How climate change will change the potential distribution of two wood-decaying fungi?

Abstract

Changes in climate, which can be understood as fluctuations in climate patterns as a reflection of natural or anthropic interventions, can generate changes in the environment and consequently affect the diversity of organisms. Fungi are extremely important in organic matter cycling in different environments, mainly forest areas, decomposing dead wood. To better understand the effects of climate change on two wood-degrading Agaricomycetes, their potential neotropical distribution was modeled using known occurrence data, available in the GBIF database and in specific literature, and associated with predictor variables extracted from Worldclim. A modeling package in R environment was used to analyze the present and future suitability for the optimistic and pessimistic scenarios. The results indicate the climate as an important factor in the distribution of Auricularia brasiliana and Megasporoporia neosetulosa. The suitability factors for the metrics used indicate that the models can be used to analyze climatic areas and that temperature and precipitation strongly influence the permanence of species in these locations. The results also indicate areas that can be affected by climatic effects, consequently causing a decrease in the occurrence and permanence of these fungi in the Neotropics. Our models can be useful as future guidelines in conservation studies for fungi.

Keywords:
Agaricomycetes; Conservation; Neotropics; GBIF; Species distribution modelling

Introduction

Over thousands of years, humans have been responsible for promoting changes in the environment causing significant ecological impacts and loss of its diversity, in addition to promoting the dispersion of species over long distances around the planet (Whittaker et al. 2005; Tylianakis et al. 2008; Bullock et al. 2018). Climate change (CC) is understood as changes in weather patterns caused by natural factors (Yue & Gao 2018) or by human action (Huang et al. 2020) that are mainly evident through the emission of gases into the atmosphere.

Changes in climate indicators are generally related to abiotic factors such as precipitation, temperature, rising levels and acidification of the ocean (Fawzy et al. 2020). These changes can trigger extreme natural phenomena and affect genetic diversity within populations (Pauls et al. 2012), causing extinction events for some ecological populations in tropical environments, including fungi (Sheldon 2019; Lughadha et al. 2020).

Fungi correspond to a group of organisms whose spatial patterns are still poorly explored compared to other groups of living beings (Hawksworth & Lücking 2017). In addition, they play an important role in recycling products resulting from their organic activity (Deacon 2006; Kendrick 2017) and are important bioindicators for conservation and management of forest areas (Jiménez et al. 2020).

Therefore, a fundamental question for conserving fungal diversity under the uncertainties of climate change would be the extent to which we will have compatible substitutes to perform the important ecological functions for the ecosystem dynamics in relation to genetic diversity (Nilsson et al. 2018). While the conservation of fungal species and ecosystems is the foundation of diversity preservation, there is a need to expand the concept of conservation to include new methodologies that add techniques and include an updated framework of new resources to track and catalog known species through potential predictions of their real functional and ecological diversity (Lofgren & Stajich 2021).

Species distribution model (SDM) is one of these new techniques, which uses existing species occurrence data associated with environmental predictor layers to generate presence probability models and suitable areas for new occurrences (Elith & Leathwick 2009; Guisan & Thuiller 2005). This technique has been recently developed and shows increasing use and applicability in delimitation of conservation areas (Guillera-Arroita et al. 2015; Keeley et al. 2017; Bosso et al. 2018), and that the Geographic Information System (GIS) tools can be useful in the management of areas and organisms relevant to their conservation and ecosystem balance, respectively (Brambilla & Saporetti 2014; Soberón et al. 2017; Kaky et al. 2020).

In recent years, studies have been intensified for most of organisms (Urbina-Cardona et al. 2019) mainly due to concerns related to climate change (Větrovský et al. 2019), species fertility (Walsh et al. 2019), expansion of invasive species in native areas (Barbet-Massin et al. 2018), divergence, conservation and niche delimitation of cryptic species (Culumber & Tobler 2016; Scriven et al. 2016; Zhao et al. 2019) and diversity through integrative taxonomy for wood-decomposing fungi (Fernández-López et al. 2019).

The number of works using SDM to analyze the distribution of fungi is currently increasing. Hao et al. (2020) recently reviewed 283 studies and highlighted three main approaches: 1) pathogenic fungi, 2) lichens and 3) mushrooms and other macro and microfungi of ecological importance. SDM showed to be a viable technique to analyze new potential areas of occurrence, as well as the relationship with the processes that influence them. Different methodologies and algorithms that incorporate cataloged data available, mostly of them herbarium records, have already been used for Agaricales (Wollan et al. 2008), ectomycorrhizal fungi (González-Ávila et al. 2013; Guo et al. 2017; Pietras & Kolanowska 2019), lichens (Szczepańska et al. 2015; Dymytrova et al. 2016; Oh et al. 2019; Menezes et al. 2020), Hymenochaetaceae (Yuan et al. 2015; Fernández-López et al. 2019), Ophiocordyceps (Yan et al. 2017; Wei et al. 2021), Boletales (Banasiak et al. 2019) and other organisms similar to fungi, such as Myxomycetes (Almadrones-Reyes & Dagamac 2018).

These studies showed that the presence of sporophores in certain areas is more strongly related to climatic factors, although the distribution patterns of macrofungal communities can also be correlated with other factors depending on the modeled species, and that the processes of global transformation underway can affect the richness of these organisms (Andrew et al. 2019). Thus, we assess the potential impact of climate change for two wood-degrading Agaricomycetes species in the pessimistic and optimistic scenarios in the Neotropical area, through ecological modeling for suitable areas.

Materials and methods

Occurrence data

A dataset with the records of the Agaricomycetes Auricularia brasiliana Y. C. Dai & F. Wu and Megasporoporia neosetulosa C.R.S. Lira & Gibertoni available on GBIF - Global Biodiversity Information Facility (https://www.gbif.org/pt/) was used to SDM analysis. Occurrences for A. brasiliana from 1906 to 2021 were downloaded from GBIF.org on January 24, 2022 (https://doi.org/10.15468/dl.dqmgbz) and M. neosetulosa from 1887 to 2020 from GBIF. org on May 17, 2022 (https://doi.org/10.15468/dl.6nmbfx). We only included in our analyses records of samples deposited in herbaria and with coordinates or with information about locality/city/municipality from which approximate coordinates could be found. Data with wrong or incomplete information, of doubtful reliability, i. e. arising from human, machine or empty observations, and of occurrence outside the Neotropics were excluded.

Due to the lack of reliable data on their actual absences, here we worked only with the presence data and pseudo-absence for model calibration and/or background points (Chefaoui & Lobo 2008; Phillips & Dudik 2008). In this sense, for this study, the environmental restriction method was used, which is based on the smallest suitable region predicted by a bioclimatic model (Engler et al. 2004). The application of this method has greater explanatory power in relation to models that randomly allocate pseudo-absences. Finally, the package reduced the number of duplicated points during the analysis.

