Temporary Samples Research Articles

Evaluating Alternative Temporal Survey Designs for Monitoring Wetland Area and Detecting Changes Over Time in California

Evaluation of wetland extent and changes in extent is a foundation of many wetland monitoring and assessment programs. Probabilistic sampling and mapping provides a cost-effective alternative to comprehensive mapping for large geographic areas. One unresolved challenge for probabilistic or design-based approaches is how best to monitor both status (e.g., extent at a single point in time) and trends (e.g., changes in extent over time) within a single monitoring program. Existing wetland status and trends (ST however, theoretical evaluation and limited implementation in other landscape monitoring areas suggest that alternative designs could increase statistical efficiency and overall accuracy. In particular, designs that employ both fixed and nonfixed sampling locations (alternately termed permanent and temporary samples), termed sampling with partial replacement (SPR), are considered to efficiently and effectively balance monitoring current status with detection of trends. This study utilized simulated sampling to assess the performance of fixed sampling locations, SPR, and strictly nonfixed designs for monitoring wetland S&T over time. Modeled changes in wetland density over time were used as inputs for sampling simulations. In contrast to previous evaluations of SPR, the results of this study support the use of a fixed sampling design and show that SPR may underestimate both S&T.

Modelagem da biomassa total e da lenha por unidade de área para bracatingais nativos

A bracatinga (Mimosa scabrella Benth.) é uma espécie pioneira nativa da Floresta Ombrófila Mista de rápido crescimento e cultivada, predominantemente, na forma de povoamentos puros ou consorciada com culturas agrícolas pelos pequenos e médios produtores da região metropolitana de Curitiba. O objetivo deste estudo foi selecionar equações estimativas da biomassa total e da lenha de bracatingais por unidade de área nas diferentes idades, disponibilizando, assim, uma ferramenta simples e de fácil aplicação durante o processo de comercialização dos povoamentos. Os dados são provenientes de 272 parcelas temporárias com idades variando de 3 a 18 anos. Foram testados 21 modelos tradicionais: 10 aritméticos e semilogarítmicos e 11 logarítmicos. Foram também desenvolvidas equações pelo processo Stepwise a partir de uma matriz de correlação. As equações ajustadas foram comparadas pelo Coeficiente de determinação ajustado (R²aj), Erro-padrão da estimativa percentual (Syx%), teste F e Distribuição gráfica de resíduos. Duas equações para cada caso foram selecionadas, sendo uma tradicional e a outra desenvolvida por Stepwise. Para a biomassa total, foram selecionadas a equação de Clutter e a equação aritmética desenvolvida por Stepwise; e para a biomassa da lenha, a equação da variável combinada, proposta por Spurr, e a equação aritmética de Stepwise. Por fim, realizou-se teste Qui-quadrado (x²) a partir de uma amostra de 17 parcelas extraídas da base de dados, em que as quatro equações selecionadas foram consideradas válidas para a estimativa da biomassa total e da lenha dos bracatingais da região metropolitana de Curitiba.

A Dynamic AdaBoost Algorithm With Adaptive Changes of Loss Function

AdaBoost is a method to improve a given learning algorithm's classification accuracy by combining its hypotheses. Adaptivity, one of the significant advantages of AdaBoost, makes AdaBoost maximize the smallest margin so that AdaBoost has good generalization ability. However, when the samples with large negative margins are noisy or atypical, the maximized margin is actually a “hard margin.” The adaptive feature makes AdaBoost sensitive to the sampling fluctuations, and prone to overfitting. Therefore, the traditional schemes prevent AdaBoost from overfitting by heavily damping the influences of samples with large negative margins. However, the samples with large negative margins are not always noisy or atypical; thus, the traditional schemes of preventing overfitting may not be reasonable. In order to learn a classifier with high generalization performance and prevent overfitting, it is necessary to perform statistical analysis for the margins of training samples. Herein, Hoeffding inequality is adopted as a statistical tool to divide training samples into reliable samples and temporary unreliable samples. A new boosting algorithm, which is named DAdaBoost, is introduced to deal with reliable samples and temporary unreliable samples separately. Since DAdaBoost adjusts weighting scheme dynamically, the loss function of DAdaBoost is not fixed. In fact, it is a series of nonconvex functions that gradually approach the 0-1 function as the algorithm evolves. By defining a virtual classifier, the dynamic adjusted weighting scheme is well unified into the progress of DAdaBoost, and the upper bound of training error is deduced. The experiments on both synthetic and real world data show that DAdaBoost has many merits. Based on the experiments, we conclude that DAdaBoost can effectively prevent AdaBoost from overfitting.

