QuantitativeEcology is a Research Lab in the School of Forestry at Northern Arizona University. It focuses on the application of advanced mathematical, computational and statistical tools to any number of problems spanning from modeling population dynamics to quantifying spatial patterns and species interactions. Quantitative ecologists often apply some combination of deterministic and/or stochastic mathematical models to theoretical ecological questions and commonly depend on sophisticated methods in applied statistics, mathematics and computer programming.

Our research aim is to exploit the potential of information found in increasingly larger and more complex data sets using mathematical equations, conceptual graphics, and model development. While quantitative ecology can deepen our understanding of the world, the field commonly requires strong analytical and creative problem-solving skills. QuantitativeEcology is committed to developing scientists with these skills and collaborating with other researchers and organizations devoted to quantitative analyses and rigor, open enquiry, and enhancing our understanding of ecological systems.


Andrew Sanchez Meador - P.I. (Hometown: Brandon, MS)

Dr. Sánchez Meador is an Associate Professor of Forest Biometrics and Quantitative Ecology in the School of Forestry at Northern Arizona University. His research program focuses primarily on quantitative forest ecology, spatial pattern-process interactions, applied remote sensing, forest biometrics and vegetation dynamics and modeling. His current research and teaching interests range from multi-scale forest restoration and ecology issues, to practical interpretations of large complex data sets, to neighborhood effects on individual tree growth and stand patterning, to new ways of visualizing data for science communication.

Jonathon Donager - Visiting Postdoc (Hometown: Both ends of Nevada)

Dr. Donager's is a postdoctoral scholar jointly appointed with the Quantitative Ecology Lab and the Ecological Restoration Institute and his research interests revolve around the application of remote sensing datasets, tools, and methods for understanding forests in the context of natural resource management. This work spans geographic scales, using lidar for understanding fine scale forest structure and satellite imagery for understanding forest pattern and structure across landscapes. In a nutshell, he examines how forests are arranged, their composition, and how those characteristics relate to ecological functions and processes.

Ryan Blackburn - Ph.D. (Hometown: Aurora,IL)

Ryan’s research focuses on the use of remote sensing techniques to enhance land management efforts towards creating more resilient ecosystems. Currently, his work applies machine learning methods to lidar derived datasets in order to assess the structure and composition of Southwestern forests. This research aims to provide efficient and accurate estimates of forest conditions for resource managers, which will then be used to monitor and predict outcomes from drivers of forest change (e.g., wildfires, management decisions, climate change).

Jill Beckmann - Ph.D. (Hometown: Centerville, OH)

Jill's research focuses on understanding neighborhood- or fine-scale spatial patterning, forest successional trajectories following treatments, and the role of plant interactions and climate on individual tree growth, mortality and ecological processes.

Merideth Reiser - M.Sc. (Hometown: Frederick, MD and Topsail, NC)

Merideth’s research focuses on using remote sensing technology to assess the structure of coarse woody debris on the Mogollon Rim. This research investigates the effectiveness of a variety of lidar measurement methods (airborne laser scanning, unpiloted aerial systems, and mobile lidar systems). This work aims to provide structure information to forest managers to aid in resource management.

Matt Jaquette - M.Sc. (Hometown: Issaquah, WA)

Matt’s research focuses on reference conditions in mixed-conifer forests of the Southwest. His current research investigates how environmental factors drove variation in the historical structure and composition of forests on the Mogollon Rim, Arizona. This work could help land managers improve the design of restoration treatments in the Southwest.

Alexander Spannuth - M.Sc. (Hometown: Flagstaff, AZ)

Alex’s research focuses on issues related to increasing prescribed fire and wildland fire use for resource benefit. While wildland fire is increasingly being used to accomplished USDA Forest Service ()USFS targets, two imperative pieces of the adaptive management cycle are currently missing: (1) quantifying how fire management is altering the current forest structure and composition at programmatic scales and (2) identifying treatments that are actually moving forest towards desired conditions. Without these two crucial components, the USFS is implementing landscape-scale treatments based on anecdotes rather than best available science.

Caden Chamberlain - Hooper UG Researcher - (Hometown: Athens, OH)

Caden’s research focuses on the application of remote sensing data to improve understanding of forest structure and function as it relates to fire ecology and forest restoration. Research is intended to support forest and fire management decisions in southwestern forests. Recent work has included developing methods for characterizing canopy fuels using airborne lidar and simulating wildfire and subsequent effects on forest landscape pattern.


Lucas Molina – M.Sc.
Dany Rochimi – M.Sc.
Marguerite Rapp – M.Sc.
Sushil Nepal – M.Sc.
Tim Bryant – M.Sc.
Michael O’Reilly – B.S., Hooper UG Researcher
Kyle Rodman – M.Sc.
Michael Johnson – M.Sc.
Karin Kralicek – Lab Assistant
Greg Black – B.S., Hooper UG Researcher

Jonathon James Donager, Ph.D.

Jonathon uses a broad array of remote sensing datasets and statistical tools to assess, measure, monitor and better understand forest ecosystems and ecosystem functions such as ecohydrology. He has worked with passive and active remote sensing data, often incorporating multiple datasets to answer research questions. He develops unique processing workflows and tools for those datasets, including tools for assessing individual tree and area-based metrics from lidar datasets and spatial algorithms for understand snow dynamics and ecosystem processes. He employs machine learning and non-parametric statistics for characterizing and understanding complicated datasets to examine relationships among ecological relationships and remote sensing data. His research is primarily oriented towards applications of remote sensing to inform and benefit natural resource managers, and as such is interested in creating datasets and tools which directly impact management decisions, especially within a multi-resource response framework. His expertise is in developing processes, tools, and models for processing, understanding and making inferences from remote sensing data.

