Unsolved Problems in Ecology

Andrew Dobson, David Tilman, Robert D. Holt, Andrew Dobson, David Tilman, Robert D. Holt

• Publisher: Princeton University Press
• Year: 2020
• ISBN:
  9780691195322
• Topic: Biological Sciences
• Subtopic: Ecology
• Index: Biological Sciences

Leading ecologists discuss some of the most compelling open questions in the field today
Unsolved Problems in Ecology brings together many of the world's leading ecologists to discuss the most fundamental research questions confronting the field today. This diverse and thought-provoking collection of essays spans virtually all of the key subfields of the discipline, from behavioral and evolutionary ecology to population biology, community ecology, ecosystem ecology, disease ecology, and conservation biology. These essays are intended to stoke curiosity, challenge prevailing wisdom, and provoke new ways of thinking about ecology in light of new technologies and unprecedented environmental challenges brought on by climate and land-use change. Authoritative and accessible, Unsolved Problems in Ecology is ideal for graduate students in the early stages of their scientific careers and an essential resource for seasoned ecologists looking for exciting new directions to take their research.

• Sheds light on modern ecology's most important and compelling open questions
• Features thought-provoking contributions from more than two dozen world-class ecologists
• Covers behavior, evolution, communities, ecosystems, resource management, and more
• Discusses ways to raise the financial and intellectual profile of the discipline
• An invaluable resource for graduate students as well as seasoned ecologists

PART I

POPULATIONS, VARIABILITY, AND SCALING

Ecological Scaling in Space and Time

A New Tool in Plain Sight?

Elizabeth T. Borer

Rapid changes in climate and nutrient deposition in regions around Earth are inducing equally rapid changes in the biosphere (Schimel et al. 1997, Ellis et al. 2010, Running 2012). These abiotic factors are not changing at the same rate or in the same direction in all locations, so organisms are increasingly experiencing novel combinations of precipitation, temperature, and nutrient deposition (Williams et al. 2007, Rockström et al. 2009). The reliance of humans on processes provided by organisms and their interactions within the local biotic and abiotic environment, such as carbon fixation, nutrient cycling, disease transmission, and the quantity, quality, and persistence of freshwater, provides a pressing reason for ecologists to develop a mechanistic understanding of the links between organisms, ecosystem processes, and regional and global cycles (Worm et al. 2006, Kareiva 2011, Cardinale et al. 2012). Yet, the combination of climate and nutrient supply experienced by organisms also results from feedbacks between the physiology of individual organisms and global biogeochemical cycles (Ehleringer and Field 1993, Vitousek et al. 1997, Arrigo 2005, Hooper et al. 2005), making clear our need to better understand processes that span temporal scales from minutes to millennia and spatial scales from suborganismal to Earth’s atmosphere to predict future effects of the changing environments on Earth.

The problem of scaling in ecology is not new. Three decades ago, scaling occupied the minds of many ecologists. For example, three decades ago John Wiens wrote an essay calling for a shift to multiscale thinking, development of new theory, and greater focus on collecting data to resolve discontinuities in processes across spatial scales (Wiens 1989). A few years later, Simon Levin published another excellent synthesis of the state of ecological knowledge of scaling (Levin 1992), arguing that knowledge of both large-scale constraints and the aggregate behavior of organisms will be necessary for achieving a predictive, mechanistic understanding of the feedbacks between organisms and ecosystem fluxes. A key topic raised by both Wiens and Levin also was addressed in a book published at nearly the same time (Ehleringer and Field 1993) in which many authors tackled the issue of scaling and cross-scale feedbacks from organismal physiology to global climate and back again.

In the decades since these papers were written, ecologists have continued to develop an understanding of long-term feedbacks, heterogeneity, and links across spatial scales. For example, the effects of forest warming over the short term have been demonstrated to stimulate soil respiration, whereas turnover in microbial composition can increase the carbon use efficiency of the community, leading to attenuation of soil respiration under continuous long-term warming (Melillo et al. 2002, Frey et al. 2013). The effects of diversity on productivity also function via long-term feedbacks. For example, long-term, chronic nutrient addition causes productivity to increase initially, but these effects attenuate over multiple decades because of ongoing loss of species diversity (Isbell et al. 2013). In a different subfield of ecology, research using metagenomic tools is highlighting the links and feedbacks among spatial scales that determine the resident microbial composition, or microbiome, of a host. For example, the identity and relative abundance of microbial species inhabiting an individual is determined at the regional scale by the composition and relative transmission ability of microbial species and at the local scale by the relative abundance of hosts and microbial competitive ability and fitness within individual host species (reviewed in Borer et al. 2013). Many factors, including the abiotic environment (Fenchel and Finlay 2004), host quality (Smith et al. 2005), and host behavior (Lombardo 2008), can play a role in these interactions and feedbacks across spatial scales.

In spite of the forward progress of this field, the fundamental issue of effectively using information about processes at one scale in predictions about outcomes at another scale remains unsolved. In 2011, the Macrosystems Biology program at the National Science Foundation (NSF) was launched to stimulate research and advance greater mechanistic understanding of processes spanning spatial scales (Dybas 2011). Although the availability of funding is certainly a key constraint on intellectual progress, identifying and collecting the types of data that will be useful for making predictions that span scales also represents a major challenge (Levin 1992, Ehleringer and Field 1993, Leibold et al. 2004, Elser et al. 2010, Nash et al. 2014). Perhaps most importantly, ecologists studying feedbacks and linkages across spatial scales are faced with tradeoffs in our capacity to gather data about the biosphere at any scale: the spatial extent versus the temporal extent of a study, the local replication versus the spatial extent of a study, or site-based experimental work versus large-scale observation (Soranno and Schimel 2014).

