How well is current plant trait composition predicted by modern and historical forest spatial configuration?

Kimberley, Adam; Blackburn, George Alan; Whyatt, J. Duncan; Smart, Simon M.

doi:10.1111/ecog.01607

Cited by 12 publications

(11 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While our analysis succeeded in explaining some site‐to‐site variation in plant community trends, much variation remains unexplained. Accounting for other variables, such as grazing pressures, current and previous landscape context, or land ownership, may improve the amount of variation explained in response trajectories (Bergès, Avon, Verheyen, & Dupouey, ; Kimberley, Blackburn, Whyatt, & Smart, , ). However, the implications of our results, i.e.…”

Section: Discussionmentioning

confidence: 99%

Global environmental change effects on plant community composition trajectories depend upon management legacies

Perring

Bernhardt‐Römermann

Baeten

et al. 2018

Global Change Biology

112

View full text Add to dashboard Cite

The contemporary state of functional traits and species richness in plant communities depends on legacy effects of past disturbances. Whether temporal responses of community properties to current environmental changes are altered by such legacies is, however, unknown. We expect global environmental changes to interact with land-use legacies given different community trajectories initiated by prior management, and subsequent responses to altered resources and conditions. We tested this expectation for species richness and functional traits using 1814 survey-resurvey plot pairs of understorey communities from 40 European temperate forest datasets, syntheses of management transitions since the year 1800, and a trait database. We also examined how plant community indicators of resources and conditions changed in response to management legacies and environmental change. Community trajectories were clearly influenced by interactions between management legacies from over 200 years ago and environmental change. Importantly, higher rates of nitrogen deposition led to increased species richness and plant height in forests managed less intensively in 1800 (i.e., high forests), and to decreases in forests with a more intensive historical management in 1800 (i.e., coppiced forests). There was evidence that these declines in community variables in formerly coppiced forests were ameliorated by increased rates of temperature change between surveys. Responses were generally apparent regardless of sites' contemporary management classifications, although sometimes the management transition itself, rather than historic or contemporary management types, better explained understorey responses. Main effects of environmental change were rare, although higher rates of precipitation change increased plant height, accompanied by increases in fertility indicator values. Analysis of indicator values suggested the importance of directly characterising resources and conditions to better understand legacy and environmental change effects. Accounting for legacies of past disturbance can reconcile contradictory literature results and appears crucial to anticipating future responses to global environmental change.

show abstract

Section: Discussionmentioning

confidence: 99%

Global environmental change effects on plant community composition trajectories depend upon management legacies

Perring

Bernhardt‐Römermann

Baeten

et al. 2018

Global Change Biology

112

View full text Add to dashboard Cite

show abstract

“…However, with the development of community phylogenetics (Webb et al, 2002) and trait-based approaches to studying community size and structure (Shipley, 2010), the use of intrinsic variables as both response and predictor variables in assemblage/community analyses is rapidly expanding (e.g. Swenson & Enquist, 2007;Jansson & Davies, 2008;Mayfield et al, 2010;Swenson et al, 2012Swenson et al, , 2016Dubuis et al, 2013;Stuart-Smith et al, 2013;Hawkins et al, 2014;Leing€ artner et al, 2014;Albouy et al, 2015;Belmaker & Jetz, 2015;Blonder et al, 2015;Enquist et al, 2015;Finegan et al, 2015;Godoy et al, 2015;Honorio Coronado et al, 2015;Lima-Mendez et al, 2015;Seymour et al, 2015;S ımov a et al, 2015;Stevens & Gavilanez, 2015;Zhang et al, 2015;Biswas et al, 2016;Boucher-Lalonde et al, 2016;Gonz alez-Maya et al, 2016;Kimberly et al, 2016;Marin & Hedges, 2016;Pfautsch et al, 2016;de la Riva et al, 2016). The assumption or hypothesis underlying all such analyses is that species attributes sort geographically according to their responses to the abiotic and biotic environment.…”

Section: Introductionmentioning

confidence: 99%

Structural bias in aggregated species‐level variables driven by repeated species co‐occurrences: a pervasive problem in community and assemblage data

