A multifactor ecological classification of the northern hardwood and conifer ecosystems of Sylvania Recreation Area, Upper Peninsula, Michigan

1985 ◽  
Vol 15 (5) ◽  
pp. 949-960 ◽  
Author(s):  
Thomas A. Spies ◽  
Burton V. Barnes

An ecological method of multifactor ecosystem classification was applied in the Sylvania Recreation Area, an 8500-ha tract of old-growth northern hardwood – conifer forests in upper Michigan. The uplands and wetlands were subjectively classified into 25 ecosystems by a method combining reconnaissance, plot sampling, data analysis, and ecosystem mapping. Each ecosystem was a characteristic combination of physiography, ecological species groups (ground vegetation), and soil. Discriminant analysis was used to evaluate the distinctness of the upland ecosystems and to compare the discriminating abilities of different ecosystem components (physiography, ground vegetation, and soil). The classification was corroborated in the multivariate analyses. The lowest estimated error rate (9.4%) in discriminant analysis was obtained by a model based on a combination of physiographic and soil characteristics and ecological species groups. The estimated error rates based on the species groups alone and physiography and soil alone were 42.2 and 25.0%, respectively. The discriminant analyses indicate that neither vegetation alone nor physiography and soil alone could be used with high reliability in classifying and mapping ecosystems. An additional discriminant analysis of the three ecosystem components indicated that the ecosystems could be distinguished by field characteristics without information from soil laboratory analyses. This analysis also demonstrated the particular value of the vegetation component as a readily observed, acceptable substitute for soil laboratory data in identifying and mapping ecosystem units.


1985 ◽  
Vol 15 (5) ◽  
pp. 961-972 ◽  
Author(s):  
Thomas A. Spies ◽  
Burton Barnes

A tabular, field-oriented method of developing ecological species groups was applied in a classification study of upland northern hardwood – hemlock ecosystems in the Sylvania Recreation Area, Upper Peninsula, Michigan. Sixteen species groups were formed, consisting of a total of 76 upland herb, shrub, and moss species. The groups were constituted based on patterns of presence and absence and coverage values of species along gradients of soil fertility and soil moisture. The ecological responses of species within many of the groups were very similar. The environmental tolerances of the species groups in relation to soil fertility, moisture, forest floor conditions, and shade tolerance were described and contrasted. The groups were more differentiated along a fertility gradient than along a moisture gradient. The tabular method was relatively simple, yet effective in determining the species groups. The method is suitable for extensive land-classification activities; its essential element is that physiography, soil, and vegetation are examined simultaneously in the field. Species groups are more reliable in site classification and mapping than a subset of a few key species and the groups also simplify the use of many indicator species for field workers and ecosystem mappers.



1990 ◽  
Vol 20 (10) ◽  
pp. 1570-1582 ◽  
Author(s):  
Louis Archambault ◽  
Burton V. Barnes ◽  
John A. Witter

An ecological multifactor approach was used to identify and describe oak ecosystem types in highly disturbed landscapes and fragmented forests in an area of over 19 000 km2 in southeastern Michigan, United States. Eleven upland ecosystems and 1 wetland ecosystem were identified in the field using reconnaissance, plot sampling, and test mapping. Each ecosystem type was a characteristic combination of physiography, soil, and climax vegetation (overstory and ground-cover vegetation). The ecological approach emphasized physiographic and soil factors because of the disturbed state of the vegetation. Of 222 species of ground-cover vegetation, only 68 were used in forming the 13 ecological species groups. White oak (Quercusalba L.) exhibited the largest ecological amplitude of the three major oak species; it occurred on dry to mesic sites. Red oak (Q. rubra L.) occurred on dry-mesic to mesic sites, and black oak (Q. velutina Lam.) was restricted to dry sites. Discriminant analysis was used to examine the distinctness of the upland ecosystems and to compare the error rates of different ecosystem components. The misclassification rates obtained by using all ecosystem components (physiography, soil, ecological species groups, and overstory vegetation) were the lowest: 20% in highly dissected terrain and 34% in flat to gently rolling terrain. However, results obtained with physiography–soil and ecological species group variables were nearly as good as results that added the overstory vegetation. More overlap among ecosystem types and higher misclassification rates were found than in ecosystems of old-growth forests of northern Michigan and oak forests in southwestern Wisconsin where similar methods were used. Nevertheless, for the highly disturbed forests of southern Michigan, the ecological, multifactor landscape approach is a useful and effective method of identifying, describing, and mapping ecosystem types.



