Biophysical Drivers Research Articles

Spatiotemporal patterns of burned areas, fire drivers, and fire probability across the equatorial Andes

Field-based fire studies in the equatorial Andes indicate that fires are strongly associated with biophysical and anthropogenic variables. However, fire controls and fire regimes at the regional scale remain undocumented. Therefore, this paper describes spatial and temporal burned-area patterns, identifies biophysical and anthropogenic fire drivers, and quantifies fire probability across 6° of latitude and 3° of longitude in the equatorial Andes. The spatial and temporal burned-area analysis was carried out based on 18 years (2001–2018) of the MCD64A1 MODIS burned-area product. Climate, topography, vegetation, and anthropogenic variables were integrated in a logistic regression model to identify the significance of explanatory variables and determine fire occurrence probability. A total of 5779 fire events were registered during the 18 years of this study, located primarily along the western cordillera of the Andes and spreading from North to South. Eighty-eight percent of these fires took place within two fire hotspots located in the northwestern and southwestern corners of the study area. Ninety-nine percent occurred during the second part of the year, between June and December. The largest density of fires was primarily located on herbaceous vegetation and shrublands. Results show that mean monthly temperature, precipitation and NDVI during the pre-fire season, the location of land cover classes such as forest and agriculture, distance to roads and urban areas, slope, and aspect were the most important determinants of spatial and temporal fire distribution. The logistic regression model achieved a good accuracy in predicting fire probability (80%). Probability was higher in the southwestern and northern corners of the study area, and lower towards the north in the western and eastern piedmonts of the Andes. This analysis contributes to the understanding of fires in mountains within the tropics. The results here presented have the potential to contribute to fire management and control in the region.

Evaluating low flow patterns, drivers and trends in the Delaware River Basin

In the humid, temperate Delaware River Basin (DRB) where water availability is generally reliable, summer low flows can cause competition between various human and ecological water uses. As temperatures continue to rise, population increases and development expands, it is critical to understand historical low flow variability to anticipate and plan for future flows. Using a sample of 325 U.S. Geological Survey gages, we evaluated spatial patterns in several low flow metrics, the biophysical and climatic drivers of these metrics, and trends in low flows for two periods: 1950–2018 and 1980–2018. We calculated the annual 7-day low flow and date, low flow deficit as the departure below a long-term daily flow threshold and the number of discrete low flow periods below this threshold. We also aggregated several climate metrics to watershed scale and used existing watershed properties quantifying land cover, topography, soils, geology, and human activity. Random forest models were used to assess the hierarchy of variable importance in explaining mean-annual low flow variability for each low flow metric using all gages. We find muted regional patterns in mean-annual low flow and low flow variability, likely due to the myriad of anthropogenic, landscape, and flow modifications that obscure flow regimes from their natural characteristics. In contrast, individual years show markedly different spatial patterns in low flow magnitude and severity. Coincident with increases in precipitation, 7-day low flows have generally increased and low flow deficits decreased for both 1950–2018 and 1980–2018 periods. However, 7-day low flows have decreased in the Coastal Plain physiographic province where water use and impervious area have increased in recent decades, highlighting the effects of land and water management on low flows. With continued change expected in the DRB, additional research needs are highlighted to enable estimation of future low flows and to plan for periods of prolonged low flow.

Urban Heat Island associated with Land Use/Land Cover and climate variations in Melbourne, Australia

Urbanization is known as one of the most prominent global problems that alter the atmosphere and land surface properties. The intensity of Urban Heat Island (UHI) associated with surface temperature and component attributes were assessed using the Community Land Model (CLM). The variations of UHI with Land Use/Land Cover (LULC) and climate variations were also investigated to provide a link among urbanization, surface energy balance interactions, and extreme hydroclimatic events which are drought (‘big dry’ and ‘angry summer’) and heavy rainfall (‘big wet’) in Melbourne, Australia. Generally, UHI demonstrated a uniform increasing trend with an ∼1.20 ± 0.20 °C increment, along with urbanization expansion of +14.93 % from 2001 to 2014. Furthermore, urban area showed positive contribution to UHI based on Land Contribution Index (LCI). High surface temperatures also resulted in higher sensible heat flux (Qh) and lower latent heat flux (Qle). During the multiple extreme climate events, the UHI biophysical drivers were majorly related to the convection reduction during ‘big dry’ (2001−2009), surface evaporative cooling during ‘big wet’ (2010−2011), and heat storage release during ‘angry summer’ (2012–2013). Overall, this analysis demonstrated correlation of UHI intensity and its component attributes with urban expansion, which was associated with LULC and climate variations in Melbourne.