A New Tool Hiding in Plain Sight

Over the past several decades, a fairly continuous stream of publications has identified conceptual areas of spatial scaling where our ignorance remains vast (e.g., Wiens 1989, Levin 1992, Ehleringer and Field 1993, Peters et al. 2007, Borer et al. 2013). However, ecological science has changed a great deal during this time, giving us a range of new tools and more highly resolved data to study ecological scaling relationships. Meta-analysis has become an accepted tool for quantitative synthesis of the ecological literature and has been used, for example, to examine support for a range of hypotheses about the key determinants of species diversity across spatial scales (Field et al. 2009). Sequencing technology and metagenomics is rapidly extending the conceptual realm and spatial scales being actively considered by ecologists (Borer et al. 2013). Electronic technology also has changed our ability to tackle questions about scaling in myriad ways, including computerization of data acquisition and access, satellite imagery, remote sensing, drone technology, and interpolation of a wide array of environmental data (Campbell et al. 2013). One example of the exciting cutting-edge of technology to examine scaling in ecology is research that is advancing our ability to use remotely sensed spectral variation as a tool for estimating local and regional biodiversity, and concurrently documenting leaf-level traits and functional differences among taxa (Cavender-Bares et al. 2016).

However, the change in the past 30 years that is perhaps most underappreciated for its potential to advance this field is neither statistical nor technological; it is the shift in the culture of ecological science from a field dominated by single investigator projects to one of collaboration (Hampton and Parker 2011, Goring et al. 2014).

Distributed Experimental Networks

Most ecological research is conducted by one or a few scientists over relatively short time scales and small spatial scales (Heidorn 2008), and whereas large-scale, multi-investigator collaborations have become increasingly common in ecology over the past several decades (Nabout et al. 2015), the vast majority of these collaborations generate, share, and analyze observational data (e.g. Baldocchi et al. 2001, Weathers et al. 2013). Although observations of ecological systems represent an exceptionally important tool for characterizing and comparing among systems, manipulative experiments are a far more powerful tool for forecasting a system’s behavior under novel environmental conditions. Given the pressing need to effectively forecast ecological responses in a changing global environment, multifactorial experiments measuring responses and feedbacks spanning spatial and temporal scales will be a key tool to complement meta-analyses, large-scale observations, and models.

Although most experiments in ecology are conceived of and performed by single investigators, large-scale, grassroots distributed experimental collaborations are rethinking ecological experimentation and are overcoming the historical tradeoffs in our capacity to gather long-term experimental data across multiple spatial scales (Borer et al. 2014a). By replicating the same experimental treatments and sampling protocols and openly sharing data with each other, ecologists collaborating in distributed experimental networks are able to replicate experiments and directly compare biological and abiotic responses across spatial scales ranging from centimeters to continents. Depending on the question, sampling can occur at multiple scales within sites (e.g., within individual, within plot, plot, block, site) to quantify a plethora of responses to experimental treatments that map onto future scenarios (e.g., multiple nutrients, herbivory, high-latitude warming, drought, and loss of biodiversity; see Arft et al. 1999, Borer et al. 2014b, Duffy et al. 2015, Fay et al. 2015).

This emerging approach to network science is requiring a rethinking of collaboration and a change in scientific culture (Guimerà et al. 2005, Hampton and Parker 2011, Borer et al. 2014a). By using common experimental treatment and sampling protocols and sharing data openly among collaborators, every site improves the dataset through contribution and each investigator benefits from the opportunity to contribute data and ideas as a result of their efforts (Borer et al. 2014a). As with any effective collaboration, careful fostering of a culture of trust and sharing means that contributors have confidence that their efforts will be included and rewarded (Hampton and Parker 2011). In this model, participation is voluntary, and for most distributed experiments organized as grassroots efforts, investigators at each site shoulder the cost of implementing the treatments and collecting the data rather than funding such efforts through a single centralized grant. This pay-to-play funding model means that participation, particularly by international collaborators in understudied regions of the world, is increased when costs are low. And, for a field that seems to perpetually struggle with physics envy, this model of egalitarian collaboration was once called “the dream” by the Director General of the European Center for Particle Research (CERN), Dr. Robert Aymar (Ford 2008).

To forecast future scenarios for ecological responses and feedbacks in nonanalog environmental conditions (Williams and Jackson 2007, Rockström et al. 2009), we need experiments that manipulate multiple global change factors over long periods of time, and we need to understand how novel conditions influence the resulting spatial patterns and processes across multiple scales. Without multifactorial experiments replicated across many sites, it remains difficult to effectively estimate interactions among factors and contingencies in r...

Table of contents

• Cover Page
• Title Page
• Copyright Page
• Dedication
• Contents
• Preface
• List of Contributors
• Part I: Populations, Variability, and Scaling
• Part II: Population Biology and the Ecology of Individuals
• Part III: Coexistence
• Part IV: Ecological Communities and Ecosystems
• Part V: Ecology and Health
• Part VI: Conservation Biology and Natural Resource Management
• Part VII: Final Thoughts
• Index