Biology and taxonomic status of the species

Auricularia brasiliana belongs to Auriculariales and has effuse-reflexed basidiomata usually adhered to the substrate, monomitic hyphal system surrounded by a gelatinous matrix that gives the possibility of rehydration after periods of drought. Megasporoporia neosetulosa belongs to Polyporales and presents resupinate basidiomata, completely adhered to decomposing branches. Both species are ecologically important as angiosperm decomposers in Amazon, Atlantic Rain Forest, Caatinga, including montane forests, and Cerrado. Auricularia brasiliana is potentially edible, since other species of the genus are traditionally used for food.

Auricularia brasiliana was segregated from the A. mesenterica complex and is only known in Brazilian biomes so far (Wu et al. 2015), while M. neosetulosa was described as a new species, phylogenetically separated from the morphologically similar M. setulosa (Henn.) Rajchenb., from which differs in the slightly larger basidiospores, and the occurence in the Neotropics, while M. setulosa seems to be restricted to Africa (Lira et al. 2021). However, the databases have not yet been updated and the cited records appear as A. mesenterica and M. setulosa. Thus, previous records of these species in the Neotropics are considered as A. brasiliana and M. neosetulosa, respectively.

Bioclimatic variables

All 19 environmental climate layers were included, as well as the topographic variable (Elevation) in two sets (current and future), available in WorldClim (Fick & Hijmans, 2017, available at: https://www.worldclim.org/). For future projections (2061~2080), the CNRM-CM6-1 climate model was used with a spatial resolution of 2.5 minutes, from the National Center for Meteorological Research. The package used, detailed below, carried out the current model and the future projection simultaneously.

Two scenarios of Shared Socio-economic Pathways, SSP1-2.6 - optimistic scenario and SSP5-8.5 - pessimistic scenario were assumed. SSP 1-2.6 represents the imposition of policies to mitigate global CO2 emissions, with temperature rise stabilizing at around 1.8°C by the end of the century, while SSP 5-8.5 represents one of the sets of scenarios that describe the highest emissions of CO2 gases until 2050. In this scenario, the world economy grows rapidly, driven by the exploitation of fossil fuels, with an increase in the average temperature of the planet by up to 4.4 °C to 2100 and with emissions high enough that they will produce a radioactive forcing of 8.5 W m-2 (O'Neill et al. 2016). The data used here were obtained from the Coupled Model Intercomparison Project Phase 6 (CMIP6) (Eyring et al. 2016, available at https://www.worldclim.org/). Finally, the study was conducted only for the Neotropics, here defined according to One Earth (https://www.oneearth.org/bioregions-2020/, accessed in April/04/2023). Thus, the environmental variables were cut with a focus on the studied area, through the Q-GIS program version 3.16.14 (https://www.qgis.org/pt_BR/site/forusers/download.html).

Algorithms and model calibration

For the species modeling process, the "ENMTML" package (Andrade et al. 2020) was used in the R environment (R Core Team 2023) which is divided into three stages: pre-processing, processing and post-processing (Andrade et al. 2020). In the pre-processing stage, the occurrences and predictor variables were inserted. No parameter was used to reduce the autocorrelation of the occurrence data. An important point for the construction of the model is the reduction of the collinearity of the predictor variables, since the use of many climatic variables can generate misleading models and, consequently, an increase in the commission errors of the predicted distributions, that is, the area climatically adequate where the species does not actually occur (Beaumont et al. 2005). In this sense, the Variance Inflation Factor (VIF) was used to reduce the collinearity of the predictor variables and the final set of climatic predictors used for modeling had VIF values >2 (Marquaridt 1970). The script can be accessed in https://github.com/ailtonm4th3us/Script-for-modelling-distribuition-species.

The argument (sp_accessible_area) that defines the hypothetical geographically accessible area for the species over time was also used for the construction of the models in relation to what is already known about their ecology and biogeography (Barve et al. 2011; Peterson et al. 2011). This area can be represented by the BAM (Biotic interactions, Abiotic conditions, and Movement) diagram (Soberón & Peterson 2005) which demonstrates the main factors that influence the distribution patterns of the species. However, this information is not available for the species studied here, so for this study we defined the accessible areas in a single shape-file of the ecoregions for the Neotropics, extracted from the World Wildlife Fund website (https://www.worldwildlife.org/biomes, accessed in April/26/22) in the modeling process (Peterson et al. 2001; Andrade et al. 2020). The extrapolation of the model is controlled by this method, as the SDM adjust the models based on the conditions in which the occurrences, pseudo-absence and/or background data are found. In this sense, when forecasts are made, the models will present new conditions, especially when projections are made for different time periods and/or geographic regions. In this case, as there is uncertainty, the models will perform extrapolations, as they were not adjusted for the conditions found in the occurrence data (Fitzpatrick & Hargrove 2009; Andrade et al. 2020).

Modeling process

Here, to perform the data partitioning, the Bootstrap method was used, which comprises a random partition between the training and test subsets (Fielding & Bell 1997; Andrade et al. 2020). Thus, 10 replicates of each model were performed, with 70% of the data destined for the training models and 30% for the validation of the models. In the processing step, we consider three algorithms for modeling: 1) Maximum Entropy (MXS) (linear and quadratic characteristics), 2) Support Vector Machine (SVM), both Machine Learning methods (Karatzoglou et al. 2004; Phillips et al. 2017) and 3) Random Forest (RDF), a classification method and linear regression (Liaw & Wiener 2002).

In the post-processing stage for evaluating the models, the metrics Area Under the Curve (AUC, Fielding & Bell 1997), Kappa (Cohen 1960), True Skill Statistics (TSS, Allouche et al. 2006), Jaccard (Leroy et al. 2018) and Sorensen (Leroy et al. 2018) were used. AUC, Kappa and TSS range from 0 to 1, and the closer to 1, the most excellent performance has the model, while the closer to 0, the poorer. Jaccard and Sorensen metrics, which refer to the similarity index, indicate that, if the value is close to 1, the predictions are similar to the observations, while the inverse indicates a higher number of errors, with possible extrapolations, thus, a lower similarity with a greater number of false positives and false negatives (Leroy et al. 2018). As the similarity indexes are not influenced by the prevalence of the species and by the unequal number of pseudo-absence (Leroy et al. 2018; Nascimento et al. 2022), we used the threshold that maximizes the Jaccard values, thus considering the values of sensitivity and specificity as limits for creating binary maps (Allouche et al. 2006). To adjust the models with the different algorithms and create an ensemble model, we used the Mean of the Best Models (SUP, acronym used in the ENMTML package, Andrade et al. 2020), which evaluates the best algorithms based on Jaccard values, that is, those algorithms that presented the Jaccard index above the average (Velazco et al. 2019). Subsequently, the maps generated were edited in the Q-GIS.

