| 1. |
- Ekström, Magnus, 1966-, et al.
(författare)
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Estimating density from presence/absence data in clustered populations
- 2020
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Ingår i: Methods in Ecology and Evolution. - : John Wiley & Sons. - 2041-210X. ; 11:3, s. 390-402
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Tidskriftsartikel (refereegranskat)abstract
- Inventories of plant populations are fundamental in ecological research and monitoring, but such surveys are often prone to field assessment errors. Presence/absence (P/A) sampling may have advantages over plant cover assessments for reducing such errors. However, the linking between P/A data and plant density depends on model assumptions for plant spatial distributions. Previous studies have shown, for example, how that plant density can be estimated under Poisson model assumptions on the plant locations. In this study, new methods are developed and evaluated for linking P/A data with plant density assuming that plants occur in clustered spatial patterns. New theory was derived for estimating plant density under Neyman-Scott-type cluster models such as the Matern and Thomas cluster processes. Suggested estimators, corresponding confidence intervals and a proposed goodness-of-fit test were evaluated in a Monte Carlo simulation study assuming a Matern cluster process. Furthermore, the estimators were applied to plant data from environmental monitoring in Sweden to demonstrate their empirical application. The simulation study showed that our methods work well for large enough sample sizes. The judgment of what is' large enough' is often difficult, but simulations indicate that a sample size is large enough when the sampling distributions of the parameter estimators are symmetric or mildly skewed. Bootstrap may be used to check whether this is true. The empirical results suggest that the derived methodology may be useful for estimating density of plants such as Leucanthemum vulgare and Scorzonera humilis. By developing estimators of plant density from P/A data under realistic model assumptions about plants' spatial distributions, P/A sampling will become a more useful tool for inventories of plant populations. Our new theory is an important step in this direction.
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| 2. |
- Ståhl, Göran, et al.
(författare)
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Informative plot sizes in presence-absence sampling of forest floor vegetation
- 2017
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Ingår i: Methods in Ecology and Evolution. - Hoboken : British Ecological Society. - 2041-210X. ; 8:10, s. 1284-1291
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Tidskriftsartikel (refereegranskat)abstract
- 1. Plant communities are attracting increased interest in connection with forest and landscape inventories due to society’s interest in ecosystem services. However, the acquisition of accurate information about plant communities poses several methodological challenges. Here, we investigate the use of presence-absence sampling with the aim to monitor state and change in plant density. We study what plot sizes are informative, i.e. the estimators should have as high precision as possible.2. Plant occurrences were modelled through different Poisson processes and tests were developed for assessing the plausibility of the model assumptions. Optimum plot sizes were determined by minimizing the variance of the estimators. While state estimators of similar kind as ours have been proposed in previous studies, our tests and change estimation procedures are new.3. We found that the most informative plot size for state estimation is 1.6 divided by the plant density, i.e. if the true density is 1 plant per square metre the optimum plot size is 1.6 square metres. This is in accordance with previous findings. More importantly, the most informative plot size for change estimation was smaller and depended on the change patterns. We provide theoretical results as well as some empirical results based on data from the Swedish National Forest Inventory.4. Use of too small or too large plots resulted in poor precision of the density (and density change) estimators. As a consequence, a range of different plot sizes would be required for jointly monitoring both common and rareplants using presence-absence sampling in monitoring programmes.
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| 3. |
- Ståhl, Göran, et al.
(författare)
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Presence-absence sampling for estimating plant density using survey data with variable plot size
- 2020
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Ingår i: Methods in Ecology and Evolution. - : John Wiley & Sons. - 2041-210X. ; 11:4, s. 580-590
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Tidskriftsartikel (refereegranskat)abstract
- Presence–absence sampling is an important method for monitoring state and change of both individual plant species and communities. With this method, only the presence or absence of the target species is recorded on plots and thus the method is straightforward to apply and less prone to surveyor judgement compared to other vegetation monitoring methods. However, in the basic setting, all plots must be equally large or otherwise it is unclear how data should be analysed. In this study, we propose and evaluate five different methods for estimating plant density based on presence–absence registrations from surveys with variable plot sizes.Using artificial plant population data as well as empirical data from the Swedish National Forest Inventory, we evaluated the performance of the proposed methods. The main analysis was conducted through sampling simulation in artificial populations, whereby bias and variance of density estimators for the different methods were quantified and compared.Both for state and change estimation of plant density, we found that the best method to handle variable plot size was to perform generalized least squares regression, using plot size as an independent variable. Methods where plots smaller than a certain threshold were excluded or their registrations recalculated were, however, almost as good. Using all registrations as if they were obtained from plots with the nominal plot size resulted in substantial bias.Our findings are important for plant population studies in a wide range of environmental monitoring programmes. In these programmes, plots are typically randomly laid out and may be located across boundaries between different land‐use or land‐cover classes, resulting in subplots of variable size. Such splitting of plots is common when large plots are used, for example, with the 100 m2 plots used in the Swedish National Forest Inventory. Our methods overcome problems to estimate plant density from presence–absence data observed in plots that vary in size.
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