Research Interests

  • Lidar applications, data fusion, and tool development: 3-Dimensional datasets are invaluable in understanding the physical structure of an ecosystem and how that structure relates to ecosystem function, health, and management objectives. Things I have and continue to work on are:
    • Mobile Lidar for site/stand assessment and monitoring
    • Mobile lidar (middle) compared to airborne (top) and terrestrial (bottom) lidar datasets at a thinned site west of Flagstaff, AZ.

    • Lidar and Structure from Motion (SfM) for snow dynamics
    • Lidar fusion for species identification
    • Airborne and terrestrial lidar for area- and individual tree-based estimates of forest structure
    • Using two terrestrial laser scanners, we examine how “far” we could see into forest stands which were thinned and non-thinned. Through examining many forest plots at varying distances, we were able to come up with “average” distances to the limits of quality data collection among both scanners and treatment types. Panels A and B represent those treatment types: non-thinned and thinned, respectively along the darker green strip in the central image. The size of the colored rings correspond to the standard deviation of distances among the forest plots we examined. The distances from the scanner location (the yellow dot in the central image) show the representative distance with a high likelihood of containing rasterized point densities of at least 25 points/m2 or greater.

    • Ecohydrology and ecosystem services
      • Snow Dynamics
      • Example of UAV time-series (A – C) for three dates in 2016, showing evolution of snow cover following a storm. These data are then combined to produce a snow persistence probability map by creating an average from the individual binary classifications of snow cover (D; see section 2.3.4). Outlines show locations of brief snow cover (red) and persistent snow cover (yellow). Examples of model inputs before applying focal window summaries included in the study are canopy height (E), variation in bare earth microtopography (F), tree density from individual tree locations (G), and individual canopy patch delineation (H).

      • Effects of forest structure on soil moisture
      • Soil moisture measurements at snow course measurement points (9 points per plot) for three treatment types at four sites. Error bars show the 95% confidence interval of the mean.

    • Landscape Scale Assessment and Analysis
      • The spatial arrangement of reference landscapes and comparisons to managed landscapes: Comparisons of landscape metrics within natural, functioning forest landscapes and wildfires managed for resource objectives (RO fires). Classification of forest cover was done using Sentinel-2 and Sentinel-1 time series imagery to classify vegetation cover types and limit landscape metrics to ponderosa pine canopy cover, which was the dominant cover type historically.
      • Comparison of forest structure in Southwestern ponderosa pine forests among reference landscapes (blue) and previously managed wildfire for resource benefit landscapes (red) among several landscape metrics to understand how landscapes compare in landscape ecology “data space”.

    • Time-series analysis for assessing historical change
      • Ponderosa pine ecotone change
      • Pinon–Juniper ecotone change

Caden Chamberlain's HURA Project Overview

Lidar and FlamMap facilitate exploration of fuel accumulation and simulated fire in the forests of northern Arizona


  • High-severity fires are increasing in extent and frequency in the ponderosa pine and dry-mixed conifer forests of the southwestern United States, demanding that land managers design and implement restoration treatments at an accelerated rate (Covington et al. 1997; Singleton et al. 2019).
  • However, the extent of at-risk forests necessitates that land managers prioritize restoration treatments in areas that, if high-severity fire occurred, would have the greatest ecological, social, and economic impacts (Collins et al. 2010; Vaillant and Reinhardt 2017).
  • To facilitate prioritization and inform decision making models, extensive, fine-resolution data describing fire hazard and potential fire behavior is needed (Hessburg et al. 2007).


  1. Quantify and map the distribution of wildfire fuels using lidar
  2. Determine the primary drivers of wildfire fuel accumulation
  3. Describe the spatial distribution and landscape patterns following simulated high severity wildfire under different weather scenarios


Project Area

  • 26,000 ha of predominantly ponderosa pine and dry mixed-conifer forests within the Mogollon Rim Ranger District of the Coconino National Forest.
  • Elevation ranges from 1,800 to 2,500 m. Average annual precipitation for the area is 49.47 cm, with annual temperatures ranging from -9.9 to 27.9 ° C (Western Regional Climate Center, 2020a).

Figure 1. Project area location and boundary, and important watersheds within the project boundary.

Creating Fuel and Topographic Data Layers from Lidar

  • I created eight, 20 m resolution data layers using lidar for FlamMap software: canopy base height (CBH), canopy bulk density (CBD), canopy cover (CC), canopy height (CH), surface fuel model, elevation (DEM), slope, and aspect

Figure 2. Eight data layers required to run FlamMap simulations, taken from Finney (2006).