Agricultural Land Abandonment in the Hill Agro-ecological Region of Nepal: Analysis of Extent, Drivers and Impact of Change.

Despite widely reported trends of agricultural land abandonment across many parts of the globe, this land use change phenomenon is relatively new in the context of Nepal. In recent years, rural farming communities in the hill region are gradually reducing the intensity of farming, leading to underutilisation and abandonment of agricultural lands. Adopting a mixed methods research approach, this study investigated the extent of agricultural land abandonment, its underlying causal drivers and perceived impacts in the hill agro-ecological region of Nepal. A structured survey of 374 households and six focus group discussions were carried out in three districts. The study revealed that around 40% of agricultural lands in the hill agro-ecological region have been abandoned and 60% of farmers have left at least one parcel of agricultural land abandoned. It was found that biophysical drivers (distance from homestead to parcel, slope of the parcel, land fragmentation, land quality and irrigation availability) and socio-demographic drivers (family size, higher education of the household members, domestic migration and out-migration) were responsible for agricultural land abandonment. Negative impacts of land abandonment were observed on the rural landscape, human-made farm structures, socio-economic systems, local food production and food security. In line with global studies, this research suggest that marginal land quality, demographic changes and rising alternative economic opportunities elsewhere contribute to farmland abandonment. This study also discusses land management approaches and policy implications to address the issue of agricultural land abandonment.

Biophysical and socioeconomic drivers of oil palm expansion in Indonesia

Indonesia has been the largest supplier of palm oil since 2007, and now supplies around 56% of the global market. While the existing literature has paid serious attention to the diverse impacts of oil palm plantation on socioeconomic factors and the environment, less is known about the joint role of biophysical and socioeconomic factors in shaping the temporal and spatial dynamics of oil palm expansion. This research investigates how the benefits and costs of converting other land use/ land cover (LULC) types to oil palm plantation affects these expansion patterns. We employ a spatial panel modeling approach to assess the contributions of biophysical and socioeconomic driving factors. Our modeling focuses on Sumatra and Kalimantan, two islands which have accounted for more than 90% of oil palm expansion in Indonesia since 1990, with Sumatra holding the majority of the country’s plantations, and Kalimantan having the highest growth rate since 2000. The results show that the expansion in Kalimantan, which has been strongly stimulated by the export value of palm oil products, has occurred in areas with better biophysical suitability and infrastructure accessibility, following the ‘pecking order’ sequence, whereby more productive areas are already occupied by existing agriculture and plantations, and avoiding areas with high environmental values or socioeconomic costs. As demand for palm oil continues to grow, and land resources become more limited, the expansion in Kalimantan will tend towards the dynamics observed in Sumatra, with plantation expanding into remote and fertile areas with high conversion costs or legal barriers. Bare ground seems to have served as a clearing-up tactic to meet the procedural requirements of oil palm plantation for sustainable development. This research facilitates the improved projection of potential areas liable to future expansion, and the development of strategies to manage the leading drivers of LULC in Indonesia.

Climate, Fuel, and Land Use Shaped the Spatial Pattern of Wildfire in California’s Sierra Nevada

AbstractCalifornia’s Sierra Nevada has experienced a large increase in wildfire activities over recent decades. This intensifying fire regime has coincided with a warming climate and increasing human activity, but the relative importance of the biophysical and anthropogenic drivers of wildfire remains unclear across this diverse landscape, especially at a finer spatial scale. We used multisource geospatial data sets of fire occurrence, and human, climatic, and biophysical variables to examine the spatial pattern and controls on Sierra Nevada wildfires averaged from 1984 to 2017. The maximum entropy model driven by both biophysical and anthropogenic variables predicted the spatial distribution of fire probability well, with an area under the curve (AUC) score of 0.81. Model diagnostics revealed that aspects of the climate, including vapor pressure deficit (VPD), temperature, and burning index (difficulty of control), dominated the spatial patterns of fire probability across the whole Sierra Nevada region. The VPD was the leading control, with a relative contribution of 32.1%. Population density and fuel amount were also significant drivers, each accounting for 15.8%–12.4% of relative contribution. VPD and burning index were the most important factors for fire probability in higher elevation forest, while population density was comparatively more important in the lower elevation forest regions of the Sierra Nevada. Our findings improved our understanding of the relative importance of various factors in shaping the spatial patterns of historical fire probability in the Sierra Nevada and across various subecoregions, providing insights for targeting spatially varying forest management strategies to limit potential future increases in wildfires.