Hawkins

Leroy

Rodrı́guez

et al. 2017

Journal of Biogeography

View full text Add to dashboard Cite

Aim Species attributes are often used to explain diversity patterns across assemblages/communities. However, repeated species co‐occurrences can generate spatial pattern and strong statistical relationships between aggregated attributes and richness in the absence of biological information. Our aim is to increase awareness of this problem. Location North America. Methods We generated empirical species richness patterns using two data structures: (1) birds gridded from range maps and (2) tree communities from the US Forest Service's Forest Inventory and Analysis. We analysed richness using linear regression, regression trees, generalized additive models, geographically weighted regression and simultaneous autoregression, with ‘random intrinsic variables’ as predictors generated by assigning random numbers to species and calculating averages in assemblages. We then generated simulations in which species with cohesive or patchy distributions are placed with respect to the North American temperature gradient with or without a broad‐scale richness gradient. Random intrinsic variables are again used as predictors of richness. Finally, we analysed one simulated scenario with random intrinsic variables as both response and predictor variables. Results The models of bird and tree richness often explained moderate to large proportions of the variance. Regression trees, geographically weighted regression and simultaneous autoregression were very sensitive to the problem; generalized additive models were moderately affected, as was multiple regression to a lesser extent. In the virtual data, the variance explained increased with increasing species co‐occurrences, but neither range cohesion, a richness gradient nor spatial autocorrelation in predictors had major impacts on the variance explained. The problem persisted when the response variable was also a random intrinsic variable. Main conclusions Repeated species co‐occurrences can generate strong spurious relationships between richness and aggregated species attributes. It is important to realize that models utilizing assemblage variables aggregated from species‐level values, as well as maps illustrating their spatial patterns, cannot be taken at face value.

show abstract

“…While research into the causes and consequences of eutrophication was a response to clear policy interest, analysis of CS vegetation data has also contributed evidence in response to concerns over the causes and consequences of loss of pollinators in north-west Europe and Britain Carvell et al, 2006;Baude et al, 2016). Habitat specific studies, such as those relating to woodlands (for example Petit et al, 2004;Kimberley et al, 2013Kimberley et al, , 2016 and hedgerows, McCollin et al, 2000;Garbutt and Sparks, 2002;Critchley et al, 2013) have been facilitated through the use of CS data. Interesting conclusions have been made through use of the data with regard to increasing numbers of non-native invasive species (Chytrý et al, 2008;Maskell et al, 2006).…”

Section: Wider Uses Of Data To Datementioning

confidence: 99%

Long-term vegetation monitoring in Great Britain – the Countryside Survey 1978–2007 and beyond

Wood

Smart

Bunce

et al. 2017

Earth Syst. Sci. Data

Self Cite

View full text Add to dashboard Cite

Abstract. The Countryside Survey (CS) of Great Britain provides a globally unique series of datasets, consisting of an extensive set of repeated ecological measurements at a national scale, covering a time span of 29 years. CS was first undertaken in 1978 to monitor ecological and land use change in Britain using standardised procedures for recording ecological data from representative 1 km squares throughout the country. The same sites, with some additional squares, were used for subsequent surveys of vegetation undertaken in 1990, 1998 and 2007, with the intention of future surveys. Other data records include soils, freshwater habitats and invertebrates, and land cover and landscape feature diversity and extents. These data have been recorded in the same locations on analogous dates. However, the present paper describes only the details of the vegetation surveys.The survey design is a series of gridded, stratified, randomly selected 1 km squares taken as representative of classes derived from a statistical environmental classification of Britain. In the 1978 survey, 256 one-kilometre sample squares were recorded, increasing to 506 in 1990, 569 in 1998 and 591 in 2007. Initially each square contained up to 11 dispersed vegetation plots but additional plots were later placed in different features so that eventually up to 36 additional sampling plots were recorded, all of which can be relocated where possible (unless the plot has been lost, for example as a consequence of building work), providing a total of 16 992 plots by 2007. Plots are estimated to have a precise relocation accuracy of 85 %. A range of plots located in different land cover types and landscape features (for example, field boundaries) are included.Although a range of analyses have already been carried out, with changes in the vegetation being related to a range of drivers at local and national scales, there is major potential for further analyses, for example in relation to climate change. Although the precise locations of the plots are restricted, largely for reasons of landowner confidentiality, sample sites are intended to be representative of larger areas, and many potential opportunities for further analyses remain.

show abstract

How well is current plant trait composition predicted by modern and historical forest spatial configuration?

Abstract: Bailrigg, Lancaster, United Kingdom, LA1 4YQ.

Cited by 12 publications

References 42 publications

Global environmental change effects on plant community composition trajectories depend upon management legacies

Global environmental change effects on plant community composition trajectories depend upon management legacies

Structural bias in aggregated species‐level variables driven by repeated species co‐occurrences: a pervasive problem in community and assemblage data

Long-term vegetation monitoring in Great Britain – the Countryside Survey 1978–2007 and beyond

Contact Info

Product

Resources

About