  • For CBH and CBD, I used methods developed by Chamberlain et al. (2020 – in review).
  • For CC, I used methods developed by Smith et al. (2009).
  • For CH and DEM, I used the R lidR package (Roussel and Auty 2019).
  • For slope and aspect, I used the R raster package (Hijmans and Etten 2020).
  • For surface fuel model, I developed a novel method for this study. I obtained existing surface fuel data for 497, 0.04 ha plots established by the Forest Service and Ecological Restoration Institute that fell within the project boundary. I then used the Forest Vegetation Simulator to calculate a surface fuel model for each plot (Reinhardt and Crookston 2003). Dominant surface fuel models within the project area included timber with grass and understory, timber with litter and understory, low load compact conifer litter, and long-needle litter. I then developed a parsimonious classification tree model using the R rpart package (version 4.1-15; Therneau et al. 2019). The resulting model used lidar-derived metrics to predict the fuel model within each 0.04 ha plot. This classification tree model was then used to create a 20 m resolution raster layer of fuel models across the project area using strictly lidar-derived metrics.

Determining Hazardous Fuel Accumulation Drivers

  • I used a chi-square test for independence and a random forest test for importance, and tested topographic drivers including topographic position index (Weiss 2001), elevation, slope, and aspect.
  • For the chi-square test for independence, I used a Monte Carlo method to perform repeated random sampling between overlain pairs of hazardous fuel rasters (CBH, CBD, and CC) and driver rasters (TPI, aspect, DEM, and slope), resulting in a total of 12 comparisons. For each hazardous fuel – driver combination, I performed 500 chi-square tests, each of which selected 100 random samples from the overlain raster layers. I then plotted the distribution of p-values from all chi-square tests for each hazardous fuel – driver combination. These p-value distributions were used to examine the strength of associations between fuels and drivers.
  • For the random forest test of importance, I used the R randomForest package (version 4.6-15; Liaw and Wiener 2002), to develop a random forest model for each hazardous fuel raster (CBH, CBD, and CC), using the four driver rasters (TPI, aspect, DEM, and slope) as predictor variables for each model. I then calculated the percent change in mean squared error (MSE) for each predictor variable, which indicated the relative importance of each driver in building the random forest model.

FlamMap Simulations

  • I ran FlamMap simulations under normal, high, and extreme fire weather scenarios, which were represented by 75th, 90th, and 97th percentile fuel moisture and maximum wind speed from historic weather data. Historic weather data was obtained from the Mormon Lake Remote Automated Weather Station (RAWS) for all fire season (April 23 – October 16) days between 1999 and 2019 (Western Regional Climate Center, 2020b).
  • The fuel moisture and wind speed values used for each fire weather scenario are shown in Table 1. A wind vector layer was produced from WindNinja software (Forthofer and Butler, 2013)

Table 1. Inputs used to run FlamMap simulations for normal, high, and extreme weather scenarios. Values calculated from historic weather data from the Mormon Mountain Remote Automated Weather Station, for all fire season (April 23 – October 16) days between 1999 and 2019.


Fire Weather

FlamMap Input




1-hr fuel moisture (%)




10-hr fuel moisture (%)




100-hr fuel moisture (%)




Herbaceous fuel moisture (%)




Live woody fuel moisture (%)




Foliar moisture content (%)




Mean wind speed




Mean wind direction




Landscape Metrics

  • Using the crown fire activity raster produced by FlamMap for each fire weather scenario, I calculated landscape-level metrics for crown fire patches using the R landscapemetrics package (version 1.4.2; Hesselbarth et al. 2019). I calculated metrics including percent landscape, mean patch area, and patch density, which I compared between each fire weather scenario.


Hazardous Fuel Accumulation

  • Hazardous fuels including CBH, CBD, and CC were distributed in a heterogeneous pattern across the project area.
  • Notable hazardous fuel accumulation for all three fuel categories were evident in the northern portion of the project area which represents the steep slopes surrounding Blue Ridge Reservoir.

Figure 3. Hazardous fuel accumulation represented by canopy base height (CBH), canopy bulk density (CBD), and canopy cover (CC) across the project area. Hazardous fuel accumulation is pronounced in major ravines, around Blue Ridge Reservoir, and at higher elevations. Colors represent hazardous values for each given fuel type, where lower values are more hazardous for CBH, but higher values are more hazardous for CBD and CC.

Drivers of Hazardous Fuel Accumulation

  • Both the chi-square test for independence and the random forest test for importance suggested that slope was the primary driver of hazardous fuel accumulation across the project area, though the association was weak overall.

Figure 4. Distribution of p-values for 500 Chi squared tests for each hazardous fuel – fuel driver combination, with the dotted line representing a p-value of 0.1. Slope has the strongest association with each hazardous fuel category.

Figure 5. Mean squared error of each predictor variable for three random forest models predicting hazardous fuel. High mean squared error indicates high relative importance in the model, thus slope is the most important predictor of each hazardous fuel category.

Simulated Crown Fire Distributions

  • The proportion of simulated crown fire increased as the fire weather scenario became more extreme.
  • With normal fire weather crown fire accounted for 7% of the landscape, with high fire weather crown fire accounted for 10% of the landscape, and for extreme fire weather crown fire accounted for 18% of the landscape.
  • The distribution of crown fire tended to follow similar patterns as hazardous fuel accumulation, with steep slopes, major ravines, and areas around Blue Ridge Reservoir exhibiting higher proportions of crown fire, despite the fire weather scenario.

Figure 6. Simulated crown and surface fire distributions under different fire weather scenarios across the project area. Proportion of crown fire increases as fire weather becomes more extreme. Most crown fire occurs in major ravines, around Blue Ridge Reservoir, and at higher elevations, following similar patterns as hazardous fuel accumulation.

