Firing Frequency Research Articles

Characterizing the Wildfire Patterns in the Marromeu Complex Using Remote Sensing Data

Objective: To caraterize the wildfire regime in the Marromeu Complex between 2002 and 2019. Method: Data from the MOD14 Collection 6 sensor, pertaining to thermal anomalies (MCD14ML, MCD64A1) corresponding to hot spots and burned areas, were used on a monthly basis. Only pixels with a reliability of 80% or higher were considered. Patterns of hot spots, burned area, density, intensity, frequency, and seasonality of fires were analyzed. Data processing was conducted using ArcGIS 10.8. Results and Discussion: A total of 12,227 hot spots were identified, covering 73% of the burned area. The annual average burned area was 24%, with densities ranging from moderate to very high. The fires showed low intensity across most of the Complex, with an average fire frequency of 2 to 3 times per year, and the highest fire records were observed in September. These findings are analyzed within the theoretical framework, emphasizing the implications of temporal and spatial variations in fires and their potential causes and environmental impacts. Implications of the Research: This study offers insights into wildfire management and emphasizes the necessity of fire management strategies in the Marromeu Complex. The implications include enhancing management plans and mitigating environmental impacts associated with fires. Originality/Value: This study contributes to the literature by providing a detailed characterization of the fire regime in the Marromeu Complex. The research is relevant as it offers new information on fire dynamics and their variations over time, which could impact management practices and environmental policies.

Effect of Calcium on the Characteristics of Action Potential Under Different Electrical Stimuli

This study investigates the role of calcium ions in the release of action potentials by comparing two models based on the framework: the standard HH model and a HH + Ca model that incorporates calcium ion channels. Purkinje cells’ responses to four types of electrical current stimuli—constant direct current, step current, square wave current, and sine current—were simulated to analyze the impact of calcium on action potential characteristics. The results indicate that, under the constant direct current stimulation, the action potential firing frequency of both models increased with the escalating current intensity, while the delay time of the first action potential decreased. However, when the current intensity exceeded a specific threshold, the peak amplitude of the action potential gradually diminished. The HH + Ca model exhibited a longer delay in the first action potential compared to the HH model but maintained an action potential release under stronger currents. In response to the step current, both models showed an increased action potential frequency with a higher current, but the HH + Ca model generated subthreshold oscillations under weak currents. With the square wave current, the action potential frequency increased, though the HH + Ca model experienced suppression under high-frequency weak currents. Under the sine current, the action potential frequency rose, with the HH + Ca model showing less depression near the sine peak due to calcium’s role in modulating membrane potential. These findings suggest that calcium ions contribute to a more stable action potential release under varying stimuli.

Age-related increase in the excitability of mouse layer V pyramidal neurons in the primary motor cortex is accompanied by an increased persistent inward current.

Sarcopenia, or pathological age-related loss of muscle strength and mass, contributes to physical function impairment in older adults. While current understanding of sarcopenia is centered mostly on neuromuscular mechanisms, mounting evidence supports that deficits at the level of the primary motor cortex (PMC) play a significant role. Despite the importance of the PMC to initiate movement, understanding of how age affects the excitability of layer V pyramidal neurons (LVPNs) of the PMC is limited. To address this, we used the whole-cell patch clamp technique to measure the excitability of LVPNs of the PMC in young, late adulthood, and old mice. Old LVPNs had increased firing frequency and membrane input resistance, but no differences in action potential kinetics versus young and late adulthood mice. Since changes in the persistent inward current (PIC) are known to contribute to changes in motor neuron excitability, we measured LVPN PICs as a putative contributor to LVPN excitability. The PIC amplitude was increased in old LVPN via increases in Na+ and Ca2+ PICs, in addition to being active across a wider voltage range. Given that LVPN function is integral to initiation of voluntary muscle contraction, altered LVPN excitability likely contributes to age-related impairment of physical function.