Results

Species data processing

We modeled the current distribution of two wood-degrading Basidiomycota species using SDM to predict suitable areas for their occurrence. We designed the models to analyze two future scenarios and understand which areas are expected to change over the 2061 to 2080 range due to the effects of climate change. Initially, A. brasiliana had 176 and M. neosetulosa 237 occurrence records downloaded from the database and the consulted literature. After the removal of incomplete data or incorrect information regarding the place of collection, the number of records was reduced to 55 records to A. brasiliana and 157 to M. neosetulosa. However, for the species modeling process, 40 occurrence points were used for A. brasiliana and 72 for M. neosetulosa, according to the spatial resolution of 2.5 minutes.

Model performance

The species presented a good performance of the explanatory values of suitability for all algorithms in the construction of the binary maps (Table 1). For AUC, the three algorithms presented training values of 0.946 and SVM had the best performance. For Kappa, the algorithms totaled a SUP average of 0.842, whereas the value of TSS was close to 1, with SVM once again being the algorithm with the highest performance value. For Jaccard, the average was 0.861 and for the Sorensen index the adequacy value was 0.922. For these two adequacy metrics, SVM also presented the best contribution values, so it seems that the SVM algorithm presents better applicability to the data training provided for these species.

Table 1
Contribution values of the algorithms with the best performance for the models of A. brasiliana and M. neosetulosa in bold.

Species	Model Evaluation
	Algorithms	AUC	Kappa	TSS	Jaccard	Sorensen
Auricularia brasiliana	MXS	0,854	0,550	0,550	0,681	0,808
	RDF	0,916	0,775	0,775	0,797	0,883
	SVM	0,926	0,792	0,792	0,827	0,901
	SUP	0,946	0,842	0,842	0,861	0,922
Megasporoporia neosetulosa	MXS	0,827	0,577	0,577	0,678	0,805
	RDF	0,919	0,745	0,745	0,774	0,870
	SVM	0,849	0,577	0,577	0,685	0,811
	SUP	0,920	0,755	0,755	0,782	0,876

The product of the training data thus represents the suitability in known and potential areas for the two species. Low values represent areas with less suitability and correspond to paler colors in the maps (Fig. 1), while those that correspond to stronger colors have greater climatic suitability for the occurrence of fungi and represent optimal values, which approach to 1.

Figure 1
Climate scenarios for A. brasiliana and M. neosetulosa. A- Current scenario, B- Optimistic scenario and C- Pessimistic scenario for A. brasiliana. D- Current scenario, E- Optimistic scenario and F- Pessimistic scenario for M. neosetulosa.

Contribution of biovariables

Of the 20 variables used to perform the analysis, 10 were selected by the model during execution for the two species in relation to the three algorithms used. For A. brasiliana, five predictors presented better performance in the execution of the model, being the predictor Bio2 (Mean diurnal range) with the highest corresponding value for the three algorithms, while M. neosetulosa had its performance with the best correspondence for Bio3, Bio9, Bio18, Bio19 and Elev, being Bio18 (Precipitation of warmest quarter) and Bio19 (Precipitation of coldest quarter) with higher values for the SVM algorithm, Bio18 for the MXS and Bio19 for RDF (Table 2). The climatic suitability for A. brasiliana seems to have the temperature as the greatest climatic factor influencing its occurrence, while M. neosetulosa would have the precipitation.

Table 2
Contribution of predictor variables for each algorithm to A. brasiliana and M. neosetulosa. Bio2: Mean diurnal range (mean of monthly (max temp−min temp); Bio3: Isothermality (bio2/bio7) (x100); Bio8: Mean temperature of wettest quarter; Bio9: Mean temperature of driest quarter; Bio13: Precipitation of wettest month; Bio14: Precipitation of driest month; Bio15: Precipitation seasonality (coefficient of variation), Bio18: Precipitation of warmest quarter; Bio19: Precipitation of coldest quarter Elev: Elevation

Species	Variables	Algorithms
	ID	MXS	RDF	SVM
Auricularia brasiliana	Bio2	0.390	0.226	0.236
	Bio3	0.009	0.088	0.053
	Bio8	0.041	0.055	0.069
	Bio9	0.029	0.038	0.112
	Bio13	0.130	0.154	0.161
	Bio14	0.106	0.107	0.126
	Bio15	0.022	0.154	0.074
	Bio18	0.160	0.078	0.039
	Bio19	0.027	0.067	0.100
	elev	0.080	0.034	0.025
Megasporoporia neosetulosa	Bio2	0.078	0.052	0.059
	Bio3	0.065	0.073	0.096
	Bio8	0.089	0.052	0.042
	Bio9	0.152	0.228	0.106
	Bio13	0.086	0.091	0.000
	Bio14	0.022	0.097	0.097
	Bio15	0.031	0.124	0.035
	Bio18	0.196	0.098	0.238
	Bio19	0.153	0.135	0.237
	elev	0.124	0.051	0.087

Species climate scenarios

The models showed three scenarios that allow visualization of the climatic suitability of the species in the present and over time in an optimistic and pessimistic perspective, respectively (Fig. 1).

In the Neotropical region, A. brasiliana currently has a wide distribution from Central America to the central region of South America, with points of greater climatic suitability also in the Caribbean islands (Fig. 1A). The Amazon rainforest stand out as areas of countries with a hot-humid climate. Some of these areas seem to have greater aptitude for the permanence of this species and consequently other potential areas are predicted by the model.

In an optimistic future scenario, there would be a significant reduction in the suitability of A. brasiliana, observed by the color differences (Fig. 1B). For the pessimistic scenario, most areas that maximize suitability are drastically reduced to some points at the coastal strip of South America, corresponding to the Atlantic Forest in Brazil, thus comprising a diminution in the niche of this species (Fig. 1C).

For M. neosetulosa, its current distribution is more concentrated in southern South America, in Central America and the Caribbean Islands (Fig. 1D). Brazil has a greater number of suitable areas compared to other South American countries, which, however, also have potential points of suitability for the current scenario (Fig. 1D). We were able to identify that, in addition to the humid tropical forests of Brazil, Central America, Caribbean and South America, the species seems to be well adapted to Cerrado, which is characterized by a seasonal tropical climate, with rainy periods and well-delimited drought.

