level CDC CO2-baited light traps from 2001 to 2019. (A) Point sizes represent abundance while colors represent trap-nights; (B) point sizes represent species richness while colors represent species evenness; (C) point sizes represent prevalence of single species detections. (A–C) Solid black lines represent county political boundaries. The figure was created in R V 3.6.3 using the following packages: ggplot2 and maps.

Full size image

Objective 1: annual collections of mosquito populations among sites
Our first objective was to identify spatial and temporal linear and nonlinear trends in mosquito abundance among sites. We also examined coarse-scale correlations between statewide (i.e., annual) and site-wide abundance and weather and land classification variables. All regression results and tables are provided as supporting information in Supporting Information: Regression Tables.
Mosquito abundance
Temporal regressions
After accounting for trapping effort, regression parameters estimating the relationship between site-level mosquito abundance and year of collection were positive using generalized linear mixed effects models (GLMMs) (“Year”—Estimate 0.03, t-value 9.11) and generalized additive mixed effects models (GAMMs) (“Year”—Est. 0.77, t-value 2.7, p = 0.007), suggesting that site-level mosquito abundance has increased in CT since 2001 (Fig. 2A,B): this trend resulted in a predicted 60% increase in annual abundance from 2001 to 2019. While these regressions identified possible increasing trends in site-level abundance, they provided an overall poor-fit to the data: AIC scores from fixed effect GLMMs were higher than random effects-only models (ΔAIC 415.1). This poor model fit may be in part driven by directly modeling Year as a fixed continuous effect; Year as a random categorical effect may better capture variation in mosquito collections30. Despite large differences in AIC scores between fixed and random effects-only models, we detected a pattern of increasing intercept values when examining “Year” as a random effect (S. Fig. 1), providing further evidence of an increasing temporal trend in site-level mosquito abundance.
Figure 2

Average annual mosquito abundance (A), number of trap nights (B), mosquito species richness (C), mosquito species evenness (D), the annual correlation between mosquito species richness and evenness (E), and the prevalence of single mosquito species detections (F) across 87 mosquito surveillance sites throughout Connecticut, U.S. sampled with ground level CDC CO2-baited light traps from 2001 – 2019. For (A)–(D) and (F), points represent the average across all sites, solid lines represent the standard error of the average, and dashed lines are added to aid interpreting each plot as a time series. For (E), points represent the average across all sites while solid lines represent the 95% CI of the correlation point estimate. The figure was created in R V 3.6.3 using base functions.

