Climate change may increase the suitable habitats for invasive freshwater Cichlids in a Neotropical basin

Climate change may increase the suitable habitats for invasive freshwater Cichlids in a Neotropical basin | 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 Article Climate change may increase the suitable habitats for invasive freshwater Cichlids in a Neotropical basin Cristian Martínez-González, Lúcia Aparecida de Fatima Mateus, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5050398/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Climate change is considered a driver for the spread of invasive alien species (IAS); nevertheless, research assessing this link remains limited. Recognizing suitable habitats where IAS have been introduced is crucial for biodiversity conservation and ecosystem management. Here, we integrated online, museum, and laboratory occurrence databases with local ecological knowledge (LEK) on IAS fishes from semi-structured interviews and georeferenced Instagram posts from traditional and recreational fishers to model the habitat-suitability of three voracious IAS Cichlids introduced in the Brazilian part of the Upper Paraguay River Basin (the Pantanal wetland and its tributaries). Our goal was to locate areas (regions, sub-basins, and reservoirs) most at risk from the spread of these IAS fishes in the basin. Our findings reveal extensive suitable habitats for these IAS fishes throughout the study basin, currently covering half of the Pantanal wetland and up to 90% of the upstream sub-basins. Under future climate scenarios, these suitable habitats are projected to expand further, encompassing 85% of the Pantanal floodplain - one of the most fish-rich basins in the Neotropical region (~ 300 spp). These findings underscore a potential IAS Cichlid range expansion in the Pantanal floodplains in the upcoming decades. Our study emphasizes the value of integrating Ecological Niche Models (ENMs) with Citizen Science data to identify high-risk areas during early invasion stages, inform preventive strategies, and support conservation efforts to mitigate the impacts of IAS on native biodiversity. Earth and environmental sciences/Environmental social sciences/Climate change impacts Biological sciences/Ecology/Ecological modelling Biological sciences/Ecology/Freshwater ecology Biological sciences/Ecology/Invasive species Social Networks Blue Peacock Bass Cichla piquiti Yellow Peacock Bass Cichla kelberi Nile Tilapia Oreochromis niloticus Figures Figure 1 Figure 2 Figure 3 Introduction Climate change and biological invasions pose significant threats to global biodiversity and ecosystem services 1 – 3 . Human activities have introduced over 37,000 invasive alien species (IAS) worldwide, contributing to 60% of documented global plant and animal extinctions 4 , 5 . In addition, IAS impacts food security and human health through the spread of pests and diseases, often exacerbated by changes in climate patterns, resulting in an estimated global economic cost of USD 423 billion annually 6 , 7 . The role of climate change as a key driver of the spread of IAS is now widely recognized; rising temperatures and changes in precipitation have altered ecosystems and created novel suitable habitats for establishing, surviving, and spreading IAS populations 8 , 9 . This pattern is expected to intensify, with important short-term impacts projected by 2050 10,11 . Given their limited extent, strong connectivity dependence, restricted species dispersal, and direct dependence on climate stability, freshwaters are among the most vulnerable ecosystems to climate change and IAS 12 , 13 . Furthermore, freshwater species face the highest extinction rates compared to their terrestrial and marine counterparts, with a quarter of freshwater fauna at significant risk of extinction 14 , 15 . In the Americas, particularly in Latin America, freshwater habitats face severe hydrological alterations, declining water quality, and ecosystem degradation beyond the impacts of IAS 16 , 17 . Fish species, the most commonly documented IAS in rivers, lakes, and wetlands, have rapidly spread through aquaculture, posing a major concern in freshwater ecosystems 18 , 19 . Several IAS fish species support profitable fisheries despite ecological risks, particularly in the Neotropical region 20 . However, in developing countries like Brazil, managing IAS fish species is hindered by data gaps, socio-economic interests, and limited state intervention 21 . The Upper Paraguay River Basin (UPRB) is situated at South America's geodetic center and spans Brazil, Bolivia, and Paraguay. It encompasses the extensive Pantanal wetland, featuring intricate floodplains nourished by upstream tributaries of the Paraguay River 22 . The UPRB is particularly affected by numerous hydropower plants and reservoirs, with more planned developments 23 . Over the past seven decades, the construction of dams has disrupted freshwater ecosystems by altering sediment flow and water chemistry composition, also obstructing fish migration routes, and altering fish assemblages 24 . Furthermore, hydroelectric reservoirs have provided favorable conditions for the proliferation of IAS fishes 25 , 26 . The ecological consequences associated with dam construction, including the proliferation of IAS in reservoirs, should be considered 27 . Cichlids are the best example of potential IAS fish being established in the UPRB. Since 1982, escapes and introductions of the Blue Peacock Bass, Cichla piquiti Kullander & Ferreira 2006, a Cichlid from the Araguaia-Tocantins basin, have been reported in the Brazilian part of the UPRB (hereinafter Br-UPRB) 28 , 29 . This species has progressively expanded its invasion range in the Pantanal wetland and some sub-basins like São Lourenço-Vermelho, Miranda, and Apa-Perdido 30 . Moreover, the finding of a second Araguaia-Tocantins Cichlid, the Yellow Peacock Bass, Cichla kelberi Kullander & Ferreira 2006 in lotic ecosystems, has raised concerns regarding these two species within the basin 29 . An additional Cichlid with great invasive potential, the Nile Tilapia, has been reported in the basin 31 , 32 . Oreochromis niloticus Linnaeus 1758 is cataloged as IAS in several tropical regions and is considered very dangerous for native fauna 33 . The Nile Tilapia has been extensively cultured recently in local reservoirs along Mato Grosso, and Mato Grosso do Sul states (Br-UPRB) 31 , 34 . Nevertheless, frequent escapes from these facilities have increased its presence in river catches by local artisanal and recreational fishers (pers. comm). While the short-term effects of IAS Cichlids proliferation are well documented, such as their role as aggressive predators and competitors that disrupt trophic webs, the long-term ecological impacts remain uncertain 27 , 35 – 37 . A set of correlative techniques analysis called Ecological Niche Models (ENMs) 38 has experienced a substantial increase in the last few years 39 , 40 . This increase is due in part to the recent availability of high-quality data on species occurrences and specific environmental variables and its ability to inform the magnitude of potential climate change impacts, to study past, present, and future distributions in biogeography, predict IAS invasion spread patterns by identifying the most suitable areas in the early stages of invasion and thus prioritize efforts in critical regions 41 – 43 . For IAS, the ENMs assess how an invasive species utilizes the available environmental space in its native range versus its invasive range 49 . This comparison helps to understand where invasive species might expand based on their known favored ecological conditions, predicting invasion risks and managing vulnerable ecosystems 41 . The Shared Socio-economic Pathways (SSPs): SSP1 1.9–2.6, SSP2 4.5, SSP3 7.0, and SSP5 8.5, consist of five narratives outlining potential changes in demographics, economics, technology, society, governance, and the environment over this century 50 . These pathways serve as a tool for comparing ENMs in scenarios of temperature increase ranging from 1.5°C in the near future (2021–2040) to 3.3 to 5.7°C in the distant future (2081–2100), assuming the highest greenhouse gas emissions scenario persists 51 . Attempts like Citizen Science bring scientists closer to local ecological knowledge (LEK) of IAS, finding a cost-effective approach involving community members in collecting data and understanding IAS impacts in human fishing communities 52 – 54 . The Citizen Science data has been incipient, nevertheless effectively implemented in ENMs and IAS management 55 – 57 . Estimating the potential distribution of the IAS fishes in the Br-UPRB is essential to support management actions, especially considering the predicted and ongoing climate change that threatens native species 58 . Using an ensemble of ENMs: Generalized Linear Models (GLM), Random Forest (RF), and Maximum Entropy (MaxEnt), we modeled the current and future (2050 - SSP2 4.5) suitable habitats for C. piquiti , C. kelberi , and O. niloticus across their native and introduced (Br-UPRB) ranges. The models incorporated hydroclimatic and topographic predictors with IAS occurrences from online, museum, and laboratory occurrence databases complemented by semi-structured interviews and georeferenced Instagram posts from traditional and recreational fishers. This study aimed to (i) identify the most suitable IAS habitats within Br-UPRB reservoirs and key regions, including wetland complexes in low-elevation areas (Pantanal), tributary sub-basins on the Brazilian plateau, and (ii) evaluate whether these suitable areas may expand under the 2050 - SSP2 4.5 climate scenario. Given the Nile and Araguaia -Tocantins basins' origin of the IAS Cichlids introduced in the Br-UPRB, we expect that climate change could expand the climatically suitable areas. Specifically, we suggest combining Pantanal wetland and reservoir conditions ( e.g. , stable water levels, reduced flow velocity, clear waters) and projected temperature increases, making favorable environments for these IAS spread soon along the Br-UPRB. This hypothesis is supported by evidence that lentic ecosystems and reservoirs often facilitate the dispersal of Ciclhid invasive species 59 , 60 and that rising temperatures can increase the range of neotropical IAS fishes 8 . Results Environmental variables selection and contribution to ENMs The two sets of climatic predictor variables selected derived from EarthEnv and WorldClim2 61,62 , (1) for Br-UPRB + Araguaia - Tocantins basins (for modeling the two Cichla spp. habitat suitability) and (2) for Br-UPRB + Nile basins (for modeling O. niloticus habitat suitability) are accessible along topographic predictors (elevation, flow, and slope) in the Supplementary Material: SM-1 and SM-2, all with Variance Inflation Factor (VIF) value < 5, to minimize multicollinearity and improve interpretability of the models. In the ensemble of ENMs, precipitation seasonality was the most important variable for C. kelberi and C. piquiti in both current and future 2050 - SSP2 4.5 scenarios. The mean diurnal range (mean of monthly (max temp - min temp)) was also important for O. niloticus and C. piquiti , while temperature seasonality stood out for C. kelberi . The importance of all ENMs variables used is detailed in Supplementary Material SM-3. Models' calibration, validation & forecasting performance The individual models selected to build the ENMs ensemble showed high reliability, with Sensitivity and Specificity consistently above 95%, validating the predictive capacity to identify suitable habitats (Table 1 ). The evaluation of true skill statistic (TSS) and area-under-the-curve (AUC) metrics also confirmed the best performance of the models selected, with a mean calibration AUC ≥ 0.99, validation AUC ≥ 0.95 (except for C. kelberi − 2050 validation AUC ≥ 0.74), calibration TSS ≥ 0.93, and validation TSS ≥ 0.81 for all IAS and climatic scenarios, indicate that the models are well-suited for projecting current and future (2050) habitats for the IAS Cichlids in the Br-UPRB and their native ranges (Table 1 ). The TTS and AUC values for each ENMs algorithm: GLM, RF, and MaxEnt, are in Supplementary Material SM-4. Table 1 Average performance of ecological niche models (ENMs) of IAS (Invasive Alien Species) Cichlids introduced in the Brazilian part of the Upper Paraguay River Basin (Br-UPRB) in current and future 2050 - SSP2 4.5 climate scenarios. Assessing the Sensitivity and Specificity of the models through calibration and validation metrics: AUC: area-under-the-curve and TSS: True Skill Statistic. Species Scenario Metric Sensitivity % Specificity % Calibration Validation Oreochromis niloticus Current TSS 97.20 95.83 0.93 0.86 AUC 97.37 95.86 0.99 0.96 2050 - SSP2 4.5 TSS 97.50 95.15 0.93 0.86 AUC 98.50 94.41 0.99 0.96 Cichla kelberi Current TSS 96.08 97.40 0.94 0.81 AUC 96.18 97.43 0.98 0.94 2050 - SSP2 4.5 TSS 95.20 97.67 0.93 0.86 AUC 95.20 97.78 0.99 0.75 Cichla piquiti Current TSS 95.17 95.00 0.90 0.84 AUC 95.65 94.73 0.99 0.94 2050 - SSP2 4.5 TSS 95.80 97.85 0.94 0.84 AUC 95.99 97.92 0.99 0.97 IAS suitable habitats in the Br-UPRB. The current ENMs ensembles reveal a high proportion of suitable habitats for all IAS Cichlids in the basin; the overlapped suitable habitats in binary projections (0:unsuitable/1:suitable for one or more species) of O. niloticus , C. piquiti , and C. kelberi are covering 40% the freshwater ecosystems in the basin, being C. piquiti the IAS that has the most proportion of suitable habitats available for its spread in both scenarios current and 2050 SSP2 4.5 (Table 2 ). Given the current environmental conditions of the Br-UPRB, the IAS Cichlids in the basin could use ≈ 22,853 km² of suitable lotic and lentic freshwater ecosystems, i.e. , 55% of the Pantanal, 30% of the sub-basin in the Brazilian plateau (Table 2 ). Collectively, the IAS Cichlids could exhibit an increase of 20% in suitable habitats of the Br-UPRB freshwater ecosystems (+ 10,407 km 2 ) in the future scenario 2050 SSP2 4.5. This increase is due to changes in the suitability of habitat, i.e. , the areas that may no longer be suitable, those that may remain stable, and those that may become suitable for these IAS in the future (%Loss, % Gain, and the ratio: IAS Range Change in Table 2 and Fig. 2 ). By 2050, the predicted climate change may influence the availability of suitable habitats for the three IAS differently. C. piquiti may experience the highest rate of change (IAS Range Change of 214%), expanding from current optimal environments near the middle course of the Paraguay River (main river in Br-UPRB) to the whole flood plain and São Lourenço-Vermelho and sub-basins further south (Table 3 , Fig. 2 a). On the other hand, C. kelberi has a more moderate rate of change (IAS Range Change of 50%); its suitable habitats may expand only around the sub-basin of introduction to the northwest of the basin (Jaurú-Sepotuba) and to the southeast of the Pantanal (Table 3 , Fig. 2 b). At last, in opposition, it is predicted that O. niloticus could maintain its suitable habitats stable in the future (IAS Range Change of 4%) (Table 3 , Fig. 2 c). Table 2 Current and future 2050 - SSP2 4.5 suitable areas in ≈ km2 for IAS (Invasive Alien Species) Cichlids introduced in the Brazilian part of the Upper Paraguay River Basin (Br-UPRB), based on binary projections from the ensemble of Ecological Niche Models (ENMs): Generalized Linear Models (GLM), Random Forest (RF), and Maximum Entropy (MaxEnt). The occurrences (Occ), percentages (%) that these areas represent in freshwater ecosystems in the (Br-UPRB), and the suitable areas may be Loss (%), may be Gain (%), and the change ratio expected between these areas (% loss - % gain), i.e., IAS Range Change are also shown. * The Overlap IAS is the suitable areas for one or more of these IAS in the study area. IAS Cichlids Occ Current 2050 - SSP2 4.5 % % IAS Range Change ≈km² % Br-UPRB ≈km² % Br-UPRB Loss Gain Cichla piquiti 609 17,318 30.48 25,973 45.71 39.8 253.8 214 Cichla kelberi 130 2,235 3.93 7,018 12.35 16.4 66.4 50 Oreochromis niloticus 168 8,648 15.22 8,963 15.78 37.3 41.0 4 *Overlap IAS 22,853 40.22 33,260 58.54 11.08 57.4 46 In concordance with the general basin-wide trend, there is an appreciable increase in the suitability of the habitats in the Pantanal wetland (lowlands) and its tributary sub-basins in the Brazilian plateau (highlands). Currently, more than half of the Pantanal floodplains are suitable habitats for IAS (55%), while in the tributary sub-basins, less than a third are suitable for these IAS (30%). These suitable areas may increase to almost all of the available freshwater aquatic ecosystems in the Pantanal (85%) by 2050 and (39%) in the tributary sub-basins (Table 3 ). C. piquiti in the Pantanal and O. niloticus in the south sub-basins have the most suitable habitats in both climate scenarios, coinciding with their initial introduction sub-basins (Table 3 ). Although the trend is towards an increase in suitable habitats for the future climate scenario, some sub-basins may minimally decrease these habitats ( e.g. , Taquari-Coxim), or these suitable habitats are very limited (-10% of the sub-basin, e.g. , Cuiabá) compared to the rest of the sub-basins, explained by the expected turnover of suitable habitats, i.e. , Range Size Change (Tables 2 & 3 , Fig. 2 ). Table 3 Percentage of Pantanal wetland, the Brazilian plateau, and tributary sub-basins (2–8) with suitable habitats for the overlap of IAS (Invasive Alien Species), i.e., suitable areas for one or more of the IAS Ciclhids introduced in the Brazilian part of the Upper Paraguay River Basin (Br-UPRB) in current and future 2050 - SSP2 4.5 climate scenarios, based on binary projections from the ensemble of Ecological Niche Models (ENMs): Generalized Linear Models (GLM), Random Forest (RF), and Maximum Entropy (MaxEnt). The percentage of the IAS with the most suitable habitat per each area is included. Current 2050 SSP2 4.5 Overlap % IAS % Overlap % IAS % 1 Pantanal Wetland 54.81 C. piquiti 52.94 85.04 C. piquiti 82.56 - Br-Highlands ( Plateau ) 29.70 O. niloticus 20.20 39.42 C. piquiti 23.64 2 Jaurú-Sepotuba 14.80 C. kelberi 13.21 31.31 C. kelberi 30 3 Cuiabá 6.63 C. piquiti 3.86 9.44 C. piquiti 3.78 4 São Lourenço-Vermelho 23.71 O. niloticus 18.09 26.46 O. niloticus 24.51 5 Taquari-Coxim 14.68 C. piquiti 13.57 12.50 C. piquiti 10.50 6 Aquidauana-Negro 20.86 O. niloticus 17.97 72.10 O. niloticus 68.57 7 Miranda 74.33 C. piquiti 68.27 81.12 C. piquiti 75.62 8 Apa-Perdido 91.50 O. niloticus 84.86 88.23 C. piquiti 69.69 IAS suitable habitats in Br-UPRB reservoirs Table 4 Percentage of the reservoirs area in the Brazilian part of the Upper Paraguay River Basin (Br-UPRB) with predicted suitable habitats for the overlap of IAS (Invasive Alien Species), i.e., suitable areas for one or more of the IAS Ciclhids introduced in reservoirs in current and future 2050 - SSP2 4.5 climate scenarios, based on binary projections from the ensemble of Ecological Niche Models (ENMs): Generalized Linear Models (GLM), Random Forest (RF), and Maximum Entropy (MaxEnt). Reservoirs type Current % 2050% Small reservoirs (1–3km 2 ) 10.34 24.14 Medium-sized reservoirs (3 − 13 km 2 ) 13.64 27.27 Large reservoirs (> 13 km 2 ) 28.57 42.86 Total 13.79 27.59 The IAS fish species introduced in the Br-UPRB currently exhibit limited suitable habitats in the basin's reservoirs (< 30%), with greater habitat suitability in large reservoirs (Table 4 ). Nevertheless, according to projections for 2050, the extent of suitable habitats for these species is expected to double within these environments, regardless of their size (Table 4 ). Discussion The Pantanal wetland and its tributary sub-basins exhibit an abundance of suitable habitats that are optimal for the future proliferation of introduced IAS Cichlids, even under one of the most optimistic future climate scenarios: 2050 - SSP2 4.5 63 . Conversely, the projected loss of suitable habitats for native freshwater fish species in the Br-UPRB under climate change contrasts 58 . These opposite trends highlight a critical concern: while the expected environmental conditions may facilitate the thriving of IAS Cichlids, native fish species may face a significant loss of diversity 15 , 58 , 64 . The potential proliferation of IAS Cichlids reviewed (mainly the two Cichla spp.) given suitable habitats in the basin may exacerbate the climate change challenges for native species, disrupting biological communities, altering ecosystem structure and function, and leading to important socio-economic consequences, as has been observed in other tropical basins where these IAS have been introduced 33 , 65 , 66 . The importance of climate seasonality in our ENMs is consistent with previous observations that seasonal temperature and precipitation patterns influence the potential spread of IAS fishes 67 , 68 . This antecedent could also be related to the fact that the colonization success of Cichlid species relies on abiotic factors and their plasticity and high adaptive capacity 69 , 70 . Unexpected changes in the climatic patterns and seasonality ( e.g. , El Niño-Oscilación del Sur (ENSO), droughts, etc.), such as those projected for 2050, may influence the IAS Cichlids' establishment and spread in the study area 71 – 74 . Several authors also demonstrated that in a large regional scale study, simple influences, such as altitude and basin area (in our study case, precipitation and temperature seasonality), seem to be enough for recognizing the