Organism and Environment Interactions into The Basic Theory of Community and Evolutionary Ecology
Organism and Environment Interactions into The Basic Theory of Community and Evolutionary Ecology
Abstract:
Understanding the interactions of an
organism and its environment is essential for us to integrate ultimate and
proximate causation on a global scale. Organism–environment interaction
includes all organisms including animals, plants, and non-eukaryotes, etc.
because all of them are responsive to environmental change including those that
are human-induced. Implications for climate change in which weather extremes
will become more common again suggest a mechanistic approach will be important
to understand how organisms may respond. Organism–environment interaction is a
fundamental concept that may unify ultimate and proximate causation and point
the way for future investigations striving to understand coping mechanisms in a
world where both predictable and unpredictable components of the environment
are changing. Humans have dramatic, diverse and far-reaching influences on the
evolution of other organisms. Numerous examples of this human-induced contemporary
evolution have been reported in a number of ‘contexts’, including hunting,
harvesting, fishing, agriculture, medicine, climate change, pollution,
eutrophication, urbanization, habitat fragmentation, biological invasions and
emerging or disappearing diseases.
Although numerous papers, journal special issues and books have addressed each
of these contexts individually, the time has come to consider them together and
thereby seek important similarities and differences. The goal of this special
issue, and this introductory paper, is to promote and expand this nascent
integration. We first develop predictions as to which human contexts might
cause the strongest and most consistent directional selection, the greatest
changes in evolutionary potential, the greatest genetic (as opposed to plastic)
changes and the greatest effects on evolutionary diversification. We then
develop predictions as to the contexts where human-induced evolutionary changes
might have the strongest effect on the population dynamics of the focal
evolving species, the structure of their communities, the functions of their
ecosystems and the benefits and costs for human societies.These qualitative
predictions are intended as a rallying point for broader and more detailed
future discussions of how human influences shape evolution, and how that
evolution then influences species traits, biodiversity, ecosystems and humans.
Keywords:
Stress,
Seasonality, Human, eco-evolutionary, dynamics, contemporary evolution,
anthropogenic influences, evolutionary diversification, rapid evolution,
ecosystem services.
Introduction:
As
described by Denny and Helmuth (Denny et al., 2009), integrating biomechanics
and ecology represents a scaling up from the biomechanics of individual
organisms and their interaction with their environment to the ecology of
populations and communities. A full population biology understanding requires
extension of biomechanics in two dimensions. In the ecological dimension,
scaling up from populations to interacting species provides a community- and
ecosystem-level understanding rooted in mechanistic knowledge. In the temporal
dimension, scaling up from ecological to evolutionary time provides an
evolutionary understanding also rooted in mechanistic knowledge of organisms’
interactions with their environment.
Much of the basic theory of population
biology focuses on the generality side of this trade-off, where the goal might
be to formalize a hypothesis about the factors driving a given outcome in a
mathematical framework to rigorously test and quantify the underlying logical
expectations (i.e. mathematics as ‘a way of thinking clearly’) (May, 2004). In
contrast, biomechanics, including that integrated with ecology, is rooted in a
bottom-up approach focusing on detailed mechanisms (Denny et al., 2009) and
therefore gravitates towards the realism side of this trade off. A number of
modeling frameworks, described elsewhere in this special issue of The Journal
of Experimental Biology, follow the biomechanics approach of scaling up from
mechanistic physiological [e.g. dynamic energy budget models (Nisbet et al.,
2012)] or individual-level (e.g. scale-transition and complexity theory
(Benedetti-Cecchi et al., 2012; Van de Koppel et al., 2012) dynamics to
patterns on ecological scales.
This functional response-based
approach can apply to additional modeling frameworks from basic theoretical
population biology not covered here such as non-evolutionary single-population
dynamics, including stage- or physiologically structured dynamics. Also,
scaling up in space receives only superficial attention here. The focus here on
community and evolutionary ecology is intended to provide an illustration of
the general approach centered on the scaling up of biomechanics ecologically
and temporally. In addition, it complements the population models presented
elsewhere in this special issue, such as the structured population models of
Nisbet et al. and Madin and Connolly (Nisbet et al., 2012; Madin and Connolly,
2012) as well as the models focused on scaling up community dynamics in space
discussed by Benedetti- Cecchi et al. and Van de Koppel et al.
(Benedetti-Cecchi et al., 2012; Van de Koppel et al., 2012).
The science which deals with the study
of the biota, their environment and interactions is known as Ecology. Ecology
derived from the Greek word Oikos meaning habitation, and logos meaning study,
i.e., study of the habitations of organisms (Figure 1). This is the study of
ecosystem, which describe the relation between the organisms with different
habitats. The environment of an organism includes both biotic and abiotic
factors. These two factors have to coordinate each other to share the resources
that are present within the environmental ecosystem. To understand about this
mutual relationship we have to study ecology. Human being is also the part of
ecosystem.
Fig.
1: Components of environment.
Types of
Ecology:
The ecological studies are all about
connections of all life forms in earth and their various types of ecology
(Figure 2). Organism Ecology This is the study of organism respond to stimuli
caused by physical environment. The organisms adapt the environment either
happily or ignoring away from its effect. A physical change in environment will
show the change in behaviour or physical attributes.
(i)
Population Ecology: The natural process is that, all
organisms will grow and die. The factors by which they will populate are the
size of colony, birth and death rate, population growth rate.
(ii) Community
Ecology: The association of populations of two or more
different species occupying the same geographical area. Competition, mutualisms
are key interactions to maintaining a community.
(iii)
Ecosystem Ecology: This is the community of living
organisms along with non-living environment like air, water, soil.
(iv)
Landscape Ecology: The exchange of energy, materials,
organisms and other products between the ecosystems is known as landscape
ecology.
(v)
Global Ecology: The effect of change in energy and matter
exchange on the function and distribution of organism in environment.