Full size image

Spatial regressions
After accounting for trapping effort, regression parameters estimating the relationship between site-level mosquito abundance and latitude/longitude were positive using a GLMM (“Latitude (centered)”—Est. 0.49, t-value 5.48; Longitude (centered)”—Est. 0.20, t-value 4.78), indicating that mosquito abundance tends to increase on a south to north and west to east gradient (which reflects the overall transition in land cover from developed to forested in CT). The best fitting fixed effect GAMM included Longitude by Latitude smoothing terms, which also predicted positive relationships between abundance and site coordinates (Smoothing term 1: Est. 0.24, p = 0.06; Smoothing term 2: Est. 0.05, p = 0.67). GAMM predictions of site-level mosquito abundance were considerably more complex than GLMM predictions, yet still supported the overall trend of increasing abundance from south to north and west to east (S. Fig. 2). Overall, the fixed effect GLMMs provided an extremely poor fit to the data compared to random effects-only GLMMs (Latitude—ΔAIC 1092.7; Longitude—ΔAIC 1099.8). These poor model fits may be in part driven by directly modeling coordinate (i.e., site) as a fixed continuous effect: GAMM predictions that account for nonlinear relationships between abundance and spatial location may provide a more appropriate fit to the data while site as a categorical random effect in the GLMMs may better capture variation in mosquito collections30.
Weather correlations
When comparing statewide annual mosquito abundance to weather variables, we found no correlations between summer temperatures, spring temperatures or precipitation. This was despite detecting a slight annual increase in temperatures across all three seasons examined (average daily temperature GLMM Est., Season/Summer: 0.05 °C, Prior Spring: 0.02 °C, Prior Winter: 0.07 °C) and a slight annual decline in within season and prior spring precipitation (total precipitation GLMM Est., Season/Summer: − 4.23 mm, Prior Spring: − 3.38 mm; Prior Winter: 2.22 mm) in CT since 2001. However, we did find a positive correlation between total summer precipitation and annual statewide mosquito abundance (r = 0.50, CI 0.07–0.78).
Land cover correlations
When comparing total site-wide abundance to land cover classifications, we found positive correlations between percent land cover categorized as barren (r = 0.22, CI 0.01–0.41), forested wetland (r = 0.34, 0.14–0.52), and non-forested wetland (r = 0.21, 0.004–0.41). We also found a negative association in total site-level abundance and percent land cover categorized as grass (r = − 0.35, − 0.52 to − 0.15).
Species richness
Temporal regressions
After accounting for trapping effort, regression parameters estimating the relationship between site-level species richness and year of collection were positive using both GLMMs (“Year (centered)”—Est. 0.10, t-value 9.46) and GAMMs (“Year”—Est. 1.78, t-value 1.93, p = 0.05) (Fig. 2C): this trend resulted in a predicted 10% increase in site-level species richness from 2001 to 2019. Overall, fixed effects GLMMs of species richness provided an overall poor fit to the data when compared to a random effects-only model (ΔAIC 319.37). However, we did observe a pattern of increasing intercept values when examining “Year” as a random effect (S. Fig. 3), further indicating that mosquito species richness has annually increased across sites in CT since 2001.
Spatial regressions
Similar to models of site-level mosquito abundance, GLMMs of species richness by coordinate predicted positive relationships (Latitude (centered): Est. 0.63, t-value = 2.11; Longitude (centered): Est. 1.26, t-value = 9.34), indicating the species richness tends to increase along a south to north, west to east gradient. The best fitting GAMM included Longitude by Latitude smoothing terms, which also predicted positive relationships between species richness and site coordinate (Smoothing term 1: Est. 1.45, p = 0.0001; Smoothing term 2: Est. 0.70, p = 0.05). The GAMM further predicted a complex relationship of species richness among sites, yet overall predicted richness was lowest in the southwest/central portions of CT (areas of greatest development) and highest along coastal/eastern portions of CT (areas of non-forested and forested wetlands) (S. Fig. 4). The fixed effect GLMMs provided very poor fits to the data compared with random effects-only models (Latitude: ΔAIC 953.01; Longitude: ΔAIC 871.93; see the above results for Site-level collections: spatial regressions for possible reasons for these poor fits).
