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Effects of light intensity and temperatureThe germination of P. paradoxa (91 to 95%) and wheat (93 to 97%) was not affected by light intensity (data not shown). Our results conform to previous studies which revealed that light intensity had little role in influencing P. paradoxa germination24.The germination of wheat and P. paradoxa was influenced by temperature regimes (Fig. 1). At temperature regimes of 15/5 °C and 20/10 °C, germination of wheat and P. paradoxa did not vary. Seed germination in wheat remained similar at temperatures ranging between 15/5 °C to 30/20 °C. However, in P. paradoxa, germination was reduced at higher temperature regimes (35/25 C) compared with lower temperature regimes (15/5 °C to 25/15 °C). At the highest temperature regime (35/25 °C), the germination of wheat was 79%, while, at this temperature regime, the germination of P. paradoxa was only 1%. This suggests that wheat can germinate at high-temperature ranges, while, germination of P. paradoxa may be reduced at high temperatures (35/25 °C). These results implied that at the time of planting wheat in Australia if the air temperature is low, the chances of emergence of P. paradoxa are very high. This suggests that efforts should be made towards early control of P. paradoxa in wheat if the air temperature in the winter season falls early. These results also suggest that early planting of wheat could reduce the emergence of P. paradoxa as the prevailing temperature conditions are relatively high in early planting (e.g., end of April). In the Indo-Gangetic Plains, better control of P. minor was observed in the early planting of wheat (high-temperature conditions) due to less emergence of P. minor25.Figure 1Effect of alternating day/night temperatures (15/5 to 35/25 °C) on germination of Phalaris paradoxa and wheat seeds (incubated for 21 d) under light/dark (12-h photoperiod). LSD: Least significant difference at the 5% level of significance.Full size imagePrevious studies have also revealed that germination of P. paradoxa was highest at 10 °C and then failed to germinate at 30 °C 24,26, however, these studies were conducted at constant temperatures and the germination response of P. paradoxa was not studied in comparison with wheat in those studies.Effect of radiant heatThe germination of P. paradoxa seeds that were stored at room temperature (25 °C) was 97%, which reduced to 88% after exposure to the 100 °C pretreatment for 5 min and became nil at 150 °C (Fig. 2). About 88% of P. paradoxa at 100 °C suggests that it can tolerate heat stress for short periods.Figure 2Effect of high-temperature pretreatment for 5 min (℃) on germination of Phalaris paradoxa seeds. LSD: Least significant difference at the 5% level of significance.Full size imageGermination was nil at 150 °C and above, suggesting that burning could help in managing P. paradoxa, particularly in a no-till field where seeds are on the soil surface or at shallow depths. Exposure of seeds to fire could inhibit germination by desiccating the seed coat or by damaging the embryo27,28,29.Burning of residue in the fields could kill weed seeds and other pests in the topsoil layer30. Windrow burning proved to be an effective tool for killing weed seeds in paddocks31. However, the crop residue burning may cause environmental destruction by killing microbes and polluting the air. Also, it reduces the amount of soil organic matter due to the high heat, causing soil degradation. Therefore, these aspects should also be considered while formulating weed management strategies through crop residue burning. Burning may also release the dormancy of other weed seeds present in the subsoil and thus may increase infestation; therefore, this technique should be used cautiously32,33.Effect of osmotic stressGermination of P. paradoxa was highest (95%) in the control treatment and germination reduced to 75% at an osmotic potential of −0.8 MPa, and became nil at −1.6 MPa (Fig. 3). However, in wheat, germination did not reduce with an increase in water potential and it was 94% in the control treatment.Figure 3Effect of osmotic potential on germination of Phalaris paradoxa and wheat seeds at alternating day/night temperatures of 20/10 °C under 12 h photoperiod. Seeds were incubated for 21 d. LSD: Least significant difference at the 5% level of significance.Full size imageAt a very high concentration of PEG, the metabolic activity of P. paradoxa might be reduced due to water stress. Seed germination is affected when seeds are not able to get critical moisture threshold levels for imbibitions34,35. These results indicate that high water stress may inhibit the seed germination of