Fig.2:
Hierarchical organization of ecology.
Types
of environment:
The term environment describes the sum
total of physical, chemical and biotic conditions surrounding and influencing
the living organisms. Environment is classified into three types-
(i) Biotic (Biological):
Biotic elements refer to the
biological component of the ecosystem, having the population of plants, animals
and microorganisms. The biotic component of the ecosystem consists of 3 groups
of organisms, the producers, consumers and decomposers. The producers are the
organisms those are capable for photosynthesis (plants). The consumers depend
on the producers (all herbivores). The
decomposers are the organisms that are rely on dead organisms for their
existence (bacteria, virus, and yeast).
(ii)
Abiotic (Physical):
It includes the flow of energy necessary to maintain any organism, the physical factor(climate, temperature, rain, snow, hills) that affects it and the supply of molecules required for its life functions(carbon, hydrogen, nitrogen, sulphur, phosphorus). Cultural The interaction between human and environment also influence the ecosystem. Background of different cultures put different values on natural world.
Importance
of studying Ecology:
(i)
Environmental Conversation
By studying ecology, the emphasis is
put on how every organism needs other for peaceful coexistence. Having no ideas
on ecology will responsible for degradation of land and environment, which is
the living place of other species leading to their destruction.
(ii) Resource Allocation
All plants and animals have roles in
the environment as they sharing limited natural resources such as air,
minerals, space. Lack of ecological studies may be the cause of deprivation and
looting of these natural resources.
(iii) Energy Conservation
The entire living organism needs
energy such as nutrition, light, radiation etc. So lack of ecological studies
will be the cause for destruction of the energy resources. Oil, coal, and
natural gases are the non-renewable sources which will destruct the ozone layer.
(iv) Eco-friendliness
It helps to appreciate living among
the organisms; this will follow natural order of things.
Evolutionary
ecology
In the community models described
above, mechanistic knowledge about organisms’ interactions with their environment
could factor into an environment-dependent functional response for a variety of
dynamics, from intraspecific dynamics such as population growth rate to
interaction dynamics such as predation rate. For a single species model of
evolutionary change, it is the mechanistic functional response in terms of the
environment-dependent population growth rate (i.e. function that describes how
the population growth rate responds to the environment) that holds particular
relevance as it provides a metric of fitness. If this function appropriately
describes both population growth and fitness, then it can serve to couple
ecological and evolutionary dynamics. Below, I describe two broad frameworks
for integrating such a mechanistic functional response into evolutionary
dynamics: quantitative genetics (with two modeling approaches described) and
game theory. This is not intended to be an exhaustive accounting of all models
relevant to these frameworks but rather an example illustration of a handful of
commonly used models. The focus here is on the micro-evolutionary dynamics of
changing gene frequencies within a population rather than the macro-evolutionary
processes of speciation and extinction because microevolution has greater
ecological relevance and is therefore more relevant to this special issue. In
addition, for simplicity, this section focuses on single-species models; for an
integrative review of both population genetic and game theoretic frameworks in
the context of coevolutionary questions of evolutionary ecology (Abrams, 2001)
and Day (Day, 2005).
In addition to describing the generic
mathematical formulations with an indication of which term can be an
environment-dependent functional response in all cases, for the first
quantitative genetics modeling approach we discuss existing theoretical
constructs (specifically, phenotype performance fitness framework and
function-valued traits) that are particularly relevant to the integration of
biomechanics. For the second quantitative genetics modelling approach we
analyze an example based on the evolution of thermal tolerance in a changing
environment in order to illustrate the mechanistic functional response
approach. Finally, for the game theory approach we use a discussion of
dispersal, a topic often explored in both game theory and biomechanics, to
exemplify how a biomechanics-based understanding might shift the focus of
evolutionary models and allow better integration of models with data.
Quantitative
Genetics:
Population genetic models follow gene
frequencies over time as they depend on relative fitness, which encompasses
both survivorship and reproductive success. While the simplest possible model
construction of density-independent fitness leads to genetic dynamics
independent of population dynamics (i.e. gene frequency dynamics do not depend
on population size), the average population fitness (relative frequency of each
genotype multiplied by its fitness, then summed over all possible genotypes)
could be considered a metric of the population growth rate, thus coupling the
population (ecological) dynamics to the genetic (evolutionary) dynamics. In
addition, consideration of density-dependent influences on fitness inevitably
leads to population size-dependent genotype fitness and therefore necessitates
fully coupled population and genetic dynamics (Day, 2005).
Conservation
applications:
Both ecological and evolutionary
processes occur in the context of human-driven global change (Palumbi, 2001).
One proposed strength of a more mechanistic approach to ecological and
evolutionary modeling, especially that related to biomechanics, is the enhanced
predictive power under novel environmental conditions, such as physiological
understanding informing predicted responses to climate change (Helmuth et al.,
2005; Denny et al., 2009; Hofmann et al., 2010; Hoffmann et al., 2011). However, the types of
models described here, even with added realism from mechanistic functional
responses, remain at the general end of the modeling trade-off described in the
Introduction and therefore tend not to have the level of both realism and precision
necessary for predictions (Levins, 1966). In addition, the key challenge for
ecologists and conservation biologists is not just predicting what will happen
under climate change, but informing local science-based management decisions
(e.g. reserve design, invasive species control) under a changing climate
(Dawson et al., 2011; Heller et al., 2009). Vulnerability estimated from
predictive models can provide a first step towards conservation management
under a changing climate (Rowland et al., 2011), but insight into relative
vulnerability and sensitivity to different processes can stem from the more
general models described here as well.
