David L.R. Affleck

A review of the challenges and opportunities in estimating above ground forest biomass using tree-level models.

Accurate biomass measurements and analyses are critical components in quantifying carbon stocks and sequestration rates, assessing potential impacts due to climate change, locating bio-energy processing plants, and mapping and planning fuel treatments. To this end, biomass equations will remain a key component of future carbon measurements and estimation. As researchers in biomass and carbon estimation, we review the present scenario of aboveground biomass estimation, focusing particularly on estimation using tree-level models and identify some cautionary points that we believe will improve the accuracy of biomass and carbon estimates to meet societal needs. In addition, we discuss the critical challenges in developing or calibrating tree biomass models and opportunities for improved biomass. Some of the opportunities to improve biomass estimate include integration of taper and other attributes and combining different data sources. Biomass estimation is a complex process, when possible, we should make use of already available resources such as wood density and forest inventory databases. Combining different data-sets for model development and using independent data-sets for model verification will offer opportunities to improve biomass estimation. Focus should also be made on belowground biomass estimation to accurately estimate the full forest contribution to carbon sequestration. In addition, we suggest developing comprehensive biomass estimation methods that account for differences in site and stand density and improve forest biomass modeling and validation at a range of spatial scales.

Improving ecosystem productivity modeling through spatially explicit estimation of optimal light use efficiency

A common assumption of remote sensing-based light use efficiency (LUE) models for estimating vegetation gross primary productivity (GPP) is that plants in a biome matrix operate at their photosynthetic capacity under optimal climatic conditions. A prescribed constant biome maximum light use efficiency parameter (LUE max ) defines the maximum photosynthetic carbon conversion rate under these conditions and is a large source of model uncertainty. Here we used tower eddy covariance measurement-based carbon (CO 2 ) fluxes for spatial estimation of optimal LUE (LUE opt ) across North America. LUE opt was estimated at 62 Flux Network sites using tower daily carbon fluxes and meteorology, and satellite observed fractional photosynthetically active radiation from the Moderate Resolution Imaging Spectroradiometer. Ageostatistical model was fitted to 45 flux tower-derived LUE opt data points using independent geospatial environmental variables, including global plant traits, soil moisture, terrain aspect, land cover type, and percent tree cover, and validated at 17 independent tower sites. Estimated LUE opt shows large spatial variability within and among different land cover classes indicated from the sparse tower network. Leaf nitrogen content and soil moisture regime are major factors explaining LUE opt patterns. GPP derived from estimated LUE opt shows significant correlation improvement against tower GPP records (R2 = 76.9%; mean root-mean-square error (RMSE) = 257gCm-2yr-1), relative to alternative GPP estimates derived using biome-specific LUE max constants (R2 = 34.0%; RMSE = 439gCm-2yr-1). GPP determined from the LUE opt map also explains a 49.4% greater proportion of tower GPP variability at the independent validation sites and shows promise for improving understanding of LUE patterns and environmental controls and enhancing regional GPP monitoring from satellite remote sensing.

Design unbiased estimation in line intersect sampling using segmented transects

In many applications of line intersect sampling, transects consist of multiple, connected segments in a prescribed configuration. The relationship between the transect configuration and the selection probability of a population element is illustrated and a consistent sampling protocol, applicable to populations composed of arbitrarily shaped elements, is proposed. It is shown that this protocol obviates the arbitrary practice of treating multiple intersections of a single particle as independent probabilistic events and preserves the design-unbiasedness of Kaiser’s (1983, Biometrics39, 965–976) conditional and unconditional estimators, suitably generalized to segmented transect designs. The relative efficiency and utility of segmented transect designs are also discussed from a fixed population perspective.

A line intersect distance sampling strategy for downed wood inventory

Perpendicular distance sampling (PDS) has emerged as a compelling alternative to line intersect sampling (LIS) for the inventory of forest fuels and other downed woody materials (DWM), particularly where the aggregate volume of DWM is of primary interest. This article develops a selection protocol and design-unbiased estimators for a new probability proportional-to-volume sampling strategy, termed line intersect distance sampling (LIDS). LIDS combines the distance sampling protocol of PDS with the transect sampling protocol of LIS and provides unbiased estimates of aggregate DWM volume from counts of selected logs or log fragments. Simulations indicate that LIDS along multidirectional (e.g., Y-shaped) transects should perform similarly to PDS in terms of sampling error; however, it remains unclear how LIDS and PDS compare with LIS, especially when interest is attached to multiple DWM population parameters. It is argued that LIDS will be most useful in reducing implementation errors, particularly detection...

Assessing the performance of sampling designs for measuring the abundance of understory plants

Accurate estimation of responses of understory plants to disturbance is essential for understanding the efficacy of management activities. However, the ability to assess changes in the abundance of plants may be hampered by inappropriate sampling methodologies. Conventional methods for sampling understory plants may be precise for common species but may fail to adequately characterize abundance of less common species. We tested conventional (modified Whittaker plots and Daubenmire and point-line intercept transects) and novel (strip adaptive cluster sampling [SACS]) approaches to sampling understory plants to determine their efficacy for quantifying abundance on control and thinned-and-burned treatment units in Pinus ponderosa forests in western Montana, USA. For species grouped by growth-form and for common species, all three conventional designs were capable of estimating cover with a 50% relative margin of error with reasonable sample sizes (3-36 replicates for growth-form groups; 8-14 replicates for common species); however, increasing precision to 25% relative margin of error required sample sizes that may be infeasible (11-143 replicates for growth-form groups; 28-54 replicates for common species). All three conventional designs required enormous sample sizes to estimate cover of nonnative species as a group (29-60 replicates) and of individual less common species (62-118 replicates), even with a 50% relative margin of error. SACS was the only design that efficiently sampled less common species, requiring only 6-11% as many replicates relative to conventional designs. Conventional designs may not be effective for estimating abundance of the majority of forest understory plants, which are typically patchily distributed with low abundance, or of newly establishing nonnative plants. Novel methods such as SACS should be considered in investigations when cover of these species is of concern.