Landscape Metrics

  • Landscape-level metrics of crown fire patches demonstrated noticeable changes between fire weather scenarios.
  • Percent of the landscape composed of crown fire patches increased from 7% to 18% as fire weather became increasingly extreme. Mean patch area was about 0.37 ha under normal fire weather and 0.45 ha under extreme fire weather.
  • Crown fire patch density also increased as fire weather became increasingly extreme, with normal fire weather producing about 20 crown fire patches/100 ha, and extreme fire weather producing about 40 crown fire patches/100 ha.

Figure 7. Landscape-level metrics calculated for simulated crown fire patches within the project area for normal, high, and extreme fire weather scenarios. Extreme fire weather scenarios result in increased extent, size, and density of crown fire patches.


  • This study demonstrates the utility of lidar for producing canopy fuel data layers across large extents (~26,000 ha) and at fine resolution (20 m).
  • Maps of hazardous fuel accumulation, drivers of fuel accumulation, and maps of potential crown fire suggest that steep slopes, major ravines, and areas surrounding Blue Ridge Reservoir should be considered foremost for restoration.
  • Prescribed burning or managed wildfires may produce desirable burn severity patterns under normal or high fire weather scenarios, especially in certain areas within the landscape.
  • The method developed to derive surface fuel model in this study had low accuracy, so other research could work to improve this method.
  • Only four, topographic drivers of hazardous fuel were considered in this study. Other drivers, including past management, climate, or soil should also be investigated.
  • Possible compounding error may have occurred with lidar-derived fuel data layers, various data sources used for FlamMap inputs, and complex models behind FlamMap.

References Cited

Chamberlain, C.C., Sánchez Meador, A.J., Thode, A.E., 2020. Airborne lidar easily provides improved estimates of canopy base height and canopy bulk density in southwestern ponderosa pine forests. For. Ecol. Manage. In Review.

Collins, B.M., Stephens, S.L., Moghaddas, J.J., Battles, J. 2010. Challenges and approaches in planning fuel treatments across fire-excluded forested landscapes. J. Forest. 108(1), 24-31.

Covington, W.W., Fulé, P.Z., Moore, M.M., Hart, S.C., Kolb, T.E., Mast, J.N., Sackett, S.S., Wagner, M.R., 1997. Restoring ecosystem health in ponderosa pine forests of the southwest. J. Forest 95, 23-29.

Finney, M.A., 2015. An overview of FlamMap fire modeling capabilities. USDA For. Serv. Proc. RMRS-P-41, pp. 213-220.

Forthofer, J., Butler, B., 2013. Windninja. US Forest Service Joint Fire Science Program.

Hessburg, P.F., Reynolds, K.M., Keane, R.E., James, K.M., Salter, R.B. 2007. Evaluating wildland fire danger and prioritizing vegetation and fuels treatments. For. Ecol. Manage. 247(1-3), 1-17.

Hesselbarth, M.H.K., 2020. landscapemetrics: Landscape metrics for categorical map patterns. R package version 1.4.3.

Hijmans, R.J., Etten J.V., 2020. raster: Geographic analysis and modeling with raster data. R package version 3.0-7.

Liaw, A., Wiener, M., 2018. randomForest: Breiman and Cutler’s Random Forests for Classification and Regression. R package version 4.6-14.

Reinhardt, E., Crookston, N.L., 2003. The fire and fuels extension to the forest vegetation simulator. USDA For. Serv. Gen. Tech. Rep. RMRS-GTR-116.

Roussel, J.R., Auty, D., 2019. lidR: Airborne LiDAR Data Manipulation and Visualization for Forestry Applications. R package version 2.1.4.

Singleton, M.P., Thode, A.E., Meador, A.J.S., Iniguez, J.M., 2019. Increasing trends in high-severity fire in the southwestern USA from 1984 to 2015. For. Ecol. Manage. 433, 709-719.

Smith, A.M., Falkowski, M.J., Hudak, A.T., Evans, J.S., Robinson, A.P., Steele, C.M., 2009. A cross-comparison of field, spectral, and lidar estimates of forest canopy cover. Can. J. Remote Sens. 35(5), 447-459.

Therneau, T., Atkinson, B., Ripley., 2019. rpart: Recursive partitioning and regression trees. R package version 4.1-15.

Vaillant, N. M., Reinhardt, E. D., 2017. An evaluation of the Forest Service Hazardous Fuels Treatment Program—Are we treating enough to promote resiliency or reduce hazard?. J. Forest. 115(4), 300-308.

Weiss, A., 2001. Topographic position and landforms analysis. Poster presentation, ESRI user conference, San Diego, CA (Vol. 200).

Western Regional Climate Center, 2020a. Blue Ridge R S, Arizona; Period of record monthly climate summary 07/01/1965 o 06/10/2016. Retrieved from http://wrcc.dri.edu/climatedata/climsum/

Western Regional Climate Center, 2020b. Mormon Lake R S, Arizona; RAWS USA Climate Archive. Retrieved from https://wrcc.dri.edu/cgi-bin/rawMAIN.pl?azAMOL


QuantitativeEcology develops research projects centered on quantifying how natural and anthropogenic disturbances shape forested ecosystems and the application of novel quantitative approaches to answer questions regarding these ecosystems. Our research interests include applied statistics, restoration ecology, ecosystem pattern-process interactions, vegetation demographics and dynamics, exploring factors influencing the economics of fire suppression and treatment costs, and emerging technology – specifically mobile devices and sensors, open-source hardware, and unmanned aerial systems.