P. paradoxa. However, under no water stress or mild water stress conditions, P. paradoxa may infest the wheat crop.Contrary to these results, previous studies reported that germination of P. paradoxa was reduced by 90% at an osmotic potential of −0.25 MPa25. Good germination of wheat at high osmotic potential indicates that the wheat variety used in this study may have water stress tolerance traits for germination. It was observed that wheat could germinate well (75%) at a high-water stress level (−1.6 MPa)36. This suggests that it is possible to menace P. paradoxa by growing stress-tolerant varieties of wheat and manipulating irrigation. In a previous study, less infestation of P. paradoxa was observed in drip-irrigated wheat crops due to optimal soil moisture conditions for the crop37.Effect of salt stressGermination of P. paradoxa was highest (93%) in the control treatment, and at a NaCl of 150 mM, germination was reduced to 76% (Fig. 4). Similarly, in wheat, germination was highest (94%) in the control treatment and at a salt concentration of 150 and 200 mM, germination was reduced to 84 and 79%, respectively. These results suggest that at a high salt concentration, P. paradoxa may infest the wheat crop owing to its ability to germinate under high salt concentrations.Figure 4Effect of sodium chloride concentration on germination of Phalaris paradoxa and wheat seeds at alternating day/night temperatures of 20/10 °C under 12 h photoperiod. Seeds were incubated for 21 d. LSD: Least significant difference at the 5% level of significance.Full size imageContrary to this, in Iran, it was observed that germination of P. paradoxa was reduced by 70% at a NaCl of 160 mM24. Most of the Australian soils are saline; therefore, it is quite possible that P. paradoxa in Australia might have developed traits for salt tolerance38. The variable response of populations of P. paradoxa to salt concentrations in Iran and Australia might be due to genetic differences between the P. paradoxa populations38. These observations suggest that P. paradoxa could invade the agroecosystem under the saline conditions of Australia.Effect of seed burial depth on emergenceGermination of P. paradoxa was very low (10%) on the soil surface, and seedling emergence was highest (74%) at a soil burial depth of 0.5 cm (Fig. 5). Seedling emergence was similar when seeds were buried in the soil at a depth ranging from 0.5 to 4 cm. Seedling emergence was 32% at a burial depth of 8 cm.Figure 5Effect of seed burial depth on seedling emergence of Phalaris paradoxa. LSD: Least significant difference at the 5% level of significance.Full size imageThe results from this experiment suggest that a no-till production system may inhibit the germination of P. paradoxa. This study also suggests that deep tillage ( > 4 cm) could reduce the emergence of P. paradoxa to some extent; therefore, inversion tillage could be a weed management strategy if the seedbank is in the shallow layer of the soil. It has been reported that the emergence of small-seeded weeds is reduced from deeper burial depths, as the soil-gas exchange is limited 21. However, it is important to know the seed longevity of this weed in different soil and environmental conditions when considering tillage operations39.Likewise, previous studies also reported that seed germination of P. paradoxa was lowest on the soil surface and no seedlings emerged from a soil depth of 10-cm2,40. Contrary to this in Iran, germination of P. paradoxa was found to be  > 65% on the soil surface 24.Evaluation of PRE-herbicidesResults revealed that cinmethylin, pyroxasulfone, and trifluralin provided 100% control of P. paradoxa. Atrazine, bixlozone, imazethapyr, isoxaflutole, prosulfocarb + s-metolachlor, and s-metolachlor were not found to be effective against P. paradoxa (Table 1). Pendimethalin and triallate controlled P. paradoxa by 80 and 42%, respectively, compared with the nontreated control.Table 1 Effect of PRE herbicides on the survival of Phalaris paradoxa and wheat seedlings (28 d after spray).Full size tableIn wheat, all tested herbicides performed similarly for plant survival except dimethenamid-P and prosulfocarb + s-metolachlor, which caused wheat mortality by 41 and 16%, respectively, compared with the nontreated control. These results suggest that pyroxasulfone, pendimethalin, and trifluralin can be successfully used for the management of P. paradoxa in wheat. Alternative use of these herbicides in wheat crops could provide sustainable weed control of P. paradoxa. In previous studies conducted in Australia, herbicides namely cinmethylin, pyroxasulfone, and trifluralin were found safe for wheat and provided excellent grass weed control41.Efficacy