distribution and suitable habitats for freshwater fish species 75 , 76 . Our results support the growing evidence that climate change impacts on habitat suitability are species-specific, driven by habitat preferences, physiological variability, and thermal plasticity 44 , 77 , 78 . For instance, although Cichla spp. use habitats similarly 79 , 80 ; our ENMs show that most suitable areas in the basin are exclusive to each species (Fig. 2 ). Specifically, 80% of the suitable habitats ( * Overlap IAS in Table 2 ) are occupied by a single species. This background suggests that under climate change, IAS Cichlids in the Br-UPRB may not compete with each other for habitats; instead, they could compete with native species 58 . Contrary to expectations, suitable habitats for O. niloticus may remain stable in the future (IAS Range Change of 4%) despite the well-documented invasive potential of this IAS 33 , 81 . Our results are challenged by the limited number of records and the considerable geographical distance between the northernmost and southernmost occurrences of O. niloticus in the basin. This occurrence distribution pattern suggests the possibility of multiple events of introduction and diverse routes of spread within the study area. Given their recent economic importance in the basin, this potential IAS should receive increased research and early management actions 33 . The reservoirs in the basin appear to offer limited habitats for introducing IAS Cichlids (> 30% of reservoir habitats could be suitable for IAS; Table 4 ), well below natural environments in the Pantanal wetland ( ≈ 85% Table 3 ). Nevertheless, with greater habitat suitability in large reservoirs, where there are usually more fish farms, and more in planning ( ≈ 42% Table 4 ) 82 , 83 . The proliferation of IAS Cichlids in Br-UPRB reservoirs may be determined by interaction with native species, including predation, competition, and upstream ecosystems, as has been happening in other tropical reservoirs 25 , 84 – 86 . In the case of Cichla spp., our projections of suitable areas for both species are consistent with their reported areas of introduction, while C. piquiti was introduced in the early 80s in the Piquiri River and is well-established in the floodplain of Paraguay River 27 , 28 ; C. kelberi was introduced most recently in the Padre Inácio and Caramujo rivers (northern of Br-UPRB) and has been expanding its invasion range toward the south of the basin 29 . Both Cichlid species are very aggressive piscivores, and if these IAS establish viable populations in all suitable habitats along the basin where our models predicted, these species could drastically impact the native fish species populations 79 , 80 . This is particularly concerning given that these IAS have already caused severe ecological damage in other ecosystems where they have been introduced 85 , 87 . The previous hypothesis that the invasion range of C. piquiti was restricted by the turbid waters, operating as an ecological barrier for the spread, was refuted recently 28 , 88 , 89 . Besides, our models' predictions include sub-basins and main rivers with turbid waters as suitable habitats for this species in the Br-UPRB, where the IAS were fished regularly (12,961 kg in 2009, 994 kg in 2010, and 16,591 kg in 2018) 30 , 90 . The decline in catches of 2010 could be related to cold fronts in 2010 July and August (4.6–9.2 ºC); since this species prefers warmer waters (physiological optimum above 15°C) 90 , 91 , an increase in a temperature of 1.5°C in the near future (2021–2040), may favor the proliferation of these IAS. We compiled information on public datasets built from traditional scientific activity (field and laboratory occurrence records, online and museum databases) complemented by data from Citizen Science (Structured interviews and Instagram posts). We highlight the recent use of fishers's local ecological knowledge (LEK) as a valuable alternative for gathering information on Cichla spp. 92 . Of all the occurrences documented in the study area through this research, 74.4% were contributed by Citizen Data. We are aware that the approach used is not always easy to implement; some fishers were reluctant to participate in interviews and share information on fishing locations, primarily due to their unfamiliarity with our research or apprehension regarding fishing regulations, including conservation areas and spawning season, regardless of the IAS status 36 , 93 . Despite the drawbacks, the easy way to visually identify the IAS Cichlids through the fishers and photographs is another advantage of the methodology used 94 ; nevertheless, it's recognized that confidently visually identifying several unknown species is challenging ( i.e. , cryptic species). Using Citizen Data with the E-ENMs' capacity to predict the regions at risk of invasion by IAS is a promising and cost-effective method to develop practical preventative actions and planning management in the basin 95 , 96 . In line with some researchers, we recommend conducting field validation of habitat-suitability models for IAS 97 . This involves testing whether these models adequately predict observed data and, if accurate, using them to estimate the potential range of the invasive species 98 . Field reports conducted in 2021 30 , after our field data collection (2018–2019), confirm the presence of C. piquiti in several habitats that were predicted as suitable in our models. Besides, we believe that the continuous feeding of the models with new data sources, through some automation processes by machine learning algorithms and artificial intelligence (AI), could produce dynamic and robust models that allow a better understanding of the invasion patterns and IAS ecology 99 , 100 . Conclusion and Recommendations Past research on IAS Ciclhids introduced in the basin was based primarily on the fishing catch reports, focusing on a single species within a specific area or sub-basin 28 – 30 . Our study provides a comprehensive patio-temporal perspective on the IAS Ciclhids' potential invadable areas, covering inter-regions and intra-basin levels. The model outputs shown here present a state-of-the-art estimation of suitable habitats for the three main fish species introduced in the Br-UPRB. It represents a second step in understanding the potential IAS impacts on native Pantanal's freshwater fish community, one of the most diverse in the Neotropical region (~ 300 species). The Mato Grosso State (Br-UPRB) restricts fishing, transport, and storage of introduced Cichla spp. until 2028 (Statal law 12.434) 101 . This law, in addition to 16 other regulations protecting invasive Peacock bass ( Cichla spp.) in Brazil, contributes to biodiversity loss, disrupts freshwater ecosystems, and generates social conflicts. 36 , 102 . Considering the economic costs of biological invasions in the Neotropical basins 103 and the unintended consequences of valuing non-native freshwater species 36 , we recommend the implementation of better control and management protocols in fish farm facilities and reservoirs to avoid the escapes of additional IAS fishes in suitable areas 104 . Among other actions, we advocate for expanding the studies of the impacts of these IAS in the Bolivian and Paraguayan part of the UPRB, as well as increasing the sampling of O. niloticus , given its high invasive potential, developing joint monitoring and control plans with local fisher communities, government agencies, universities, research institutes, and recreational fishing tourism companies, e.g . by encouraging massive recreational and artisanal fishing and consumption, intending to reduce populations as a strategy to control the spread of IAS fish species 105 . We also promote a gradual change to the commercial production (fish farms) of native species and their repopulation in areas where they have been affected by introduced IAS Cichlids, safeguarding ecosystem services generated by native freshwater fishes 36 . The early detection of a potential IAS and the ability to map its invasion range are fundamental to effective management decisions 53 . Our study also demonstrates the advantage of integrating LEK with ENMs to enhance IAS detection 38 , 92 . This combined approach improves the identification of suitable habitats, enabling precise predictions of potential invadible areas 41 . This integration of Citizen science data with GIS methodologies is critical for early detection and control, as many IAS may not yet occupy all predicted suitable habitats 94 , 106 , 107 . Leveraging these methodologies allows for targeted management strategies, mitigating ecological during initial invasion stages 108 . Materials and methods Study area The study is focused on the Br-UPRB. The Paraguay River, along with its main upstream tributaries of the Pantanal wetland 81 (Fig. 3 ). This Ramsar site begins in the Brazilian plateau, covering an area of 361,666 km 2 74 . The basin experiences a tropical humid climate, averaging an annual temperature of 22.5–26.5°C. Seasonal precipitation, mainly during the rainy period (October-March), contributes 70% of the total annual precipitation (800–1600 mm). These climatic dynamics impact substantial water level fluctuations of approximately 3.1 ± 0.9 meters 74 . Br-UPRB, over decades, faces overfishing, deforestation, ecosystem homogenization, habitat loss, fragmentation, biodiversity decline, and the introduction of IAS, impacting regional wildlife persistence 23 , 81 . Mapping the hydroelectric reservoirs in the basin To assess if the hydroelectric reservoirs' conditions are suitable for the IAS fishes, we used the Brazilian Electric Energy Agency's (ANEEL) georeferenced database and Landsat-8 satellite imagery to confirm the presence of reservoirs. We selected reservoirs with an area over 1 km 2 to match the environmental variables' spatial resolution (Table 5 and Fig. 3 ). Table 5. Identified reservoirs in the Brazilian part of the Upper Paraguay River Basin (Br-UPRB). Small reservoirs, medium-sized reservoirs, and large reservoirs. Reservoirs type # of reservoirs Area Small reservoirs 20 1–3km 2 Medium-sized reservoirs 29 3 − 13 km 2 Large reservoirs 6 > 13 km 2 Species occurrence records We collected data on IAS Cichlid occurrences (1989–2019) from specimens, field catches, and databases in native and study areas (Supplementary Material SM-5). Evidence on Blue Peacock Bass: C. piquiti , Yellow Peacock Bass: C. kelberi , and Tilapia: O. niloticus was obtained. Record gaps were filled through Citizen Science data, obtaining information from interviews with local fishers and georeferenced photos of sport anglers obtained on the social network Instagram (Meta Platforms, Inc.) 96 . The semi-structured interviews were done following the Rapid Assessment Protocol (RAP) developed for detecting IAS fish species in Brazilian lakes 94 ; we conducted 257 interviews with experienced local fishers between 2018 and 2019, covering most of the study area (Main rivers and sub-basins). To ensure accuracy, cross-validation of interviews involved presenting images of common fish species absent in the basin and avoiding false-positive occurrence records (model of the interview in Supplementary Material SM-6-7). Each validated interview was transformed into an occurrence of IAS only if the fisher could specify the nearest water body section where the species was found. Beyond interviews, Instagram IAS georeferenced photo analysis was conducted in the study area. Original posts, official accounts, and fishing-related hashtags were cataloged. Communicated with photographers and fishers to confirm the location details. Out of 326 potential IAS records, only 80 were validated through direct messages, ensuring precise georeferencing. IAS occurrences within 1km of the next water body were relocated to align with gridded environmental variables 61 . Records not meeting this criterion or duplicating within the same 1km² cell were eliminated, retaining only the most recent. Environmental variables and climate scenarios To model the current and future suitable habitats for IAS Ciclhid species, we selected topographic and bioclimatic variables relevant to climate change and IAS modeling in 30 arc-seconds of resolution (≈ 1km 2 ). For current conditions, we used the database of freshwater-specific environmental variables for biodiversity analysis 61 . For the future climatic scenario, we employed WorldClim bioclimatic variables from CMIP6 (Coupled Model Intercomparison Project) simulations representing 2050 conditions (average for 2041–2060) in the Shared Socio-economic Pathways SSP2 4.5, a mid-term scenario 51 . We selected the SSP2 4.5, which foresees a moderate path for climate action, aligning with countries' emissions commitments 63 . This outlook estimates a global temperature rise of 2.1–3.5°C from 1950, with a population of 9.6 billion. Radiative forcing in SSP2 4.5 peaks at 4.5 Wm − 2 by 2050, then decreases if priority in international cooperation for environmental aims; nevertheless, inequalities persist 51 To reduce the uncertainty associated with a single global climate model, we used an ensemble (unweighted mean) of CMCC-ESM2 (Centro Euro-Mediterraneo sui Cambiamenti Climatici Earth System Model version 2) and HadGEM3-GC31-LL (Hadley Centre Global Environment Model version 3, Global Coupled configuration 3.1, Low-Resolution) projections 109 , 110 . This approach provides more robust predictions and better represents the potential future scenarios 111 , 112 . Additionally, it improves accuracy by capturing different climate sensitivities and regional patterns 113 . We divide the climatic and topographic predictor variables into two sets: (1) for Br-UPRB + Araguaia - Tocantins basins (for modeling the two Cichla spp. habitat suitability) and (2) for Br-UPRB + Nile basins (for modeling O. niloticus habitat suitability) We utilized the variance inflation factor (VIF) to address multicollinearity, selecting the bioclimatic variables with VIF values > 5 114 (Supplementary Material SM-1). Additionally, we included elevation, slope, and flow accumulation in models, considering their impact on IAS fish habitat selection and dispersal 115 . These climatic data, used in various ENMs, are considered standard for modeling freshwater species distributions 75 , 116 (Supplementary Material SM-3). Modeling Process and Ensemble Forecasting Approach. Using Ecological Niche Models (ENMs), we identify the suitable habitats for the selected IAS Cichlid species in their native and introduced region (Br-UPRB) (Supplementary Material SM-8) 117 . ENM is based on the ecological niche concept, which posits that a species will have a greater preference for a location where the environmental conditions necessary for its survival are maximized 45 . ENMs have proven effective in evaluating fish species distribution, contributing to native conservation and non-native population control 47 . We applied the ensemble forecasting approach of ENMs, yielding a consensus result from multiple ENMs 118 . IAS Cichlid occurrences and environmental variables were linked using three ENMs algorithms: Generalized Linear Models (GLM), Random Forest (RF), and Maximum Entropy (MaxEnt) 119 . These ENMs enhance projections for IAS with limited occurrence points in undersampled regions 41 . Unlike maximizing the projected range, the approach relies on overlapping regions of employed ENMs, which is favorable for dealing with uncertainty and species with changing niche tolerances 118 , 119 . Models were fitted using the ensemble platform for species distribution modeling "Biomod2" in the R environment 120 . We developed as many models as possible, as allowed by the modeling package (10 Runs + 1 All runs), considering the number of occurrences of each species and the maximum number of sets (repetitions and cross-validations). In total, 198 models were run: 10 + 1 * three algorithms (RF, GLM, MaxEnt) * three IAS ( C. piquiti , C. kelberi , O. niloticus ) * two Climatic scenarios (Current and 2050 - SSP2 4.5). Models' assessment We employed the bootstrap technique for model evaluation, randomly dividing species occurrences into 75% for model generation and 25% for testing 120 . We assessed models' performance through spatial cross-validation, measuring sensitivity (accuracy in identifying occurrences), specificity (accuracy in identifying background points), area-under-the-curve (AUC; ability to differentiate occurrences from background, where AUC > 0.5 indicates good model performance), and the True Skill Statistic (TSS; ranging from − 1 to 1, with values > 0.5 indicating strong performance). The test group assessed omission or commission errors, evaluating the model's predictive ability. The True Skill Statistics (TSS) estimate the sensitivity and specificity, assessing overfitting and underfitting risks and overall predictive power. TSS values range from − 1 to 1, with 1 indicating perfect prediction 121 . Considering a 'good' value as 0.5 or above, we selected a 0.7 TSS threshold to select the ENMs of each IAS in climatic scenarios (current & 2050 - SSP2 4.5). IAS binary presence maps We converted ENMs continuous frequencies into binary projections, transforming probability values (0-100) into binary (1: Predicted suitable habitat / 0: Predicted unsuitable uabitat). Applying the TSS > 0.7 threshold, we selected values (70–100) as suitable habitat (conservative predictions) 118 . This scenario reasonably forecasts the IAS potential range with few occurrences, as in our study, which is widely used in invasion ecology 41 . Suitable / Unsuitable values were extracted for each sub-basin and reservoir, analyzing IAS habitat-suitability patterns in the study area. Analysis of climate change scenarios We utilized the R package " BIOMOD_RangeSize" for a climate scenario analysis of our IAS overlap 120 . Comparing binary projections (current vs. 2050 SSP2 4.5), we assessed changes in four habitat types: Areas that may no longer be suitable: predicted to be lost. Areas that may remain unsuitable: predicted to remain stable. Areas that may gain suitability: predicted to be gained. Areas that may remain suitable: predicted to remain stable. Model outputs have a 0.926 km² resolution (pixel size at equator latitude). Nevertheless, the study area's size and distance from the equator alter potential suitable habitat values, presenting results in (≈ km²) rather than exact values (Supplementary Material SM-9). Declarations Acknowledgments CM-G a thanks OAS, GCUB & CNPq: Organization of American States, Coimbra Group of Brazilian Universities, and Brazilian Council for Scientific Research for the master scholarship (#130674/2018-4 CNPq) TS-S c thanks FAPEMAT (project FAPEMAT-PRO.000274/2023) and CECAV (project TCCE ICMBio-CECAV nº. 001/2023). Funding Brazilian National Water and Basic Sanitation Agency (ANA: Agência Nacional de Águas e Saneamento Básico) for funding the field interviews. Author information Authors and Affiliations Graduate Program in Ecology and Biodiversity Conservation, Institute of Biological Sciences, Federal University of Mato Grosso, Av. Fernando Corrêa, 2367, Cuiabá, Mato Grosso, 78060-900, Brazil. Cristian Martínez-González Laboratory of Ecology and Management of Fisheries Resources, Institute of Biological Sciences, Federal University of Mato Grosso, Cuiabá, Mato Grosso, Brazil, Av. Fernando Corrêa, 2367, Cuiabá, Mato Grosso, 78060-900, Brazil. Lúcia Aparecida de Fatima Mateus, Jerry Magno Ferreira Penha Department of Botany, Institute of Biological Sciences, Federal University of Mato Grosso, Cuiabá, Brazil, Av. Fernando Corrêa, 2367, Cuiabá, Mato Grosso, 78060-900, Brazil. Thadeu Sobral-Souza State University of Mato Grosso do Sul, Mato Grosso do Sul, Rod. Dourados-Itahum, Km 12, Dourados, Mato Grosso do Sul, 79804-970, Brazil. Yzel Rondon Súarez Corresponding author * Correspondence to Cristian Martínez-González Author contributions CM-G and JMFP conceived, designed, and approved the study. LAdFM and YRS provided the data collection in the field. TS-S contributed to the development of the models and/or revisions. CM-G carried out the analysis and wrote the first draft of the manuscript. All authors contributed to subsequent versions and the interpretation of the data and results. All authors approved the final version of the manuscript. Competing Interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Ethics declarations This study included the participation of local fishing communities of artisanal and recreational fishermen and was approved by the Federal University of Mato Grosso's ethical research committee (register: 3.511.327). All the interviews were performed following relevant guidelines and regulations, protecting the identity of the interviewed fishers. Additionally, we confirmed that informed consent was assigned from all interviewed fishermen. Code availability The script codes generated in this study have been deposited in the following Figshare repository: https://doi.org/10.6084/m9.figshare.26961754. Data availability The occurrence data of the introduced Cichlid species studied in its native distribution area was obtained from available biodiversity databases and georeferenced research papers (Supplementary Material S-1), and the occurrence data in its introduced range are available in the Figshare repository: https://doi.org/10.6084/m9.figshare.26961754. The climatic and topographic data used are available at https://www.earthenv.org/streams and https://www.worldclim.org/data/bioclim.html. Additionally, upon request, the corresponding author could provide the high-resolution rasters generated in this study. References Mainka, S. A. & Howard, G. W. Climate change and invasive species: double jeopardy. Integr. Zool. 5 , 102–111 (2010). Pyšek, P. et al. Scientists’ warning on invasive alien species. Biol. Rev. 95 , 1511–1534 (2020). Weiskopf, S. R. et al. Climate change effects on biodiversity, ecosystems, ecosystem services, and natural resource management in the United States. Sci. Total Environ. 