In the optimistic scenario, the species has its niche reduced almost entirely in the northern region of South America, being most likely to be restricted to dry regions in the Andes and the southern region of South America (Fig. 1E). We also found that for northern South America, a small area of the Guiana plateau remained suitable for the species, which could lead to the isolation of the species (Fig. 1E). The niche was also significantly reduced to the central region on the map which previously presented several potential areas for the species. For the pessimistic future scenario, the model admits that this reduction to the north could be even more severe, and that the species will only occur in small fragments along Central and South America, and the Cerrado with greater potential of its occurrence, in addition to the coastal strip of the country where we find the Atlantic Forest (Fig. 1F).

Our analyses showed that the two species share, in some cases, equal access points for each suitability grid, but with different values for each distribution range, although A. brasiliana seems to be better distributed in the northern part of the Neotropics while M. neosetulosa in the southern part. Our suitability values for both models performed excellently and the maps indicate that most suitable areas are in Amazonian Forest for A. brasiliana and Cerrado and mountainous areas for M. neosetulosa. Furthermore, some areas of Atlantic Forest in Brazil would remain suitable for both species in different scenarios.

Discussion

Although the distribution of fungi is still poorly explained and there are difficulties in predicting climate change for these organisms, the use of SDM presents a good technique to predict the occurrence of these organisms in space over time (Hao et al. 2020). We found that the challenges to apply SDM to fungi are linked to detection and sampling, as well as the taxonomic changes underway. This is due to the inclusion of genetic data to better delimit and identify the species, which causes uncertainties in the choice of the taxa to be modeled (Stefani et al. 2014; Hawksworth & Lücking 2017) Therefore, the species in this study were selected following recent literature that confirmed their taxonomic position and delimitation from DNA sequences. Although A. brasiliana and M. neosetulosa have phylogenetic support that justify their delimitation to the Neotropics, the confirmation of their distribution is not fully resolved and new complementary studies with broader DNA sampling of material collected in other regions of the Neotropics other Brazil are needed. Furthermore, revising deposited material in herbaria and updating the databases is of paramount importance to optimize this process. Despite being an initial study, our work paves the way for the ecological understanding and the limits of the distribution of the studied species, knowing that for fungi the effects of taxonomic inaccuracies have been little studied when dealing with distribution models (Elith et al. 2013; Fernández-López et al. 2019).

There is currently an emerging interest in predicting new environments in which fungal species have not been observed, mainly to guide biodiversity management and conservation processes (Sequeira et al. 2018). Studies show that the areas modeled according to the points of occurrence correspond to places in which the species already exists and that the models introduce new areas climatically similar (Nascimento et al. 2022) which can be reached by the species and serve as potential areas for their establishment depending on their dispersion capacity (Barve et al. 2011). The planet is undergoing transformations resulting from climate change, which are a reflection of different impacts. Among these, forest deforestation is one of the aggravating factors that lead to loss of substrate and temperature rising, generating extreme phenomena and atypical events that can lead to the extinction of organisms such as fungi (Walker et al. 2018; Lughadha et al. 2020). Additionally, other important factors may be already affecting the distribution of these organisms more than climate changes, such as land and sea use that reduces biodiversity on the planet, followed by the exploitation of natural resources and pollution (Jaureguiberry et al. 2022). However, the results obtained here demonstrate the current panorama of the distribution caused by the effect of climate change and alert to the causes of alterations in the future, which come from the set of successive actions and events listed above that drive the global loss of biodiversity and compromising the conservation of organisms.

Our models show that the areas of greatest climatic suitability are reduced and that for A. brasiliana and M. neosetulosa the potential range will change in some regions, mainly central and northern South America in response to climate change. Although these areas remain suitable for both species over the years, the decrease in suitability for central-northern regions including countries in this territorial range, especially Brazil, is worrying, as they are areas that hold a large part of Amazon Forest and shelter a good part of the diversity of fungi (Gazis & Chaverri 2015) that act in different roles in ecosystem dynamics (Fukasawa & Matsukura 2021).

With the reduction of the Amazon Forest in these areas, it is assumed that the occurrence of A. brasiliana will drastically be reduced, since dependence on climatic conditions can alter its presence and interfere with ecological interactions with plants due to its role as a decomposer, and also affecting its diversity (Větrovský et al. 2019; Naranjo-Ortiz & Gabaldón 2019). For M. neosetulosa, the Cerrado biome in Brazil seems to have the highest percentage of suitable areas, which indicates that the conditions present in this environment may favor the permanence of the species in this habitat. The Cerrado is the second largest biome in Brazil, a hostspost that has suffered strong pressure from agribusiness through legal and illegal deforestation and has few protected areas compared to other biomes in the country such as the Amazon (Colli et al. 2020; Rajão et al. 2020). Even for an optimistic future scenario, the suitability of this species seems to disappear in the central-north region of South America, leaving few areas appropriate for its occurrence.

In addition, areas of the Atlantic Forest biome show to be suitable for both species even in the pessimistic scenario. It is worth mentioning that this Brazilian hotspot is currently undergoing changes in its remnants as a result of habitat reduction and fragmentation that negatively impact species richness (Püttker et al. 2020). It is, therefore, an area that urgently needs conservation. This concern extends to climate change that can further exacerbate and reduce the size of distribution areas for fungal species (Bidartondo et al. 2018).

Areas of importance for conservation in South America, such as the islands of the Caribbean Sea, the Chilean forests, the coastal zone of Colombia, the Amazon rainforest and the Brazilian Cerrado are considered biodiversity hotspots and constantly suffer from threats over the years that reflect in the reduction of existing lives in these environments (Higgins 2007; Brum et al. 2019; Zabel et al. 2019; Rosa et al. 2021). The two future scenarios show that suitable biomes in Brazil for the analyzed species appear to be the most important centers for conservation. It can be seen that even for the optimistic scenario, the reduction of climatically suitable habitat is dramatic and with that, our models suggest climate impacts on the populations of A. brasiliana and M. neosetulosa. Finally, we dialogue with the need for actions that prioritize the conservation of areas and biodiversity in order to avoid future devastation and disturbances that affect the diversity of wood-decomposing fungi (Fukasawa & Matsukura 2021) and, consequently, the ecological relationships they establish mainly with plants in the Neotropics.

The application of ecological modeling to wood-decomposing fungi brings a new perspective to analyze the distribution of species in areas of ecological importance. These areas have a high degree of biological diversity, showing strong signs of disturbance. Our models indicate climatic suitability for regions considered hostspots of biodiversity in the Neotropics and the need for intervention to preserve, at least, the species modeled here. The values of the tests performed were explanatory and reliable, showing that the annual temperature and the level of quarterly precipitation are strongly influencing the species in the study and that all these factors may probably be associated with the effects of anthropic changes and consequently of the climate change.