Random Forest Spatial-Temporal and State Space Models to Assess the Impact of Bushfire-Induced Aerosol Events on Ozone Depletion in Australia

Serious concerns exist that the increasing frequency of fires may delay the recovery of ozone given increasing temperatures due to climate change. Australian bushfires from September 2019 to February 2020 were catastrophic. A random forest spatial-temporal (RF sp) analysis using satellite data to detect an association between Australian bushfires and stratosphere ozone on the local depletion of ozone in the vicinity of fires in three regions of Australia (Pacific Ocean, Victoria, NSW) has shown a significant reduction in ozone attributable to aerosols from fires. By intervention analysis, increases in aerosols in all three regions were shown to have a significant and ongoing impact 1–5 days later on reducing ozone (p < 0.0001). Intervention analysis also gave similar periods of aerosol exceedance to those found by Hidden Markov models (HMMs). HMMs established a significant and quantifiable decline in ozone due to bushfire-induced aerosols, with significant lags of 10–25 days between times of aerosol exceedance and subsequent ozone level decline in all three regions.

Nanomagnetic Guidance Shapes the Structure-Function Relationship of Developing Cortical Networks.

In this study, we implement large-scale nanomagnetic guidance on cortical neurons to guide dissociated neuronal networks during development. Cortical networks cultured over microelectrode arrays were exposed to functionalized magnetic nanoparticles, followed by magnetic field exposure to guide neurites over 14 days in vitro. Immunofluorescence of the axonal protein Tau revealed a greater number of neurites that were longer and aligned with the nanomagnetic force relative to nonguided networks. This was further confirmed through brightfield imaging on the microelectrode arrays during development. Spontaneous electrophysiological recordings revealed that the guided networks exhibited increased firing rates and frequency in force-aligned connectivity identified through Granger Causality. Applying this methodology across networks with nonuniform force directions increased local activity in target regions, identified as regions in the direction of the nanomagnetic force. Altogether, these results demonstrate that nanomagnetic forces guide the structure and function of dissociated cortical neuron networks at the millimeter scale.

Assessing the Cascading Post-Earthquake Fire-Risk Scenario in Urban Centres

The frequency of urban fires has grown in recent years everywhere, especially in historic districts, including in Portugal, due to the existence of sensitive igniting materials, the proximity of buildings, the complex urban layout, and the presence of many people. The current study proposes a technique, applied in the Baixa Pombalina (downtown) area in Lisbon, to undertake an appropriate evaluation of the post-earthquake fire cascading effect, which may cause major damage. The earthquake vulnerability and damage scenario were carried out using the Risk-UE method. An empirical fire ignition model was then applied to determine the quantity and location of fire ignitions for different return periods. Furthermore, the simple fire spread Hamada’s model was applied to both the equally spaced grid buildings, as in the original Hamada procedure, and the current study area layout for different time thresholds. Finally, the risk assessment for both models was carried out, allowing for the estimation of earthquake and fire losses, respectively. The results demonstrated that the models are comparable, showing that the Hamada model might be a useful tool for large-scale evaluations aimed at disaster-risk reduction and management since it gives useful information for managing and reducing natural and anthropogenic hazards.

Dynamics of standing dead wood and severe fire in north Australian savannas: implications for carbon management

Background Many fires in north Australian savannas are severe enough to cause canopy scorch, tree death and removal of stags. Better fire management may increase carbon sequestration in trees, perhaps including stags. Aims To describe and analyse dynamics of stags in tropical savannas (600–1000 mm annual rainfall) in relation to fire and better understand their role in biomass sequestration. Methods We monitored marked populations of live and dead trees over 12 years. Statistical models describing influences on stag creation and loss are applied in stag dynamics simulations. Key results Immediately following severe fire, stag biomass increases acutely because many more live trees are killed than stags removed. Between severe fires, stag losses exceed tree deaths, so peaks are quite short. Many ‘new’ stags are lost (fallen or consumed) quickly. Conclusions Between fires, stags comprise ~7.5–8.9% of standing above-ground biomass, more under dry conditions and during recovery from severe fire or other drivers of increased tree mortality. Fire management is unlikely to increase proportions of total woody biomass in stags unless it also reduces live biomass. Implications Reducing frequency of severe fires can increase total carbon sequestration in dry tropical savannas. Prediction uncertainties and management risks around sequestration present daunting challenges for policy-makers and fire management practitioners.