733 , 137782 (2020). Bellard, C., Cassey, P. & Blackburn, T. M. Alien species as a driver of recent extinctions. Biol. Lett. 12 , 20150623 (2016). Roy, H. E., Pauchard, A., Stoett, P. & Renard Truong, T. IPBES Invasive Alien Species Assessment: Full Report . (2024). https://zenodo.org/doi/10.5281/zenodo.7430682 doi:10.5281/ZENODO.7430682. IBPES. Summary for Policymakers of the Thematic Assessment Report on Invasive Alien Species and Their Control of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. https://zenodo.org/record/8314303 (2023). 10.5281/ZENODO.8314303 Pecl, G. T. et al. Biodiversity redistribution under climate change: Impacts on ecosystems and human well-being. Science 355 , eaai9214 (2017). Bellard, C., Jeschke, J. M., Leroy, B. & Mace, G. M. Insights from modeling studies on how climate change affects invasive alien species geography. Ecol. Evol. 8 , 5688–5700 (2018). Essl, F. et al. Drivers of future alien species impacts: An expert-based assessment. Glob. Change Biol. 26 , 4880–4893 (2020). Roura-Pascual, N. et al. Alternative futures for global biological invasions. Sustain. Sci. 16 , 1637–1650 (2021). Seebens, H. et al. Projecting the continental accumulation of alien species through to 2050. Glob. Change Biol. 27 , 970–982 (2021). Burgiel, S. W., Muir, A. A. & Invasive Species Climate Change and Ecosystem-Based Adaptation: Addressing Multiple Drivers of Global Change Global Invasive Species Programme. Preprint at (2010). https://doi.org/10.13140/2.1.1460.8161 Kernan, M. Climate change and the impact of invasive species on aquatic ecosystems. Aquat. Ecosyst. Health Manag. 18 , 321–333 (2015). Harrison, I. et al. The freshwater biodiversity crisis. Science 362 , 1369–1369 (2018). Sayer, C. A. et al. One-quarter of freshwater fauna threatened with extinction. Nature 638 , 138–145 (2025). Castellanos, E. et al. Climate Change 2022-Impacts, Adaptation and Vulnerability Regional Factsheet Central and South America . (2022). http://hdl.handle.net/20.500.12123/12589 Torremorell, A. et al. Current and future threats for ecological quality management of South American freshwater ecosystems. Inland. Waters . 11 , 125–140 (2021). Bernery, C. et al. Freshwater Fish Invasions: A Comprehensive Review. Annu. Rev. Ecol. Evol. Syst. 53 , 427–456 (2022). Muñoz-Mas, R. et al. Two centuries of spatial and temporal dynamics of freshwater fish introductions. Global Ecol. Biogeogr. 32 , 1632–1644 (2023). Vitule, J. R. S. et al. Comment on ‘Fish biodiversity and conservation in South America by Reis et al. ’. Journal of Fish Biology 90, 1182–1190 (2017). (2016). Fragoso-Moura, E. N., Oporto, L. T., Maia-Barbosa, P. M. & Barbosa, F. A. R. Loss of biodiversity in a conservation unit of the Brazilian Atlantic Forest: the effect of introducing non-native fish species. Braz J. Biol. 76 , 18–27 (2016). Wantzen, K. M. et al. UNESCO,. The Pantanal: How long will there be Life in the Rhythm of the Waters? in River culture: life as a dance to the rhythm of the waters (ed. Wantzen, K. M.) 497–536 (2023). 10.54677/DYRD7304 Zanatta, S. C. & Maciel, J. Pantanal ameaçado: a construção de represas e a insignificância da energia hídrica produzida. Inter 333–348. 10.20435/inter.v22i2.2811 (2021). Agostinho, A. A., Pelicice, F. M. & Gomes, L. C. Dams and the fish fauna of the Neotropical region: impacts and management related to diversity and fisheries. Braz J. Biol. 68 , 1119–1132 (2008). Pereira, L. S. et al. Non-native species in reservoirs: how are we doing in Brazil? Hydrobiologia 817 , 71–84 (2018). Rocha, B. S., García-Berthou, E. & Cianciaruso, M. V. Non-native fishes in Brazilian freshwaters: identifying biases and gaps in ecological research. Biol. Invasions . 25 , 1643–1658 (2023). Carvalho, D. C., Oliveira, D. A. A., Sampaio, I. & Beheregaray, L. B. Analysis of propagule pressure and genetic diversity in the invasibility of a freshwater apex predator: the peacock bass (genus Cichla). Neotrop. ichthyol. 12 , 105–116 (2014). Resende, E., Marques, D. & Ferreira Lksg. A successful case of biological invasion: the fish Cichla piquiti, an Amazonian species introduced into the Pantanal, Brazil. Braz J. Biol. 68 , 799–805 (2008). Ortega, J. First record of Peacock bass Cichla kelberi Kullander & Ferreira, 2006 in the Brazilian Pantanal. BIR 4, 133–138 (2015). Cordova Junior, R. F. & D. Introdução e distribuição do tucunaré-azul e do tambaqui no Pantanal Corumbaense. Acta 19 , 01–15 (2021). Nobile, A. B. et al. Status and recommendations for sustainable freshwater aquaculture in Brazil. Reviews Aquaculture . 12 , 1495–1517 (2020). Moreira, E. A. & Silva, D. D. P. A dozen fish: some exotic invasive species found in Brazilian waters and their impacts. Ciênc anim. bras. 24 , e–74647E (2023). Cassemiro, F. A. S., Bailly, D., Graça, D., Agostinho, A. A. & W. J. & The invasive potential of tilapias (Osteichthyes, Cichlidae) in the Americas. Hydrobiologia 817 , 133–154 (2018). Lima Junior, D. P. et al. Aquaculture expansion in Brazilian freshwaters against the Aichi Biodiversity Targets. Ambio (2018). 10.1007/s13280-017-1001-z De Medinas, M. et al. Predicted impacts of proposed hydroelectric facilities on fish migration routes upstream from the Pantanal wetland (Brazil). River Res. Apps . 36 , 452–464 (2020). Pelicice, F. M. et al. Unintended consequences of valuing the contributions of non-native species: misguided conservation initiatives in a megadiverse region. Biodivers. Conserv. 32 , 3915–3938 (2023). Pelicice, F. M., Latini, J. D. & Agostinho, A. A. Fish fauna disassembly after the introduction of a voracious predator: main drivers and the role of the invader’s demography. Hydrobiologia 746 , 271–283 (2015). Peterson, A. T. Predicting the Geography of Species’ Invasions via Ecological Niche Modeling. Q. Rev. Biol. 78 , 419–433 (2003). Elith, J. & Franklin, J. Species Distribution Modeling. in Encyclopedia of Biodiversity 558–572 (Elsevier, doi: 10.1016/B978-0-12-822562-2.00379-0 . (2024). Thuiller, W. et al. Niche-based modelling as a tool for predicting the risk of alien plant invasions at a global scale. Glob. Change Biol. 11 , 2234–2250 (2005). Elith, J. Predicting Distributions of Invasive Species. in Invasive Species (eds Robinson, A. P., Walshe, T., Burgman, M. A. & Nunn, M.) 93–129 (Cambridge University Press, doi: 10.1017/9781139019606.006 . (2017). Franklin, J. Species distribution modelling supports the study of past, present and future biogeographies. J. Biogeogr. 50 , 1533–1545 (2023). Sinclair, S. J., White, M. D. & Newell, G. R. How Useful Are Species Distribution Models for Managing Biodiversity under Future Climates? E&S 15, art8 (2010). Peterson, A. T. & Soberón, J. M. Integrating fundamental concepts of ecology, biogeography, and sampling into effective ecological niche modeling and species distribution modeling. Plant. Biosystems - Int. J. Dealing all Aspects Plant. Biology . 146 , 789–796 (2012). Soberón, J. M. Niche and area of distribution modeling: a population ecology perspective. Ecography 33 , 159–167 (2010). Nori, J. & Rojas-Soto, O. On the environmental background of aquatic organisms for ecological niche modeling: a call for caution. Aquat. Ecol. 53 , 595–605 (2019). Valencia-Rodríguez, D., Jiménez-Segura, L., Rogéliz, C. A. & Parra, J. L. Ecological niche modeling as an effective tool to predict the distribution of freshwater organisms: The case of the Sabaleta Brycon henni (Eigenmann, 1913). PLoS ONE . 16 , e0247876 (2021). Saupe, E. E. et al. Variation in niche and distribution model performance: The need for a priori assessment of key causal factors. Ecol. Model. 237–238 , 11–22 (2012). Thuiller, W. Ecological niche modelling. Curr. Biol. 34 , R225–R229 (2024). IPCC. Climate Change 2021 – The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge University Press, 2023). 10.1017/9781009157896 Meinshausen, M. et al. The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500. Geosci. Model. Dev. 13 , 3571–3605 (2020). Feldman, M. J. et al. Trends and gaps in the use of citizen science derived data as input for species distribution models: A quantitative review. PLoS ONE . 16 , e0234587 (2021). Gervazoni, P. et al. Citizen Science Improves the Known and Potential Distribution of a Strong Wetland Invader: Implications for Niche Modeling and Invasion Management. Environ. Manage. 71 , 1176–1187 (2023). Hallwass, G., Schiavetti, A. & Silvano, R. A. M. Fishers’ knowledge indicates temporal changes in composition and abundance of fishing resources in Amazon protected areas. Anim. Conserv. 23 , 36–47 (2020). Howard, L., Van Rees, C. B., Dahlquist, Z., Luikart, G. & Hand, B. K. A review of invasive species reporting apps for citizen science and opportunities for innovation. NB 71 , 165–188 (2022). Phillips, T. B., Bailey, R. L., Martin, V., Faulkner-Grant, H. & Bonter, D. N. The role of citizen science in management of invasive avian species: What people think, know, and do. J. Environ. Manage. 280 , 111709 (2021). Pocock, M. J. O. et al. Citizen science is a vital partnership for invasive alien species management and research. iScience 27 , 108623 (2024). Peluso, L. M., Mateus, L., Penha, J., Súarez, Y. & Lemes, P. Climate change may reduce suitable habitat for freshwater fish in a tropical watershed. Clim. Change . 176 , 44 (2023). Havel, J. E., Kovalenko, K. E., Thomaz, S. M. & Amalfitano, S. Kats, L. B. Aquatic invasive species: challenges for the future. Hydrobiologia 750 , 147–170 (2015). Sultan, M. & Pei, D. S. Invasive Alien Species Problem in the Reservoir. in Reservoir Ecotoxicology 219–228 (Springer International Publishing, Cham, doi: 10.1007/978-3-031-26344-6_19 . (2023). Domisch, S., Amatulli, G. & Jetz, W. Near-global freshwater-specific environmental variables for biodiversity analyses in 1 km resolution. Sci. Data . 2 , 150073 (2015). Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Intl J. Climatology . 37 , 4302–4315 (2017). Nazarenko, L. S. et al. Future Climate Change Under SSP Emission Scenarios With GISS-E2.1. J. Adv. Model. Earth Syst. 14 , e2021MS002871 (2022). Peluso, L. M. et al. Climate change negative effects on the Neotropical fishery resources may be exacerbated by hydroelectric dams. Sci. Total Environ. 828 , 154485 (2022). Leal, L. B. et al. Changes in ecosystem functions generated by fish populations after the introduction of a non-native predator (Cichla kelberi) (Perciformes: Cichlidae). Neotrop. ichthyol. 19 , e210041 (2021). Rauber, R. G. et al. Changes in fish functional diversity in a neotropical floodplain associated with the invasive Cichla spp.: a long-term perspective. Hydrobiologia 10.1007/s10750-024-05725-w (2024). Mofu, L. et al. Impacts of non-native fishes under a seasonal temperature gradient are forecasted using functional responses and abundances. NB 49 , 57–75 (2019). Sloan, T. J. & Turingan, R. G. The Effects of Temperature on the Feeding Performance of Invasive Fishes. AJLS 2 , 63 (2014). Luz, S. C. S. et al. Reduced genetic diversity and the success of the invasive peacock bass (Cichliformes: Cichlidae). Braz J. Biol. 84 , e248656 (2024). Burress, E. D. Cichlid fishes as models of ecological diversification: patterns, mechanisms, and consequences. Hydrobiologia 748 , 7–27 (2015). Alves, J. C., Andreotti, G. F., Agostinho, A. A. & Gomes, L. C. Effects of the El Niño Southern Oscillation (ENSO) on fish assemblages in a Neotropical floodplain. Hydrobiologia 848 , 1811–1823 (2021). Andreotti, G. F., Alves, J. C., Alves, D. C., Agostinho, A. A. & Gomes, L. C. The response of fish functional diversity to the El Niño Southern Oscillation (ENSO) in a Neotropical floodplain. Hydrobiologia 848 , 1207–1218 (2021). Silva, C. B., Silva, M. E. S. & Ambrizzi, T. Climatic variability of river outflow in the Pantanal region and the influence of sea surface temperature. Theor. Appl. Climatol . 129 , 97–109 (2017). Thielen, D. et al. Quo vadis Pantanal? Expected precipitation extremes and drought dynamics from changing sea surface temperature. PLoS ONE . 15 , e0227437 (2020). Barradas, J. R. S., Silva, L. G. & Harvey, B. C. Fontoura, N. F. Estimating migratory fish distribution from altitude and basin area: a case study in a large Neotropical river. Freshw. Biol. 57 , 2297–2305 (2012). Comte, L. & Grenouillet, G. Distribution shifts of freshwater fish under a variable climate: comparing climatic, bioclimatic and biotic velocities. Divers. Distrib. 21 , 1014–1026 (2015). Hirzel, A. H. & Le Lay, G. Habitat suitability modelling and niche theory. J. Appl. Ecol. 45 , 1372–1381 (2008). Comte, L. & Olden, J. D. Evolutionary and environmental determinants of freshwater fish thermal tolerance and plasticity. Glob. Change Biol. 23 , 728–736 (2017). Agostinho, A. A. et al. Introduced Cichlids in the Americas: Distribution Patterns, Invasion Ecology, and Impacts. in The Behavior, Ecology and Evolution of Cichlid Fishes (eds Abate, M. E. & Noakes, D. L. G.) 313–361 (Springer Netherlands, Dordrecht, doi: 10.1007/978-94-024-2080-7_10 . (2021). Franco, A. C. S., Petry, A. C. & Tavares, M. R. De Fátima Ramos Guimarães, T. & Dos Santos, L. N. Global distribution of the South American peacock basses Cichla spp. follows human interference. Fish and Fisheries 23, 407–421 (2022). Tomas, W. M. et al. Sustainability Agenda for the Pantanal Wetland: Perspectives on a Collaborative Interface for Science, Policy, and Decision-Making. Trop. Conserv. Sci. 12 , 194008291987263 (2019). Latini, A. O. et al. Brazil’s new fish farming Decree threatens freshwater conservation in South America. Biol. Conserv. 263 , 109353 (2021). Ikeda-Castrillon, S. K., Oliveira-Junior, E. S., Rossetto, O. C., Saito, C. H. & Wantzen, K. M. The Pantanal: A Seasonal Neotropical Wetland Under Threat. in The Palgrave Handbook of Global Sustainability 1–27 (Springer International Publishing, Cham, doi: 10.1007/978-3-030-38948-2_36-1 . (2022). Agostinho, A. A., Gomes, L. C., Santos, N. C. L., Ortega, J. C. G. & Pelicice, F. M. Fish assemblages in Neotropical reservoirs: Colonization patterns, impacts and management. Fish. Res. 173 , 26–36 (2016). Espínola, L. A., Minte-Vera, C. V. & Júlio, H. F. Invasibility of reservoirs in the Paraná Basin, Brazil, to Cichla kelberi Kullander and Ferreira, 2006. Biol. Invasions . 12 , 1873–1888 (2010). Pelicice, F. M. & Agostinho, A. A. Fish fauna destruction after the introduction of a non-native predator (Cichla kelberi) in a Neotropical reservoir. Biol. Invasions . 11 , 1789–1801 (2009). Carvalho, T. L., De Almeida Ferreira, E., Pelicice, F. M. & Fernandes, R. Comparative functional responses predict the predatory impact of the highly invasive fish Cichla kelberi. Hydrobiologia 848 , 2203–2211 (2021). Matias, R. et al. Estudo do tucunaré (Cichla ocellaris) em duas baías marginais do Rio Piquiri (Pantanal do Paiaguás, MS): parâmetros físico- químicos e análise histológica da pele. 5, (2001). Marques, D. K. S. & Resende, E. K. Ocorrência do Tucunaré Cichla cf. monoculus no Pantanal. (2005). https://www.infoteca.cnptia.embrapa.br/infoteca/handle/doc/812032 Clímaco, T. S., Soriano, B. M. A. & Catella, A. C. Mortandade do tucunaré-azul (Cichla piquiti) no Pantanal em função de frentes frias: informações preliminares. (2022). Catella, A. C., Albuquerque, S., Campos, F. & Santos, D. Sistema de Controle da Pesca de Mato Grosso do Sul - CPESCA/MS . 54 (2018). http://www.infoteca.cnptia.embrapa.br/infoteca/handle/doc/980999 Catelani, P. A., Petry, A. C. & Pelicice, F. M. Azevedo Matias Silvano, R. Fishers’ knowledge on the ecology, impacts and benefits of the non-native peacock bass Cichla kelberi in a coastal river in southeastern Brazil. Ethnobio Conserv. 10.15451/ec2020-09-10.04-1-16 (2021). Alves, G. H. Z., Tófoli, R. M., Message, H. J., Lima-Júnior, D. P. & Hoeinghaus, D. J. New decree promotes fish invasion in Amazon and Pantanal. Biodivers. Conserv. 27 , 2449–2450 (2018). Latini, A. O. & Petrere Júnior, M. Efficiency of rapid field methods for detecting non-native fish in Eastern Brazilian lakes. Hydrobiologia 817 , 85–96 (2018). Coxen, C. L., Frey, J. K., Carleton, S. A. & Collins, D. P. Species distribution models for a migratory bird based on citizen science and satellite tracking data. Global Ecol. Conserv. 11 , 298–311 (2017). Pocock, M. J. O., Roy, H. E., Fox, R., Ellis, W. N. & Botham, M. Citizen science and invasive alien species: Predicting the detection of the oak processionary moth Thaumetopoea processionea by moth recorders. Biol. Conserv. 208 , 146–154 (2017). West, A. M., Kumar, S., Brown, C. S., Stohlgren, T. J. & Bromberg, J. Field validation of an invasive species Maxent model. Ecol. Inf. 36 , 126–134 (2016). Ortega-Huerta, M. A. & Vega‐Rivera, J. H. Validating distribution models for twelve endemic bird species of tropical dry forest in western Mexico. Ecol. Evol. 7 , 7672–7686 (2017). Hutchinson, R., He, L. & Emerson, S. Species Distribution Modeling of Citizen Science Data as a Classification Problem with Class-Conditional Noise. AAAI 31, (2017). Silvestro, D., Goria, S., Sterner, T. & Antonelli, A. Improving biodiversity protection through artificial intelligence. Nat. Sustain. 5 , 415–424 (2022). Mendes, M. LEI No 12.434, DE 01 DE MARÇO DE 2024. 1 (2024). Ota, R. R. et al. Brazilian fish fauna threatened by a proposal for a new law protecting invaders. Biodivers. Conserv. 28 , 787–789 (2019). Adelino, J. R. P. et al. The economic costs of biological invasions in Brazil: a first assessment. NB 67 , 349–374 (2021). Shirley, E. A. & Gore, M. L. Trust in scientists and rates of noncompliance with a fisheries rule in the Brazilian Pantanal. PLoS ONE . 14 , e0207973 (2019). Moore, A. The Aquatic Invaders: Marine Management Figuring Fishermen, Fisheries, and Lionfish in The Bahamas. Cult. Anthropol. 27 , 667–688 (2012). Mohd Dali, M. Z. et al. Detection and Management of Freshwater Invasive Alien Species through Environmental DNA Analysis and Geographic Information Systems: A Review. Sustainability 15 , 9497 (2023). Kousteni, V. et al. Citizen scientists contributing to alien species detection: the case of fishes and mollusks in European marine waters. Ecosphere 13 , e03875 (2022). Jeschke, J. M. & Strayer, D. L. Usefulness of Bioclimatic Models for Studying Climate Change and Invasive Species. Ann. N. Y. Acad. Sci. 1134 , 1–24 (2008). Lovato, T. et al. CMIP6 Simulations With the CMCC Earth System Model (CMCC-ESM2). J Adv Model Earth Syst 14, eMS002814 (2022). (2021). Andrews, M. B. et al. Historical Simulations With HadGEM3-GC3.1 for CMIP6. J Adv Model Earth Syst 12, eMS001995 (2020). (2019). Tanimu, B. et al. Enhancing reliability in climate projections: A novel approach for selecting global climate models. Phys. Chem. Earth Parts A/B/C . 134 , 103598 (2024). Castaneda-Gonzalez, M., Poulin, A., Romero-Lopez, R. & Turcotte, R. Weighting climate models for hydrological projections: effects on contrasting hydroclimatic regions. Clim. Change . 176 , 170 (2023). Firpo, M. Â. F. et al. Assessment of CMIP6 models’ performance in simulating present-day climate in Brazil. Front. Clim. 4 , 948499 (2022). Dormann, C. F. et al. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36 , 27–46 (2013). Camana, M., Dala-Corte, R. B. & Becker, F. G. Relation between species richness and stream slope in riffle fish assemblages is dependent on spatial scale. Environ. Biol. Fish. 99 , 603–612 (2016). Alvarez, F., Gerhard, P., Paiva Silva, D., Spacek Godoy, B. & Montag, L. F. D. A. Effects of different variable sets on the potential distribution of fish species in the Amazon Basin. Ecol. Freshw. Fish . 29 , 764–778 (2020). Peterson, A. T. Predicting the Geography of Species’ Invasions via Ecological Niche Modeling. Q. Rev. Biol. 78 , 419–433 (2003). Hao, T., Elith, J., Guillera-Arroita, G. & Lahoz‐Monfort, J. J. A review of evidence about use and performance of species distribution modelling ensembles like BIOMOD. Divers. Distrib. 