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A dispersal-induced paradox: synchrony and stability in stochastic metapopulations
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Understanding how dispersal influences the dynamics of spatially distributed populations is a major priority of both basic and applied ecologists. Two well-known effects of dispersal are spatial synchrony (positively correlated population dynamics at different points in space) and dispersal-induced stability (the phenomenon whereby populations have simpler or less extinction-prone dynamics when they are linked by dispersal than when they are isolated). Although both these effects of dispersal should occur simultaneously, they have primarily been studied separately. Herein, I summarise evidence from the literature that these effects are expected to interact, and I use a series of models to characterise that interaction. In particular, I explore the observation that although dispersal can promote both synchrony and stability singly, it is widely held that synchrony paradoxically prevents dispersal-induced stability. I show here that in many realistic scenarios, dispersal is expected to promote both synchrony and stability at once despite this apparent destabilising influence of synchrony. This work demonstrates that studying the spatial and temporal impacts of dispersal together will be vital for the conservation and management of the many communities for which human activities are altering natural dispersal rates.
Keywords
Autoregressive model, correlated environmental stochasticity, dispersal, dispersal-induced stability, metapopulation, negative binomial model, Ricker model, spatial heterogeneity, synchrony.
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A general integrative model for scaling plant growth, carbon flux, and functional trait spectra
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Linking functional traits to plant growth is critical for scaling attributes of organisms to the dynamics of ecosystems (1,2) and for understanding how selection shapes integrated botanical phenotypes (3). However, a general mechanistic theory showing how traits specifically influence carbon and biomass flux within and across plants is needed. Building on foundational work on relative growth rate (4–6), recent work on functional trait spectra (7–9), and metabolic scaling theory (10,11), here we derive a generalized trait-based model of plant growth. In agreement with a wide variety of empirical data, our model uniquely predicts how key functional traits interact to regulate variation in relative growth rate, the allometric growth normalizations for both angiosperms and gymnosperms, and the quantitative form of several functional trait spectra relationships. The model also provides a general quantitative framework to incorporate additional leaf-level trait scaling relationships (7,8) and hence to unite functional trait spectra with theories of relative growth rate, and metabolic scaling. We apply the model to calculate carbon use efficiency. This often ignored trait, which may influence variation in relative growth rate, appears to vary directionally across geographic gradients. Together, our results show how both quantitative plant traits and the geometry of vascular transport networks can be merged into a common scaling theory. Our model provides a framework for predicting not only how traits covary within an integrated allometric phenotype but also how trait variation mechanistically influences plant growth and carbon flux within and across diverse ecosystems.
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A holistic approach to climate targets
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An assessment of allowable carbon emissions that factors in multiple climate targets finds smaller permissible emission budgets than those inferred from studies that focus on temperature change alone.
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A paradigm shift in understanding and quantifying the effects of forest harvesting on floods in snow environments
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A well-established precept in forest hydrology is that any reduction of forest cover will always have a progressively smaller effect on floods with increasing return period. The underlying logic in snow environments is that during the largest snowmelt events the soils and vegetation canopy have little additional storage capacity and under these conditions much of the snowmelt will be converted to runoff regardless of the amount or type of vegetation cover. Here we show how this preconceived physical understanding, reinforced by the outcomes of numerous paired watershed studies, is indefensible because it is rationalized outside the flood frequency distribution framework. We conduct a meta-analysis of postharvest data at four catchments (3–37 km2) with moderate level of harvesting (33%–40%) to demonstrate how harvesting increases the magnitude and frequency of all floods on record (19–99 years) and how such effects can increase unchecked with increasing return period as a consequence of changes to both the mean (þ11% to þ35%) and standard deviation (12% to þ19%) of the flood frequency distribution. We illustrate how forest harvesting has substantially increased the frequency of the largest floods in all study sites regardless of record length and this also runs counter to the prevailing wisdom in hydrological science. The dominant process responsible for these newly emerging insights is the increase in net radiation associated with the conversion from longwave-dominated snowmelt beneath the canopy to shortwave-dominated snowmelt in harvested areas, further amplified or mitigated by basin characteristics such as aspect distribution, elevation range, slope gradient, amount of alpine area, canopy closure, and drainage density. Investigating first order environmental controls on flood frequency distributions, a standard research method in stochastic hydrology, represents a paradigm shift in the way harvesting effects are physically explained and quantified in forest hydrology literature.