In general, we adopt an open approach to quantitative investigations, working from exploratory data analyses to mathematical-based analytics. Quantitative analyses are, thus, treated more like a proper language than tools and we use this language to enhance our understating of ecological systems. QuantitativeEcology is a flexible and resilient research lab, open to collaborations with federal and state land management agencies, private institutions, NGOs, other universities, and private companies.

Example Research Areas...

Ecological Restoration, Forest Dynamics and Spatial Pattern-Forest Process Interaction

Assisting disturbance regimes and forest structure, composition and function towards desired conditions is a central tenant for ecological restoration and a primary objective for federal land management. While it is well accepted that forest ecosystem functions and processes are sensitive to spatial patterning, particularly in ponderosa pine and dry mixed-conifer of the southwest US, the scientific basis for understanding the influence of spatial patterns within forest and restoration ecology is limited to a handful of studies over small geographic areas. Key to understanding the drivers of forest dynamics and the development of prescriptions intended to mimic desired environmental change are scale-dependent spatial information at the stand-, group-, and tree-level.

Quantifying Forest Conditions: Monitoring Implementation and Effects of Treatment

Treatments targeting ecological restoration in frequent-fire forests of the Southwest are generally aimed at approximating conditions prior to fire-exclusion conditions by reducing tree densities and hazardous fuels, and reintroducing surface fire. The treatments are typically mechanical in nature, but increasingly managers are utilizing natural fire ignitions (sometimes called “managed” fire) to achieve restoration and hazardous fuels reduction objectives. However, approaches minimizing risk and public scrutiny may result in undesirable outcomes or not achieve desired objectives altogether, thus underscoring the need for rapid forest assessment and/or sound adaptive management methodologies. Understanding how best to utilize field-based inventories and samples, integrate landscape ecology and remote sensing techniques and approaches, and merge monitoring results into future actions in an adaptive management framework are essential to monitoring the effectiveness and success of management actions.

The Future of Forestry: Big Data, Remote Sensing, and Mobile Technology

Accurate and reliable forestry data are obtained by an increasing variety of means (e.g., wireless sensor networks, lidar, smartphone and tablet applications) providing an opportunity for the development of new approaches and analytical techniques in forestry. However, increases in volume and speed of acquisition, along with uncertainty in precision and bias from new technologies, creating challenges for traditional analytical and statistical approaches for current forestry applications. Through various projects, QuantitativeEcology.org is evaluating and adopting technologies such as mobile lidar and machine learning approaches such as random forests and mask convolutional neural networks, to organize, store, query, summarize and analyze increasing larger and more complex datasets for forestry and ecological restoration applications.

Forest Measurements and Tree Allometry

Assessing basic tree and forest characteristics is essential for many aspects of forestry and natural resources management. Practitioners “measuring” the forest provide data that are used to support land management decisions concerning not only timber resources, but also wildlife habitat, recreation, forest health, watershed condition and ecosystem processes. Tree allometry focuses on quantitative relationships between key tree dimensional characteristics (which are usually easy to measure) and other properties (which are often difficult to assess) and is key to assessments of product volume, forest biomass, carbon stocks and understanding tree structure. Understanding past and present quantities of volume, biomass, and carbon is necessary to understand forests' capacity to provide ecosystem services including wood and fiber production for traditional timber products or bioenergy and better understanding of forest carbon stores is essential to greenhouse-gas accounting related to climate change mitigation and adaptation strategies.

Structural and Functional Influence of Dead Wood in Southwestern Forest Ecosystems

Changes in climate and shifts in insect and disease dynamics are expected to result in increased tree mortality, inevitability resulting in increases in coarse woody debris (CWD; i.e., snags and logs) populations. These structures are known to serve important roles in biodiversity, trophic chains, forest regeneration, nutrient cycling and overall carbon storage, yet CDW abundance and distributions are poorly understood and snag fall and downed log decay rates are rarely quantified. Understanding the condition, variability and dynamics of CWD has traditionally been done by extrapolating from field inventories and through the utilization of chronosequences. These approaches, due to high variability in localized CWD and difficulties sampling across vast forest condition and environmental gradients, can result in problematic inaccuracies and are both time consuming and costly. Through various ongoing studies, QuantitativeEcology.org is exploring the accuracy and effectiveness of (1) long-term ecological studies as a sources documenting CDW dynamics, (2) the use of various modeling approaches for the estimation of short- and long-term snag fall and downed wood decay rates, and (3) the use of ALS, UAS, and MLS lidar, as well as new field-based sample designs, to assess CWD loading, condition, and size distribution in frequent-fire ecosystems of the southwestern US.


A key feature of the QuantitativeEcology Lab is strong integration between instruction and research activities. Teaching is where we being to develop our thoughts, conceptual tools, and methodologies and remains the arena where we verify our findings. QuantitativeEcology owes its origins to to many late-night (over pints) discussions where the the flow of information increases and the integration between instruction and research activities blurs. Throughout our courses and workshops, we use an interdisciplinary approach to enable students to master quantitative and visual representation techniques in the widest sense. We draw on the expertise of professors and professionals from several disciplines, from forest ecology to remote sensing and statistics to computer science.

