Pinus taeda L. dynamics, migration patterns and genetic structure were investigated over geological time scale (the past 21,000 years), historical time scale (the past 500 years) and recent time scale (the past 50 years ago) using multi-source data and an interdisciplinary approach. Population genetics, microsatellite markers, DNA fingerprinting, fossil records, geological history, historical records, aerial photographs, soil maps, weather data, remote sensing and geographic information systems (GIS) were used to assess the dynamics of P. taeda populations especially for the Lost Pines (LP), a disjunct population at the westernmost edge of the species range. Pinus taeda populations east and west of the Mississippi River Valley are genetically differentiated. Eastern populations had higher allelic diversity and diagnostic alleles than western populations. Gene flow estimates are high. Allelic diversity and diagnostic alleles patterns are attributed to the prevailing wind direction. Differentiation east and west of the MRV was attributed to separation to two refugia during the Pleistocene. The Lost Pines population is believed to have undergone one or more bottleneck events with loss of rare alleles. Despite the bottleneck, allelic richness was similar for the LP and the control population from the Western Gulf (WG) population. Population size contraction of the LP was attributed to climate change in central Texas over geological time scale. The natural origin of the Lost Pines was investigated. Multivariate and clustering techniques and assignment and exclusion methods using DNA markers show that the LP population shared ancestry with the WG populations with no evidence for admixture from other sources. Historical records parallel this conclusion. With the absence of logging within Bastrop and Buescher State Parks, P. taeda area and patch size increased from 1949 to 1995. Thirty six percent of the pine patches observed in 1949 had disappeared by 1995 by merging. Landscape pattern analysis shows significant dynamics. The distribution of P. taeda in Bastrop County was associated with sandy light topsoils, clayey heavy sub-soils and high permeable soils. Pinus taeda grow on various soil types as well. Growing on these soils under current climatic conditions may compensate for the precipitation regime in this area.

The Canary Island pine, Pinus canariensis, is an endemic tree that forms one of the main forest ecosystems within the archipelago, and whose distribution has been reduced in the last five centuries by clear cutting for the extraction of timber and tar. It was in the XXth century that exploitation declined and reforestation programs were brought forward for the restoration of an ecosystem that harbours a number of endangered endemic species of plants and animals. In addition to reforestation efforts, an understanding of population genetic processes is also necessary for the successful conservation management of the Canarian pine forest, particularly in light of gathering evidence for local adaptation.<br /><br />In this thesis historical and contemporary gene flow within P. canariensis was studied with nuclear and chloroplast microsatellite markers. High immigration rates (0.68–0.75) were estimated as expected for an outcrossing windpollinated tree. Nevertheless, significant population differentiation (theta = 0.019, RST = 0.044) was detectable for sites separated by only a few kilometres. Within the context of reforestation programs the high levels of gene flow detected would appear to have a positive effect on reforested stands by facilitating the immigration of local alleles from natural stands into potentially genetically depauperate first generation gene pools of reforested stands.<br /><br />Historical population growth was revealed with chloroplast microsatellites for most populations of P. canariensis. Population expansions for the pine parasite weevil Brachyderes rugatus were also detected, broadly coinciding with the population expansions within the Canary Island pine forests. Given the estimated times of expansion, these population demographic increases would seem likely related to the process of colonisation of newly emerged islands or local patches after volcanic disturbance. Detection and dating of these expansions from chloroplast microsatellites was, to some degree, negatively affected by homoplasy (i.e. parallel and back mutations).<br /><br />Coalescent simulations of the evolution of chloroplast microsatellites were applied to study the effects of homoplasy in the statistical analysis of population structuring. Measures of genetic diversity based on number of haplotypes and genetic distances were differently affected. Genetic distances were underestimated but were proportional to the actual value. These effects help to explain the lower performance of statistical analyses for the detection and dating of population expansions. Further research on the effects of homoplasy in the analysis of population differentiation using chloroplast microsatellites is essential.