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Allowable carbon emissions lowered by multiple climate targets
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Climate targets are designed to inform policies that would limit the
magnitude and impacts of climate change caused by anthropogenic
emissions of greenhouse gases and other substances. The target
that is currently recognized by most world governments1 places a
limit of two degrees Celsius on the global mean warming since
preindustrial times. This would require large sustained reductions
in carbon dioxide emissions during the twenty-first century and
beyond2–4. Such a global temperature target, however, is not sufficient
to control many other quantities, such as transient sea level
rise5
, ocean acidification6,7 and net primary production on land8,9.
Here, using an Earth system model of intermediate complexity
(EMIC) in an observation-informed Bayesian approach, we show
that allowable carbon emissions are substantially reduced whenmultiple
climate targets are set. We take into account uncertainties in
physical and carbon cycle model parameters, radiative efficiencies10,
climate sensitivity11 and carbon cycle feedbacks12,13 along with a
large set of observational constraints. Within this framework, we
explore a broad range of economically feasible greenhouse gas scenarios
from the integrated assessment community14–17 to determine
the likelihood of meeting a combination of specific global
and regional targets under various assumptions. For any given
likelihood of meeting a set of such targets, the allowable cumulative
emissions are greatly reduced from those inferred from the temperature
target alone. Therefore, temperature targets alone are unable
to comprehensively limit the risks from anthropogenic emissions.
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An emerging movement ecology paradigm
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1st 2 paragraphs: Movement of individual organisms, one of the most fundamental features of life on Earth, is a crucial component of almost any ecological and evolutionary process, including major problems associated with habitat fragmentation, climate change, biological invasions, and the spread of pests and diseases. The rich variety of movement modes seen among microorganisms, plants, and animals has fascinated mankind since time immemorial. The prophet Jeremiah (7th century B.C.),for instance, described the temporal consistency in migratory patterns of birds, and Aristotle (4th centur y B.C.) searched for common features unifying animal movements (see ref. 1). Modern movement research, however, is characterized by a broad range of specialized scientific approaches, each developed to explore a different type of movement carried out by a specific group of organisms (2). Beyond this separation across movement types and taxonomic (or functional) groups, movement research divides into four different ‘‘paradigms,’’ the random, biomechanical, cognitive, and optimality approaches (1), which are loosely linked to each other. Although movement research is extensive and is growing rapidly (2),
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Analysis of monotonic greening and browning trends from global NDVI time-series
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Remotely sensed vegetation indices are widely used to detect greening and browning trends; especially the global coverage of time-series normalized difference vegetation index (NDVI) data which are available from 1981. Seasonality and serial auto-correlation in the data have previously been dealt with by integrating the data to annual values; as an alternative to reducing the temporal resolution, we apply harmonic analyses and non-parametric trend tests to the GIMMS NDVI dataset (1981–2006). Using the complete dataset, greening and browning trends were analyzed using a linear model corrected for seasonality by subtracting the seasonal component, and a seasonal non-parametric model. In a third approach, phenological shift and variation in length of growing season were accounted for by analyzing the time-series using vegetation development stages rather than calendar days. Results differed substantially between the models, even though the input data were the same. Prominent regional greening trends identified by several other studies were confirmed but the models were inconsistent in areas with weak trends. The linear model using data corrected for seasonality showed similar trend slopes to those described in previous work using linear models on yearly mean values. The non-parametric models demonstrated the significant influence of variations in phenology; accounting for these variations should yield more robust trend analyses and better understanding of vegetation trends.
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Another reason for concern: regional and global impacts on ecosystems for different levels of climate change
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Between 1C and 2C increases in global mean temperatures most species, ecosystems and landscapes will be impacted and adaptive capacity will become limited. With the already ongoing high rate of climate change, the decline in biodiversity will therefore accelerate and simultaneously many ecosystem services will become less abundant.
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Appalachian LCC PI and Clemson scientists unveil software that revolutionizes wildlife habitat connectivity modeling
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A trio of Clemson University scientists has unveiled a groundbreaking computational software called “GFlow” that makes wildlife habitat connectivity modeling vastly faster, more efficient and superior in quality and scope.
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Appalachian LCC Science Delivery Workshop
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The Appalachian LCC is setting up a science delivery workshop to share recently develop products and train partners in decision support planning tools.
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