Predicting the future of the endemic cactus Melocactus pachyacanthus in a semiarid landscape

Predicting the future of the endemic cactus Melocactus pachyacanthus in a semiarid landscape | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Predicting the future of the endemic cactus Melocactus pachyacanthus in a semiarid landscape Flávia dos Santos Bomfim, Luisa Maria Diele-Viegas, Thieres Santos-Almeida, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9109033/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Anthropogenic activities have increasingly pressured biodiversity worldwide. The cactus Melocactus pachyacanthus , an endemic and endangered species of Brazil´s Caatinga biome, is particularly vulnerable. This study assessed the effects of climate change, land use, and fire on the distribution of M. pachyacanthus across three ecoregions in the Chapada Diamantina region, Bahia, Brazil. We employed Ecological Niche Modeling (ENM) to evaluate current climatic suitability and to project future scenarios, overlaying ENM outputs with land-use and fire data. The current model identified a concentration of suitable habitat in the Southern Sertaneja Depression. At the same time, future projections reveal a reduction in this region and an expansion toward the Chapada Diamantina Complex. Although land use significantly contributed to habitat loss, fire had a lesser impact. Our findings underscore the importance of ENM as a tool for identifying priority conservation areas and guiding the creation of Environmental Protection Areas. These results highlight the urgent need for conservation policies to safeguard endemic species like M. pachyacanthus amid ongoing environmental changes. Our research provides insights into how climate and human activities reshape species distributions in semi-arid regions worldwide. Anthropic activities Caatinga Cactus Ecological Niche Modeling Environmental suitability Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Given the constant changes driven by human activities related to climate change, land use and occupation, and fires in the Caatinga biome, measuring the potential impacts of these anthropogenic activities on local biodiversity has become increasingly necessary (Silva, 2023)[ 32 ]. The Caatinga is a seasonally dry tropical forest located in a semi-arid tropical climate marked by high average annual temperatures and long periods of drought (Fernandes, 2018)[ 14 ]. Its specific edaphoclimatic characteristics have given it a rich diversity and endemic species (Taiz & Zeiger, 2006)[ 34 ]. As one of the world’s most biodiverse semi-arid biomes (Tabarelli et al., 2018)[ 33 ], the Caatinga hosts a rich flora and fauna adapted to extreme conditions, making the conservation of endemic species essential for ecological resilience (Fernandes & Queiroz, 2018)[ 14 ]. Representing the endemic species of the Caatinga, Melocactus pachyacanthus Buining & Brederoo is a cactus (Martinelli & Moraes, 2013)[ 26 ] with a succulent subshrub habit that is found on flat rocky outcrops (Zappi & Taylor, 2023)[39 ], where the exposed limestone is exploited (Zappi et al., 2011)[ 38 ]. The known uses of this species are ecological, in the foraging of animals typical of the Caatinga, with frugivorous and nectarivorous habits (Cavalcante et al., 2013)[ 8 ], mystical-religious, and economic as an ornamental plant (Martinelli & Moraes, 2013)[ 26 ]. Slow-growing, this species is quite vulnerable to environmental disturbances, including increases in temperature and loss of habitat quality during its initial stages of development (Cavalcante et al., 2013)[ 8 ]. This vulnerability is quite worrying, given that projections of future climate change for the Caatinga biome are pessimistic and that frequent emissions of greenhouse gases (GHGs) are expected. Reductions in precipitation and increases in annual temperature tend to desertify this region (Tavares & Ramos, 2016; Tavares et al., 2019)[ 36 , 35 ]. Soil exploitation for agricultural purposes is another factor that significantly impacts this biome (Martinelli & Moraes, 2013)[ 26 ]. Intensive farming practices are among the main factors responsible for converting areas of native vegetation into monocultures and pasture plantations (Martinelli & Moraes, 2013)[ 26 ]. In this context, illegal deforestation for land use and occupation is one of the causes of the increasing expansion of fire (Silva et al., 2023)[ 32 ]. In addition, fire outbreaks have been increasing significantly in the Caatinga and may be directly associated with climate change; they can impoverish the nutritional attributes of local soils (Diele-Viegas et al., 2022; Boff, 2018; Macedo, 2023)[ 9 , 4 , 23 ]. Ecological Niche Modeling (ENM) is an approach widely used by ecologists to predict species' distribution and occurrence patterns (Barbet-Massin, 2012)[ 3 ]. Using ENM, it is possible to generate maps of species' environmental suitability based on their realized niche and project these models for different environmental scenarios (Barbet-Massin, 2012; Santos et al., 2024)[ 3 , 31 ]. Identifying this suitability is essential to defining the potential areas of occurrence of the species to be restored and protected. Thus, to conserve Melocactus pachyacanthus , an “Endangered” of extinction (EP) in the wild (Brasil, 2022) [ 7 ], we measure the impacts of human activities related to climate change, land use, and fire on its occurrence. The specific objectives were I. to map the potential areas of bioclimatic suitability for the occurrence of the species M. pachyacanthus in the current scenario; II. to predict the potential bioclimatic suitability for the occurrence of the species M. pachyacanthus considering optimistic future scenarios about GHG emissions, and, III. to assess the impact of land use and occupation and fire on the potential occurrence of M. pachyacanthus in the current scenario. 2. Methodology 2.1 Study area The study area is delineated around the three ecoregions of Chapada Diamantina in Bahia, Brazil: Southern Sertaneja Depression (SSD), Chapada Diamantina Complex (CDC), and São Francisco Dunes (SFD). The selection considered the locations of known occurrence points for Melocactus pachyacanthus (Fig. 1). (Fig. 1 Location of occurrence points Melocactus pachycanthus in ecoregions) The DSM ecoregion covers an area of ​​373,900 km², except the Campo Maior Complex ecoregion, which borders all the ecoregions of Chapada. It encompasses the CDC and the SFD. Its vegetation is more typical of the semiarid to arid Northeast, and the predominant climate is hot and semiarid (Velloso et al., 2002)[ 37 ]. The CDC covers an area of ​​50,610 km². Located in the center-south of the Caatinga, it is considered the biome's highest region. The extensive reworking of these areas has created deep valleys. The climate of this ecoregion ranges from hot to tropical in the western part and tropical to semiarid in the eastern part (Velloso et al., 2002)[ 37 ]. The SFD area is part of the central-western biome, covering ​​36,170 km². With a scorching, semiarid climate, this ecoregion has high soil temperatures and is characterized by extensive wind farms with caatinga vegetation grouped into thickets (Velloso et al., 2002)[ 37 ]. 2.2 Geographical space The species' occurrences were collected from the Global Biodiversity Information Facility (GBIF, https://www.gbif.org/ ). A total of 33 occurrences were found. The occurrence points were filtered to remove points in the ocean, municipal centroids, and duplicate occurrences. The remaining 16 points were used to perform correlative modeling with bioclimatic variables (Table 1 ). Table 1 Spatial data on the occurrence of the species Melocactus pachycanthus in the ecoregions and municipalities of Bahia. ID Spatial Location of Occurrence points Ecorregiões Lat Long Municipality 1 Southern Sertaneja Depression -11.01333 -41.676246 São Gabriel 2 Southern Sertaneja Depression -10.883588 -41.160171 Ourolândia 3 Chapada Diamantina complex -11.525812 -41.182406 Morro do Chapéu 4 Southern Sertaneja Depression -9.594404 -40.315713 Juazeiro 5 Southern Sertaneja Depression -11.305921 -41.838276 Irecê 6 Southern Sertaneja Depression -11.681666 -41.673889 Canarana 7 Chapada Diamantina complex -11.089444 -41.085278 Varzéa Nova 8 Southern Sertaneja Depression -9.977638 -40.884175 Campo Formoso 9 Southern Sertaneja Depression -11.414721 -41.406111 América Dourada 10 Chapada Diamantina complex -11.013611 -40.407778 Saúde 11 Chapada Diamantina complex -11.41361 -40.407778 Miguel Calmon 12 Southern Sertaneja Depression -11.50000 -41.70000 13 Southern Sertaneja Depression -10.046919 -41.126198 Campo Formoso 14 Southern Sertaneja Depression -11.3000000 -41.420000 América Dourada 15 Southern Sertaneja Depression -10.153381 -40.732417 Campo Formoso 16 Southern Sertaneja Depression -10.856956 -41.660072 São Gabriel 2.3 Environmental Space The 19 bioclimatic variables of WorldClim 2.0 were collected with 2.5’ arc-minutes (~ 5 x 5km) (Fick & Hijmans, 2017)[ 15 ]. We cropped the variables to the study area and removed bioclimatic variables 8, 9, 18, and 19. These variables combine precipitation and temperature data and present anomalies as unrealistic spatial discontinuities (Escobar et al., 2014)[ 13 ]. To reduce collinearity, we removed correlated variables (r > 0.7) using Pearson’s Correlation (Dormann et al., 2013)[ 11 ]. The uncorrelated bioclimatic variables selected for the model were BIO3- Isothermality (BIO2/BIO7) (×100), BIO10- Average temperature of the warmest quarter, and BIO12 - Annual precipitation. 