25 , 839–852 (2019). Diniz-Filho, J. A. F. et al. Partitioning and mapping uncertainties in ensembles of forecasts of species turnover under climate change. Ecography 32 , 897–906 (2009). Thuiller, W. BIOMOD – optimizing predictions of species distributions and projecting potential future shifts under global change. Glob. Change Biol. 9 , 1353–1362 (2003). Ruete, A. & Leynaud, G. C. Goal-Oriented Evaluation of Species Distribution Models’ Accuracy and Precision: True Skill Statistic Profile and Uncertainty Maps . (2015). https://peerj.com/preprints/1208v1 Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformation.NonnativeCichlidsinthePantanalRevised.docx Cite Share Download PDF Status: Published Journal Publication published 27 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 12 May, 2025 Reviews received at journal 02 May, 2025 Reviews received at journal 11 Apr, 2025 Reviewers agreed at journal 10 Apr, 2025 Reviewers agreed at journal 10 Apr, 2025 Reviews received at journal 09 Apr, 2025 Reviewers agreed at journal 09 Apr, 2025 Reviewers invited by journal 08 Apr, 2025 Submission checks completed at journal 04 Apr, 2025 First submitted to journal 26 Mar, 2025 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5050398","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":434314189,"identity":"22d954af-9c0c-4506-be44-26abc2f8d195","order_by":0,"name":"Cristian Martínez-González","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYPACCSBmPvgAxOQjTkcCSAtbsgGIzUakFhDBYyZBlBb+9tOJnyt/WOTx8x8wq+apYZAnqEXiTO5myTMJEsWSDQfSbvMcYzBsI6TFQIJ3g2RDgkTihoMNx27zNjAkELQFqGXzT5CW/YcZ24qJ1bINYgsbMxszUVqAftlm2ZAmkTjjDBuz5JxjEoT9wt9+dvPNBpu6xP7+8x8/vKmxkecnpAXDVlI1jIJRMApGwSjABgAunjYwUzrwpQAAAABJRU5ErkJggg==","orcid":"","institution":"Federal University of Mato Grosso","correspondingAuthor":true,"prefix":"","firstName":"Cristian","middleName":"","lastName":"Martínez-González","suffix":""},{"id":434314190,"identity":"7a0fe88b-2c29-4683-9ecd-0b662d8d4501","order_by":1,"name":"Lúcia Aparecida de Fatima Mateus","email":"","orcid":"","institution":"Federal University of Mato Grosso","correspondingAuthor":false,"prefix":"","firstName":"Lúcia","middleName":"Aparecida de Fatima","lastName":"Mateus","suffix":""},{"id":434314192,"identity":"51dd165b-d753-4cf4-9627-419e25a1ee9b","order_by":2,"name":"Thadeu Sobral-Souza","email":"","orcid":"","institution":"Federal University of Mato Grosso","correspondingAuthor":false,"prefix":"","firstName":"Thadeu","middleName":"","lastName":"Sobral-Souza","suffix":""},{"id":434314194,"identity":"7783c0cc-cafe-44f0-959d-18efe7677ee8","order_by":3,"name":"Yzel Rondon Súarez","email":"","orcid":"","institution":"State University of Mato Grosso do Sul","correspondingAuthor":false,"prefix":"","firstName":"Yzel","middleName":"Rondon","lastName":"Súarez","suffix":""},{"id":434314195,"identity":"14f15ce5-1b59-487f-a1f7-affc05b34f8b","order_by":4,"name":"Jerry Magno Ferreira Penha","email":"","orcid":"","institution":"Federal University of Mato Grosso","correspondingAuthor":false,"prefix":"","firstName":"Jerry","middleName":"Magno Ferreira","lastName":"Penha","suffix":""}],"badges":[],"createdAt":"2024-09-07 21:23:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5050398/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5050398/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-30425-3","type":"published","date":"2025-11-27T15:57:19+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79736467,"identity":"64b97875-81fc-4a14-956e-0a30c808f03f","added_by":"auto","created_at":"2025-04-02 07:11:10","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":182507,"visible":true,"origin":"","legend":"\u003cp\u003eModified BAM (Biotic, Abiotic, Mobility) diagram showing the typical freshwater landscape in a geographic region under consideration \u003cstrong\u003eG\u003c/strong\u003e. Region \u003cstrong\u003eA\u003c/strong\u003e is where scenopoetic conditions favor a species. Region \u003cstrong\u003eB\u003c/strong\u003e is the area where the bionomic conditions suit the species. The blue area represents accessible geographic spaces for a freshwater species (\u003cstrong\u003eM\u003c/strong\u003e). The red area represents sites in the ecological space where M overlaps with A and B (realized niche of the species) \u003csup\u003e45,46\u003c/sup\u003e.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5050398/v1/89f711f51d6de9daa2b5f494.jpg"},{"id":79736470,"identity":"7ace60b4-f5a2-48be-b501-0329723871c0","added_by":"auto","created_at":"2025-04-02 07:11:11","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":24295476,"visible":true,"origin":"","legend":"\u003cp\u003eProjected changes in habitat-suitability for: \u003cstrong\u003ea)\u003c/strong\u003e \u003cem\u003eCichla piquiti\u003c/em\u003e, \u003cstrong\u003eb)\u003c/strong\u003e \u003cem\u003eCichla kelberi\u003c/em\u003e, and \u003cstrong\u003ec)\u003c/strong\u003e \u003cem\u003eOreochromis niloticus\u003c/em\u003e in the Brazilian part of the Upper Paraguay River Basin (Br-UPRB) between Current and Future 2050 - SSP2 4.5 climate scenarios, based on binary projections from the ensemble of Ecological Niche Models (ENMs): Generalized Linear Models (GLM), Random Forest (RF), and Maximum Entropy (MaxEnt). In light blue, the areas that may no longer be suitable (predicted to be lost); in dark blue, the areas that may remain unsuitable (predicted to remain stable); in light red, the areas that may gain suitability (predicted to be gained), and in dark red, the areas that may remain suitable (predicted to remain stable). Orange dots indicate the presence of each species.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5050398/v1/e37efa6b14edf9ee07207e80.jpg"},{"id":79737067,"identity":"4f777302-0891-4a0c-9321-f3de35ff1f40","added_by":"auto","created_at":"2025-04-02 07:19:13","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":13911158,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area - Brazilian part of the Upper Paraguay River Basin (Br-UPRB). \u003cstrong\u003e1.\u003c/strong\u003e Pantanal wetland and the main tributary sub-basins in the Brazilian plateau (2-8). \u003cstrong\u003e2.\u003c/strong\u003e Jaurú-Sepotuba, \u003cstrong\u003e3.\u003c/strong\u003eCuiabá, \u003cstrong\u003e4.\u003c/strong\u003e São Lourenço-Vermelho, \u003cstrong\u003e5.\u003c/strong\u003e Taquari-Coxim, \u003cstrong\u003e6.\u003c/strong\u003e Aquidauana-Negro, \u003cstrong\u003e7.\u003c/strong\u003e Miranda, and \u003cstrong\u003e8.\u003c/strong\u003e Apa-Perdido. The location of reservoirs and Introduced Alien Species (IAS) occurrences are included.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5050398/v1/ab3b9bb5fb149f714b6c4e36.jpg"},{"id":97178919,"identity":"f4f2d279-d683-43cb-aa67-d1ef530e87bb","added_by":"auto","created_at":"2025-12-01 16:14:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":40098492,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5050398/v1/a7d32edb-8a45-4aa3-9ee0-912b94b31c49.pdf"},{"id":79737062,"identity":"3d5ba008-4809-40f3-a86b-094079e80703","added_by":"auto","created_at":"2025-04-02 07:19:11","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":3150640,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.NonnativeCichlidsinthePantanalRevised.docx","url":"https://assets-eu.researchsquare.com/files/rs-5050398/v1/dd9f05a2f4e10df3355fe142.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eClimate change may increase the suitable habitats for invasive freshwater Cichlids in a Neotropical basin\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eClimate change and biological invasions pose significant threats to global biodiversity and ecosystem services \u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Human activities have introduced over 37,000 invasive alien species (IAS) worldwide, contributing to 60% of documented global plant and animal extinctions \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. In addition, IAS impacts food security and human health through the spread of pests and diseases, often exacerbated by changes in climate patterns, resulting in an estimated global economic cost of USD 423\u0026nbsp;billion annually \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe role of climate change as a key driver of the spread of IAS is now widely recognized; rising temperatures and changes in precipitation have altered ecosystems and created novel suitable habitats for establishing, surviving, and spreading IAS populations\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. This pattern is expected to intensify, with important short-term impacts projected by 2050 \u003csup\u003e10,11\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGiven their limited extent, strong connectivity dependence, restricted species dispersal, and direct dependence on climate stability, freshwaters are among the most vulnerable ecosystems to climate change and IAS \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Furthermore, freshwater species face the highest extinction rates compared to their terrestrial and marine counterparts, with a quarter of freshwater fauna at significant risk of extinction \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. In the Americas, particularly in Latin America, freshwater habitats face severe hydrological alterations, declining water quality, and ecosystem degradation beyond the impacts of IAS \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFish species, the most commonly documented IAS in rivers, lakes, and wetlands, have rapidly spread through aquaculture, posing a major concern in freshwater ecosystems \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Several IAS fish species support profitable fisheries despite ecological risks, particularly in the Neotropical region \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. However, in developing countries like Brazil, managing IAS fish species is hindered by data gaps, socio-economic interests, and limited state intervention \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe Upper Paraguay River Basin (UPRB) is situated at South America's geodetic center and spans Brazil, Bolivia, and Paraguay. It encompasses the extensive Pantanal wetland, featuring intricate floodplains nourished by upstream tributaries of the Paraguay River \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. The UPRB is particularly affected by numerous hydropower plants and reservoirs, with more planned developments \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Over the past seven decades, the construction of dams has disrupted freshwater ecosystems by altering sediment flow and water chemistry composition, also obstructing fish migration routes, and altering fish assemblages \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Furthermore, hydroelectric reservoirs have provided favorable conditions for the proliferation of IAS fishes \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. The ecological consequences associated with dam construction, including the proliferation of IAS in reservoirs, should be considered \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCichlids are the best example of potential IAS fish being established in the UPRB. Since 1982, escapes and introductions of the Blue Peacock Bass, \u003cem\u003eCichla piquiti\u003c/em\u003e Kullander \u0026amp; Ferreira 2006, a Cichlid from the Araguaia-Tocantins basin, have been reported in the Brazilian part of the UPRB (hereinafter Br-UPRB) \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. This species has progressively expanded its invasion range in the Pantanal wetland and some sub-basins like S\u0026atilde;o Louren\u0026ccedil;o-Vermelho, Miranda, and Apa-Perdido \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Moreover, the finding of a second Araguaia-Tocantins Cichlid, the Yellow Peacock Bass, \u003cem\u003eCichla kelberi\u003c/em\u003e Kullander \u0026amp; Ferreira 2006 in lotic ecosystems, has raised concerns regarding these two species within the basin \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAn additional Cichlid with great invasive potential, the Nile Tilapia, has been reported in the basin \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eOreochromis niloticus\u003c/em\u003e Linnaeus 1758 is cataloged as IAS in several tropical regions and is considered very dangerous for native fauna \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. The Nile Tilapia has been extensively cultured recently in local reservoirs along Mato Grosso, and Mato Grosso do Sul states (Br-UPRB) \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Nevertheless, frequent escapes from these facilities have increased its presence in river catches by local artisanal and recreational fishers (pers. comm). While the short-term effects of IAS Cichlids proliferation are well documented, such as their role as aggressive predators and competitors that disrupt trophic webs, the long-term ecological impacts remain uncertain \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA set of correlative techniques analysis called Ecological Niche Models (ENMs) \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e has experienced a substantial increase in the last few years \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. This increase is due in part to the recent availability of high-quality data on species occurrences and specific environmental variables and its ability to inform the magnitude of potential climate change impacts, to study past, present, and future distributions in biogeography, predict IAS invasion spread patterns by identifying the most suitable areas in the early stages of invasion and thus prioritize efforts in critical regions \u003csup\u003e\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor IAS, the ENMs assess how an invasive species utilizes the available environmental space in its native range versus its invasive range \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. This comparison helps to understand where invasive species might expand based on their known favored ecological conditions, predicting invasion risks and managing vulnerable ecosystems \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe Shared Socio-economic Pathways (SSPs): SSP1 1.9\u0026ndash;2.6, SSP2 4.5, SSP3 7.0, and SSP5 8.5, consist of five narratives outlining potential changes in demographics, economics, technology, society, governance, and the environment over this century \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. These pathways serve as a tool for comparing ENMs in scenarios of temperature increase ranging from 1.5\u0026deg;C in the near future (2021\u0026ndash;2040) to 3.3 to 5.7\u0026deg;C in the distant future (2081\u0026ndash;2100), assuming the highest greenhouse gas emissions scenario persists \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAttempts like Citizen Science bring scientists closer to local ecological knowledge (LEK) of IAS, finding a cost-effective approach involving community members in collecting data and understanding IAS impacts in human fishing communities \u003csup\u003e\u003cspan additionalcitationids=\"CR53\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. The Citizen Science data has been incipient, nevertheless effectively implemented in ENMs and IAS management \u003csup\u003e\u003cspan additionalcitationids=\"CR56\" citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Estimating the potential distribution of the IAS fishes in the Br-UPRB is essential to support management actions, especially considering the predicted and ongoing climate change that threatens native species \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eUsing an ensemble of ENMs: Generalized Linear Models (GLM), Random Forest (RF), and Maximum Entropy (MaxEnt), we modeled the current and future (2050 - SSP2 4.5) suitable habitats for \u003cem\u003eC. piquiti\u003c/em\u003e, \u003cem\u003eC. kelberi\u003c/em\u003e, and \u003cem\u003eO. niloticus\u003c/em\u003e across their native and introduced (Br-UPRB) ranges. The models incorporated hydroclimatic and topographic predictors with IAS occurrences from online, museum, and laboratory occurrence databases complemented by semi-structured interviews and georeferenced Instagram posts from traditional and recreational fishers. This study aimed to (i) identify the most suitable IAS habitats within Br-UPRB reservoirs and key regions, including wetland complexes in low-elevation areas (Pantanal), tributary sub-basins on the Brazilian plateau, and (ii) evaluate whether these suitable areas may expand under the 2050 - SSP2 4.5 climate scenario.\u003c/p\u003e \u003cp\u003eGiven the Nile and Araguaia -Tocantins basins' origin of the IAS Cichlids introduced in the Br-UPRB, we expect that climate change could expand the climatically suitable areas. Specifically, we suggest combining Pantanal wetland and reservoir conditions (\u003cem\u003ee.g.\u003c/em\u003e, stable water levels, reduced flow velocity, clear waters) and projected temperature increases, making favorable environments for these IAS spread soon along the Br-UPRB. This hypothesis is supported by evidence that lentic ecosystems and reservoirs often facilitate the dispersal of Ciclhid invasive species \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e and that rising temperatures can increase the range of neotropical IAS fishes \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eEnvironmental variables selection and contribution to ENMs\u003c/h2\u003e \u003cp\u003eThe two sets of climatic predictor variables selected derived from EarthEnv and WorldClim2 \u003csup\u003e61,62\u003c/sup\u003e, (1) for Br-UPRB\u0026thinsp;+\u0026thinsp;Araguaia - Tocantins basins (for modeling the two \u003cem\u003eCichla\u003c/em\u003e spp. habitat suitability) and (2) for Br-UPRB\u0026thinsp;+\u0026thinsp;Nile basins (for modeling \u003cem\u003eO. niloticus\u003c/em\u003e habitat suitability) are accessible along topographic predictors (elevation, flow, and slope) in the Supplementary Material: SM-1 and SM-2, all with Variance Inflation Factor (VIF) value\u0026thinsp;\u0026lt;\u0026thinsp;5, to minimize multicollinearity and improve interpretability of the models.\u003c/p\u003e \u003cp\u003eIn the ensemble of ENMs, precipitation seasonality was the most important variable for \u003cem\u003eC. kelberi\u003c/em\u003e and \u003cem\u003eC. piquiti\u003c/em\u003e in both current and future 2050 - SSP2 4.5 scenarios. The mean diurnal range (mean of monthly (max temp - min temp)) was also important for \u003cem\u003eO. niloticus\u003c/em\u003e and \u003cem\u003eC. piquiti\u003c/em\u003e, while temperature seasonality stood out for \u003cem\u003eC. kelberi\u003c/em\u003e. The importance of all ENMs variables used is detailed in Supplementary Material SM-3.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eModels' calibration, validation \u0026 forecasting performance\u003c/h3\u003e\n\u003cp\u003eThe individual models selected to build the ENMs ensemble showed high reliability, with Sensitivity and Specificity consistently above 95%, validating the predictive capacity to identify suitable habitats (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The evaluation of true skill statistic (TSS) and area-under-the-curve (AUC) metrics also confirmed the best performance of the models selected, with a mean calibration AUC\u0026thinsp;\u0026ge;\u0026thinsp;0.99, validation AUC\u0026thinsp;\u0026ge;\u0026thinsp;0.95 (except for \u003cem\u003eC. kelberi\u003c/em\u003e \u0026minus;\u0026thinsp;2050 validation AUC\u0026thinsp;\u0026ge;\u0026thinsp;0.74), calibration TSS\u0026thinsp;\u0026ge;\u0026thinsp;0.93, and validation TSS\u0026thinsp;\u0026ge;\u0026thinsp;0.81 for all IAS and climatic scenarios, indicate that the models are well-suited for projecting current and future (2050) habitats for the IAS Cichlids in the Br-UPRB and their native ranges (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The TTS and AUC values for each ENMs algorithm: GLM, RF, and MaxEnt, are in Supplementary Material SM-4.\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\u003eAverage performance of ecological niche models (ENMs) of IAS (Invasive Alien Species) Cichlids introduced in the Brazilian part of the Upper Paraguay River Basin (Br-UPRB) in current and future 2050 - SSP2 4.5 climate scenarios. Assessing the Sensitivity and Specificity of the models through calibration and validation metrics: AUC: area-under-the-curve and TSS: True Skill Statistic.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"left\" 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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScenario\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMetric\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSensitivity %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpecificity %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCalibration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eValidation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cem\u003eOreochromis niloticus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e95.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e95.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2050 -\u003c/p\u003e \u003cp\u003eSSP2 4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e95.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e98.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e94.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cem\u003eCichla kelberi\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e96.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e97.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e96.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e97.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2050 -\u003c/p\u003e \u003cp\u003eSSP2 4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e97.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e97.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cem\u003eCichla piquiti\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e95.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e94.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2050 -\u003c/p\u003e \u003cp\u003eSSP2 4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e97.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e97.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.97\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\u003e \u003cb\u003eIAS suitable habitats in the Br-UPRB.