1984 ◽  
Vol 23 (01) ◽  
pp. 15-22
Author(s):  
Y. Sekita ◽  
T. Ohta ◽  
M. Inoue ◽  
H. Takeda

SummaryJudgements of examinees’ health status by doctors and by the examinees themselves are compared applying multiple discriminant analysis. The doctors’ judgements of the examinees’ health status are studied comparatively using laboratory data and the examinees’ subjective symptom data.This data was obtained in an Automated Multiphasic Health Testing System. We discuss the health conditions which are significant for the judgement of doctors about the examinees. The results show that the explanatory power, when using subjective symptom data, is fair in the case of the doctors’ judgement. We found common variables, such as nervousness, lack of perseverance etc., which form the first canonical axis.



BMC Genomics ◽  
2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Julie E. Hernández-Salmerón ◽  
Gabriel Moreno-Hagelsieb

Abstract Background Finding orthologs remains an important bottleneck in comparative genomics analyses. While the authors of software for the quick comparison of protein sequences evaluate the speed of their software and compare their results against the most usual software for the task, it is not common for them to evaluate their software for more particular uses, such as finding orthologs as reciprocal best hits (RBH). Here we compared RBH results obtained using software that runs faster than blastp. Namely, lastal, diamond, and MMseqs2. Results We found that lastal required the least time to produce results. However, it yielded fewer results than any other program when comparing the proteins encoded by evolutionarily distant genomes. The program producing the most similar number of RBH to blastp was diamond ran with the “ultra-sensitive” option. However, this option was diamond’s slowest, with the “very-sensitive” option offering the best balance between speed and RBH results. The speeding up of the programs was much more evident when dealing with eukaryotic genomes, which code for more numerous proteins. For example, lastal took a median of approx. 1.5% of the blastp time to run with bacterial proteomes and 0.6% with eukaryotic ones, while diamond with the very-sensitive option took 7.4% and 5.2%, respectively. Though estimated error rates were very similar among the RBH obtained with all programs, RBH obtained with MMseqs2 had the lowest error rates among the programs tested. Conclusions The fast algorithms for pairwise protein comparison produced results very similar to blast in a fraction of the time, with diamond offering the best compromise in speed, sensitivity and quality, as long as a sensitivity option, other than the default, was chosen.



Biometrics ◽  
1974 ◽  
Vol 30 (2) ◽  
pp. 239 ◽  
Author(s):  
G. J. McLachlan


2018 ◽  
Vol 48 (3) ◽  
pp. 179-190 ◽  
Author(s):  
Henrique Luis Godinho CASSOL ◽  
Yosio Edemir SHIMABUKURO ◽  
João Manuel de Brito CARREIRAS ◽  
Elisabete Caria MORAES

ABSTRACT This paper presents a novel approach for estimating the height of individual trees in secondary forests at two study sites: Manaus (central Amazon) and Santarém (eastern Amazon) in the Brazilian Amazon region. The approach consists of adjusting tree height-diameter at breast height (H:DBH) models in each study site by ecological species groups: pioneers, early secondary, and late secondary. Overall, the DBH and corresponding height (H) of 1,178 individual trees were measured during two field campaigns: August 2014 in Manaus and September 2015 in Santarém. We tested the five most commonly used log-linear and nonlinear H:DBH models, as determined by the available literature. The hyperbolic model: H = a.DBH/(b+DBH) was found to present the best fit when evaluated using validation data. Significant differences in the fitted parameters were found between pioneer and secondary species from Manaus and Santarém by F-test, meaning that site-specific and also ecological-group H:DBH models should be used to more accurately predict H as a function of DBH. This novel approach provides specific equations to estimate height of secondary forest trees for particular sites and ecological species groups. The presented set of equations will allow better biomass and carbon stock estimates in secondary forests of the Brazilian Amazon.





