NECROMASS PRODUCTION: STUDIES IN UNDISTURBED AND LOGGED AMAZON FORESTS

Necromass stocks account for up to 20% of carbon stored in tropical forests and have been estimated to be 14–19% of the annual aboveground carbon flux. Both stocks and fluxes of necromass are infrequently measured. In this study, we directly measured the production of fallen coarse necromass (≥2 cm diameter) during 4.5 years using repeated surveys in undisturbed forest areas and in forests subjected to reduced‐impact logging at the Tapajos National Forest, Belterra, Brazil (3.08° S, 54.94° W). We also measured fallen coarse necromass and standing dead stocks at two times during our study. The mean (SE) annual flux into the fallen coarse necromass pool in undisturbed forest of 6.7 (0.8) Mg·ha−1·yr−1 was not significantly different from the flux under a reduced‐impact logging of 8.5 (1.3) Mg·ha−1·yr−1. With the assumption of steady state, the instantaneous decomposition constants for fallen necromass in undisturbed forests were 0.12 yr−1 for large, 0.33 yr−1 for medium, and 0.47 yr−1 for small size classes. The mass weighted decomposition constant was 0.15 yr−1 for all fallen coarse necromass. Standing dead wood had a residence time of 4.2 years, and ∼0.9 Mg·ha−1·yr−1 of this pool was respired annually to the atmosphere through decomposition. Coarse necromass decomposition at our study site accounted for 12% of total carbon re‐mineralization, and total aboveground coarse necromass was 14% of the aboveground biomass. Use of mortality rates to calculate production of coarse necromass leads to an underestimation of coarse necromass production by 45%, suggesting that nonlethal disturbance such as branch fall contributes significantly to this flux. Coarse necromass production is an important component of the tropical forest carbon cycle that has been neglected in most previous studies or erroneously estimated

Tropical forest structure: Ground measurements of coarse necromass and satellite observations of crown geometry

Forests are structurally diverse, but these structures derive from the same processes of disturbance and growth. Understanding forest structure can help unlock the history, function, and future of a forested ecosystem. Components of forest structure include tree size distributions, foliage distributions and variation in canopy density, and coarse woody debris (coarse necromass). Tropical rainforests are structurally the most complex of all ecosystems. In addition to having high biological diversity, Amazon forests are marked by complex vegetation dynamics and a diverse forest stand structures, which play an important role in the interactions of water and carbon between the biosphere and atmosphere. Two aspects of forest structure in Amazonia are examined in this thesis, canopy geometry and coarse necromass. A crown delineation algorithm was developed that uses high resolution satellite image data. This algorithm was applied to two forests with field based data on forest structure and then applied at seven locations across the Amazon basin. The algorithm provided forest structure based on crown geometry across vast areas the where field based studies would be prohibitive due to cost and time. Coarse necromass dynamics were studied through a combination of field work using novel techniques that measured necromass density, volume, and calculated mass for fallen and standing coarse necromass stocks at two tropical forested sites. For both sites, the effect that reduced impact logging (RIL) had on coarse necromass pools was found to generate 50% more coarse necromass. Density and void space estimates were found to not be significantly different between sites. Standing dead and fallen coarse necromass were found to be proportionally related across sites and forest types. The production of necromass at one site, Tapajos, was examined over a 4.5 year period, providing an estimate of necromass cycling. RIL and undisturbed forests were found to similar coarse necromass production. Mortality rates used to estimate coarse necromass production tend to underestimate the amount by about 55%. Finally a review of current literature dealing with coarse necromass dynamics in tropical forests was conducted

Geospatial modeling approach to monument construction using Michigan from A.D. 1000–1600 as a case study

