Effects of life-state on detectability in a demographic study of the terrestrial orchid Cleistes bifaria

M. Kery; K.B. Gregg

doi:10.1046/j.1365-2745.2003.00759.x

Home/Archive/USGS

USGS · 5224234

Effects of life-state on detectability in a demographic study of the terrestrial orchid Cleistes bifaria

M. Kery·K.B. Gregg

View original source ↗Export metadata ↓

Abstract

1. Most plant demographic studies follow marked individuals in permanent plots. Plots tend to be small, so detectability is assumed to be one for every individual. However, detectability could be affected by factors such as plant traits, time, space, observer, previous detection, biotic interactions, and especially by life - state . 2. We used a double-observer survey and closed population capture-recapture modelling to estimate state -specific detectability of the orchid Cleistes bifaria in a long-term study plot of 41.2 m 2 . Based on AIC c model selection, detectability was different for each life - state and for tagged vs. previously untagged plants. There were no differences in detectability between the two observers. 3. Detectability estimates (SE) for one-leaf vegetative, two-leaf vegetative, and flowering/fruiting states correlated with mean size of these states and were 0.76 (0.05), 0.92 (0.06), and 1 (0.00), respectively, for previously tagged plants, and 0.84 (0.08), 0.75 (0.22), and 0 (0.00), respectively, for previously untagged plants. (We had insufficient data to obtain a satisfactory estimate of previously untagged flowering plants). 4. Our estimates are for a medium-sized plant in a small and intensively surveyed plot. It is possible that detectability is even lower for larger plots and smaller plants or smaller life -states (e.g. seedlings) and that detectabilities < I are widespread in plant demographic studies. 5. State -dependent detectabilities are especially worrying since they will lead to a size- or state -biased sample from the study plot. Failure to incorporate detectability into demographic estimation methods introduces a bias into most estimates of population parameters such as fecundity, recruitment, mortality, and transition rates between life -states. We illustrate this by a simple example using a matrix model, where a hypothetical population was stable but, due to imperfect detection, wrongly projected to be declining at a rate of 8% per year. 6. Almost all plant demographic studies are based on models for discrete states. State and size are important predictors both for demographic rates and detectability . We suggest that even in studies based on small plots, state - or size-specific detectability should be estimated at least at some point to avoid biased inference about the dynamics of the population sampled.

Explore related subjects

Journal of Ecology Article

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

M. Kery, K.B. Gregg. 2003. Effects of life-state on detectability in a demographic study of the terrestrial orchid Cleistes bifaria. https://doi.org/10.1046/j.1365-2745.2003.00759.x

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

USGS2026

Long-term aridity shapes grassland drought resistance and modulates the roles of plant diversity and functional composition

Understanding the drivers of plant community stability is crucial for predicting ecosystem responses to extreme drought events. In grasslands, drought resistance supports the maintenance of key functions such as above-ground primary productivity, making the identification of resistance drivers essential to guide management under climate change. Proposed factors contributing to grassland stability include multiple diversity facets, functional traits and long-term climate, but most assessments focus on temporal invariability under historical disturbance regimes, leaving mechanisms of extreme drought resistance and their variation across climatic contexts relatively underexplored. Here, we analysed data from 54 grassland sites of the International Drought Experiment to examine the resistance of above-ground net primary productivity to a short-term (i.e. 1 year) extreme drought. We investigated the relative importance and joint influence of functional composition (i.e. community-weighted means of leaf and root traits), plant diversity facets (taxonomic, functional and phylogenetic) and climate (aridity and rainfall variability) on drought resistance. We used structural equation models to disentangle direct, indirect, and moderating pathways linking these drivers to drought resistance. Long-term aridity appeared as one of the most important drivers of grassland resistance to drought, with more arid sites showing lower resistance. Moreover, aridity impacted resistance through indirect effects by shaping functional composition and plant diversity, and by moderating the influence of plant diversity and functional composition. Functional composition related to dehydration avoidance and dehydration tolerance was also positively associated with resistance, while diversity had a weaker relationship with resistance, mostly through functional and phylogenetic facets. Interannual rainfall variability also influenced resistance, with different effects in more arid versus humid and less arid sites. Synthesis . Widely studied stability drivers such as plant diversity and functional composition have only partial explanatory power for short-term drought resistance of above-ground productivity in grasslands at a global scale. The abiotic context, particularly long-term aridity, is crucial for understanding ecosystem responses to rainfall variation and can improve predictive models for advancing the study of ecosystem resistance to drought. Along with management practices that target high species diversity or specific traits, restoration and conservation practices should support vulnerable sites experiencing high aridity

