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To compare soil viral community composition within and across terrestrial habitats on a regional scale, viromes were generated from 34 near-surface (top 15 cm) soil samples, with a total of 30 viromes included in downstream ecological analyses (see Supplementary Methods). The analyzed viromes were collected from four distinct habitats (wetlands, grasslands, chaparral shrublands, and woodlands, each with 7, 14, 4, and 5 viromes, respectively) across five field sites (Fig. S1 for sampling scheme, Table S1 for soil properties). Following quality filtering, the 30 viromes generated an average of 72,950,833 reads and 416 contigs ≥10 Kbp per virome (Table S2). Wetland viromes yielded more contigs ≥10 Kbp than viromes from other habitats, both in total and on average per virome (Table S2). We used VIBRANT to identify 3490 viral contigs in our assemblies, which were clustered into 3,432 viral operational taxonomic units (vOTUs), defined as ≥10 Kbp viral contigs sharing ≥ 95% average nucleotide identity over 85% contig length [17]. Constrained analysis of principal coordinates (CAP analysis) revealed strong clustering by habitat rather than by site, implying that, where environmental parameters are substantially different, environmental conditions are stronger drivers of viral community composition than geographic distance (Fig. S2).Multiple lines of evidence suggest that wetter soils harbored greater viral diversity than drier soils. We recovered the most vOTUs from wetlands, both in total (56% of all vOTUs were from wetlands) and per virome (on average, 307 vOTUs were recovered per wetland virome, compared to 116 from all habitats) (Fig. 1A). Unsurprisingly, wetlands had significantly greater moisture content than other habitats (Fig. 1B; ANOVA followed by Tukey multiple comparisons of means, p 100 Km distances here. Taken together, we propose that soil viral communities often display high heterogeneity within and among habitats, presumably due to a combination of host adaptations and microdiversity, dispersal limitation, and fluctuating environmental conditions over space and time. More

Sample collection and RNA preparationWe analyzed 6 adult yellow-bellied toad individuals representative of distinct behavioral profiles, i.e. prolonged unken-reflex display vs no unken-reflex display (thereafter referred as “ + ” and “-“, respectively). Behavioral profiles were scored as in Chiocchio et al.12: 3 toads showed prolonged unken-reflex (+), whereas the other 3 did not show unken-reflex (−), as reported in Table 1. Sampling procedures were approved by the Italian Ministry of Ecological Transition and the Italian National Institute for Environmental Protection and Research (ISPRA; permit number: 20824, 18-03-2020). After dissection, brain tissue was immediately stored in RNAprotect Tissue Reagent (Quiagen) until RNA extraction. RNA extractions were performed using the RNeasy Plus Kit (Quiagen), according to the manufacturer’ instructions. RNA quality and concentration were assessed by means of both a spectrophotometer and a Bioanalyzer (Agilent Cary60 UV-vis and Agilent 2100, respectively – Agilent Technologies, Santa Clara, USA).Table 1 Summary of the 6 libraries deposited in the Sequence Read Archive (SRA) of NCBI, in terms of number of raw and trimmed reads per sample.Full size tableLibrary preparation and sequencingLibrary preparation and RNA sequencing were performed by NOVOGENE (UK) COMPANY LIMITED using Illumina NovaSeq platform. Library construction was carried out using the NEBNext® Ultra ™ RNA Library Prep Kit for Illumina®, following manufacturer instructions. Briefly, after the quality control check, the mRNA sample was isolated from the total RNA by using magnetic beads made of oligos d(T)25 (i.e. polyA-tail mRNA enrichment). Subsequently, mRNA was randomly fragmented, and a cDNA synthesis step proceeded using random hexamers and the reverse transcriptase enzyme. Once the synthesis of the first chain has finished, the second chain was synthesized with the addition of the Illumina buffer, dNTPs, RNase H and polymerase I of E.coli, by means of the Nick translation method. Then, the resulting products went through purification, repair, A-tailing and adapter ligation. Fragments of the appropriate size were enriched by PCR, the indexed P5 and P7 primers were introduced, and the final products were purified. Finally, the Illumina Novaseq 6000 sequencing system was used to sequence the libraries, through a paired-end 150 bp (PE150) strategy. We obtained on average 52.7 million reads for each library. The sequencing data are available at the NCBI Sequence Read Archive (project ID PRJNA76401320).Pre-assembly processing stageA total of 316,329,573 pairs of reads was generated by Illumina sequencing. All of them went to a cleaning analytic step. The quality of the raw reads was assessed with the FastQC 0.11.5 tool (http://www.bioinformatics.bbsrc.ac.uk/projects/fastqc), in order to estimate the RNAseq quality profiles. The quality estimators were generated for both the raw and trimmed data. The quality assessment metrics for trimmed data were aggregated across all samples into a single report for a summary visualization with MultiQC software tool21 v.1.9 (see Fig. 1). To remove low quality bases and adapter sequences, raw reads were also analyzed through a quality trimming step with Trimmomatic22, v.0.39 (setting the option SLIDINGWINDOW: 4: 15, MINLEN: 36, and HEADCROP: 13). All the unpaired reads were discarded. After the cleaning step and removal of low-quality reads, 297,354,405 clean reads (i.e. 94% of raw reads) were maintained for building the de novo transcriptome assembly (see Table 1).Fig. 1The cleaned reads from all samples were assessed with FastQC and visualized with MultiQC. (a) Read count distribution for mean sequence quality. (b) Mean quality scores distribution. (c) Read length distribution. (d) Per Sequence GC Content.Full size image
