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<journal-id journal-id-type="publisher-id">Front. Bee Sci.</journal-id>
<journal-title>Frontiers in Bee Science</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Bee Sci.</abbrev-journal-title>
<issn pub-type="epub">2813-5911</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
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<article-meta>
<article-id pub-id-type="doi">10.3389/frbee.2025.1510451</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Bee Science</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Climate change will lead to local extinctions and mismatched range contractions disrupting bee-dependent crop pollination</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Oliveira</surname>
<given-names>Willams</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="author-notes" rid="fn003">
<sup>&#x2020;</sup>
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<name>
<surname>Cruz-Neto</surname>
<given-names>Oswaldo</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="author-notes" rid="fn003">
<sup>&#x2020;</sup>
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<contrib contrib-type="author">
<name>
<surname>Silva</surname>
<given-names>J&#xe9;ssica L. S.</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
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<xref ref-type="author-notes" rid="fn003">
<sup>&#x2020;</sup>
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<contrib contrib-type="author">
<name>
<surname>Tabarelli</surname>
<given-names>Marcelo</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="author-notes" rid="fn003">
<sup>&#x2020;</sup>
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<contrib contrib-type="author">
<name>
<surname>Peres</surname>
<given-names>Carlos A.</given-names>
</name>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
<xref ref-type="aff" rid="aff4">
<sup>4</sup>
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<contrib contrib-type="author" corresp="yes">
<name>
<surname>Lopes</surname>
<given-names>Ariadna V.</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="author-notes" rid="fn001">
<sup>*</sup>
</xref>
<xref ref-type="author-notes" rid="fn003">
<sup>&#x2020;</sup>
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<aff id="aff1">
<sup>1</sup>
<institution>Programa de P&#xf3;s-Gradua&#xe7;&#xe3;o em Biologia Vegetal, Departamento de Bot&#xe2;nica, Universidade Federal de Pernambuco</institution>, <addr-line>Recife, Pernambuco</addr-line>, <country>Brazil</country>
</aff>
<aff id="aff2">
<sup>2</sup>
<institution>Departamento de Bot&#xe2;nica, Universidade Federal de Pernambuco</institution>, <addr-line>Recife, Pernambuco</addr-line>, <country>Brazil</country>
</aff>
<aff id="aff3">
<sup>3</sup>
<institution>School of Environmental Sciences, University of East Anglia</institution>, <addr-line>Norwich</addr-line>, <country>United Kingdom</country>
</aff>
<aff id="aff4">
<sup>4</sup>
<institution>Instituto Juru&#xe1;</institution>, <addr-line>Manaus</addr-line>, <country>Brazil</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>Edited by: Vera Lucia Imperatriz- Fonseca, University of S&#xe3;o Paulo, Brazil</p>
</fn>
<fn fn-type="edited-by">
<p>Reviewed by: Robert Chlebo, Slovak University of Agriculture, Slovakia</p>
<p>Rose Sagwe, Institute of Primate Research, Kenya</p>
<p>Ricardo Caliari Oliveira, Autonomous University of Barcelona, Spain</p>
</fn>
<fn fn-type="corresp" id="fn001">
<p>*Correspondence: Ariadna V. Lopes, <email xlink:href="mailto:ariadna.lopes@ufpe.br">ariadna.lopes@ufpe.br</email>
</p>
</fn>
<fn fn-type="other" id="fn003">
<p>&#x2020;ORCID: Willams Oliveira, <uri xlink:href="https://orcid.org/0000-0001-8345-7986">orcid.org/0000-0001-8345-7986</uri>; Oswaldo Cruz-Neto, <uri xlink:href="https://orcid.org/0000-0002-6625-7568">orcid.org/0000-0002-6625-7568</uri>; J&#xe9;ssica L. S. Silva, <uri xlink:href="https://orcid.org/0000-0001-8519-0891">orcid.org/0000-0001-8519-0891</uri>; Marcelo Tabarelli, <uri xlink:href="https://orcid.org/0000-0001-7573-7216">orcid.org/0000-0001-7573-7216</uri>; Carlos A. Peres, <uri xlink:href="https://orcid.org/0000-0002-1588-8765">orcid.org/0000-0002-1588-8765</uri>; Ariadna V. Lopes, <uri xlink:href="https://orcid.org/0000-0001-5750-5913">orcid.org/0000-0001-5750-5913</uri>
</p>
</fn>
</author-notes>
<pub-date pub-type="epub">
<day>29</day>
<month>05</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="collection">
<year>2025</year>
</pub-date>
<volume>3</volume>
<elocation-id>1510451</elocation-id>
<history>
<date date-type="received">
<day>13</day>
<month>10</month>
<year>2024</year>
</date>
<date date-type="accepted">
<day>05</day>
<month>05</month>
<year>2025</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2025 Oliveira, Cruz-Neto, Silva, Tabarelli, Peres and Lopes</copyright-statement>
<copyright-year>2025</copyright-year>
<copyright-holder>Oliveira, Cruz-Neto, Silva, Tabarelli, Peres and Lopes</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<p>Climate change is one of the main drivers of biological reorganization, population decline of pollinators, and disruption of species interactions. These impacts represent a major threat to crop pollination and human food security. Here, we tested the hypothesis that the spatial mismatches between Neotropical food plant species and their bee pollinators are exacerbated under scenarios of projected climate change. To investigate this hypothesis we performed species distribution modeling to simulate the effects of climate change on suitable habitats for the occurrence of both native food plants and their main pollinators. We selected three economically important food plants native to Brazil bearing a self-incompatible reproductive system that is strictly dependent on pollinators: (1) <italic>Bertholletia excelsa</italic>, (2) <italic>Eugenia uniflora</italic>, and (3) <italic>Passiflora edulis</italic>; and we selected the main effective bee pollinators of each plant species: (1) <italic>Apis mellifera</italic> (i.e., pollinator of <italic>E. uniflora</italic>), (2) <italic>Eulaema mocsaryi</italic> (i.e., pollinator of <italic>B. excelsa</italic>), and (3) <italic>Xylocopa frontalis</italic> (i.e., pollinator of <italic>P. edulis</italic>). We documented that climate change will likely distinctly affect areas of suitable habitats for food plants and their main bee pollinators across Brazil, in which all species will likely experience contractions in their ecological niches. In addition, we also documented that suitable habitats were reduced for the co-occurrence of all food plants and their pollinators. Specifically, 51.5% for <italic>P. edulis</italic> and <italic>X. frontalis</italic>, 76% for <italic>B. excelsa</italic> and <italic>E. mocsaryi</italic>, and 54% for <italic>E. uniflora</italic> and <italic>A. mellifera</italic>. Therefore, these findings underscore that plausible climate change scenarios can act as a potential driver of spatial mismatches between food plants and their main pollinators, disrupting the pollination of these food plants. Our results show that plant and pollinator species respond negatively to the impacts of climate change under all scenarios, which can result in alarming projections for strictly bee-pollinated self-incompatible plant species. This study reaffirms that bees are sensitive to climate change, highlighting the negative impact even for the exotic European honeybee, <italic>Apis mellifera</italic>. Finally, climate change could impact crop pollination, with detrimental implications for food production and food security.</p>
</abstract>
<kwd-group>
<kwd>
<italic>Apis mellifera</italic>
</kwd>
<kwd>Brazil-nut</kwd>
<kwd>food plants</kwd>
<kwd>food security</kwd>
<kwd>distribution modeling</kwd>
<kwd>mismatched mutualism</kwd>
<kwd>passion fruit</kwd>
<kwd>Pitanga</kwd>
</kwd-group>
<contract-num rid="cn001">#141954/2020-5, #175071/2023-3, #308832/2014-0, #309505/2018-6</contract-num>
<contract-num rid="cn002">001</contract-num>
<contract-num rid="cn003">BCT-34 0208-2.05/17</contract-num>
