Estimating acorn crops using visual surveys

1994 ◽  
Vol 24 (10) ◽  
pp. 2105-2112 ◽  
Author(s):  
Walter D. Koenig ◽  
Johannes M.H. Knops ◽  
William J. Carmen ◽  
Mark T. Stanback ◽  
Ronald L. Mumme
Keyword(s):  
High Efficiency ◽  
Sampling Error ◽  
Population Variation ◽  
Conifer Species ◽  
Acorn Production ◽  
Visual Survey ◽  
Annual Productivity ◽  
Level Data ◽  
Survey Technique ◽  
Individual Trees

We describe a visual survey technique for evaluating acorn production. In contrast with previously proposed methods, our technique yields ratio-level data on annual productivity that are analyzable with standard statistics and, by sampling the same trees each year, data on the reproductive patterns of individual trees. We compared this technique with two independent sets of acorn-trap data acquired on oaks of three species at Hastings Reservation in central coastal California. Correlations between acorns counted by the visual surveys and collected from acorn traps under the same trees were significant for all three species. Most scatter in the data appeared to be attributable to three causes: (1) sampling error, especially among trees with very small crops, (2) finite counting speed, leading to a lack of discrimination among trees with very large crops by the visual surveys, and (3) arboreal acorn removal by animals. This latter factor can be particularly large, rendering visual surveys more reliable than the use of traps. Furthermore, only the high efficiency of visual surveys allows for the practical assessment of samples large enough to accommodate high within-population variation and detect widespread geographic variation in acorn production. Visual surveys offer a method of assessing the fruit or cone crops of many hardwood and conifer species that is not only more efficient but also more accurate than the use of traps.

Distinguishing error from chaos in ecological time series

1990 ◽  
Vol 330 (1257) ◽  
pp. 235-251 ◽  
Keyword(s):  
New York ◽  
Population Density ◽  
Sampling Error ◽  
Natural Populations ◽  
Real Data ◽  
Environmental Stochasticity ◽  
Population Variation ◽  
Data Sets ◽  
Spatial Effects ◽  
Low Dimensional

Over the years, there has been much discussion about the relative importance of environmental and biological factors in regulating natural populations. Often it is thought that environmental factors are associated with stochastic fluctuations in population density, and biological ones with deterministic regulation. We revisit these ideas in the light of recent work on chaos and nonlinear systems. We show that completely deterministic regulatory factors can lead to apparently random fluctuations in population density, and we then develop a new method (that can be applied to limited data sets) to make practical distinctions between apparently noisy dynamics produced by low-dimensional chaos and population variation that in fact derives from random (high-dimensional)noise, such as environmental stochasticity or sampling error. To show its practical use, the method is first applied to models where the dynamics are known. We then apply the method to several sets of real data, including newly analysed data on the incidence of measles in the United Kingdom. Here the additional problems of secular trends and spatial effects are explored. In particular, we find that on a city-by-city scale measles exhibits low-dimensional chaos (as has previously been found for measles in New York City), whereas on a larger, country-wide scale the dynamics appear as a noisy two-year cycle. In addition to shedding light on the basic dynamics of some nonlinear biological systems, this work dramatizes how the scale on which data is collected and analysed can affect the conclusions drawn.

Development of Predictive Models for Identifying Potential S100A9 Inhibitors Based on Machine Learning Methods

2019 ◽  
Author(s):  
Jihyeun Lee ◽  
Surendra Kumar ◽  
Sang-Yoon Lee ◽  
Sung Jean Park ◽  
Mi-hyun Kim
Keyword(s):  
Machine Learning ◽  
Predictive Models ◽  
Large Scale ◽  
High Efficiency ◽  
Inhibitory Effect ◽  
Rational Drug Design ◽  
Feature Sets ◽  
Protein Protein Interaction ◽  
Level Data ◽  
Random Forest Models

S100A9 is a potential therapeutic target for various disease including prostate cancer, colorectal cancer, and Alzheimer’s disease. However, the sparsity of atomic level data such as protein-protein interaction of S100A9 with MD2/TLR4/CD147 makes rational drug design of S100A9 inhibitors more challengeable. Herein we firstly report predictive models of S100A9 inhibitory effect by applying machine learning classifiers on 2D-molecular descriptors. The models were optimized through feature selectors as well as classifiers to produce the top eight random forest models with robust predictability as well as high cost-effectiveness. Notably, the optimal feature sets were obtained after the reduction of 2798 features into dozens of features with the chopping of fingerprint bits. In addition, the high efficiency of compact feature sets allowed us to further screen a large-scale dataset (over 6,000,000 compounds) within a week. Through the consensus vote of the top models, 46 hits (hit rate = 0.000713%) were identified as potential S100A9 inhibitors. We expect that our models will facilitate the drug discovery process by providing high predictive power as well as cost-reduction ability and give insights into the design of the novel drugs targeting S100A9.

scholarly journals Estimating Mast Production: An Evaluation of Visual Surveys and Comparison with Seed Traps using White Oaks