Building monuments was one way that past societies reconfigured their landscapes in response to shifting social and ecological factors. Understanding the connections between those factors and monument construction is critical, especially when multiple types of monuments were constructed across the same landscape. Geospatial technologies enable past cultural activities and environmental variables to be examined together at large scales. Many geospatial modeling approaches, however, are not designed for presence-only (occurrence) data, which can be limiting given that many archaeological site records are presence only. We use maximum entropy modeling (MaxEnt), which works with presence-only data, to predict the distribution of monuments across large landscapes, and we analyze MaxEnt output to quantify the contributions of spatioenvironmental variables to predicted distributions. We apply our approach to co-occurring Late Precontact (ca. A.D. 1000–1600) monuments in Michigan: (i) mounds and (ii) earthwork enclosures. Many of these features have been destroyed by modern development, and therefore, we conducted archival research to develop our monument occurrence database. We modeled each monument type separately using the same input variables. Analyzing variable contribution to MaxEnt output, we show that mound and enclosure landscape suitability was driven by contrasting variables. Proximity to inland lakes was key to mound placement, and proximity to rivers was key to sacred enclosures. This juxtaposition suggests that mounds met local needs for resource procurement success, whereas enclosures filled broader regional needs for intergroup exchange and shared ritual. Our study shows how MaxEnt can be used to develop sophisticated models of past cultural processes, including monument building, with imperfect, limited, presence-only data

Estimating Tropical Forest Structure Using a Terrestrial Lidar

Forest structure comprises numerous quantifiable biometric components and characteristics, which include tree geometry and stand architecture. These structural components are important in the understanding of the past and future trajectories of these biomes. Tropical forests are often considered the most structurally complex and yet least understood of forested ecosystems. New technologies have provided novel avenues for quantifying biometric properties of forested ecosystems, one of which is LIght Detection And Ranging (lidar). This sensor can be deployed on satellite, aircraft, unmanned aerial vehicles, and terrestrial platforms. In this study we examined the efficacy of a terrestrial lidar scanner (TLS) system in a tropical forest to estimate forest structure. Our study was conducted in January 2012 at La Selva, Costa Rica at twenty locations in a predominantly undisturbed forest. At these locations we collected field measured biometric attributes using a variable plot design. We also collected TLS data from the center of each plot. Using this data we developed relative vegetation profiles (RVPs) and calculated a series of parameters including entropy, Fast Fourier Transform (FFT), number of layers and plant area index to develop statistical relationships with field data.We developed statistical models using a series of multiple linear regressions, all of which converged on significant relationships with the strongest relationship being for mean crown depth (r2 = 0.88, p \u3c 0.001, RMSE = 1.04 m). Tree density was found to have the poorest significant relationship (r2 = 0.50, p \u3c 0.01, RMSE = 153.28 n ha-1). We found a significant relationship between basal area and lidar metrics (r2 = 0.75, p \u3c 0.001, RMSE = 3.76 number ha-1). Parameters selected in our models varied, thus indicating the potential relevance of multiple features in canopy profiles and geometry that are related to field-measured structure. Models for biomass estimation included structural canopy variables in addition to height metrics. Our work indicates that vegetation profiles from TLS data can provide useful information on forest structure

Validation of Satellite Rainfall Products for Western Uganda.

Central equatorial Africa is deficient in long-term, ground-based measurements of rainfall; therefore, the aim of this study is to assess the accuracy of three high-resolution, satellite-based rainfall products in western Uganda for the 2001–10 period. The three products are African Rainfall Climatology, version 2 (ARC2); African Rainfall Estimation Algorithm, version 2 (RFE2); and 3B42 from the Tropical Rainfall Measuring Mission, version 7 (i.e., 3B42v7). Daily rainfall totals from six gauges were used to assess the accuracy of satellite-based rainfall estimates of rainfall days, daily rainfall totals, 10-day rainfall totals, monthly rainfall totals, and seasonal rainfall totals. The northern stations had a mean annual rainfall total of 1390 mm, while the southern stations had a mean annual rainfall total of 900 mm. 3B42v7 was the only product that did not underestimate boreal-summer rainfall at the northern stations, which had ~3 times as much rainfall during boreal summer than did the southern stations. The three products tended to overestimate rainfall days at all stations and were borderline satisfactory at identifying rainfall days at the northern stations; the products did not perform satisfactorily at the southern stations. At the northern stations, 3B42v7 performed satisfactorily at estimating monthly and seasonal rainfall totals, ARC2 was only satisfactory at estimating seasonal rainfall totals, and RFE2 did not perform satisfactorily at any time step. The satellite products performed worst at the two stations located in rain shadows, and 3B42v7 had substantial overestimates at those stations

Anthropogenic Habitats Facilitate Dispersal of an Early Successional Obligate: Implications for Restoration of an Endangered Ecosystem