of PRE-herbicides in relation to crop residue coverCinmethylin, pendimethalin, and pyroxasulfone were proven to be very effective against P. paradoxa under no residue cover conditions (Table 2). However, at the residue cover of 6 t ha-1 (high output systems), the efficacy of these herbicides decreased and these three herbicides failed to provide effective control of P. paradoxa. At the residue cover of 2 t ha-1 (low output systems), the efficacy of pyroxasulfone in controlling P. paradoxa was not affected; however, cinmethylin and pendimethalin at the residue load of 2 t ha-1 did not control P. paradoxa. These results suggest that in a residue-retained, no-till system, pyroxasulfone could provide better control of P. paradoxa compared with cinmethylin and pendimethalin.Table 2 The interaction of PRE herbicides and wheat residue amount on the survival of Phalaris paradoxa seedlings at 28 d after spray.Full size tableThe crop residue binds some herbicides, which results in a reduced dose to target weeds and provides poor weed control42. A crop residue cover of 1 t ha-1 may prevent 50% of the herbicide from reaching the target weed seeds in the soil and thus provide poor weed control43.Efficacy of POST herbicides in relation to plant sizeWhen plants were sprayed at the 4-leaf stage, the herbicides clodinafop and propaquizafop were not effective against P. paradoxa compared with the other tested herbicides (Table 3). The efficacy of clethodim, glyphosate, haloxyfop, and paraquat in controlling P. paradoxa was not decreased even when plants were sprayed at the 10-leaf stage. In previous studies, poor control of P. paradoxa was observed with ACCase-inhibiting herbicides44,45. These results also suggest that under noncropped or fallow situations, early and late cohorts of P. paradoxa can be controlled successfully by delaying applications of clethodim, paraquat, haloxyfop, and glyphosate.Table 3 The interaction effect of plant size (large plants-10 leaves and small plants-4 leaves) and herbicide treatments on the survival of Phalaris paradoxa seedlings at 28 d after spray.Full size tableGermination of P. paradoxa at 25/15 °C (day/night) was lower compared with 20/10 °C. This suggests that early sowing of wheat (relatively high-temperature conditions) could reduce the emergence of P. paradoxa in fields. Phalaris paradoxa did not germinate after exposure to radiant heat of 150 °C (for 5 min), which suggests that burning may be a useful tool for managing P. paradoxa, particularly when seeds are on the soil surface or at the shallow surface. A high level of tolerance of P. paradoxa to water and salt stress was observed. These observations suggest that this weed can dominate under saline and water stress conditions in Australia. Low germination of P. paradoxa was observed on the soil surface, suggesting that a no-till system could provide better control of P. paradoxa. PRE herbicides cinmethylin, pyroxasulfone, pendimethalin, and trifluralin were effective for control of P. paradoxa in wheat; however, under a conservation tillage system, pyroxasulfone provided better control of P. paradoxa compared with other herbicides. Haloxyfop and clethodim were the most effective herbicides among the ACCase-inhibiting herbicides. Under noncropped or fallow land situations, larger plants of P. paradoxa can be successfully controlled with the application of clethodim, glyphosate, and paraquat. More

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The giant panda’s preference for culm over leaves occurred even though leaves had far more protein than did culm, which is inconsistent with the suggestion that giant pandas are high protein carnivores1. The giant panda’s preference for culm over leaves in the spring was likely driven by the increased availability of mono- and polysaccharides in culm relative to leaves31. This preference by giant pandas for a high-carbohydrate, low protein diet is similar to the brown bear’s preference for carbohydrate-rich but protein-poor berries or apples over protein- and energy-rich salmon, although both needed to be consumed to produce the most efficient diet2,10. The preference for culm over leaves created a protein ME in the diet of giant pandas from January to March (~ 20%) when digestible carbohydrates were most plentiful and for the entire year (27 ± 10%) that was comparable to the macronutrient proportions in giant panda milk and the milk and diets selected by other ursids (Table 1, Fig. 3) that minimize energy expenditure and maximize the efficiency of gain3.Table 1 The protein and fat metabolizable energy concentrations (%) in ursid milks and in the diets selected by brown bears, polar bears, and sloth bears when given