De novo transcriptome assembly and quality assessmentAs there is no reference genome for B. pachypus, we performed a de novo transcriptome assembly procedure. The workflow of the bioinformatic pipelines is shown in Fig. 2. All the described bioinformatics analyses were performed on the high-performance computing systems provided by ELIXIR-IT HPC@CINECA23.Fig. 2Workflow of the bioinformatic pipeline, from raw input data to annotated contigs, for the de novo transcriptome assembly of B. pachypus.Full size imageTo construct an optimized de novo transcriptome, avoiding chimeric transcripts, we employed rnaSPAdes24, a tool for de novo transcriptome assembly from RNA-Seq data implemented in the SPAdes v.3.14.1 package. rnaSPAdes automatically detected two k-mer sizes, approximately one third and half of the maximal read length (the two detected k-mer sizes were 45 and 67 nucleotides, respectively). At this stage, a total of 1,118,671 assembled transcripts were generated by rnaSPAdes runs, with an average length of 689.41 bp and an N50 of 1474 bp (Table 2).Table 2 Similarity rate of newly assembled transcripts versus the de novo transcriptome of B. pachypus.Full size tableResults from the assembly procedures were validated through three independent validator algorithms implemented in BUSCO25 v.4.1.4, DETONATE26 v.1.11 and TransRate27 v.1.0.3. These tools generate several metrics used as a guide to evaluate error sources in the assembly process and provide evidence about the quality of the assembled transcriptome. Busco provides a quantitative measure of transcriptome quality and completeness, based on evolutionarily-informed expectations of gene content from the near-universal, ultra-conserved eukaryotic proteins (eukaryota_odb9) database. Detonate (DE novo TranscriptOme rNa-seq Assembly with or without the Truth Evaluation) is a reference-free evaluation method based on a novel probabilistic model that depends only on the assembly and the RNA-Seq reads used to construct it. Transrate generates standard metrics and remapping statistics. No reference protein sequences were used for the assessment with Transrate. The main metrics resulted from the assembly validators are shown in Table 2 (“Before CD-HIT-est” column). After this triple assessment validation step, the result of the assembly procedure become the input for the CD-HIT-est v.4.8.128 program, a hierarchical clustering tool used to avoid redundant transcripts and fragmented assemblies common in the process of de novo assembly, providing unique genes. CD-HIT-est was run using the default parameters, corresponding to a similarity of 95%. Subsequently, a second validation step was launched on the CD-HIT-est output file. To refine the final transcriptome dataset, a further hierarchical clustering step was performed by running CORSET v1.0629. Then, the output of CORSET was validated by BUSCO, and quality assessment was performed with HISAT230,31 by mapping the trimmed reads to the reference transcriptome (unigenes). Results from all validation steps are shown in Table 2 and discussed in the “Technical Validation” paragraph.Finally, the CORSET output was run on TransDecoder32,33, the current standard tool that identifies long open read frames (ORFs) in assembled transcripts, using default parameters. TransDecoder by default performs ORF prediction on both strands of assembled transcripts regardless of the sequenced library. It also ranks ORFs based on their completeness, and determines if the 5 ‘end is incomplete by looking for any length of AA codons upstream of a start codon (M) without a stop codon. We adopted the “Longest ORF” rule and selected the highest 5 AUG (relative to the inframe stop codon) as the translation start site.Transcriptome annotationWe employed different kinds of annotations for the de novo assembly. We introduced DIAMOND34, an open-source algorithm based on double indexing that is 20,000 times faster than BLASTX on short reads and has a similar degree of sensitivity. Like BLASTX, DIAMOND attempts to determine exhaustively all significant alignments for a given query. Most sequence comparison programs, including BLASTX, follow the seed-and-extend paradigm. In this two-phase approach, users search first for matches of seeds (short stretches of the query sequence) in the reference database, and this is followed by an ‘extend’ phase that aims to compute a full alignment. The following parameter settings were applied: DIAMOND-fast DIAMOND BLASTX-t 48 -k 250 -min-score 40; DIAMOND-sensitive: DIAMOND BLASTX -t 48 -k 250 -sensitive -min-score 40.Contigs were aligned with DIAMOND on Nr, SwissProt and TrEMBL to retrieve the corresponding best annotations. An annotation matrix was then generated by selecting the best hit for each database. Following the analysis of BLASTX against Nr, SwissProt and TremBL, we obtained respectively: 123,086 (64.57%), 77,736 (40.78%), 122,907 (64.48%) contigs. The results obtained following the analysis with BLASTP against Nr, SwissProt and TrEMBL were 96,321 (50.53%), 57,877 (30.36%) and 97,256 (51.02%) contigs respectively. All the information on the resulting datasets is resumed in Table 3.Table 3 Summary of homology annotation hits on the different databases queried in this study.Full size tableThe output obtained by the BLASTX annotation consisted in a total of 77391 sequences simultaneously mapped on the three queried databases (i.e., Nr, SwissProt and TrEMBL). The output obtained following the BLASTP annotation consisted in a total of 57704 sequences simultaneously mapped on the three databases. Venn diagrams are presented in Fig. 3, showing the redundancy of the annotations in the different databases for both DIAMOND BLASTX (Fig. 3a) and DIAMOND BLASTP (Fig. 3b). Furthermore, the ten most represented species and the ten hits of the gene product obtained respectively with BLASTX and BLASTP by mapping the transcripts against the reference database Nr are shown in Figs. 4 and 5. Since BLASTX translated nucleotide sequence searches against protein sequences the BLASTX results are more exhaustive than BLASTP results. Contigs were also processed with InterProScan35 to detect InterProScan signatures. The InterPro database (http://www.ebi.ac.uk/interpro/) integrates together predictive models or ‘signatures’ representing protein domains, families and functional sites from multiple, diverse source databases: Gene3D, PANTHER, Pfam, PIRSF, PRINTS, ProDom, PROSITE, SMART, SUPERFAMILY and TIGRFAMs. The obtained InterProScan results for all the unigenes are available on Figshare in the form of Tab Separated Values (tsv) file format, which includes the GO and KEGG annotated contigs, respectively.Fig. 3Venn diagrams for the number of contigs annotated with DIAMOND (BLASTX (a) and BLASTP (b) functions) against the three databases: Nr, SwissProt, TREMBL.Full size imageFig. 