Dynamics of land use and land cover changes in Huluka watershed of Oromia Regional State, Ethiopia

BackgroundLand use and land cover changes in urbanized watersheds of developing countries like Ethiopia are underpinned by the complex interaction of different actors, driving forces, and the land itself. Land conversion due to residential development, economic growth, and transportation is identified as the most serious environmental pressure on urbanized landscapes of the world. It results in the degradation of natural vegetation and significant increases in impervious surfaces. The purpose of the study was to analyze spatio-teporal changes in land use and land cover in the Huluka watershed with implications to sustainable development in the watershed.ResultsForest land, cultivated land, urban built-up, bush/shrub land, bare land, grassland, and water body were identified as the seven types of land use and land cover in the Huluka watershed. Forest land decreased by 59.3% at an average rate of 164.52 ha/year between 1979 and 2017. Bush/ shrub land decreased by 68.2% at an average rate of 318.71 ha/year between 1979 and 2017. Grassland decreased by 32.7% at an average rate of 228.65 ha/year between 1979 and 2017. Water body decreased by 5.1% at an average rate of 1.06 ha/year between 1979 and 2017. Urban built-up area increased by 351% at an average rate of 16.20 ha/year between 1979 and 2017. Cultivated land increased by 105.3% at an average rate of 692.76 ha/year between 1979 and 2017. Bare land increased by 41.9% at an average rate of 4.00 ha/year between 1979 and 2017. Infrastructural and agricultural expansion, increased demand for wood, local environmental and biophysical drivers, rapid human population growth, economic drivers, technological drivers, policy and institutional drivers, and local socio-cultural drivers were perceived by residents as drivers of land use and land cover changes. Increased flooding risk, increased soil erosion, increased sedimentation into water resources like lakes and rivers, decrease in soil fertility, loss of biodiversity, loss of springs, decrease in annual rainfall, and increase in heat during the dry season were perceived by residents as negative local effects of land use and land cover changes.ConclusionsChanges in land use and land cover in the study water shade imply the need for integrating sustainable watershed planning and management into natural resources management strategies. In other words, practices of appropriate land use planning and management, family planning, participatory planning and management, appropriate environmental impact assessment (EIA), and proper planning and management of development projects and programmes are of paramount importance to promote sustainable development in the Huluka watershed and beyond.

Biophysical drivers of net ecosystem and methane exchange across phenological phases in a tidal salt marsh

Salt marshes are large carbon reservoirs as part of blue carbon ecosystems. Unfortunately, there is limited information about the net ecosystem (NEE) and methane (CH4) exchange between salt marshes and the atmosphere to fully understand their carbon dynamics. We tested the influence of biophysical drivers by plant phenological phases (i.e., Greenup, Maturity, Senescence and Dormancy) on NEE and CH4 exchange in a grass-dominated temperate tidal salt marsh. We used three years of data derived from eddy covariance, PhenoCam (to measure vegetation phenology), and ancillary meteorological and water/soil variables. Overall, NEE showed significant differences among all phenological phases (p < 0.05), while CH4 exchange had significant differences among all phases except for Greenup and Dormancy. Net CO2 uptake was higher across Maturity (-61 g C-CO2 m2), while CO2 emissions were higher during Dormancy (182 g C-CO2 m2). The lower but constant CO2 emissions during Dormancy overshadowed the CO2 uptake during the growing season and contributed to >72% of the annual CO2 emissions in this ecosystem. Net CH4 emissions were higher during Maturity (3.7 g C-CH4 m2) and Senescence (4.2 g C-CH4 m2). Photosynthetically active radiation (PAR) substantially influenced (r2 > 0.57) daytime NEE across phenological phases, but a combination of variables including water table level (WTL), water temperature and atmospheric pressure were relevant to explain CH4 exchange. The study site was an overall net carbon source to the atmosphere with annual emissions of 13-201 g C-CO2 m−2yr−1 and 8.5-15.2 g C-CH4 m−2yr−1. Our findings provide insights on: a) the role of plant phenological phases on ecosystem-scale CO2 and CH4 fluxes; b) challenges for modeling ecosystem-scale CO2 and CH4 fluxes in salt marshes; and c) the potential net loss of carbon to the atmosphere that should be considered for carbon management and accounting in these ecosystems.