Heterozygous expression of a Kcnt1 gain-of-function variant has differential effects on somatostatin- and parvalbumin-expressing cortical GABAergic neurons

More than 20 recurrent missense gain-of-function (GOF) mutations have been identified in the sodium-activated potassium (KNa) channel gene KCNT1 in patients with severe developmental and epileptic encephalopathies (DEEs), most of which are resistant to current therapies. Defining the neuron types most vulnerable to KCNT1 GOF will advance our understanding of disease mechanisms and provide refined targets for precision therapy efforts. Here, we assessed the effects of heterozygous expression of a Kcnt1 GOF variant (Kcnt1Y777H) on KNa currents and neuronal physiology among cortical glutamatergic and GABAergic neurons in mice, including those expressing vasoactive intestinal polypeptide (VIP), somatostatin (SST), and parvalbumin (PV), to identify and model the pathogenic mechanisms of autosomal dominant KCNT1 GOF variants in DEEs. Although the Kcnt1Y777H variant had no effects on glutamatergic or VIP neuron function, it increased subthreshold KNa currents in both SST and PV neurons but with opposite effects on neuronal output; SST neurons became hypoexcitable with a higher rheobase current and lower action potential (AP) firing frequency, whereas PV neurons became hyperexcitable with a lower rheobase current and higher AP firing frequency. Further neurophysiological and computational modeling experiments showed that the differential effects of the Kcnt1Y777H variant on SST and PV neurons are not likely due to inherent differences in these neuron types, but to an increased persistent sodium current in PV, but not SST, neurons. The Kcnt1Y777H variant also increased excitatory input onto, and chemical and electrical synaptic connectivity between, SST neurons. Together, these data suggest differential pathogenic mechanisms, both direct and compensatory, contribute to disease phenotypes, and provide a salient example of how a pathogenic ion channel variant can cause opposite functional effects in closely related neuron subtypes due to interactions with other ionic conductances.

Heterozygous expression of a Kcnt1 gain-of-function variant has differential effects on somatostatin- and parvalbumin-expressing cortical GABAergic neurons.

More than 20 recurrent missense gain-of-function (GOF) mutations have been identified in the sodium-activated potassium (KNa) channel gene KCNT1 in patients with severe developmental and epileptic encephalopathies (DEEs), most of which are resistant to current therapies. Defining the neuron types most vulnerable to KCNT1 GOF will advance our understanding of disease mechanisms and provide refined targets for precision therapy efforts. Here, we assessed the effects of heterozygous expression of a Kcnt1 GOF variant (Kcnt1Y777H) on KNa currents and neuronal physiology among cortical glutamatergic and GABAergic neurons in mice, including those expressing vasoactive intestinal polypeptide (VIP), somatostatin (SST), and parvalbumin (PV), to identify and model the pathogenic mechanisms of autosomal dominant KCNT1 GOF variants in DEEs. Although the Kcnt1Y777H variant had no effects on glutamatergic or VIP neuron function, it increased subthreshold KNa currents in both SST and PV neurons but with opposite effects on neuronal output; SST neurons became hypoexcitable with a higher rheobase current and lower action potential (AP) firing frequency, whereas PV neurons became hyperexcitable with a lower rheobase current and higher AP firing frequency. Further neurophysiological and computational modeling experiments showed that the differential effects of the Kcnt1Y777H variant on SST and PV neurons are not likely due to inherent differences in these neuron types, but to an increased persistent sodium current in PV, but not SST, neurons. The Kcnt1Y777H variant also increased excitatory input onto, and chemical and electrical synaptic connectivity between, SST neurons. Together, these data suggest differential pathogenic mechanisms, both direct and compensatory, contribute to disease phenotypes, and provide a salient example of how a pathogenic ion channel variant can cause opposite functional effects in closely related neuron subtypes due to interactions with other ionic conductances.