4Most represented species and gene product hits. Top 10 best species (a) and protein (b) hits present in the reference database (Nr, BLASTX).Full size imageFig. 5Most represented species and gene product hits. Top 10 best species (a) and protein (b) hits present in the reference database (Nr, BLASTP).Full size imageComparison with Bombina orientalis brain transcriptomeWe compared the brain de novo transcriptome of B. pachypus with the brain de novo transcriptome of B. orientalis, recently produced in the frame of a prey-catching conditioning experiment17,18. The B. orientalis transcriptome resource was downloaded from GEO archive of NCBI (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE171766). To make the datasets comparable, we first performed ORF prediction on B. orientalis trascriptome using Transdecoder, using default settings. Results from the B. orientalis trascriptome ORF prediction are available in Figshare at the following link https://doi.org/10.6084/m9.figshare.20319633/). We also applied the makedb function implemented in DIAMOND to create the protein database index. Then, we aligned the B. pachypus predicted coding sequences and proteins (query files) against the B. orientalis protein database (reference) using DIAMOND BLASTX and BLASTP, respectively. We obtained 167041 matches from BLASTX and 156248 matches for BLASTP. Results from the BLASTX and BLASTP comparisons, and the most matched proteins, are available on Figshare36 (link available in next paragraph). More

Global climate change combined with regional and local anthropic activities suggest an increase in recurrence and extent of wildfires on ecosystems worldwide31,47,48, affecting in particular regions with higher likelihood of fire occurrences31 and making natural systems more prone to fire occurrences21. Estimates of accumulated burned area in Brazil between 1985–2020 revealed that, among the Brazilian biomes, the Pantanal is the most affected by the fires (with accumulated burned area equivalent to 57.5% of the biome within Brazil)46. But 43% of 2020 burned area (≈13% of the Pantanal) had not burned since 200319. Therefore, it is impressive that nearly 1/3 of the Pantanal burned in a single year17,18,19 (Figs. 1, 2 and S1, S2). The high number of human-induced fires17,18,19,21 combined with the hottest and driest conditions since 198017,22,38,49 led 2020 to record the highest daily severity rating (DSR) index of fires for this time period17,49. With documented increase of 2 °C in the average temperature22 and a 40% shortage in rainfall26,38. But the fire risk got even higher with the simultaneous occurrence of dry and hot spells, between August and November, when the maximum temperature reached, on average, 6 °C above the normal, accounting for 55% of the burned area of 202049.Most fires started close to the agriculture frontiers21, but they predominantly affected the natural vegetation (reaching between 91–95% of it in occurrence of fire50,51 and 96% of it in estimated burned area)31,46, with tragic consequences for jaguars and the Pantanal biota17,19,26. Along with the fires, the severity of the 2020 drought22,52,53 dropped minimum river depths at around 86% below normal25,54 (Fig. 2 and S1, S3, S4). Consequently, resulting in several records of animal starvation, dehydration, and death17,19,26. And late mortality from indirect causes of fires certainly increased these numbers26. Besides, post-fire ecosystem and hydrology changes also had ecological effects with long-term impacts on ecosystem recovery and fire risk31, impacting resource quality, availability, and productivity26,31. Vegetation productivity declined below −1.5 σ over more than 30% of the natural areas and evaporation decreased (by ~ 9%)31. Burned vegetation made the soil more vulnerable to erosion, increasing the runoff (by ~ 5%) over the natural areas31, and the resulting charcoal and ash contaminated rivers17.Many reasons may have contributed to the intensity of the 2020 drought in the Pantanal, from climate8,22,24,49 to direct and indirect human impacts in the Upper Paraguay River Basin (UPRB)21,55,56. In fact, anthropic changes in land use also increased the biome sensitivity to fire-climate extremes)31. The shortage of rain throughout the UPRB, particularly in the summer season, is among the main factors, as the basin water balance controls the hydroclimatological dynamics in the Pantanal (Fig. 2 and S3–S9)22. The shortage of rain may also be a consequence of increased deforestation in the Amazon rainforest57,58, as summer rainfall in the Pantanal is strongly associated with the climate of the Amazon59. Furthermore, the reduction in wetland flooded areas is historically correlated with the spread of fires (Fig. 2 and S1)22,28,29. Low water levels led to the absence of flooding and reduced wetland areas, and the remaining dry vegetation provided flammable material and created favourable conditions for fires to occur22,23,24. In addition, the lack of governmental and human resources and delayed response at federal and local levels58,60,61 amplified the negative effects of water shortage17,19,58.Although historical