Vegetation patterns and their underlying processes: where are we now?

Helm, Aveliina; Kalamees, Reïn; Zobel, Martin

doi:10.1111/jvs.12206

Cited by 6 publications

(6 citation statements)

References 22 publications

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“…Hence, the peak of herb richness occurs at mid-elevations because these elevations offer maximum light availability and moderate temperatures. This hypothesis is derived from Grime (1973) and has been confirmed by many experimental and observational studies (Fraser, Jentsch, & Sternberg, 2014;Gibb et al, 2015;Helm, Kalamees, & Zobel, 2014;Klanderud, Vandvik, & Goldberg, 2015) but has rarely been applied to the explanation of elevational richness patterns.…”

Section: Introductionmentioning

confidence: 90%

A trait‐based approach reveals the importance of biotic filter for elevational herb richness pattern

Jiang

Liu

et al. 2018

Journal of Biogeography

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Aim To evaluate how a biotic filter (overstorey cover [OC]) shapes an elevational herb richness pattern and to test whether intermediate elevations represent more suitable habitats for herb species due to the minimal biotic and moderate abiotic filtering that occured there. Location Dongling Mountain, China. Taxon Herbs. Methods We sampled herb richness along a 1,000‐m vertical transect (OC and elevation covary) and four horizontal transects (across the OC gradient with no change in elevation). Functional traits related to shade (leaf mass per area [LMA] and leaf stem ratio [LSR]) and freezing tolerance (leaf thickness [LT] and hair density [HD]) were collected based on vegetation sampling plots. We divided the richness data into below‐ and above‐timberline groups and then assessed the most important driver for each group. By estimating the variations in species richness and functional diversity along horizontal transects, we examined how these parameters responded to OC while controlling for associated differences in elevation. Results Herb richness showed a unimodal relationship with elevation, with a peak around the timberline. Below the timberline, herb richness was better predicted by OC and showed a positive relationship with the functional diversity of LMA and LSR. The frost period performed better in predicting herb richness above the timberline, and herb richness was positively related to the functional diversity of HD. Main conclusions The results support our hypothesis and highlight the importance of OC in shaping herb richness patterns. Below the timberline, OC acts as a filter, sorting species based on their ability to tolerate shade, whereas the frost period limits richness above the timberline and favours species that can tolerate low temperatures. Therefore, we suggest that biotic interactions, as well as environmental and geographical factors, should be considered the main drivers of elevational plant richness patterns.

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Section: Introductionmentioning

confidence: 90%

A trait‐based approach reveals the importance of biotic filter for elevational herb richness pattern

Jiang

Liu

et al. 2018

Journal of Biogeography

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“…Need for more field-based biodiversity assessments -In an era of readily available sophisticated analytical methods and increasing availability of global data sets, we advance in ecological knowledge, but basic biodiversity research is still lacking (Helm et al 2014). Large databases, such as sPlot (Dengler et al 2014) for plant communities or TRY (Kattge et al 2011) for functional traits, depend on collected in the field.…”

Section: Resultsmentioning

confidence: 99%

Plant species richness record in Brazilian Pampa grasslands and implications

et al. 2018

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Species richness remains one of the most valuable type of information for biodiversity conservation. Here, we report a plant richness record in the Brazilian Pampa. We found 56 vascular plant species in a single plot of 1 9 1 m in a privately owned grazed grassland in a region with shallow soils. This number is considerably higher than common species numbers in similar surveys and highlights the high plant diversity in South Brazilian grasslands. We take our record as an opportunity to reflect on some issues of high importance if we wish to reach conservation goals: much of the biodiversity is inside private lands, and these should be more in the focus of conservation efforts; field research, especially quantitative sampling, continues to be essential to improve knowledge on biodiversity and its distribution patterns; training biologists to be able to carry out biodiversity assessments and to interpret the results should be a key issue for universities. We hope that the communication of this record will encourage new scientific discoveries and raise social interest about the conservation of grasslands in South Brazil.

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“…Transitional areas between different biogeographic regions are especially relevant in terms of plant diversity and sensitivity to climate change (Cadenasso et al 2003;. Therefore, thorough information on current vegetation patterns into these areas is especially important to track the effects of global change and human impact on ecosystems , and to preserve plant biodiversity in the future (Hernández-Stefanoni et al 2012;Helm et al 2014;Amici et al 2015).…”

Section: Management Implicationsmentioning

confidence: 99%

“…The increasing amount of information on climate, soil properties and human influence enables building better models to reveal the underlying factors that shape vegetation patterns (Saura et al 2011;Helm et al 2014), and geostatistics represent a suitable method for understanding spatial variability and interpolation of vegetation distribution maps . Taking into account that many of the experimental forest variables (e.g.…”

Section: Management Implicationsmentioning

confidence: 99%

“…There is an increasing awareness of the importance of the spatial distribution of variables and processes in ecology (Helm et al 2014). The capacity to acquire measurements with precise spatial location provided by global positioning systems (GPS), as well as to store, analyse, visualize, and interpret those data within a geographical information system (GIS), provide for an ever increasing number of datasets spatially referenced and susceptible of spatial analysis (Wagner 2003).…”

Section: Introductionmentioning

confidence: 99%

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