2.4 Ecological niche model We used three algorithms in the modeling process: Bioclim (Rangel & Loyola, 2012)[ 30 ], Support Vector Machine (SVM) (Rangel & Loyola, 2012)[ 30 ], and Generalized Linear Model (GLM) (McCullagh & Nelder, 2019)[ 25 ]. For each algorithm, we ran 10 bootstrap replicates, where 70% of the occurrences were used for training and 30% for testing. During model testing, we generated random pseudo-absences: 10,000 points for the GLM and 160 points (10 times the number of presences) for the Bioclim and SVM models (Barbet-Massin et al., 2012)[ 3 ]. The models were evaluated using the Area Under the Curve (AUC/ROC; Fielding & Bell, 1997). This metric ranges from 0 to 1, where values below 0.5 indicate random predictions and values above 0.75 indicate good predictions. Based on AUC, we selected the best replicates for each algorithm and combined them into an ensemble model using an average weighted by AUC 0,75 (Diniz-Filho et al., 2010)[ 10 ]. 2.5 Model projection and overlay We project the consensus model for the years 2041–2060 (hereafter referred to as 2050) using the shared socioeconomic pathways (SSP) scenario SSP2 45 and SSPS5 85 (IPCC, 2021)[ 18 ]. The SSP2 45 scenario predicts intermediate greenhouse gas emissions that stabilize in 2050, resulting in a 2.0°C increase, while the SSP5 85 scenario predicts very high greenhouse gas emissions that triple from 2075 onwards, with a 2.4°C increase in 2050 (IPCC, 2021)[ 18 ]. Future climate projections were performed with the ensemble (average) of three global circulation models (GCMs) from the Coupled Model Intercomparison Project Phase Six (CMIP6) (Eyring et al., 2016)[ 12 ]. We selected the best performing GCMS in South America (Oliveira et al., 2023)[ 27 ]: (1) Beijing Climate Center model (BCC-CSM2-MR); (2) Institute Pierre Simon Laplace model (IPSL-CM6A-LR); and (3) Model for Interdisciplinary Research on Climate (MIROC6). Using the model generated to date, we estimated the suitability threshold that maximizes the True Skill Statistic (TSS; Andrade et al., 2020)[ 1 ] validation metric. The threshold was used to transform the present models and the projections for the future into binary maps (presence/absence). To understand the impact of land use and fires on the species' habitat, we separately overlapped the binarized models on the land use and fire maps. Thematic maps from collection 8 of the MapBiomas project (Time series 1985–2024), (Jr-Souza et al., 2020)[ 20 ] and fire maps (MapBiomas, https://plataforma.brasil.mapbiomas.org/monitor-do-fogo ) were used to overlay the present models. 3. Results 3.1 Mapping the bioclimatic suitability of the species Melocactus pachyacanthus for the current scenario In the current scenario, the MNE for the species M. pachyacanthus predicted a bioclimatic suitability area of approximately 16.278,90 km² across the three ecoregions of interest. In the SSD ecoregion, the area was approximately 41.300,93 km², followed by the CDC with 16.278,90 km² and the SFD region with 54.136,20 km² (Table 2 ). Table 2 Calculation of areas of climatic suitability without interference from the variables fire and land use and occupation, and calculation of suitability with interference from the variables fire and land use and occupation, considering the years 1995, 2005, 2015, and 2022 for each variable. CCD - Chapada Diamantina Complex (CCD); DSF - São Francisco Dunes (DSF), and DSM - Southern Sertaneja Depression. Type map Ecorregions year CCD DSF DSM Sdm 16278.90278 54.136204 41300.93056 Fire 1995 16278.90278 54.136204 41300.93056 Fire 2005 16278.90278 54.136204 41300.93056 Fire 2015 16173.88851 54.136204 41279.86483 Fire 2022 16154.33523 54.136204 41278.30835 Land use 1995 13130.16218 54.136204 26579.2041 Land use 2005 13370.66189 54.136204 26239.04563 Land use 2015 12375.45984 54.136204 23976.94566 Land use 2022 12554.6199 54.136204 24902.58022 This model showed an increase in suitability variation from 0.6 to 0.8. In the SSD ecoregion, where 81.25% of the species' known occurrence points are located, a larger area of ​​bioclimatic suitability was observed, with a value close to 0.8. In the CDC ecoregion, where 18.75% of the species' known occurrence points are located, suitability was closer to 0.6, a value higher than the DSF. In this ecoregion, where there are no records of the species' occurrence, suitability was below 0.6. 3.2 Prediction of the bioclimatic suitability of the species Melocactus pachyacanthus for the most and least optimistic future climate change scenarios In climate projections for the most optimistic future scenario, the model predicted suitability for greenhouse gas (GHG) emissions ranging from 0.6 to 0.8. In the SSD ecoregion, the suitability percentage ranged from 0.7 to 0.75. In the CDC ecoregion, where the suitability area expanded, the observed value was close to 0.6, higher than SFD, which was below 0.6 (Fig. 2). In the current scenario, a reduction in the suitability area was observed, with values close to 0.8 in SSD. Climate projections for the less optimistic future scenario of greenhouse gas emissions also predicted a range of 0.6 to 0.8 in suitability. In the DSM ecoregion, suitability ranged from 0.6 to 0.8. In the CDC ecoregion, suitability was closer to 0.6; in SFD, it was below 0.5 (Fig. 2). On this map, there was an even greater reduction in suitability, with values near 0.8 in DSF. However, an expansion of areas of suitability with percentages close to 0.7 was observed in CDC. (Fig. 2 Climate projections for future scenarios) 3.3 Interference of land use and occupation on the occurrence of the species Melocactus pachyacanthus in the present bioclimatic scenario The land-use and occupation maps indicated overlapping areas with altered vegetation cover and possible bioclimatic suitability for Melocactus pachyacanthus (Fig. 3). These altered areas also overlapped with the niche occupied by some populations. For the SSD ecoregion, the percentage of overlapping decreased areas in bioclimatic suitability in 1995 was approximately 35.65%. (Fig. 3 Land-use and occupation maps) Following this, the land use maps for the years (2005, 2015, and 2022) showed an overlap of approximately 36.47%, 41.95%, and 39.70%, respectively, with the area of ​​bioclimatic suitability predicted in the present model. At the CDC, the overlap between anthropized areas mapped in 1995 and bioclimatic suitability areas was approximately 19.34%. In 2005, the overlap was approximately 17.87%. In 2015, it was approximately 23.97%; in 2022, approximately 22.88%. The map did not predict overlap between anthropized areas and ​​bioclimatic suitability for the SFD ecoregion. 3.4 Fire interference in the occurrence of Melocactus pachyacanthus in the present bioclimatic scenario In the SSD ecoregion, the fire maps for 1995 and 2005 did not identify any overlap between fires and potential areas of the species' bioclimatic suitability. However, for 2015 and 2022, the overlap percentages were 0.051% and 0.054%, respectively (Fig. 4). (Fig. 4 Fire maps) Regarding the CDC ecoregion, the fire maps did not identify any fire outbreaks in the species' potential areas of occurrence during 1995–2005. For 2015 and 2022, overlaps of 0.64% and 0.77% in fire outbreaks were identified within the potential bioclimatic suitability areas (Fig. 3). The DSF results for fire, which overlap with areas of present bioclimatic suitability, for the periods 1995, 2005, 2015, and 2022, were negative. The fire map showed no overlap of the burned areas with the occurrence points of the species under study (Fig. 3). 