Landscape modification and habitat fragmentation disrupt the connectivity of natural landscapes, with major consequences for biodiversity. Species that require patchily distributed habitats, such as those that specialize on early successional ecosystems, must disperse through a landscape matrix with unsuitable habitat types. We evaluated landscape effects on dispersal of an early successional obligate, the New England cottontail (Sylvilagus transitionalis). Using a landscape genetics approach, we identified barriers and facilitators of gene flow and connectivity corridors for a population of cottontails in the northeastern United States. We modeled dispersal in relation to landscape structure and composition and tested hypotheses about the influence of habitat fragmentation on gene flow. Anthropogenic and natural shrubland habitats facilitated gene flow, while the remainder of the matrix, particularly development and forest, impeded gene flow. The relative influence of matrix habitats differed between study areas in relation to a fragmentation gradient. Barrier features had higher explanatory power in the more fragmented site, while facilitating features were important in the less fragmented site. Landscape models that included a simultaneous barrier and facilitating effect of roads had higher explanatory power than models that considered either effect separately, supporting the hypothesis that roads act as both barriers and facilitators at all spatial scales. The inclusion of LiDAR-identified shrubland habitat improved the fit of our facilitator models. Corridor analyses using circuit and least cost path approaches revealed the importance of anthropogenic, linear features for restoring connectivity between the study areas. In fragmented landscapes, human-modified habitats may enhance functional connectivity by providing suitable dispersal conduits for early successional specialists

Population pressure and global markets drive a decade of forest cover change in Africa\u27s Albertine Rift

Africa\u27s Albertine Rift region faces a juxtaposition of rapid human population growth and protected areas, making it one of the world\u27s most vulnerable biodiversity hotspots. Using satellite-derived estimates of forest cover change, we examined national socioeconomic, demographic, agricultural production, and local demographic and geographic variables, to assess multilevel forces driving local forest cover loss and gain outside protected areas during the first decade of this century. Because the processes that drive forest cover loss and gain are expected to be different, and both are of interest, we constructed models of significant change in each direction. Although rates of forest cover change varied by country, national population change was the strongest driver of forest loss for all countries – with a population doubling predicted to cause 2.06% annual cover loss, while doubling tea production predicted to cause 1.90%. The rate of forest cover gain was associated positively with increased production of the local staple crop cassava, but negatively with local population density and meat production, suggesting production drivers at multiple levels affect reforestation. We found a small but significant decrease in loss rate as distance from protected areas increased, supporting studies suggesting higher rates of landscape change near protected areas. While local population density mitigated the rate of forest cover gain, loss was also correlated with lower local population density, an apparent paradox, but consistent with findings that larger scale forces outweigh local drivers of deforestation. This implicates demographic and market forces at national and international scales as critical drivers of change, calling into question the necessary scales of forest protection policy in this biodiversity hotspot. Using a satellite derived estimate of forest cover change for both loss and gain added a dynamic component to more traditionally static and unidirectional studies, significantly improving our understanding of landscape processes and drivers at work

Evaluating multiple causes of persistent low microwave backscatter from Amazon forests after the 2005 drought

Amazonia has experienced large-scale regional droughts that affect forest productivity and biomass stocks. Space-borne remote sensing provides basin-wide data on impacts of meteorological anomalies, an important complement to relatively limited ground observations across the Amazon’s vast and remote humid tropical forests. Morning overpass QuikScat Ku-band microwave backscatter from the forest canopy was anomalously low during the 2005 drought, relative to the full instrument record of 1999–2009, and low morning backscatter persisted for 2006–2009, after which the instrument failed. The persistent low backscatter has been suggested to be indicative of increased forest vulnerability to future drought. To better ascribe the cause of the low post-drought backscatter, we analyzed multiyear, gridded remote sensing data sets of precipitation, land surface temperature, forest cover and forest cover loss, and microwave backscatter over the 2005 drought region in the southwestern Amazon Basin (4°-12°S, 66°-76°W) and in adjacent 8°x10° regions to the north and east. We found moderate to weak correlations with the spatial distribution of persistent low backscatter for variables related to three groups of forest impacts: the 2005 drought itself, loss of forest cover, and warmer and drier dry seasons in the post-drought vs. the pre-drought years. However, these variables explained only about one quarter of the variability in depressed backscatter across the southwestern drought region. Our findings indicate that drought impact is a complex phenomenon and that better understanding can only come from more extensive ground data and/or analysis of frequent, spatially-comprehensive, high-resolution data or imagery before and after droughts