A Social-Ecological Systems Understanding of Drivers of Degradation in the Tsitsa River Catchment to Inform Sustainable Land Management

Understanding the interactions of the social and biophysical drivers of land degradation is crucial for developing adaptive management actions for future sustainability. A research-praxis project, the ‘Tsitsa Project’ (TP), applies a social-ecological systems (SES) approach where researchers, natural resource managers, and residents collaborate to support sustainable livelihoods and improved natural resource management for the degraded Tsitsa River Catchment (TRC) in South Africa. A system diagramming approach was coupled with findings from interviews, workshops, literature, and two conceptual frameworks. Data inputs were qualitatively integrated to provide a systemic snapshot of how the context-specific social and biophysical drivers are interlinked and how they interact, revealing multiple processes that operate simultaneously to cause and exacerbate land degradation. Physical and climatic variables, changes in land use and cover, and overgrazing were identified as key factors leading to degradation. Additionally, poverty and disempowerment were also important. While little can be done to influence the physical aspects (steep topography and duplex soils) and climatic variables (extreme rainfall and drought), carefully planned changes in land use and management could produce dual-benefits for improving landscape conditions and sustainable livelihoods. This analysis will inform integrated planning processes to monitor, avoid, reduce and reverse land degradation.

Is Alaska’s Yukon–Kuskokwim Delta Greening or Browning? Resolving Mixed Signals of Tundra Vegetation Dynamics and Drivers in the Maritime Arctic

AbstractAlaska’s Yukon–Kuskokwim Delta (YKD) is among the Arctic’s warmest, most biologically productive regions, but regional decline of the normalized difference vegetation index (NDVI) has been a striking feature of spaceborne Advanced High Resolution Radiometer (AVHRR) observations since 1982. This contrast with “greening” prevalent elsewhere in the low Arctic raises questions concerning climatic and biophysical drivers of tundra productivity along maritime–continental gradients. We compared NDVI time series from AVHRR, the Moderate Resolution Imaging Spectroradiometer (MODIS), and Landsat for 2000–19 and identified trend drivers with reference to sea ice and climate datasets, ecosystem and disturbance mapping, field measurements of vegetation, and knowledge exchange with YKD elders. All time series showed increasing maximum NDVI; however, whereas MODIS and Landsat trends were very similar, AVHRR-observed trends were weaker and had dissimilar spatial patterns. The AVHRR and MODIS records for time-integrated NDVI were dramatically different; AVHRR indicated weak declines, whereas MODIS indicated strong increases throughout the YKD. Disagreement largely arose from observations during shoulder seasons, when there is partial snow cover and very high cloud frequency. Nonetheless, both records shared strong correlations with spring sea ice extent and summer warmth. Multiple lines of evidence indicate that, despite frequent disturbances and high interannual variability in spring sea ice and summer warmth, tundra productivity is increasing on the YKD. Although climatic drivers of tundra productivity were similar to more continental parts of the Arctic, our intercomparison highlights sources of uncertainty in maritime areas like the YKD that currently, or soon will, challenge historical concepts of “what is Arctic.”

Modelled larval supply predicts coral population recovery potential following disturbance

It is hypothesized that spatio-temporal variability in larval supply is caused by multiple biophysical drivers which correlate with the occurrence of recruitment pulses, influencing the recovery potential of coral reefs following large-scale disturbances. Here, we used a larval dispersal model to explore coral larvae dispersal patterns under variable oceanographic conditions, densities of parental colonies, and taxon-specific biology of propagules. Model predictions were validated with observed settlement and recruitment data to test the robustness of larval dispersal modelling for forecasting the recovery potential of study reefs. The model was applied to the western Pacific archipelago of Palau for 3 yr before and after major typhoon disturbances, and simulations were run and validated for 2 major broadcast-spawning reef-building taxa:AcroporaandPorites.Investigations into the relative role of physical (currents, wind, waves) and biological (taxa, disturbance impact) parameters on overall larval supply show that low wind speeds and the intermittent occurrence of north and southwest oceanic currents contributed significantly to enhancing larval supply at the scale of the archipelago. Reduced parental colony densities on eastern reefs following disturbances did not have a major impact on predicted larval supply patterns. Relatively low larval supply to most of the disturbed eastern reefs is predicted during the most common oceanographic conditions, forecasting low recovery potential through larval recruitment. Mapping the spatio-temporal dynamics of larval supply and identifying barriers to dispersal from intact to disturbed reefs can help predict recovery patterns across reef communities.