4. Discussions The need for more favorable temperatures in Melocactus Pachyacanthus is related to the species' low tolerance to disturbance in the early stages of its development and its slow growth (Cavalcante et al., 2013)[ 8 ]. The ecoregion with the greatest potential for bioclimatic suitability for the species' occurrence was the SSD. The species' chances of survival are possibly greater in this location (Zappi et al., 2011)[ 38 ]. This map also showed that some populations occur in areas with lower bioclimatic suitability in the current scenario. It is possible that the climate experienced by the species in the CDC ecoregion is being influenced by its local microclimate (Kearney et al., 2014) [ 21 ]. In this region, soil and climate conditions are somewhat different from those in the other regions due to the formation of islands through isolation processes (Velloso et al., 2002)[ 37 ]. For the SSD ecoregion, the intensification of climate change (Cavalcante et al., 2013)[ 8 ] is reflected in the reduction of the bioclimatic suitability predicted for this location in future scenarios (Pritchard & Harrot, 2010)[ 29 ]. With this loss of habitat quality, maintaining Melocactus pachyacanthus in these locations would require adaptation to this new scenario (Pritchard & Harrot, 2010)[ 29 ]. However, this is unlikely given the short time over which climate change has occurred (Pritchard & Harrot, 2010)[ 29 ]. In this bioclimatic context, migration or dispersal could be an alternative to prevent species extinction. Evaluating the influence of land use and occupation is essential to consider migration as a survival measure, as it strongly shapes species' conservation processes (Pritchard & Harrop, 2010)[ 29 ]. Currently, agricultural practices are among the leading causes of habitat destruction and fragmentation for this species (Tabarelli et al., 2018)[ 33 ], making it less viable for populations to colonize new areas (Pritchard & Harrop, 2010)[ 29 ]. The land-use and occupation map shows the growth of degraded regions over the years (1995, 2005, 2012, and 2022). This growth was observed in the species' fundamental niche and in the areas where the species occurs. Although they still occur in these degraded areas, the species' populations are declining (Martinelli & Moraes, 2013)[ 26 ]. Fire has also been increasingly present in the Caatinga Biome (Silva et al., 2023; Jesus et al., 2020)[ 19 , 32 ]. The maps showing the impacts of fire showed an increase in fire outbreaks in areas of climatic suitability predicted for the current scenario. Still, the percentage values indicating the presence of these outbreaks are insignificant. Despite this, fire may pose an imminent risk to the species due to uncontrolled burning practices that spread fire to surrounding areas, damaging soil quality and vegetation (Baker et al., 2013; Luz et al., 2023)[ 2 , 22 ]. Based on these results, it is possible to begin considering measures to minimize the impacts of anthropogenic activities on the occurrence of Melocactus pachyacanthus (Zappi et al., 2011)[ 38 ]. One of them would be the creation of recovery plans for degraded areas in potential bioclimatic suitability areas. Since bioclimatic suitability is expected to decline in future scenarios, restoring degraded, climatically suitable areas would be one possible path. In conjunction with this action, creating Protected Areas (PAs) of Integral Protection (IP) that guarantee the protection of these areas is fundamental to this process, primarily because PAs are legally protected areas established by law to preserve biodiversity (Boff, 2018; Brasil, 2000) [ 4 , 6 ]. The prediction of the potential regions of environmental suitability also contributes positively to a more effective management plan for the conservation of the species M. pachyacanthus (Rangel & Loyola, 2012)[ 30 ] by mapping the areas that have the most favorable environmental conditions for the survival of this species (Paglia et al., 2012)[ 28 ]. It is also essential that the management plan for this PA addresses these needs in its preparation (Rangel & Loyola, 2012)[ 30 ], so that these areas receive the appropriate categorization of their uses (Brasil, 2000)[ 6 ]. In conclusion, the persistence of Melocactus pachyacanthus is closely tied to the interplay between its ecological constraints and rapidly changing environmental conditions. Although the SSD ecoregion currently represents the most favorable area for the species, future climate scenarios indicate a decline in suitability, while populations already occurring in less suitable regions may depend on localized microclimatic conditions for survival. Limited dispersal capacity, combined with increasing habitat degradation driven by agricultural expansion and, to a lesser extent, fire, further restricts the species’ ability to track suitable environments. Given these constraints, conservation efforts should prioritize restoring degraded yet climatically suitable areas and establishing well-managed protected areas to safeguard remaining populations. Integrating bioclimatic predictions with land-use planning will be essential to enhance the effectiveness of conservation strategies and mitigate the impacts of ongoing environmental change on this vulnerable species. Declarations Ethics, Consent to Participate, and Consent to Publish Not applicable. Competing interests The authors have no relevant financial or non-financial interests to disclose. Funding statement This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author Contribution Flávia dos Santos Bomfim: Methodology, Research, Formal analysis, Data curation, Writing original draft, Writing final document. Luisa Maria Diele-Viegas: Methodology, Research, Formal analysis, Data curation, Conceptualization, Writing original draft, Writing final document, Review & editing. Thieres Santos Almeida: Methodology, Formal analysis, Data curation, Writing original draft, Review & editing. Bianca Barros Zaballa: Research, Methodology, Data curation, Writing original draft. Mateus Almeida dos Santos: Methodology, Data curation, Writing original draft, Review & editing. Hugo Andrade: Data curation, Writing original draft, Review & editing. Fernanda Melo Gomes: Methodology, Research, Conceptualization, Writing original draft. Acknowledgement We are grateful to colleagues from the BioDivA laboratory for their vital contributions to previous versions of this manuscript. Data Availability ll data supporting the findings of this study are available within the paper. References Andrade AFA, Velazco SJE, JUNIOR PDM.. ENMTML: An R package for a straightforward construction of complex ecological niche models. Environ Model Softw. 2020;125:104615. https://doi.org/10.1016/j.envsoft.2019.104615 . Baker SC, Espiões TA, Wardlaw TJ, Balmer J, Franklin JF, Jordan GJ. 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Rev Arvore. 2023;47. https://doi.org/10.1590/1806-908820230000002 . Mapbiomas, Brasil. 2024. Dados monitor mensais do fogo. https://brasil.mapbiomas.org/dados-monitor-mensal-do-fogo . (Accessed 10 February 2024). McCullagh P, Nelder JA. 2019. Generalized linearmodels.Chapman and Hall. Martinelli G, Moraes A. 2013. Livro Vermelho da Flora do Brasil, Rio de Janeiro, primeira edição 2013. https://www.researchgate.net/profile/Marcelo_Menezes2/publication/273000307_Cactaceae/links/54f48fca0cf2f28c1361e233.pdf Oliveira DM, Ribeiro JGM, Faria LF, Reboita MS. Performance dos modelos climáticos do CMIP6 em simular a precipitação em subdomínios da América do Sul no período histórico. Revista Brasileira de Geografia Física. Revista Brasileira de Geografia Física. 2023;16(01):116–33. https://periodicos.ufpe.br/revistas/rbgfe/article/view/255100 . Paglia AP, Rezende DT, Koch I, Kortz AR, Donatti C, editors. 2012. Modelos de Distribuição de Espécie em Estratégia de Conservação da Biodiversidade para Adaptação Baseada no Ecossistema Frente a mudanças climáticas. Natureza & Conservação 10(2):231–234, December 2012 Copyright© 2012 ABECO Handling Editor: Paulo De Marco Jr. http://dx.doi.org/10.4322/natcon.2012.031 Pritchard DJ, Harrop SR. 2010. Ex situ conservation: the value of plant collection. BGCI BGjournal V7. n 1.https://www.bgci.org/resources/bgci-tools-and-resources/bgjournal/ Rangel TF, Loyola RD, editors. 2012. Modelos de nicho ecológico de rotulagem. Natureza & Conservação 10(2) 119–126. Revista Brasileira de Conservação da Natureza. Copyright© 2012 ABECO Handling Editor: Paulo De Marco Jr. http://dx.doi.org/10.4322/natcon.2012.030 Santos MA, Zaballa BB, Bomfim FS, Almeida TS, Andrade H, Gomes FM, Diele-Viegas LM. Navigating climate change from a lizard endemic to a semi-arid environment. J Arid Environ. 2024;226:105281. https://doi.org/10.1016/j.jaridenv.2024.105281 . Silva AC, Juvanhol RS, Miranda JR. 2023. Variabilidade espaço-temporal de ocorrência e recorrência de fogo no bioma Caatinga usando dados do sensor MODIS. Ciência Florestal, Santa Maria, 33(1), e 70195, pp. 1–23, 2023. 