Analyzing Destination Images: A Perceptual Charting Approach

“…The optimal number of groups was determined according to the highest Simple Structure Index (SSI) following Dolnicar et al . (). We obtained eight synthetic variables by averaging inside each group determined by the k‐means clustering.…”

Section: Methodsmentioning

confidence: 99%

Combining projected changes in species richness and composition reveals climate change impacts on coastal Mediterranean fish assemblages

Albouy

Guilhaumon

Araújo

et al. 2012

Global Change Biology

108

View full text Add to dashboard Cite

Species Temporal Turnover (STT) is one of the most familiar metrics to assess changes in assemblage composition as a consequence of climate change. However, STT mixes two components in one metric, changes in assemblage composition caused by a process of species loss or gain (i.e. the nestedness component) and changes in assemblage composition caused by a process of species replacement (i.e. the species replacement component). Drawing on previous studies investigating spatial patterns of beta diversity, we propose measures of STT that allow analysing each component (species replacement vs. nestedness), separately. We also present a mapping strategy to simultaneously visualize changes in species richness and assemblage composition. To illustrate our approach, we used the Mediterranean coastal fish fauna as a case study. Using Bioclimatic Envelope Models (BEMs) we first projected the potential future climatic niches of 288 coastal Mediterranean fish species based on a global warming scenario. We then aggregated geographically the species-level projections to analyse the projected changes in species richness and composition. Our results show that projected changes in assemblage composition are caused by different processes (species replacement vs. nestedness) in several areas of the Mediterranean Sea. In addition, our mapping strategy highlights that the coastal fish fauna in several regions of the Mediterranean Sea could experience a 'cul-de-sac' effect if exposed to climate warming. Overall, the joint exploration of changes in species richness and composition coupled with the distinction between species replacement and nestedness bears important information for understanding the nature of climate change impacts on biodiversity. These methodological advances should help decision-makers in prioritizing action in the areas facing the greatest vulnerability to climate.

show abstract

“…To ensure clustering robustness, we performed 1000 iterations of the k-means algorithm. We determined the optimal number of clusters (radial growth patterns) using the simple structure index (SSI; [75]) criterion. We then compared the growth attributes among the different growth radial patterns using a Kruskal-Wallis non-parametric analysis of variance, followed by a non-parametric Fisher's least significant difference post-hoc test [76].…”

Section: Statistical Analysesmentioning

confidence: 99%

Unveiling the Diversity of Tree Growth Patterns in Boreal Old-Growth Forests Reveals the Richness of Their Dynamics

Martin

Krause

Fenton

et al. 2020

Forests

View full text Add to dashboard Cite

Research Highlights: Radial growth patterns of trees growing in old-growth boreal forests in eastern Canada can be grouped into a small number of simple patterns that are specific to different old-growth forest types or successional stages. Background and Objectives: Identifying the main radial growth trends in old-growth forests could help to develop silvicultural treatments that mimic the complex dynamics of old-growth forests. Therefore, this study aimed to identify the main radial growth patterns and determine how their frequencies change during forest succession in old-growth forests, focusing on boreal landscapes in eastern Canada. Materials and Methods: We used dendrochronological data sampled from 21 old-growth stands in the province of Quebec, Canada. Tree-ring chronologies were simplified into chronologies of equal length to retain only primary growth trends. We used k-means clustering to identify individual growth patterns and the difference in growth-pattern frequency within the studied stands. We then used non-parametric analyses of variance to compare tree or stand characteristics among the clusters. Results: We identified six different growth patterns corresponding to four old-growth forest types, from stands at the canopy breakup stage to true old-growth stands (i.e., when all the pioneer cohort had disappeared). Secondary disturbances of low or moderate severity drove these growth patterns. Overall, the growth patterns were relatively simple and could be generally separated into two main phases (e.g., a phase of limited radial increment size due to juvenile suppression and a phase of increased radial increment size following a growth release). Conclusions: The complexity of old-growth forest dynamics was observed mainly at the stand level, not at the tree level. The growth patterns observed in true old-growth forests were similar to those observed following partial or stem-selection cuts in boreal stands; thus, these silvicultural treatments may be effective in mimicking old-growth dynamics.

show abstract

Analyzing Destination Images: A Perceptual Charting Approach

Cited by 25 publications

References 18 publications

Spatial analysis of the trophic interactions between two juvenile fish species and their preys along a coastal–estuarine gradient

Spatial analysis of the trophic interactions between two juvenile fish species and their preys along a coastal–estuarine gradient

Combining projected changes in species richness and composition reveals climate change impacts on coastal Mediterranean fish assemblages

Unveiling the Diversity of Tree Growth Patterns in Boreal Old-Growth Forests Reveals the Richness of Their Dynamics

Contact Info

Product

Resources

About