Coupled Effects of High Temperatures and Droughts on Forest Fires in Northeast China

High temperatures and droughts are two natural disasters that cause forest fires. During climate change, the frequent occurrence of high temperatures, droughts, and their coupled effects significantly increase the forest fire risk. To reveal the seasonal and spatial differences in the coupled effects of high temperatures and droughts on forest fires, this study used the Copula method and proposed the compound extremely high-temperature and drought event index (CTDI). The results indicated that the study area was subject to frequent forest fires in spring (71.56%), and the burned areas were mainly located in forests (40.83%) and the transition zone between farmland and forests (36.91%). The probability of forest fires in summer increased with high temperatures and drought intensity, with high temperatures playing a dominant role. The highest forest fire hazard occurred in summer (>0.98). The probability of a forest fire occurring under extreme meteorological conditions in summer and fall was more than twice as high as that in the same zone under non-extreme conditions. Droughts play a significant role in the occurrence and spread of forest fires during fall. These results can provide decision-making support for forest fire warnings and fire fighting in the Northeast China forest zone.

Contemporary fires are less frequent but more severe in dry conifer forests of the southwestern United States

Wildfires in the southwestern United States are increasingly frequent and severe, but whether these trends exceed historical norms remains contested. Here we combine dendroecological records, satellite-derived burn severity, and field measured tree mortality to compare historical (1700-1880) and contemporary (1985-2020) fire regimes at tree-ring fire-scar sites in Arizona and New Mexico. We found that contemporary fire frequency, including recent, record fire years, is still <20% of historical levels. Since 1985, the fire return interval averages 58.8 years, compared to 11.4 years before 1880. Fire severity, however, has increased. At sites where trees historically survived many fires over centuries, 42% of recent fires resulted in high tree mortality. Suppressed wildfires tended to burn more severely than prescribed burns and wildfires managed for resource benefit. These findings suggest that expanded use of low-severity prescribed and managed fire would help restore forest resilience and historical fire regimes in dry conifer forests.

Short Spatiotemporal Fire History Explains the Occurrence of Beetles Favoured by Fire.

The number and area of forest fires in northern Europe have been dramatically reduced during the past century, and several fire-favoured species are now threatened. To promote the recovery of these species, prescribed burning is often used as a conservation measure, and to optimise the use of these conservation burns, knowledge is needed on suitable fire frequency, size and placement in the landscape. The aim of this study was to analyse the effect of recent fire history (12 yrs) on beetles sampled using smoke attraction traps at 21 forest sites in a 10,000 km2 region. We analysed the odds of finding a fire-favoured beetle species or individual among the beetles in each trap using a new spatiotemporal connectivity measure and compared the results to non-fire-favoured and saproxylic species. For fire-favoured beetles, both the number of species and individuals significantly increased with connectivity to previous fires, while the other two groups did not. The spatiotemporal connectivity that best explained the patterns suggests that fire-favoured beetles mainly respond to fires within a 2 km range up to 2-3 years after the fire. Hence, to preserve fire-favoured insects, prescribed fires must be close in space and time to other fires-whether prescribed or natural.