10.5902/1980509870195 . https://doi.org/10.5902/1980509870195. Tabarelli M, Leal LIR, Scarano FR, Silva JMC. 2018. Caatinga: legado, trajetória e desafios rumo à sustentabilidade. Cienc.cult.v70.n 4.São Paulo. http://dx.doi.org/10.21800/2317-66602018000400009%3E Taiz L, Zeiger E. 2006. Fisiología vegetal. Artmed, terceira edição, Califórnia, 2006. Tavares VC, Arruda AIRP, Silva DG. 2019. Desertificação, mudanças climáticas e seca no semiárido brasileiro: uma revisão bibliográfica. GEOSUL. 34,(70), 385–405, Florianópolis. https://doi.org/10.5007/2177-5230.2019v34n70p385 Tavares VC, Ramos LN. 2016. A desertificação em São João do Cariri (PB): Uma análise das vulnerabilidades (Desertification in São João do Cariri (PB): analyses of vulnerabilities). https://doi.org/10.5935/1984-2295.20160095 Velloso AL, Sampaio EVSB, Pareyn FGC. 2002. Ecorregiões propostas para o bioma da caatinga. Recife: Associação Plantas do Nordeste; Instituto de Conservação Ambiental The Nature Conservancy do Brasil, vigésima segunda edição, 2002. http://www.bibliotecaflorestal.ufv.br/bitstream/handle/123456789/5391/Ecorregioes-Propostas-para-o-bioma-da-caatinga.pdf?sequence=1&isAllowed=y ZAPPI D et al. 2011. Plano de ação nacional para a conservação das Cactaceae. Organizadores: Suelma Ribeiro Silva,Brasília. Instituto Chico Mendes de Conservação da Biodiversidade, Icmbio.Série Espécies Ameaçadas. https://www.researchgate.net/publication/305467448_Plano_de_Acao_Nacional_para_a_Conservacao_das_Cactaceas . (Accessed 10 February 2024). Zappi D, Taylor NP. 2023. Cactaceae in Flora e Funga do Brasil. Jardim Botânico do Rio de Janeiro. https://floradobrasil.jbrj.gov.br/FB1582 . (Accessed 10 October 2024). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 18 May, 2026 Reviews received at journal 17 May, 2026 Reviewers agreed at journal 15 May, 2026 Reviewers agreed at journal 14 May, 2026 Reviews received at journal 04 May, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers agreed at journal 17 Apr, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviewers invited by journal 13 Apr, 2026 Editor assigned by journal 02 Apr, 2026 Submission checks completed at journal 26 Mar, 2026 First submitted to journal 26 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9109033","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":625149050,"identity":"71a0c040-8d97-4fab-96ac-c839dd1d5c5a","order_by":0,"name":"Flávia dos Santos Bomfim","email":"","orcid":"","institution":"(Bio)Diversity in the Anthropocene Lab, Federal University of Bahia","correspondingAuthor":false,"prefix":"","firstName":"Flávia","middleName":"dos Santos","lastName":"Bomfim","suffix":""},{"id":625149052,"identity":"2164ce7e-b891-4126-b961-5fbfd279165b","order_by":1,"name":"Luisa Maria Diele-Viegas","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYBACPgbGBjCDDUxWADEzcwNeLWyoWs6AtDAS0oIMGNvAJAEtEsnNH34wbEvsY2+/+PHnvNpo/naglh8V2/BoSWyT7GG4ndjGc6ZYmnfb8dwZhxkbGHvO3MarhYGH4bYxm0ROgjTjtmO5DUAtzIxteLU0f/wD0iL/JvnnzznHcucToaVBGmiLHJsE+zEJ3oaa3A0EtfA8bJOWMQBq4clhs+Y5diB3I1DLQXx+4WdPf/zxTcVtHvn2449v/qipy513/vDBBz8qcGuBAAMQwQMiD4P5BwiohwH2B0CijkjFo2AUjIJRMJIAADSJVpvXEZuQAAAAAElFTkSuQmCC","orcid":"","institution":"(Bio)Diversity in the Anthropocene Lab, Federal University of Bahia","correspondingAuthor":true,"prefix":"","firstName":"Luisa","middleName":"Maria","lastName":"Diele-Viegas","suffix":""},{"id":625149055,"identity":"72188279-1c76-4e73-a1db-65a73f17be01","order_by":2,"name":"Thieres Santos-Almeida","email":"","orcid":"","institution":"Integrative Biodiversity Research Laboratory, Federal University of Sergipe","correspondingAuthor":false,"prefix":"","firstName":"Thieres","middleName":"","lastName":"Santos-Almeida","suffix":""},{"id":625149058,"identity":"3cfdb83b-7426-4dea-8c4e-dfb92b84597c","order_by":3,"name":"Bianca Barros Zaballa","email":"","orcid":"","institution":"(Bio)Diversity in the Anthropocene Lab, Federal University of Bahia","correspondingAuthor":false,"prefix":"","firstName":"Bianca","middleName":"Barros","lastName":"Zaballa","suffix":""},{"id":625149062,"identity":"31092187-6714-4468-bc8e-778388c27d58","order_by":4,"name":"Mateus Almeida Santos","email":"","orcid":"","institution":"(Bio)Diversity in the Anthropocene Lab, Federal University of Bahia","correspondingAuthor":false,"prefix":"","firstName":"Mateus","middleName":"Almeida","lastName":"Santos","suffix":""},{"id":625149066,"identity":"3beef021-cda4-437f-928c-b16bcb71d098","order_by":5,"name":"Hugo Andrade","email":"","orcid":"","institution":"Vertebrate Biology and Ecology Lab, Federal University of Sergipe","correspondingAuthor":false,"prefix":"","firstName":"Hugo","middleName":"","lastName":"Andrade","suffix":""},{"id":625149072,"identity":"577315c2-4762-48dc-8d7f-fa77091fb52e","order_by":6,"name":"Fernanda Melo Gomes","email":"","orcid":"","institution":"(Bio)Diversity in the Anthropocene Lab, Federal University of Bahia","correspondingAuthor":false,"prefix":"","firstName":"Fernanda","middleName":"Melo","lastName":"Gomes","suffix":""}],"badges":[],"createdAt":"2026-03-13 01:23:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9109033/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9109033/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107420991,"identity":"252de0a2-ec61-4f8a-ab82-cfb20443d532","added_by":"auto","created_at":"2026-04-21 10:30:20","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":422563,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of occurrence points \u003cem\u003eMelocactus pachycanthus \u003c/em\u003ein ecoregions\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9109033/v1/57406751ef1a279f7e8d2a85.jpeg"},{"id":107489201,"identity":"287787ba-e15f-4d1c-97b4-82073d4efefe","added_by":"auto","created_at":"2026-04-22 02:46:51","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":249059,"visible":true,"origin":"","legend":"\u003cp\u003eClimate projections for future scenarios\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9109033/v1/147be23993ec927607076e8e.jpeg"},{"id":107420992,"identity":"1e361634-633c-4fde-992b-af95562e37fa","added_by":"auto","created_at":"2026-04-21 10:30:21","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":288438,"visible":true,"origin":"","legend":"\u003cp\u003eLand-use and occupation maps\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9109033/v1/b515ff8593480ff8dbcd857a.jpeg"},{"id":107420993,"identity":"baac8df8-8459-4bd9-8a23-a5ec32de83d7","added_by":"auto","created_at":"2026-04-21 10:30:21","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":264025,"visible":true,"origin":"","legend":"\u003cp\u003eFire maps\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9109033/v1/159a433d313bc0fbb5ecd580.jpeg"},{"id":107490089,"identity":"067b33d1-58e4-4e60-891b-6d1dce3151f3","added_by":"auto","created_at":"2026-04-22 02:50:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1630222,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9109033/v1/3e705cfe-6837-4d28-8084-c38564875c3f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predicting the future of the endemic cactus Melocactus pachyacanthus in a semiarid landscape","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGiven the constant changes driven by human activities related to climate change, land use and occupation, and fires in the Caatinga biome, measuring the potential impacts of these anthropogenic activities on local biodiversity has become increasingly necessary (Silva, 2023)[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The Caatinga is a seasonally dry tropical forest located in a semi-arid tropical climate marked by high average annual temperatures and long periods of drought (Fernandes, 2018)[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Its specific edaphoclimatic characteristics have given it a rich diversity and endemic species (Taiz \u0026amp; Zeiger, 2006)[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. As one of the world\u0026rsquo;s most biodiverse semi-arid biomes (Tabarelli et al., 2018)[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], the Caatinga hosts a rich flora and fauna adapted to extreme conditions, making the conservation of endemic species essential for ecological resilience (Fernandes \u0026amp; Queiroz, 2018)[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRepresenting the endemic species of the Caatinga, \u003cem\u003eMelocactus pachyacanthus\u003c/em\u003e Buining \u0026amp; Brederoo is a cactus (Martinelli \u0026amp; Moraes, 2013)[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] with a succulent subshrub habit that is found on flat rocky outcrops (Zappi \u0026amp; Taylor, 2023)[39 ], where the exposed limestone is exploited (Zappi et al., 2011)[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The known uses of this species are ecological, in the foraging of animals typical of the Caatinga, with frugivorous and nectarivorous habits (Cavalcante et al., 2013)[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], mystical-religious, and