A Variance Estimator for Constrained Estimates of Change in Relative Categorical Frequencies

Consistent estimators of change and state becomes an issue when sample data come from a mix of permanent and temporary observation units. A joint maximum likelihood estimator of state and change creates estimates of state that depend on antecedent viz. posterior survey results and may differ from estimates of state derived from a single-date analysis of the sample data. A constrained estimator of change in relative categorical frequencies that eliminates this potential inconsistency is proposed and a model based estimator of their sampling variance is developed. The performance of the constrained estimator is quantified against six criteria and a joint maximum likelihood estimator in simulated sampling from 15 populations with three combinations of permanent and temporary samples, four to six categorical class attributes, and constant size between sampling dates. Bias of the constrained estimators was negligible but larger than for joint maximum likelihood estimators. Mean absolute deviations and variances of constrained estimators were generally at par with the joint estimators. Constrained estimators of root mean square errors and achieved coverage of nominal confidence intervals of constrained estimators were occasionally better. A generalized variance function for the constrained estimates of change is provided as a computational shortcut.

Mitochondrial DNA variation, effective female population size and population history of the endangered Chinese sturgeon, Acipenser sinensis

The anadromous Chinese sturgeon (Acipenser sinensis), mainly endemic to the Yangtze River in China, is an endangered fish species. The natural population has declined since the Gezhouba Dam blocked its migratory route to the spawning grounds in 1981. In the near future, the completion of the Three Gorges Dam, the world's largest hydroelectric project, may further impact this species by altering the water flow of the Yangtze River. Little is currently known about the population genetic structure of the Chinese sturgeon. In this study, DNA sequence data were determined from the control region (D-loop) of the mitochondrial genome of adult sturgeons (n = 106) that were collected between 1995–2000. The molecular data were used to investigate genetic variation, effective female population size and population history of the Chinese sturgeon in the Yangtze River. Our results indicate that the reduction in abundance did not change genetic variation of the Chinese sturgeon, and that the population underwent an expansion in the past. AMOVA analysis indicated that 98.7% of the genetic variability occurred within each year's spawning populations, the year of collection had little influence on the diversity of annual temporary samples. The relative large effective female population size (N ef) indicates that good potential exists for the recovery of this species in the future. Strikingly, the ratio of N ef to the census female population size (N f) is unusually high (0.77–0.93). This may be the result of a current bottleneck in the population of the Chinese sturgeon that is likely caused by human intervention.

A calibrateable site index model for Pinus kesiya plantations in southeastern Africa

The aim of the study was to develop a site index model for Pinus kesiya Royle ex Gordon plantations in southeastern Africa based on the relationship between the dominant height and stand age. Conversely, analysis of dominant height and age data showed that the growth patterns of plantations were different. In addition, the asymptotes and forms of standwise dominant height curves varied within plantations. In developing a common site index model, instead of using the more common approach of estimating separate dominant height–age models for different plantations or sites, a mean curve approach based on a linear random parameter model with fixed and random parameters was applied. The random parameter model of this study was calibrated by predicting random parameters for the plantation and stand effects, in accordance with the standard linear prediction theory. The analyses showed that the calibration of the dominant height model was an efficient method to obtain reliable dominant height predictions of a stand, particularly when several dominant height–age observations from different stands of a plantation and at least one measured dominant height and stand age of a target stand are available. This is the case in many forest inventories based on temporary samples, i.e., cross-sectional data. The new site index model is a useful tool for use in different mensurational applications, and its properties can efficiently be utilized for example in forest inventories of P. kesiya plantations in southeastern Africa.