7170 Plasticity of the Hypothalamic Tuberoinfundibular Dopaminergic Neuronal Network in Lactating Rats

Abstract Disclosure: P. Papaioannou: None. T. Georgescu: None. D.R. Grattan: None. S. Bunn: None. S. Yip: None. The tuberoinfundibular dopaminergic (TIDA) neurons play a crucial role in regulating prolactin secretion. Their synchronized network activity with slow but highly rhythmic firing pattern is important for dopamine release in rats[1]. While this unique TIDA network activity is vital for prolactin negative feedback in non-lactating conditions, its behaviour during lactation, when prolactin demand is high, remains unknown. Our hypothesis posited that the TIDA neuronal network in lactating rats becomes desynchronized, disrupting the negative feedback loop. To test this, we utilized ex-vivo Ca2+ imaging to simultaneously monitor population-wide TIDA neuron activity in non-lactating (NL; n=13) and lactating (L; n=10) rats injected with a cre-inducible adeno-associated virus (AAV) containing the Ca2+ indicator, GCaMP6s, into their arcuate nucleus. Analysis of neuronal network synchronicity revealed significant desynchronization in the TIDA network during lactation, as indicated by a markedly lower mean correlation coefficient matrix (CM) compared to non-lactating conditions (NL: 0.87±0.02, n= 26 sections vs L: 0.22±0.03, n=29 sections; p&amp;lt;0.001, Student’s t-test). Furthermore, the oscillatory activity patterns of these desynchronized TIDA neurons displayed a significantly lower cell rhythmicity index (RI) in lactating animals compared to non-lactating ones (NL: 0.17±0.01, n=186 cells vs L: 0.70±0.02, n=77 cells; p&amp;lt;0.001, Student’s t-test). Interestingly, within these low rhythmic neurons, some exhibited higher while others showed lower firing frequencies compared to non-lactating TIDA neurons. After 7 days post-weaning (n=4 animals), the TIDA network activity returned to non-lactating behaviour (CM = 0.85 ± 0.04, n=6 sections and RI = 0.17±0.01; n=47 cells). In summary, our findings demonstrate a reversible reconfiguration of the TIDA neuronal networks during lactation, characterized by low rhythmic oscillations and individual unique firing patterns, leading to diminished intercellular synchrony that may impede dopamine release, ultimately facilitating prolactin release to support lactation. Once weaned, the TIDA neuronal network reset to NL state to allow a new reproductive cycle. [1] Lyons, D. J., Horjales-Araujo, E. &amp; Broberger, C. Synchronized network oscillations in rat tuberoinfundibular dopamine neurons: switch to tonic discharge by thyrotropin-releasing hormone. Neuron 65, 217-229, doi:10.1016/j.neuron.2009.12.024 (2010). Presentation: 6/2/2024

Interactions between soil and other environmental variables modulate forest expansion and ecotone dynamics in humid savannas of Central Africa.

Forest expansion into savanna is a pervasive phenomenon in West and Central Africa, warranting comparative studies under diverse environmental conditions. We collected vegetation data from the woody and grassy components within 73 plots of 0.16 ha distributed along a successional gradient from humid savanna to forest in Central Africa. We associated spatially collocated edaphic parameters and fire frequency derived from remote sensing to investigate their combined influence on the vegetation. Soil texture was more influential in shaping savanna structure and species distribution than soil fertility, with clay-rich soils promoting higher grass productivity and fire frequency. Savanna featuring woody aboveground biomass surpassing 40 Mg ha-1 could escape the grass-fire feedback loop, by depressing grass biomass below 4 Mg ha-1. This thicker woody layer also favoured the establishment of fire-tolerant forest pioneers, which synergically contributed to the expansion of forests. Conversely, savannas below this fire suppression threshold sustained a balance between trees and grasses through the grass-fire feedback mechanism. This hysteresis loop, particularly pronounced on clayey soils, suggests that the contrast between grassy savanna and young forests might represent alternative ecosystem states, although savannas with low woody biomass remained vulnerable to forest edge encroachment.

The impacts of fire vary among vertical strata: Responses of ant communities to long-term experimental burning.