Weather correlations
We found no correlations of note between mosquito species richness and seasonal temperatures and precipitation.
Land cover correlations
Positive correlations of note for site-level species richness included: coniferous forest (r = 0.25, 0.04–0.43), deciduous forest (r = 0.56, 0.40–0.69), and forested wetland (r = 0.43, 0.23–0.58). Negative correlations included: barren (r = − 0.30, − 0.48 to − 0.10), developed (r = − 0.66, − 0.77 to − 0.53), grass (r = − 0.24, − 0.43 to − 0.03), and open water (r = − 0.31, − 0.49 to − 0.11).
Species evenness
Temporal regressions
Trends in species evenness were negative using both GLMMs (“Year”—Est. − 0.01, t-value − 7.86) and GAMMs (“Year (centered)”—Est. − 0.04, t-value − 5.58, p = 0.000) (Fig. 2D): this trend resulted in a predicted 12% decrease in site-level species evenness from 2001 to 2019. Similar to fixed effects GLMMs of species richness, fixed effects GLMMs of species evenness were less informative than a random effects-only model (ΔAIC 66.5). Declining intercept values were evident when evaluating “Year” as a random effect (S. Fig. 5), further supporting an overall annual decline in species evenness estimates among sites.
Spatial regressions
Similar to spatial models of species richness, GLMMs predicted positive relationships between species evenness and coordinate (Latitude (centered): Est. 0.36, t-value = 7.63; Longitude (centered): Est. 0.18, t-value = 8.54); the best fitting GAMM, which included Longitude by Latitude smoothing terms, also predicted positive relationships (Smoothing term 1: Est. 0.12, p = 0.01; Smoothing term 2: Est. 0.16, p = 0.004). GAMM predictions of site-level species evenness were equally complex to predictions of abundance and richness, and predicted evenness to be highest in southcentral and eastern CT (S. Fig. 6). Fixed effect GLMMs provided very poor fits to the data compared with random effects-only models (Latitude: ΔAIC 502.6; Longitude: ΔAIC 488.4; see the above results for Site-level collections: spatial regressions for possible reasons for these poor fits).
Weather correlations
We did find a negative correlation between statewide prior spring minimum temperatures and mosquito species evenness (r = − 0.49, − 0.77 to − 0.04).
Land cover correlations
Positive correlations of note for species evenness included: deciduous forest (r = 0.46, 0.28–0.61) and forested wetland (r = 0.22, 0.01–0.41). Negative correlations included: barren (r = − 0.37, − 0.54 to − 0.18), developed (r = − 0.45, − 0.60 to − 0.26), and open water (r = − 0.32, − 0.50 to − 0.12).
Correlations between abundance, richness, and evenness
The relationships between abundance, richness, and evenness varied depending on the scale examined. Across all years of data at the site-level, the correlation between abundance and richness was positive (r = 0.53, 0.36–0.67), the correlation between abundance and evenness as negative (r = − 0.35, − 0.52 to − 0.15), and there was no correlation of note between richness and evenness. Across all sites at the year-level, there were no correlations of note between abundance, richness, and evenness. Annual statewide correlations between richness and evenness (RRE) were positive for all years yet there was no noticeable annual trend in these correlations (Fig. 2E). Spatially, the average site-level RRE was 0.15 (± 0.03 SE). Furthermore, the magnitude and direction of RRE tended to increase on a south to north gradient (r = 0.31, 0.11–0.49), yet there was no apparent relationship in RRE along a west to east gradient (S. Fig. 7). We did detect a positive correlation between RRE and average maximum spring temperatures (r = 0.46, 0.01–0.76) as well as a positive correlation between RRE and percent land cover classified as coniferous forest (r = 0.23, 0.02–0.42).
Single detection events
Single detection events were defined as the prevalence of single species detections at a site (i.e., number of species with a single pool divided by species richness). Changes in single species detections could indirectly indicate range expansion among species (i.e., the prevalence of single detections decreases with time) and/or areas of unique mosquito diversity (i.e., the prevalence of single detections changes across space).
Temporal regressions
We detected no overall pattern of increasing/decreasing annual prevalence of single-species detections among sites (GLMM, “Year”—Est. − 0.13, t-value = − 1.12, p = 0.22; GAMM, “Year”—Est. 0.02, t value = − 0.31, p = 0.75) (Fig. 2F). These models were considered equivalent to a random effects-only GLMM (ΔAIC  More