The species mostly occur in places of climatic vulnerability, with more critical points for the north of South America, as well as for the Central America region. In both optimistic and pessimistic future scenarios, A. brasiliana will have its niche drastically reduced, while M. neosetulosa will be restricted to dry regions of the Andes and southern South America. Thus, our models may serve as guides for conservation plans that seek to protect areas from their expansive reduction, as well as presenting factors associated with climatic interferences that impact the occurrence of fungal species and their ecosystem biodiversity.

Acknowledgments

The authors thank CNPq (302941/2019-3) for funding this research and for the AMAO (UFPE 200215443) scholarship and FACEPE for the AMAO (BIC-0510-2.05/21), DCS (IBPG-1309-2.00/21) and RLMA (BFP-0207-2.03/20) scholarships.

References

• Allouche O, Tsoar A, Kadmon R. 2006. Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS). Journal of Applied Ecology 43: 1223-1232. doi: 10.1111/j.1365-2664.2006.01214.x
  » https://doi.org/10.1111/j.1365-2664.2006.01214.x
• Almadrones-Reyes A, Dagamac NHA. 2018. Predicting Local Habitat Suitability in Changing Climate Scenarios: Applying Species Distribution Modelling for Diderma Hemisphaericum Current Research in Environmental & Applied Mycology 8: 492-500. doi: 10.5943/cream/8/5/2
  » https://doi.org/10.5943/cream/8/5/2
• Andrade AFA, Velazco SJE, De Marco Júnior P. 2020. ENMTML: An R package for a straightforward construction of complex ecological niche models. Environmental Modelling & Software 125: 104615. doi: 10.1016/j.envsoft.2019.104615
  » https://doi.org/10.1016/j.envsoft.2019.104615
• Andrew C, Büntgen U, Egli S et al 2019. Open-source data reveal how collections-based fungal diversity is sensitive to global change. Applications in Plant Sciences 7: e01227. doi: 10.1002/aps3.1227
  » https://doi.org/10.1002/aps3.1227
• Banasiak Ł, Pietras M, Wrzosek M et al 2019.Aureoboletus projectellus (Fungi, Boletales) - Occurrence Data, Environmental Layers and Habitat Suitability Models for North America and Europe. Data in Brief 23: 103779. doi: 10.1016/j.dib.2019.103779
  » https://doi.org/10.1016/j.dib.2019.103779
• Barbet-Massin M, Rome Q, Villemant C, Courchamp F. 2018. Can species distribution models really predict the expansion of invasive species? PLoS One 13: e0193085. doi: 10.1371/journal.pone.0193085
  » https://doi.org/10.1371/journal.pone.0193085
• Barve N, Barve V, Jiménez-Valverde A et al 2011. The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecological Modelling 222: 1810-1819. doi: 10.1016/j.ecolmodel.2011.02.011
  » https://doi.org/10.1016/j.ecolmodel.2011.02.011
• Beaumont LJ, Hughes L, Poulsen M. 2005. Predicting species distributions: Use of climatic parameters in BIOCLIM and its impact on predictions of species’ current and future distributions. Ecological Modelling 186: 251-270. doi: 10.1016/j.ecolmodel.2005.01.030
  » https://doi.org/10.1016/j.ecolmodel.2005.01.030
• Bidartondo MI, Ellis C, Kauserud H et al 2018. Climate change: Fungal responses and effects. In: Willis JK (ed.). State of the World's Fungi 2018. Kew, UK, Royal Botanical Gardens. p. 62-69.
• Bosso L, Smeraldo S, Rapuzzi P, Sama G, Garonna AP, Russo D. 2018. Nature protection areas of Europe are insufficient to preserve the threatened beetle Rosalia alpina (Coleoptera: Cerambycidae): Evidence from species distribution models and conservation gap analysis. Ecological. Entomology 43: 192-203. doi: 10.1111/een.12485
  » https://doi.org/10.1111/een.12485
• Brambilla M, Saporetti F. 2014. Modelling distribution of habitats required for different uses by the same species: Implications for conservation at the regional scale. Biological Conservation 174: 39-46. doi: 10.1016/j.biocon.2014.03.018
  » https://doi.org/10.1016/j.biocon.2014.03.018
• Brum FT, Pressey RL, Bini LM, Loyola R. 2019. Forecasting conservation impact to pinpoint spatial priorities in the Brazilian Cerrado. Biological Conservation 240: 108283. doi: 10.1016/j.biocon.2019.108283
  » https://doi.org/10.1016/j.biocon.2019.108283
• Bullock JM, Bonte D, Pufal G et al 2018. Human-Mediated Dispersal and the Rewiring of Spatial Networks. Trends in Ecology & Evolution 33: 958-970. doi: 10.1016/j.tree.2018.09.008
  » https://doi.org/10.1016/j.tree.2018.09.008
• Chefaoui RM, Lobo JM. 2008. Assessing the effects of pseudo-absences on predictive distribution model performance. Ecological Modelling 210: 478-486. doi: 10.1016/j.ecolmodel.2007.08.010
  » https://doi.org/10.1016/j.ecolmodel.2007.08.010
• Cohen J. 1960. A coefficient of agreement for nominal scales. Educational and Psychological Measurement 20: 37-46. doi: 10.1177/001316446002000104
  » https://doi.org/10.1177/001316446002000104
• Colli GR, Vieira CR, Dianese JC. 2020. Biodiversity and conservation of the Cerrado: Recent advances and old challenges. Biodiversity and Conservation 29: 1465-1475. doi: 10.1007/s10531-020-01967-x
  » https://doi.org/10.1007/s10531-020-01967-x
• Culumber ZW, Tobler M. 2016. Ecological divergence and conservatism: Spatiotemporal patterns of niche evolution in a genus of livebearing fishes (Poeciliidae: Xiphophorus). BMC Evolutionary Biology 16: 44. doi: 10.1186/s12862-016-0593-4
  » https://doi.org/10.1186/s12862-016-0593-4
• Deacon JW. 2006. Fungal biology. 4th. edn. Massachusetts, Blackwell Publishing.
• Dymytrova L, Stofer S, Ginzler C, Breiner FT, Scheidegger C. 2016. Forest-structure data improve distribution models of threatened habitat specialists: Implications for conservation of epiphytic lichens in forest landscapes. Biological Conservation 196: 31-38. doi: 10.1016/j.biocon.2016.01.030