Courses Taught

Class Class Title Semester
FOR 413 & 414C Ecosystem Assessment I & II Fall
FOR 606 Ecological Data Analysis Fall (Odd Years)
FOR 641 Data Metaphors and Visualization Fall
FOR 313 Silviculture (Forest Resource Sampling) Fall
FOR 324W Forest Management I (Forest Biometrics) Spring

Workshops Taught

Year Class Title Semester
2020 Big Data Analytics in Forestry Spring


  1. Huffman, D.W., Stoddard, M.T., Springer, J.D., Crouse, J.E., Sánchez Meador, A.J. and S. Nepal. 2019. Stand dynamics of pinyon-juniper woodlands following hazardous fuels reduction treatments in Arizona. Rangeland Ecology and Management. 72(5): 757-767.
  2. Yazzie, J.O., Fulé, P.Z., Kim, Y.-S., and A.J. Sánchez Meador. 2019. Diné kinship for conserving native tree species in climate change. Ecological Applications. 29(6):e01944.
  3. Putraditama, A., Kim, Y.-S., Sánchez Meador, A.J., and H. Baral. 2019. Evaluating effectiveness of Community Forest scheme in reducing deforestation in Indonesia. Forest Policy and Economics. 106(2019) 101976. 
  4. Vaughan, D., Auty, D., Kolb, T., Sánchez Meador, A.J., Mackes, K., Dahlen, J. and K. Moser. 2019. Climate alters ponderosa pine (Pinus ponderosa) wood density more than stand basal area in a replicated stand density experiment in the southwestern USA. Annals of Forest Science 76:85 1-12. 
  5. Wasserman, T.N., Sánchez Meador, A.J. and A.E.M. Waltz. 2019. Grain and extent considerations are integral for monitoring landscape-scale desired conditions in fire-adapted forests. 10(6) 465. Forests.
  6. Singleton, M.E., Thode, A.E., Sánchez Meador, A.J., and J. Iniguez. 2019. Increasing trends in high-severity fire in the southwestern USA from 1984-2015. Forest Ecology and Management. 433: 709-719.
  7. Stoddard, M.T., Huffman, D.W., Fulé, P.Z., Crouse, J.E., and A.J. Sánchez Meador. 2018. Forest structure and regeneration 15 years after wildfire in a ponderosa pine and mixed conifer ecotone, Arizona, USA. Fire Ecology. 14:12.
  8. Azpeleta Tarancón, A., Fulé, P.Z., Sánchez Meador, A.J., Kim, Y-S., and T. Padilla. 2018. Spatiotemporal variability of fire regimes in adjacent Native American and public forests, New Mexico, USA. Ecosphere. 9(11):e02492.
  9. Donager, J. Sankey, T.T., Sankey, J., Sánchez Meador, A.J., Springer, A. and J.D. Bailey. 2018. Examining forest structure with terrestrial lidar: suggestions and novel techniques based on comparisons between scanners and forest treatments. Earth and Space Science. 5(11): 753-776.
  10. Whitehair, L., Fulé, P.Z., Sánchez Meador, A.J., Tarancón2, A.A., and Yeon-Su Kim. 2018. Fire regime on a cultural landscape: Navajo Nation. Ecology and Evolution. 2018(8): 9848–9858.
  11. Mockta, T.K., Fulé, P.Z., Sánchez Meador, A.J., Padilla, T., and Yeon-Su Kim. 2018 Sustainability of teepee pole stands on Mescalero Apache Tribal Lands: characteristics and climate change effects. Forest Ecology and Management 530: 250-258.
  12. Burch, B.D. and A.J. Sánchez Meador. In Press. Comparison of forest age estimators using k-tree, fixed-radius, and variable-radius plot sampling. Canadian Journal of Forest Research 48 (8): 942-951.
  13. Springer, J.D., Huffman, D.W., Stoddard M.T., Sánchez Meador, A.J., and Waltz, A.E.M. 2018. Plant community dynamics following hazardous fuel treatments and mega-wildfire in a southwestern warm-dry mixed-conifer forest. Forest Ecology and Management 429: 278-286.
  14. Roccaforte, J.P., Sánchez Meador, A.J., Waltz, A.E.M., Gaylord, M.L., Stoddard, M.T., and D.W. Huffman. 2018. Delayed tree mortality, bark beetle activity, and regeneration dynamics five years following the Wallow Fire, Arizona, USA: assessing trajectories towards resiliency. Forest Ecology and Management 248: 20-26.
  15. Goodrich, B.A., Waring, K.M., Auty, A. and A.J. Sánchez Meador. In Press. Interactions of management and white pine blister rust on Pinus strobiformis regeneration abundance in southwestern United States. Forestry.
  16. Pommerening, A. and A.J. Sánchez Meador. 2018. Tree interactions between myth and reality. Forest Ecology and Management. 424: 164-176.
  17. Kralicek, K., Sánchez Meador, A.J., and L. Rathbun. 2018. Development and assessment of regeneration imputation models for forest in Oregon and Washington. Forest Ecology and Management. 409 667-682.
  18. Huffman, D.W., Crouse, J.E., Sánchez Meador, A.J., Springer, J.D., and M.T. Stoddard. 2018. Restoration benefits of re-entry with resource objective wildfire on a ponderosa pine landscape in northern Arizona, USA. Forest Ecology and Management. 408: 16-24.