One possible component of local
management under climate change is to protect the capacity of natural systems
to respond to the global changes (Heller et al., 2009). On the evolutionary
level, protecting response capacity means protecting the potential for genetic
adaptation as it depends on properties such as genetic variance, population
size and the level of gene flow (Hoffmann et al., 2011) where genetic
adaptation is one aspect of the population-level response to climate change,
along with movement and acclimation (Parmesan, 2006). On the community level,
protecting the response capacity involves protecting community-level
resistance, resilience and robustness to environmental change as it depends on
properties and processes such as diversity, modularity, redundancy and feedback
loops (Levin et al., 2008).Sensitivity of basic models to different assumptions
and parameters can provide insight into which processes and properties are most
important to the overall response and therefore inform this aspect of
management under climate change.
Conservation management efforts [e.g.
Crouse and colleagues] (Crouse et al., 1987) provide a quintessential example
of this approach. For an example related to the question of physiological
response to climate change, we constructed a model, rooted in the thermal
tolerance fitness function described above, of coral reef ecological and evolutionary
response to the future thermal stress expected with climate change (Baskett et
al., 2010). Sensitivity analysis of a variety of model constructs and of all
model parameters provided an integrative and quantitative comparison of
existing recommendations (previously considered qualitatively and occasionally
contradictory) for local protection of coral reef resistance, resilience and
adaptive capacity in a changing climate. Generally, incorporating mechanistic
functional responses into basic models allows greater connection of model
parameters to biologically relevant characteristics (Schoener, 1986), and
therefore increases the potential to connect model outcomes to an empirical
understanding of important traits or physiological processes. Furthermore,
mechanistic functional responses that include interactions with the physical
environment will also indicate sensitivity to empirically relevant
environmental conditions. Such insights can provide a sense both of which
patterns or processes might best allow response capacity and of which local
stressors are more likely to interact synergistically with global change, which
can suggest prioritization in terms of which locations and processes to protect
and which local stressors to protect against. This enhanced realism and
biological relevance in the context of basic models could help add much-needed
specificity to general recommendations, allowing them to strike the
difficult-to-achieve balance between broad applicability and concreteness for
recommendations for management under a changing climate (Heller et al., 2009).
Governance
Considerations:
The equilibrating processes that
buffer the effects of the burgeoning human population density are extracted
from a diminishing area of global natural environments that supply the air we
breathe, the seafood we consume, and the land we cultivate. Many of these
environments provide resources that are shared by multiple governing states and
have influences beyond country boundaries. Perhaps the greatest barrier to
protecting these environmental systems is the temptation of states or other
stakeholders to contribute to a “tragedy of the commons,” wherein a common
resource is overexploited because stakeholders with unfettered access have an
incentive to exploit the resource as quickly as possible before the other
stakeholders do the same (Bull et al., 2016; Cairns et al., 2017). Guarding
against exploitation requires a system of governance, whether collectively
imposed by the stakeholders or externally imposed (Bull et al., 2016; Cairns et
al., 2017). Common pool resource problems necessitate considering strategic
interactions in a group, as can be formalized and analyzed using game theory.
Using game theory, Barrett (Barrett et al., 2016) outlines three forms of
international law that can be used to circumvent environmental degradation: (i)
treaties that implore states to act in a collective interest; (ii) treaties
that impel cooperating states to punish the uncooperative; and (iii) treaties
that coordinate the behaviour of states. Noting the failures of the first two
forms, Barrett applies game theory to demonstrate elegantly the potential for
“coordination games” to ensure cooperative protection of our shared global
environment. Barrett details how effective cooperation can be facilitated both
by international policy that coordinates all governing parties to realize
mutual gains, and by trade agreements or technical standards formulated such
that all parties can coordinate in their own interest to realize mutual gains.
The failure of international treaties with weak accountability is highlighted
by Mangel (Mangel et al., 2016) in the specific context of whaling.
Simultaneously, Mangel underscores the complexity of enforcement, which entails
a role for evidence-based scientific expertise at the interface of law and
species preservation. Similarly, Castro et al., 2016 call for engagement of the
academic community at the corporate interface to assess objectively the complex
impacts of the industrial activities. Analogous to the conflict between
governing states over shared resources highlighted by Barrett (Barrett et al.,
2016), conflicts similarly arise between corporations and communities, as
illuminated by Castro et al. with case studies from extractive industries and
hydropower development. Castro et al. (Castro et al., 2016) also highlight a
particularly vexing long-standing issue: the problem of obtaining unbiased
environmental, social, and health impact assessments for corporate projects
where the results of the assessments influence decisions on whether or not a
project is to move forward. Furthermore, follow-up impact assessments,
confirming or overriding the impact predictions, are also needed to form a
basis for corporate accountability once project implementation is underway. An
important and balanced framework for this process comes from an unexpected
source: the Roman Catholic Church in several sections of the Papal Encyclical (Chapin,
2003; Brulle et al., 2015; Castro et al,
2016; Cheptou et al., 2017; Colautti et al, 2017) “Laudato Si,” recently issued
by Pope Francis. Operationalization of the segment of the encyclical devoted to
“Environmental Impact Assessment” is a major challenge, where success could
have profound positive impact on human–environmental interactions. Among
natural resources that are shared among states, ocean ecosystems are one of the
most prominent. The essential roles that ocean ecosystems play in food
security, economic development, and climatic processes led the United Nations Sustainable
Development Goals to include the conservation of marine reserves as an explicit
priority for their most recent agenda. Hurdles to this conservation include
overfishing, climate change, and population growth, all of which are captured
and addressed by the complex adaptive-systems framework advanced by Lubchenco
et al., 2016, which demonstrates how complexity can be navigated to generate
thoughtful incentive structures that align conservation and economic benefits.