Remote Sensing Derived Fire Frequency, Soil Moisture and Ecosystem Productivity Explain Regional Movements in Emu over Australia.

Species distribution modeling has been widely used in studying habitat relationships and for conservation purposes. However, neglecting ecological knowledge about species, e.g. their seasonal movements, and ignoring the proper environmental factors that can explain key elements for species survival (shelter, food and water) increase model uncertainty. This study exemplifies how these ecological gaps in species distribution modeling can be addressed by modeling the distribution of the emu (Dromaius novaehollandiae) in Australia. Emus cover a large area during the austral winter. However, their habitat shrinks during the summer months. We show evidence of emu summer habitat shrinkage due to higher fire frequency, and low water and food availability in northern regions. Our findings indicate that emus prefer areas with higher vegetation productivity and low fire recurrence, while their distribution is linked to an optimal intermediate (~0.12 m3 m-3) soil moisture range. We propose that the application of three geospatial data products derived from satellite remote sensing, namely fire frequency, ecosystem productivity, and soil water content, provides an effective representation of emu general habitat requirements, and substantially improves species distribution modeling and representation of the species’ ecological habitat niche across Australia.

On the efficiency of line intersect distance sampling

Sampling strategies commonly used for coarse woody debris (CWD) inventories, including line intersect sampling (LIS), typically require large sample sizes to estimate aggregate volume with reasonable precision. Line intersect distance sampling (LIDS) is a recently developed strategy based on a probability proportional-to-volume design and a linear sampling unit. In principle, the design augments the precision of volume estimators by increasing the intensity with which bulkier particles are sampled, while the transect-based protocol facilitates the search for qualifying particles. This study reports on the relative performances of LIDS and LIS in seven stands in Montana, USA. Particles selected by LIDS were consistently less numerous but larger in cross section than those selected at the same locations by LIS. In timed field trials, LIDS required more time than LIS, but CWD volume estimates from LIDS were generally more precise, more than offsetting the time differential. Conversely, aggregate length and a...

Edge effects in line intersect sampling with segmented transects

Transects consisting of multiple, connected segments with a prescribed configuration are commonly used in ecological applications of line intersect sampling. The transect configuration has implications for the probability with which population elements are selected and for how the selection probabilities can be modified by the boundary of the tract being sampled. As such, the transect configuration also affects the performance of methods designed to eliminate edge-effect bias. We show that the reflection method solves the edge-problem for designs that use symmetric radial transects (e.g., straight-line and X-shaped transects centered at the sample point). This method also applies to designs that use asymmetric radial transects, provided the orientation is selected uniformly at random. Asymmetric radial transects include straight lines emanating from the sample point, and L- and Y-shaped transects. The reflection method does not apply to designs where polygonal transects (e.g., triangular, square, and hexagonal transects) are used, or where the orientations of asymmetric radial transects are fixed. We provide a new method that eliminates edge-effect bias for designs that use asymmetric radial transects with fixed orientation, but a workable solution for polygonal transects remains elusive.

Future global productivity will be affected by plant trait response to climate

Plant traits are both responsive to local climate and strong predictors of primary productivity. We hypothesized that future climate change might promote a shift in global plant traits resulting in changes in Gross Primary Productivity (GPP). We characterized the relationship between key plant traits, namely Specific Leaf Area (SLA), height, and seed mass, and local climate and primary productivity. We found that by 2070, tropical and arid ecosystems will be more suitable for plants with relatively lower canopy height, SLA and seed mass, while far northern latitudes will favor woody and taller plants than at present. Using a network of tower eddy covariance CO2 flux measurements and the extrapolated plant trait maps, we estimated the global distribution of annual GPP under current and projected future plant community distribution. We predict that annual GPP in northern biomes (≥45 °N) will increase by 31% (+8.1 ± 0.5 Pg C), but this will be offset by a 17.9% GPP decline in the tropics (−11.8 ± 0.84 Pg C). These findings suggest that regional climate changes will affect plant trait distributions, which may in turn affect global productivity patterns.

Development of Height-Volume Relationships in Second Growth Abies grandis for Use with Aerial LiDAR

Abstract Following typical forest inventory protocols, individual tree volume estimates are generally derived via diameter-at-breast-height (DBH)-based allometry. Although effective, measurement of DBH is time consuming and potentially a costly element in forest inventories. The capacity of airborne light detection and ranging (LiDAR) to provide individual tree-level information poses options for estimating tree-level attributes to enhance the information content of forest inventories. LiDAR provides excellent height measurements and, given the physiologic scaling connection of plant height and volume, using individual tree height-volume relationships could overcome errors associated with the intermediate step of inferring DBH from LiDAR. In this study, 60 Abies grandis (grand fir: 6 cm–64 cm DBH) were destructively sampled to assess stem volume across the Intermountain West in order to develop individual tree height-to-stem volume relationships. Results show DBH (r2 > 0.98) and height (r2 > 0.94) are significantly (p < 0.001) related to stem volume via power relationships. LiDAR-derived heights provided a 12 % RMSE improvement in accuracy of individual tree volume over LiDAR-regressed DBH estimates. Comparing height-based estimates with an existing regional allometry by mapping stem volume in a grand fir-dominated stand yielded a 6.3 % difference in total volume. This study demonstrates LiDARs potential to estimate individual stem volume at forest management scales, utilizing height-volume relationships.