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Global land-use data
For the historical time period 1700–2016, we used reconstructions of global cropland, pasture, and urban areas from the HYDE 3.2 dataset49 (available from https://doi.org/10.17026/dans-25g-gez3). Whilst HYDE 3.2 provides land-use data as far back as 10,000 BCE, we began our analysis in the year 1700, prior to which global land-use data are subject to increased uncertainty49,50. A total of 47 maps, including lower and upper uncertainty bounds, are available at 10-year intervals between 1700 and 2000, and at 1-year intervals between 2000 and 2016. These data were upscaled from their original spatial resolution of 0.083° to a 0.5° grid by summing up the cropland, pasture, or urban areas of all 0.083° grid cells contained in a given 0.5° cell.
For the period 2020–2100, we used 0.5°-resolution 10-year time-step projections of global cropland, pasture, and urban areas from the AIM model51 (available from https://doi.org/10.7910/DVN/4NVGWA), covering Representative Concentration Pathways (RCPs) 2.6, 4.5, 6.0 and 8.5, and Shared Socio-economic Pathways (SSPs) 1–5. The dataset contains all possible combinations of these emission and socio-economic trajectories with the exception of RCP 2.6/SSP 3, and RCP 8.5/SSPs 1–4. The data were harmonised with the HYDE 3.2 data by adding the differences between HYDE 3.2 and AIM cropland, pasture and urban area maps in the year 2010 to the AIM future land use projections. We refer to refs. 27,28,29,52 for details of the emission and socio-economic pathways, and to ref. 28 for a comparison between the AIM model and other integrated assessment models.
Global biome data
We used the BIOME4 vegetation model53 (available from https://pmip2.lsce.ipsl.fr/synth/biome4.shtml) to simulate the distribution of global potential natural biomes between the years 1700 and 2000, and between 2020 and 2100 for each of the four climate-change scenarios considered here (RCPs 2.6, 4.5, 6.0, 8.5), at a spatial resolution of 0.5°. Inputs required by BIOME4 include global mean atmospheric CO2 concentration, and gridded monthly means of temperature, precipitation, and percent sunshine. Past and RCP-specific future CO2 levels were obtained from refs. 54 and 55, respectively. The climatic data were generated as follows. For the period 1700–1900, we used annual simulations from the HadCM3 climate model56 (available from https://esgf-node.llnl.gov/search/cmip5/; Experiments ‘past1000’ and ‘historical’, Ensemble ‘r1i1p1’). For the period 1901–2016, we used 0.5° resolution annual observational data57 (available from https://doi.org/10.5285/10d3e3640f004c578403419aac167d82). For the period 2020–2100, and for each RCP (2.6, 4.5, 6.0, 8.5), we used annual simulations from the HadGEM2-ES climate model58, the MIROC5 climate model59 and the CSIRO-Mk3.6.0 climate model60 (available from https://esgf-node.llnl.gov/search/cmip5/; for each climate model and each RCP, we used averages from Ensembles ‘r1i1p1’, ‘r2i1p1’, ‘r3i1p1’, ‘r4i1p1’). We downscaled and bias-corrected both the pre-1901 HadCM3 simulations and the future HadGEM2-ES, MIROC5, and CSIRO-Mk3.6.0 simulations using the delta method61. This method is based on applying the difference between simulated and observed climate at times at which both are available (here we used the 1900–1930 period for the historical data, and the year 2006 for the future data) to the simulated climate at points in time at which only simulated data exist (i.e., pre-1901 and post-2016) in order to correct systematic biases in the climate model61,62. The delta method also serves to spatially downscale the simulated climate to the 0.5° resolution of the observational data.
For the computation of the global biome distribution at a point in time t, we used as inputs for BIOME4 the atmospheric CO2 concentration and gridded monthly climate values averaged across the time interval [t – 30 years, t]. Biome simulations were performed at 10-year intervals for both the historical and the future period. The complete time series of global biome simulations are available as Supplementary Movies 1–13.
Estimation of species’ habitat ranges
We estimated the geographic habitat ranges of an individual bird, mammal, and amphibian species through time following the general methodology in ref. 23. Our approach combines the following data:
I.
Spatial polygon data of species-specific extents of occurrence of all known birds63 (available from http://datazone.birdlife.org/species/requestdis), mammals, and amphibians64 (available from https://www.iucnredlist.org/).