hydrological series show that extreme drought events occurred in the past22,25,38,62 (e.g., from the late 1960s to early 1970s, Fig. S3), they also show that the recovery of the Pantanal was conditioned to the subsequent 15 years of regular to exceptional floods (1974 to early 1990s, Figs. S1, S3). Savanna-like vegetation, the predominant vegetation type in the Pantanal, usually recovers from the effects of fires in relatively short periods (months to a few years)23, depending on the severity and frequency of fires and climate conditions in the subsequent years23,28,29. But the resilience of many species may decrease with the annual repetition of extreme fire events28,29,30. Thus, human interventions to prevent (instead to promote) sequential fire events in the same area are paramount19,23,62,63.Estimating the effects that uncontrolled extensive fires can cause to the apex predator of the Neotropics in a region considered one of the strongholds for the species can contribute to the conservation of jaguar and other wildlife species, as well as to the debate regarding potential cumulative impact of recurrent wildfires on ecosystems26,31,51,62,63. Our results revealed the drastic impact of fire on estimated numbers of jaguars, home ranges, and priority areas for jaguar conservation in the Pantanal was exceptionally high in 2020 and proportionally more severe than the nominal 31% of burned area across the Pantanal (e.g., fires affected 45% of the jaguars and 79% of their HRs). Moreover, the annual comparison showed that 2019 was the second-worst year regarding fire impacts (only behind 2020) and equally extreme compared to historical means22. Although the Pantanal is well known for its annual and pluri-annual cycles of wet and dry seasons7,64, the unusual levels of droughts22,25,65,66 and fires17,20,21 in subsequent years are alarming. Furthermore, climate assessment and projections of warmer and dryer conditions for the region in the coming years are equally worrying22,24,37,38.We found that 45% of the jaguar population estimated for the Pantanal occupied areas affected by the 2020 fires (Fig. 1). This finding suggests that the fires heavily impacted the jaguars in the Pantanal, even if we assume that the major effects were only temporary displacement. This potential displacement may make it more difficult for jaguars to find new suitable areas, thus increasing territorial disputes and decreasing survival and reproductive success. Furthermore, 2019 ranked as the second-highest year of impact of fire on jaguar population estimates among the 16 years considered (Table 1, Fig. 1). Importantly, we did not consider cumulative impacts on sequential years or fire recurrence in these estimates. Moreover, the available estimates for jaguar abundance we used36 are very conservative and probably underestimated jaguar populations from the Pantanal by a maximum of 3 jaguars/100 km2. However, the reported density of jaguars may reach up to 12.4 jaguars/100 km2 in northern PAs5,67,68 and up to 6.5–7 jaguars/100 km2 in the southern Pantanal farms5,69,70. Considering that PAs in the northern Pantanal were severely damaged by the 2020 fires, our results show conservative figures for the actual number of jaguars affected by fires.We used densities estimated from an ecosystem-wide assessment of impacts as a proxy of the proportion of total population reached by fire each year on a regional scale. Fires affected a substantial proportion of estimated individuals in the Pantanal in 2019–2020. In 2020, for instance, 87% of all jaguars affected by fire were in the Brazilian Pantanal. In contrast, the smaller population in the Paraguayan and Bolivian Pantanal had a higher median percentage of individuals affected by fire between 2005–2019. While 45% of jaguars were affected by fire in a single year (2020) in the Pantanal, a study45 using the same conservative estimates36 for jaguar abundance in the Brazilian Amazon revealed that 1.8% of the population (1422 individuals) was killed or displaced by fire between 2016–2019. Another report estimated that more than 500 individuals were affected by the 2019 fires in the Brazilian and Bolivian Amazon71,72. Based on the same density estimates we found that in the Pantanal — a much smaller biome — more jaguars were affected by fire in single years (n = 513 in 2019 and n = 746 in 2020). This recent increase in the number of jaguars affected by fire raises a red flag to the supposed stability of the species in the Pantanal, which is currently globally and locally classified as Near Threatened1,5. Therefore, we recommend that future assessments by IUCN specialists carefully consider the frequency and intensity of fires as a potentially significant and growing threat to jaguars in the Pantanal, and their effects on long-term populational trends.Quantifying the occurrence of fire on HRs introduced a functional perspective to understanding the impact of fire on individual jaguars. Similarly, our estimates of the number of affected jaguars revealed a vast amount and extent of affected HRs in the last two years (Figs. 2 and 3). Jaguars are apex predators, often considered as a keystone73,74,75,76 and umbrella species45,77, highly dependent on large habitat areas78, dense native vegetation cover35,79,80, and abundance of prey67,81. Considering that jaguars often select areas with high environmental integrity35,68,78,79,80, the higher impact of recent fires on HRs corroborates reports showing the increase of natural areas burned in the Pantanal31,46,50,51. The proportion of burned forests, for instance, was 10 times higher in 2020 than the estimated median between 1985 and 201931. Sadly, it is likely that much of these burned forests in Northern Pantanal included areas pointed as suitable jaguar habitat and of great interest to the creation of additional PAs82.In the Pantanal, HRs are smaller35,83 and population densities are high5,67,68,69,70 because the biome is a highly productive system7,55,67, with