Search USGS⌕ Search
Home/Archive/USGS

USGS · 70031459

The accuracy of matrix population model projections for coniferous trees in the Sierra Nevada, California

P. J. van Mantgem·N.L. Stephenson
View original source ↗Export metadata ↓

Also available from

Abstract

1 We assess the use of simple, size-based matrix population models for projecting population trends for six coniferous tree species in the Sierra Nevada, California. We used demographic data from 16 673 trees in 15 permanent plots to create 17 separate time-invariant, density-independent population projection models, and determined differences between trends projected from initial surveys with a 5-year interval and observed data during two subsequent 5-year time steps. 2 We detected departures from the assumptions of the matrix modelling approach in terms of strong growth autocorrelations. We also found evidence of observation errors for measurements of tree growth and, to a more limited degree, recruitment. Loglinear analysis provided evidence of significant temporal variation in demographic rates for only two of the 17 populations. 3 Total population sizes were strongly predicted by model projections, although population dynamics were dominated by carryover from the previous 5-year time step (i.e. there were few cases of recruitment or death). Fractional changes to overall population sizes were less well predicted. Compared with a null model and a simple demographic model lacking size structure, matrix model projections were better able to predict total population sizes, although the differences were not statistically significant. Matrix model projections were also able to predict short-term rates of survival, growth and recruitment. Mortality frequencies were not well predicted. 4 Our results suggest that simple size-structured models can accurately project future short-term changes for some tree populations. However, not all populations were well predicted and these simple models would probably become more inaccurate over longer projection intervals. The predictive ability of these models would also be limited by disturbance or other events that destabilize demographic rates. ?? 2005 British Ecological Society.

Explore related subjects

Journal of EcologyArticle

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

P. J. van Mantgem, N.L. Stephenson. 2005-05-03. The accuracy of matrix population model projections for coniferous trees in the Sierra Nevada, California. https://doi.org/10.1111/j.1365-2745.2005.01007.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↗