  » https://doi.org/10.1016/j.biocon.2016.01.030
• Elith J, Leathwick JR. 2009. Species distribution models: Ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution, and Systematics 40: 677-697. doi: 10.1146/annurev.ecolsys.110308.120159
  » https://doi.org/10.1146/annurev.ecolsys.110308.120159
• Elith J, Simpson J, Hirsch M, Burgman MA. 2013. Taxonomic uncertainty and decision making for biosecurity: Spatial models for myrtle/guava rust. Australasian Plant Pathology 42: 43-51. doi: 10.1007/s13313-012-0178-7
  » https://doi.org/10.1007/s13313-012-0178-7
• Engler R, Guisan A, Rechsteiner L. 2004. An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data. Journal of Applied Ecology 41: 263-274. doi: 10.1111/j.0021-8901.2004.00881.x
  » https://doi.org/10.1111/j.0021-8901.2004.00881.x
• Eyring V, Bony S, Meehl GA, Senior CA, Stevens B, Stouffer RJ, Taylor KE. 2016. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development 9: 1937-1958. doi: 10.5194/gmd-9-1937-2016
  » https://doi.org/10.5194/gmd-9-1937-2016
• Fawzy S, Osman AI, Doran J, Rooney DW. 2020. Strategies for mitigation of climate change: A review. Environmental Chemistry Letters 18: 2069-2094. doi: 10.1007/s10311-020-01059-w
  » https://doi.org/10.1007/s10311-020-01059-w
• Fernández-López J, Telleria MT, Dueñas Met al 2019. Addressing the diversity of Xylodon raduloides complex through integrative taxonomy. IMA Fungus 10: 9. doi: 10.1186/s43008-019-0010-x
  » https://doi.org/10.1186/s43008-019-0010-x
• Fick SE, Hijmans RJ. 2017. WorldClim 2: New 1km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37: 4302-4315. doi: 10.1002/joc.5086
  » https://doi.org/10.1002/joc.5086
• Fielding AH, Bell JF. 1997. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation 24: 38-49. doi: 10.1017/S0376892997000088
  » https://doi.org/10.1017/S0376892997000088
• Fitzpatrick MC, Hargrove WW. 2009. The projection of species distribution models and the problem of non-analog climate. Biodiversity and Conservation 18: 2255-2261. doi: 10.1007/s10531-009-9584-8
  » https://doi.org/10.1007/s10531-009-9584-8
• Fukasawa Y, Matsukura K. 2021. Decay stages of wood and associated fungal communities characterise diversity-decomposition relationships. Scientific Reports 11: 8972. doi: 10.1038/s41598-021-88580-2
  » https://doi.org/10.1038/s41598-021-88580-2
• Gazis R, Chaverri P. 2015. Wild trees in the Amazon basin harbor a great diversity of beneficial endosymbiotic fungi: Is this evidence of protective mutualism? Fungal Ecology 17: 18-29. doi: 10.1016/j.funeco.2015.04.001
  » https://doi.org/10.1016/j.funeco.2015.04.001
• González-Ávila PA, Luna-Vegas I, Ríos MV, Saade RL, Blanco JC. 2013. Current knowledge and importance of the order Gomphales (Fungi: Basidiomycota) in Mexico. Nova Hedwigia 97: 55-86. doi: 10.1127/0029-5035/2013/0099
  » https://doi.org/10.1127/0029-5035/2013/0099
• Guillera-Arroita G, Lahoz‐Monfort JJ, Elith J et al 2015. Is my species distribution model fit for purpose? Matching data and models to applications. Global Ecology and Biogeography 24: 276-292. doi: 10.1111/geb.12268
  » https://doi.org/10.1111/geb.12268
• Guisan A, Thuiller W. 2005. Predicting species distribution: Offering more than simple habitat models. Ecology Letters 8: 993-1009. doi: 10.1111/j.1461-0248.2005.00792.x
  » https://doi.org/10.1111/j.1461-0248.2005.00792.x
• Guo Y, Li X, Zhao Z, Wei H, Gao B, Gu W. 2017. Prediction of the Potential Geographic Distribution of the Ectomycorrhizal Mushroom Tricholoma matsutake under multiple climate change scenarios. Scientific Reports 7: 46221. doi: 10.1038/srep46221
  » https://doi.org/10.1038/srep46221
• Hao T, Guillera-Arroita G, May TW, Lahoz-Monfort JJ, Elith J. 2020. Using species distribution models for fungi. Fungal Biology Reviews 34: 74-88. doi: 10.1016/j.fbr.2020.01.002
  » https://doi.org/10.1016/j.fbr.2020.01.002
• Hawksworth DL, Lücking R. 2017. Fungal diversity revisited: 2.2 to 3.8 million species. Microbiology Spectrum 5: 5-4. doi: 10.1128/9781555819583.ch4
  » https://doi.org/10.1128/9781555819583.ch4
• Higgins PA. 2007. Biodiversity loss under existing land use and climate change: An illustration using northern South America. Global Ecology and Biogeography 16: 197-204. doi: 10.1111/j.1466-8238.2006.00278.x
  » https://doi.org/10.1111/j.1466-8238.2006.00278.x
• Huang J, Zhang G, Zhang Y, Guan X, Wei Y, Guo R. 2020. Global desertification vulnerability to climate change and human activities. Land Degradation & Development 31: 1380-1391. doi: 10.1002/ldr.3556
  » https://doi.org/10.1002/ldr.3556
• Jaureguiberry P, Titeux N, Wiemers M et al 2022. The direct drivers of recent global anthropogenic biodiversity loss. Science Advances 8: eabm9982. doi: 10.1126/sciadv.abm9982
  » https://doi.org/10.1126/sciadv.abm9982
• Jiménez PG, Fernández AR, Sánchez JS, de la Cruz DR. 2020. Mycological Indicators in Evaluating Conservation Status: The Case of Quercus spp Dehesas in the Middle-West of the Iberian Peninsula (Spain). Sustainability 12: 10442. doi: 10.3390/su122410442
  » https://doi.org/10.3390/su122410442
• Kaky E, Nolan V, Alatawi A, Gilber F. 2020. A comparison between Ensemble and MaxEnt species distribution modelling approaches for conservation: A case study with Egyptian medicinal plants. Ecological Informatics 60: 101150. doi: 10.1016/j.ecoinf.2020.101150
  » https://doi.org/10.1016/j.ecoinf.2020.101150
• Karatzoglou A, Smola A, Hornik K, Zeileis A. 2004. kernlab-an S4 package for kernel methods in R. Journal Statistical Software 11: 1-20. doi: 10.18637/jss.v011.i09
  » https://doi.org/10.18637/jss.v011.i09