This research examined the fire history of ponderosa pine (Pinus ponderosa Douglas ex P. Lawson & C. Lawson) forests in northwestern New Mexico. The study area included three sites in the Zuni Mountains of Cibola National Forest and one site along the boundary of El Malpais National Monument. I crossdated over 800 fire scars on 75 samples to reconstruct spatial and temporal characteristics of historic wildfire regimes. The Weibull Median Interval, Weibull Modal Interval, and Mean Fire Interval ranged from five to eight years across all sites and percent-scarred classes (all fires, 10% scarred, and 25% scarred) and indicated that low-severity wildfires occurred frequently in the study area during the period 1700 to 1880. Wildfires were historically driven by climatic variability. Superposed Epoch Analyses revealed that wetter conditions typically occurred one to three years prior to a fire event and were followed by drought during the fire year. No relationship was found between the Pacific Decadal Oscillation (PDO) and wildfire occurrence. These findings implied that shorter-term fluxes between wet and dry conditions, rather than longer-term climatic variability, were historically most conducive to fire occurrence. Fire frequency decreased suddenly in the late 19th century across the study area, and results indicated that fire has been absent at all sites since the 1920s. Anthropogenic disturbances including livestock grazing, timber harvesting, and fire suppression likely explain observed differences between historic and contemporary wildfire regimes in the Zuni Mountains.This research has important implications for forest management. In ponderosa pine forests of the southwestern United States, land managers often aim to restore historic ecological conditions. The reintroduction of a frequent, low-severity wildfire regime might restore some ecological patterns and processes, but given the strong legacy of human disturbances and the influences of human-induced climate change, a complete return to historic conditions may be neither possible nor desired.

Thesis (doctoral)--Universität, Göttingen, 2000.

Diss. (sammanfattning) Alnarp : Sveriges lantbruksuniversitet, 2009. / Härtill 5 uppsatser.

Background: The objective of this study was to verify the effect that plot locational errors, termed plot co-registration errors, have on the strength of regression between LiDAR canopy metrics and the measured total standing volume (TSV) of plots in a Pinus radiata forest. Methods: A 737 hectare plantation of mature Pinus radiata located in Northern Hawkes Bay was selected for the study. This forest had been measured in a pre-harvest inventory and had aerial LiDAR assessment. The location of plots was verified using a survey-grade GPS. Least square linear regression models were developed to predict TSV from LiDAR canopy metrics for a sample of 204 plots. The regression strength, accuracy and bias was compared for models developed using either the actual (verified) or the incorrect (intended) locations for these plots. The change to the LiDAR canopy metrics after the plot co-registration errors was also established. Results: The plot co-registration error in the sample ranged from 0.7 m to 70.3 m, with an average linear spatial error of 10.6 m. The plot co-registration errors substantially reduced the strength of regression between LiDAR canopy metrics and TSV, as the model developed from the actual plot locations had an R2 of 44%, while the model developed from the incorrect plot locations had an R2 of 19%. The greatest reductions in model strength occurred when there was less than a 60% overlap between the plots defined by correct and incorrect locations. Higher plot co-registration errors also caused significant changes to the height and density LiDAR canopy metrics that were used in the regression models. The lower percentile elevation LiDAR metrics were more sensitive to plot co- registration errors, compared to higher percentile metrics. Conclusion: Plot co-registration errors have a significant effect on the strength of regressions formed between TSV and LiDAR canopy metrics. This indicates that accurate measurements of plot locations are necessary to fully utilise LiDAR for inventory purposes in forests of Pinus radiata.