economic as an ornamental plant (Martinelli \u0026amp; Moraes, 2013)[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Slow-growing, this species is quite vulnerable to environmental disturbances, including increases in temperature and loss of habitat quality during its initial stages of development (Cavalcante et al., 2013)[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis vulnerability is quite worrying, given that projections of future climate change for the Caatinga biome are pessimistic and that frequent emissions of greenhouse gases (GHGs) are expected. Reductions in precipitation and increases in annual temperature tend to desertify this region (Tavares \u0026amp; Ramos, 2016; Tavares et al., 2019)[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Soil exploitation for agricultural purposes is another factor that significantly impacts this biome (Martinelli \u0026amp; Moraes, 2013)[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Intensive farming practices are among the main factors responsible for converting areas of native vegetation into monocultures and pasture plantations (Martinelli \u0026amp; Moraes, 2013)[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In this context, illegal deforestation for land use and occupation is one of the causes of the increasing expansion of fire (Silva et al., 2023)[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In addition, fire outbreaks have been increasing significantly in the Caatinga and may be directly associated with climate change; they can impoverish the nutritional attributes of local soils (Diele-Viegas et al., 2022; Boff, 2018; Macedo, 2023)[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEcological Niche Modeling (ENM) is an approach widely used by ecologists to predict species' distribution and occurrence patterns (Barbet-Massin, 2012)[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Using ENM, it is possible to generate maps of species' environmental suitability based on their realized niche and project these models for different environmental scenarios (Barbet-Massin, 2012; Santos et al., 2024)[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Identifying this suitability is essential to defining the potential areas of occurrence of the species to be restored and protected.\u003c/p\u003e \u003cp\u003eThus, to conserve \u003cem\u003eMelocactus pachyacanthus\u003c/em\u003e, an \u0026ldquo;Endangered\u0026rdquo; of extinction (EP) in the wild (Brasil, 2022) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], we measure the impacts of human activities related to climate change, land use, and fire on its occurrence. The specific objectives were I. to map the potential areas of bioclimatic suitability for the occurrence of the species \u003cem\u003eM. pachyacanthus\u003c/em\u003e in the current scenario; II. to predict the potential bioclimatic suitability for the occurrence of the species \u003cem\u003eM. pachyacanthus\u003c/em\u003e considering optimistic future scenarios about GHG emissions, and, III. to assess the impact of land use and occupation and fire on the potential occurrence of \u003cem\u003eM. pachyacanthus\u003c/em\u003e in the current scenario.\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study area\u003c/h2\u003e \u003cp\u003eThe study area is delineated around the three ecoregions of Chapada Diamantina in Bahia, Brazil: Southern Sertaneja Depression (SSD), Chapada Diamantina Complex (CDC), and S\u0026atilde;o Francisco Dunes (SFD). The selection considered the locations of known occurrence points for \u003cem\u003eMelocactus pachyacanthus\u003c/em\u003e (Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(Fig.\u0026nbsp;1 Location of occurrence points \u003cem\u003eMelocactus pachycanthus\u003c/em\u003e in ecoregions)\u003c/p\u003e \u003cp\u003eThe DSM ecoregion covers an area of ​​373,900 km\u0026sup2;, except the Campo Maior Complex ecoregion, which borders all the ecoregions of Chapada. It encompasses the CDC and the SFD. Its vegetation is more typical of the semiarid to arid Northeast, and the predominant climate is hot and semiarid (Velloso et al., 2002)[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe CDC covers an area of ​​50,610 km\u0026sup2;. Located in the center-south of the Caatinga, it is considered the biome's highest region. The extensive reworking of these areas has created deep valleys. The climate of this ecoregion ranges from hot to tropical in the western part and tropical to semiarid in the eastern part (Velloso et al., 2002)[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe SFD area is part of the central-western biome, covering ​​36,170 km\u0026sup2;. With a scorching, semiarid climate, this ecoregion has high soil temperatures and is characterized by extensive wind farms with caatinga vegetation grouped into thickets (Velloso et al., 2002)[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Geographical space\u003c/h2\u003e \u003cp\u003eThe species' occurrences were collected from the Global Biodiversity Information Facility (GBIF, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gbif.org/\u003c/span\u003e\u003cspan address=\"https://www.gbif.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). A total of 33 occurrences were found. The occurrence points were filtered to remove points in the ocean, municipal centroids, and duplicate occurrences. The remaining 16 points were used to perform correlative modeling with bioclimatic variables (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSpatial data on the occurrence of the species \u003cem\u003eMelocactus pachycanthus\u003c/em\u003e in the ecoregions and municipalities of Bahia.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eSpatial Location of Occurrence points\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEcorregi\u0026otilde;es\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLat\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLong\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMunicipality\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouthern Sertaneja Depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-11.01333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-41.676246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS\u0026atilde;o Gabriel\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouthern Sertaneja Depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-10.883588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-41.160171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOurol\u0026acirc;ndia\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChapada Diamantina complex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-11.525812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-41.182406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMorro do Chap\u0026eacute;u\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouthern Sertaneja Depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-9.594404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-40.315713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJuazeiro\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouthern Sertaneja Depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-11.305921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-41.838276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIrec\u0026ecirc;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouthern Sertaneja Depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-11.681666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-41.673889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCanarana\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChapada Diamantina complex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-11.089444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-41.085278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVarz\u0026eacute;a Nova\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouthern Sertaneja Depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-9.977638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-40.884175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCampo Formoso\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouthern Sertaneja Depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-11.414721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-41.406111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAm\u0026eacute;rica Dourada\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChapada Diamantina complex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-11.013611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-40.407778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSa\u0026uacute;de\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChapada Diamantina complex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-11.41361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-40.407778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMiguel Calmon\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouthern Sertaneja Depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-11.50000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-41.70000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouthern Sertaneja Depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-10.046919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-41.126198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCampo Formoso\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouthern Sertaneja Depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-11.3000000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-41.420000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAm\u0026eacute;rica Dourada\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouthern Sertaneja Depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-10.153381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-40.732417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCampo Formoso\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouthern Sertaneja Depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-10.856956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-41.660072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS\u0026atilde;o Gabriel\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Environmental Space\u003c/h2\u003e \u003cp\u003eThe 19 bioclimatic variables of WorldClim 2.0 were collected with 2.5\u0026rsquo; arc-minutes (~\u0026thinsp;5 x 5km) (Fick \u0026amp; Hijmans, 2017)[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. We cropped the variables to the study area and removed bioclimatic variables 8, 9, 18, and 19. These variables combine precipitation and temperature data and present anomalies as unrealistic spatial discontinuities (Escobar et al., 2014)[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. To reduce collinearity, we removed correlated variables (r\u0026thinsp;\u0026gt;\u0026thinsp;0.7) using Pearson\u0026rsquo;s Correlation (Dormann et al., 2013)[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The uncorrelated bioclimatic variables selected for the model were BIO3- Isothermality (BIO2/BIO7) (\u0026times;100), BIO10- Average temperature of the warmest quarter, and BIO12 - Annual precipitation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Ecological niche model\u003c/h2\u003e \u003cp\u003eWe used three algorithms in the modeling process: Bioclim (Rangel \u0026amp; Loyola, 2012)[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], Support Vector Machine (SVM) (Rangel \u0026amp; Loyola, 2012)[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], and Generalized Linear Model (GLM) (McCullagh \u0026amp; Nelder, 2019)[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. For each algorithm, we ran 10 bootstrap replicates, where 70% of the occurrences were used for training and 30% for testing. During model testing, we generated random pseudo-absences: 10,000 points for the GLM and 160 points (10 times the number of presences) for the Bioclim and SVM models (Barbet-Massin et al., 2012)[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The models were evaluated using the Area Under the Curve (AUC/ROC; Fielding \u0026amp; Bell, 1997). This metric ranges from 0 to 1, where values below 0.5 indicate random predictions and values above 0.75 indicate good predictions. Based on AUC, we selected the best replicates for each algorithm and combined them into an ensemble model using an average weighted by AUC 0,75 (Diniz-Filho et al., 2010)[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Model projection and overlay\u003c/h2\u003e \u003cp\u003eWe project the consensus model for the years 2041\u0026ndash;2060 (hereafter referred to as 2050) using the shared socioeconomic pathways (SSP) scenario SSP2 45 and SSPS5 85 (IPCC, 2021)[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The SSP2 45 scenario predicts intermediate greenhouse gas emissions that stabilize in 2050, resulting in a 2.0\u0026deg;C increase, while the SSP5 85 scenario predicts very high greenhouse gas emissions that triple from 2075 onwards, with a 2.4\u0026deg;C increase in 2050 (IPCC, 2021)[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Future climate projections were performed with the ensemble (average) of three global circulation models (GCMs) from the Coupled Model Intercomparison Project Phase Six (CMIP6) (Eyring et al., 2016)[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. We selected the best performing GCMS in South America (Oliveira et al., 2023)[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]: (1) Beijing Climate Center model (BCC-CSM2-MR); (2) Institute Pierre Simon Laplace model (IPSL-CM6A-LR); and (3) Model for Interdisciplinary Research on Climate (MIROC6).\u003c/p\u003e \u003cp\u003eUsing the model generated to date, we estimated the suitability threshold that maximizes the True Skill Statistic (TSS; Andrade et al., 2020)[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] validation metric. The threshold was used to transform the present models and the projections for the future into binary maps (presence/absence). To understand the impact of land use and fires on the species' habitat, we separately overlapped the binarized models on the land use and fire maps. Thematic maps from collection 8 of the MapBiomas project (Time series 1985\u0026ndash;2024), (Jr-Souza et al., 2020)[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and fire maps (MapBiomas, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://plataforma.brasil.mapbiomas.org/monitor-do-fogo\u003c/span\u003e\u003cspan address=\"https://plataforma.brasil.mapbiomas.org/monitor-do-fogo\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) were used to overlay the present models.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Mapping the bioclimatic suitability of the species \u003cem\u003eMelocactus pachyacanthus\u003c/em\u003e for the current scenario\u003c/h2\u003e \u003cp\u003eIn the current scenario, the MNE for the species \u003cem\u003eM. pachyacanthus\u003c/em\u003e predicted a bioclimatic suitability area of approximately 16.278,90 km\u0026sup2; across the three ecoregions of interest. In the SSD ecoregion, the area was approximately 41.300,93 km\u0026sup2;, followed by the CDC with 16.278,90 km\u0026sup2; and the SFD region with 54.136,20 km\u0026sup2; (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCalculation of areas of climatic suitability without interference from the variables fire and land use and occupation, and calculation of suitability with interference from the variables fire and land use and occupation, considering the years 1995, 2005, 2015, and 2022 for each variable. CCD - Chapada Diamantina Complex (CCD); DSF - S\u0026atilde;o Francisco Dunes (DSF), and DSM - Southern Sertaneja Depression.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eType map\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eEcorregions\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCCD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDSF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDSM\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSdm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16278.90278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54.136204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41300.93056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16278.90278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54.136204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41300.93056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16278.90278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54.136204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41300.93056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16173.88851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54.136204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41279.86483\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16154.33523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54.136204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41278.30835\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13130.16218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54.136204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26579.2041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13370.66189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54.136204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26239.04563\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12375.45984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54.136204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23976.94566\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12554.6199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54.136204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24902.58022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThis model showed an increase in suitability variation from 0.6 to 0.8. In the SSD ecoregion, where 81.25% of the species' known occurrence points are located, a larger area of ​​bioclimatic suitability was observed, with a value close to 0.8. In the CDC ecoregion, where 18.75% of the species' known occurrence points are located, suitability was closer to 0.6, a value higher than the DSF. In this ecoregion, where there are no records of the species' occurrence, suitability was below 0.6.