<contract-sponsor id="cn001">Conselho Nacional de Desenvolvimento Cient&#xed;fico e Tecnol&#x3cc;gico<named-content content-type="fundref-id">10.13039/501100003593</named-content>
</contract-sponsor>
<contract-sponsor id="cn002">Coordena&#xe7;&#xe3;o de Aperfei&#xe7;oamento de Pessoal de N&#xed;vel Superior<named-content content-type="fundref-id">10.13039/501100002322</named-content>
</contract-sponsor>
<contract-sponsor id="cn003">Funda&#xe7;&#xe3;o de Amparo &#xe1; Ci&#xea;ncia e Tecnologia do Estado de Pernambuco<named-content content-type="fundref-id">10.13039/501100006162</named-content>
</contract-sponsor>
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<fig-count count="3"/>
<table-count count="2"/>
<equation-count count="0"/>
<ref-count count="101"/>
<page-count count="13"/>
<word-count count="5501"/>
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<custom-meta>
<meta-name>section-in-acceptance</meta-name>
<meta-value>Bees in Pollination</meta-value>
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</custom-meta-wrap>
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</front>
<body>
<sec id="s1" sec-type="intro">
<label>1</label>
<title>Introduction</title>
<p>Climate change is rapidly altering biodiversity at all levels, from genes to ecosystems (<xref ref-type="bibr" rid="B85">Scheffers et&#xa0;al., 2016</xref>; <xref ref-type="bibr" rid="B45">IPCC, 2021</xref>), with significant consequences for essential ecological services like pollination. It is expected that by 2050 climate change will lead to an average temperature increase of up to 4&#x2da;C (<xref ref-type="bibr" rid="B44">IPCC, 2014</xref>). Overall, climate change has been associated with modifications at different levels of biological organization, affecting the maintenance of ecological processes and the provision of ecosystem services (<xref ref-type="bibr" rid="B42">Houghton et&#xa0;al., 2001</xref>; <xref ref-type="bibr" rid="B57">Malhi and Wright, 2004</xref>; <xref ref-type="bibr" rid="B56">Malhi et&#xa0;al., 2008</xref>; <xref ref-type="bibr" rid="B85">Scheffers et&#xa0;al., 2016</xref>; <xref ref-type="bibr" rid="B29">Ferreira et&#xa0;al., 2021</xref>), such as pollination (e.g., <xref ref-type="bibr" rid="B10">Botkin et&#xa0;al., 2007</xref>; <xref ref-type="bibr" rid="B53">Loarie et&#xa0;al., 2008</xref>; <xref ref-type="bibr" rid="B40">Giannini et&#xa0;al., 2017</xref>, <xref ref-type="bibr" rid="B39">2020</xref>; <xref ref-type="bibr" rid="B90">Silva et&#xa0;al., 2019</xref>, <xref ref-type="bibr" rid="B91">2022</xref>; <xref ref-type="bibr" rid="B81">Sales et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B62">Millard et&#xa0;al., 2023</xref>). In response to climate change, some species move to new more suitable habitats, as a way of adjusting their climatic envelope (e.g., <xref ref-type="bibr" rid="B96">Urban, 2015</xref>; <xref ref-type="bibr" rid="B17">Chen et&#xa0;al., 2011</xref>; <xref ref-type="bibr" rid="B71">Pecl et&#xa0;al., 2017</xref>). In this way, climate change acts as a strong environmental driver capable of reorganizing biological assemblages, affecting many species interactions, leading to incompatible geographic distributions between interacting species (<xref ref-type="bibr" rid="B87">Schweiger et&#xa0;al., 2008</xref>; <xref ref-type="bibr" rid="B98">Valiente-Banuet et&#xa0;al., 2015</xref>; <xref ref-type="bibr" rid="B40">Giannini et&#xa0;al., 2017</xref>, <xref ref-type="bibr" rid="B39">2020</xref>; <xref ref-type="bibr" rid="B81">Sales et&#xa0;al., 2021</xref>). Then, climate change can cause spatiotemporal mismatches, changes in ecological networks with the formation of new interactions, and shifts in dispersal capacity (<xref ref-type="bibr" rid="B60">Memmott et&#xa0;al., 2007</xref>; <xref ref-type="bibr" rid="B86">Schleuning et&#xa0;al., 2020</xref>). In addition, incompatibilities in plant-animal interactions are also driven by changes in phenology, such as flowering time or animal emergence and migration (<xref ref-type="bibr" rid="B31">Fitter and Fitter, 2002</xref>; <xref ref-type="bibr" rid="B49">Kharouba and Vellend, 2015</xref>; <xref ref-type="bibr" rid="B18">Cohen et&#xa0;al., 2018</xref>; <xref ref-type="bibr" rid="B48">Kharouba et&#xa0;al., 2018</xref>).</p>
<p>Bees are widely reported to play a key role in ecosystem functioning, contributing to the pollination of most wild plants and crops (<xref ref-type="bibr" rid="B50">Klein et&#xa0;al., 2007</xref>; <xref ref-type="bibr" rid="B69">Ollerton et&#xa0;al., 2011</xref>; <xref ref-type="bibr" rid="B67">Oliveira et&#xa0;al., 2024</xref>). However, bees have experienced globally declines in abundance and richness due to land use change, biological invasions, pesticides, and agricultural expansion (<xref ref-type="bibr" rid="B75">Potts et&#xa0;al., 2010</xref>; <xref ref-type="bibr" rid="B22">Dicks et&#xa0;al., 2016</xref>; <xref ref-type="bibr" rid="B101">Zattara and Aizen, 2021</xref>; <xref ref-type="bibr" rid="B2">Albacete et&#xa0;al., 2023</xref>). In addition to these stressors, climate change has also been widely reported as one of the main causes of pollinator declines across the globe (<xref ref-type="bibr" rid="B43">IPBES, 2016</xref>; <xref ref-type="bibr" rid="B75">Potts et&#xa0;al., 2010</xref>, <xref ref-type="bibr" rid="B76">2016</xref>; <xref ref-type="bibr" rid="B40">Giannini et&#xa0;al., 2017</xref>; <xref ref-type="bibr" rid="B52">Lima and Marchioro, 2021</xref>; <xref ref-type="bibr" rid="B81">Sales et&#xa0;al., 2021</xref>), as observed for honeybees and bumblebees (<xref ref-type="bibr" rid="B7">Becher et&#xa0;al., 2013</xref>; <xref ref-type="bibr" rid="B47">Kerr et&#xa0;al., 2015</xref>; <xref ref-type="bibr" rid="B92">Soroye et&#xa0;al., 2020</xref>). Warmer temperatures, changes in precipitation regimes, and higher frequency of extreme weather events affect the behavior and survival of bee species (<xref ref-type="bibr" rid="B41">Goulson et&#xa0;al., 2015</xref>; <xref ref-type="bibr" rid="B92">Soroye et&#xa0;al., 2020</xref>; <xref ref-type="bibr" rid="B45">IPCC, 2021</xref>). In general, rising temperatures can negatively impact plant-pollinator interactions through reduced visitation rates (e.g., <xref ref-type="bibr" rid="B77">Rader et&#xa0;al., 2013</xref>; <xref ref-type="bibr" rid="B58">Maluf et&#xa0;al., 2022</xref>). For example, watermelon crops under extreme climate change scenarios incur a 14.5% decline in pollination services provided by managed bees (<xref ref-type="bibr" rid="B77">Rader et&#xa0;al., 2013</xref>). Then, plant responses to climate change are also influenced by how animals respond to these changes (<xref ref-type="bibr" rid="B86">Schleuning et&#xa0;al., 2020</xref>). Thus, disruptions in plant-animal interactions intensified by the effects of climate change can impact biodiversity and the status quo of species interactions.</p>
<p>As a major threat to the maintenance of natural and managed populations globally (<xref ref-type="bibr" rid="B70">Pacifici et&#xa0;al., 2017</xref>; <xref ref-type="bibr" rid="B76">Potts et&#xa0;al., 2016</xref>; <xref ref-type="bibr" rid="B78">Rafferty, 2017</xref>), climate change is a major potential driver of reduced agricultural yields (<xref ref-type="bibr" rid="B43">IPBES, 2016</xref>; <xref ref-type="bibr" rid="B11">BPBES/REBIPP, 2019</xref>; <xref ref-type="bibr" rid="B40">Giannini et&#xa0;al., 2017</xref>, <xref ref-type="bibr" rid="B39">2020</xref>; <xref ref-type="bibr" rid="B81">Sales et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B62">Millard et&#xa0;al., 2023</xref>). The reduction in pollinator populations driven by climate change affects the provision of pollination services for many agricultural crops that are pollinator-dependent and economically important, such as tomato, guava, coffee and avocado (<xref ref-type="bibr" rid="B20">Costanza et&#xa0;al., 2014</xref>; <xref ref-type="bibr" rid="B43">IPBES, 2016</xref>; <xref ref-type="bibr" rid="B11">BPBES/REBIPP, 2019</xref>; <xref ref-type="bibr" rid="B40">Giannini et&#xa0;al., 2017</xref>, <xref ref-type="bibr" rid="B39">2020</xref>; <xref ref-type="bibr" rid="B81">Sales et&#xa0;al., 2021</xref>), all of which could reduce the productivity of agricultural crops and negatively affect the global economy (<xref ref-type="bibr" rid="B43">IPBES, 2016</xref>). Since pollinators play an important role in the production of many fruits and seeds that are consumed by humans, making a large and strong contribution to global agricultural production (<xref ref-type="bibr" rid="B38">Giannini et&#xa0;al., 2015</xref>; <xref ref-type="bibr" rid="B22">Dicks et&#xa0;al., 2016</xref>; <xref ref-type="bibr" rid="B66">Novais et&#xa0;al., 2018</xref>), compromising the pollination process in pollinator-dependent crops can result in low economic yields (<xref ref-type="bibr" rid="B35">Garibaldi et&#xa0;al., 2013</xref>), which directly affects human food security (<xref ref-type="bibr" rid="B43">IPBES, 2016</xref>; <xref ref-type="bibr" rid="B68">Oliveira et&#xa0;al., 2023</xref>).</p>
