A Novel Hierarchical Multinomial Approach to Modeling Age-Specific Harvest Data

2018 ◽  
pp. 29-48
Author(s):  
Khurram Nadeem ◽  
Entao Chen ◽  
Ying Zhang
Keyword(s):  
Harvest Data

scholarly journals Efficient use of harvest data: a size‐class‐structured integrated population model for exploited populations

Ecography ◽  
2021 ◽  
Author(s):  
Marlène Gamelon ◽  
Chloé R. Nater ◽  
Éric Baubet ◽  
Aurélien Besnard ◽  
Laura Touzot ◽  
...  
Keyword(s):  
Population Model ◽  
Size Class ◽  
Integrated Population Model ◽  
Harvest Data

What can harvest data tell us about Montana's black bears?

Ursus ◽  
2012 ◽  
Vol 23 (1) ◽  
pp. 30-41 ◽  
Author(s):  
Julie A. Beston ◽  
Richard D. Mace
Keyword(s):  
Black Bears ◽  
Harvest Data

Global Value Chain Data for Mexico in the Automotive and Electronics Sectors

2021 ◽  
Author(s):  
Mirjana Stankovic ◽  
Agustín Ignacio Filippo
Keyword(s):  
Data Protection ◽  
Value Chain ◽  
Digital Transformation ◽  
Global Perspective ◽  
High Tech ◽  
Global Value Chain ◽  
Current Position ◽  
Data Framework ◽  
Harvest Data ◽  
Data Transfers

This report uses the Global Value Chain (GVC) data framework to provide scoping review and analysis of Mexico's current position and potential for using and harvesting GVC data in the automotive and electronics sectors. By conducting the study on GVCs data, we hope to broaden the understanding of the importance of data transfers for GVCs, production, and trade, underlining that data are critical to all companies and not only to the so-called "high-tech companies." Data protection, sharing, and security are also central to manufacturers in the automotive and electronics sectors. This report will review how datafication, data protection, sharing, and security impact Mexico's automotive and electronics industry. This information is analyzed from a global perspective and the viewpoint of Mexico to provide a holistic picture of the situation when identifying trajectories for entry, growth, and upgrading along GVCs that rely on datafication and digital transformation. It will also offer recommendations for regulators and policymakers on how to facilitate successful GVCs' data functioning and guidance for businesses on how to harvest data for growth and digital transformation.

scholarly journals HONEY CROP ESTIMATION FROM SPACE: DETECTION OF LARGE FLOWERING EVENTS IN WESTERN AUSTRALIAN FORESTS

2018 ◽  
Vol XLII-1 ◽  
pp. 79-86
Author(s):  
T. Campbell ◽  
P. Fearns
Keyword(s):  
Remotely Sensed ◽  
Spectral Space ◽  
Remotely Sensed Data ◽  
Response Curves ◽  
Real World Data ◽  
Distance Analysis ◽  
Peak Separation ◽  
Flower Density ◽  
Harvest Data ◽  
Detailed Assessment

<p><strong>Abstract.</strong> Recent studies have shown that in the spectral space there is often a better spectral separation between leaves and flowers and even between flowers of different species than between leaves of different species. In this study we assess the ability of satellite remotely sensed data to detect the flowering of Red Gum trees (<i>Corymbia calophylla</i>) in Western Australia, the state’s largest annual honey crop. Spectroradiometer measurements of flowers, leaves and groundcover from Red Gum forests were subjected to ANOVA analysis, which showed that flowers are spectrally different to their environment for 92<span class="thinspace"></span>% of the wavelengths between 350<span class="thinspace"></span>nm and 1800<span class="thinspace"></span>nm. A more detailed assessment, using the JM Distance calculation, showed that the spectra can be reliably separated using 10<span class="thinspace"></span>% of the wavelengths, with peak separation between 518<span class="thinspace"></span>nm and 557<span class="thinspace"></span>nm. To assess the ability of satellite-borne sensors to detect the presence of flowers, the spectroradiometer data were convolved with satellite instruments’ response curves to create synthetic remotely sensed datasets on which JM Distance analysis was performed. MODIS blue bands achieved a median JM Distance of greater than 1.9 and therefore should be able to detect the presence of flowers from the environment. Further assessment showed that the shortest wavelength bands for MODIS, VIIRS and Sentinel 3 all occur where the flower spectra have lower reflectance than their natural background. A sensitivity analysis of percentage flower cover for a pixel showed that the highest sensitivity was obtained by dividing the band closest to 520<span class="thinspace"></span>nm by the shortest wavelength band for data from these three sources. The MODIS band 10/band 8 metric was tested for its ability to detect flowers in real-world data using 15 years of qualitative honey harvest data from one apiary site as a proxy for flower density. This test was successful as, while there was some overlap between good, moderate and poor years, the poor years could be separated from the other years with nearly 80<span class="thinspace"></span>% accuracy.</p>