an abundance of prey species and quality habitat, thus allowing jaguars to meet their spatial needs using smaller areas35,68,83. Consequently, floodplain jaguars are also usually larger44,84. However, a trend of increasing drought, rising temperatures, and repeated occurrences of exceptional fires would weaken the Pantanal’s resilience22,32. Importantly to note as well that the occurrence and intensity of fires are frequently higher in the dry season, peaking within jaguars HRs in the years with intense fire occurrence in the Pantanal. This apparent higher impact over jaguar habitat agrees with studies pointing out highest damage in PAs17,27 (Fig. S20), natural vegetation and particularly in forested areas in 202031,46,50,51. Recurrent impacts may particularly affect the most sensitive species28,29,30, resulting in a less productive environment32, which ultimately decreases the habitat quality of many species. These effects would likely push jaguars to expand their HRs, which would increase disputes for territories and favour a decrease in body size, consequently decreasing reproductive rates and population size.The extent of protected areas burned is another indicator of how fire can impact biodiversity. Like the HRs, the Pantanal PAs were affected differently in space and time, but the greatest fires occurred in recent years (2019 and 2020). In 2020, fires occurred in 62% of Brazilian PAs — particularly in northern Pantanal — where several portions of PAs overlapping with jaguar HRs were entirely or almost entirely affected by fires (Figs. 1–3). In 2019, however, fires affected the Pantanal PAs in Bolivia, Paraguay and southern Brazil more severely in areas that also overlapped with HRs (Figs. 1–3). Several causes can explain the spread of fires across PAs, including a combination of heat, drought, miscalculated human use of fires, lack of resources and personnel for surveillance and fire control improvement17,18,19,20,21,22,23.The displacement, injuries, and deaths caused by fire to animals within PAs are worrying because these areas are reportedly richer in diversity and biomass85,86 (including higher jaguars densities36,67,87 and are fundamental to safeguarding biodiversity and ensuring the long-term provision of ecosystem services88,89. Protected areas are important to jaguars because they provide larger continuous areas of natural dense vegetation cover (such as forests and shrublands), flooded habitats and limit contact with humans, attributes of great influence in jaguar habitat selection35,78,79,80,82, and particularly important to females90,91. However, although some PAs support up to 12.4 jaguars/100 km2 (e.g., Taiamã Ecological Station – TES)67, the currently availability of Pantanal PAs alone would not support viable jaguar populations for more than 50 years87. Therefore, sustainable management that allows coexistence in private lands is also fundamental for the conservation of jaguars in the Pantanal5,9,10,11. Protected areas of integral protection, such as TES, currently occupy only 5.7% of the Pantanal7 but were the most affected by fires in absolute area (Fig. S20, Table S5)27. The total number of PAs, including the sustainable use ones, corresponds to only 5% of the Brazilian Pantanal (Tables S1–S3)7,92,93,94,95,96 and around 10% of the entire Pantanal7, most of it in Bolivia97. These percentages are much lower than the minimum of 17% recommended in the Aichi goals for terrestrial ecosystems7,56. Furthermore, PAs are also scarce in the Pantanal headwaters (6% of the surrounding Cerrado uplands) (Tables S1–S3, Fig. S19)7,92,93,94,95,96. To make matters worse, PAs were reduced by almost 20% in the Brazilian Pantanal in 2007 and have not been expanded in the Cerrado uplands since 2006 (Tables S1–S3, Fig. S19)93. The relatively small coverage of protected areas in the Pantanal, which serve as refuges, increases the negative effects of fires, as jaguars are likely displaced into sub-optimal habitats. Consequently, jaguars and other species may struggle to find equally resource-rich sites after being displaced from PAs.For the long-term survival of the jaguar, it is essential to implement conservation plans that consider the dispersal and reproduction of the species along the Paraguay River98, increase the network and size of PAs82, and adequately allocate funding and personnel to maintain the PAs. Furthermore, careful implementation of strategies to mitigate the risk of fire18,19,62 and other human impacts outside PAs5,6,7,8,9,10,11,12,13,14,15,16,89,99 are urgent needs for conservation of the Pantanal. In any case, our results highlight that to sustain viable populations of jaguars and other species, conservation plans for the Pantanal must account for fire impact on PAs and other vital areas for biodiversity.Although jaguar HRs often overlap with PAs67,68,87, some individuals may settle in unprotected areas69,70. In our sample, we found that 38 HRs partially overlapped with PAs (Fig. 1) and 10 HRs did not. On the other hand, considering the sum of the HR extents and the total area overlapped with the PAs, we found that 20% of the HR extent matched the PAs. Notably, jaguars coexist with different levels of anthropic pressures outside the PAs4,5,9,10,11,12,13,14,15,16. Jaguar distribution range has been restricted to 63% of the Pantanal5 and even more restricted in the UPRB100. Agriculture expansion, particularly cattle ranching and soybean cultivation (Figs. S17, S18)65, has been identified as the main causes of jaguars’ disappearance or decline due to killing and habitat loss5,9,13.Sustainable use has been advocated as a conservation strategy in the Pantanal, mainly due to the characteristics of the region, where cattle ranching uses as pastures the natural areas restricted by the Pantanal flooding regime since the 17th century7,23. In recent years, ecotourism has also gained great importance55,101,102. However, there are risks in relying on sustainable use as a core strategy for 90% of