1999 ◽  
Vol 23 (3) ◽  
pp. 164-169 ◽  
Author(s):  
Roger W. Perry ◽  
Ronald E. Thill
Keyword(s):  
Survey Methods ◽  
Quercus Alba ◽  
Ouachita Mountains ◽  
Acorn Production ◽  
Visual Survey ◽  
Tree Canopies ◽  
Seed Trap ◽  
Highly Correlated ◽  
Mast Production

Abstract We compared five types of visual mast surveys with seed trap data from 105 white oaks (Quercus alba L.) during 1996-1997 in the Ouachita Mountains of Arkansas. We also evaluated these visual survey methods for their usefulness in detecting differences in acorn density among areas. Indices derived from all five methods were highly correlated with acorn densities derived from traps, and the Koenig method had the highest r-values. Categorical surveys using fewer than six categories yielded significantly different acorn densities among all categories, whereas surveys using nine or ten categories did not. All survey methods detected moderate to large acorn density differences among four study areas. We found no difference in the effectiveness of visual surveys in dense versus open-forested conditions. Visual surveys are an effective method for evaluating acorn production and may be superior to seed traps for comparisons of acorn production in tree canopies since they are not affected as greatly by wildlife removal. However, visual surveys can be biased by observer differences, whereas trap data are not. South. J. Appl. For. 16(3):164-169.

The value of timber inventory information for management planning

2008 ◽  
Vol 38 (8) ◽  
pp. 2287-2294 ◽  
Author(s):  
B. E. Borders ◽  
W. M. Harrison ◽  
M. L. Clutter ◽  
B. D. Shiver ◽  
R. A. Souter
Keyword(s):  
United States ◽  
Net Present Value ◽  
Sampling Error ◽  
Management Plan ◽  
Southern United States ◽  
Inventory Data ◽  
Timber Management ◽  
Level Data ◽  
Management Plans ◽  
Timber Inventory

Timber inventory data is the basis for many monetary transactions related to timber and timberland sale and (or) purchase as well as for development of timber management plans. The value of such data is well known and much appreciated for sale and (or) purchase of standing merchantable timber. Unfortunately, the value of timber inventory data for planning purposes is less well understood. We report on the results of a large simulation study that was undertaken to evaluate the utility and value of timber inventory data for timber management plan development for a typical timberland ownership in the southern United States. Our results indicate that timberland managers are likely producing management plans that do not maximize the profitability of their timberland holdings. Specifically, our results indicate it is likely that timber management organizations that develop timber management plans with stand level data that has a sampling error of 25% are experiencing expected losses in net present value in excess of 170 US$·ha–1 on a large proportion of the acreage found on typical timberland parcels in the southern United States.

scholarly journals Toward an Evolutionarily Appropriate Null Model: Jointly Inferring Demography and Purifying Selection

Genetics ◽  
2020 ◽  
Vol 215 (1) ◽  
pp. 173-192 ◽  
Author(s):  
Parul Johri ◽  
Brian Charlesworth ◽  
Jeffrey D. Jensen
Keyword(s):  
Large Scale ◽  
Demographic History ◽  
Null Model ◽  
Purifying Selection ◽  
Population Level ◽  
Population Variation ◽  
Theoretical Development ◽  
Level Data ◽  
Constant Operation ◽  
Adaptive Processes

The question of the relative evolutionary roles of adaptive and nonadaptive processes has been a central debate in population genetics for nearly a century. While advances have been made in the theoretical development of the underlying models, and statistical methods for estimating their parameters from large-scale genomic data, a framework for an appropriate null model remains elusive. A model incorporating evolutionary processes known to be in constant operation, genetic drift (as modulated by the demographic history of the population) and purifying selection, is lacking. Without such a null model, the role of adaptive processes in shaping within- and between-population variation may not be accurately assessed. Here, we investigate how population size changes and the strength of purifying selection affect patterns of variation at “neutral” sites near functional genomic components. We propose a novel statistical framework for jointly inferring the contribution of the relevant selective and demographic parameters. By means of extensive performance analyses, we quantify the utility of the approach, identify the most important statistics for parameter estimation, and compare the results with existing methods. Finally, we reanalyze genome-wide population-level data from a Zambian population of Drosophila melanogaster, and find that it has experienced a much slower rate of population growth than was inferred when the effects of purifying selection were neglected. Our approach represents an appropriate null model, against which the effects of positive selection can be assessed.

Estimation of crown form for six conifer species of northern California

1990 ◽  
Vol 20 (8) ◽  
pp. 1137-1142 ◽  
Author(s):  
Greg S. Biging ◽  
Lee C. Wensel
Keyword(s):  
Sierra Nevada ◽  
Tree Height ◽  
Wildlife Habitat ◽  
Conifer Species ◽  
Damage Potential ◽  
Cross Sectional ◽  
Individual Tree ◽  
Crown Volume ◽  
Using Data ◽  
Individual Trees