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe current ENMs ensembles reveal a high proportion of suitable habitats for all IAS Cichlids in the basin; the overlapped suitable habitats in binary projections (0:unsuitable/1:suitable for one or more species) of \u003cem\u003eO. niloticus\u003c/em\u003e, \u003cem\u003eC. piquiti\u003c/em\u003e, and \u003cem\u003eC. kelberi\u003c/em\u003e are covering 40% the freshwater ecosystems in the basin, being \u003cem\u003eC. piquiti\u003c/em\u003e the IAS that has the most proportion of suitable habitats available for its spread in both scenarios current and 2050 SSP2 4.5 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGiven the current environmental conditions of the Br-UPRB, the IAS Cichlids in the basin could use\u0026thinsp;\u003cb\u003e\u0026asymp;\u003c/b\u003e\u0026thinsp;22,853 km\u0026sup2; of suitable lotic and lentic freshwater ecosystems, \u003cem\u003ei.e.\u003c/em\u003e, 55% of the Pantanal, 30% of the sub-basin in the Brazilian plateau (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Collectively, the IAS Cichlids could exhibit an increase of 20% in suitable habitats of the Br-UPRB freshwater ecosystems (+\u0026thinsp;10,407 km\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e) in the future scenario 2050 SSP2 4.5. This increase is due to changes in the suitability of habitat, \u003cem\u003ei.e.\u003c/em\u003e, the areas that may no longer be suitable, those that may remain stable, and those that may become suitable for these IAS in the future (%Loss, % Gain, and the ratio: IAS Range Change in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBy 2050, the predicted climate change may influence the availability of suitable habitats for the three IAS differently. \u003cem\u003eC. piquiti\u003c/em\u003e may experience the highest rate of change (IAS Range Change of 214%), expanding from current optimal environments near the middle course of the Paraguay River (main river in Br-UPRB) to the whole flood plain and S\u0026atilde;o Louren\u0026ccedil;o-Vermelho and sub-basins further south (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). On the other hand, \u003cem\u003eC. kelberi\u003c/em\u003e has a more moderate rate of change (IAS Range Change of 50%); its suitable habitats may expand only around the sub-basin of introduction to the northwest of the basin (Jaur\u0026uacute;-Sepotuba) and to the southeast of the Pantanal (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). At last, in opposition, it is predicted that \u003cem\u003eO. niloticus\u003c/em\u003e could maintain its suitable habitats stable in the future (IAS Range Change of 4%) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003ec).\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\u003eCurrent and future 2050 - SSP2 4.5 suitable areas in \u0026asymp;\u0026thinsp;km2 for IAS (Invasive Alien Species) Cichlids introduced in the Brazilian part of the Upper Paraguay River Basin (Br-UPRB), based on binary projections from the ensemble of Ecological Niche Models (ENMs): Generalized Linear Models (GLM), Random Forest (RF), and Maximum Entropy (MaxEnt). The occurrences (Occ), percentages (%) that these areas represent in freshwater ecosystems in the (Br-UPRB), and the suitable areas may be Loss (%), may be Gain (%), and the change ratio expected between these areas (% loss - % gain), i.e., IAS Range Change are also shown. * The Overlap IAS is the suitable areas for one or more of these IAS in the study area.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIAS Cichlids\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOcc\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e2050 - SSP2 4.5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIAS\u003c/p\u003e \u003cp\u003eRange Change\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026asymp;km\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003cp\u003eBr-UPRB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026asymp;km\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%\u003c/p\u003e \u003cp\u003eBr-UPRB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLoss\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGain\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCichla piquiti\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17,318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25,973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e253.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e214\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCichla kelberi\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7,018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e66.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eOreochromis niloticus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8,963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e37.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e41.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e*Overlap IAS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22,853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33,260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e58.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e57.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e46\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\u003eIn concordance with the general basin-wide trend, there is an appreciable increase in the suitability of the habitats in the Pantanal wetland (lowlands) and its tributary sub-basins in the Brazilian plateau (highlands). Currently, more than half of the Pantanal floodplains are suitable habitats for IAS (55%), while in the tributary sub-basins, less than a third are suitable for these IAS (30%). These suitable areas may increase to almost all of the available freshwater aquatic ecosystems in the Pantanal (85%) by 2050 and (39%) in the tributary sub-basins (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eC. piquiti\u003c/em\u003e in the Pantanal and \u003cem\u003eO. niloticus\u003c/em\u003e in the south sub-basins have the most suitable habitats in both climate scenarios, coinciding with their initial introduction sub-basins (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Although the trend is towards an increase in suitable habitats for the future climate scenario, some sub-basins may minimally decrease these habitats (\u003cem\u003ee.g.\u003c/em\u003e, Taquari-Coxim), or these suitable habitats are very limited (-10% of the sub-basin, \u003cem\u003ee.g.\u003c/em\u003e, Cuiab\u0026aacute;) compared to the rest of the sub-basins, explained by the expected turnover of suitable habitats, \u003cem\u003ei.e.\u003c/em\u003e, Range Size Change (Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u0026amp; \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePercentage of Pantanal wetland, the Brazilian plateau, and tributary sub-basins (2\u0026ndash;8) with suitable habitats for the overlap of IAS (Invasive Alien Species), i.e., suitable areas for one or more of the IAS Ciclhids introduced in the Brazilian part of the Upper Paraguay River Basin (Br-UPRB) in current and future 2050 - SSP2 4.5 climate scenarios, based on binary projections from the ensemble of Ecological Niche Models (ENMs): Generalized Linear Models (GLM), Random Forest (RF), and Maximum Entropy (MaxEnt). The percentage of the IAS with the most suitable habitat per each area is included.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e2050 SSP2 4.5\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOverlap %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eIAS %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOverlap %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eIAS %\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePantanal Wetland\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eC. piquiti\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e85.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eC. piquiti\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e82.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBr-Highlands (\u003cb\u003ePlateau\u003c/b\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eO. niloticus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eC. piquiti\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJaur\u0026uacute;-Sepotuba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eC. kelberi\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eC. kelberi\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCuiab\u0026aacute;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eC. piquiti\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eC. piquiti\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS\u0026atilde;o Louren\u0026ccedil;o-Vermelho\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eO. niloticus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eO. niloticus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTaquari-Coxim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eC. piquiti\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eC. piquiti\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAquidauana-Negro\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eO. niloticus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e72.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eO. niloticus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e68.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiranda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eC. piquiti\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e81.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eC. piquiti\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e75.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApa-Perdido\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eO. niloticus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e88.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eC. piquiti\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e69.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eIAS suitable habitats in Br-UPRB reservoirs\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePercentage of the reservoirs area in the Brazilian part of the Upper Paraguay River Basin (Br-UPRB) with predicted suitable habitats for the overlap of IAS (Invasive Alien Species), i.e., suitable areas for one or more of the IAS Ciclhids introduced in reservoirs in current and future 2050 - SSP2 4.5 climate scenarios, based on binary projections from the ensemble of Ecological Niche Models (ENMs): Generalized Linear Models (GLM), Random Forest (RF), and Maximum Entropy (MaxEnt).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReservoirs type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurrent %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2050%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmall reservoirs (1\u0026ndash;3km\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium-sized reservoirs (3 \u0026minus;\u0026thinsp;13 km\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLarge reservoirs (\u0026gt;\u0026thinsp;13 km\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e13.79\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e27.59\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe IAS fish species introduced in the Br-UPRB currently exhibit limited suitable habitats in the basin's reservoirs (\u0026lt;\u0026thinsp;30%), with greater habitat suitability in large reservoirs (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Nevertheless, according to projections for 2050, the extent of suitable habitats for these species is expected to double within these environments, regardless of their size (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e "},{"header":"Discussion","content":"\u003cp\u003eThe Pantanal wetland and its tributary sub-basins exhibit an abundance of suitable habitats that are optimal for the future proliferation of introduced IAS Cichlids, even under one of the most optimistic future climate scenarios: 2050 - SSP2 4.5 \u003csup\u003e63\u003c/sup\u003e. Conversely, the projected loss of suitable habitats for native freshwater fish species in the Br-UPRB under climate change contrasts \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. These opposite trends highlight a critical concern: while the expected environmental conditions may facilitate the thriving of IAS Cichlids, native fish species may face a significant loss of diversity \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e,\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. The potential proliferation of IAS Cichlids reviewed (mainly the two \u003cem\u003eCichla\u003c/em\u003e spp.) given suitable habitats in the basin may exacerbate the climate change challenges for native species, disrupting biological communities, altering ecosystem structure and function, and leading to important socio-economic consequences, as has been observed in other tropical basins where these IAS have been introduced \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e,\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe importance of climate seasonality in our ENMs is consistent with previous observations that seasonal temperature and precipitation patterns influence the potential spread of IAS fishes \u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e,\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. This antecedent could also be related to the fact that the colonization success of Cichlid species relies on abiotic factors and their plasticity and high adaptive capacity \u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e,\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. Unexpected changes in the climatic patterns and seasonality (\u003cem\u003ee.g.\u003c/em\u003e, El Ni\u0026ntilde;o-Oscilaci\u0026oacute;n del Sur (ENSO), droughts, etc.), such as those projected for 2050, may influence the IAS Cichlids' establishment and spread in the study area \u003csup\u003e\u003cspan additionalcitationids=\"CR72 CR73\" citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. Several authors also demonstrated that in a large regional scale study, simple influences, such as altitude and basin area (in our study case, precipitation and temperature seasonality), seem to be enough for recognizing the distribution and suitable habitats for freshwater fish species \u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e,\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur results support the growing evidence that climate change impacts on habitat suitability are species-specific, driven by habitat preferences, physiological variability, and thermal plasticity \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e,\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e. For instance, although \u003cem\u003eCichla\u003c/em\u003e spp. use habitats similarly \u003csup\u003e\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e,\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e; our ENMs show that most suitable areas in the basin are exclusive to each species (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Specifically, 80% of the suitable habitats (\u003cb\u003e*\u003c/b\u003eOverlap IAS in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) are occupied by a single species. This background suggests that under climate change, IAS Cichlids in the Br-UPRB may not compete with each other for habitats; instead, they could compete with native species \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eContrary to expectations, suitable habitats for \u003cem\u003eO. niloticus\u003c/em\u003e may remain stable in the future (IAS Range Change of 4%) despite the well-documented invasive potential of this IAS \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e. Our results are challenged by the limited number of records and the considerable geographical distance between the northernmost and southernmost occurrences of \u003cem\u003eO. niloticus\u003c/em\u003e in the basin. This occurrence distribution pattern suggests the possibility of multiple events of introduction and diverse routes of spread within the study area. Given their recent economic importance in the basin, this potential IAS should receive increased research and early management actions \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe reservoirs in the basin appear to offer limited habitats for introducing IAS Cichlids (\u0026gt;\u0026thinsp;30% of reservoir habitats could be suitable for IAS; Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), well below natural environments in the Pantanal wetland (\u003cb\u003e\u0026asymp;\u003c/b\u003e\u0026thinsp;85% Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Nevertheless, with greater habitat suitability in large reservoirs, where there are usually more fish farms, and more in planning (\u003cb\u003e\u0026asymp;\u003c/b\u003e\u0026thinsp;42% Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) \u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e,\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e. The proliferation of IAS Cichlids in Br-UPRB reservoirs may be determined by interaction with native species, including predation, competition, and upstream ecosystems, as has been happening in other tropical reservoirs \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan additionalcitationids=\"CR85\" citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn the case of \u003cem\u003eCichla\u003c/em\u003e spp., our projections of suitable areas for both species are consistent with their reported areas of introduction, while \u003cem\u003eC. piquiti\u003c/em\u003e was introduced in the early 80s in the Piquiri River and is well-established in the floodplain of Paraguay River \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e; \u003cem\u003eC. kelberi\u003c/em\u003e was introduced most recently in the Padre In\u0026aacute;cio and Caramujo rivers (northern of Br-UPRB) and has been expanding its invasion range toward the south of the basin \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Both Cichlid species are very aggressive piscivores, and if these IAS establish viable populations in all suitable habitats along the basin where our models predicted, these species could drastically impact the native fish species populations \u003csup\u003e\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e,\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e. This is particularly concerning given that these IAS have already caused severe ecological damage in other ecosystems where they have been introduced \u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e,\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe previous hypothesis that the invasion range of \u003cem\u003eC. piquiti\u003c/em\u003e was restricted by the turbid waters, operating as an ecological barrier for the spread, was refuted recently \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e,\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e. Besides, our models' predictions include sub-basins and main rivers with turbid waters as suitable habitats for this species in the Br-UPRB, where the IAS were fished regularly (12,961 kg in 2009, 994 kg in 2010, and 16,591 kg in 2018) \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e\u003c/sup\u003e. The decline in catches of 2010 could be related to cold fronts in 2010 July and August (4.6\u0026ndash;9.2 \u0026ordm;C); since this species prefers warmer waters (physiological optimum above 15\u0026deg;C) \u003csup\u003e\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e,\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u003c/sup\u003e, an increase in a temperature of 1.5\u0026deg;C in the near future (2021\u0026ndash;2040), may favor the proliferation of these IAS.\u003c/p\u003e \u003cp\u003eWe compiled information on public datasets built from traditional scientific activity (field and laboratory occurrence records, online and museum databases) complemented by data from Citizen Science (Structured interviews and Instagram posts). We highlight the recent use of fishers's local ecological knowledge (LEK) as a valuable alternative for gathering information on \u003cem\u003eCichla\u003c/em\u003e spp. \u003csup\u003e\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e\u003c/sup\u003e. Of all the occurrences documented in the study area through this research, 74.4% were contributed by Citizen Data. We are aware that the approach used is not always easy to implement; some fishers were reluctant to participate in interviews and share information on fishing locations, primarily due to their unfamiliarity with our research or apprehension regarding fishing regulations, including conservation areas and spawning season, regardless of the IAS status \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e\u003c/sup\u003e. Despite the drawbacks, the easy way to visually identify the IAS Cichlids through the fishers and photographs is another advantage of the methodology used \u003csup\u003e\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e\u003c/sup\u003e; nevertheless, it's recognized that confidently visually identifying several unknown species is challenging (\u003cem\u003ei.e.