• Keeley AT, Beier P, Keeley BW, Fagan ME. 2017. Habitat suitability is a poor proxy for landscape connectivity during dispersal and mating movements. Landscape Urban Planning 161: 90-102. doi: 10.1016/j.landurbplan.2017.01.007
  » https://doi.org/10.1016/j.landurbplan.2017.01.007
• Kendrick B. 2017. The fifth kingdom. 2nd. edn. Newburyport, Focus Information Group, Inc.
• Leroy B, Delsol R, Hugueny B, Meynard CN, Barhoumi C, Barbet-Massin M, Bellard C. 2018. Without quality presence - absence data, discrimination metrics such as TSS can be misleading measures of model performance. Journal of Biogeography 45: 1994-2002. doi: 10.1111/jbi.13402
  » https://doi.org/10.1111/jbi.13402
• Liaw A, Wiener M. 2002. Classification and regression by randomForest. R. News 2: 18-22.
• Lira CRS, Alvarenga RLM, Ryvarden L, Gibertoni TB. 2021. Phylogeny of Megasporoporia s. lat. and related genera of Polyporaceae: New genera, new species and new combinations. Mycosphere 12: 1262-1289. doi: 10.5943/mycosphere/12/1/16
  » https://doi.org/10.5943/mycosphere/12/1/16
• Lofgren LA, Stajich JE. 2021. Fungal biodiversity and conservation mycology in light of new technology, big data, and changing attitudes. Current Biology 19: 1312-1325. doi: 10.1016/j.cub.2021.06.083
  » https://doi.org/10.1016/j.cub.2021.06.083
• Lughadha EN, Bachman SP, Leão TCC et al 2020. Extinction risk and threats to plants and fungi. Plants Peoples Planet 2: 389-408. doi: 10.1002/ppp3.10146
  » https://doi.org/10.1002/ppp3.10146
• Marquaridt DW. 1970. Generalized inverses, ridge regression, biased linear estimation, and nonlinear estimation. Technometrics 12: 591-612. doi: 10.1080/00401706.1970.10488699
  » https://doi.org/10.1080/00401706.1970.10488699
• Menezes AA, Cáceres MES, Bastos CJP, Lücking R. 2020. Modeled lichen metacommunities in the Brazilian Atlantic Forest: do geopolitical regions and the Southern Tropic division reflect natural entities? Phytocoenologia 50: 211-233. doi: 10.1127/phyto/2020/0358
  » https://doi.org/10.1127/phyto/2020/0358
• Naranjo‐Ortiz MA, Gabaldón T. 2019. Fungal evolution: Major ecological adaptations and evolutionary transitions. Biological Reviews of the Cambridge Philosophical Society 94: 1443-1476. doi: 10.1111/brv.12510
  » https://doi.org/10.1111/brv.12510
• Nascimento AC, Montalva J, Ascher JS, Engel MS, Silva DP. 2022. Current and future distributions of a native Andean bumble bee. Journal of Insect Conservation 26: 559-569. doi: 10.1007/s10841-022-00395-2
  » https://doi.org/10.1007/s10841-022-00395-2
• Nilsson RH, Anslan S, Bahram M, Wurzbacher C, Baldrian P, Tedersoo L. 2018. Mycobiome diversity: High-throughput sequencing and identification of fungi. Nature Reviews Microbiology 2: 95-109. doi: 10.1038/s41579-018-0116-y
  » https://doi.org/10.1038/s41579-018-0116-y
• Oh S-Y, Woo J-J, Hur J-S. 2019. Distribution of Foliicolous Lichen Strigula and Genetic Structure of S. multiformis on Jeju Island, South Korea. Microorganisms 7: 430. doi: 10.3390/microorganisms7100430
  » https://doi.org/10.3390/microorganisms7100430
• O'Neill BC, Tebaldi C, Van Vuuren DP et al 2016. The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6, Geoscientific. Model Development 9: 3461-3482. doi: 10.5194/gmd-9-3461-2016
  » https://doi.org/10.5194/gmd-9-3461-2016
• Pauls SU, Nowak C, Bálint M, Pfenninger M. 2012. The impact of global climate change on genetic diversity within populations and species. Molecular Ecology 22: 925-946. doi: 10.1111/mec.12152
  » https://doi.org/10.1111/mec.12152
• Peterson AT, Sanchez-Cordero V, Soberon J, Bartley J, Buddemeier RW, Navarro-Sigüenza AG. 2001. Effects of global climate change on geographic distributions of Mexican Cracidae. Ecological Modelling 144: 21-30. doi: 10.1016/S0304-3800(01)00345-3
  » https://doi.org/10.1016/S0304-3800(01)00345-3
• Peterson AT, Soberón J, Pearson RG, Anderson RP, Martínez-Meyer E, Nakamura M, Araújo MB. 2011. Ecological Niches and Geographic Distributions. Princeton, Princeton University Press. doi: 10.23943/princeton/9780691136868.001.0001
  » https://doi.org/10.23943/princeton/9780691136868.001.0001
• Phillips SJ, Anderson RP, Dudík M, Schapire RE, Blair ME. 2017. Opening the black box: An open‐source release of Maxent. Ecography 40: 887-893. doi: 10.1111/ecog.03049
  » https://doi.org/10.1111/ecog.03049
• Phillips SJ, Dudik M. 2008. Modeling of species distributions with Maxent: New extensions and a comprehensive evaluation. Ecography 31: 161-175. doi: 10.1111/j.0906-7590.2008.5203.x
  » https://doi.org/10.1111/j.0906-7590.2008.5203.x
• Pietras M, Kolanowska M. 2019. Predicted Potential Occurrence of the North American False Truffle Rhizopogon salebrosus in Europe. Fungal Ecology 39: 225-230. doi: 10.1016/j.funeco.2018.12.002
  » https://doi.org/10.1016/j.funeco.2018.12.002
• Püttker T, Crouzeilles R, Almeida-Gomes M et al 2020. Indirect effects of habitat loss via habitat fragmentation: A cross-taxa analysis of forest-dependent species. Biological Conservation 241: 108368. doi: 10.1016/j.biocon.2019.108368
  » https://doi.org/10.1016/j.biocon.2019.108368
• R Core Team. 2023. R: A Language and Environment for Statistical Computing. Viena, Áustria, R Foundation for Statistical Computing.
• Rajão R, Soares-Filho B, Nunes F et al 2020. The rotten apples of Brazil's agribusiness. Science 369: 246-248. doi: 10.1126/science.aba6646
  » https://doi.org/10.1126/science.aba6646
• Rosa MR, Brancalion PHS, Crouzeilles R et al 2021. Hidden destruction of older forests threatens Brazil’s Atlantic Forest and challenges restoration programs. Science Advances 7: eabc4547. doi: 10.1126/sciadv.abc4547
  » https://doi.org/10.1126/sciadv.abc4547
• Scriven JJ, Whitehorn PR, Goulson D, Tinsley MC. 2016. Niche partitioning in a sympatric cryptic species complex. Ecology and Evolution 6: 1328-1339. doi: 10.1002/ece3.1965