ad libitum access to foods rich in protein, fat, and digestible carbohydrates (PFC) or protein and fat only (PF)1,3,4,29,32,40,54,55.Full size tableRelative to the suggestion that giant pandas are not well adapted to consuming the more omnivorous macronutrient proportions characteristic of the diets of other ursids1, captive giant pandas are often fed various combinations of bamboo and high-carbohydrate supplements that include rice, baby cereal, bread, beans, wheat, millet, apples, carrots, ground corn, sorghum, sugar cane, and sugar in addition to milk, eggs, vegetables, and various meats5,32,33. The dry matter of giant panda diets in five Chinese zoos in which successful reproduction occurred (i.e., Beijing Zoo, Chengdu Zoo, China Conservation and Research Center, Fuzhou Zoo, and Xian Zoo) averaged 11.6 ± 2.4% protein, 39.0 ± 13.6% neutral detergent fiber (NDF) or cell wall, 5.0 ± 2.0% fat, and 5.4 ± 0.6% ash32. If we estimate soluble carbohydrates as 100 – (NDF + protein + fat + ash)3, the soluble carbohydrate content was 39.0 ± 11.2%. This approach likely underestimates digestible carbohydrates in that it assumes a zero digestibility for the hemicellulose fraction of the NDF. However, even with these assumptions, the average macronutrient ME distribution was 19 ± 4% protein, 18 ± 7% fat, and 63 ± 18% carbohydrate, or again a low-protein macronutrient ratio typical of the other ursid diets (Table 1).Several errors may have been made in the previous giant panda study1 that likely influenced their conclusion. These included initially air-drying their bamboo samples in a dark room prior to laboratory drying and analyses34. When plants are cut and allowed to dry slowly, soluble carbohydrates are lost as they are metabolized to carbon dioxide, water, and energy until death of the plant cells35,36. The loss of soluble carbohydrates increases when drying occurs slowly, as would occur with air-drying in a dark room. Protein also may be metabolized, but the nitrogen remains and is only converted to different nitrogen-containing compounds, such as amides, free amino acids and peptides that would be part of a crude protein estimate36.Thus, if there are significant amounts of soluble carbohydrates in fresh bamboo, air-drying of bamboo samples will lead to an underestimate of the importance of carbohydrates and thereby an overestimate of the importance of protein. Indeed, starch accounted for 16 ± 11% of the digestible macronutrients and 23 ± 13% of the digestible carbohydrates in bamboo during the current study. Also, the previous study1 assumed a hemicellulose digestibility of 22%37, which significantly underestimated that found in our digestion studies (46 ± 9%).Another potential error in the previous study1 was in using a concept they termed “relative efficiencies” of macronutrient absorption in which the macronutrient profiles of bamboo were directly compared to that of giant panda feces. Such a comparison is often meaningless without knowing the amounts of food consumed and feces produced because the proportions of macronutrients in the feces reflect the extraordinarily complex interaction between the variable absorption of digestible products, passage of indigestible components, and excretion of metabolic products. Thus, only by providing data showing a close linkage between relative efficiencies and digestibility or measuring digestibility as we did can one be certain of estimating the relative importance of macronutrients.The macronutrient intake of wild sloth bears has not been measured, although the dietary proportions and energy content of termites, ants, and fruits have been estimated17. Soldiers and worker termites and ants are generally low in fat and high in protein (excluding the nitrogen in their chitin exoskeleton), whereas alate and alate nymphs (winged reproductive termites) can be very low in protein and high in fat (i.e.,  > 50% fat)38. Joshi et al.17 surmised that sloth bears consumed primarily termite eggs and defending soldiers based on the residues in bear feces and the absence of eggs and soldiers at termite mounds after sloth bear feeding bouts. Although not measured, the dry matter of termite eggs is likely high in both protein and fat, which would create a high fat ME because of the much greater energy content of fat than protein39. The high fruit diet of the summer will be low in protein and fat and high in carbohydrates if not supplemented with other fat-rich foods (e.g., grubs or insect larvae)17. Thus, depending on season and which stage of the ant and termite life cycle the bears consume, wild sloth bears could be consuming either high or low-protein or fat diets.The preference for fat that we observed differs