Lubchenco et al.’s framework can motivate positive shifts in behavior at the
individual, corporate, or national level, as illustrated by recent progress
toward fishery restoration. In a contrasting example of unsuccessful incentive
structures, Walsh et al., 2016 examine how policies surrounding ecotourism
within the iconic haven of the Galapagos have been heavily weighted toward
supporting the growth of tourism, benefiting the local economy at the expense
of environmental decay. Reforming incentive structures simultaneously to
promote human well-being and alleviate inequalities across and within nations,
both in the near and longer-term, has the potential to resolve the conflicts
described in the Galapagos by Walsh et al., 2016, in the extractive industries
by Castro et al., 2016, and in Japanese whaling practices by Mangel (Mangel et
al., 2016).
Monitoring
and Modeling Human:
Environment Feedbacks for Ecological
Resources One of the greatest challenges facing humans is how to feed our
growing population while sustaining what remains of biodiversity and ecosystem
services. Conversion of natural areas to agricultural use is the leading cause
of forest loss globally. Natural grassland areas are under even greater threat
(Pagnutti et al., 2013). Two papers in this issue examine the power of applying
new monitoring tools and modelling approaches to conserve biodiversity in
agriculturally dominated ecosystems. Mendenhall et al., 2016 assess a region in
Costa Rica across which nearly 50% of forest cover is embedded in rural
agricultural land. The preservation of forest thus requires that diverse
farming systems that preserve biodiversity be highly valued by the landowners.
To determine the ecological value of arboreal and agricultural coexistence,
Mendenhall et al. develop models that quantify the relationships between local
tree cover and biodiversity. They stress the importance of compensating farmers
for the ecosystem services provided by their lands, another example where the
architecture of incentives is crucial to the sustenance of ecosystem health and
viability in coupled human–environment systems. Similarly, Henderson et al.
(Henderson et al., 2016) examine a region in southern Brazil where natural mosaics
of forests interspersed with grasslands are gradually converted to agricultural
and silvicultural use. The authors study long-term trajectories of these
mosaics by coupling the ecological dynamics to a model of human behavior,
calibrated with ecological and sociological data. Henderson et al. (2016) show
that the sustainability of forest–grassland–agriculture mosaics depends on a
comprehensive valuation of land-use types that includes both economic and
ecological dimensions, a theme that is underscored by a number of papers in
this series. Technological innovations promise transformation of our lives and
economies, but innovations can concurrently destabilize our natural environment.
As just one example, hydraulic fracking facilitates the extraction of otherwise
inaccessible fossil fuel and has been credited with catalyzing the economies of
a number of US states following financial crises. Nevertheless, the staggering
costs of air pollution, drinking water contamination, and global warming
exacerbation are unacceptable. Sustainable environmental equilibria can be
subject to sudden and catastrophic regime shifts that could arise from
technological innovations that accelerate the rate at which resources can be
extracted. The detection of early warning signals is a vital component of
adaptation strategies that mitigate environmental disruption. Bauch et al., (2016)
demonstrate that coupled human–environment systems can also exhibit the same
types of early warning signals that occur in uncoupled ecological systems.
Important differences arise, however. For example, early warning signals could
herald shifts toward either collapse or conservation regimes, depending on
parameter values. Moreover, human vacillation between complacency and concern
in response to perceived resource availability can threaten the viability of
long-term conservation and keep the human–environment system perpetually in the
vicinity of a dangerous tipping point. This danger underscores the need for
long-term thinking to replace reactionary behavior. Hastings et al., (2016)
emphasizes the importance of considering short- and long-term temporal
dynamics, including time delays and tipping points that arise from population
demography, in the recovery of ecological systems under alternative management
practices. The time scale pertinent to optimizing outcomes of management and
sustainability is highly dependent upon the specific ecological system in
question. Hastings provides a broadly applicable optimization approach that
addresses the issue of time scale for environmental management, ranging from
invasive species to fisheries.
Human–Environmental
Health:
The field of epidemiology is rooted in
ecological theory. The principles of species conservation are fundamental to
infectious disease epidemiology, except the goal is reversed: we aim to push a
pathogen species to extinction. The increasingly mobile and dense human
population represents a continuously expanding niche for infectious diseases.
Similarly, agricultural, and domestic animal species have increased alongside
humans, whereas most other species have declined. To an impressive extent, we
have been able to keep pace with pathogen emergence and spread by virtue of our
ingenuity, underlying the development of vaccines and therapeutics. Nonetheless,
pharma-ceutical innovations are only as effective as the degree to which humans
are able and willing to adhere to the recommended implementation. Vaccine
refusal has plagued the control of childhood disease and eradication efforts
against polio (Bauch et al., 2013). Also critical is population adherence to
non-pharmaceutical interventions, such as animal movement bans during the
United Kingdom foot-and-mouth outbreak and avoidance of the culturally
important traditional burials during the West African Ebola outbreak.
Frameworks, such as the one developed by Lubchenco et al., (2016) to facilitate
the alignment of otherwise opposing interests and enhance synergies between
disparate entities in fisheries, are equally important to the arena of public
health. The unifying One Health paradigm incorporates the human species as a
component in an interdependent health ecosystem, where we can both affect and
be affected by changes in the environment and in zoonotic communities. Within
our lifetimes, we have seen HIV and Ebola jump from primates to humans, as well
as antimicrobial resistance spread in response to our livestock care practices.
Beyond these high-profile recent examples, many of history’s greatest scourges
originated via zoonosis, including rabies, leprosy, and the bubonic plague. The
One Health movement seeks to make the human connection to other species an
explicit part of analysis and planning. In support of this paradigm, governmental
agencies and academic organizations have devised a variety of ways for uniting
expertise across traditionally separate fields, usually by economic
quantification of projected costs and benefits. For example, the Indian state
of Tamil Nadu has pioneered the establishment of a state-level One Health
coordination committee. This committee brings together leaders from the human
health, veterinary, and animal welfare sectors to develop rabies control
strategies that transcend sectoral boundaries. To inform cooperative resource
allocation and decision making, Fitzpatrick et al., (2016) were commissioned to
evaluate the various strategies under consideration by this committee,
quantifying the impact of veterinary sector efforts on human health. In comparison
with economic analyses of rabies campaigns in sub- Saharan Africa, the
vaccination coverage found to be effective and efficient for Tamil Nadu was
also highly feasible to implement, even more so than rabies control strategies
advocated by the World Health Organization and implemented in other countries.