<p>The total area cultivated with pollinator-dependent crops has expanded in recent decades (<xref ref-type="bibr" rid="B1">Aizen et&#xa0;al., 2019</xref>; <xref ref-type="bibr" rid="B68">Oliveira et&#xa0;al., 2023</xref>), and, consequently, the need for pollinators and pollination has also increased. Thus, it is urgent to understand how to mitigate the effects of climate change on crop pollination to ensure biodiversity persistence and safeguard food sovereignty. In Brazil, for example, 60% of all crops depend on pollinators (<xref ref-type="bibr" rid="B38">Giannini et&#xa0;al., 2015</xref>). Furthermore, Brazil ranks 5<sup>th</sup> in its human population, is the 3<sup>rd</sup> largest agricultural producer and exporter, and is arguably Earth&#x2019;s most megadiverse country (e.g., <xref ref-type="bibr" rid="B54">Lopes et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B74">Porto et&#xa0;al., 2020</xref>; <xref ref-type="bibr" rid="B68">Oliveira et&#xa0;al., 2023</xref>a). However, food insecurity and hunger are still a major problem in Brazil, which returned to the hunger map in 2021, with over 60 million people facing moderate to severe food insecurity in 2020 (<xref ref-type="bibr" rid="B28">FAO, 2021</xref>). Understanding crop-pollinator interactions occurring in Brazil thus appears as an excellent opportunity to examine the effects of climate change on selected cases of self-incompatible or highly pollinator-dependent crops and discuss the implications for crop pollination and food security in light of the future scenarios.</p>
<p>In this study, we seek to understand how climate change simultaneously affects the range and spatial distribution of three economically important food plants native to Brazil and their respective main pollinators. Here we focus on <italic>Passiflora edulis</italic> (Passionfruit), <italic>Bertholletia excelsa</italic> (Brazil-nut), and <italic>Eugenia uniflora</italic> (Pitanga or Brazilian cherry) as model species to investigate the impacts of climate change on plant-pollinator interactions. These species are key in agroforestry systems, which restore ecosystems by enhancing soil productivity and supporting biodiversity while mitigating climate change (<xref ref-type="bibr" rid="B97">Urruth et&#xa0;al., 2022</xref>; <xref ref-type="bibr" rid="B51">Lima et&#xa0;al., 2023</xref>). In addition, these three food plant species are also excellent models because they are self-incompatible (i.e., rely on cross-pollination to set fruits and seeds), and essentially dependent on pollinators; in other words, yields are reduced by at least 90% in the absence of pollinators (<xref ref-type="bibr" rid="B50">Klein et&#xa0;al., 2007</xref>). We tested the hypothesis that the current spatial distribution of self-incompatible food plant species and their main pollinating bees will be highly modified under different scenarios of projected climate change, resulting in a mismatch in their new regions of occurrence, thereby threatening crop pollination services. Therefore, we expected a reconfiguration of the areas of occurrence leading to a spatial mismatch in plant-pollinator interactions.</p>
</sec>
<sec id="s2" sec-type="materials|methods">
<label>2</label>
<title>Materials and methods</title>
<sec id="s2_1">
<label>2.1</label>
<title>Selection of food plant and pollinator species</title>
<p>To conduct our climate modeling, we selected the three native Brazilian food plants bearing self-incompatible reproductive system (i.e., obligatory cross-pollination) and with the highest Economic Value of Pollination (EVP) (<xref ref-type="bibr" rid="B11">BPBES/REBIPP, 2019</xref>; <xref ref-type="bibr" rid="B67">Oliveira et&#xa0;al., 2024</xref>); these species are strictly dependent on pollinators to set seed (<italic>sensu</italic> <xref ref-type="bibr" rid="B50">Klein et&#xa0;al., 2007</xref>): (1) <italic>Passiflora edulis</italic> (EVP= US$284,549,580/yr); (2) <italic>Bertholletia excelsa</italic> (EVP= US$75,586,750/yr); and (3) <italic>Eugenia uniflora</italic> (EVP= US$264,600/yr). Additionally, we selected their main effective pollinators: (1) the native carpenter bee <italic>Xylocopa frontalis</italic>, for <italic>P. edulis</italic>, (2) the native orchid bee <italic>Eulaema mocsaryi</italic>, for <italic>B. excelsa</italic>, and (3) the introduced honeybee <italic>Apis mellifera</italic>, for <italic>Eugenia uniflora</italic> (<xref ref-type="bibr" rid="B11">BPBES/REBIPP, 2019</xref>; <xref ref-type="bibr" rid="B23">Diniz and Buschini, 2016</xref>; <xref ref-type="bibr" rid="B16">Cavaltante et&#xa0;al., 2018</xref>; <xref ref-type="bibr" rid="B81">Sales et&#xa0;al., 2021</xref>).</p>
</sec>
<sec id="s2_2">
<label>2.2</label>
<title>Occurrence data</title>
<p>We compiled a comprehensive database on the georeferenced range data of the three food plants and their pollinators. For plant and pollinators presence-only occurrence we used the Global Biodiversity Information Facility (GBIF), an international platform that provides open access to data on species occurrence (<xref ref-type="bibr" rid="B36">GBIF, 2021</xref>) (<ext-link ext-link-type="uri" xlink:href="https://www.gbif.org">https://www.gbif.org</ext-link>, last accessed May 2023). For plant occurrence, we also used the REFLORA - Herb&#xe1;rio Virtual, a virtual herbarium catalogue containing information on Brazilian plants (<ext-link ext-link-type="uri" xlink:href="http://reflora.jbrj.gov.br/reflora/herbarioVirtual">http://reflora.jbrj.gov.br/reflora/herbarioVirtual</ext-link>, last accessed May 2023); and the Botanical Information and Ecology Network Platform (BIEN), a global database that provides data on plant diversity, trait records, and species distribution, including georeferenced plant observation data from herbarium, field plot, and survey inventory records (<ext-link ext-link-type="uri" xlink:href="https://bien.nceas.ucsb.edu/bien/biendata">https://bien.nceas.ucsb.edu/bien/biendata</ext-link>, last accessed May 2023). To access the BIEN database, we use the BIEN package (<xref ref-type="bibr" rid="B55">Maitner et&#xa0;al., 2018</xref>; <xref ref-type="bibr" rid="B91">Silva et&#xa0;al., 2022</xref>) available from the R 4.1.1 platform (<xref ref-type="bibr" rid="B79">R Core Team, 2021</xref>). To perform our analysis, we excluded all repeated and mismatched occurrence data for each plant and pollinator species. We obtained a total of 3,479 and 2,241 geographic coordinates for plant and pollinator species, respectively. <xref ref-type="supplementary-material" rid="SF1">
<bold>Supplementary Table S1</bold>
</xref> contains the DOI of each occurrence search by species from GBIF (e.g.,&#xa0;<xref ref-type="bibr" rid="B94">Taheri et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B90">Silva et&#xa0;al., 2019</xref>, <xref ref-type="bibr" rid="B91">2022</xref>).</p>
</sec>
<sec id="s2_3">
<label>2.3</label>
<title>Current and future environmental variables</title>