II.
Species-specific biome requirements63,64 (data also available from the above websites).

III.
Maps of global potential natural biome distributions corresponding to the relevant climatic conditions through time (i.e., reconstructions for the past, and RCP-specific projections for the future).

IV.
Maps of global cropland, pasture, and urban areas through time (i.e., reconstructions for the past, and RCP- and SSP-specific projections for the future).

The data I–IV were used to estimate the habitat range of individual species at a given point in time as illustrated in Fig. 4 and detailed in the following. In a first step, we used species-specific extents of occurrence (data I), which represent the outermost geographic limits of species’ observed, inferred or projected occurrences1. These spatial envelopes do not account for the distribution of natural or artificial land cover within that area, and therefore generally extend substantially beyond a species’ actual area of occupancy65,66. We first remapped extents of occurrence from their original spatial polygon format to a 0.083° resolution grid using the ‘rasterise’ function of the ‘raster’ package in R, which maps spatial polygons to those raster grid cells whose centres are contained within the polygons. For each species, we then determined the proportion of 0.083° cells contained in each 0.5° grid cell that represents the species’ extent of occurrence. This provides an estimate of the proportion of each 0.5° grid cell that is contained in the species’ extent of occurrence. Compared to the rasterising extent of occurrence directly to a 0.5° grid, this approach provides for more accurate estimates of species’ ranges and reduces the number of species that are not included in our analysis because their extents of occurrence do not overlap with any grid cell centre.
Fig. 4: Method of estimating potential natural and actual range for the example of the bat-eared fox (Otocyon megalotis) in the year 1900.