scholarly journals Estimating brown bear abundance and harvest rate on the southern Alaska Peninsula

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245367
Author(s):  
Earl F. Becker ◽  
David W. Crowley
Keyword(s):  
Sampling Method ◽  
Distance Sampling ◽  
Brown Bear ◽  
Aerial Survey ◽  
Abundance Estimation ◽  
Game Management ◽  
Geographic Scale ◽  
Detection Function ◽  
Harvest Rate ◽  
Harvest Data

Abundance estimation of hunted brown bear populations should occur on the same geographic scale as harvest data analyses for estimation of harvest rate. Estimated harvest rates are an important statistic for managing hunted bear populations. In Alaska, harvest data is collected over large geographic units, called Game Management Units (GMUs) and sub-GMUs. These sub GMUs often exceed 10,000 km2. In the spring of 2002, we conducted an aerial survey of GMU 9D (12,600 km2) and GMU 10 (4,070 km2) using distance sampling with mark-resight data. We used a mark-resight distance sampling method with a two-piece normal detection function to estimate brown bear abundance as 1,682.9 (SE = 174.29) and 316.9 (SE = 48.25) for GMU 9D and GMU 10, respectively. We used reported hunter harvest to estimate harvest rates of 4.35% (SE = 0.45%) and 3.06% (SE = 0.47%) for GMU 9D and GMU 10, respectively. Management objective for these units support sustained, high quality hunting opportunity which harvest data indicate are met with an annual harvest rate of approximately 5–6% or less.

The Use of Contemporary Harvest Data as a Means of Thinking about Prehistoric Resource Depression

2011 ◽  
Vol 32 (1) ◽  
pp. 1-13
Author(s):  
Corrina I. Smith ◽  
Cynthia A. Bradbury ◽  
Mark G. Plew
Keyword(s):  
Resource Depression ◽  
Harvest Data

scholarly journals Statistical methods for analysis of multi-harvest data from perennial pasture variety selection trials

2015 ◽  
Vol 66 (9) ◽  
pp. 947 ◽  
Author(s):  
Joanne De Faveri ◽  
Arūnas P. Verbyla ◽  
Wayne S. Pitchford ◽  
Shoba Venkatanagappa ◽  
Brian R. Cullis
Keyword(s):  
Statistical Methods ◽  
Temporal Correlation ◽  
Field Trials ◽  
Model Fit ◽  
Natural Sources ◽  
Variety Selection ◽  
Sources Of Variation ◽  
Harvest Data ◽  
Insight Into

Variety selection in perennial pasture crops involves identifying best varieties from data collected from multiple harvest times in field trials. For accurate selection, the statistical methods for analysing such data need to account for the spatial and temporal correlation typically present. This paper provides an approach for analysing multi-harvest data from variety selection trials in which there may be a large number of harvest times. Methods are presented for modelling the variety by harvest effects while accounting for the spatial and temporal correlation between observations. These methods provide an improvement in model fit compared to separate analyses for each harvest, and provide insight into variety by harvest interactions. The approach is illustrated using two traits from a lucerne variety selection trial. The proposed method provides variety predictions allowing for the natural sources of variation and correlation in multi-harvest data.