<p>We downloaded climate data summarizing aspects of precipitation, temperature, and elevation, at 2.5 min spatial resolution, from WorldClim project 2.1 for contemporary times (average for the years 1970 - 2000) and the future in 2090 (average 2081 - 2100) (<xref ref-type="bibr" rid="B30">Fick and Hijmans, 2017</xref>; <xref ref-type="bibr" rid="B91">Silva et&#xa0;al., 2022</xref>). In our analysis of future climate projections spanning the period from 2081 to 2100, we relied upon data derived from the Coupled Model Intercomparison Project Phase 6 (CMIP6) (<xref ref-type="bibr" rid="B27">Eyring et&#xa0;al., 2016</xref>). The CMIP6 models are currently accessible and exhibit a heightened sensitivity to climate compared to their predecessors (for more details see <xref ref-type="bibr" rid="B27">Eyring et&#xa0;al., 2016</xref>). For this study, we deliberately selected three distinct Shared Socioeconomic Pathways (SSPs) scenarios (<xref ref-type="bibr" rid="B80">Riahi et&#xa0;al., 2017</xref>; <xref ref-type="bibr" rid="B45">IPCC, 2021</xref>).</p>
<p>The three SSPs we considered here are as follows: (1) the &#x201c;middle of the road&#x201d; scenario, SSP2-4.5, which embodies optimism in terms of mitigation and adaptation, envisaging that social, economic, and technological trends will remain relatively consistent with historical patterns. It assumes moderate population growth, reduced income inequality, and limited environmental degradation; (2) the &#x201c;regional rivalry&#x2013;rocky road&#x201d; scenario, SSP3-7.0, which presents a more pragmatic view of the challenges associated with mitigation and adaptation. It assumes limited investments in education, healthcare, and technological progress in the world&#x2019;s poorest regions, coupled with rapid population growth, rising inequality, and significant environmental degradation. This pathway aims for radiative forcing stabilization by 2100; and (3) the &#x201c;fossil-fueled development&#x2014;taking the highway&#x201d; scenario, SSP5-8.5, which is pessimistic assuming substantial challenges for mitigation, while those for adaptation are minimal. SSP5-8.5 is characterized by the exploitation of abundant fossil fuel reserves, resulting in the highest greenhouse gas emissions and a significant increase in terrestrial temperatures (<xref ref-type="bibr" rid="B80">Riahi et&#xa0;al., 2017</xref>; <xref ref-type="bibr" rid="B45">IPCC, 2021</xref>). Our selection of SSP scenarios was based on the BCCCSM2-MR (<xref ref-type="bibr" rid="B100">Wu et&#xa0;al., 2021</xref>), CanESM5 (<xref ref-type="bibr" rid="B93">Swart et&#xa0;al., 2019</xref>), and MIROC6 (<xref ref-type="bibr" rid="B95">Tatebe et&#xa0;al., 2019</xref>) general circulation models, which have shown superior performance in Neotropical regions, as corroborated by previous studies (e.g., <xref ref-type="bibr" rid="B14">Cai et&#xa0;al., 2020</xref>; <xref ref-type="bibr" rid="B33">Fuentes-Castillo et&#xa0;al., 2020</xref>; <xref ref-type="bibr" rid="B61">Men&#xe9;ndez-Guerrero et&#xa0;al., 2020</xref>; <xref ref-type="bibr" rid="B81">Sales et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B90">Silva et&#xa0;al., 2019</xref>, <xref ref-type="bibr" rid="B91">2022</xref>). Furthermore, we incorporated elevation data as an essential edaphic variable, sourced from the WorldClim project 2.1 (<xref ref-type="bibr" rid="B30">Fick and Hijmans, 2017</xref>).</p>
<p>To reduce issues related to overfitting and collinearity among the 19 climatic variables and elevation, our approach involved conducting separate modeling for each species based on uncorrelated variables within both the present and future climate scenarios. The assessment of correlations among predictor variables was achieved through principal component analysis (PCA) within R 4.1.1 (<xref ref-type="bibr" rid="B79">R Core Team, 2021</xref>), and we opted to use the principal components responsible for 95% of the overall variation in environmental variables (<xref ref-type="bibr" rid="B21">De Marco and N&#xf3;brega, 2018</xref>). Ultimately, selected predictor variables for our models encompassed the following: (1) Temperature seasonality (BIO4); (2) Annual precipitation (BIO12); (3) Precipitation of the wettest quarter (BIO16); (4) Precipitation of the warmest quarter (BIO18); (5) Precipitation of the coldest quarter (BIO19); and (6) Elevation. These specific variables have been established as significant factors in modeling plant species distributions, as substantiated by previous studies (<xref ref-type="bibr" rid="B59">Marengo et&#xa0;al., 2017</xref>; <xref ref-type="bibr" rid="B90">Silva et&#xa0;al., 2019</xref>, <xref ref-type="bibr" rid="B91">2022</xref>; <xref ref-type="bibr" rid="B15">Cavalcante et&#xa0;al., 2020</xref>).</p>
</sec>
<sec id="s2_4">
<label>2.4</label>
<title>Species distribution modeling</title>
<p>We applied three distinct algorithms to predict habitat suitability to food plant species native to Brazil and their main pollinators. These algorithms included: (1) The maximum entropy method, the most widely used method for predicting species distributions (e.g., <xref ref-type="bibr" rid="B32">Fonseca et&#xa0;al., 2015</xref>; <xref ref-type="bibr" rid="B90">Silva et&#xa0;al., 2019</xref>), based on the principle of maximum entropy, in which the best approximation to an unknown probability distribution is the one that satisfies any constraint on the distribution (<xref ref-type="bibr" rid="B73">Phillips et&#xa0;al., 2006</xref>); (2) The support vector machine method, which presents performance compatible with or superior to methods that consider only the presence data and with moderate amounts of data, avoiding common problems and limitations (<xref ref-type="bibr" rid="B24">Drake et&#xa0;al., 2006</xref>); and (3) The random forest method, non-parametric analysis, which uses a low number of test samples, and provides high performance compared to other traditional regression algorithms (<xref ref-type="bibr" rid="B82">Sahragard et&#xa0;al., 2018</xref>). These selected algorithms are well-suited for working with presence and pseudo-absence data, an approach that aligns with the characteristics of the data collected within the localities surveyed in this study (<xref ref-type="bibr" rid="B5">Andrade et&#xa0;al., 2020</xref>). The combined use of these three algorithms improves the quality of the generated distribution models (e.g., <xref ref-type="bibr" rid="B91">Silva et&#xa0;al., 2022</xref>). To execute these analyses, we utilized the &#x201c;ENMTML&#x201d; R package (<xref ref-type="bibr" rid="B5">Andrade et&#xa0;al., 2020</xref>). A total of 15 replicates were generated for each algorithm, corresponding to each climate scenario (current, SSP-2.45, SSP-3.70, and SSP5-8.5) for each food plant and pollinator species. Each occurrence dataset was partitioned into two groups: a training set, comprising 70% of the sampled occurrence data for each plant and pollinator species, and a test or validation set, encompassing the remaining 30% of the sampled occurrence data. This partitioning was achieved through the bootstrap method. Given that the algorithms applied are predicated on the basis of presence and pseudo-absence data, we configured the distribution models to identify 500 pseudo-absences in grid cells characterized by lower climatic suitability for the presence of each target species (<xref ref-type="bibr" rid="B6">Barbet-Massin et&#xa0;al., 2012</xref>).</p>
<p>The evaluation of each model&#x2019;s performance involved the use of two widely recognized metrics: True Skill Statistics (TSS) and the Area Under the Curve (AUC). These metrics have previously been applied in studies assessing plant distribution models (<xref ref-type="bibr" rid="B81">Sales et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B90">Silva et&#xa0;al., 2019</xref>, <xref ref-type="bibr" rid="B91">2022</xref>). TSS and AUC are established measures for gauging the goodness-of-fit of species distribution models (Allouche et&#xa0;al., 2006). The TSS offers a comprehensive evaluation of model accuracy by considering its performance relative to random accuracy. It yields a score within the range of -1 to 1, with values close to 1 indicating optimal model performance. Typically, TSS values exceeding 0.5 are considered indicative of satisfactory model performance. On the other hand, the AUC values fall within the 0 to 1 range, with the accuracy of the model being classified as: (1) low accuracy (0.5-0.7), (2) moderate accuracy (0.7-0.9), and (3) high accuracy (&gt;0.9) (<xref ref-type="bibr" rid="B99">Wakie et&#xa0;al., 2014</xref>). Based on the final models, we created binary maps using the suitability that gives the highest values of TSS threshold, in which the sum of sensitivity and specificity is maximum. The binary maps for each species in each climatic scenario were cut for the Brazil boundaries. Only areas of high suitability appear on binary maps.</p>