Here, for visualisation purposes, cropland, pasture, and urban areas were aggregated into one map; in reality, our method checks each of them separately against species’ artificial habitat preferences.

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In a second step, we refined the derived species-specific maps of the proportion of 0.5° grid cells contained in species’ extents of occurrence by combining them with species-specific biome requirements and maps of global biome distributions. Species-specific biome requirements (data II) include one or more habitat categories (cf. Supplementary Table 1), in which each species is known to occur. A species was estimated as being present in a grid cell contained in its previously derived extent of occurrence under the potential natural biome at a given point in time if the species’ list of habitat categories contained the local (i.e., grid cell-specific) potential natural biome at the relevant time (data III; see above). This required matching IUCN habitat categories (https://www.iucnredlist.org/resources/habitat-classification-scheme) with the biome categories of the Biome4 vegetation model, which was done as shown in Supplementary Table 1. In this way, we subset extents of occurrences by only retaining grid cells where the natural biome type is included in a species’ list of suitable habitat categories. The result of this step represents a species’ estimated potential natural habitat range (i.e., in the hypothetical absence of anthropogenic land use) at a given point in time.
In a third step, we estimated actual habitat ranges by including maps of global land use through time. Each species’ actual habitat range at a given time was derived by removing any unsuitable anthropogenic land from the previously estimated potential natural range. Historical and projected future land use maps (data IV; see above) provide the fraction of each grid cell that is occupied by cropland, pasture or urban areas. These data were combined with information on which of these three artificial land cover types, if any, species can occur in, which is also included in the list of species’ biome requirements (data II). This allowed us, for each grid cell contained in a species’ potential natural range at a given time, to estimate the proportion of the grid cell that contained suitable habitat. A species’ actual habitat range size was then obtained as the sum of the areas of the remaining suitable habitat from all relevant grid cells.
We applied the above method at each point in time for which global land use data is available (see above). In this way, we obtained potential natural ranges and actual ranges for 47 points in time between 1700 and 2016—using the baseline as well as lower and upper uncertainty bounds of the HYDE 3.2 land-use reconstructions—, and for nine points in time between 2020 and 2100—using the 16 combinations of future climatic and socio-economic pathways (see above), each of which, in turn, was considered based on climate data from three alternative models. Thus, we considered a total of 141 historical and 432 future scenarios.
Since the global distribution of natural biomes varies over time as the result of (naturally or anthropogenically) changing climatic conditions, the sizes of potential natural habitat ranges are time-dependent. This motivates to consider range changes in relation to the potential natural ranges estimated at a particular reference time, for which we chose the year t0 = 1850, representing a modern pre-industrial baseline. Denoting the potential natural range and the actual range of a species i at a time t by (A_i^{{mathrm{potential}}}(t)) and (A_i^{{mathrm{actual}}}(t)), respectively, the range change associated with species i at time t as the result of the distribution of biomes and land use at that time was calculated at as

$${Delta}A_ileft( t right) = 100{mathrm{% }} cdot left( {frac{{A_i^{{mathrm{actual}}}(t)}}{{A_i^{{mathrm{potential}}}(t_0)}} – 1} right).$$
(1)