  » https://doi.org/10.1002/ece3.1965
• Sequeira AMM, Bouchet PJ, Yates KL, Mengersen K, Caley MJ. 2018. Transferring biodiversity models for conservation: Opportunities and challenges. Methods in Ecology and Evolution 9: 1250-1264. doi: 10.1111/2041-210X.12998
  » https://doi.org/10.1111/2041-210X.12998
• Sheldon KS. 2019. Climate Change in the Tropics: Ecological and Evolutionary Responses at Low Latitudes. Annual Review of Ecology, Evolution, and Systematics 50: 303-333. doi: 10.1146/annurev-ecolsys-110218-025005
  » https://doi.org/10.1146/annurev-ecolsys-110218-025005
• Soberón J, Osorio-Olvera L, Peterson T. 2017. Diferencias Conceptuales entre Modelación de Nichos y Modelación de Áreas de Distribución. Revista Mexicana de Biodiversidad 88: 437-441. doi: 10.1016/j.rmb.2017.03.011
  » https://doi.org/10.1016/j.rmb.2017.03.011
• Soberón J, Peterson AT. 2005. Interpretation of Models of Fundamental Ecological Niches and Species Distributional Areas. Biodiversity Informatics 2: 1-10. doi: 10.17161/bi.v2i0.4
  » https://doi.org/10.17161/bi.v2i0.4
• Stefani FOP, Jones RH, May TW. 2014. Concordance of seven gene genealogies compared to phenotypic data reveals multiple cryptic species in Australian dermocyboid Cortinarius (Agaricales). Molecular Phylogenetics and Evolution 71: 249-260. doi: 10.1016/j.ympev.2013.10.019
  » https://doi.org/10.1016/j.ympev.2013.10.019
• Szczepańska K, Pruchniewicz D, Kossowska M. 2015. Modeling the potential distribution of three lichens of the Xanthoparmelia pulla group (Parmeliaceae, Ascomycota) in Central Europe. Acta Societatis Botanicorum Poloniae 84: 431. doi: 10.5586/asbp.2015.035
  » https://doi.org/10.5586/asbp.2015.035
• Tylianakis JM, Didham RK, Bascompte J, Wardle DA. 2008. Global change and species interactions in terrestrial ecosystems. Ecology Letters 11: 1351-1363. doi: 10.1111/j.1461-0248.2008.01250.x
  » https://doi.org/10.1111/j.1461-0248.2008.01250.x
• Urbina-Cardona N, Blair ME, Londoño MC, Loyola R, Velásquez-Tibatá J, Morales-Devia H. 2019. Species Distribution Modeling in Latin America: A 25-Year Retrospective Review. Tropical Conservation Science 12: 194008291985405. doi: 10.1177/1940082919854058
  » https://doi.org/10.1177/1940082919854058
• Velazco SJE, Villalobos F, Galvão F, De Marco Júnior P. 2019. A dark scenario for Cerrado plant species: effects of future climate, land use and protected areas ineffectiveness. Diversity and Distributions 25: 660-673. doi: 10.1111/ddi.12886
  » https://doi.org/10.1111/ddi.12886
• Větrovský T, Kohout P, Kopecký M et al 2019. A meta-analysis of global fungal distribution reveals climate-driven patterns. Nature Communications 10: 5142. doi: 10.1038/s41467-019-13164-8
  » https://doi.org/10.1038/s41467-019-13164-8
• Walker TWN, Kaiser C, Strasser F et al 2018. Microbial temperature sensitivity and biomass change explain soil carbon loss with warming. Nature Climate Change 8: 885-889. doi: 10.1038/s41558-018-0259-x
  » https://doi.org/10.1038/s41558-018-0259-x
• Walsh BS, Parratt SR, Hoffmann AA et al 2019. The impact of climate change on fertility. Trends in Ecology & Evolution 34: 249-259. doi: 10.1016/j.tree.2018.12.002
  » https://doi.org/10.1016/j.tree.2018.12.002
• Wei Y, Zhang L, Wang J et al 2021. Chinese caterpillar fungus (Ophiocordyceps sinensis) in China: Current distribution, trading, and futures under climate change and overexploitation. Science of The Total Environment 755: 142548. doi: 10.1016/j.scitotenv.2020.142548
  » https://doi.org/10.1016/j.scitotenv.2020.142548
• Whittaker RJ, Araújo MB, Jepson P, Ladle RJ, Watson JEM, Willis KJ. 2005. Conservation Biogeography: Assessment and prospect. Diversity and Distributions 11: 3-23. doi: 10.1111/j.1366-9516.2005.00143.x
  » https://doi.org/10.1111/j.1366-9516.2005.00143.x
• Wollan AK, Bakkestuen V, Kauserud H, Gulden G, Halvorsen R. 2008. Modelling and predicting fungal distribution patterns using herbarium data. Journal of Biogeography 35: 2298-2310. doi: 10.1111/j.1365-2699.2008.01965.x
  » https://doi.org/10.1111/j.1365-2699.2008.01965.x
• Wu F, Yuan Y, Rivoire B, Dai Y-C. 2015. Phylogeny and diversity of the Auricularia mesenterica (Auriculariales, Basidiomycota) complex. Mycological Progress 14: 42. doi: 10.1007/s11557-015-1065-8
  » https://doi.org/10.1007/s11557-015-1065-8
• Yan Y, Li Y, Wang W-J et al 2017. Range Shifts in Response to Climate Change of Ophiocordyceps sinensis, a Fungus Endemic to the Tibetan Plateau. Biological Conservation 206: 143-150. doi: 10.1016/j.biocon.2016.12.023
  » https://doi.org/10.1016/j.biocon.2016.12.023
• Yuan HS, Wei HJ, Wang XG. 2015. Maxent modeling for predicting the potential distribution of Sanghuang, an Important Group of Medicinal Fungi in China. Fungal Ecology 17: 140-145. doi: 10.1016/j.funeco.2015.06.001
  » https://doi.org/10.1016/j.funeco.2015.06.001
• Yue XL, Gao QX. 2018. Contributions of natural systems and human activity to greenhouse gas emissions. Advances Climate Change Research 9: 243-252. doi: 10.1016/j.accre.2018.12.003
  » https://doi.org/10.1016/j.accre.2018.12.003
• Zabel F, Delzeit R, Schneider Jet al 2019. Global impacts of future cropland expansion and intensification on agricultural markets and biodiversity. Nature Communications 10: 2844. doi: 10.1038/s41467-019-10775-z
  » https://doi.org/10.1038/s41467-019-10775-z
• Zhao Q, Zhang H, Wei J. 2019. Climatic niche comparison across a cryptic species complex. PeerJ 7: e7042. doi: 10.7717/peerj.7042
  » https://doi.org/10.7717/peerj.7042

Publication Dates

• Publication in this collection
  25 Sept 2023
• Date of issue
  2023

History

• Received
  26 Jan 2023
• Accepted
  26 July 2023