<p>Utilizing an ensemble method, we harnessed consensus maps to depict habitat suitability for both food plant and pollinator species across various software packages and climate scenarios, a process informed by the principal component analysis (PCA). To merge the three maps pertaining to each species within a given future scenario, we leveraged the functionality of the &#x201c;Spatial Analyst Tools&#x201d; followed by &#x201c;Cell Statistics - Overlay Statistic (Mean)&#x201d; within the ArcGIS 10.0 software (<xref ref-type="bibr" rid="B26">ESRI, 2019</xref>). Subsequently, we subjected all consensus maps to a cutting process, enabling a detailed examination of the repercussions of climate change on the distribution of food plants and pollinators within the entire Brazilian domain. The extent of suitable habitat was calculated individually for each species within each scenario. To ensure the accuracy and reliability of our modeling outcomes, we meticulously scrutinized and refined all results using ArcGIS 10.0 software (<xref ref-type="bibr" rid="B26">ESRI, 2019</xref>). Notably, our models strongly underscored the pivotal influence of climatic variables in delineating the limits of species occurrence, meaning that the models generated aptly captured the underlying biological factors governing population persistence within this environment (<xref ref-type="bibr" rid="B88">Searcy and Shaffer, 2016</xref>).</p>
</sec>
</sec>
<sec id="s3" sec-type="results">
<label>3</label>
<title>Results</title>
<p>Considering food plants, model performance experienced robust support from the occurrence data given that mean values for AUC and TSS were high at 0.84 and 0.62, respectively (<xref ref-type="table" rid="T1">
<bold>Table&#xa0;1</bold>
</xref>). The model performance for pollinating bees was also robust with 0.82 and 0.81 mean values for AUC and TSS, respectively (<xref ref-type="table" rid="T2">
<bold>Table&#xa0;2</bold>
</xref>). The ecological niche models generated showed that the variables BIO12 (annual precipitation) and elevation had the highest contribution to the current, SSP-3.70, and SSP-5.85 scenarios for all food plant and pollinator species compared to any other predictor. In addition, elevation and BIO19 (coldest quarter precipitation) explained more of the variation in species distribution under the SSP-2.45 scenario.</p>
<table-wrap id="T1" position="float">
<label>Table&#xa0;1</label>
<caption>
<p>Evaluation of species distribution model performance considering the adjustments between the occurrence data of food plant species native to Brazil and climatic scenarios for the current period (averaged for 1970 - 2000) and three future scenarios for 2090 (averaged for 2081 - 2100): SSP-2.45, SSP-3.70, and SSP-5.85.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" rowspan="2" align="left">Food plant species</th>
<th valign="middle" rowspan="2" align="left">Climate scenarios</th>
<th valign="middle" colspan="2" align="center">AUC</th>
<th valign="middle" colspan="2" align="center">TSS</th>
</tr>
<tr>
<th valign="middle" align="center">Mean</th>
<th valign="middle" align="center">SD</th>
<th valign="middle" align="center">Mean</th>
<th valign="middle" align="center">SD</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" rowspan="4" align="center">
<italic>Bertholletia excelsa</italic>
</td>
<td valign="top" align="left">Current</td>
<td valign="middle" align="center">0.745425</td>
<td valign="middle" align="center">0.020797</td>
<td valign="middle" align="center">0.678888</td>
<td valign="middle" align="center">0.036982</td>
</tr>
<tr>
<td valign="top" align="left">SSP 2.45</td>
<td valign="middle" align="center">0.745914</td>
<td valign="middle" align="center">0.023186</td>
<td valign="middle" align="center">0.588148</td>
<td valign="middle" align="center">0.031842</td>
</tr>
<tr>
<td valign="top" align="left">SSP 3.70</td>
<td valign="middle" align="center">0.738957</td>
<td valign="middle" align="center">0.02037</td>
<td valign="middle" align="center">0.570741</td>
<td valign="middle" align="center">0.031127</td>
</tr>
<tr>
<td valign="top" align="left">SSP 5.85</td>
<td valign="middle" align="center">0.751241</td>
<td valign="middle" align="center">0.01987</td>
<td valign="middle" align="center">0.668519</td>
<td valign="middle" align="center">0.036485</td>
</tr>
<tr>
<td valign="middle" rowspan="4" align="center">
<italic>Eugenia uniflora</italic>
</td>
<td valign="top" align="left">Current</td>
<td valign="middle" align="center">0.852107</td>
<td valign="middle" align="center">0.008809</td>
<td valign="middle" align="center">0.634848</td>
<td valign="middle" align="center">0.018385</td>
</tr>
<tr>
<td valign="top" align="left">SSP 2.45</td>
<td valign="middle" align="center">0.866712</td>
<td valign="middle" align="center">0.006953</td>
<td valign="middle" align="center">0.587302</td>
<td valign="middle" align="center">0.011674</td>
</tr>
<tr>
<td valign="top" align="left">SSP 3.70</td>
<td valign="middle" align="center">0.865433</td>
<td valign="middle" align="center">0.007682</td>
<td valign="middle" align="center">0.573882</td>
<td valign="middle" align="center">0.015696</td>
</tr>
<tr>
<td valign="top" align="left">SSP 5.85</td>
<td valign="middle" align="center">0.861379</td>
<td valign="middle" align="center">0.007053</td>
<td valign="middle" align="center">0.578355</td>
<td valign="middle" align="center">0.017138</td>
</tr>
<tr>
<td valign="middle" rowspan="4" align="center">
<italic>Passiflora edulis</italic>
</td>
<td valign="top" align="left">Current</td>
<td valign="middle" align="center">0.886668</td>
<td valign="middle" align="center">0.005253</td>
<td valign="middle" align="center">0.628844</td>
<td valign="middle" align="center">0.013346</td>
</tr>
<tr>
<td valign="top" align="left">SSP 2.45</td>
<td valign="middle" align="center">0.905698</td>
<td valign="middle" align="center">0.004946</td>
<td valign="middle" align="center">0.670296</td>
<td valign="middle" align="center">0.013301</td>
</tr>
<tr>
<td valign="top" align="left">SSP 3.70</td>
<td valign="middle" align="center">0.910879</td>
<td valign="middle" align="center">0.005528</td>
<td valign="middle" align="center">0.682967</td>
<td valign="middle" align="center">0.015784</td>
</tr>
<tr>
<td valign="top" align="left">SSP 5.85</td>
<td valign="middle" align="center">0.910463</td>
<td valign="middle" align="center">0.004942</td>
<td valign="middle" align="center">0.678765</td>
<td valign="middle" align="center">0.013924</td>
</tr>
</tbody>
</table>
</table-wrap>
<table-wrap id="T2" position="float">
<label>Table&#xa0;2</label>
<caption>
<p>Evaluation of species distribution model performance considering the adjustments between the occurrence data of pollinating bee species and climatic scenarios for the current period (averaged for 1970 - 2000) and three future scenarios for 2090 (averaged for 2081 - 2100): SSP-2.45, SSP-3.70, and SSP-5.85.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" rowspan="2" align="left">Pollinating bee species</th>
<th valign="middle" rowspan="2" align="left">Climate scenarios</th>
<th valign="middle" colspan="2" align="center">AUC</th>
<th valign="middle" colspan="2" align="center">TSS</th>
</tr>
<tr>
<th valign="middle" align="center">Mean</th>
<th valign="middle" align="center">SD</th>
<th valign="middle" align="center">Mean</th>
<th valign="middle" align="center">SD</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" rowspan="4" align="center">
<italic>Apis mellifera</italic>
</td>
<td valign="top" align="left">Current</td>
<td valign="middle" align="center">0.895465</td>
<td valign="middle" align="center">0.008126</td>
<td valign="middle" align="center">0.848437</td>
<td valign="middle" align="center">0.020500</td>
</tr>
<tr>
<td valign="top" align="left">SSP 2.45</td>
<td valign="middle" align="center">0.868566</td>