the biome (95% of Brazilian Pantanal), and exposure to human-induced fires is one of them21,31.Fire is a fundamental factor acting on the dynamics of the Pantanal vegetation23,28,29. However, repeated uncontrolled fires can drastically impact forests and other habitats critical to the jaguars and increase the area for cattle ranching, therefore increasing the risk of livestock depredation and retaliatory hunting11. Thus, the conservation of the jaguar and other animal species in the Pantanal is critically linked to fire management and the use of private lands because the increased fire may extend and aggravate other anthropic impacts (Fig. 4). This work highlights the significant increase in the extent and severity of recent fires in the Pantanal and how these fires have affected jaguars. Further studies that estimate natural habitat recovery and fire recurrence and assess real-time and long-term effects of fire on jaguars and other species are critical to guide fire management and conservation.Fig. 4: Scheme summarizing the main impacts of fires in the Pantanal.The red arrows are intentionally larger and show a feedback loop linking increased negative human impacts, climate change, and drought to increased fires and burned areas, with a consequent negative impact on biodiversity. The blue arrows describe a feedback loop for fire control and impact mitigation. The dashed arrows denote other relevant effects in the biome (e.g., cumulative effects from infrastructure such as hydroelectric power plants, river waterways, water and soil pollution from legal and illegal mining and agriculture, poaching and illegal wildlife trade, opportunistic exploitation of burned areas, as well as natural climate constraints.Full size imageChanges in the climate8,22,24,37,38, landscape and water use in the UPRB over the last four decades7,18,56,65 are cumulative threats that may interfere with water recharge and vegetation resilience in the Pantanal. Global temperatures may increase up to 1.5 °C over the next five years37, in addition to the 2 °C already recorded since 1980. By the end of the 21st century, scientists estimate increases of 5 − 7 °C in the temperature and the frequency of climatic extremes and a 30% reduction in average rainfall8,37,38. Until 2019, pastures covered 15.5% of the Brazilian Pantanal and agriculture about 0.14%25. However, agriculture and pastures occupied 60–65% of the surrounding Cerrado uplands within the UPRB7,55,56, an occupation similar to the adjacent Paraguayan Chaco and Bolivian Chiquitano Forest7,103,104. And future projections estimate a loss of 14,005 km2 of native vegetation from 2018 through 2050105. Consequently, this land occupation impacted the main headwaters of the Pantanal rivers and ultimately the entire Pantanal6,56,106,107. Furthermore, by 2019, 47 hydroelectric power plants were installed or in operation, and another 133 were planned, totalling about 180 potential dam projects in the Brazilian UPRB108. Besides, most of these projected hydropower infrastructures will overlap with the distribution of jaguars, also in the adjacent biomes, impacting negatively the species particularly in Brazil15. These economic and infrastructure activities in the surrounding highlands frequently ignore their cumulative impacts109 and affect the Pantanal in different ways (Fig. 4, S17, S18), including its drainage dynamics and flood pulses, with consequent impacts on drought duration and fire spread17,19,22,23,24(Figs. 1–4, SI). This combination of factors probably intensifies the Pantanal droughts, particularly the periodic sequence of dry years.Therefore, a critical point is how human actions can exacerbate such extreme events7,21,31,55,106,110 and make fire control even more difficult19,23,62 or, on the opposite, contribute to minimize the overall impacts of drought and fires and promote biodiversity conservation19,63 (Fig. 4). Given that the rainfall remained below average in the last wet seasons53 (Figs. S1, S3–S8) and that a severe drought persisted in 2021111, a surveillance protocol for rapid response and programs for fire management, mitigation of human impacts and ecosystem recovery are needed19,23,62,63. If such measures keep lacking, a tragedy similar to the 2020 fires may be repeated in the coming years (Fig. 4). And Pantanal native vegetation may be reduced to only about 62% by 203021. To make matters worse, the government budget allocated for fire control and firefighting for 2021 was reduced to 65.5% of the 2019 budget61 and all funds allocated to the environment were reduced to the lowest level in 20 years61,112, with serious complaints of misuse113, embezzlement114 and wood-smuggling probe115.The extent of the recent wildfire in the Pantanal has signalled that fire is a potential threat to the long-term conservation of the jaguar. Furthermore, fires severely affected other species and human activities17,19,23, demanding an immediate mitigation plan18,19,62. In fact, permanent fire brigades have been established, and an animal rescue centre is under construction in response to the effects of the recent extensive fires in the Pantanal. Although actions are underway at local levels, the warming and drying trend22,24,37,38 is also a combination of global warming8,37 and rapid land-use changes7,18,65 (Figs. S17, S18), with cumulative impacts in the UPRB and Pantanal wetlands (Fig. 4). Therefore, the immediate reduction of deforestation in the Amazon and Pantanal and the establishment of a forest restoration plan in the UPRB are critical. The lack of sufficient mitigatory actions may throw the Pantanal into a perverse vortex (increasing feedback of cumulative negative impacts, (Fig. 4), thus affecting the survival of jaguars and the various species under their umbrella, as well as human welfare. More

