Éditeur(s) : Black Studies Department, Amherst College Department of English, University of Florida Black Studies Department, Amherst College ( Amherst, MA ) Department of English, University of Florida ( Gainesville, FL )
Résumé : Based on the reading form for BLST 111 from the Black Studies Department, Amherst College.

Droits : [cc by-nc-sa] This item is licensed with the Creative Commons Attribution Non-Commercial Share Alike License. This license lets others remix, tweak, and build upon this work non-commercially, as long as they credit the author and license their new creations under the identical terms.
http://ufdc.ufl.edu/AA00015986/00001

Éditeur(s) : Black Studies Department, Amherst College Black Studies Department, Amherst College ( Amherst, MA )

Droits : [cc by-nc-sa] This item is licensed with the Creative Commons Attribution Non-Commercial Share Alike License. This license lets others remix, tweak, and build upon this work non-commercially, as long as they credit the author and license their new creations under the identical terms.
http://ufdc.ufl.edu/AA00015986/00001

Éditeur(s) : HAL CCSD Wiley
Résumé : International audience
1. The Before-After Control-Impact Paired Series (BACIPS) design distinguishes natural spatial and temporalvariability from variation induced by an environmental impact (or intervention) of interest. BACIPS is a powerfultool to derive inferences about interventions when classic experimental approaches (e.g. which rely on spatialreplicates and random assignment of treatments) are not feasible or desirable. Previously applied BACIPSdesigns generally assume that effects are sudden, constant and long-lived: that is, that systems exhibit ‘stepchanges’in response to interventions. However, complex ecological interactions or gradual interventions maycreate delayed and/or progressive responses, potentially impeding the reliability of classic (step-change) analyses.2. We propose a novel approach, the Progressive-Change BACIPS, which generalizes and expands the scope ofBACIPS analyses.We evaluate the relative performance of this approach using both simulated and real data thatexhibit step-change, linear, asymptotic and sigmoid responses following an intervention.We quantify the statisticalpower and accuracy of the Progressive-Change BACIPS under varying initial population densities, intensityof spatial sampling, effect sizes and number of sampling dates After the intervention.3. We show that Progressive-Change BACIPS identified the correct model among the set of candidate modelsunder most conditions and led to accurate estimates of the parameters that were used to generate the simulateddata. When data were sparse, and the dynamics complex, simpler (more parsimonious) models were favouredover the more complex models that actually generated the simulated data. Application of the Progressive-Change BACIPS to existing data sets from the literature led to strong support for specific models (over alternatives)and led to more specific inferences than possible under the classic BACIPS approach.4. The Progressive-Change BACIPS proposed here is more flexible than the original BACIPS formulationbecause the data are used to inform the form of the final model, rather than having the form of the modelimposed on the data. This leads to better estimates of the effects of environmental impacts and the time-scalesover which they operate. As a result, the Progressive-Change BACIPS should be applicable to a wide range ofstudies and should help improve investigation of time-dependent effects. R code to perform Progressive-ChangeBACIPS analysis is provided.
ISSN: 2041-210X

hal-01416668
https://hal-univ-perp.archives-ouvertes.fr/hal-01416668
DOI : 10.1111/2041-210X.12655