\u003c/p\u003e \u003cp\u003e \u003cb\u003e3.2 Prediction of the bioclimatic suitability of the species\u003c/b\u003e \u003cb\u003eMelocactus pachyacanthus\u003c/b\u003e \u003cb\u003efor the most and least optimistic future climate change scenarios\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn climate projections for the most optimistic future scenario, the model predicted suitability for greenhouse gas (GHG) emissions ranging from 0.6 to 0.8. In the SSD ecoregion, the suitability percentage ranged from 0.7 to 0.75. In the CDC ecoregion, where the suitability area expanded, the observed value was close to 0.6, higher than SFD, which was below 0.6 (Fig.\u0026nbsp;2). In the current scenario, a reduction in the suitability area was observed, with values close to 0.8 in SSD.\u003c/p\u003e \u003cp\u003eClimate projections for the less optimistic future scenario of greenhouse gas emissions also predicted a range of 0.6 to 0.8 in suitability. In the DSM ecoregion, suitability ranged from 0.6 to 0.8. In the CDC ecoregion, suitability was closer to 0.6; in SFD, it was below 0.5 (Fig.\u0026nbsp;2). On this map, there was an even greater reduction in suitability, with values near 0.8 in DSF. However, an expansion of areas of suitability with percentages close to 0.7 was observed in CDC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(Fig.\u0026nbsp;2 Climate projections for future scenarios)\u003c/p\u003e \u003cp\u003e \u003cb\u003e3.3 Interference of land use and occupation on the occurrence of the species\u003c/b\u003e \u003cb\u003eMelocactus pachyacanthus\u003c/b\u003e \u003cb\u003ein the present bioclimatic scenario\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe land-use and occupation maps indicated overlapping areas with altered vegetation cover and possible bioclimatic suitability for \u003cem\u003eMelocactus pachyacanthus\u003c/em\u003e (Fig.\u0026nbsp;3). These altered areas also overlapped with the niche occupied by some populations. For the SSD ecoregion, the percentage of overlapping decreased areas in bioclimatic suitability in 1995 was approximately 35.65%.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(Fig.\u0026nbsp;3 Land-use and occupation maps)\u003c/p\u003e \u003cp\u003eFollowing this, the land use maps for the years (2005, 2015, and 2022) showed an overlap of approximately 36.47%, 41.95%, and 39.70%, respectively, with the area of ​​bioclimatic suitability predicted in the present model.\u003c/p\u003e \u003cp\u003eAt the CDC, the overlap between anthropized areas mapped in 1995 and bioclimatic suitability areas was approximately 19.34%. In 2005, the overlap was approximately 17.87%. In 2015, it was approximately 23.97%; in 2022, approximately 22.88%.\u003c/p\u003e \u003cp\u003eThe map did not predict overlap between anthropized areas and ​​bioclimatic suitability for the SFD ecoregion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Fire interference in the occurrence of \u003cem\u003eMelocactus pachyacanthus\u003c/em\u003e in the present bioclimatic scenario\u003c/h2\u003e \u003cp\u003eIn the SSD ecoregion, the fire maps for 1995 and 2005 did not identify any overlap between fires and potential areas of the species' bioclimatic suitability. However, for 2015 and 2022, the overlap percentages were 0.051% and 0.054%, respectively (Fig.\u0026nbsp;4).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(Fig.\u0026nbsp;4 Fire maps)\u003c/p\u003e \u003cp\u003eRegarding the CDC ecoregion, the fire maps did not identify any fire outbreaks in the species' potential areas of occurrence during 1995\u0026ndash;2005. For 2015 and 2022, overlaps of 0.64% and 0.77% in fire outbreaks were identified within the potential bioclimatic suitability areas (Fig.\u0026nbsp;3).\u003c/p\u003e \u003cp\u003eThe DSF results for fire, which overlap with areas of present bioclimatic suitability, for the periods 1995, 2005, 2015, and 2022, were negative. The fire map showed no overlap of the burned areas with the occurrence points of the species under study (Fig.\u0026nbsp;3).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussions","content":"\u003cp\u003eThe need for more favorable temperatures in \u003cem\u003eMelocactus Pachyacanthus\u003c/em\u003e is related to the species' low tolerance to disturbance in the early stages of its development and its slow growth (Cavalcante et al., 2013)[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The ecoregion with the greatest potential for bioclimatic suitability for the species' occurrence was the SSD. The species' chances of survival are possibly greater in this location (Zappi et al., 2011)[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis map also showed that some populations occur in areas with lower bioclimatic suitability in the current scenario. It is possible that the climate experienced by the species in the CDC ecoregion is being influenced by its local microclimate (Kearney et al., 2014) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In this region, soil and climate conditions are somewhat different from those in the other regions due to the formation of islands through isolation processes (Velloso et al., 2002)[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor the SSD ecoregion, the intensification of climate change (Cavalcante et al., 2013)[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] is reflected in the reduction of the bioclimatic suitability predicted for this location in future scenarios (Pritchard \u0026amp; Harrot, 2010)[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. With this loss of habitat quality, maintaining \u003cem\u003eMelocactus pachyacanthus\u003c/em\u003e in these locations would require adaptation to this new scenario (Pritchard \u0026amp; Harrot, 2010)[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. However, this is unlikely given the short time over which climate change has occurred (Pritchard \u0026amp; Harrot, 2010)[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In this bioclimatic context, migration or dispersal could be an alternative to prevent species extinction.\u003c/p\u003e \u003cp\u003eEvaluating the influence of land use and occupation is essential to consider migration as a survival measure, as it strongly shapes species' conservation processes (Pritchard \u0026amp; Harrop, 2010)[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Currently, agricultural practices are among the leading causes of habitat destruction and fragmentation for this species (Tabarelli et al., 2018)[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], making it less viable for populations to colonize new areas (Pritchard \u0026amp; Harrop, 2010)[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The land-use and occupation map shows the growth of degraded regions over the years (1995, 2005, 2012, and 2022). This growth was observed in the species' fundamental niche and in the areas where the species occurs. Although they still occur in these degraded areas, the species' populations are declining (Martinelli \u0026amp; Moraes, 2013)[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFire has also been increasingly present in the Caatinga Biome (Silva et al., 2023; Jesus et al., 2020)[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The maps showing the impacts of fire showed an increase in fire outbreaks in areas of climatic suitability predicted for the current scenario. Still, the percentage values indicating the presence of these outbreaks are insignificant. Despite this, fire may pose an imminent risk to the species due to uncontrolled burning practices that spread fire to surrounding areas, damaging soil quality and vegetation (Baker et al., 2013; Luz et al., 2023)[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBased on these results, it is possible to begin considering measures to minimize the impacts of anthropogenic activities on the occurrence of \u003cem\u003eMelocactus pachyacanthus\u003c/em\u003e (Zappi et al., 2011)[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. One of them would be the creation of recovery plans for degraded areas in potential bioclimatic suitability areas. Since bioclimatic suitability is expected to decline in future scenarios, restoring degraded, climatically suitable areas would be one possible path.