Animals, housing, and treatmentsAll procedures involving animals were approved by the University of Florida Institutional Animal Care and Use Committee (Protocol #201709925). All methods were performance in accordance with the relevant guidelines and regulations, and permission and informed consent was obtained from the University of Florida (owners) for the use of the steers in this experiment.The experiment was carried out during July and August of 2017 at the Feed Efficiency Facility of University of Florida, North Florida Research and Education Center, located in Marianna, Florida (30°52′N, 85°11″W, 35 m asl). Both ‘Argentine’ bahiagrass and ‘Florigraze’ rhizoma peanut hays were obtained from commercial producers. The hay bales were stored in enclosed barns throughout the duration of the experiment.Twenty-five Brahman × Angus crossbred steers (Bos sp.) were utilized (average BW = 341 ± 17 kg, approx. 16 months of age). The steers were grazing bermudagrass (Cynodon dactylon) pastures, a C4 grass, prior to the start of the study. The day prior to the start of the experiment (e.g. day-1), steers brought to working facilities, where they remained 16 h off feed and water, in order to obtain shrunk bodyweights. On day 0 of the experiment, steers were weighed, blocked by bodyweight, and allocated to five treatments (5 steers per treatment) and housed in grouped pens. Hay intake was recorded utilizing GrowSafe© systems (GrowSafe Systems Ltd., Calgary, AB, Canada), which utilize radio frequency identification to record feed intake by weight change measured to the nearest gram. Water was available ad libitum. Forage treatments were offered ad libitum by providing sufficient hay to maintain full feed troughs throughout each day of the experiment. Treatments were five proportions of ‘Florigraze’ rhizoma peanut hay in ‘Argentine’ bahiagrass hay: (1) 100% bahiagrass hay (0% RP); (2) 25% rhizoma peanut hay + 75% bahiagrass hay (25% RP); (3) 50% rhizoma peanut hay + 50% bahiagrass hay (50% RP); (4) 75% rhizoma peanut hay + 25% bahiagrass hay (75% RP); (5) 100% rhizoma peanut hay (100% RP). Diet chemical composition is presented in Table 1. All treatment proportions were weighed and mixed on as-fed basis. Mixing of diets was done manually; no hay mixers or choppers were used, to minimize leaf shatter.Sample collectionSteers were housed for 32 days and sampling occurred on 0, 8, 16, 24, and 32 days after initiation of treatment diets; exception was for feces, which were collected on d-1 given steers were fasted on d-0 of the experiment. The hay mixtures offered to the steers were collected (10 samples of each diet) and analyzed for nutritive value (Table 1), at the start of the experiment. All sampling occurred between 0700 and 1000 h on each of the sampling days.Fecal samples were collected directly from the rectum and placed in quart-sized plastic bags to avoid contamination. The feces were frozen at −20 °C. All fecal samples were thawed, dried at 55 °C for 72 h, and ground to pass a 2-mm stainless steel screen using a Wiley Mill (Model 4, Thomas-Wiley Laboratory Mill, Thomas Scientific, Swedesboro, NJ, USA). Samples were then ball milled using a Mixer Mill MM400 (Retsch GmbH, Haan, Germany) at 25 Hz for 9 min.Blood was obtained through jugular venipuncture using 10-mL K2 EDTA vials (Becton Dickinson and Company, Franklin Lakes, NJ, USA), and stored in ice until centrifugation. All tubes were centrifuged at 714 G for 20 min using an Allegra X-22R centrifuge (Beckman Coulter, Brea, CA, USA). A 10-mL sample of plasma was collected and placed in a separate glass vial, the remaining plasma, white blood cell, and platelet fractions were discarded. The remaining RBC pellet was re-suspended with 9 vol. 0.9% NaCl solution and mixed at room temperature for 15 min at 2 Hz orbital shaker. The tubes were then centrifuged at 714 G for 20 min. The saline solution from the centrifuged tubes was discarded after centrifugation. The rinse procedure was repeated two more times for a total of three rinses. After the third rinse procedure, a 500-µL sample was removed, frozen at −20 °C, and subsequently freeze-dried for isotopic analyses.Hair clippings were obtained from an area of 20 × 20 cm on the left hindquarter, utilizing veterinary hair clippers (Sunbeam-Oster Inc., Boca Raton, FL, USA). Hair clippings were collected, placed in nylon bags (Ankom Technology, Macedon, NY, USA), and frozen for subsequent analysis. Clippings were always collected in the same location from each animal in order to ensure new hair growth would be analyzed. All hair samples were cleaned using soapy water and defatted following protocols for other keratin-based tissues 31,34. Each sample was sonicated twice for 30 min in a methanol and chloroform solution (2:1, v/v), rinsed with distilled water, and oven dried overnight at 60 °C. Each hair sample was ball milled using a Mixer Mill MM400 (Retsch GmbH, Haan, Germany) at 25 Hz for 9 min.CalculationsAfter processing, all samples were analyzed for total C and N using a CHNS analyzer through the Dumas dry combustion method (Vario MicroCube, Elementar Americas Inc., Ronkonkoma, NY, USA) coupled to an isotope ratio mass spectrometer (IsoPrime 100, Elementar, Elementar Americas Inc., Ronkonkoma, NY, USA). Certified standards of L-glutamic acid (USGS40, USGS41; United States Geological Survey) were used for isotope ratio mass spectrometer calibration. Isotope ratios were as follows: δ13C of −26.39, + 37.63‰, and δ15N of −4.52, 47.57‰ for USGS40 and USGS41, respectively. Calibration of the IRMS was conducted according to Cook, et al. 35, with an accuracy of ≤ 0.06 ‰ for 15N and ≤ 0.08 ‰ for 13C.The isotope ratio for 13C/12C was calculated as:$$delta^{{{13}}} {text{C}} = , left( {^{{{13}}} {text{C}}/^{{{12}}} {text{C}}_{{{text{sample}}}} {-}^{{{13}}} {text{C}}/^{{{12}}} {text{C}}_{{{text{reference}}}} } right)/ , left( {^{{{13}}} {text{C}}/^{{{12}}} {text{C}}_{{{text{reference}}}} times { 1}000} right)$$
(1)