The case study of rabies control in Tamil Nadu demonstrates the value of even
modest investments in zoonotic disease prevention, and highlights the
importance of tailoring infectious disease control policies to specific
settings. At the same time, Fitzpatrick et al.’s framework (2016) for the
evaluation of the effectiveness and cost-effectiveness of One Health strategies
is applicable to other multi sectoral solutions to address public health and
environmental challenges.
A controversial ethical issue
underlying cost-effectiveness analysis specifically, and resource allocation
trade-offs between different points in time generally, is the rate of
discounting the future that should be applied to integrate both costs and
values over time, given both uncertainty about future events and the
opportunity costs from forgoing alternative investments. For considerations of
health economics, the World Health Organization stipulates that a 3% annual
discounting rate should be applied (Dibattista et al, 2008). This has become
the standard in cost-effectiveness. However, compounding of the 3% discounting
every year leads to diminishingly small valuation for the future beyond a
couple of decades. This low valuation stands in contrast to the degree of
concern that most people feel for the future that their children and their
children’s children will experience. It has been argued that for environmental
considerations, the discounting rate that is ethical to future generations
should be extremely low, to properly treat the interests of future generations
(Roemer et al.,2010).
Economic discounting is partly
motivated by the uncertainty of what the future holds. Mechanistic and
statistical models are often developed with the goal of predicting future
trends in human– environment systems. The focus of several papers in this issue
was predictive modeling, particularly how the interrelated dynamics of disease
transmission and human behavior influence the ecology, evolution, and control
of infectious diseases (Bauch et al., 2013) across spatial, temporal, and
organizational scales. Using a model of intrapatch disease spread and
interpatch mobility, Castillo-Chavez et al. (Castillo et al., 2016) illustrate
the limitations of existing theoretical frameworks with respect to modeling
such complex adaptive systems. The authors call for the formulation of improved
theoretical frameworks that can encompass such processes and disentangle the
role of epidemiological and socio-economic forces. Although there is
overwhelming evidence for anthropogenic climate change, the multilayered
repercussions on physical and biological systems are likely so extensive that
they are still being realized. As an example of this concern, Fisman et al., (2016)
identify externalities of climate change on disease trends in the United States
that have previously gone unappreciated. Specifically, their analysis of
temporal trends of hospitalization data reveals that vector-borne and enteric
disease in the United States are impacted by climatic shifts associated with El
Ni~no Southern Oscillations. Given that this relationship between climate
change and vectorborne, as well as enteric diseases, is significant even in a
country with high levels of sanitation and relatively low prevalence of these
diseases, the influence of climate change on such diseases are expected to be even
greater in developing countries. Becker et al., (2016) point out that
modeling-coupled human–environment interactions requires understanding how
natural system dynamics unfold at both small and large spatial scales, such as
individual house-holds versus entire cities. Applying a stochastic disease
transmission model to a 1904 measles outbreak in London, as well as to the
2014–2015 Disneyland, California measles outbreak, Becker et al. find that
disease transmission within schools and within age classes is higher than has
been estimated from population-level serological analyses. Population dynamics
not only vary at different spatial scales, as in Becker et al. (Becker et al.,
2016), and at different time scales, as in Hastings (Hastings et al., 2016), but
can also be affected by rapid evolutionary processes. Lewnard and Townsend
(Lewnard et al., 2016) demonstrate that the evolution of disease resistance in
a disease vector can drive shifts in outbreak seasonality. To capture these
complex interactions, they analyze extensive data from the Indian Plague
Commission on climate, rat infection and resistance, and survival of flea
vectors at different temperatures. Integrating this data into a model that
combines environmentally forced plague dynamics with selection for a
quantitative resistance trait in rats, Lewnard and Townsend demonstrate that
the observed phase shifts in epidemic dynamics were modulated by the evolution
of resistance over time. Moreover, incorporating the evolution of plague
resistance among rats into their model reproduces observed changes in seasonal
epidemic patterns. Furthermore, it captures experimentally observed
associations between climate and flea population dynamics in India. Similar to
Becker et al., (2016), Lewnard and Townsend (Lewnard et al., 2016) demonstrate
that historical datasets can yield insights into the epidemiological,
ecological, and evolutionary dynamics of re-emerging disease agents, insights
that will help to guide the design of preparedness and response strategies that
mitigate future outbreaks.
The
Need for Cooperation in Protecting Human:
Environment Systems Fragile ecosystems
are subject not only to conflicts between short term rewards and long-term
conservation goals, but also are subject to the vagary of human responses to
environmental challenges. Given that many environmental problem- including
those explored in this issue- represent a common pool resource problem, their
solution will require improved cooperation between humans. The human mind has
spent most of its evolutionary history in a hunter-gatherer setting, and it is
in this localized setting that our penchant for cooperation evolved.
Consequently, a pressing challenge for the current phase in the evolutionary
journey of our species is to promote the scale-up of cooperation far beyond
localized settings. Cross-sectoral, collaborative, and integrated approaches
can be powerful tools to bolstering the sustainability, resiliency, and
equitability of natural resources within and between generations globally.
Public health, conservation, agricultural security, and economic development
are deeply intertwined in ways that are not immediately obvious. Understanding
the interplay is fundamental to the development of an architecture of
incentives and rewards that aligns disparate interests to optimize outcomes
over the long-term. In the precarious balance between improving the standard of
living across the globe while minimizing the negative externalities associated
with the resources that we extract to do so, it is imperative to identify
synergies that make effective solutions cost-effective as well. The human
species has unparalleled capacities of ingenuity, foresight, and compassion
that can be used to direct the current trajectory of the world’s ecosystems from
rapid deterioration and destabilization toward equity and sustainability.