This thesis investigates the past extent, structure and dynamics of Mar Lodge Caledonian pine (Pinus sylvestris L.) woodland, near Braemar in the south-eastern Cairngorms. The geographical extent and Holocene history of the Scottish pinewoods are generally understood, but the Mar pinewoods are relatively obscure. This thesis is concerned with the Holocene history of the Mar pinewoods; the timing and reasons for first appearance, the increase in abundance of Pinus to become a dominant species, the spatial extent of the woodland and its structure and form. The investigation includes changes to the woodland assemblage during its fragmentation and disappearance in the late Holocene and possible influences on the woodland from people living in the area. At the heart of this thesis is an understanding of the factors underpinning the ecology of Pinus and the response of the species to competition with other taxa. This is related to the spatial and temporal changes in climate that contribute to the location and development of Pinus in Scotland and Mar Lodge. Areas comparable to Mar Lodge are defined as ‘core areas’ of pine woodland rather than ‘native areas’. This avoids the necessity of considering every short period of colonisation by Pinus in areas distal to large populations. The location, extent, form and behaviour of woodland according to macro sub-fossils and micro sub-fossils is used to define core woodland as those with a long presence of Pinus, often continuing to the present day. Areas with a long history but no extant population are regarded as peripheral areas. This thesis consists of extensive palaeoecological investigations of three peat sequences: from within extant pine woodland (Doire Bhraghad), from just beyond its edge (White Bridge) and from peat with sub-fossil pine stumps located 10km west of the modern range of Pinus (Geldie Lodge). A range of techniques, including loss of mass on ignition and colorimetric light transmission analysis are applied to the peat, but palynological techniques form the basis of the investigation. Stomatal counts are used in conjunction with pollen counts to explore the process of Pinus colonisation, and its increase in abundance to form woodland. Pinus percentage and influx, together with the ratio of arboreal to non-arboreal pollen and the percentage of Empetrum are used to define the density of the woodland canopy. The stability of the Doire Bhraghad assemblage confirms the area as core Pinus woodland. Pinus is present from c. 9600 cal BP and dominates woodland from c. 9150 cal BP. Woodland here is a closed, solely Pinus canopy from c. 8600 until 4000 cal BP. Arrival of Pinus at Geldie Lodge is undated but occurs before c. 7550 cal BP. Woodland is always more open; Pinus is co-dominant with Betula, showing affinity with other peripheral areas. Pinus woodland fragments at all Mar Lodge sites from c. 3900 cal BP, disappearing from Geldie Lodge by c. 2800 cal BP and White Bridge by c. 1900 cal BP. Calluna replaces Pinus as the dominant species at all three sites. The disappearance of Pinus is thought to relate to regional climatic change toward wetter conditions. At Geldie Lodge a prior Coleopteran study suggests Pinus growing on the mire surface to be small and short lived. These may not have been the only trees growing in the area but they perhaps contributed to the major fluctuations in arboreal and non-arboreal pollen. Early canopy fluctuations (c. 7550 to 6000 cal BP) at Geldie Lodge may be related to Mesolithic human activity; there is stronger evidence of human presence from c. 4000 cal BP, possibly including cereal cultivation. Evidence from Doire Bhraghad and White Bridge is indicative only of low intensity grazing activity. It is unlikely that human activity instigated the fragmentation and disappearance of woodland, but may have contributed to the process.

No description available.

No description available.

The mapping of forest age is important for effective forest inventory as age is indicative of a number of plant physiological processes. Field survey techniques have traditionally been used to collect forest inventory data, but these methods are costly and time-consuming. Remote sensing offers an alternative which is time-effective and cost-effective and can cover large areas. The aim of this research was to assess the capabilities of multispectral and hyperspectral remotely sensed image data and the statistical method, random forest, for Pinus patula age prediction. The first section of this study used spatial and spectral data derived from multispectral QuickBird imagery to predict forest age. Five co-occurrence texture measures (variance, contrast, correlation, homogeneity, and dissimilarity) were calculated on QuickBird panchromatic imagery (0.6 m spatial resolution) using 12 moving window sizes. The spectral data was extracted from visible and near infrared (NIR) QuickBird imagery (2.4 m spatial resolution). Using the random forest ensemble, various methods of combining the spectral and texture variables were evaluated. The best model was achieved using backward variable selection which aims to find the fewest number of input bands while maintaining the highest predictive accuracy. Only five of the original 64 variables were used in the final model (R2 = 0.68). The second part of this study examined the utility of the random forest ensemble and AISA Eagle hyperspectral image data to predict P. patula age. Random forest was used to determine the optimal subset of hyperspectral bands that could predict P. patula age. Two sequential variable selection methods were tested: forward and backward variable selection. Although both methods resulted in the same root mean square error (3.097), the backward variable selection method was unable to significantly reduce the large hyperspectral dataset and selected 206 variables for the model. The forward variable selection method successfully reduced the large dataset to only nine optimal bands while maintaining the highest predictive accuracy from the hyperspectral dataset (R2 = 0.6). Overall, we concluded that (i) remotely sensed data can produce accurate models for P. patula age prediction, (ii) random forest is an effective tool for the combination of spectral and spatial multispectral data, (iii) random forest is an effective tool for variable selection of a high dimensional hyperspectral dataset, and (iv), although random forest has mainly been used as a classifier, it is also a very effective tool for prediction. / Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2010.