markedly from current zoo diets. Zoo diets can be classified into two macronutrient types: 1) high carbohydrate, low protein, low fat diets that use grains, often in cooked porridges or soups, with fruits and vegetables or 2) diets having more modest or intermediate levels of protein, fat, and carbohydrates that include dog food, bear chows, or omnivore dry or canned products supplemented with fruits and vegetables (Fig. 3). Examples of the first type of diet are more common in Germany [e.g., Leipzig Zoo (ME protein 11%, fat 5%, and carbohydrate 84%)] and the various bear rescue centers in India [e.g., Bannerghatta Bear Rescue Centre (ME protein 10%, fat 9%, and carbohydrate 81%)]. Examples of the second type of diet are more common in US and other European zoos and have more protein and fat than the high grain diets but are much lower in fat than what bears selected in the current study22 (Fig. 3). Nevertheless, bears consuming all past and current zoo diets are prone to developing hepatobiliary cancer and inflammatory bowel disease.If these problems are dietary in origin and not due to something unique to feeding on termites and ants (e.g., development of a unique gastrointestinal microbiome or consumption of formic acid in ants or chitin in both ants and termites), there are two broad types of diets not fed in captivity (i.e., high protein diets and high fat diets) (Fig. 3). In evaluating if either one of those might be more suitable for sloth bears, the protein ME ratios of ursid milks and the diets voluntarily selected by brown bears, polar bears, giant pandas, and sloth bears are low and do not differ from each other (t(3) = 2.449, p = 0.092), which minimizes maintenance energy requirements and maximizes the efficiency of gain1,3,4,29,40 (Table 1). Additionally, brown bears and sloth bears prefer high fat, low carbohydrate diets when given a choice between foods rich in either carbohydrates or fats3 (Table 1, Fig. 3). This fat preference in the adult ursid diet is virtually identical to that occurring in ursid milks (t(2) = -0.726, p = 0.543) even though omnivorous ursids likely have a strong preference for sweet flavors41.While an understanding of the link between dietary macronutrient content and biliary cancer is lacking, we hypothesize that bears, such as polar bears and apparently sloth bears that prefer or evolved to consume high-fat diets, have high resting rates of bile production. Consequently, when sloth bears consume a high-carbohydrate, low-fat diet long term, bile is not secreted into the digestive tract as fast as it is being produced and may back up in the bile ducts, cause bile duct dilation and inflammation, and ultimately biliary cancer. An example of this process is a rare congenital disease in humans and other animals known as choledochal cyst disease. Sacs or outpocketings may develop along the bile ducts in this disease. Bile sitting in those sacs or in the bile ducts causes inflammation of the duct walls and, if not treated by surgical excision, biliary cancer42.If we assume the macronutrient characteristics of ursid milks and the preferences for low protein, low carbohydrate, high fat diets exhibited by brown bears, polar bears, and sloth bears are healthy, current and past sloth bear zoo diets have provided too little fat, too much digestible carbohydrate, and often too much protein (Fig. 3). While this mismatch between the diets fed in captivity and what sloth bears prefer might explain the high incidence of hepatobiliary cancer, inflammatory bowel disease, and poor reproduction world-wide, we cannot dismiss the possibility that the bears’ preference for avocados and fat and the avoidance of apples, baked yams, and digestible carbohydrates in the current study has nothing to do with their macronutrient content and would be unhealthy long-term. Thus, additional feeding studies are needed to determine if a high fat, low protein, low carbohydrate diet might be the key to improving the health, reproduction, and longevity of captive sloth bears.Finally, the selection of lower protein diets by giant pandas, polar bears, sloth bears, and brown bears and the often low-protein omnivorous diets of the other four ursids indicate that all ursids can modulate liver catabolic enzyme activity when needed to conserve protein. This would suggest that this ability to conserve protein occurred early in the evolution of ursids from a high protein carnivore ancestor and may have been critical to the spread of ursids world-wide by opening niches that could not be filled by another high protein carnivore. While all ursids at times may consume foods with a much higher protein content than that of a low protein omnivore, that selection process can only be evaluated relative to the other available dietary choices interacting with foraging and metabolic constraints and does not indicate their preferred diet is that of a high protein carnivore2,43,44. More