Geometric models are presented for the prediction of crown volume and width at any height in the crown of six conifer species in the Sierra Nevada. Crown volume is defined as the geometric space occupied by the crown and is allometrically related to the diameter, height, and crown ratio of individual trees. Crown diameter is derived from crown volume, tree height, and crown ratio. The crown volumes and associated measures can be used to compute indices of individual tree competition such as those used in the CACTOS (California Conifer Timber Output Simulator) system or to compute other measures such as wildlife habitat suitability or insect damage potential. Estimation equations are developed by regression using data collected on crowns of 593 felled trees. The equations use dbh, total height, and crown ratio to estimate total crown volume, crown volume above a specified height, and cumulative crown cross sectional area at a specified height.

scholarly journals Seamless integration of above- and under-canopy unmanned aerial vehicle laser scanning for forest investigation

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yunsheng Wang ◽  
Antero Kukko ◽  
Eric Hyyppä ◽  
Teemu Hakala ◽  
Jiri Pyörälä ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Laser Scanning ◽  
High Efficiency ◽  
Low Cost ◽  
Seamless Integration ◽  
Aerial Vehicle ◽  
Individual Trees ◽  
Mobile Mapping Systems ◽  
Aerial Perspective

Abstract Background Current automated forest investigation is facing a dilemma over how to achieve high tree- and plot-level completeness while maintaining a high cost and labor efficiency. This study tackles the challenge by exploring a new concept that enables an efficient fusion of aerial and terrestrial perspectives for digitizing and characterizing individual trees in forests through an Unmanned Aerial Vehicle (UAV) that flies above and under canopies in a single operation. The advantage of such concept is that the aerial perspective from the above-canopy UAV and the terrestrial perspective from the under-canopy UAV can be seamlessly integrated in one flight, thus grants the access to simultaneous high completeness, high efficiency, and low cost. Results In the experiment, an approximately 0.5 ha forest was covered in ca. 10 min from takeoff to landing. The GNSS-IMU based positioning supports a geometric accuracy of the produced point cloud that is equivalent to that of the mobile mapping systems, which leads to a 2–4 cm RMSE of the diameter at the breast height estimates, and a 4–7 cm RMSE of the stem curve estimates. Conclusions Results of the experiment suggested that the integrated flight is capable of combining the high completeness of upper canopies from the above-canopy perspective and the high completeness of stems from the terrestrial perspective. Thus, it is a solution to combine the advantages of the terrestrial static, the mobile, and the above-canopy UAV observations, which is a promising step forward to achieve a fully autonomous in situ forest inventory. Future studies should be aimed to further improve the platform positioning, and to automatize the UAV operation.

scholarly journals THIS (METHOD) IS (NOT) FINE

2018 ◽  
Vol 50 (6) ◽  
pp. 872-874
Author(s):  
Jelte M. Wicherts
Keyword(s):  
Recent Article ◽  
Sampling Error ◽  
Level Data ◽  
Item Level ◽  
Restriction Of Range

SummaryIn their response to my criticism of their recent article in Journal of Biosocial Science (te Nijenhuis et al., 2017), te Nijenhuis and van den Hoek (2018) raise four points none of which concerns my main point that the method of correlated vectors (MCV) applied to item-level data represents a flawed method. Here, I discuss te Nijenhuis and van den Hoek’s four points. First, I argue that my previous application of MCV to item-level data showed that the method can yield nonsensical results. Second, I note that meta-analytic corrections for sampling error, imperfect measures, restriction of range and unreliability of the vectors are futile and cannot help fix the method. Third, I note that even with perfect data, the method can yield negative correlations. Fourth, I highlight the irrelevance of te Nijenhuis and van den Hoek (2018)’s point that my comment had not been published in a peerreviewed journal by referring to my articles in 2009 and 2017 on MCV in peer-reviewed journals.

Development of Predictive Models for Identifying Potential S100A9 Inhibitors Based on Machine Learning Methods

2019 ◽  
Author(s):  
Jihyeun Lee ◽  
Surendra Kumar ◽  
Sang-Yoon Lee ◽  
Sung Jean Park ◽  
Mi-hyun Kim
Keyword(s):  
Machine Learning ◽  
Predictive Models ◽  
Large Scale ◽  
High Efficiency ◽  
Inhibitory Effect ◽  
Rational Drug Design ◽  
Feature Sets ◽  
Protein Protein Interaction ◽  
Level Data ◽  
Random Forest Models

S100A9 is a potential therapeutic target for various disease including prostate cancer, colorectal cancer, and Alzheimer’s disease. However, the sparsity of atomic level data such as protein-protein interaction of S100A9 with MD2/TLR4/CD147 makes rational drug design of S100A9 inhibitors more challengeable. Herein we firstly report predictive models of S100A9 inhibitory effect by applying machine learning classifiers on 2D-molecular descriptors. The models were optimized through feature selectors as well as classifiers to produce the top eight random forest models with robust predictability as well as high cost-effectiveness. Notably, the optimal feature sets were obtained after the reduction of 2798 features into dozens of features with the chopping of fingerprint bits. In addition, the high efficiency of compact feature sets allowed us to further screen a large-scale dataset (over 6,000,000 compounds) within a week. Through the consensus vote of the top models, 46 hits (hit rate = 0.000713%) were identified as potential S100A9 inhibitors. We expect that our models will facilitate the drug discovery process by providing high predictive power as well as cost-reduction ability and give insights into the design of the novel drugs targeting S100A9.

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