where δ13C is the C isotope ratio of the sample relative to Pee Dee Belemnite (PDB) standard (‰), 13C/12Csample is the C isotope ratio of the sample, and 13C/12Creference is the C isotope ratio of PDB standard 5. The isotope ratio for 15N/14N was calculated as:$$delta^{{{15}}} {text{N}} = , left( {^{{{15}}} {text{N}}/^{{{14}}} {text{N}}_{{{text{sample}}}} -^{{{15}}} {text{N}}/^{{{14}}} {text{N}}_{{{text{reference}}}} } right)/left( {^{{{15}}} {text{N}}/^{{{14}}} {text{N}}_{{{text{reference}}}} times { 1}000} right)$$
(2)
where δ15N is the N isotope ratio of the sample relative to atmospheric nitrogen (‰), 15N/14Nsample is the N isotope ratio of the sample, and 15N/14Nreference is the N isotope ratio of atmospheric N (standard) 5. The fraction factor (Δ) in this study was considered to be the difference between the diet isotopic composition (δ13C or δ15N) and that of the given sample 5.The dietary proportion of rhizoma peanut hay was back-calculated using δ13C and δ15N of each plant on a DM basis 3. This method is advantageous in that it does not require further tissue processing and facilitates implementation at the field scale. The proportion of rhizoma peanut was estimated using Eq. (3), described by Jones et al. 3:$$%RP=100-left{100 times frac{A-C}{B-C}right}$$
(3)
where %RP is the proportion of RP in the diet, A is the δ13C or δ15N of the sample obtained, B is the δ13C or δ15N of bahiagrass, and C is the δ13C or δ15N of RP.Statistical analysisAll response variables were analyzed using linear mixed model procedures as implemented in SAS PROC GLIMMIX (SAS/STAT 15.1, SAS Institute). Individual animals were considered the experimental unit. Treatment, collection day, and their interaction were considered fixed effects, and block was considered a random effect in the model. The data were analyzed as repeated measures, considering collection day as the repeated measure. The best covariance matrix was selected according to the lowest AICC fit statistic. Least squares treatment means were compared through pairwise t test using the PDIFF option of the LSMEAN statement in the aforementioned procedure. Based on the recommendations by Milliken and Johnson 36 and Saville 37, no adjustment for multiple comparisons was made. Orthogonal polynomial contrasts (linear and quadratic effects) were used to test effects of RP inclusion on isotopic responses. The slice option was used when the treatment × collection day interaction was significant (P ≤ 0.05), using collection day as the factor, to test treatment effects across collection days. Significance was declared at P ≤ 0.05. The contrast statement was used to test for linear or quadratic effects. Regression analyses for the dietary predictions were conducted using PROC REG from SAS.Predictions of dietary proportions based on Eq. (3) were generated for 16 subgroups reflecting combinations of isotope (13C or 15N), day (8 or 32), and sample type (feces, plasm, RBC, or hair). Analyses comparing predicted versus actual diet proportions included several components. First, we computed the concordance correlation coefficient (CCC) following the recommendations from Crawford, et al. 38. The CCC is a measure of agreement that encompasses both precision and accuracy, along with corresponding 95% bias accelerated and corrected (BCa) bootstrap confidence intervals. For comparative purposes we calculated the Pearson correlation coefficient which only reflects precision. Both correlation coefficients range from −1.0 to 1.0 and we interpreted values ≥ 0.80 as indicating strong positive agreement/correlation. Next, we regressed the actual percentages on the predicted percentages using linear regression. Perfect prediction corresponds to the estimated regression line having an intercept of zero and a slope of 1.0. We then partitioned the mean square error (MSE) of the predicted (from Eq. (3), not the above linear regression) versus actual percentages as described in Rice and Cochran 39. This partitioning entails calculating the proportion of MSE attributable to three sources of error: the difference in mean predicted and actual values (mean component, denoted “MC”), the error resulting from the slope of the above linear regression deviating from 1.0 (slope component, denoted “SC”), and random error (random component, denoted “RC”). The results from the above analyses were examined to identify subgroups whose predictions were sufficiently good to warrant hypothesis testing. In this context “good” means that the predicted percentages were strongly correlated with the actual percentages and the magnitudes of the predicted percentages were similar to the actual percentages. The objective of the hypothesis testing was to formally evaluate whether dietary proportions could be directly predicted from Eq. (3) (in contrast to generating predictions using the equation from regressing actual dietary percentages on the predicted percentages from Eq. (3)). Paired two one-sided test (TOST) equivalence tests were conducted for the selected subgroups with α = 0.0540. These tests are formulated such that the null hypothesis is “non-equivalence” and the alternative hypothesis is “equivalence”. An input parameter to the test is the equivalence region, a range for which we consider the mean actual minus predicted difference to be unimportant (“equivalent”) from a practical standpoint. For each equivalence test we also computed the 90% confidence interval for the mean actual minus predicted difference which we refer to as the “minimum equivalence region”. Based on the structure of TOST equivalence tests, to reject the null hypothesis at the 0.05 level, the equivalence region specified for the test must completely contain the minimum equivalence region. For example, if the pre-specified equivalence region is (−15%, 15%) and the computed minimum equivalence region is (−16%, −6%) the null hypothesis would not be rejected. Finally, we re-ran all of the analyses described above for the selected subgroups where, prior to analysis, predicted percentages outside of the valid range were assigned the appropriate boundary value (i.e., predicted percentages  100% were assigned a value of 100%). More