Freshwater, Fish and the Future: Proceedings of the Global Cross-Sectoral Conference

2016 ◽  
Keyword(s):  
Environmental Flows ◽  
Economic Valuation ◽  
Assessment Method ◽  
Fiscal Constraints ◽  
Use Of Data ◽  
Recreational Fishery ◽  
Murray Darling Basin ◽  
History Of ◽  
Harvest Data ◽  
Murray Cod

<em>Abstract</em> .—The collection and use of data to manage the freshwater fisheries of Australia’s Murray–Darling basin (MDB) has a poor history of success. While there was limited assessment data for early subsistence and commercial fisheries, even after more robust data became available during the 1950s its quality varied across jurisdictions and was often poorly collated, assessments were not completed, and the data were underutilized by management. The fishery for Murray Cod <em>Maccullochella peelii </em> is given as an example, where the fishery declined to the point of closure and then the decline continued to the extent that Murray Cod was listed as a threatened species and all harvest now only occurs through the recreational fishery. Lessons from such poor population assessments have not been fully learned, however, as there remains a paucity of harvest data for this recreational fishery. Without a proper assessment, a true economic valuation of this fishery has not been made. As the MDB is Australia’s food bowl, there are competing demands for water use by agriculture, and without a proper assessment of the worth of the fishery, it is difficult for Murray Cod to be truly considered in either economic or sociopolitical discussions. The poor state of MDB rivers and their fish populations (including Murray Cod) has, however, resulted in political pressure for the development of the sustainable rivers audit, a common assessment method for riverine environmental condition monitoring. This audit undertakes standardized sampling for fish and a range of other variables at a number of fixed and randomly selected sites on a 3-year rotating basis. While the sustainable rivers audit has provided a range of data indicating that the condition of rivers is generally very poor, these data have yet to be fully utilized to determine the potential state of the fisheries (such as Murray Cod) or to set targets for rehabilitation, such as for environmental flows. While, to date, data analyses have been somewhat restricted by fiscal constraints, more comprehensive use of data, together with full fishery valuations, should be seen as the way forward for improved management.

Paddlefish Management, Propagation, and Conservation in the 21st Century

2009 ◽  
Keyword(s):  
Mississippi River ◽  
First Year ◽  
State Agencies ◽  
Ohio River ◽  
Independent Data ◽  
Arkansas River ◽  
Commercial Harvest ◽  
Harvest Data ◽  
Sport Fisheries ◽  
Anthropogenic Alterations

<em>Abstract</em>.—Paddlefish <em>Polyodon spathula </em>have been intensively harvested in both sport and commercial fisheries. Recent harvests (2000–2006) were surveyed from state agencies and compared to historical harvests (1965– 1975). Seven major sport fisheries had recent annual harvests greater than 1,000 fish, and most large sport fisheries appeared to have sustainable harvests due to intensive management. Recent commercial harvest was greater than sport harvest across the species’ range. Most of the commercial harvest was from Arkansas, Kentucky, and Tennessee. Annual commercial harvest from the Ohio River increased from 6,000 to 196,000 kg from 1965–1975 to 2000–2006. Annual harvest remained substantial from the Arkansas River (37,000 kg), the lower Tennessee River (121,000 kg), and the Mississippi River (103,000 kg). Harvests of paddlefish (sport and commercial) compiled from the literature were highly variable and ranged between 0.01 and 5.06 fish/ ha and 0.04–43.43 kg/ha (median = 0.12 fish/ha, 1.73 kg/ha). Stock depression has been associated with a first-year harvest as low as 1.46 kg/ha, and harvests greater than 5 kg/ha were usually associated with overfishing or opening a previously closed fishery. Case histories from the Tennessee and Ohio River systems documented that paddlefish were susceptible to overharvest in lentic waters and river reservoirs, but the threat posed by commercial harvest from large rivers will remain unresolved until more fisheries-independent data becomes available. Anthropogenic alterations to habitat, overreliance on harvest data, and lack of fisheries-independent data limit our historical understanding of the degree of threat that harvest is to paddlefish populations.

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