Fire is a powerful tool for conservation management at a landscape scale, but a rigorous evidence base is often lacking for understanding its impacts on biodiversity in different biomes. Fire-induced changes to habitat openness have been identified as an underlying driver of responses of faunal communities, including for ants. However, most studies of the impacts of fire on ant communities consider only epigeic (foraging on the soil surface) species, which may not reflect the responses of species inhabiting other vertical strata. Here, we examine how the responses of ant communities vary among vertical strata in a highly fire-prone biome. We use a long-term field experiment to quantify the effects of fire on the abundance, richness, and composition of ant assemblages of four vertical strata (subterranean, leaf litter, epigeic, and arboreal) in an Australian tropical savanna. We first document the extent to which each stratum harbors distinct assemblages. We then assess how the assemblage of each stratum responds to three fire-related predictors: fire frequency, fire activity, and vegetation cover. Each stratum harbored a distinct ant assemblage and showed different responses to fire. Leaf litter and epigeic ants were most sensitive to fire because it directly affects their microhabitats, but they showed contrasting negative and positive responses, respectively. Subterranean ants were the least sensitive because of the insulating effects of soil. Our results show that co-occurring species of the same taxonomic group differ in the strength and direction of their response to fire depending on the stratum they inhabit. As such, effective fire management for biodiversity conservation should consider species in all vertical strata.

Fire and ant interactions mediated by honeydew and extrafloral nectar in an australian tropical savanna

Fire is a major disturbance affecting ecosystems globally, but its impact on mutualisms has received minimal attention. Here, we use a long-term field experiment to investigate the impact of different fire regimes on globally important ant–honeydew and ant–extrafloral nectar (EFN) mutualistic interactions in an Australian tropical savanna. These interactions provide ants with a key energy source, while their plant and hemipteran hosts receive protection services. We examined ant interactions on species of Eucalyptus (lacking EFNs) and Acacia (with EFNs) in three replicate plots each of burning every 2 and 3 years early in the dry season, burning late in the dry season every 2 years, and unburnt for > 25 years. The proportions of plants with ant–honeydew interactions in Acacia (44.6%) and Eucalyptus (36.3%) were double those of Acacia plants with ant–EFN interactions (18.9%). The most common ants, representing 85% of all interactions, were behaviourally dominant species of Oecophylla, Iridomyrmex and Papyrius. Fire promoted the incidence of ant interactions, especially those involving EFNs on Acacia, which occurred on only 3% of plants in unburnt plots compared with 24% in frequently burnt plots. Fire also promoted the relative incidence of behaviourally dominant ants, which are considered the highest quality mutualists. Contrary to expectations, frequent fire did not result in a switching of behaviourally dominant ant partners from forest-adapted Oecophylla to arid-adapted Iridomyrmex. Our findings that frequent fire increases ant interactions mediated by honeydew and extrafloral nectar, and promotes the quality of ant mutualists, have important implications for protective services provided by ants in highly fire-prone ecosystems.

UAV forest fire detection based on CNN and InceptionV3

Abstract Forest fires have created a severe hazard in our ecological environment with abrasive climate change all over the world. What is even more concerning, the frequency of forest fires is increasing year by year. By far, a great number of methodologies, such as satellite systems, IR, and WSN, have been created to resolve this rigorous problem. However, most of these detection methods are costly and structurally complex, making them difficult to quickly learn and use, thus not effectively reducing personnel and property injuries. Our research proposed using UAVs (Unmanned Aerial Vehicles) as visual detection carriers and using InceptionV3 for visual processing of aerial images. The flame dataset from IEEE Dataport, containing 63178 JPEG pictures, is used in our research. Our proposed forest fire detection methodology using Convolutional Neural Network (CNN) and InceptionV3 achieved a remarkable 89.90% validation accuracy. Moreover, our methodology gained an ideal precision and recall ratio.

Projecting Large Fires in the Western US With an Interpretable and Accurate Hybrid Machine Learning Method