\u003c/p\u003e \u003cp\u003eIn conjunction with this action, creating Protected Areas (PAs) of Integral Protection (IP) that guarantee the protection of these areas is fundamental to this process, primarily because PAs are legally protected areas established by law to preserve biodiversity (Boff, 2018; Brasil, 2000) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The prediction of the potential regions of environmental suitability also contributes positively to a more effective management plan for the conservation of the species \u003cem\u003eM. pachyacanthus\u003c/em\u003e (Rangel \u0026amp; Loyola, 2012)[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] by mapping the areas that have the most favorable environmental conditions for the survival of this species (Paglia et al., 2012)[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. It is also essential that the management plan for this PA addresses these needs in its preparation (Rangel \u0026amp; Loyola, 2012)[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], so that these areas receive the appropriate categorization of their uses (Brasil, 2000)[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn conclusion, the persistence of \u003cem\u003eMelocactus pachyacanthus\u003c/em\u003e is closely tied to the interplay between its ecological constraints and rapidly changing environmental conditions. Although the SSD ecoregion currently represents the most favorable area for the species, future climate scenarios indicate a decline in suitability, while populations already occurring in less suitable regions may depend on localized microclimatic conditions for survival. Limited dispersal capacity, combined with increasing habitat degradation driven by agricultural expansion and, to a lesser extent, fire, further restricts the species\u0026rsquo; ability to track suitable environments. Given these constraints, conservation efforts should prioritize restoring degraded yet climatically suitable areas and establishing well-managed protected areas to safeguard remaining populations. Integrating bioclimatic predictions with land-use planning will be essential to enhance the effectiveness of conservation strategies and mitigate the impacts of ongoing environmental change on this vulnerable species.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics, Consent to Participate, and Consent to Publish\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding statement\u003c/h2\u003e \u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eFl\u0026aacute;via dos Santos Bomfim: Methodology, Research, Formal analysis, Data curation, Writing original draft, Writing final document. Luisa Maria Diele-Viegas: Methodology, Research, Formal analysis, Data curation, Conceptualization, Writing original draft, Writing final document, Review \u0026amp; editing. Thieres Santos Almeida: Methodology, Formal analysis, Data curation, Writing original draft, Review \u0026amp; editing. Bianca Barros Zaballa: Research, Methodology, Data curation, Writing original draft. Mateus Almeida dos Santos: Methodology, Data curation, Writing original draft, Review \u0026amp; editing. Hugo Andrade: Data curation, Writing original draft, Review \u0026amp; editing. Fernanda Melo Gomes: Methodology, Research, Conceptualization, Writing original draft.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe are grateful to colleagues from the BioDivA laboratory for their vital contributions to previous versions of this manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003ell data supporting the findings of this study are available within the paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAndrade AFA, Velazco SJE, JUNIOR PDM.. ENMTML: An R package for a straightforward construction of complex ecological niche models. 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(Accessed 10 October 2024).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e "}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Ecology](https://link.springer.com/journal/44396)","snPcode":"44396","submissionUrl":"https://submission.nature.com/new-submission/44396/3","title":"Discover Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Anthropic activities, Caatinga, Cactus, Ecological Niche Modeling, Environmental suitability","lastPublishedDoi":"10.21203/rs.3.rs-9109033/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9109033/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAnthropogenic activities have increasingly pressured biodiversity worldwide. The cactus \u003cem\u003eMelocactus pachyacanthus\u003c/em\u003e, an endemic and endangered species of Brazil\u0026acute;s Caatinga biome, is particularly vulnerable. This study assessed the effects of climate change, land use, and fire on the distribution of \u003cem\u003eM. pachyacanthus\u003c/em\u003e across three ecoregions in the Chapada Diamantina region, Bahia, Brazil. We employed Ecological Niche Modeling (ENM) to evaluate current climatic suitability and to project future scenarios, overlaying ENM outputs with land-use and fire data. The current model identified a concentration of suitable habitat in the Southern Sertaneja Depression. At the same time, future projections reveal a reduction in this region and an expansion toward the Chapada Diamantina Complex. Although land use significantly contributed to habitat loss, fire had a lesser impact. Our findings underscore the importance of ENM as a tool for identifying priority conservation areas and guiding the creation of Environmental Protection Areas. These results highlight the urgent need for conservation policies to safeguard endemic species like \u003cem\u003eM. pachyacanthus\u003c/em\u003e amid ongoing environmental changes. Our research provides insights into how climate and human activities reshape species distributions in semi-arid regions worldwide.\u003c/p\u003e","manuscriptTitle":"Predicting the future of the endemic cactus Melocactus pachyacanthus in a semiarid landscape","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-21 10:30:16","doi":"10.21203/rs.3.rs-9109033/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-18T11:35:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-17T21:42:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"75792449512834369652498468141034580346","date":"2026-05-15T05:51:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"221055696528404367762375932731881336307","date":"2026-05-14T16:29:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-04T05:56:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"45249081588283385244494940886726479317","date":"2026-04-23T15:19:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"243849874991991616685401186369130188281","date":"2026-04-17T17:56:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"232951086603444714447058153274761851860","date":"2026-04-13T19:05:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-13T17:35:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-02T11:40:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-26T15:52:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Ecology","date":"2026-03-26T15:48:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Ecology](https://link.springer.com/journal/44396)","snPcode":"44396","submissionUrl":"https://submission.nature.com/new-submission/44396/3","title":"Discover Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"76e04c0d-9337-4d7a-b28f-a29fc2203b6a","owner":[],"postedDate":"April 21st, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-18T11:35:15+00:00","index":66,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-17T21:42:12+00:00","index":63,"fulltext":""},{"type":"reviewerAgreed","content":"75792449512834369652498468141034580346","date":"2026-05-15T05:51:27+00:00","index":61,"fulltext":""},{"type":"reviewerAgreed","content":"221055696528404367762375932731881336307","date":"2026-05-14T16:29:15+00:00","index":59,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-04T05:56:13+00:00","index":25,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-21T10:30:16+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-21 10:30:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9109033","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9109033","identity":"rs-9109033","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}