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Our understanding of how plant communities will respond to current and future climate change has advanced considerably since the use of early climate models to predict specific local temperature and precipitation changes, and thereby plant transitions, at the beginning of climate change recognition and research. The collection, availability and spatial distribution of pollen records across the Americas has recently allowed us to investigate plant compositional transitions during climate change periods in the past. Results from these models and pollen record investigations have provided formulated hypotheses, led by László Orlóci, in palynology and climate change ecology predicting that high latitude, high elevation and species-rich communities have shown greater plant compositional transitions during periods of climate change in the past, show greater change today, and will show greater change in the future. To address hypotheses for the past, and infer changes for the future, we used 238 pollen records across North and South America to test if latitude, elevation and taxa richness defined plant compositional transitions and their rates of change over the past 10,000 years, representing the majority of the Holocene (11.7k — 150 ybp), and if transitions were identifiable as hotspots of change. Contradicting Orlóci’s hypothesis we found low latitude records to show greater transitions during the late Holocene, associated with increased aridity leading to Amazon, deciduous and Atlantic forest — Caatinga, Cerrado shifts at low latitudes. Higher elevations showed greater plant compositional transitions during the late Holocene, providing support for Orlóci’s hypothesis of greater plant transitions at higher elevations, associated with Andean cooling over the past 5k years. Richness explained less of plant compositional transition than latitude and elevation and showed positive, negative and no relationships showing no clear conclusive pattern. Climate change research solely at high latitudes and high elevations overlooks consequences of climate shifts on other ecosystems, such as tropical forests of north-eastern South America showing past increased aridity and predicted future drought probability of 80% by 2050 leading to predicted 20% forest loss by the end of the century.
Forest biomes have expanded and contracted in response to past climate fluctuations, but it is not clear how they will respond to human-induced atmospheric change. We provide a review of the literature, describing historical links between biogeographical and atmospheric patterns, comparing characteristics of forest biomes and describing expected changes in climate forcings from observed range shifts. Over the geological history, climate fluctuations prompted changes in forest distribution that, in turn, stabilized the atmosphere. Over the past century, warming-induced stress has caused widespread declines of mature forests, but new forests have expanded into open areas of boreal, tropical and temperate regions. Historically, forest expansion happened at much faster rates in cold than in warm regions. Across biomes, species interactions control the use of limiting resources, regulating community dynamics and expansion rates in response to climate variability. Modern impacts of land use change on the distribution of forest biomes are well understood, but the expansion of new forests and their role in stabilizing the atmosphere are yet to be accounted for in global models. Expansion of tropical and temperate forests would yield a negative climate forcing through increased carbon sequestration and evaporative cooling, but in the boreal region forest expansion could amplify climate warming due to changes in albedo. Although qualitative descriptions of forest-atmosphere interactions are possible based on existing records, the net climate forcing from forest range shifts remains uncertain. Three critical gaps in knowledge hinder rigorous evaluations of causality necessary to probe for linkages between climatic and biogeographical patterns: (i) reconstructions of vegetation dynamics have not sufficiently represented warm biomes; (ii) climate and vegetation dynamics are typically assessed at non-comparable scales; and (iii) single-proxies are normally used to simultaneously infer changes in climate and vegetation distribution, leading to redundancy in interpretation. Addressing these issues would improve our ability to decipher past and predict future outcomes of forest-atmosphere interactions.
Spatial patterns of tropical tree populations may contribute to the identification of underlying ecological mechanisms such as dispersal, competition and pest and pathogen pressure. Classical structure functions for quantifying spatial patterns have limitations, for example, although Ripley’s K function is useful for identifying differences in spatial patterns through space, this makes it difficult to identify differences in spatial patterns through time. To complement Ripley’s K function, we use the Thomas process, a point pattern model, to quantify temporal changes in spatial patterns for five tropical tree species known to have density-dependent mortality from species-specific pests and/or pathogens on Barro Colorado Island, Panama. We fit the data of each species to the model to estimate the cluster size (σ) and density of cluster centres (ρ). We compared spatial patterns based on the pair correlation function of the Thomas process determined from σ and ρ. We found reduced clustering over the past 20 years for Ocotea whitei (Lauraceae) and Quararibea asterolepis (Bombacaceae), the two species with the best-documented cases of pathogen and pest outbreaks on BCI, suggesting support for the Janzen-Connell Hypothesis; however, we reject the hypothesis because neither species showed spatial regularity throughout the census period. We found that the Thomas process point pattern model used in concordance with Ripley’s K function and other classical structure functions in ecology can be a simplified and powerful method for testing hypotheses regarding changing spatial patterns of populations through time.
The recovery process of a Dutch heathland after fire is investigated. The study area, 12 m x 20 m, has been surveyed yearly between 1963 and 1993. Previous work has shown that a stationary Markov chain models the observed recovery process well. However, the Markov model fails to capture an important observation, the existence of a phase structure. The process begins deterministically, but small random (non-Markov) effects accumulate through time and at some point the process suddenly becomes noisy. Here we make use of the spatial information contained in vegetation maps to examine dynamics at a fine spatial scale. We find that the phases observed at a large spatial scale separate themselves out distinctly at finer spatial scales. This spatial information allows us to investigate hypotheses about the mechanisms governing deterministic versus noisy vegetation dynamics.