Data sources and pre-processingEach of the predictor variables used in our analysis (Table 1), as well as the dependent variable (fire hotspots) underwent pre-processing to transform the data into a format suitable to be passed to our CNN model for prediction. Here we briefly outline these processes and describe the method of generating a training and validation data set for model development. For further details about each predictor variable pre-processing, see Horton et al. (2021).Table 1 Model input data sources, citation, original resolution, and date ranges.Full size tableFire hotspotsWe used both Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) fire hotspot data as the dependent variable for use in our model development. As fire hotspots do not give precise locations, but rather indicate that a fire hotspot occurred within a grid cell of the size of the dataset (MODIS 1 km, VIIRS 375 m), we represented each fire hotspot as a 500 m buffered area around the centre point of each grid square identified. We used all fire hotspot occurrences with a confidence rating >50%.LandcoverWe use a collection of historic land cover maps generated by the Ministry of Forestry Indonesia from 1996 to 2016 at 2–3 year intervals38. Before use, we re-designated the land cover map classifications to reduce the number from 25 to just 8 (supplementary Table S2), which are ‘Primary and secondary dry forest’, ‘Swamp forest, ‘Swamp scrubland’, ‘Scrubland, Transition, and bare land’, ‘Riceland’, ‘Plantation’, ‘Settlements’, ‘water, and Cloud’.In addition to these 8 land cover classifications, we also derived a forest clearance index, which identifies areas cleared of forest and assigns an index value that is large negative (−10) immediately after clearing and degrades back towards 0 as time since clearing increases yearly. Areas that are re-forested are assigned large positive values (10) that degrade towards 0 yearly as time since afforestation increase25.Vegetation indicesAll vegetation indices were taken as pre-fire season 3-month averages from May to July. In addition to the original MODIS ET, PET, NDVI, and EVI products, we also included ‘normalised’ variables, whereby each vegetation index was expressed as the ratio of the same index taken at a reference site. The reference site was an area of dense primary forest outside of the EMRP area.Proximity to anthropogenic factorsThe distance to roads and settlement rasters were derived from OpenStreetMap data as the Euclidean distance to nearest feature in 250 m resolution. The same was done for all water bodies, which were then classified by hand into either canals or rivers. These features are taken as those shown in 2015 for all years, and therefore may misrepresent earlier years. However, the majority of canal development in the region took place between 1996 and 1998 and so should not differ dramatically from this date onwards.Oceanic Niño Index (ONI)We use a single value for the entire study area taken as the three-month average for the early fire season each year (July–September).Number of cloud daysUsing the state_1km band in the daily MODIS terra product (MOD09GA version 6), which classifies each pixel as either ‘no cloud’, ‘cloud’, ‘mixed’, or ‘unknown’, we counted the number of ‘cloud’ or ‘mixed’ designations for each pixel for the pre-fire season period May–July.Cross year normalisationAll predictor variables are normalised to be represented between 0 and 1 as the range between the minimum and maximum values for each variable that occur across all years, such that:$${V}_{{{{{{rm{norm}}}}}}}=frac{V-{V}_{{min }}}{{V}_{{max }}-{V}_{{min }}}$$where ({V}_{{{{{{rm{norm}}}}}}}) is the normalised version of the predictor variable (V), ({V}_{{max }}) is the maximum value within the training dataset across all years (2002–2019), and ({V}_{{min }}) is the minimum value within the training dataset across all years.Training and validation dataset assemblyOnce pre-processed, all predictor variable rasters were resampled to the same dimensions (with a resolution of 0.002 degrees in the WGS84 co-ordinate system) and stacked yearly, so that each year (2002–2019) comprised of a 31 feature maps input as a raster stack, with each feature map representing a different predictor variable. Each yearly stack was then split into tiles matching the input dimensions of the CNN model. Our final model was built to take an input size of 32 × 32 pixels (raster cells). Therefore, each yearly raster stack