<td valign="middle" align="center">0.010529</td>
<td valign="middle" align="center">0.856771</td>
<td valign="middle" align="center">0.024392</td>
</tr>
<tr>
<td valign="top" align="left">SSP 3.70</td>
<td valign="middle" align="center">0.873402</td>
<td valign="middle" align="center">0.009943</td>
<td valign="middle" align="center">0.871905</td>
<td valign="middle" align="center">0.021923</td>
</tr>
<tr>
<td valign="top" align="left">SSP 5.85</td>
<td valign="middle" align="center">0.869870</td>
<td valign="middle" align="center">0.010662</td>
<td valign="middle" align="center">0.869010</td>
<td valign="middle" align="center">0.023211</td>
</tr>
<tr>
<td valign="middle" rowspan="4" align="center">
<italic>Eulaema mocsaryi</italic>
</td>
<td valign="top" align="left">Current</td>
<td valign="middle" align="center">0.799639</td>
<td valign="middle" align="center">0.025718</td>
<td valign="middle" align="center">0.820930</td>
<td valign="middle" align="center">0.050340</td>
</tr>
<tr>
<td valign="top" align="left">SSP 2.45</td>
<td valign="middle" align="center">0.760760</td>
<td valign="middle" align="center">0.030667</td>
<td valign="middle" align="center">0.774057</td>
<td valign="middle" align="center">0.047746</td>
</tr>
<tr>
<td valign="top" align="left">SSP 3.70</td>
<td valign="middle" align="center">0.771172</td>
<td valign="middle" align="center">0.030774</td>
<td valign="middle" align="center">0.790077</td>
<td valign="middle" align="center">0.055122</td>
</tr>
<tr>
<td valign="top" align="left">SSP 5.85</td>
<td valign="middle" align="center">0.782453</td>
<td valign="middle" align="center">0.034663</td>
<td valign="middle" align="center">0.748372</td>
<td valign="middle" align="center">0.067554</td>
</tr>
<tr>
<td valign="middle" rowspan="4" align="center">
<italic>Xylocopa frontalis</italic>
</td>
<td valign="top" align="left">Current</td>
<td valign="middle" align="center">0.853935</td>
<td valign="middle" align="center">0.010464</td>
<td valign="middle" align="center">0.800660</td>
<td valign="middle" align="center">0.020500</td>
</tr>
<tr>
<td valign="top" align="left">SSP 2.45</td>
<td valign="middle" align="center">0.792887</td>
<td valign="middle" align="center">0.019255</td>
<td valign="middle" align="center">0.761544</td>
<td valign="middle" align="center">0.037645</td>
</tr>
<tr>
<td valign="top" align="left">SSP 3.70</td>
<td valign="middle" align="center">0.819174</td>
<td valign="middle" align="center">0.016241</td>
<td valign="middle" align="center">0.853838</td>
<td valign="middle" align="center">0.032127</td>
</tr>
<tr>
<td valign="top" align="left">SSP 5.85</td>
<td valign="middle" align="center">0.786730</td>
<td valign="middle" align="center">0.016820</td>
<td valign="middle" align="center">0.782882</td>
<td valign="middle" align="center">0.030181</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>We observed that climate change was associated with contractions of suitable habitats for all plant and pollinator species under any climate scenario examined here. Comparing the current and any future scenarios, climate change was associated with a reduction in suitable habitat areas for all three food plant species. Specifically, suitable habitats may be reduced by up to 52.5% for <italic>Passiflora edulis</italic>, 70% for <italic>Bertholletia excelsa</italic>, and 54.5% for <italic>Eugenia uniflora</italic> (<xref ref-type="fig" rid="f1">
<bold>Figure&#xa0;1</bold>
</xref>). We documented that reductions in suitable habitat areas were greatest under the SSP-5.85 scenario for <italic>P. edulis</italic> and <italic>B. excelsa</italic> (<xref ref-type="fig" rid="f1">
<bold>Figure&#xa0;1</bold>
</xref>), and the SSP-3.70 scenario for <italic>E. uniflora</italic> (<xref ref-type="fig" rid="f1">
<bold>Figure&#xa0;1</bold>
</xref>). Considering the pollinators, climate change also marked reduced suitable habitat areas for all three pollinator species. Overall, habitats could be reduced by up to 39.6% for <italic>Xylocopa frontalis</italic>, 51.4% for <italic>Eulaema mocsaryi</italic>, and 44.1% for <italic>Apis mellifera</italic> (<xref ref-type="fig" rid="f2">
<bold>Figure&#xa0;2</bold>
</xref>). The highest reductions were under the SSP-2.45 scenario for <italic>X. frontalis</italic> and <italic>E. mocsaryi</italic> and the SSP-3.70 scenario for <italic>A. mellifera</italic> (<xref ref-type="fig" rid="f2">
<bold>Figure&#xa0;2</bold>
</xref>).</p>
<fig id="f1" position="float">
<label>Figure&#xa0;1</label>
<caption>
<p>Geographic projection of suitable habitat areas for food plant species native to Brazil under both one contemporary <bold>(A&#x2013;C)</bold> and three future scenarios [SSP-2.45 <bold>(D&#x2013;F)</bold>, SSP-3.70 <bold>(G&#x2013;I)</bold>, and SSP-5.85 <bold>(J&#x2013;L)</bold>] for 2090 (averaged for 2081-2100).</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="frbee-03-1510451-g001.tif"/>
</fig>
<fig id="f2" position="float">
<label>Figure&#xa0;2</label>
<caption>
<p>Geographic projection of suitable habitat areas for pollinators under both one contemporary <bold>(A&#x2013;C)</bold> and three future scenarios [SSP-2.45 <bold>(D&#x2013;F)</bold>, SSP-3.70 <bold>(G&#x2013;I)</bold>, and SSP-5.85 <bold>(J&#x2013;L)</bold>] for 2090 (averaged for 2081-2100).</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="frbee-03-1510451-g002.tif"/>
</fig>
<p>Overall, suitable habitats for <italic>P. edulis</italic> will likely be across the northern and southern regions of Brazil within the Atlantic forest. For <italic>B. excelsa</italic>, relictual suitable habitats contracted from the seasonally-dry peripheral to the core portions of the species distribution within the Amazon. For <italic>E. uniflora</italic>, we observed that suitable habitats also moved towards the central-southern portions of the species range, with species distributions within the Atlantic forest. Regarding pollinators, suitable habitats for <italic>X. frontalis</italic> moved to the northeastern and southeastern range limits, with marked losses in suitable habitats central-western Brazil, whereas suitable habitats for <italic>E. mocsaryi</italic> shifted to northwestern Brazil. For honeybees, remaining habitat patches shifted to southeastern and southern Brazil, following marked loss of suitable habitats in the semi-arid northeast.</p>
<p>Overlaying species distribution maps, we observed that climate change is clearly likely to induce marked niche mismatches between food plants and their pollinators. Specifically, suitable habitats for the co-occurrence of plant-pollinator interactions could be reduced by up to 51.5% for <italic>P. edulis</italic> and <italic>X. frontalis</italic>, 76% for <italic>B. excelsa</italic> and <italic>E. mocsaryi</italic>, and 54% for <italic>E. uniflora</italic> and <italic>A. mellifera</italic> (<xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3</bold>
</xref>). The niche mismatches between the food plants and their pollinating bees will impact the plant-pollinator interactions and, consequently, plant reproduction. These reductions in co-occurrence were most exacerbated under the SSP-2.45 scenario for <italic>P. edulis</italic> and <italic>X. frontalis</italic>, and for <italic>E. uniflora</italic> and <italic>A. mellifera</italic>. The worst predicted co-occurrence forecast for Brazil-nut trees and their orchid bee pollinators was under the SSP-5.85 scenario (<xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3</bold>
</xref>). Our findings highlight not only how climate change can impact ecological networks by disrupting plant-pollinator interactions, but also their potential to pose threats to food security by reducing pollination and, consequently, the productivity of pollinator-dependent food plants that essentially rely on pollinators to set fruits and seeds.</p>
<fig id="f3" position="float">
<label>Figure&#xa0;3</label>
<caption>
