Howard L. Wright

Bio: Howard L. Wright is an academic researcher. The author has contributed to research in topics: Site index & Desk. The author has an hindex of 2, co-authored 3 publications receiving 32 citations.

Modelling dominant height growth of Douglas‐fir (Pseudotsuga menziesii (Mirb.) Franco) in Portugal

Abstract: Summary The first plantations of the North-West American conifer Douglas-fir were established in Portugal in 1904. Investigations into growth and yield patterns were started in 1969. Since then others have carried out further studies. This study includes data from the previous studies and covers the whole range of site conditions where Douglas-fir grows in Portugal. Dominant height data from 87 stem analysis trees from 12 sites were used to model dominant height growth of Douglas-fir using the McDill‐Amateis, Chapman‐Richards and Lundqvist‐Korf growth functions. The Chapman‐Richards and Lundqvist‐Korf growth functions were used in their integral and difference forms. For the evaluation of the candidate growth models’ performance, three steps were adopted: (1) all the candidate growth equations were fitted with the data available from stem analysis from 87 trees, eliminating growth equations with non-logical and non-biological consistency and poor statistical properties; (2) the remaining growth equations were cross-validated using two sub-samples of the stem analysis data and selection of the growth equations with the best statistical results; (3) validation, using the whole data set from stem analysis for fitting growth models and the stand data for calculation of the prediction errors, was carried out. Out of the nine models evaluated five were rejected in the first step and two in the second step. The two best models had similar results in the third step and were compared with previous Douglas-fir site index curves. The McDill‐Amateis function performed best.

Operational implementation of a LiDAR inventory in Boreal Ontario

Abstract: An existing Light Detection and Ranging (LiDAR) data set captured on the Romeo Malette Forest near Timmins, Ontario, was used to explore and demonstrate the feasibility of such data to enrich existing strategic forest-level resource inventory data. Despite suboptimal calibration data, stand inventory variables such as top height, average height, basal area, gross total volume, gross merchantable volume, and above-ground biomass were estimated from 136 calibration plots and validated on 138 independent plots, with root mean square errors generally less than 20% of mean values. Stand densities (trees per ha) were estimated with less precision (30%). These relationships were used as regression estimators to predict the suite of variables for each 400 m2 tile on the 630 000-ha forest, with predictions capable of being aggregated in any user-defined manner—for a stand, block, or forest—with appropriate estimates of statistical precision. This pilot study demonstrated that LiDAR data may satisfy growing needs f...

Statistical Techniques for Sampling And Monitoring Natural Resources

Abstract: We present the statistical theory of inventory and monitoring from a probabilistic point of view. We start with the basics and show the interrelationships between designs and estimators illustrating the methods with a small artificial population as well as with a mapped realistic population. For such applications, useful open source software is given in Appendix 4. Various sources of ancillary information are described and applications of the sampling strategies are discussed. Classical and bootstrap variance estimators are discussed also. Numerous problems with solutions are given, often based on the experiences of the authors. Key additional references are cited as needed or desired.

Ecological research and conservation management in the Cape Floristic Region between 1945 and 2015: History, current understanding and future challenges

Abstract: In 1945, the Royal Society of South Africa published a wide-ranging report, prepared by a committee led by Dr C.L. Wicht, dealing with the preservation of the globally unique and highly diverse vegetation of the south-western Cape. The publication of the Wicht Committee’s report signalled the initiation of a research programme aimed at understanding, and ultimately protecting, the unique and diverse ecosystems of the Cape Floristic Region. This programme has continued for over 70 years, and it constitutes the longest history of concerted scientific endeavour aimed at the conservation of an entire region and its constituent biota. This monograph has been prepared to mark the 70th anniversary of the Wicht Committee report. It provides a detailed overview of the circumstances that led up to the Wicht Committee’s report, and the historical context within which it was written. It traces the development of new and substantial scientific understanding over the past 70 years, particularly with regard to catchment...

Nonlinear mixed-effects (NLME) diameter growth models for individual China-Fir (Cunninghamia lanceolata) trees in Southeast China.

Abstract: An individual-tree diameter growth model was developed for Cunninghamia lanceolata in Fujian province, southeast China. Data were obtained from 72 plantation-grown China-fir trees in 24 single-species plots. Ordinary non-linear least squares regression was used to choose the best base model from among 5 theoretical growth equations; selection criteria were the smallest absolute mean residual and root mean square error and the largest adjusted coefficient of determination. To account for autocorrelation in the repeated-measures data, we developed one-level and nested two-level nonlinear mixed-effects (NLME) models, constructed on the selected base model; the NLME models incorporated random effects of the tree and plot. The best random-effects combinations for the NLME models were identified by Akaike's information criterion, Bayesian information criterion and −2 logarithm likelihood. Heteroscedasticity was reduced with two residual variance functions, a power function and an exponential function. The autocorrelation was addressed with three residual autocorrelation structures: a first-order autoregressive structure [AR(1)], a combination of first-order autoregressive and moving average structures [ARMA(1,1)] and a compound symmetry structure (CS). The one-level (tree) NLME model performed best. Independent validation data were used to test the performance of the models and to demonstrate the advantage of calibrating the NLME models.