Species whose potential natural habitat range size in the reference year t0 = 1850 (i.e., the range size estimated in the absence of anthropogenic land use and based on the global distribution of biomes in 1850) is zero, (A_i^{{mathrm{potential}}}left( {t_0} right) = 0), were not included in the analysis as, in this case, changes in range size are not defined. Based on the set (left{ {{Delta}A_ileft( t right)} right}_{i = 1,2, ldots }) of the individual range changes of all species through time, we calculated range change percentiles at each point in time (Fig. 1a), and determined the proportion of species that have experienced the loss of a given percentage of their baseline range (Fig. 1b). Similarly as in Eq. (1), we also computed the range change attributed only to climate-change-induced biome changes, (100% cdot left( {A_i^{{mathrm{potential}}}(t)/A_i^{{mathrm{potential}}}(t_0) – 1} right)) (Supplementary Fig. 1).
Analyses were conducted using Matlab R2019a67 and R v3.6.368.
Method discussion
Whilst the available climate data for a given point in time only allows us to assign one primary natural biome type to each 0.5° grid cell, microclimates within cells may, in reality, result in the presence of different biomes in parts of a cell that are not represented in our data. By design of the approach used here, grid cells containing a non-primary biome that is suitable for a species, whilst the estimated primary biome is not, do not contribute to our estimation of the species’ habitat range. Conversely, grid cells containing a non-primary biome that is not suitable for a species, whilst the primary biome is suitable, would be included in their entirety in the species’ estimated range. This may lead us to underestimate the range sizes of species typically occurring in non-primary biomes in areas in which the estimated primary biomes are not suitable for the species, and to overestimate the range sizes of species typically occurring in the estimated primary biome in areas where other biomes also occur that are not suitable. Higher-resolution biome data could, in principle, reduce inaccuracies; however, generating such data in a reliable manner is not trivial. We are not aware of indications that this aspect of the approach would either systematically increase or decrease our overall estimates for range size changes across species in Fig. 1a.
Our estimation of species’ habitat range sizes does not take into account habitat connectivity within or across grid cells. In principle, this can result in disconnected patches being included in a species’ estimated range, despite in reality being too small to represent potentially suitable habitat. However, neither species-specific data on the minimum size that spatially connected areas must not fall below before becoming non-viable nor reliable very-high-resolution land use and biome data, both of which would be needed to fully accommodate this issue, are currently available.
Although species’ extents of occurrence are based not only on known, but also inferred and projected occurrences, the data remain very likely biased as the result of range contractions that occurred before the beginning of the systematic collection and mapping of species’ distributions, and that cannot be fully reconstructed. Whilst this may lead us to underestimate the absolute range sizes of species, it does not necessarily imply that we either systematically underestimate or overestimate the percentage change of species’ ranges through time.
We chose the 0.5° resolution for our analysis as both the 1901–2016 observational climate data (and therefore also the pre-1901 and future climate data, which were downscaled using the observational data) and the projections of future land use are only available at this resolution. Attempts to further downscale these data would likely involve significant additional uncertainties. We are not aware of indications that an increase in the resolution of the analysis (if indeed the necessary datasets were available) would result in a systematic increase or decrease of either the absolute range sizes or the percentage change of range sizes relative to the baseline sizes, estimated here, at any point in time.
Species-specific extents of occurrence and habitat preferences have been argued to be subject to uncertainty69; however, uncertainty estimates (quantitative or otherwise) are not provided with the data. In our main analysis, we therefore used the available data at face value. However, to verify that our results are not overly impacted by specific species, we performed the following bootstrapping analysis. Based on the set of species-specific range changes of all 16,919 species, estimated for the year 2016, we randomly sampled 16,919 values from this set with replacement a total of 104 times. For each of these 104 sets of range change estimates, we calculated 10%–90% percentiles analogous to Fig. 1a. For each percentile, we then calculated the mean and standard deviation of the computed 104 values. The result, shown in Supplementary Fig. 5, demonstrates that the uncertainties of our estimates with respect to specific species are very small, indicating that our results are robust with respect to potential uncertainties in the species data.
Estimates of temporal delays in biome shifts in response to climatic changes70 are currently not available with the global coverage that would allow us to further refine our approach of assuming that biomes at a given point in time are determined by the climatic conditions in the preceding 30 years. This also applies to data on the dispersal speeds of plant functional types, and their effect on potential delays in colonisations of previously climatically unsuitable areas33; current studies on this topic are too spatially scarce to inform our approach. In our main analysis, we therefore followed the assumption commonly made in global vegetation models of no seed dispersal limitations71. However, to explore the impact of this assumption, we also repeated our analysis based on the extreme scenario of biomes not shifting at all between the present (year 2016) and 2100. The estimated range size changes (Supplementary Fig. 6) are quantitatively similar to the results of our main analysis (Fig. 3), consistent with our assessment of the overall stronger impact of land use compared to climate-driven biome changes. Qualitatively, i.e., in terms of how different RCP/SSP scenarios rank relative to each other, results are equivalent to those of our main analysis.
As noted in the Introduction, our estimates of future habitat ranges represent upper estimates of species’ actual geographic distributions. In particular, our main analysis does not account for species’ ability to migrate to areas that will become suitable habitat at a future point in time but are not at present. However, our framework allows us to examine the effect of excluding such areas from the estimated habitat range. We repeated our analysis of future changes in habitat range sizes, but considered a grid cell as part of a species’ range only if the local biomes estimated for both the relevant point in the future and for the present (year 2016) were included in the species’ list of biome requirements. In other words, grid cells outside of species’ current potential natural habitat ranges were not counted towards their future range sizes, assuming that species are not able to migrate at all. This represents an extreme scenario that will underestimate most species’ mobility (e.g., over half of the species considered here can fly) and their ability to track biome shifts. Since the habitat range derived for a species in this manner is a subset of the one estimated in our main analysis, projected range losses based on this approach are, by design, higher (Supplementary Fig. 7). Qualitatively, results are equivalent to those in Fig. 3 in terms of how different RCP/SSP scenarios rank relative to each other.
As the empirical data on species’ habitat preferences only provide categorical biome requirements, not continuous climatic envelopes, the method used here does not account for range changes due to changes in climatic conditions that are too small to manifest as biome changes. However, estimating precise climatic envelopes of species can be subject to considerable uncertainty and be highly sensitive to the way in which they are estimated (see below). By construction of the method used here, species’ ranges over time vary within the extents of occurrence provided with the empirical data, and do not exceed those. Justification for this assumption is provided by the fact that potential natural ranges (and, much more, actual ranges) are generally well-contained within extents of occurrence, with the former accounting for an average of 64% of the area of the latter in the reference year 1850, thus providing ample space for range shifts and expansions within the boundaries. Additional evidence that the restriction of habitat ranges to the extents of occurrence does not prevent significant range expansions can be seen in the sizeable number of species that have already experienced such range expansions (Fig. 1a and Supplementary Fig. 1) or are predicted to do so in future scenarios of strong global warming (Supplementary Fig. 1 and Supplementary Fig. 3a).
Climate niche models estimate statistical relationships between climatic conditions and species’ spatial distributions, and apply these to climate projections in order to estimate future distribution patterns72. By design, they have great potential for mapping species’ distributions under a high degree of complexity in terms of possible predictor variables and their interactions, which has made the approach very useful in scenarios where the number of species, the geographic region and/or the temporal scale considered is relatively small so that statistical challenges are well-manageable73,74,75. In an analysis involving a large number of species, points in time, and different climatic and land-use scenarios considered here, the challenges commonly faced by climate nice models, specifically in terms of ensuring the robustness of the underlying statistical model and the estimated parameters, and avoiding unwanted artefacts in the extrapolation behaviour76,77,78,79,80,81, would be very difficult to manage. By operating directly and transparently on the empirical data of species’ extents of occurrence and biome requirements, and not being reliant on any particular statistical model or parameterisation, the approach used here provides the robustness needed at this scale of data23,82.
Reporting summary
Further information on research design is available in the Nature Research Reporting Summary linked to this article. More

