DOI: 10.5061/dryad.b5mkkwhnk
title: Data from: Importance of accounting for imperfect detection of plants in the estimation of population growth rates

description: Detection of plant individuals is imperfect. Not accounting for this issue
 can result in biased estimates of demographic parameters as important as
 population growth rates. In mobile organisms, a common practice is to
 explicitly account for detection probability during the estimation of most
 demographic parameters, but no study in plant populations has examined the
 consequences of ignoring imperfect detectability on the estimation of
 population growth rates. The lack of accounting for detection probability
 occurs because plant demographers have frequently assumed that detection
 is perfect, and because there is a scarcity of studies that formally
 compare the performance of estimation methods that incorporate detection
 probabilities with respect to methods that ignore detectabilities. Based
 on field data of five plant species and data simulations, we compared the
 performance of three methods that estimate population growth rates, two
 that do not estimate detection probabilities (direct counts of individuals
 and the minimum-number-alive method) and the other that explicitly
 accounts for detection probabilities (temporal symmetry models). Our aims
 were (1) to estimate detection probabilities, and (2) to evaluate the
 performance of these three methods by calculating bias, accuracy, and
 precision in their estimates of population growth rates. Our five plant
 species had imperfect detection. Estimates of population growth rates that
 explicitly incorporate detectabilities had better performance (less biased
 estimates, with higher accuracy and precision) than those obtained with
 the two methods that do not calculate detection probabilities. In these
 latter methods, bias increases as detection probability decreases. Our
 findings highlight the importance of using robust analytical methods that
 account for detection probability of plants during the estimation of
 critical demographic parameters such as population growth rates. In this
 way, estimates of plant population parameters will reliably indicate their
 actual status and quantitative trends.
description: # Data from: Importance of accounting for imperfect detection of plants in
 the estimation of population growth rates
 [https://doi.org/10.5061/dryad.b5mkkwhnk](https://doi.org/10.5061/dryad.b5mkkwhnk) ## Description of the data and file structure Each supplementary data file (.inp) contains the following information. The name of the files is indicated with the four first letter of the genus and the four first letters of its specific epithet. * The first columns (12 for *F. chlorifolia*, five for *M. hernandezii*, six for *N. macrocephala*, four for *G. lagenophora*, and nine for *C. tenuiflora*) correspond to detection histories of the species specified in the name of the file. Detection (1) and non-detection (0). * The following blank column is to separate the penultimate column. * Penultimate column indicates the number of each observed detection history. * The last column contains only semicolons that are necessary for loading data as .inp files in Program MARK ([https://www.phidot.org/software/mark/](https://www.phidot.org/software/mark/)). Simulation procedure is detailed in the main text. Values used for simulation are showed in Fig. 1 (detection probability, *p*) and in Table S3 (apparent survival, φ, and recruitment, *f*).