  19. Laughlin, D.C., Strahan, R.T., Huffman, D.W., and A.J. Sánchez Meador. 2017. Using trait-based ecology to restore resilient ecosystems: historical conditions and the future of montane forests in western North America. Restoration Ecology. 25 (S2): S135-S146.
  20. Owen, S.M., Sieg, C.H., Sánchez Meador, A.J., Fulé, P.Z., Iniguez, J.M., Baggett, L.S., Fornwalt, P.J., and M.A. Battaglia. 2017. Spatial patterns of ponderosa pine regeneration in high-severity burn patches. Forest Ecology and Management. 405: 134-149.
  21. Rodman, K.C., Sánchez Meador, A.J., Moore, M.M., and D.W. Huffman. 2017. Influence of Forest Type and Abiotic Factors on Variability in Reference Conditions Across Pinus ponderosa-Dominated Forests in the Southwestern United States. Forest Ecology and Management. 404: 316-329.
  22. Sánchez Meador, A.J., Springer J.D., Huffman, D.W., Crouse J.E., and M.A. Bowker. 2017. Ecological restoration treatments improve soil function in frequent-fire forests of the western United States: A systematic review. Restoration Ecology. 25: 497-508
  23. Massey, R., Sankey, T.T., Congalton, R.G., Yadav, K., Thenkabail, P.S., Ozdogan, M., and A.J. Sánchez Meador. 2017. MODIS phenology-derived, multi-year distribution of conterminous U.S. crop types. Remote Sensing of Environment. 198: 490-503.
  24. Bagdon, B.A., Huang, C.-H., Sánchez Meador, A.J., and S. Dewhurst. 2017. Climate change constrains the efficiency frontier when managing forests to reduce fire severity and maximize carbon storage. Ecological Economics 140: 201-214.
  25. Huffman, D.W., Sánchez Meador, A.J., Stoddard, M.T., Crouse, J.E., and J.P. Roccaforte. 2017. Efficacy of resource objective wildfires for restoring ponderosa pine (Pinus ponderosa) forests of northern Arizona. Forest Ecology and Management. 389:395-403. Download
  26. Strahan, R.T., Sanchez Meador, A.J., Huffman, D.W., and D.C. Laughlin. 2016. Shifts in community-level traits and functional diversity in a mixed conifer forest: a legacy of land-use change. Journal of Applied Ecology. 53(6) 1755-1765. Download
  27. Rodman, K.C., Sánchez Meador, A.J. Huffman, D.W. and K.M. Waring. 2016 Reference conditions and historical fine-scale spatial dynamics in a southwestern mixed-conifer forest, Arizona, USA. Forest Science 62(3): 268-280.
  28. Schneider, E., Sánchez Meador, A.J. and W.W. Covington. 2016. Reference conditions and historical changes in an unharvested ponderosa pine stand on sedimentary soil. Restoration Ecology 24: 212-221.
  29. Taylor, M.H., Sánchez Meador, A.J., Kim, Y.S. Rollins, K., and H. Will. 2015. The economics of ecological restoration and hazardous fuel reduction treatments in the ponderosa pine forest ecosystem. Forest Science 61: 988-1008.
  30. Stoddard, M.T., Sánchez Meador, A.J., Fulé, P.Z., and J.E. Korb. 2015. 5-year post-restoration treatment conditions and simulated forest trajectories under alternative climate scenarios in a southwestern warm/dry mixed-conifer forest. Forest Ecology and Management 356: 253-261
  31. Ouzts, J., Kolb, T. Sánchez Meador, A.J. and D.W. Huffman. 2015. Post-fire ponderosa pine plantings in Arizona and New Mexico. Forest Ecology and Management 354: 281-290.
  32. Tuten, M.C., Sánchez Meador, A.J. and P.Z. Fulé. 2015. Ecological restoration and fine-scale structural regulation in Southwestern ponderosa pine forests. Forest Ecology and Management 348: 57–67.
  33. Sánchez Meador, A.J., Waring, K.M., and E.L. Kalies. 2015. Implications of diameter caps on multiple forest resource responses in the context of 4FRI: Results from the Forest Vegetation Simulator. Journal of Forestry 113(2): 219–230.
  34. Waltz, A.E.M., Stoddard, M.T., Kalies, E.L., Springer, J.D., Huffman, D.W. and A.J. Sánchez Meador. 2014. Effectiveness of fuel reduction treatments: assessing metrics of forest resiliency and wildfire severity after the Wallow Fire, AZ. Forest Ecology and Management 334(15): 43-52.
  35. S. Nepal, B.R. Ojha, A.J. Sánchez Meador, S.P. Gaire, and C. Shilpakar. 2014. Effect of gamma rays on germination and photosynthetic pigments of maize (Zea mays L.) inbreds. International Journal of Research. 1(5): 511-545.
  36. Azpeleta Tarancón, A., P.Z. Fulé, K.L. Shive, C.H. Sieg, A.J. Sánchez Meador, and B. Strom. 2014. Simulating post-wildfire forest trajectories under alternative climate and management scenarios. Ecological Applications. 24(7): 1626–1637.
  37. Reynolds, R.T., A.J. Sánchez Meador, J.A., Youtz, T. Nicolet, M.S. Matonis, P.L. Jackson, D.G. DeLorenzo, and A.D. Graves. 2013. Restoring composition and structure in Southwestern frequent-fire forests: A science-based framework for improving ecosystem resiliency. Gen. Tech. Rep. RMRS-GTR-310. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 76 p.