Human
influences on evolution:
(a)
In which contexts will directional selection be the strongest and most
consistent?
(b)Directional selection favouring
evolutionary change is expected to be the strongest and most consistent when
population phenotypes rest persistently on the slopes of steep adaptive peaks
(Fig. 1). This state can arise when the elevations and gradients of peaks are
the greatest, and when their displacement from current phenotypes is the
farthest, fastest and most sustained. One confluence of these conditions occurs
when selection consistently favours the most extreme trait values (e.g. the
largest body sizes) independent of the specific distribution of trait values.
Another confluence occurs in antagonistic coevolution, where the evolution of
one species (e.g. a parasite) to better exploit another species (e.g. a host)
leads to the evolution of countermeasures and, hence, the continual evolution
of both species (Brockhurst et al., 2014; Carmona et al., 2015). These co-evolutionary
arms races come in two general forms: escalating arms races (Nuismer et al.,
2007) and cyclical Red Queen dynamics. Both forms of antagonistic coevolution
can impose strong directional selection, but escalating arms races might more
often lead to consistent directional change.
|
. |
Strong
and consistent directional selection might arise in any of the human disturbance
contexts, but we specifically wish to highlight hunting or harvesting, climate
change and certain agricul-tural and human health situations. For
hunting or harvesting, selection can actively target and disproportionately
remove the largest individuals each generation, regardless of average body size
(Kuparinen et al., 2017). Strong directional selection for smaller size thus
should persist even as evolution proceeds. Matching this expectation,
phenotypic changes in wild populations are greatest when humans act as
predators (Bull et al., 2016, Darimont et al., 2009) and when fishing intensity
is strongest (Sharpe et al., 2009). For climate change, one might similarly assume
that, because warming is ongoing, optimal trait values should continue to shift
in the same direction, with a clear example being the advancement of spring
phenology (Lancaster et al., 2017, Parmesan et al., 2003). However,
year-to-year climate vari- ation can exceed the overall warming trend, and so
selection should be variable from year to year (Yang et al., 2010). Of course,
directional selection will not be eternal in either context, because harvesting
often ceases when fish get too small or rare, and because phenology cannot
advance indefinitely. Hence, we instead expect the strongest and most
consistent directional selection to arise when humans instigate or intensify
escalating arms races, such as when we kill or control our enemies (weeds, pests,
pathogens), which then evolve resistance, which thus necessi- tates newer or
stronger control measures and so on (Turcotte et al., 2017, Hiltunen et al.,
2017, Rogalski et al., 2017). Even here, directional selection might stop or at
least weaken if, for example, enemies evolve tolerance instead of resistance
(Langwig et al., 2017, Svensson et al., 2010) if they evolve to become friends
or neigh- bours (e.g. domestication of wild animals or plants), or if we wipe
them out.
In
which contexts will evolutionary potential be the most dramatically altered?
Evolutionary potential is determined by the
distribution of genetic (co)variance across the adaptive landscape, with
greater variance and better alignment with the direction of selection both
expected to speed evolution (Wood et al., 2016). Any perturbation that alters genetic (co)variance can thus influence
evolutionary dynamics on a given adaptive landscape. In some cases, genetic
variation can increase, including through greater gene flow, hybridization or
mutation. Such increases can be beneficial if enough of the new variation is
adaptive versus detrimental if too much of it is maladaptive. In other cases,
genetic variation can decrease, most obviously through strong selection or
reduced population size (which might or might not be coupled), with the latter
increasing gen- etic drift and inbreeding. Such decreases could be beneficial
(e.g. if they reflect precise adaptation) or detrimental (e.g. if they limit
future responses to selection).
We suggest that the greatest increases in genetic
variation will attend contexts where population sizes increase most
dramatically, such as for introduced species experiencing ‘enemy release’ (Colautti et
al., 2017) and for native species
benefiting from ‘disappearing diseases’ (Rogalski et al., 2017). Increases are also expected when diverse
source populations are brought together in new locations (Colautti et
al., 2017, Roy et al., 2015) and in the case of exposure to mutagens
(e.g. pollution (Moller et al., 2015). We suggest that the greatest decreases in genetic variation will
attend contexts where selection is strong and consistent, and/or when
population sizes decrease dramatically. Some contexts that generate strong
selection were discussed earlier, especially hunting/harvesting and some
coevolutionary arms races. However, even exceptionally strong selection does
not always deplete relevant genetic variation, especially when the selection is
variable in time and space. Some contexts that can greatly reduce population
size include habitat loss (e.g. urbanization (Cheptou et al., 2017), strong abiotic stressors (e.g. pollution (Hamilton
et al., 2017, Alexander et al.,
2017) and strong biotic stres- sors
(e.g. invasive predators/parasites (Colautti et al., 2017) and emerging diseases. Additionally,
genetic variation within populations can decrease owing to fragmentation that
reduces gene flow.
In
which contexts will evolutionary diversification be most altered?
Biodiversity evolves through adaptation to (i)
different environments (i.e. different
peaks on
an adaptive landscape) and (ii) similar environments in
different locations (i.e. the same peak on different adaptive landscapes).
Thus, human influences that change the number, type and distinctiveness of
environments and locations will effectively change the number, position and
shape of adaptive peaks, which will thereby influence the evolution of
biodiversity (Fig.1)These effects could arise in three basic ways. First,
humans can change how populations in different locations experience divergent
selection by, for example, translocating species into new locations (Herrel et al.,
2008) or altering habitats in some
locations (Dibattista et al.,
2011, Salmo et al., 2016).