<p>Geographic projection of suitable habitat areas for the co-occurrence of food plant species native to Brazil and their main pollinators under both one contemporary <bold>(A&#x2013;C)</bold> and three future scenarios [SSP-2.45 <bold>(D&#x2013;F)</bold>, SSP-3.70 <bold>(G&#x2013;I)</bold>, and SSP-5.85 <bold>(J&#x2013;L)</bold>] for 2090 (averaged for 2081-2100). Green areas indicate the occurrence of food plants; blue areas indicate the occurrence of pollinators; and red areas indicate the spatial overlap between food plants and pollinators.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="frbee-03-1510451-g003.tif"/>
</fig>
</sec>
<sec id="s4" sec-type="discussion">
<label>4</label>
<title>Discussion</title>
<p>Our results clearly indicate that climate change will likely reduce suitable habitats and alter the spatial distribution for pollinator-dependent food plants native to Brazil and their primary bee pollinators. Our findings are evidenced by the pattern of contractions in occurrence areas, which were most severe under some climate change scenarios, with divergent responses among the species examined here. Furthermore, our findings show that climate change can act as a potential driver of decoupling between each crop and its main pollinating bee, leading to co-occurrence mismatches. Reductions in suitable habitat areas were documented for all food plants and their pollinators under all climate change scenarios, indicating that climate change can potentially affect the occurrence and co-occurrence of plants and their pollinators, which corroborates our hypotheses regardless of the scenario we used. Therefore, shifts in habitat suitability induced by climate change can result in severe spatial mismatches, potentially exacerbating the risks of pollination failure.</p>
<p>Our results reinforce now widely observed and predicted patterns of climate-induced shifts species ranges in terms of suitability, some of which involving potential impacts on pollination (e.g., <xref ref-type="bibr" rid="B37">Giannini et&#xa0;al., 2013</xref>, <xref ref-type="bibr" rid="B40">2017</xref>; <xref ref-type="bibr" rid="B89">Settele et&#xa0;al., 2016</xref>; <xref ref-type="bibr" rid="B25">Elias et&#xa0;al., 2017</xref>; <xref ref-type="bibr" rid="B62">Millard et&#xa0;al., 2023</xref>). The effects of climate change have been linked to significant reductions in pollination services (<xref ref-type="bibr" rid="B89">Settele et&#xa0;al., 2016</xref>; <xref ref-type="bibr" rid="B81">Sales et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B62">Millard et&#xa0;al., 2023</xref>). This poses intractable challenges to food production and food security as biotic pollination is a major ecosystem service ensuring crop yields in a large number of food plants (<xref ref-type="bibr" rid="B50">Klein et&#xa0;al., 2007</xref>; <xref ref-type="bibr" rid="B38">Giannini et&#xa0;al., 2015</xref>; <xref ref-type="bibr" rid="B67">Oliveira et&#xa0;al., 2024</xref>). From this perspective, our results are consistent with previously documented effects of climate change on agricultural crops and pollinators in Brazil. Specifically, projected climate change could reduce suitable areas for the occurrence of bees, which pollinate some crops, including tomato, persimmon, and passionfruit (e.g., <xref ref-type="bibr" rid="B37">Giannini et&#xa0;al., 2013</xref>, <xref ref-type="bibr" rid="B40">2017</xref>; <xref ref-type="bibr" rid="B25">Elias et&#xa0;al., 2017</xref>). In addition, the interaction between agricultural land use and climate change is associated with population declines in insect pollinators, and these impacts are more severe in tropical regions (<xref ref-type="bibr" rid="B62">Millard et&#xa0;al., 2023</xref>). Watermelon production is also expected to decline due to climate change when crops are pollinated by managed honeybees, but the opposite was shown for native bee pollinators, reinforcing the importance of the native biota in mitigating climate change (<xref ref-type="bibr" rid="B77">Rader et&#xa0;al., 2013</xref>). Some species could therefore respond positively to climate change. In contrast to this study, it has been suggested that Brazil-nut trees could expand their distribution area by up to 6%, depending on the climate change scenario (<xref ref-type="bibr" rid="B83">Sales et&#xa0;al., 2020</xref>). However, when interactions with pollinators were examined, declines in pollinator diversity may still lead to a spatial mismatch and threaten the pollination of Brazil-nut tree stands (<xref ref-type="bibr" rid="B83">Sales et&#xa0;al., 2020</xref>). Regardless of the climate scenario, the distribution of wild Brazil-nut tree populations across the Brazilian, Peruvian and the Bolivian Amazon is likely to be constrained, not least because of mounting threats from widespread forest degradation through logging and wildfires (C.A. Peres, unpubl. data).</p>
<p>Climate change could be a strong environmental driver altering ecosystem functioning (<xref ref-type="bibr" rid="B57">Malhi and Wright, 2004</xref>), often leading to an ecological imbalance that favors generalist species over specialists (e.g., <xref ref-type="bibr" rid="B13">Burkle et&#xa0;al., 2013</xref>; <xref ref-type="bibr" rid="B90">Silva et&#xa0;al., 2019</xref>). The latter, in turn, have narrower ecological niches and become more susceptible to suitable habitat contraction or undergo local extinction (<xref ref-type="bibr" rid="B9">Blois et&#xa0;al., 2013</xref>; <xref ref-type="bibr" rid="B90">Silva et&#xa0;al., 2019</xref>). We observed a marked decline in suitable habitats. For <italic>Passiflora edulis</italic> and <italic>Eugenia uniflora</italic>, we observed a range contraction in the Atlantic forest region, whereas <italic>Bertholletia excelsa</italic> succumbs to substantial habitat loss across the Amazon. The Atlantic forest is one of the most susceptible biodiversity hotspots to climate change (<xref ref-type="bibr" rid="B8">B&#xe9;llard et&#xa0;al., 2014</xref>), which is intensified by deforestation in this biome (<xref ref-type="bibr" rid="B84">Scarano and Ceotto, 2015</xref>). The Atlantic forest is expected to face pervasive biotic reorganization followed by a savannization process (<xref ref-type="bibr" rid="B46">Joly et&#xa0;al., 2014</xref>; <xref ref-type="bibr" rid="B84">Scarano and Ceotto, 2015</xref>), particularly in the Brazilian northeast where aridity is a major issue, which could explain the patterns we find for <italic>P. edulis</italic> and <italic>E. uniflora</italic>, but also for&#xa0;their pollinators (<italic>Xylocopa frontalis</italic> and <italic>Apis mellifera</italic>). Additionally, the risk of pollinator shortages for native crops has been documented in some counties in northern Brazil, although this is mainly concentrated in the northeast and the southeast, with the Atlantic forest and the Caatinga dry forest at higher risk of pollinator shortages (<xref ref-type="bibr" rid="B67">Oliveira et&#xa0;al., 2024</xref>). Considering Brazil-nut trees, reduced precipitation and increasing frequency of drought are expected in the eastern and southeast Amazon, the regions most affected by deforestation (<xref ref-type="bibr" rid="B56">Malhi et&#xa0;al., 2008</xref>). This is aggravated given that Brazil-nut tree populations have been widely affected by deforestation, habitat fragmentation, wildfires, and overexploitation of their seed crops (<xref ref-type="bibr" rid="B72">Peres et&#xa0;al., 2003</xref>; <xref ref-type="bibr" rid="B12">Brand&#xe3;o et&#xa0;al., 2021</xref>). Therefore, deforestation is a major factor boosting the effects of climate change on pollinators, which is reinforced by climate projections in the eastern Amazon indicating that 95% of all bee species will face declines in their area of occurrence, affecting crop pollination (<xref ref-type="bibr" rid="B39">Giannini et&#xa0;al., 2020</xref>).</p>