Land Suitability Analysis for Potato Crop in the Jucusbamba and Tincas Microwatersheds (Amazonas, NW Peru): AHP and RS–GIS Approach

Agricultural productivity in the Peruvian region of Amazonas is being jeopardized by conflicts and inadequate land use, that are ultimately contributing to environmental degradation. Therefore, our aim is to assess land suitability for potato (Solanum tuberosum L.) farming in the Jucusbamba and Tincas microwatersheds located in Amazonas, in order to improve land-use planning and enhance the crop productivity of small-scale farmers. The site selection methodology involved a pair-wise comparison matrix (PCM) and a weighted multicriteria analysis using the Analytical Hierarchy Process (AHP) on selected biophysical and socioeconomical drivers. Simultaneously, land cover mapping was conducted using field samples, remote sensing (RS), geostatistics and geographic information systems (GIS). The results indicated that for potato crop farming, the most important criteria are climatological (30.14%), edaphological (29.16%), topographical (25.72%) and socioeconomical (14.98%) in nature. The final output map indicated that 8.2% (22.91 km2) was highly suitable, 68.5% (190.37 km2) was moderately suitable, 21.6% (60.11 km2) was marginally suitable and 0.0% was not suitable for potato farming. Built-up areas (archaeological sites, urban and road networks) and bodies of water were discarded from this study (4.64 km2). This study intends to promote and guide sustainable agriculture through agricultural land planning.

Quantitative assessment of the importance of bio-physical drivers of land cover change based on a random forest method

The spatial distribution patterns of land cover greatly influence the ecological balance of the Loess Plateau. Understanding the bio-physical drivers of land cover change is important for ecological restoration in the context of climate change. However, in the analysis of the drivers of land cover change in the Loess Plateau, the role of bio-physical drivers has not been quantitatively evaluated. Using remote sensing data, machine learning, and statistical methods, this study analyzed the spatial and temporal patterns of land cover from 2001 to 2018 in the Loess Plateau of China. We used a random forest (RF) model to quantify the importance of bio-physical drivers of land cover. Our results demonstrated that the RF model has good performance and high reliability (model accuracy score > 0.8). Our simulation experiment revealed that evapotranspiration was the most important driver (importance score, IS >0.2), temperature and precipitation had regional heterogeneity, and slope was the least important (IS <0.05). We suggest that evapotranspiration can be regulated by properly allocating the type of land cover, so as to rationally allocate water resources on the Loess Plateau. This study provides a new foundation for quantitatively evaluating the drivers of land cover change and regulating the distribution of water resources on the Loess Plateau, China.

Surface Urban Heat and Cool Islands and Their Drivers: An Observational Study in Nanjing, China

AbstractUrban heat islands (UHIs) are caused by a multitude of changes induced by urbanization. However, the relative importance of biophysical and atmospheric factors in controlling the UHI intensity remains elusive. In this study, we quantify the magnitude of surface UHIs (SUHIs), or surface urban cool islands (SUCIs), and elucidate their biophysical and atmospheric drivers on the basis of observational data collected from one urban site and two rural grassland sites in and near the city of Nanjing, China. Results show that during the daytime a strong SUCI effect is observed when the short grassland site is used as the reference site whereas a moderate SUHI effect is observed when the tall grassland is used as the reference site. We find that the former is mostly caused by the lower aerodynamic resistance for convective heat transfer at the urban site and the latter is primarily caused by the higher surface resistance for evapotranspiration at the urban site. At night, SUHIs are observed when either the short or the tall grassland site is used as the reference site and are predominantly caused by the stronger release of heat storage at the urban site. In general, the magnitude of SUHI is much weaker, and even becomes SUCI during daytime, with the short grassland site being the reference site because of its larger aerodynamic resistance. The study highlights that the magnitude of SUHIs and SUCIs is mostly controlled by urban–rural differences of biophysical factors, with urban–rural differences of atmospheric conditions playing a minor role.

