Most cited article - PubMed ID 28503083
Vegetation resurvey is robust to plot location uncertainty
Biodiversity time series reveal global losses and accelerated redistributions of species, but no net loss in local species richness. To better understand how these patterns are linked, we quantify how individual species trajectories scale up to diversity changes using data from 68 vegetation resurvey studies of seminatural forests in Europe. Herb-layer species with small geographic ranges are being replaced by more widely distributed species, and our results suggest that this is due less to species abundances than to species nitrogen niches. Nitrogen deposition accelerates the extinctions of small-ranged, nitrogen-efficient plants and colonization by broadly distributed, nitrogen-demanding plants (including non-natives). Despite no net change in species richness at the spatial scale of a study site, the losses of small-ranged species reduce biome-scale (gamma) diversity. These results provide one mechanism to explain the directional replacement of small-ranged species within sites and thus explain patterns of biodiversity change across spatial scales.
- MeSH
- Biodiversity MeSH
- Ecosystem * MeSH
- Forests * MeSH
- Plants MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Geographicals
- Europe MeSH
QUESTIONS: Did high densities of wild ungulates cause a decline in plant species richness in a temperate oakwood? How did species composition change after nearly five decades? Did ungulates facilitate the spread of ruderal species and supress endangered species? Did dispersal strategies play a role in these processes? LOCATION: Krumlov Wood, SE Czech Republic. METHODS: In 2012, we resampled 58 quasi-permanent vegetation plots first surveyed in 1960s. Between the surveys, 36 plots were enclosed in a game preserve with artificially high density of ungulates (mostly deer, mouflon and wild boar; ca. 55 animals per square km). We analysed the differences in temporal changes between plots inside and outside the game preserve, focusing on species diversity and composition. We assessed species characteristics relevant to grazing to understand compositional changes. RESULTS: Ungulates significantly increased alpha and gamma diversity and caused significant vegetation homogenization inside the game preserve. Vegetation homogenization and the increase in species richness resulted from massive enrichment by ruderal species. However, richness of endangered species decreased. Species dispersed by animals internally (endozoochory) increased, while species dispersed externally (epizoochory) or by wind (anemochory) decreased. CONCLUSIONS: Contrary to our expectations, our long-term data showed that artificially high ungulate densities substantially increased plant species richness. Apparently, the establishment of ruderal herbs was supported by frequent disturbances and ungulate-mediated dispersal. At the same time, species richness of non-ruderal plants did not change, probably because ungulates hindered the regeneration of woody species and maintained an open forest canopy. In conclusion, high ungulate density led to the spread of ruderal species, which in turn strongly contributed to the observed shift towards nutrient-richer conditions and taxonomically more homogenous communities.
- Keywords
- deer, disturbance, game preserve, long-term change, plant-herbivore interactions, semi-permanent plots, species richness, taxonomic homogenization, vegetation resurvey,
- Publication type
- Journal Article MeSH
More and more ecologists have started to resurvey communities sampled in earlier decades to determine long-term shifts in community composition and infer the likely drivers of the ecological changes observed. However, to assess the relative importance of, and interactions among, multiple drivers joint analyses of resurvey data from many regions spanning large environmental gradients are needed. In this paper we illustrate how combining resurvey data from multiple regions can increase the likelihood of driver-orthogonality within the design and show that repeatedly surveying across multiple regions provides higher representativeness and comprehensiveness, allowing us to answer more completely a broader range of questions. We provide general guidelines to aid implementation of multi-region resurvey databases. In so doing, we aim to encourage resurvey database development across other community types and biomes to advance global environmental change research.
- Keywords
- (quasi-)permanent plots, community ecology, ground layer vegetation, legacy data, temperate forest,
- Publication type
- Journal Article MeSH
BACKGROUND: Resurveying historical vegetation plots has become more and more popular in recent years as it provides a unique opportunity to estimate vegetation and environmental changes over the past decades. Most historical plots, however, are not permanently marked and uncertainty in plot location, in addition to observer bias and seasonal bias, may add significant error to temporal change. These errors may have major implications for the reliability of studies on long-term environmental change and deserve closer attention of vegetation ecologists. MATERIAL & METHODS: Vegetation data obtained from the resurveying of non-permanently marked plots are assessed for their potential to study environmental-change effects on plant communities and the challenges the use of such data have to meet. We describe the properties of vegetation resurveys distinguishing basic types of plots according to relocation error, and we highlight the potential of such data types for studying vegetation dynamics and their drivers. Finally, we summarise the challenges and limitations of resurveying non-permanently marked vegetation plots for different purposes in environmental change research. RESULTS AND CONCLUSIONS: Resampling error is caused by three main independent sources of error: error caused by plot relocation, observer bias, and seasonality bias. For relocation error, vegetation plots can be divided into permanent and non-permanent plots, while the latter are further divided into quasi-permanent (with approximate relocation) and non-traceable (with random relocation within a sampled area) plots. To reduce the inherent sources of error in resurvey data, the following precautions should be followed: (i) resurvey historical vegetation plots whose approximate plot location within a study area is known; (ii) consider all information available from historical studies in order to keep plot relocation errors low; (iii) resurvey at times of the year when vegetation development is comparable to the historical survey to control for seasonal variability in vegetation; (iv) keep a high level of experience of the observers to keep observer bias low; and (v) edit and standardise datasets before analyses.