\u003c/em\u003e, cryptic species). Using Citizen Data with the E-ENMs' capacity to predict the regions at risk of invasion by IAS is a promising and cost-effective method to develop practical preventative actions and planning management in the basin \u003csup\u003e\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e,\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn line with some researchers, we recommend conducting field validation of habitat-suitability models for IAS \u003csup\u003e\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e\u003c/sup\u003e. This involves testing whether these models adequately predict observed data and, if accurate, using them to estimate the potential range of the invasive species \u003csup\u003e\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e\u003c/sup\u003e. Field reports conducted in 2021\u003csup\u003e30\u003c/sup\u003e, after our field data collection (2018\u0026ndash;2019), confirm the presence of \u003cem\u003eC. piquiti\u003c/em\u003e in several habitats that were predicted as suitable in our models. Besides, we believe that the continuous feeding of the models with new data sources, through some automation processes by machine learning algorithms and artificial intelligence (AI), could produce dynamic and robust models that allow a better understanding of the invasion patterns and IAS ecology \u003csup\u003e\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e,\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Conclusion and Recommendations","content":"\u003cp\u003ePast research on IAS Ciclhids introduced in the basin was based primarily on the fishing catch reports, focusing on a single species within a specific area or sub-basin \u003csup\u003e\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Our study provides a comprehensive patio-temporal perspective on the IAS Ciclhids' potential invadable areas, covering inter-regions and intra-basin levels. The model outputs shown here present a state-of-the-art estimation of suitable habitats for the three main fish species introduced in the Br-UPRB. It represents a second step in understanding the potential IAS impacts on native Pantanal's freshwater fish community, one of the most diverse in the Neotropical region (~\u0026thinsp;300 species).\u003c/p\u003e \u003cp\u003eThe Mato Grosso State (Br-UPRB) restricts fishing, transport, and storage of introduced \u003cem\u003eCichla\u003c/em\u003e spp. until 2028 (Statal law 12.434)\u003csup\u003e\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e\u003c/sup\u003e. This law, in addition to 16 other regulations protecting invasive Peacock bass (\u003cem\u003eCichla\u003c/em\u003e spp.) in Brazil, contributes to biodiversity loss, disrupts freshwater ecosystems, and generates social conflicts. \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e\u003c/sup\u003e. Considering the economic costs of biological invasions in the Neotropical basins \u003csup\u003e\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e\u003c/sup\u003e and the unintended consequences of valuing non-native freshwater species \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, we recommend the implementation of better control and management protocols in fish farm facilities and reservoirs to avoid the escapes of additional IAS fishes in suitable areas \u003csup\u003e\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAmong other actions, we advocate for expanding the studies of the impacts of these IAS in the Bolivian and Paraguayan part of the UPRB, as well as increasing the sampling of \u003cem\u003eO. niloticus\u003c/em\u003e, given its high invasive potential, developing joint monitoring and control plans with local fisher communities, government agencies, universities, research institutes, and recreational fishing tourism companies, \u003cem\u003ee.g\u003c/em\u003e. by encouraging massive recreational and artisanal fishing and consumption, intending to reduce populations as a strategy to control the spread of IAS fish species \u003csup\u003e\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e\u003c/sup\u003e. We also promote a gradual change to the commercial production (fish farms) of native species and their repopulation in areas where they have been affected by introduced IAS Cichlids, safeguarding ecosystem services generated by native freshwater fishes \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. The early detection of a potential IAS and the ability to map its invasion range are fundamental to effective management decisions \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur study also demonstrates the advantage of integrating LEK with ENMs to enhance IAS detection \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e\u003c/sup\u003e. This combined approach improves the identification of suitable habitats, enabling precise predictions of potential invadible areas \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. This integration of Citizen science data with GIS methodologies is critical for early detection and control, as many IAS may not yet occupy all predicted suitable habitats \u003csup\u003e\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e,\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e,\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e\u003c/sup\u003e. Leveraging these methodologies allows for targeted management strategies, mitigating ecological during initial invasion stages \u003csup\u003e\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy area\u003c/h2\u003e\n \u003cp\u003eThe study is focused on the Br-UPRB. The Paraguay River, along with its main upstream tributaries of the Pantanal wetland \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). This Ramsar site begins in the Brazilian plateau, covering an area of 361,666 km\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e \u003cspan class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. The basin experiences a tropical humid climate, averaging an annual temperature of 22.5\u0026ndash;26.5\u0026deg;C. Seasonal precipitation, mainly during the rainy period (October-March), contributes 70% of the total annual precipitation (800\u0026ndash;1600 mm). These climatic dynamics impact substantial water level fluctuations of approximately 3.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9 meters \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. Br-UPRB, over decades, faces overfishing, deforestation, ecosystem homogenization, habitat loss, fragmentation, biodiversity decline, and the introduction of IAS, impacting regional wildlife persistence \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eMapping the hydroelectric reservoirs in the basin\u003c/h3\u003e\n\u003cp\u003eTo assess if the hydroelectric reservoirs\u0026apos; conditions are suitable for the IAS fishes, we used the Brazilian Electric Energy Agency\u0026apos;s (ANEEL) georeferenced database and Landsat-8 satellite imagery to confirm the presence of reservoirs. We selected reservoirs with an area over 1 km\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e to match the environmental variables\u0026apos; spatial resolution (Table 5 and Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;5. Identified reservoirs in the Brazilian part of the Upper Paraguay River Basin (Br-UPRB). Small reservoirs, medium-sized reservoirs, and large reservoirs.\u003c/p\u003e\n\u003ctable id=\"Tabb\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eReservoirs type\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e# of reservoirs\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eArea\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmall reservoirs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026ndash;3km\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium-sized reservoirs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 \u0026minus;\u0026thinsp;13 km\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLarge reservoirs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;13 km\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eSpecies occurrence records\u003c/h2\u003e\n \u003cp\u003eWe collected data on IAS Cichlid occurrences (1989\u0026ndash;2019) from specimens, field catches, and databases in native and study areas (Supplementary Material SM-5). Evidence on Blue Peacock Bass: \u003cem\u003eC. piquiti\u003c/em\u003e, Yellow Peacock Bass: \u003cem\u003eC. kelberi\u003c/em\u003e, and Tilapia: \u003cem\u003eO. niloticus\u003c/em\u003e was obtained. Record gaps were filled through Citizen Science data, obtaining information from interviews with local fishers and georeferenced photos of sport anglers obtained on the social network Instagram (Meta Platforms, Inc.) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e96\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eThe semi-structured interviews were done following the Rapid Assessment Protocol (RAP) developed for detecting IAS fish species in Brazilian lakes \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e94\u003c/span\u003e\u003c/sup\u003e; we conducted 257 interviews with experienced local fishers between 2018 and 2019, covering most of the study area (Main rivers and sub-basins). To ensure accuracy, cross-validation of interviews involved presenting images of common fish species absent in the basin and avoiding false-positive occurrence records (model of the interview in Supplementary Material SM-6-7). Each validated interview was transformed into an occurrence of IAS only if the fisher could specify the nearest water body section where the species was found.\u003c/p\u003e\n \u003cp\u003eBeyond interviews, Instagram IAS georeferenced photo analysis was conducted in the study area. Original posts, official accounts, and fishing-related hashtags were cataloged. Communicated with photographers and fishers to confirm the location details. Out of 326 potential IAS records, only 80 were validated through direct messages, ensuring precise georeferencing.\u003c/p\u003e\n \u003cp\u003eIAS occurrences within 1km of the next water body were relocated to align with gridded environmental variables \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. Records not meeting this criterion or duplicating within the same 1km\u0026sup2; cell were eliminated, retaining only the most recent.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eEnvironmental variables and climate scenarios\u003c/h2\u003e\n \u003cp\u003eTo model the current and future suitable habitats for IAS Ciclhid species, we selected topographic and bioclimatic variables relevant to climate change and IAS modeling in 30 arc-seconds of resolution (\u0026asymp;\u0026thinsp;1km\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e). For current conditions, we used the database of freshwater-specific environmental variables for biodiversity analysis \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. For the future climatic scenario, we employed WorldClim bioclimatic variables from CMIP6 (Coupled Model Intercomparison Project) simulations representing 2050 conditions (average for 2041\u0026ndash;2060) in the Shared Socio-economic Pathways SSP2 4.5, a mid-term scenario \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eWe selected the SSP2 4.5, which foresees a moderate path for climate action, aligning with countries\u0026apos; emissions commitments \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. This outlook estimates a global temperature rise of 2.1\u0026ndash;3.5\u0026deg;C from 1950, with a population of 9.6\u0026nbsp;billion. Radiative forcing in SSP2 4.5 peaks at 4.5 Wm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e by 2050, then decreases if priority in international cooperation for environmental aims; nevertheless, inequalities persist \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003eTo reduce the uncertainty associated with a single global climate model, we used an ensemble (unweighted mean) of CMCC-ESM2 (Centro Euro-Mediterraneo sui Cambiamenti Climatici Earth System Model version 2) and HadGEM3-GC31-LL (Hadley Centre Global Environment Model version 3, Global Coupled configuration 3.1, Low-Resolution) projections \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e109\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e110\u003c/span\u003e\u003c/sup\u003e. This approach provides more robust predictions and better represents the potential future scenarios\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e111\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e112\u003c/span\u003e\u003c/sup\u003e. Additionally, it improves accuracy by capturing different climate sensitivities and regional patterns\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e113\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eWe divide the climatic and topographic predictor variables into two sets: (1) for Br-UPRB\u0026thinsp;+\u0026thinsp;Araguaia - Tocantins basins (for modeling the two \u003cem\u003eCichla\u003c/em\u003e spp. habitat suitability) and (2) for Br-UPRB\u0026thinsp;+\u0026thinsp;Nile basins (for modeling \u003cem\u003eO. niloticus\u003c/em\u003e habitat suitability)\u003c/p\u003e\n \u003cp\u003eWe utilized the variance inflation factor (VIF) to address multicollinearity, selecting the bioclimatic variables with VIF values\u0026thinsp;\u0026gt;\u0026thinsp;5 \u003csup\u003e114\u003c/sup\u003e (Supplementary Material SM-1). Additionally, we included elevation, slope, and flow accumulation in models, considering their impact on IAS fish habitat selection and dispersal \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e115\u003c/span\u003e\u003c/sup\u003e. These climatic data, used in various ENMs, are considered standard for modeling freshwater species distributions \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e75\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e116\u003c/span\u003e\u003c/sup\u003e (Supplementary Material SM-3).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eModeling Process and Ensemble Forecasting Approach.\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eUsing Ecological Niche Models (ENMs), we identify the suitable habitats for the selected IAS Cichlid species in their native and introduced region (Br-UPRB) (Supplementary Material SM-8) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e117\u003c/span\u003e\u003c/sup\u003e. ENM is based on the ecological niche concept, which posits that a species will have a greater preference for a location where the environmental conditions necessary for its survival are maximized \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. ENMs have proven effective in evaluating fish species distribution, contributing to native conservation and non-native population control \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eWe applied the ensemble forecasting approach of ENMs, yielding a consensus result from multiple ENMs \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e118\u003c/span\u003e\u003c/sup\u003e. IAS Cichlid occurrences and environmental variables were linked using three ENMs algorithms: Generalized Linear Models (GLM), Random Forest (RF), and Maximum Entropy (MaxEnt) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e119\u003c/span\u003e\u003c/sup\u003e. These ENMs enhance projections for IAS with limited occurrence points in undersampled regions \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Unlike maximizing the projected range, the approach relies on overlapping regions of employed ENMs, which is favorable for dealing with uncertainty and species with changing niche tolerances \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e118\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e119\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eModels were fitted using the ensemble platform for species distribution modeling \u0026quot;Biomod2\u0026quot; in the R environment \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e120\u003c/span\u003e\u003c/sup\u003e. We developed as many models as possible, as allowed by the modeling package (10 Runs\u0026thinsp;+\u0026thinsp;1 All runs), considering the number of occurrences of each species and the maximum number of sets (repetitions and cross-validations). In total, 198 models were run: 10\u0026thinsp;+\u0026thinsp;1 * three algorithms (RF, GLM, MaxEnt) * three IAS (\u003cem\u003eC. piquiti\u003c/em\u003e, \u003cem\u003eC. kelberi\u003c/em\u003e, \u003cem\u003eO. niloticus\u003c/em\u003e) * two Climatic scenarios (Current and 2050 - SSP2 4.5).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eModels\u0026apos; assessment\u003c/h2\u003e\n \u003cp\u003eWe employed the bootstrap technique for model evaluation, randomly dividing species occurrences into 75% for model generation and 25% for testing \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e120\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eWe assessed models\u0026apos; performance through spatial cross-validation, measuring sensitivity (accuracy in identifying occurrences), specificity (accuracy in identifying background points), area-under-the-curve (AUC; ability to differentiate occurrences from background, where AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.5 indicates good model performance), and the True Skill Statistic (TSS; ranging from \u0026minus;\u0026thinsp;1 to 1, with values\u0026thinsp;\u0026gt;\u0026thinsp;0.5 indicating strong performance).\u003c/p\u003e\n \u003cp\u003eThe test group assessed omission or commission errors, evaluating the model\u0026apos;s predictive ability. The True Skill Statistics (TSS) estimate the sensitivity and specificity, assessing overfitting and underfitting risks and overall predictive power. TSS values range from \u0026minus;\u0026thinsp;1 to 1, with 1 indicating perfect prediction \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e121\u003c/span\u003e\u003c/sup\u003e. Considering a \u0026apos;good\u0026apos; value as 0.5 or above, we selected a 0.7 TSS threshold to select the ENMs of each IAS in climatic scenarios (current \u0026amp; 2050 - SSP2 4.5).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eIAS binary presence maps\u003c/h2\u003e\n \u003cp\u003eWe converted ENMs continuous frequencies into binary projections, transforming probability values (0-100) into binary (1: Predicted suitable habitat / 0: Predicted unsuitable uabitat). Applying the TSS\u0026thinsp;\u0026gt;\u0026thinsp;0.7 threshold, we selected values (70\u0026ndash;100) as suitable habitat (conservative predictions)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e118\u003c/span\u003e\u003c/sup\u003e. This scenario reasonably forecasts the IAS potential range with few occurrences, as in our study, which is widely used in invasion ecology \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Suitable / Unsuitable values were extracted for each sub-basin and reservoir, analyzing IAS habitat-suitability patterns in the study area.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch3\u003eAnalysis of climate change scenarios\u0026nbsp;\u003c/h3\u003e\n \u003cp\u003eWe utilized the R package \u0026quot; BIOMOD_RangeSize\u0026quot; for a climate scenario analysis of our IAS overlap\u0026nbsp;\u003csup\u003e120\u003c/sup\u003e.\u0026nbsp;Comparing binary projections (current vs. 2050 SSP2 4.5), we assessed changes in four habitat types:\u003c/p\u003e\n \u003col class=\"decimal_type\" style=\"list-style-type: lower-alpha;\"\u003e\n \u003cli\u003eAreas that may no longer be suitable: predicted to be lost.\u003c/li\u003e\n \u003cli\u003eAreas that may remain unsuitable: predicted to remain stable.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eAreas that may gain suitability: predicted to be gained.\u003c/li\u003e\n \u003cli\u003eAreas that may remain suitable: predicted to remain stable.\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003cp\u003eModel outputs have a 0.926 km\u0026sup2; resolution (pixel size at equator latitude). Nevertheless, the study area\u0026apos;s size and distance from the equator alter potential suitable habitat values, presenting results in (\u0026asymp; km\u0026sup2;) rather than exact values (Supplementary Material SM-9).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgments\u003c/h2\u003e\n\u003cul class=\"decimal_type\"\u003e\n \u003cli\u003eCM-G\u003csup\u003ea\u003c/sup\u003e thanks OAS, GCUB \u0026amp; CNPq: Organization of American States, Coimbra Group of Brazilian Universities, and Brazilian Council for Scientific Research for the\u0026nbsp;master scholarship (#130674/2018-4 CNPq)\u003c/li\u003e\n \u003cli\u003eTS-S\u003csup\u003ec\u003c/sup\u003e thanks FAPEMAT (project FAPEMAT-PRO.000274/2023) and CECAV (project\u0026nbsp;TCCE ICMBio-CECAV n\u0026ordm;. 001/2023).\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cul\u003e\n \u003cli\u003eBrazilian National Water and Basic Sanitation Agency (ANA: Ag\u0026ecirc;ncia Nacional de \u0026Aacute;guas e Saneamento B\u0026aacute;sico) for funding the field interviews.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003eAuthor information\u003c/h2\u003e\n\u003ch3\u003eAuthors and Affiliations\u003c/h3\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eGraduate Program in Ecology and Biodiversity Conservation,\u0026nbsp;Institute of Biological Sciences,\u0026nbsp;Federal University of Mato Grosso, Av. Fernando Corr\u0026ecirc;a, 2367, Cuiab\u0026aacute;, Mato Grosso, 78060-900, Brazil.\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eCristian Mart\u0026iacute;nez-Gonz\u0026aacute;lez\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eLaboratory of Ecology and Management of Fisheries Resources,\u0026nbsp;Institute of Biological Sciences, Federal University of Mato Grosso, Cuiab\u0026aacute;, Mato Grosso, Brazil, Av. Fernando Corr\u0026ecirc;a, 2367, Cuiab\u0026aacute;, Mato Grosso, 78060-900, Brazil.\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eL\u0026uacute;cia Aparecida de Fatima Mateus, Jerry Magno Ferreira Penha\u0026nbsp;\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eDepartment of Botany, Institute of Biological Sciences, Federal University of Mato Grosso, Cuiab\u0026aacute;, Brazil, Av. Fernando Corr\u0026ecirc;a, 2367, Cuiab\u0026aacute;, Mato Grosso, 78060-900, Brazil.