<p>Our projections indicate that climate change is a potential driver of niche mismatch between food plants and their pollinators. Brazil is one of the countries at highest risk of crop yield losses due to the impacts of climate change on the abundance of insect pollinators (<xref ref-type="bibr" rid="B62">Millard et&#xa0;al., 2023</xref>). The contraction in areas of biotic overlap may therefore represent a severe threat to Brazilian agriculture, given that the tropics will likely experience the highest risks of pollinator loss, which threatens the production of key pollinator-dependent crops, such as cocoa, mango, watermelon, passionfruit, tomato, and coffee (<xref ref-type="bibr" rid="B37">Giannini et&#xa0;al., 2013</xref>, <xref ref-type="bibr" rid="B40">2017</xref>; <xref ref-type="bibr" rid="B25">Elias et&#xa0;al., 2017</xref>; <xref ref-type="bibr" rid="B62">Millard et&#xa0;al., 2023</xref>). As such, our worst-case scenario highlights the severe impacts on ecosystem functioning and food production, with downstream socioeconomic implications. Such negative effects of climate change on the pollination of food plants affect not only biodiversity but also subsistence and cash-crop yields, the national economy, and the livelihoods of local communities. The decline in local food production tends to increase the value of national trade, which may benefit the national economy, but reinforces inequality and rural poverty by eroding per capita purchase power (<xref ref-type="bibr" rid="B19">Cornelsen et&#xa0;al., 2015</xref>; <xref ref-type="bibr" rid="B64">Murphy et&#xa0;al., 2022</xref>). These effects will be even more intense in Brazil, which is a leading producer and exporter of tropical fruit crops (<xref ref-type="bibr" rid="B68">Oliveira et&#xa0;al., 2023</xref>) and one of the countries most vulnerable to climate change (<xref ref-type="bibr" rid="B62">Millard et&#xa0;al., 2023</xref>). A decline in pollination interactions induced by climate change heightens food insecurity (<xref ref-type="bibr" rid="B62">Millard et&#xa0;al., 2023</xref>). Furthermore, the reduction in crop diversity driven by the expansion of monoculture lands is a key driver of pollinator declines, which in turn could threaten the resilience of ecosystems and agricultural yields (e.g., <xref ref-type="bibr" rid="B1">Aizen et&#xa0;al., 2019</xref>; <xref ref-type="bibr" rid="B68">Oliveira et&#xa0;al., 2023</xref>). Overall, the combined impacts of land-use and climate change reduce long-term agricultural sustainability by increasing the likelihood of disrupted plant-pollinator interactions, which can ultimately lead to food shortages. Accordingly, the loss of pollinators will increase trade risks for major food importers such as China, western Europe and the USA, as the lack of pollinators will disrupt agricultural yields of pollinator-dependent cash crops, such as coffee and cocoa (<xref ref-type="bibr" rid="B62">Millard et&#xa0;al., 2023</xref>).</p>
<p>Therefore, decision-makers need to focus on developing public policies that prioritize climate change mitigation and encourage sustainable agricultural practices to protect key mutualistic interactions that benefit human societies, such as pollination. Resilient ecosystems not only benefit plants and their pollinators but are also vital in ensuring agricultural productivity and food security (<xref ref-type="bibr" rid="B43">IPBES, 2016</xref>). Thus, we need to rethink our agricultural practices, implementing biodiversity conservation as a crucial pillar to mitigate the ecological challenges imposed by climate change, while also ensuring resilience and food sovereignty. To achieve this, policy makers should urgently include the remaining areas where key mutualists are expected to co-occur into action plans for conservation, while maintaining species abundance and richness, and building more sustainable agricultural production pathways (<xref ref-type="bibr" rid="B3">Altieri and Nicholls, 2008</xref>, <xref ref-type="bibr" rid="B4">2017</xref>; <xref ref-type="bibr" rid="B34">Garibaldi et&#xa0;al., 2016</xref>; <xref ref-type="bibr" rid="B65">Nicholls and Altieri, 2018</xref>; <xref ref-type="bibr" rid="B68">Oliveira et&#xa0;al., 2023</xref>). Furthermore, sustainable agriculture practices should be adaptable to a variety of production methods and conditions, and consider the entire agricultural system in their design (<xref ref-type="bibr" rid="B63">Muhie, 2022</xref> and references therein). These practices can include climate-smart agriculture (a relatively new approach that ensures improved agricultural practices while lowering greenhouse gas emissions to assist underprivileged people in increasing agricultural productivity and income), organic farming (which promotes resource sustainability, environmental protection, animal welfare, food quality and safety, social justice, and market-driven payments for internalized costs), sustainable intensification (which increases crop yields without threatening the environment or converting natural areas into farmland), regenerative agriculture (a food and agricultural system focused on rehabilitation and preservation), and integrated nutrient management (a technique for safely disposing of agricultural wastes by incorporating inorganic and organic fertilizers in a balanced and integrated manner to preserve soil fertility and give plants the right amount of nutrients). These practices can achieve the goals of life on land and zero-hunger of the Sustainable Development Goals, especially in major emergent agricultural economies such as Brazil.</p>
</sec>
</body>
<back>
<sec id="s5" sec-type="data-availability">
<title>Data availability statement</title>
<p>The original contributions presented in the study are included in the article/<xref ref-type="supplementary-material" rid="SF1">
<bold>Supplementary Material</bold>
</xref>. Further inquiries can be directed to the corresponding author.</p>
</sec>
<sec id="s6" sec-type="author-contributions">
<title>Author contributions</title>
<p>WO: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing &#x2013; original draft, Writing &#x2013; review &amp; editing. OC: Conceptualization, Data curation, Formal analysis, Methodology, Supervision, Validation, Visualization, Writing &#x2013; review &amp; editing. JS: Data&#xa0;curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing &#x2013; review &amp; editing. MT: Funding acquisition, Validation, Visualization, Writing &#x2013; review &amp; editing. CP: Writing &#x2013; review &amp; editing, Supervision. AL: Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Validation, Visualization, Writing &#x2013; review &amp; editing.</p>
</sec>
<sec id="s7" sec-type="funding-information">
<title>Funding</title>
<p>The author(s) declare that financial support was received for the research and/or publication of this article. This study was supported by the Conselho Nacional de Desenvolvimento Cient&#xed;fico e Tecnol&#xf3;gico (CNPq) through PhD studentship #141954/2020-5 awarded to WO and #175071/2023-3 awarded to JS; PQ #308832/2014-0 and #309505/2018-6 awarded to AL; the Coordena&#xe7;&#xe3;o de Aperfei&#xe7;oamento de Pessoal de N&#xed;vel Superior (CAPES) (for all authors grant code: 001; postdoc fellowship awarded to OC-N, grant number: APQ-0789-2.05/16), and the Funda&#xe7;&#xe3;o de Amparo &#xe0; Ci&#xea;ncia e Tecnologia do Estado de Pernambuco (FACEPE) (postdoc fellowship to OC-N, grant number: BCT-34 0208-2.05/17).</p>
</sec>
<sec id="s8" sec-type="COI-statement">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec id="s9" sec-type="ai-statement">
<title>Generative AI statement</title>
<p>The author(s) declare that no Generative AI was used in the creation of this manuscript.</p>
</sec>
<sec id="s10" sec-type="disclaimer">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors&#xa0;and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
<sec id="s11" sec-type="supplementary-material">
<title>Supplementary material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/frbee.2025.1510451/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/frbee.2025.1510451/full#supplementary-material</ext-link>
</p>
<supplementary-material xlink:href="Table1.pdf" id="SF1" mimetype="application/pdf">
<label>Supplementary Table&#xa0;1</label>
<caption>
<p>Download information of occurrence data of three food plants native to Brazil and two pollinators obtained from the GBIF repository (source: The Global Biodiversity Information Facility 2021 - GBIF; <ext-link ext-link-type="uri" xlink:href="https://www.gbif.org">https://www.gbif.org</ext-link>).</p>
</caption>
</supplementary-material>
</sec>
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