Second, humans can modify the number and distinc- tiveness of alternative
environments in a given location, thereby altering disruptive selection. Third,
humans can modify the evolutionary independence of populations and species by
altering hybridization, gene flow and introgression (Seehausen et al.,
2008). In each case, the effects can be
‘positive’ by facilitating the evolution of increased biodiversity (reviewed in
(Hendry et al., 2007) or
‘negative’ by causing the evolution of decreased biodiversity (Seehausen
et al., 2008). Each of the contexts for
human influence could have the above ramifications; yet they seem particularly
to converge for species introductions that lead to biological invasions.
Specifically, introduced species often experience novel selective pressures,
especially new biotic interactions, and have considerable evolutionary
independence from source populations in the native range. In addition,
introduced species can provide new ‘environments’ for adaptation by native
species, with an exemplar being new insect host races - and their associated
species - on introduced plants (Forbes et al., 2009). Alternatively, or additionally, species
introductions can cause the evolution of decreased biodiversity by altering the
distinctiveness of natural environments, with an exemplar being new food types
for birds that diminish the distinctiveness of native food types. Introductions
can also increase or decrease biodiversity by altering patterns of gene flow,
hybrid- ization and gene flow within and among species (Vonlanthen et
al., 2012). Other contexts that merit
special mention are fragmentation, which imposes strong selection and increases
evolutionary independence (Cheptou et al., 2017), and urbanization and pollution, which create especially novel
environments (Hamilton et al., 2017, Alberti et al.,2017 , Alberti et al., 2014).
Ecological
and societal consequences:
(i)
In which contexts will human-induced evolution most alter population dynamics?
Humans have direct effects on species that alter
aspects of their population structure ranging from age distributions to overall
abundance. Beyond these direct demographic effects, humans can indirectly
modify species’ population dynamics by influencing their evolution. For
instance, environmental change should render many populations maladapted,
leading to decreased individual fitness, which should decrease population size potentially
causing extirpation or extinction. Yet this mal-adaptation should also generate
selection, which should promote adaptation that increases individual fitness, which
should increase population size potentially allowing ‘evolutionary rescue’ (Carls on et
al.,2014; Gonzalez et al., 2013).
However, these potential evolutionary benefits are not inevitable, nor are they
necessarily sufficient for recovery. First, evolutionary rescue depends on
sufficient adaptive genetic variation, which might or might not be present.
Second, strong selection can impose a mortality cost (i.e. ‘hard’ selection)
that dramatically reduces population size, which can decrease genetic variation
and increase inbreeding, drift and stochastic extinction (Saccheri et
al., 2006). As an additional effect,
human activities can lead to the evolution of increased (or decreased) carrying
capacity in particular species, such as through adaptation to new environments
or resources.
Evolution occurring in any of the contexts for human
influence could alter species’ population dynamics, with several effects being
especially clear. First, the negative effects of pollution (e.g. toxic
chemicals) should often impose hard selection that can influence population
size (Lotze
et al., 2004). Second, attempts to
reduce or eliminate enemies in agriculture and medicine are specifically
designed to decrease the target’s absolute fitness and should therefore also
impose hard selection. With respect to the evolution of carrying capacity,
several other human activities seem likely to be particularly potent. For
instance, humans often generate novel environments (e.g. urbanization (Cheptou
et al., 2017) and agriculture (Turcotte
et al., 2017) and novel species
interactions (e.g. invasions/extinctions and emerging/disappearing diseases (Rogalski
et al., 2017) that can provide
opportunities for evolutionary niche expansion. Putative examples might be
mosquitoes adapting to the London Underground and again new insect host races
on introduced plants (Forbes et al., 2009). In all of these scenarios and others the evolution caused by human
activities can substantially alter the abundance, age structure and population
growth rate.
(b)
In which contexts will human-induced evolution most alter community structure?
As was the case for population dynamics, humans
often have direct demographic influences on community structure, whereas we are
here interested in the evolutionary effects. We distinguish two main scenarios.
First, human activities can have broad effects that simultaneously influence
the evolution of manyspecies, thus providing multiple points of entry for influence
on a given community. Second, human activities can have strong effects on the
evolution of particular ‘important’ species, which can then have cascading
effects on the broader community (Cairns et al., 2017). These cascading effects could be a direct
result of trait change in the important species; that is, trait- mediated
effects of an evolving species on the community in which it is embedded.
Alternatively or additionally, the evolution of an important species could
alter its population dynamics, which could thereby influence the rest of the
community.
Simultaneous multispecies evolutionary effects seem
possible in many contexts, but especially so for wholesale alterations of the
environment. One such context is climate change, which is reshaping the
phenology (and other traits) of large sets of interacting species, which
thereby alters relative species abundances and the structure of food webs (Both et al.,
2009). Wholesale environmental
alterations are also typical in urbanization, fragmentation, pollution and
agriculture (Laurance et al., 2014). Cascading effects of an important species seem most likely when humans
influence the evolution of particular ‘keystone’ species, ‘foundation’ species,
‘niche constructing’ species, ‘ecosystem engineers’, ‘strong interactors’ and
so on. These important species could be those having very large effects as
individuals (e.g. beavers, elephants and sea otters) or large effects owing to
their high abundance (e.g. weeds, pests, pathogens and migratory species). We
suggest that the community consequences of evolution in important species such
as these are particularly likely for hunting/harvesting and human health, where
humans often directly target specific large-effect friends or enemies. The same
should be true when climate change or the bioaccumulation of toxins influences
the evolution of apex predators (Zarnetske et al., 2012).
(c)
In which contexts will human-induced evolution most alter ecosystem function?