\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThadeu Sobral-Souza\u0026nbsp;\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eState University of Mato Grosso do Sul, Mato Grosso do Sul, Rod. Dourados-Itahum, Km 12, Dourados, Mato Grosso do Sul, 79804-970, Brazil.\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYzel Rondon S\u0026uacute;arez\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eCorresponding author\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003e*\u003c/strong\u003eCorrespondence to Cristian Mart\u0026iacute;nez-Gonz\u0026aacute;lez\u003c/p\u003e\n\u003ch3\u003eAuthor contributions\u003c/h3\u003e\n\u003cp\u003eCM-G and JMFP conceived, designed, and approved the study. LAdFM and YRS provided the data collection in the field. TS-S contributed to the development of the models and/or revisions. CM-G carried out the analysis and wrote the first draft of the manuscript. All authors contributed to subsequent versions and the interpretation of the data and results. All authors approved the final version of the manuscript.\u003c/p\u003e\n\u003ch2\u003eCompeting Interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003ch2\u003eEthics declarations\u003c/h2\u003e\n\u003cp\u003eThis study included the participation of local fishing communities of artisanal and recreational fishermen and was approved by the Federal University of Mato Grosso\u0026apos;s ethical research committee (register: 3.511.327). All the interviews were performed following relevant guidelines and regulations, protecting the identity of the interviewed fishers. Additionally, we confirmed that informed consent was assigned from all interviewed fishermen.\u003c/p\u003e\n\u003ch2\u003eCode availability\u003c/h2\u003e\n\u003cp\u003eThe script codes generated in this study have been deposited in the following Figshare repository: https://doi.org/10.6084/m9.figshare.26961754.\u003c/p\u003e\n\u003ch2\u003eData availability\u003c/h2\u003e\n\u003cp\u003eThe occurrence data of the introduced Cichlid species studied in its native distribution area was obtained from available biodiversity databases and georeferenced research papers (Supplementary Material S-1), and the occurrence data in its introduced range are available in the Figshare repository: https://doi.org/10.6084/m9.figshare.26961754. The climatic and topographic data used are available at https://www.earthenv.org/streams and https://www.worldclim.org/data/bioclim.html. Additionally, upon request, the corresponding author could provide the high-resolution rasters generated in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMainka, S. A. \u0026amp; Howard, G. W. Climate change and invasive species: double jeopardy. \u003cem\u003eIntegr. Zool.\u003c/em\u003e \u003cb\u003e5\u003c/b\u003e, 102\u0026ndash;111 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePyšek, P. et al. Scientists\u0026rsquo; warning on invasive alien species. \u003cem\u003eBiol. Rev.\u003c/em\u003e \u003cb\u003e95\u003c/b\u003e, 1511\u0026ndash;1534 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeiskopf, S. R. et al. Climate change effects on biodiversity, ecosystems, ecosystem services, and natural resource management in the United States. \u003cem\u003eSci. Total Environ.\u003c/em\u003e \u003cb\u003e733\u003c/b\u003e, 137782 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBellard, C., Cassey, P. \u0026amp; Blackburn, T. M. Alien species as a driver of recent extinctions. \u003cem\u003eBiol. Lett.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 20150623 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoy, H. E., Pauchard, A., Stoett, P. \u0026amp; Renard Truong, T. \u003cem\u003eIPBES Invasive Alien Species Assessment: Full Report\u003c/em\u003e. (2024). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://zenodo.org/doi/10.5281/zenodo.7430682\u003c/span\u003e\u003cspan address=\"https://zenodo.doi/10.5281/zenodo.7430682\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e doi:10.5281/ZENODO.7430682.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIBPES. \u003cem\u003eSummary for Policymakers of the Thematic Assessment Report on Invasive Alien Species and Their Control of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services.\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://zenodo.org/record/8314303\u003c/span\u003e\u003cspan address=\"https://zenodo.org/record/8314303\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5281/ZENODO.8314303\u003c/span\u003e\u003cspan address=\"10.5281/ZENODO.8314303\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePecl, G. T. et al. Biodiversity redistribution under climate change: Impacts on ecosystems and human well-being. \u003cem\u003eScience\u003c/em\u003e \u003cb\u003e355\u003c/b\u003e, eaai9214 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBellard, C., Jeschke, J. M., Leroy, B. \u0026amp; Mace, G. M. Insights from modeling studies on how climate change affects invasive alien species geography. \u003cem\u003eEcol. Evol.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e, 5688\u0026ndash;5700 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEssl, F. et al. Drivers of future alien species impacts: An expert-based assessment. \u003cem\u003eGlob. Change Biol.\u003c/em\u003e \u003cb\u003e26\u003c/b\u003e, 4880\u0026ndash;4893 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoura-Pascual, N. et al. Alternative futures for global biological invasions. \u003cem\u003eSustain. Sci.\u003c/em\u003e \u003cb\u003e16\u003c/b\u003e, 1637\u0026ndash;1650 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeebens, H. et al. Projecting the continental accumulation of alien species through to 2050. \u003cem\u003eGlob. Change Biol.\u003c/em\u003e \u003cb\u003e27\u003c/b\u003e, 970\u0026ndash;982 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurgiel, S. W., Muir, A. A. \u0026amp; Invasive Species Climate Change and Ecosystem-Based Adaptation: Addressing Multiple Drivers of Global Change Global Invasive Species Programme. Preprint at (2010). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.13140/2.1.1460.8161\u003c/span\u003e\u003cspan address=\"10.13140/2.1.1460.8161\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKernan, M. Climate change and the impact of invasive species on aquatic ecosystems. \u003cem\u003eAquat. Ecosyst. Health Manag.\u003c/em\u003e \u003cb\u003e18\u003c/b\u003e, 321\u0026ndash;333 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarrison, I. et al. The freshwater biodiversity crisis. \u003cem\u003eScience\u003c/em\u003e \u003cb\u003e362\u003c/b\u003e, 1369\u0026ndash;1369 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSayer, C. A. et al. One-quarter of freshwater fauna threatened with extinction. \u003cem\u003eNature\u003c/em\u003e \u003cb\u003e638\u003c/b\u003e, 138\u0026ndash;145 (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCastellanos, E. \u003cem\u003eet al. Climate Change 2022-Impacts, Adaptation and Vulnerability Regional Factsheet Central and South America\u003c/em\u003e. (2022). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://hdl.handle.net/20.500.12123/12589\u003c/span\u003e\u003cspan address=\"http://hdl.handle.net/20.500.12123/12589\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTorremorell, A. et al. Current and future threats for ecological quality management of South American freshwater ecosystems. \u003cem\u003eInland. Waters\u003c/em\u003e. \u003cb\u003e11\u003c/b\u003e, 125\u0026ndash;140 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBernery, C. et al. Freshwater Fish Invasions: A Comprehensive Review. \u003cem\u003eAnnu. Rev. Ecol. Evol. Syst.\u003c/em\u003e \u003cb\u003e53\u003c/b\u003e, 427\u0026ndash;456 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMu\u0026ntilde;oz-Mas, R. et al. Two centuries of spatial and temporal dynamics of freshwater fish introductions. \u003cem\u003eGlobal Ecol. Biogeogr.\u003c/em\u003e \u003cb\u003e32\u003c/b\u003e, 1632\u0026ndash;1644 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVitule, J. R. S. et al. Comment on \u0026lsquo;Fish biodiversity and conservation in South America by Reis et al. \u0026rsquo;. \u003cem\u003eJournal of Fish Biology\u003c/em\u003e 90, 1182\u0026ndash;1190 (2017). (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFragoso-Moura, E. N., Oporto, L. T., Maia-Barbosa, P. M. \u0026amp; Barbosa, F. A. R. Loss of biodiversity in a conservation unit of the Brazilian Atlantic Forest: the effect of introducing non-native fish species. \u003cem\u003eBraz J. Biol.\u003c/em\u003e \u003cb\u003e76\u003c/b\u003e, 18\u0026ndash;27 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWantzen, K. M. et al. UNESCO,. The Pantanal: How long will there be Life in the Rhythm of the Waters? in \u003cem\u003eRiver culture: life as a dance to the rhythm of the waters\u003c/em\u003e (ed. Wantzen, K. M.) 497\u0026ndash;536 (2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.54677/DYRD7304\u003c/span\u003e\u003cspan address=\"10.54677/DYRD7304\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZanatta, S. C. \u0026amp; Maciel, J. Pantanal amea\u0026ccedil;ado: a constru\u0026ccedil;\u0026atilde;o de represas e a insignific\u0026acirc;ncia da energia h\u0026iacute;drica produzida. \u003cem\u003eInter\u003c/em\u003e 333\u0026ndash;348. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.20435/inter.v22i2.2811\u003c/span\u003e\u003cspan address=\"10.20435/inter.v22i2.2811\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgostinho, A. A., Pelicice, F. M. \u0026amp; Gomes, L. C. Dams and the fish fauna of the Neotropical region: impacts and management related to diversity and fisheries. \u003cem\u003eBraz J. Biol.\u003c/em\u003e \u003cb\u003e68\u003c/b\u003e, 1119\u0026ndash;1132 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePereira, L. S. et al. Non-native species in reservoirs: how are we doing in Brazil? \u003cem\u003eHydrobiologia\u003c/em\u003e \u003cb\u003e817\u003c/b\u003e, 71\u0026ndash;84 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRocha, B. S., Garc\u0026iacute;a-Berthou, E. \u0026amp; Cianciaruso, M. V. Non-native fishes in Brazilian freshwaters: identifying biases and gaps in ecological research. \u003cem\u003eBiol. Invasions\u003c/em\u003e. \u003cb\u003e25\u003c/b\u003e, 1643\u0026ndash;1658 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarvalho, D. C., Oliveira, D. A. A., Sampaio, I. \u0026amp; Beheregaray, L. B. Analysis of propagule pressure and genetic diversity in the invasibility of a freshwater apex predator: the peacock bass (genus Cichla). \u003cem\u003eNeotrop. ichthyol.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 105\u0026ndash;116 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eResende, E., Marques, D. \u0026amp; Ferreira Lksg. A successful case of biological invasion: the fish Cichla piquiti, an Amazonian species introduced into the Pantanal, Brazil. \u003cem\u003eBraz J. Biol.\u003c/em\u003e \u003cb\u003e68\u003c/b\u003e, 799\u0026ndash;805 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrtega, J. First record of Peacock bass Cichla kelberi Kullander \u0026amp; Ferreira, 2006 in the Brazilian Pantanal. \u003cem\u003eBIR\u003c/em\u003e 4, 133\u0026ndash;138 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCordova Junior, R. F. \u0026amp; D. Introdu\u0026ccedil;\u0026atilde;o e distribui\u0026ccedil;\u0026atilde;o do tucunar\u0026eacute;-azul e do tambaqui no Pantanal Corumbaense. \u003cem\u003eActa\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e, 01\u0026ndash;15 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNobile, A. B. et al. Status and recommendations for sustainable freshwater aquaculture in Brazil. \u003cem\u003eReviews Aquaculture\u003c/em\u003e. \u003cb\u003e12\u003c/b\u003e, 1495\u0026ndash;1517 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoreira, E. A. \u0026amp; Silva, D. D. P. A dozen fish: some exotic invasive species found in Brazilian waters and their impacts. \u003cem\u003eCi\u0026ecirc;nc anim. bras.\u003c/em\u003e \u003cb\u003e24\u003c/b\u003e, e\u0026ndash;74647E (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCassemiro, F. A. S., Bailly, D., Gra\u0026ccedil;a, D., Agostinho, A. A. \u0026amp; W. J. \u0026amp; The invasive potential of tilapias (Osteichthyes, Cichlidae) in the Americas. \u003cem\u003eHydrobiologia\u003c/em\u003e \u003cb\u003e817\u003c/b\u003e, 133\u0026ndash;154 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLima Junior, D. P. et al. Aquaculture expansion in Brazilian freshwaters against the Aichi Biodiversity Targets. \u003cem\u003eAmbio\u003c/em\u003e (2018). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s13280-017-1001-z\u003c/span\u003e\u003cspan address=\"10.1007/s13280-017-1001-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Medinas, M. et al. Predicted impacts of proposed hydroelectric facilities on fish migration routes upstream from the Pantanal wetland (Brazil). \u003cem\u003eRiver Res. Apps\u003c/em\u003e. \u003cb\u003e36\u003c/b\u003e, 452\u0026ndash;464 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePelicice, F. M. et al. Unintended consequences of valuing the contributions of non-native species: misguided conservation initiatives in a megadiverse region. \u003cem\u003eBiodivers. Conserv.\u003c/em\u003e \u003cb\u003e32\u003c/b\u003e, 3915\u0026ndash;3938 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePelicice, F. M., Latini, J. D. \u0026amp; Agostinho, A. A. Fish fauna disassembly after the introduction of a voracious predator: main drivers and the role of the invader\u0026rsquo;s demography. \u003cem\u003eHydrobiologia\u003c/em\u003e \u003cb\u003e746\u003c/b\u003e, 271\u0026ndash;283 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeterson, A. T. Predicting the Geography of Species\u0026rsquo; Invasions via Ecological Niche Modeling. \u003cem\u003eQ. Rev. Biol.\u003c/em\u003e \u003cb\u003e78\u003c/b\u003e, 419\u0026ndash;433 (2003).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElith, J. \u0026amp; Franklin, J. Species Distribution Modeling. in Encyclopedia of Biodiversity 558\u0026ndash;572 (Elsevier, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/B978-0-12-822562-2.00379-0\u003c/span\u003e\u003cspan address=\"10.1016/B978-0-12-822562-2.00379-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThuiller, W. et al. Niche-based modelling as a tool for predicting the risk of alien plant invasions at a global scale. \u003cem\u003eGlob. Change Biol.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 2234\u0026ndash;2250 (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElith, J. Predicting Distributions of Invasive Species. in Invasive Species (eds Robinson, A. P., Walshe, T., Burgman, M. A. \u0026amp; Nunn, M.) 93\u0026ndash;129 (Cambridge University Press, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1017/9781139019606.006\u003c/span\u003e\u003cspan address=\"10.1017/9781139019606.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFranklin, J. Species distribution modelling supports the study of past, present and future biogeographies. \u003cem\u003eJ. Biogeogr.\u003c/em\u003e \u003cb\u003e50\u003c/b\u003e, 1533\u0026ndash;1545 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSinclair, S. J., White, M. D. \u0026amp; Newell, G. R. How Useful Are Species Distribution Models for Managing Biodiversity under Future Climates? \u003cem\u003eE\u0026amp;S\u003c/em\u003e 15, art8 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeterson, A. T. \u0026amp; Sober\u0026oacute;n, J. M. Integrating fundamental concepts of ecology, biogeography, and sampling into effective ecological niche modeling and species distribution modeling. \u003cem\u003ePlant. Biosystems - Int. J. Dealing all Aspects Plant. Biology\u003c/em\u003e. \u003cb\u003e146\u003c/b\u003e, 789\u0026ndash;796 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSober\u0026oacute;n, J. M. Niche and area of distribution modeling: a population ecology perspective. \u003cem\u003eEcography\u003c/em\u003e \u003cb\u003e33\u003c/b\u003e, 159\u0026ndash;167 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNori, J. \u0026amp; Rojas-Soto, O. On the environmental background of aquatic organisms for ecological niche modeling: a call for caution. \u003cem\u003eAquat. Ecol.\u003c/em\u003e \u003cb\u003e53\u003c/b\u003e, 595\u0026ndash;605 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eValencia-Rodr\u0026iacute;guez, D., Jim\u0026eacute;nez-Segura, L., Rog\u0026eacute;liz, C. A. \u0026amp; Parra, J. L. Ecological niche modeling as an effective tool to predict the distribution of freshwater organisms: The case of the Sabaleta Brycon henni (Eigenmann, 1913). \u003cem\u003ePLoS ONE\u003c/em\u003e. \u003cb\u003e16\u003c/b\u003e, e0247876 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaupe, E. E. et al. Variation in niche and distribution model performance: The need for a priori assessment of key causal factors. \u003cem\u003eEcol. Model.\u003c/em\u003e \u003cb\u003e237\u0026ndash;238\u003c/b\u003e, 11\u0026ndash;22 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThuiller, W. Ecological niche modelling. \u003cem\u003eCurr. Biol.\u003c/em\u003e \u003cb\u003e34\u003c/b\u003e, R225\u0026ndash;R229 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIPCC. \u003cem\u003eClimate Change 2021 \u0026ndash; The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change\u003c/em\u003e (Cambridge University Press, 2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1017/9781009157896\u003c/span\u003e\u003cspan address=\"10.1017/9781009157896\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeinshausen, M. et al. The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500. \u003cem\u003eGeosci. Model. Dev.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e, 3571\u0026ndash;3605 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeldman, M. J. et al. Trends and gaps in the use of citizen science derived data as input for species distribution models: A quantitative review. \u003cem\u003ePLoS ONE\u003c/em\u003e. \u003cb\u003e16\u003c/b\u003e, e0234587 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGervazoni, P. et al. Citizen Science Improves the Known and Potential Distribution of a Strong Wetland Invader: Implications for Niche Modeling and Invasion Management. \u003cem\u003eEnviron. Manage.\u003c/em\u003e \u003cb\u003e71\u003c/b\u003e, 1176\u0026ndash;1187 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHallwass, G., Schiavetti, A. \u0026amp; Silvano, R. A. M. Fishers\u0026rsquo; knowledge indicates temporal changes in composition and abundance of fishing resources in Amazon protected areas. \u003cem\u003eAnim. Conserv.\u003c/em\u003e \u003cb\u003e23\u003c/b\u003e, 36\u0026ndash;47 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoward, L., Van Rees, C. B., Dahlquist, Z., Luikart, G. \u0026amp; Hand, B. K. A review of invasive species reporting apps for citizen science and opportunities for innovation. \u003cem\u003eNB\u003c/em\u003e \u003cb\u003e71\u003c/b\u003e, 165\u0026ndash;188 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePhillips, T. B., Bailey, R. L., Martin, V., Faulkner-Grant, H. \u0026amp; Bonter, D. N. The role of citizen science in management of invasive avian species: What people think, know, and do. \u003cem\u003eJ. Environ. Manage.\u003c/em\u003e \u003cb\u003e280\u003c/b\u003e, 111709 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePocock, M. J. O. et al. Citizen science is a vital partnership for invasive alien species management and research. \u003cem\u003eiScience\u003c/em\u003e \u003cb\u003e27\u003c/b\u003e, 108623 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeluso, L. M., Mateus, L., Penha, J., S\u0026uacute;arez, Y. \u0026amp; Lemes, P. Climate change may reduce suitable habitat for freshwater fish in a tropical watershed. \u003cem\u003eClim. Change\u003c/em\u003e. \u003cb\u003e176\u003c/b\u003e, 44 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHavel, J. E., Kovalenko, K. E., Thomaz, S. M. \u0026amp; Amalfitano, S. Kats, L. B. Aquatic invasive species: challenges for the future. \u003cem\u003eHydrobiologia\u003c/em\u003e \u003cb\u003e750\u003c/b\u003e, 147\u0026ndash;170 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSultan, M. \u0026amp; Pei, D. S. Invasive Alien Species Problem in the Reservoir. in Reservoir Ecotoxicology 219\u0026ndash;228 (Springer International Publishing, Cham, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/978-3-031-26344-6_19\u003c/span\u003e\u003cspan address=\"10.1007/978-3-031-26344-6_19\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDomisch, S., Amatulli, G. \u0026amp; Jetz, W. Near-global freshwater-specific environmental variables for biodiversity analyses in 1 km resolution. \u003cem\u003eSci. Data\u003c/em\u003e. \u003cb\u003e2\u003c/b\u003e, 150073 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFick, S. E. \u0026amp; Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. \u003cem\u003eIntl J. Climatology\u003c/em\u003e. \u003cb\u003e37\u003c/b\u003e, 4302\u0026ndash;4315 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNazarenko, L. S. et al. Future Climate Change Under SSP Emission Scenarios With GISS-E2.1. \u003cem\u003eJ. Adv. Model. Earth Syst.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, e2021MS002871 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeluso, L. M. et al. Climate change negative effects on the Neotropical fishery resources may be exacerbated by hydroelectric dams. \u003cem\u003eSci. Total Environ.