AbstractMore frequent and widespread large fires are occurring in the western United States (US), yet reliable methods for predicting these fires, particularly with extended lead times and a high spatial resolution, remain challenging. In this study, we proposed an interpretable and accurate hybrid machine learning (ML) model, that explicitly represented the controls of fuel flammability, fuel availability, and human suppression effects on fires. The model demonstrated notable accuracy with a F1‐score of 0.846 ± 0.012, surpassing process‐driven fire danger indices and four commonly used ML models by up to 40% and 9%, respectively. More importantly, the ML model showed remarkably higher interpretability relative to other ML models. Specifically, by demystifying the “black box” of each ML model using the explainable AI techniques, we identified substantial structural differences across ML fire models, even among those with similar accuracy. The relationships between fires and their drivers, identified by our model, were aligned closer with established fire physical principles. The ML structural discrepancy led to diverse fire predictions and our model predictions exhibited greater consistency with actual fire occurrence. With the highly interpretable and accurate model, we revealed the strong compound effects from multiple climate variables related to evaporative demand, energy release component, temperature, and wind speed, on the dynamics of large fires and megafires in the western US. Our findings highlight the importance of assessing the structural integrity of models in addition to their accuracy. They also underscore the critical need to address the rise in compound climate extremes linked to large wildfires.

Predicting Fractional Shrub Cover in Heterogeneous Mediterranean Landscapes Using Machine Learning and Sentinel-2 Imagery

Wildfires pose a growing threat to Mediterranean ecosystems. This study employs advanced classification techniques for shrub fractional cover mapping from satellite imagery in a fire-prone landscape in Quinta da França (QF), Portugal. The study area is characterized by fine-grained heterogeneous land cover and a Mediterranean climate. In this type of landscape, shrub encroachment after land abandonment and wildfires constitutes a threat to ecosystem resilience—in particular, by increasing the susceptibility to more frequent and large fires. High-resolution mapping of shrub cover is, therefore, an important contribution to landscape management for fire prevention. Here, a 20 cm resolution land cover map was used to label 10 m Sentinel-2 pixels according to their shrub cover percentage (three categories: 0%, &gt;0%–50%, and &gt;50%) for training and testing. Three distinct algorithms, namely Support Vector Machine (SVM), Artificial Neural Networks (ANNs), and Random Forest (RF), were tested for this purpose. RF excelled, achieving the highest precision (82%–88%), recall (77%–92%), and F1 score (83%–88%) across all categories (test and validation sets) compared to SVM and ANN, demonstrating its superior ability to accurately predict shrub fractional cover. Analysis of confusion matrices revealed RF’s superior ability to accurately predict shrub fractional cover (higher true positives) with fewer misclassifications (lower false positives and false negatives). McNemar’s test indicated statistically significant differences (p value &lt; 0.05) between all models, consolidating RF’s dominance. The development of shrub fractional cover maps and derived map products is anticipated to leverage key information to support landscape management, such as for the assessment of fire hazard and the more effective planning of preventive actions.

Phenology forecasting models for detection and management of invasive annual grasses

AbstractNon‐native annual grasses can dramatically alter fire frequency and reduce forage quality and biodiversity in the ecosystems they invade. Effective management techniques are needed to reduce these undesirable invasive species and maintain ecosystem services. Well‐timed management strategies, such as grazing, that are applied when invasive grasses are active prior to native plants can control invasive species spread and reduce their impact; however, anticipating the timing of key phenological stages that are susceptible to management over vast landscapes is difficult, as the phenology of these species can vary greatly over time and space. To address this challenge, we created range‐wide phenology forecasts for two problematic invasive annual grasses: cheatgrass (Bromus tectorum), and red brome (Bromus rubens). We tested a suite of 18 mechanistic phenology models using observations from monitoring experiments, volunteer science, herbarium records, timelapse camera imagery, and downscaled gridded climate data to identify the models that best predicted the dates of flowering and senescence of the two invasive grass species. We found that the timing of flowering and senescence of cheatgrass and red brome were best predicted by photothermal time models that had been adjusted for topography using gridded continuous heat‐insolation load index values. Phenology forecasts based on these models can help managers make decisions about when to schedule management actions such as grazing to reduce undesirable invasive grasses and promote forage production, quality, and biodiversity in grasslands; to predict the timing of greatest fire risk after annual grasses dry out; and to select remote sensing imagery to accurately map invasive grasses across topographic and latitudinal gradients. These phenology models also have the potential to be operationalized for within‐season or within‐year decision support.