Andes, Bofedales, and the Communities of Huascarán National Park, Peru

Mountain wetlands are abundant in the high elevations of the tropical Andes. Wetlands occupy ~11% of the total park area and are mostly found in the large mountain valleys. Wetlands occur up to 5000 m asl, but most occur between 4,000–4,700 m asl. The highest elevation wetlands are typically dominated by cushion plants, while lower elevation wetlands are more commonly occupied by graminoids. About 60% of all wetlands are peatlands and the remainder are mineral soil wet meadows. The peatlands are up to 11 m deep and 12,000 years old, storing an average of 2,101 Mg C ha-1, which is comparable to lowland tropical peatlands. Our work in Huascarán National Park in Peru is also showing the importance of wetlands in a coupled natural-human system. These wetlands and alpine landscapes are shaped in part by legacies of past human land use, including ancient pastoralism and farming, and are also affected by millions of downstream users dependent upon wetlands and glacier-fed streams for water and energy production. Biodiversity and endemism is high among taxonomic groups such as plants, birds, fish, amphibians and insects. Currently the tropical Andes are in ecological flux due to rapid land cover changes caused by both biophysical and socioeconomic drivers. In addition, the high Andes are experiencing warming and rapid glacial retreat that is resulting in hydroecological changes and socioeconomic changes to the traditional Andean societies that feed back to changes in wetland sustainability.

Functional Representation of Biological Components in Methane‐Cycling Processes in Wetlands Improves Modeling Predictions

AbstractWetlands are important natural sources of methane. However, methane predictions are unconstrained due in part to simplified representations in biogeochemical models of complex interactions between biological components and biophysical drivers. Chang et al. (2019, https://doi.org/10.1029/2019JG005355) demonstrate how mechanistic descriptions of processes mediated by biological components and their drivers help improve methane prediction in the thawing permafrost peatlands, where biological components are shifting as a response to current climate change. These findings underscore the need for accounting for the different methanogenic pathways in biogeochemical models to predict methane cycling under a changing climate scenario. This comment presents some implications of considering the methanogenic pathways when simulating different wetland ecosystems and discusses the relevance of including other processes mediated by biological components in the methane cycle in models.

Estimating the probability of wildfire occurrence in Mediterranean landscapes using Artificial Neural Networks

Wildfires are a major disturbance in the Mediterranean Basin and an ecological factor that constantly alters the landscape. In this context, it is crucial to understand where wildfires are more likely to occur as well as the drivers guiding them in complex landscapes such as the Mediterranean area. The objectives of this study are to estimate wildfire probability occurrence as a function of biophysical and human-related drivers, to provide an assessment of the relative impact of each driver and analyze the performance of machine learning techniques compared to traditional regression modeling. By employing an Artificial Neural Network model and fire data (2004–2012), we estimated wildfire probability across two geographical regions covering most of the Italian territory: Alpine and subalpine region and Insular and peninsular region. The high classification accuracy (0.68 for the Alpine and subalpine region and 0.76 for the Insular and peninsular region) and good performances of the technique (AUC values of 0.82 and 0.76, respectively) suggest that our model can be used in the areas studied to assess wildfire probability occurrence. We compared our model with a logistic function, which showed a weaker predictive power (AUC values of 0.78 for the Alpine and subalpine region and 0.65 for the Insular and peninsular region) compared to the Artificial Neural Network. In addition, we assessed the importance of each variable by isolating it in the model. The importance of an individual variable differed between the two regions, underscoring the high diversity of wildfire occurrence drivers in Mediterranean landscapes. Results show that in the Alpine and subalpine region, the presence of forest is the most important variable, while climate resulted as being the most important variable in the Insular and peninsular region. The majority of areas recently affected by large wildfires in both regions have been correctly classified by the ANN model as ‘high fire probability’. Hence, the use of an Artificial Neural Network is efficient and robust for understanding the probability of wildfire occurrence in Italy and other similar complex landscapes.