Vegetatio ◽  
1988 ◽  
Vol 75 (1-2) ◽  
pp. 81-86 ◽  
Author(s):  
M. D. Swaine ◽  
T. C. Whitmore


2020 ◽  
Vol 22 (1) ◽  
Author(s):  
NAOUEL MOUALKI ◽  
Nadhra Sirine

Abstract. Moualki N, Boukrouma N. 2021. The influence of environmental factors on the distribution and composition of plant species in Oued Charef dam, North East of Algeria. Biodiversitas 22: 346-353. Identification of the primary factors that influence the ecological distribution of species groups is important to managers of Oued Charef dam in northern Algeria. This study aimed to identify main ecological species groups, describe the site conditions associated with these species groups, and the relationships between environmental factors and the distribution of ecological species groups using Ward’s cluster analysis for classification and principal component analysis (PCA). For this purpose, 50 plots (200 m2 each) were sampled using the Braun- Blanquet method. Soil samples were collected and analyzed to study soil properties. Multivariate analysis methods were used to classify and determine the relationship between species composition and environmental factors and to recognize ecological species groups. The R i386 (version 4.0.3) software was used for data analyzing. Ward's cluster analysis when applied on terrestrial species data gives three groups distinctly distributed on ordination plan. In cluster groups of terrestrial species Group (1) is dominated by Daisies chrysanthemum, Group (2) by Cynodon dactylon L, and Group (3) dominated by Fumana thymifolia. The groups of terrestrial species are readily superimposed on PCA ordination plane. The most important environmental factors associated with terrestrial species composition in Oued Charef dam communities were conductivity (EC), FSA, FSI, clay, salinity, phosphorus (PO4), TN (nitrogen), nitrates (NO3), and nitrites (NO2). While among the edaphic factors only pH showed a negative correlation to plant species this may due to the anthropogenic disturbances however further studies are needed to explore the rest of parts of the said regions. This study gives important insights on ecological relationships between plant biodiversity and soil chemical in a primary wetland ecosystem in northeast of Algeria.



Author(s):  
Kristian Skau Bjerreskov ◽  
Thomas Nord-Larsen ◽  
Rasmus Fensholt

Mapping forest extent and forest cover classification are important for the assessment of forest resources in socio-economic as well as ecological terms. Novel developments in the availability of remotely sensed data, computational resources, and advances in areas of statistical learning have enabled fusion of multi-sensor data, often yielding superior classification results. Most former studies of nemoral forests fusing multi-sensor and multi-temporal data have been limited in spatial extent and typically to a simple classification of landscapes into major land cover classes. We hypothesize that multi-temporal, multi-censor data will have a specific strength in further classification of nemoral forest landscapes owing to the distinct seasonal patterns of the phenology of broadleaves. This study aimed to classify the Danish landscape into forest/non-forest and further into forest types (broadleaved/coniferous) and species groups, using a cloud-based approach based on multi-temporal Sentinel 1 and 2 data and machine learning (random forest) trained with National Forest Inventory (NFI) data. Mapping of non-forest and forest resulted in producer accuracies of 99% and 90 %, respectively. The mapping of forest types (broadleaf and conifer) within the forested area resulted in producer accuracies of 95% for conifer and 96% for broadleaf forest. Tree species groups were classified with producer accuracies ranging 34-74%. Species groups with coniferous species were the least confused whereas the broadleaf groups, especially Oak, had higher error rates. The results are applied in Danish National accounting of greenhouse gas emissions from forests, resource assessment and assessment of forest biodiversity potentials.



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