was split into many 32 × 32 × 31 raster stack tiles that span the defined study area. These were then converted to 3D arrays holding the values of all predictor variables for each raster stack tile.The same process was repeated for the yearly fire hotspot rasters used as the dependent variable in building our model. Each year was split into 32 × 32 × 1 tiles across the study area, and then converted to 3D arrays, each of which pairs with one predictor variable array.The 3D predictor variable arrays (dimensions: 32 × 32 × 31) were then stacked into one large 4D array containing all these individual tiles across all years (dimensions: W × 32 × 32 × 31, where W is a large value). The same was done with the 3D dependent variable arrays (dimension: 32 × 32 × 1), preserving the order so that each element in this large 4D array (dimensions: W × 32 × 32 × 1) matches with its counterpart in the predictor variable array.The order of this large 4D training data array was then randomised along the first dimension to avoid bias in passing to the CNN training algorithm, but the randomised re-ordering was repeated with the dependent variable array so as to preserve the elementwise pairing for cross-validation.Model development and applicationFire prediction requires the combination of spatial and temporal indicators to generate a probabilistic output for each location within a given study area. There is a need to preserve a certain level of proximity information, as the location of variables in relation to one another may have a substantial impact on the results. For example, a patch of secondary forest that is immediately adjacent to an area recently deforested may have a significantly higher probability of fire occurrence than an area surrounded entirely by primary forest.CNNs retain spatial features by employing a moving window of reference, known as a kernel, over the input image that captures these proximity relationships within the model structure. For this reason, CNNs are often used for image classification problems, and is an ideal model configuration for the problem of fire prediction across an area. Therefore, we have developed a CNN binary classification model using the Keras API package39 that builds on the TensorFlow machine learning platform40.Model structureCNN models typically apply a combination of kernel layers and dense layers that perform a series of transformations on the multi-channel input to either reduce it down to a single value, or to output an image the same width and height as the input with a single channel. These classification models can either assign a single value (binary classifier), or return one of many possible classifications.Kernels act on a subsection of the input stack (31 feature maps), assigning weights according to each cell’s position within the subsection to transform and combine the values into a new format to pass forward. As the kernel is applied to all subsections of the input stack, it transforms them to the new format, and builds a reconstituted image with dimensions that usually differ from the input. A dense layer will do the same operation, but acting only on a single grid cell of the input stack, acting at the same location upon all input feature maps within the stack at a time—using all values at that location (i.e., the 1 × 1 subsection) and transforming them according to assigned weights to pass forward a new set of channels to a single grid cell on the output stack. Each layer, either kernel or dense, may expand or contract the number of channels it passes forward. A kernel layer may also change the width and height dimensions of the subsection it passes forwards.We require an output that corresponds to a map of fire-occurrences; therefore our model needs to perform a series of transforms that preserve the width and height of the input, but reduce it to a single channel. The single channel in the output then represents the probability of each cell being classified as fire or not-fire (0–1).Our CNN model is comprised of 5 kernel layers (K1–K5 in Fig. 5), each acts on a 3 × 3 subsection and preserves width and height, passing forwards a transformed 3 × 3 section. Kernel K1 takes an input of 31 channels (predictor variables) but passes forward 128 channels to form the transformation T1 (Fig. 6). Kernels K2–K4 take inputs of 128 channels and pass forward 128 channels (T2–T4). Kernel K5 takes an input of 128 channels but passes forward 1 channel—the output. After each kernel applies its weights, there is an activation function applied before the values are passed on, which modify the answer to fit the necessary criteria to be a valid input to the next process. Kernels K1–K4 have a rectified linear (relu) activation function, which returns the input value if positive, and 0 if negative. Kernel K5 has a sigmoid activation function, that transforms the input values to between 0 and 1 such that negative values are transformed to 0.5.Fig. 6: Model structural diagram.Model structural diagram showing the input, 3 × 3 kernel layers (K1–K5), each transformation passed forwards (T1–T4) and the output, with all dimensions labelled.Full size imageModel training and validationWe used a stochastic gradient descent optimising function called Adam41 combined with a binary cross-entropy loss function to train the model against our fire-hotspot dataset iterated over 20 epochs. We split the data 70/30, using 70% as training data and 30% as validation data, recording accuracy, precision, and recall as the performance metrics, as well as the loss function itself.After model training, we applied the model to each yearly raster stack and compared the output against the fire-hotspot data for further model validation. Before validating the model outputs, we applied a simple 3 × 3 moving average window as a smoothing function to reduce the edge effects of tiling that are a by-product of having to split the study area into smaller tiles (32 × 32) for passing to the model. For this yearly validation, we again used the metrics accuracy, precision, and recall, such that:$${{{{{rm{Accuracy}}}}}}=100({{{{{rm{TP}}}}}}+{{{{{rm{TN}}}}}})/({{{{{rm{TP}}}}}}+{{{{{rm{TN}}}}}}+{{{{{rm{FP}}}}}}+{{{{{rm{FN}}}}}})$$$${{{{{rm{Precision}}}}}}=100({{{{{rm{TP}}}}}})/({{{{{rm{TP}}}}}}+{{{{{rm{FP}}}}}})$$$${{{{{rm{Recall}}}}}}=100({{{{{rm{TP}}}}}})/({{{{{rm{TP}}}}}}+{{{{{rm{FN}}}}}})$$where TP is true positive, TN is true negative, FP is false positive, and FN is false negative. These comparisons were made on a raster cell to raster cell basis after designating a 500 m buffer around each fire hotspot observation (MODIS and VIIRS data) and converting the buffers to a raster image of the same resolution and extent as the model prediction.ScenariosAfter validating the model performance, we built future scenarios to investigate the impact on fire occurrence of managing key anthropogenic features of the landscape: canals and land cover (Table 2).Table 2 Future scenario types and descriptions.Full size tableStudies have shown that unmanaged areas of heavily degraded or cleared swamp-forest are most susceptible to fires16,17,25,26,33,42. Therefore, we have built scenarios that investigate the possible impact of managing these areas by altering the model inputs to re-assign the land-cover designations ‘Swamp shrubland’ and ‘Scrubland’, as well as other land designation alterations. The first such restoration scenario investigates the impact of reforesting these areas by re-assigning the designations to ‘Swamp forest’. The second such scenario investigates the impact of converting these unmanaged areas to plantations by re-assigning the designations to ‘Plantation’. We also built two further land cover scenarios to investigate the impact of continued deforestation in the region by re-assigning the ‘Swamp forest’ designation to ‘Swamp shrubland’ and ‘Plantation’.We then built a scenario to investigate the impact of canal blocking on fire occurrence, modifying the proximity to canals model input by reducing the number of canals included in our proximity analysis to just two major canals, one that runs north-south, and one that runs west-east (Fig. 1). These canals could not practically be blocked due to their size and importance as navigation conduits.The final scenario simulates the combined impact of both re-foresting unmanaged degraded and cleared forest areas and the blocking of canals simultaneously.To evaluate the impact of each scenario on fire occurrences, we calculated the ratio of model predictions >0.5 probability (i.e., that a fire would occur in that raster cell) for each year for each scenario against the same year for the baseline scenario.Model use as a predictive toolTo evaluate the model’s potential to predict future fire distribution across the wider ex-Mega Rice Project area, we trained a second version of the model following the same methodology outlined above, but included only data from 2002 to 2018 in the training and test data passed to the model fitting algorithm. We then applied the model to the predictor variables corresponding to 2019 and compared model outputs to the observations of fire-occurrences by again looking at the metrics accuracy, precision, and recall. We also present a visual comparison of the outputs from the full model (2019 included in training data), the predictive model (2019 not included), and the observation data (MODIS and VIIRS hotspots). More