We sought to compare the efficacy of the stationary Markov model and conventional ordination techniques in describing compositional and structural changes in forest communities along natural and manmade spatial gradients at two scales, local and regional. Vegetation abundance and structure data are from six sites spanning a spatial gradient in the Great Lakes-St. Lawrence forests near Sudbury, Ontario, Canada. Ordination did not detect slope-related local gradients despite the general trend that, as distance from the pollution source increases, vegetation along the slopes begins to display Markovian spatial dynamics. We suggest that this is due to information loss resulting from static ordination analyses: information regarding transitions between observations along the natural ordering of quadrats is not maintained. Both ordination techniques and the Markov analyses detected strong regional pollution-induced gradients in abundance and structure.
Many taxa show their highest species richness at intermediate elevations, but the underlying reasons for this remain unclear. Here, we suggest that the physiological tolerance hypothesis can explain species richness patterns along elevational gradients, and we used functional diversity to test this hypothesis. We analyzed herb species richness, functional diversity, and environmental conditions along a 1300 m elevational gradient in a temperate forest, Beijing, China. We found that herb richness exhibited a “hump-shaped” relationship with elevation, with peak richness at approximately 1800 m. Functional diversity showed a significant unimodal relationship with elevation. The duration of high temperatures (≥ 300C: DHT) was the best predictor for herb richness and functional diversity along the gradient from 1020 to 1800 m, which suggest richness is limited by high temperature at low elevations. While along the gradient from 1800 to 2300 m, the duration of low temperatures (≤ 0°C: DLT) was the most powerful explanatory variable, which indicated at high elevations, richness reduced due to low temperature. Our analyses showed that the functional diversity of traits related to drought-tolerance (leaf mass per area, leaf area, and leaf hardiness) exhibited negative relationships with DHT, while functional diversity of traits related to freezing-tolerance (leaf thickness and hair density) exhibited negative relationships with DLT. Taken together, our results demonstrated that the richness-elevation relationship is consistent with the physiological tolerance hypothesis: at low elevations, richness is limited by high temperatures, and at high elevations, richness is reduced due to low temperatures. We concluded that our results provide trait-based support for the physiological tolerance hypothesis, suggesting that mid-elevations offer the most suitable environmental conditions, thus higher numbers of species are able to persist.
Gradients of physical conditions and biological interactions of species may generate assembly patterns of trait-convergence and trait-divergence in the structure of plant communities. Here we report evidence on the effect of canopy closure on non-random patterns in the functional structure of herbaceous plant communities in temperate forest. We evaluated SLA (specific leaf area), leaf area and shape, dry matter content, presence of rhizomes, and plant height and inclination. In one of the three sites surveyed we found clear patterns of both trait-convergence and trait-divergence. Along the canopy closure gradient we observed communities formed by species with large SLA and long and narrow leaves being replaced by communities formed by species with smaller SLA and rounded leaves, which we interpret as environmental filtering producing such a trait-convergence. Further, communities located in more open sites contained more distinct species in terms of SLA, leaf area and leaf shape, i.e., indicating a divergence pattern along the canopy closure gradient. The other study sites showed no significant patterns when analyzed alone. When the three sites were analyzed jointly, a significant pattern of convergence for plant inclination was found. Although subjected to local variation and historical agents, our study presents consistent patterns of both trait-convergence and divergence and evidence of assembly rules and non-random patterns in communities of herbaceous plants along a canopy closure gradient.
The paper responds to the question: How should one go about designing the statistical analysis of biodiversity if it had to be done across scales in time and space? The conceptual basis of the design is the definition of biodiversity as a convolution of two community components. One of the components is richness, the product of species evolution, and the other structure, the consequence of environmental sorting (biotic, physical). The method of choice takes information in the manner of frequency distributions, and decomposes the associated total diversity into additive components specific to the deemed sorting factors. Diversity quantities are supplied by the analysis by which the relative importance of sorting factors can be measured and the dynamic oscillations which they generate in diversity can be traced. Examples support the a priori idea that the velocity of compositional change in the community during the late quaternary period has co-varied closely with the specific components of Kolmogorov-type complexity, Anand's structural complexity and Rényi's entropy of order one. The paper explains what is involved and why is it important.
Alpha, beta, and gamma diversity are three fundamental biodiversity components in ecology, but most studies focus only on the scale issues of the alpha or gamma diversity component. The beta diversity component, which incorporates both alpha and gamma diversity components, is ideal for studying scale issues of diversity. We explore the scale dependency of beta diversity and scale relationship, both theoretically as well as by application to actual data sets. Our results showed that a power law exists for beta diversity-area (spatial grain or spatial extent) relationships, and that the parameters of the power law are dependent on the grain and extent for which the data are defined. Coarse grain size generates a steeper slope (scaling exponent z) with lower values of intercept (c), while a larger extent results in a reverse trend in both parameters. We also found that, for a given grain (with varying extent) or a given extent (with varying grain) the two parameters are themselves related by power laws. These findings are important because they are the first to simultaneously relate the various components of scale and diversity in a unified manner.
