scholarly journals Species variables or environmental variables as indicators of forest biodiversity: a case study using saproxylic beetles in Maritime pine plantations

2009 ◽  
Vol 66 (3) ◽  
pp. 306 ◽  
Author(s):  
Antoine Brin ◽  
Hervé Brustel ◽  
Hervé Jactel
2020 ◽  
pp. 153465012098345
Author(s):  
Mirela Cengher ◽  
Joy C. Clayborne ◽  
Adrianna E. Crouch ◽  
Julia T. O’Connor

Over 60% of children diagnosed with selective mutism are also diagnosed with Autism Spectrum Disorder. Previous research established that behavioral interventions are effective at increasing speech in children with both diagnoses. However, few studies conducted assessments to determine environmental variables that inhibit speech, and such assessments are necessary for the development of effective and efficient treatments. This case study describes an assessment that evaluated the function(s) of selective mutism. The results confirmed that the participant did not talk to avoid social interaction and that mutism occurred primarily in the presence of multiple, unfamiliar people. Our first treatment focused on increasing tolerance for social interaction, demonstrated by an increase in speech production in the presence of unfamiliar people. Our second treatment focused on increasing qualitative aspects of the participant’s speech (i.e., both responses and initiations). Finally, we taught the participant’s parents to implement the treatment in naturalistic settings, and the participant demonstrated generalization of treatment effects across people and settings. Implications for clinical practice and future research are discussed.


2021 ◽  
pp. 181-196
Author(s):  
Edgar J. González ◽  
Dylan Z. Childs ◽  
Pedro F. Quintana-Ascencio ◽  
Roberto Salguero-Gómez

Integral projection models (IPMs) allow projecting the behaviour of a population over time using information on the vital processes of individuals, their state, and that of the environment they inhabit. As with matrix population models (MPMs), time is treated as a discrete variable, but in IPMs, state and environmental variables are continuous and are related to the vital rates via generalised linear models. Vital rates in turn integrate into the population dynamics in a mechanistic way. This chapter provides a brief description of the logic behind IPMs and their construction, and, because they share many of the analyses developed for MPMs, it only emphasises how perturbation analyses can be performed with respect to different model elements. The chapter exemplifies the construction of a simple and a more complex IPM structure with an animal and a plant case study, respectively. Finally, inverse modelling in IPMs is presented, a method that allows population projection when some vital rates are not observed.


2018 ◽  
Vol 39 (2) ◽  
Author(s):  
Ignacio Ramos Gutiérrez ◽  
Juan Manuel Martínez Labarga ◽  
José Araújo Díaz ◽  
Alejandro G. Fernández de Castro ◽  
Juan Carlos Moreno Saiz

2021 ◽  
Vol 221 ◽  
pp. 110874
Author(s):  
Houssain Zitouni ◽  
Alae Azouzoute ◽  
Charaf Hajjaj ◽  
Massaab El Ydrissi ◽  
Mohammed Regragui ◽  
...  

2011 ◽  
Vol 20 (5) ◽  
pp. 929-943 ◽  
Author(s):  
Evangelia G. Drakou ◽  
Athanasios S. Kallimanis ◽  
Antonios D. Mazaris ◽  
Evangelia Apostolopoulou ◽  
John D. Pantis

2003 ◽  
Vol 95 (1) ◽  
pp. 379-386 ◽  
Author(s):  
J.W.H. Ferguson ◽  
A.S. van Jaarsveld ◽  
R. Johnson ◽  
G.J. Bredenkamp ◽  
S.H. Foord ◽  
...  

2015 ◽  
Vol 54 (7) ◽  
pp. 1637-1662 ◽  
Author(s):  
Jason M. Apke ◽  
Daniel Nietfeld ◽  
Mark R. Anderson

AbstractEnhanced temporal and spatial resolution of the Geostationary Operational Environmental Satellite–R Series (GOES-R) will allow for the use of cloud-top-cooling-based convection-initiation (CI) forecasting algorithms. Two such algorithms have been created on the current generation of GOES: the University of Wisconsin cloud-top-cooling algorithm (UWCTC) and the University of Alabama in Huntsville’s satellite convection analysis and tracking algorithm (SATCAST). Preliminary analyses of algorithm products have led to speculation over preconvective environmental influences on algorithm performance. An objective validation approach is developed to separate algorithm products into positive and false indications. Seventeen preconvective environmental variables are examined for the positive and false indications to improve algorithm output. The total dataset consists of two time periods in the late convective season of 2012 and the early convective season of 2013. Data are examined for environmental relationships using principal component analysis (PCA) and quadratic discriminant analysis (QDA). Data fusion by QDA is tested for SATCAST and UWCTC on five separate case-study days to determine whether application of environmental variables improves satellite-based CI forecasting. PCA and significance testing revealed that positive indications favored environments with greater vertically integrated instability (CAPE), less stability (CIN), and more low-level convergence. QDA improved both algorithms on all five case studies using significantly different variables. This study provides an examination of environmental influences on the performance of GOES-R Proving Ground CI forecasting algorithms and shows that integration of QDA in the cloud-top-cooling-based algorithms using environmental variables will ultimately generate a more skillful product.


2014 ◽  
Vol 134 (2) ◽  
pp. 223-233 ◽  
Author(s):  
Manuel Arias-Rodil ◽  
Felipe Crecente-Campo ◽  
Marcos Barrio-Anta ◽  
Ulises Diéguez-Aranda

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