Assessing and predicting shifts in mountain forest composition across 25 years of climate change

Abstract

Aim: Spatial predictions of future communities under climate change can be obtained by stacking species distribution models (S-SDM), but proper evaluation of community S-SDM predictions across time with fully independent data has rarely been carried out. The aim of this study was to evaluate the predictive abilities of S-SDMs for whole forest communities across the last 25 years in a mountain region. Location:The western Swiss Alps. Methods:We used past vegetation surveys (2,984 plots) and environmental data from the 1990s to calibrate SDMs for 364 plant species and predict changes in forest composition under contemporary conditions. These projections were then evaluated by resurveying a random subset of 92 forest plots in summer 2014.Results: Species distribution models showed the same accuracy in the past (calibration data) and present (evaluation data). The S-SDMs correctly predicted the general trends in species richness and shift of ecological conditions (i.e., temperature, moisture) at the regional level. However, it proved more difficult to identify precisely which forest communities or areas are most or least affected by climate change.Main conclusion: Our results show that, across a period of a few decades, S-SDMs can usefully predict trends in macroecological properties such as richness or average ecological conditions, but fail to accurately predict changes in composition. This is likely due to the combined effects of the stochasticity of local colonization and extinction events, dispersal limitations, community assembly rules (e.g., competition), observer bias, model and location errors and interannual variation. Furthermore, these models cannot account for potential species adaptations leading to persistence in sites predicted unsuitable. This highlights the need for developing more accurate forest community predictions as support to help prioritizing conservation actions. K E Y W O R D Scommunity predictions, ecological indicator values, global warming, monitoring, plant community, species distribution models, Switzerland

Current and projected future risks of freshwater fish invasions in China

“…With 20 calibration datasets and five modeling algorithms, we obtained 500 different sets of predicted probabilities of presence for each species and each time horizon, which were subsequently combined using a weighted average approach based on the TSS values of the individual models (after excluding models with TSS < 0.6; Marmion et al 2009). For each grid cell, we then quantified the overall threat of establishment as the sum of the probabilities of presence across all species, separately for each species group and climate scenario (Scherrer et al 2017). To identify species-specific establishment threat, we calculated the extent of suitable habitat as the sum of the area of individual grid cells weighted by the probabilities of occurrence and expressed species establishment threat as a percentage of the total area covered by China's fresh waters (6.865 × 10 6 km 2 ).…”

Long‐term monitoring reveals forest tree community change driven by atmospheric sulphate pollution and contemporary climate change

“…The use of elevational gradients is a powerful approach to investigate climate change impacts on biodiversity distribution because climate, and therefore climatically determined species distributions, change dramatically across relatively fine spatial scales (e.g., Brusca et al, 2013;Kelly & Goulden, 2008;Walther et al, 2005). Similar studies have documented elevational shifts in range along fine-scale gradients, but typically do so comparing a pair of discrete census periods (e.g., Damschen, Harrison, & Grace, 2010;Savage & Vellend, 2015;Scherrer, Massy, Meier, Vittoz, & Guisan, 2017).…”

Long‐term ecological data for conservation: Range change in the black‐billed capercaillie (Tetrao urogalloides) in northeast China (1970s–2070s)

“…Therefore, ecologists require an ecological baseline to understand the long‐term ecological responses of ecosystems to anthropogenic activities and climate change (Beaugrand, Edwards, Raybaud, Goberville, & Kirby, ; Fonzo, Collen, & Mace, ; Rick & Lockwood, ). Historical ecological data can be used to document species records over time (McClenachan, Ferretti, & Baum, ; Turvey et al., ; Yang et al., ) and help ecologists integrate future perspectives within historical contexts to provide unique insights into the long‐term dynamics of endangered species (Beaugrand et al., ; Scherrer et al., ). In fact, the application of long‐term ecological data, particularly historical data, is often hampered by data limitations, including incomplete and spatially biased datasets (Boakes et al., ; Hortal, Jimenez‐Valverde, Gomez, Lobo, & Baselga, ; McClenachan et al., ).…”

“…Historical ecological data can be used to document species records over time (McClenachan, Ferretti, & Baum, 2012;Turvey et al, 2015;Yang et al, 2016) and help ecologists integrate future perspectives within historical contexts to provide unique insights into the long-term dynamics of endangered species (Beaugrand et al, 2015;Scherrer et al, 2017).…”