Structural diversity and development in active fire regime mixed-conifer forests

Nearly a century of fire suppression in most forested land of the United States has limited researchers’ ability to construct and rigorously test conceptual models of forest structural development in mixed-conifer ecosystems. As a result, land managers must rely on conceptual models of forest development that may overemphasize idealized stand structures and developmental pathways, which ultimately hampers management of many forest systems for resilience to future climate change impacts. We sought to determine the relative importance of fire history (frequency, severity, and time since fire) and biophysical variables on forest structural diversity and development. Importantly, we conducted our study in an ecosystem with a contemporary active fire regime where wildland fire has been managed as an ecosystem process for four decades. Using data from unburned (≥80 years since fire), once-burned and twice-burned mixed-conifer forests in the Bob Marshall Wilderness of Northwest Montana, we conducted a hierarchical clustering analysis to identify forest stand structure classes. We then used a Classification and Regression Tree analysis, combined with other post-hoc analyses, to elucidate the biophysical and disturbance history drivers that lead to each structure class. The cluster analysis revealed six forest structure classes. The CART analysis indicated that time since fire plays a large role in determining forest structure, but at intermediate time scales structure is further shaped by repeat fires and interactions with biophysical variables. The CART and post-hoc analyses did not, however, indicate a singular fire history or biophysical pathway to any one structure class. We synthesize our results in a conceptual model of forest structural development under an active fire regime. This model supports existing theory that succession following severe fire plays a large role in shaping forest structure. It also recognizes the role of fire at variable severities and frequencies, the physical environment, and tree community composition in influencing forest structural development. The complexity of forest structure and development generated by an active fire regime points to the need to incorporate a process-based view of wildfire if the goal is to manage for improved resiliency and adaptive capacity to future climate change impacts.

Interannual monsoon wind variability as a key driver of East African small pelagic fisheries

Small pelagic fisheries provide food security, livelihood support and economic stability for East African coastal communities—a region of least developed countries. Using remotely- sensed and field observations together with modelling, we address the biophysical drivers of this important resource. We show that annual variations of fisheries yield parallel those of chlorophyll-a (an index of phytoplankton biomass). While enhanced phytoplankton biomass during the Northeast monsoon is triggered by wind-driven upwelling, during the Southeast monsoon, it is driven by two current induced mechanisms: coastal “dynamic uplift” upwelling; and westward advection of nutrients. This biological response to the Southeast monsoon is greater than that to the Northeast monsoon. For years unaffected by strong El-Niño/La-Niña events, the Southeast monsoon wind strength over the south tropical Indian Ocean is the main driver of year-to-year variability. This has important implications for the predictability of fisheries yield, its response to climate change, policy and resource management.

Leveraging big data for public health: Mapping malaria vector suitability in Malawi with Google Earth Engine.

In an era of big data, the availability of satellite-derived global climate, terrain, and land cover imagery presents an opportunity for modeling the suitability of malaria disease vectors at fine spatial resolutions, across temporal scales, and over vast geographic extents. Leveraging cloud-based geospatial analytical tools, we present an environmental suitability model that considers water resources, flow accumulation areas, precipitation, temperature, vegetation, and land cover. In contrast to predictive models generated using spatially and temporally discontinuous mosquito presence information, this model provides continuous fine-spatial resolution information on the biophysical drivers of suitability. For the purposes of this study the model is parameterized for Anopheles gambiae s.s. in Malawi for the rainy (December–March) and dry seasons (April–November) in 2017; however, the model may be repurposed to accommodate different mosquito species, temporal periods, or geographical boundaries. Final products elucidate the drivers and potential habitat of Anopheles gambiae s.s. Rainy season results are presented by quartile of precipitation; Quartile four (Q4) identifies areas most likely to become inundated and shows 7.25% of Malawi exhibits suitable water conditions (water only) for Anopheles gambiae s.s., approximately 16% for water plus another factor, and 8.60% is maximally suitable, meeting suitability thresholds for water presence, terrain characteristics, and climatic conditions. Nearly 21% of Malawi is suitable for breeding based on land characteristics alone and 28.24% is suitable according to climate and land characteristics. Only 6.14% of the total land area is suboptimal. Dry season results show 25.07% of the total land area is suboptimal or unsuitable. Approximately 42% of Malawi is suitable based on land characteristics alone during the dry season, and 13.11% is suitable based on land plus another factor. Less than 2% meets suitability criteria for climate, water, and land criteria. Findings illustrate environmental drivers of suitability for malaria vectors, providing an opportunity for a more comprehensive approach to malaria control that includes not only modeled species distributions, but also the underlying drivers of suitability for a more effective approach to environmental management.