Journal of Ecology↗

USGS2026

Overstorey mortality promotes juvenile piñon pine growth during favourable weather at cooler, wetter sites

Hotter droughts have resulted in widespread tree die-off events globally, frequently leading to regeneration failure. Dry forest recovery often depends on the growth and survival of extant juvenile trees. However, it is unclear how microenvironmental changes following overstorey tree die-off affect juvenile trees, particularly in dryland systems where tree recruitment is typically limited by water availability and heat stress. We simulated an overstorey tree die-off event by girdling trees in piñon-juniper woodlands across the south-western United States. We sampled juvenile piñon pine growth from live and dead overstorey treatments across six study sites spanning a regional latitudinal gradient and local elevational gradients. We examined how juvenile branch and needle growth differed between live and dead overstorey treatments, and whether responses varied with weather conditions and juvenile tree size following overstorey mortality. We found greater juvenile branch and needle growth under dead compared with live overstorey trees for 2 years following overstorey mortality at mid- and high-elevation sites which are typically cooler and wetter than the other sites. These observed growth releases were contingent on favourable post-mortality weather conditions. Higher growth under dead overstorey occurred at sites experiencing near-average climatic water deficits compared with sites experiencing above-average climatic water deficits. Growth at all sites increased from the first to second year after overstorey mortality. Across sites, growth was unrelated to juvenile tree size. Synthesis . Our results underscore differentiation in juvenile responses to overstorey tree die-off driven by local site conditions and weather across the range of Pinus edulis . Overstorey mortality resulted in consistently higher juvenile growth only at climatically favourable sites and during favourable weather, while unmeasured microsite differences could help account for variation observed at the hottest and driest site. Results from less climatically favourable sites suggest that overstorey trees neither directly limit nor facilitate juvenile growth, though further study over longer timeframes is needed to resolve the pace and magnitude of potential recovery or decline. Overall, juvenile vigour may be promoted following overstorey mortality only in a narrow spatial (site) and temporal (weather) environmental context, suggesting additional vulnerabilities for piñon populations under more arid conditions.

Arizona, Colorado↗

USGS2025

Causal interpretations can be based on mechanistic knowledge

There exists a long-standing disconnect between statistical and mechanistic approaches to the development of causal understanding. Statistical approaches, which have dominated the literature, have focused on the need to obtain perfectly unbiased estimates of causal effects often using either experimental, quasi-experimental or other methods. Mechanistic approaches have instead focused on investigating how systems work by elucidating the structures and processes whereby variations in one system property can propagate to other system properties. Explicit references to ‘causal effects’ have tended to require adherence to statistical methods and standards, inadvertently downplaying the suitability of mechanistic knowledge for that purpose. It has been recently demonstrated that both mechanistic and statistical approaches can contribute to the long-term goal of developing causal knowledge and understanding. Proponents of statistical causal inference have seldom recommended that mechanistic evidence be relied upon to support causal interpretations. This paper provides a clear and thorough example where a causal interpretation can be supported based on mechanistic knowledge. Arguing for a causal interpretation based on knowledge of mechanisms has typically been an informal process and one that has thus far infrequently led to explicit declarations of causal knowledge by scientists. To overcome this problem, we illustrate a recently described procedure referred to as ‘causal knowledge analysis’ to summarize explicit support for causal interpretations. In this paper, we first clarify the basis of the long-standing disagreement by describing the crux of the problem as viewed from a statistical perspective and by describing how it can be overcome when there is sufficient mechanistic knowledge. We then offer a proof-of-concept example based on robust documentation and description of the mechanisms whereby plants causally regulate the responses of coastal marsh elevation to changes in sea level. Synthesis —The evidential requirements for declaring a relationship to be causal have been obscured until very recently, leading to a long neglect of this issue by scientists. Meanwhile, subject matter experts have accumulated a vast body of undeclared causal knowledge that we now need to recognize in order to position scientists as essential players in defending causal interpretations.

Journal of Ecology↗