  38. Gedeon, C.I., L.C. Drickamer, and A.J. Sánchez Meador. 2012. Importance of burrow-entrance mounds of Gunnison’s prairie dogs (Cynomys gunnisoni) for vigilance and mixing of soil. Southwestern Naturalist. 57(1): 100-104.
  39. Sánchez Meador, A.J., P.F. Parysow, and M.M. Moore. 2011. A new method for delineating tree patches and assessing spatial reference conditions of ponderosa pine forests in northern Arizona. Restoration Ecology. 19(4): 490-499.
  40. Sánchez Meador, A.J. and M.M. Moore. 2010. Lessons from long-term studies of harvest methods in southwestern ponderosa pine-Gambel oak forests on the Fort Valley Experimental Forest, Arizona, U.S.A. Forest Ecology and Management. 260(2): 193-206.
  41. Sánchez Meador, A.J., P.F. Parysow and M.M. Moore. 2010. Historical stem-mapped permanent plots increase precision of reconstructed reference data in ponderosa pine forests of northern Arizona. Restoration Ecology. 18(2): 224-234.
  42. Sánchez Meador, A.J., M.M. Moore, J.D. Bakker, and P.F. Parysow. 2009. 108 years of change in spatial pattern following selective harvest of a ponderosa pine stand in northern Arizona, USA. Journal of Vegetation Science. 20(1): 79-90.
  43. Bakker, J.D., A.J. Sánchez Meador, P.Z. Fulé, D.W. Huffman, and M.M. Moore. 2008 “Growing Trees Backwards”: Description of a Stand Reconstruction Model. Pp 136-142 in Olberding, S.D., and M.M. Moore (tech coords). 2008. Fort Valley Experimental Forest – A Century of Research 1908-2008. Conference Proceedings; August 7-9, 2008; Flagstaff, AZ. Proceedings RMRS-P-53. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 408 p.
  44. De Blois, B.P., A.J. Finkral, A.J. Sánchez Meador, and M.M. Moore. 2008. Early Thinning Experiments Established by the Fort Valley Experimental Forest. Pp 197-203 in Olberding, S.D., and M.M. Moore (tech coords). 2008. Fort Valley Experimental Forest – A Century of Research 1908-2008. Conference Proceedings; August 7-9, 2008; Flagstaff, AZ. Proceedings RMRS-P-53. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 408 p.
  45. Dyer, J.H., A.J. Sánchez Meador, M.M. Moore, and J.D. Bakker. 2008. Forest Structure and Tree Recruitment Changes on a Permanent Historical Cinder Hills Plot Over a 130-Year Period. Pp 214-221 in Olberding, S.D., and M.M. Moore (tech coords). 2008. Fort Valley Experimental Forest—A Century of Research 1908-2008. Conference Proceedings; August 7-9, 2008; Flagstaff, AZ. Proceedings RMRS-P-53. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 408 p.
  46. Sánchez Meador, A.J. and M.M. Moore. 2008. 93 Years of Stand Density and Land-Use Legacy Research at the Coulter Ranch Study Site. Pp 321-330 in Olberding, S.D., and M.M. Moore (tech coords). 2008. Fort Valley Experimental Forest – A Century of Research 1908-2008. Conference Proceedings; August 7-9, 2008; Flagstaff, AZ. Proceedings RMRS-P-53. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 408 p.
  47. Sánchez Meador, A.J. and S.D. Olberding. 2008. Fort Valley’s Early Scientists: A Legacy of Distinction. Pp 331-338 in Olberding, S.D., and M.M. Moore (tech coords). 2008. Fort Valley Experimental Forest – A Century of Research 1908-2008. Conference Proceedings; August 7-9, 2008; Flagstaff, AZ. Proceedings RMRS-P-53. Fort Collins, CO: U.S. Dept. of Agriculture, Forest Service, Rocky Mountain Research Station. 408 p.
  48. Abella, S.R., W.W. Covington, P.Z. Fulé, L.B. Lentile, A.J. Sánchez Meador, and P. Morgan. 2007. Past, Present, and Future Old Growth in Frequent-Fire Conifer Forests of the Western United States. Ecology and Society 12(2): 16.


Media Coverage





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i = 0;

while (!deck.isInOrder()) {
    print 'Iteration ' + i;

print 'It took ' + i + ' iterations to sort the deck.';



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Name Description Price
Item One Ante turpis integer aliquet porttitor. 29.99
Item Two Vis ac commodo adipiscing arcu aliquet. 19.99
Item Three Morbi faucibus arcu accumsan lorem. 29.99
Item Four Vitae integer tempus condimentum. 19.99
Item Five Ante turpis integer aliquet porttitor. 29.99


Name Description Price
Item One Ante turpis integer aliquet porttitor. 29.99
Item Two Vis ac commodo adipiscing arcu aliquet. 19.99
Item Three Morbi faucibus arcu accumsan lorem. 29.99
Item Four Vitae integer tempus condimentum. 19.99
Item Five Ante turpis integer aliquet porttitor. 29.99


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