\u003c/em\u003e \u003cb\u003e828\u003c/b\u003e, 154485 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeal, L. B. et al. Changes in ecosystem functions generated by fish populations after the introduction of a non-native predator (Cichla kelberi) (Perciformes: Cichlidae). \u003cem\u003eNeotrop. ichthyol.\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e, e210041 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRauber, R. G. et al. Changes in fish functional diversity in a neotropical floodplain associated with the invasive Cichla spp.: a long-term perspective. \u003cem\u003eHydrobiologia\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10750-024-05725-w\u003c/span\u003e\u003cspan address=\"10.1007/s10750-024-05725-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMofu, L. et al. Impacts of non-native fishes under a seasonal temperature gradient are forecasted using functional responses and abundances. \u003cem\u003eNB\u003c/em\u003e \u003cb\u003e49\u003c/b\u003e, 57\u0026ndash;75 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSloan, T. J. \u0026amp; Turingan, R. G. The Effects of Temperature on the Feeding Performance of Invasive Fishes. \u003cem\u003eAJLS\u003c/em\u003e \u003cb\u003e2\u003c/b\u003e, 63 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuz, S. C. S. et al. Reduced genetic diversity and the success of the invasive peacock bass (Cichliformes: Cichlidae). \u003cem\u003eBraz J. Biol.\u003c/em\u003e \u003cb\u003e84\u003c/b\u003e, e248656 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurress, E. D. Cichlid fishes as models of ecological diversification: patterns, mechanisms, and consequences. \u003cem\u003eHydrobiologia\u003c/em\u003e \u003cb\u003e748\u003c/b\u003e, 7\u0026ndash;27 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlves, J. C., Andreotti, G. F., Agostinho, A. A. \u0026amp; Gomes, L. C. Effects of the El Ni\u0026ntilde;o Southern Oscillation (ENSO) on fish assemblages in a Neotropical floodplain. \u003cem\u003eHydrobiologia\u003c/em\u003e \u003cb\u003e848\u003c/b\u003e, 1811\u0026ndash;1823 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndreotti, G. F., Alves, J. C., Alves, D. C., Agostinho, A. A. \u0026amp; Gomes, L. C. The response of fish functional diversity to the El Ni\u0026ntilde;o Southern Oscillation (ENSO) in a Neotropical floodplain. \u003cem\u003eHydrobiologia\u003c/em\u003e \u003cb\u003e848\u003c/b\u003e, 1207\u0026ndash;1218 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSilva, C. B., Silva, M. E. S. \u0026amp; Ambrizzi, T. Climatic variability of river outflow in the Pantanal region and the influence of sea surface temperature. \u003cem\u003eTheor. Appl. Climatol\u003c/em\u003e. \u003cb\u003e129\u003c/b\u003e, 97\u0026ndash;109 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThielen, D. et al. Quo vadis Pantanal? Expected precipitation extremes and drought dynamics from changing sea surface temperature. \u003cem\u003ePLoS ONE\u003c/em\u003e. \u003cb\u003e15\u003c/b\u003e, e0227437 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarradas, J. R. S., Silva, L. G. \u0026amp; Harvey, B. C. Fontoura, N. F. Estimating migratory fish distribution from altitude and basin area: a case study in a large Neotropical river. \u003cem\u003eFreshw. Biol.\u003c/em\u003e \u003cb\u003e57\u003c/b\u003e, 2297\u0026ndash;2305 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eComte, L. \u0026amp; Grenouillet, G. Distribution shifts of freshwater fish under a variable climate: comparing climatic, bioclimatic and biotic velocities. \u003cem\u003eDivers. Distrib.\u003c/em\u003e \u003cb\u003e21\u003c/b\u003e, 1014\u0026ndash;1026 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHirzel, A. H. \u0026amp; Le Lay, G. Habitat suitability modelling and niche theory. \u003cem\u003eJ. Appl. Ecol.\u003c/em\u003e \u003cb\u003e45\u003c/b\u003e, 1372\u0026ndash;1381 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eComte, L. \u0026amp; Olden, J. D. Evolutionary and environmental determinants of freshwater fish thermal tolerance and plasticity. \u003cem\u003eGlob. Change Biol.\u003c/em\u003e \u003cb\u003e23\u003c/b\u003e, 728\u0026ndash;736 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgostinho, A. A. et al. Introduced Cichlids in the Americas: Distribution Patterns, Invasion Ecology, and Impacts. in The Behavior, Ecology and Evolution of Cichlid Fishes (eds Abate, M. E. \u0026amp; Noakes, D. L. G.) 313\u0026ndash;361 (Springer Netherlands, Dordrecht, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/978-94-024-2080-7_10\u003c/span\u003e\u003cspan address=\"10.1007/978-94-024-2080-7_10\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFranco, A. C. S., Petry, A. C. \u0026amp; Tavares, M. R. De F\u0026aacute;tima Ramos Guimar\u0026atilde;es, T. \u0026amp; Dos Santos, L. N. Global distribution of the South American peacock basses \u003cem\u003eCichla\u003c/em\u003e spp. follows human interference. \u003cem\u003eFish and Fisheries\u003c/em\u003e 23, 407\u0026ndash;421 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTomas, W. M. et al. Sustainability Agenda for the Pantanal Wetland: Perspectives on a Collaborative Interface for Science, Policy, and Decision-Making. \u003cem\u003eTrop. Conserv. Sci.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 194008291987263 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLatini, A. O. et al. Brazil\u0026rsquo;s new fish farming Decree threatens freshwater conservation in South America. \u003cem\u003eBiol. Conserv.\u003c/em\u003e \u003cb\u003e263\u003c/b\u003e, 109353 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIkeda-Castrillon, S. K., Oliveira-Junior, E. S., Rossetto, O. C., Saito, C. H. \u0026amp; Wantzen, K. M. The Pantanal: A Seasonal Neotropical Wetland Under Threat. in The Palgrave Handbook of Global Sustainability 1\u0026ndash;27 (Springer International Publishing, Cham, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/978-3-030-38948-2_36-1\u003c/span\u003e\u003cspan address=\"10.1007/978-3-030-38948-2_36-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgostinho, A. A., Gomes, L. C., Santos, N. C. L., Ortega, J. C. G. \u0026amp; Pelicice, F. M. Fish assemblages in Neotropical reservoirs: Colonization patterns, impacts and management. \u003cem\u003eFish. Res.\u003c/em\u003e \u003cb\u003e173\u003c/b\u003e, 26\u0026ndash;36 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEsp\u0026iacute;nola, L. A., Minte-Vera, C. V. \u0026amp; J\u0026uacute;lio, H. F. Invasibility of reservoirs in the Paran\u0026aacute; Basin, Brazil, to Cichla kelberi Kullander and Ferreira, 2006. \u003cem\u003eBiol. Invasions\u003c/em\u003e. \u003cb\u003e12\u003c/b\u003e, 1873\u0026ndash;1888 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePelicice, F. M. \u0026amp; Agostinho, A. A. Fish fauna destruction after the introduction of a non-native predator (Cichla kelberi) in a Neotropical reservoir. \u003cem\u003eBiol. Invasions\u003c/em\u003e. \u003cb\u003e11\u003c/b\u003e, 1789\u0026ndash;1801 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarvalho, T. L., De Almeida Ferreira, E., Pelicice, F. M. \u0026amp; Fernandes, R. Comparative functional responses predict the predatory impact of the highly invasive fish Cichla kelberi. \u003cem\u003eHydrobiologia\u003c/em\u003e \u003cb\u003e848\u003c/b\u003e, 2203\u0026ndash;2211 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatias, R. et al. Estudo do tucunar\u0026eacute; (Cichla ocellaris) em duas ba\u0026iacute;as marginais do Rio Piquiri (Pantanal do Paiagu\u0026aacute;s, MS): par\u0026acirc;metros f\u0026iacute;sico- qu\u0026iacute;micos e an\u0026aacute;lise histol\u0026oacute;gica da pele. 5, (2001).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarques, D. K. S. \u0026amp; Resende, E. K. \u003cem\u003eOcorr\u0026ecirc;ncia do Tucunar\u0026eacute; Cichla cf. monoculus no Pantanal.\u003c/em\u003e (2005). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.infoteca.cnptia.embrapa.br/infoteca/handle/doc/812032\u003c/span\u003e\u003cspan address=\"https://www.infoteca.cnptia.embrapa.br/infoteca/handle/doc/812032\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCl\u0026iacute;maco, T. S., Soriano, B. M. A. \u0026amp; Catella, A. C. Mortandade do tucunar\u0026eacute;-azul (Cichla piquiti) no Pantanal em fun\u0026ccedil;\u0026atilde;o de frentes frias: informa\u0026ccedil;\u0026otilde;es preliminares. (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCatella, A. C., Albuquerque, S., Campos, F. \u0026amp; Santos, D. \u003cem\u003eSistema de Controle da Pesca de Mato Grosso do Sul - CPESCA/MS\u003c/em\u003e. 54 (2018). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.infoteca.cnptia.embrapa.br/infoteca/handle/doc/980999\u003c/span\u003e\u003cspan address=\"http://www.infoteca.cnptia.embrapa.br/infoteca/handle/doc/980999\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCatelani, P. A., Petry, A. C. \u0026amp; Pelicice, F. M. Azevedo Matias Silvano, R. Fishers\u0026rsquo; knowledge on the ecology, impacts and benefits of the non-native peacock bass Cichla kelberi in a coastal river in southeastern Brazil. \u003cem\u003eEthnobio Conserv.\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.15451/ec2020-09-10.04-1-16\u003c/span\u003e\u003cspan address=\"10.15451/ec2020-09-10.04-1-16\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlves, G. H. Z., T\u0026oacute;foli, R. M., Message, H. J., Lima-J\u0026uacute;nior, D. P. \u0026amp; Hoeinghaus, D. J. New decree promotes fish invasion in Amazon and Pantanal. \u003cem\u003eBiodivers. Conserv.\u003c/em\u003e \u003cb\u003e27\u003c/b\u003e, 2449\u0026ndash;2450 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLatini, A. O. \u0026amp; Petrere J\u0026uacute;nior, M. Efficiency of rapid field methods for detecting non-native fish in Eastern Brazilian lakes. \u003cem\u003eHydrobiologia\u003c/em\u003e \u003cb\u003e817\u003c/b\u003e, 85\u0026ndash;96 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCoxen, C. L., Frey, J. K., Carleton, S. A. \u0026amp; Collins, D. P. Species distribution models for a migratory bird based on citizen science and satellite tracking data. \u003cem\u003eGlobal Ecol. Conserv.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 298\u0026ndash;311 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePocock, M. J. O., Roy, H. E., Fox, R., Ellis, W. N. \u0026amp; Botham, M. Citizen science and invasive alien species: Predicting the detection of the oak processionary moth Thaumetopoea processionea by moth recorders. \u003cem\u003eBiol. Conserv.\u003c/em\u003e \u003cb\u003e208\u003c/b\u003e, 146\u0026ndash;154 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWest, A. M., Kumar, S., Brown, C. S., Stohlgren, T. J. \u0026amp; Bromberg, J. Field validation of an invasive species Maxent model. \u003cem\u003eEcol. Inf.\u003c/em\u003e \u003cb\u003e36\u003c/b\u003e, 126\u0026ndash;134 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrtega-Huerta, M. A. \u0026amp; Vega‐Rivera, J. H. Validating distribution models for twelve endemic bird species of tropical dry forest in western Mexico. \u003cem\u003eEcol. Evol.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e, 7672\u0026ndash;7686 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHutchinson, R., He, L. \u0026amp; Emerson, S. Species Distribution Modeling of Citizen Science Data as a Classification Problem with Class-Conditional Noise. \u003cem\u003eAAAI\u003c/em\u003e 31, (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSilvestro, D., Goria, S., Sterner, T. \u0026amp; Antonelli, A. Improving biodiversity protection through artificial intelligence. \u003cem\u003eNat. Sustain.\u003c/em\u003e \u003cb\u003e5\u003c/b\u003e, 415\u0026ndash;424 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMendes, M. \u003cem\u003eLEI No 12.434, DE 01 DE MAR\u0026Ccedil;O DE 2024.\u003c/em\u003e 1 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOta, R. R. et al. Brazilian fish fauna threatened by a proposal for a new law protecting invaders. \u003cem\u003eBiodivers. Conserv.\u003c/em\u003e \u003cb\u003e28\u003c/b\u003e, 787\u0026ndash;789 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdelino, J. R. P. et al. The economic costs of biological invasions in Brazil: a first assessment. \u003cem\u003eNB\u003c/em\u003e \u003cb\u003e67\u003c/b\u003e, 349\u0026ndash;374 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShirley, E. A. \u0026amp; Gore, M. L. Trust in scientists and rates of noncompliance with a fisheries rule in the Brazilian Pantanal. \u003cem\u003ePLoS ONE\u003c/em\u003e. \u003cb\u003e14\u003c/b\u003e, e0207973 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoore, A. The Aquatic Invaders: Marine Management Figuring Fishermen, Fisheries, and Lionfish in The Bahamas. \u003cem\u003eCult. Anthropol.\u003c/em\u003e \u003cb\u003e27\u003c/b\u003e, 667\u0026ndash;688 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMohd Dali, M. Z. et al. Detection and Management of Freshwater Invasive Alien Species through Environmental DNA Analysis and Geographic Information Systems: A Review. \u003cem\u003eSustainability\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, 9497 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKousteni, V. et al. Citizen scientists contributing to alien species detection: the case of fishes and mollusks in European marine waters. \u003cem\u003eEcosphere\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e, e03875 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJeschke, J. M. \u0026amp; Strayer, D. L. Usefulness of Bioclimatic Models for Studying Climate Change and Invasive Species. \u003cem\u003eAnn. N. Y. Acad. Sci.\u003c/em\u003e \u003cb\u003e1134\u003c/b\u003e, 1\u0026ndash;24 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLovato, T. et al. CMIP6 Simulations With the CMCC Earth System Model (CMCC-ESM2). \u003cem\u003eJ Adv Model Earth Syst\u003c/em\u003e 14, eMS002814 (2022). (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndrews, M. B. et al. Historical Simulations With HadGEM3-GC3.1 for CMIP6. \u003cem\u003eJ Adv Model Earth Syst\u003c/em\u003e 12, eMS001995 (2020). (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTanimu, B. et al. Enhancing reliability in climate projections: A novel approach for selecting global climate models. \u003cem\u003ePhys. Chem. Earth Parts A/B/C\u003c/em\u003e. \u003cb\u003e134\u003c/b\u003e, 103598 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCastaneda-Gonzalez, M., Poulin, A., Romero-Lopez, R. \u0026amp; Turcotte, R. Weighting climate models for hydrological projections: effects on contrasting hydroclimatic regions. \u003cem\u003eClim. Change\u003c/em\u003e. \u003cb\u003e176\u003c/b\u003e, 170 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFirpo, M. \u0026Acirc;. F. et al. Assessment of CMIP6 models\u0026rsquo; performance in simulating present-day climate in Brazil. \u003cem\u003eFront. Clim.\u003c/em\u003e \u003cb\u003e4\u003c/b\u003e, 948499 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDormann, C. F. et al. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. \u003cem\u003eEcography\u003c/em\u003e \u003cb\u003e36\u003c/b\u003e, 27\u0026ndash;46 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCamana, M., Dala-Corte, R. B. \u0026amp; Becker, F. G. Relation between species richness and stream slope in riffle fish assemblages is dependent on spatial scale. \u003cem\u003eEnviron. Biol. Fish.\u003c/em\u003e \u003cb\u003e99\u003c/b\u003e, 603\u0026ndash;612 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlvarez, F., Gerhard, P., Paiva Silva, D., Spacek Godoy, B. \u0026amp; Montag, L. F. D. A. Effects of different variable sets on the potential distribution of fish species in the Amazon Basin. \u003cem\u003eEcol. Freshw. Fish\u003c/em\u003e. \u003cb\u003e29\u003c/b\u003e, 764\u0026ndash;778 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeterson, A. T. Predicting the Geography of Species\u0026rsquo; Invasions via Ecological Niche Modeling. \u003cem\u003eQ. Rev. Biol.\u003c/em\u003e \u003cb\u003e78\u003c/b\u003e, 419\u0026ndash;433 (2003).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHao, T., Elith, J., Guillera-Arroita, G. \u0026amp; Lahoz‐Monfort, J. J. A review of evidence about use and performance of species distribution modelling ensembles like BIOMOD. \u003cem\u003eDivers. Distrib.\u003c/em\u003e \u003cb\u003e25\u003c/b\u003e, 839\u0026ndash;852 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiniz-Filho, J. A. F. et al. Partitioning and mapping uncertainties in ensembles of forecasts of species turnover under climate change. \u003cem\u003eEcography\u003c/em\u003e \u003cb\u003e32\u003c/b\u003e, 897\u0026ndash;906 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThuiller, W. BIOMOD \u0026ndash; optimizing predictions of species distributions and projecting potential future shifts under global change. \u003cem\u003eGlob. Change Biol.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, 1353\u0026ndash;1362 (2003).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuete, A. \u0026amp; Leynaud, G. C. \u003cem\u003eGoal-Oriented Evaluation of Species Distribution Models\u0026rsquo; Accuracy and Precision: True Skill Statistic Profile and Uncertainty Maps\u003c/em\u003e. (2015). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://peerj.com/preprints/1208v1\u003c/span\u003e\u003cspan address=\"https://peerj.com/preprints/1208v1\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Social Networks, Blue Peacock Bass, Cichla piquiti, Yellow Peacock Bass, Cichla kelberi, Nile Tilapia, Oreochromis niloticus","lastPublishedDoi":"10.21203/rs.3.rs-5050398/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5050398/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eClimate change is considered a driver for the spread of invasive alien species (IAS); nevertheless, research assessing this link remains limited. Recognizing suitable habitats where IAS have been introduced is crucial for biodiversity conservation and ecosystem management. Here, we integrated online, museum, and laboratory occurrence databases with local ecological knowledge (LEK) on IAS fishes from semi-structured interviews and georeferenced Instagram posts from traditional and recreational fishers to model the habitat-suitability of three voracious IAS Cichlids introduced in the Brazilian part of the Upper Paraguay River Basin (the Pantanal wetland and its tributaries). Our goal was to locate areas (regions, sub-basins, and reservoirs) most at risk from the spread of these IAS fishes in the basin. Our findings reveal extensive suitable habitats for these IAS fishes throughout the study basin, currently covering half of the Pantanal wetland and up to 90% of the upstream sub-basins. Under future climate scenarios, these suitable habitats are projected to expand further, encompassing 85% of the Pantanal floodplain - one of the most fish-rich basins in the Neotropical region (~\u0026thinsp;300 spp). These findings underscore a potential IAS Cichlid range expansion in the Pantanal floodplains in the upcoming decades. Our study emphasizes the value of integrating Ecological Niche Models (ENMs) with Citizen Science data to identify high-risk areas during early invasion stages, inform preventive strategies, and support conservation efforts to mitigate the impacts of IAS on native biodiversity.\u003c/p\u003e","manuscriptTitle":"Climate change may increase the suitable habitats for invasive freshwater Cichlids in a Neotropical basin","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-02 07:11:06","doi":"10.21203/rs.3.rs-5050398/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-12T13:53:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-02T18:13:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-11T22:46:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"315427641408751577084447631115762739950","date":"2025-04-10T22:40:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"30947343226964589929327089810326052817","date":"2025-04-10T13:06:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-09T16:46:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"216910137540956256442734416539175433721","date":"2025-04-09T16:16:49+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-08T21:03:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-04T12:37:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-26T12:40:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9e144785-5011-4ffc-9bf4-c46d56a467a6","owner":[],"postedDate":"April 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":46555693,"name":"Earth and environmental sciences/Environmental social sciences/Climate change impacts"},{"id":46555694,"name":"Biological sciences/Ecology/Ecological modelling"},{"id":46555695,"name":"Biological sciences/Ecology/Freshwater ecology"},{"id":46555696,"name":"Biological sciences/Ecology/Invasive species"}],"tags":[],"updatedAt":"2025-12-01T16:07:29+00:00","versionOfRecord":{"articleIdentity":"rs-5050398","link":"https://doi.org/10.1038/s41598-025-30425-3","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-11-27 15:57:19","publishedOnDateReadable":"November 27th, 2025"},"versionCreatedAt":"2025-04-02 07:11:06","video":"","vorDoi":"10.1038/s41598-025-30425-3","vorDoiUrl":"https://doi.org/10.1038/s41598-025-30425-3","workflowStages":[]},"version":"v1","identity":"rs-5050398","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5050398","identity":"rs-5050398","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}