Stratigraphic context of the Portfjeld Formation
The Portfjeld Formation is the lowermost formation of the Franklinian Basin in southern Peary Land (Figs. 1 and 2a, b), resting unconformably on Mesoproterozoic sandstones of the Independence Fjord Group and localized erosionally truncated outliers of Neoproterozoic tillites and associated carbonates of inferred Marinoan affinity (for regional stratigraphic reviews, see17,18). The carbonate-dominated Portfjeld Formation is overlain, at a karstified unconformity (Fig. 2c), by transgressive fluvial to marine shelf siliciclastics of the Buen Formation. The sandstone-dominated lower member of the Buen Formation yields trace fossils of early Cambrian age19, while the mudstone-dominated upper member contains rich faunas of Cambrian Series 2 (Stage 3–4) age20.
Fig. 1: Field photographs of locality of the Portfjeld biota.

a Portfjeld Formation—basal Buen sandstones at Midsommersøer, notice the conspicuous band of dark cherty dolomites. b Detail of Portfjeld Formation, west of Midsommersøer, with the same darky cherty dolomites overlain by thrombolitic mounds, showing the lithostratigraphic horizon yielding the Portfjeld biota. c View looking east along Wandel Dal with Midsommersøer, taken from the fossil locality (off shot left). Scale bar valid for a.

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Fig. 2: Geography, geology, and stratigraphy of the study area, North Greenland.

a Geological map showing the sample locality at Midsommersøer, North Greenland. b Stratigraphic schemes in northern Ellesmere Island and North Greenland. c Stratigraphic section through the Portfjeld Formation at the western end of Midsommersøer compared with δ13Ccarb (o/oo PDB) values indicating the Shuram–Wonoka anomaly (about 570–560 Ma), “F” indicates the sample locality.

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The Portfjeld Formation comprises two discrete stratigraphic packages separated by a regionally developed karstic unconformity. The lower succession, about 170-m thick, is dominated by dolostones with rare limestones and represents two transgressive–regressive cycles of a carbonate ramp. Typical facies include hummocky cross-stratified intraclast-rich grainstones and cherty dark dolostones of the mid- and outer ramp, and ooid–pisoid grainstones and varied microbial facies of the inner ramp, including columnar and meter-scale domal stromatolites and thrombolitic bioherms. The capping hiatal surface shows penetrative and multi-generational karstic features extending some 40 m beneath the surface (Fig. 2c), including extensive interstratal solution, brecciation, and successive cave/vug/fracture fills and cementation, testifying to a protracted period of subaerial exposure. The transgressive succession of the upper Portfjeld Formation (c. 70–90 m thick) comprises fluvial sandstones and mudstones succeeded by high-energy shallow marine carbonate and siliciclastic facies, truncated upwards by dolines and karstic collapse structures at the Portfjeld–Buen formation boundary.
Chemostratigraphy shows that the δ13C of the Portfjeld Formation carbonate samples range from +4‰ to −8‰. Positive δ13C values persist over the lower c. 40 m of the formation, before a marked negative shift of 12‰ down to values of −8‰. A more gradual increase characterizes the δ13C values up-section through the karstified strata to the karstic unconformity at 167 m, following which there is a clear stabilization in δ13C values to values around 0 to −1‰ for the remainder of the succession (Fig. 2c).
The δ13C database for Neoproterozoic carbonate sections has proliferated within the last 30 years to the point where a δ13C compilation curve can act as a chemostratigraphic correlation tool for newly studied sections. Utilizing chemostratigraphy as a chronology tool involves the correlation of globally coherent geochemical perturbations and trends in vertical carbonate successions, within a broadly understood timeframe. This is particularly useful when attempting to refine age estimates for successions that lack abundant biostratigraphical and/or radiometric data. Utilizing the most up-to-date δ13C chemostratigraphic framework21, the asymmetric negative δ13C excursion and more gradual recovery displayed by the mid-section of the Portfjeld Formation can be aligned with the most extreme C-isotope variation recorded in Earth’s history: the Shuram–Wonoka anomaly. The form and magnitude (∼12‰) of this δ13C excursion, as well as a nadir value of −8‰, are unique to the Shuram–Wonoka anomaly and deter its alignment with other Neoproterozoic excursions, as well as the Basal Cambrian Isotope Excursion, BACE21. The Shuram–Wonoka anomaly is recognized intercontinentally in Late Ediacaran strata22 and provides a broad chronostratigraphic marker to constrain the biostratigraphy presented in this study. Williams and Schmidt22 noted that the Wonoka excursion spanned an interval of up to 10 Myr. from about 570 to 560 Ma and was recognized in shallow marine shelf environments on three palaeocontinents with low palaeolatitudes ( More