Human influences on evolution that then have consequences
for the dynamics of populations and species and the structure of communities
might thereby alter various aspects of ecosystem function, such as primary
productivity, nutrient cycling, decomposition rates and carbon sequestration (Bailey et al.,
2009). In parallel to our above
suggestions for community structure, these effects could arise through
wholesale environmental manipulations that influence the evolution of many
species, or through effects on the evolution of specific important species,
which could then have trait- or density- mediated effects on ecosystems. Additionally,
the evolutionary effects on community structure considered in the previous
question could cascade to have ecosystem consequences. Finally, any
evolutionary influences on a particular ecosystem function could have cascading
influences on other ecosystem functions, including through feedbacks that
influence community structure, population dynamics and trait evolution.
The most important contexts for human-caused
evolutionary effects on ecosystem function might be similar to those described
above for community structure. First, wholesale environmental change that
causes the evolution of many species that together have important ecosystem
effects seems particularly likely for climate change, urbanization, pollution
and agriculture. Second, cascading effects of the evolution of important
species seem particularly likely for hunting/harvesting, agriculture and human
health. Although evolutionary effects on ecosystem function could be strong
(e.g. for plant size affecting nutrient and carbon cycling (Chapin et al.,
2003), theory and empirical assessments
have suggested that such effects might be weaker at the ecosystem level than at
the community level. The hypothesized reason is that additional external
variables are expected to strongly influence ecosystem processes, and thereby
swamp, or at least obscure, the effects of contemporary evolution. As examples,
potential ecosystem effects of evolution might be swamped for climate change by
the direct abiotic effects of varying temperature and precipitation, for
agriculture by the direct effects of fertilizer and irrigation and for
eutrophication by the direct effects of nutrients. Thus, we might expect the
effects of evolution on ecosystems to be strongest, or at least the most
obvious, in contexts where external drivers are not changing dramatically at
the same time. Two such contexts might be introduced/invasive species and
hunting/harvesting, where changes in biotic conditions could be more important
than changes in abiotic factors. Of course, it is also possible for altered
biotic interactions to swamp or obscure underlying eco-evolutionary dynamics.
(d)
In which contexts will human-induced evolution most alter human societies?
We have thus far addressed how human influences on
evolution can alter ecological processes at the population, community and
ecosystem levels. It is now time to evaluate when these effects might have the
greatest consequences for humans themselves, with respect either to services
(from our friends) or disservices (from our enemies). First, some organismal
traits are of specific interest to humans, such as the size of hunted/harvested
animals, the concentration of useful plant chemicals, the nutrient content of
agricultural products or the resistance of weeds/pests/pathogens to control
measures. Second, humans can derive costs or benefits from evolutionary effects
on the population dynamics of focal organisms, such as the biomass of harvested
or cultivated species, the abundance of weeds/pests/pathogens, and the density
and spread of undesirable invasive species. Third, evolutionary effects on
communities can interact with our desire to preserve biodiversity (Carroll et
al., 2014, Hendry et al., 2010).
Fourth, evolutionary changes can influence emergent ecosystem properties
that humans care about, such as carbon sequestration, water clarity or air
quality.
The consequences of human-induced evolution for
human societies are most obvious when the evolving organisms provide direct
benefits as friends (e.g. hunting/harvesting and domestication) or direct costs
as enemies (e.g. weeds/pests/ pathogens, invasive species and emerging
diseases). A less direct conduit for societal impacts occurs when humans influence
the evolution of our ‘neighbours’, which can thereby influence our appreciation
of nature or provide a mechanism for emerging enemies or friends. Importantly,
all of the contexts for human-induced evolution have the potential to feedback
to influence human societies through for example- biodiversity, nutrient
cycling and productivity. Stated plainly, all ecosystem services and
disservices are shaped by past and future evolution, making them -more
properly—EVO system services and disservices (Hendry et al., 2010). We further suggest that societal impacts
will be strongly shaped by the nature and strength of feedbacks, such as when
humans cause the evolution of organisms in ways that have societal impacts,
which then induces humans to further modify the evolution of those organisms.
Those feedbacks could be positive (reinforcing), which will tend to destabilize
eco-evolutionary systems, or negative (opposing), which will tend to stabilize
eco-evolutionary systems (Strauss et al., 2014). An especially clear example of positive feed- back is the race between
organisms detrimental to humans and our attempts to control them.
Conclusions:
Organism–environment
interaction represents a biological phenomenon that integrates ultimate and
proximate causation on a global scale. Furthermore, it includes all organisms
including plants and non-eukaryotes because all organisms are being affected by
human-induced rapid environmental change. A mechanistic approach will be
critical to understand why some organisms can cope with change and others
cannot. New opportunities are developing to study organism–environment
interaction. Improved methods for gene profiling mean it is more efficient and
cheaper to sequence transcriptomes in tissues from individuals in different
habitats, social conditions, etc.
Emerging techniques in
proteomics and biochemical aspects (e.g., metabolome) allow additional
perspectives on organism–environment interaction downstream of gene expression.
Recent investigations on maternal effects (and paternal) reveal highly
significant effects on phenotypes of offspring that can be passed on to other
generations. Effects of diet and endocrine disrupters on gene methylation
patterns ecome pertinent here. There is also a great need for mathematical
models to provide a framework at the mechanistic levels underlying
organism–environment interaction.
In summary, mechanistic
functional responses can readily fit into existing frameworks for modeling
species interactions and, in the guise of fitness functions, evolutionary
dynamics. Furthermore, mechanistic functional responses that incorporate
organisms’ interactions with their environment can extend these frameworks to
explore environmental influences, including multispecies or adaptive responses
to changing environments and the evolution of phenotypic plasticity. For both
community and evolutionary models, this approach ties into existing theoretical
frameworks such as trait-mediated interactions and the evolution of
function-valued traits.
References:
1. Abrams P. A. (2001). Modelling the
adaptive dynamics of traits involved in inter- and intraspecific interactions:
An assessment of three methods. Acad Sci
USA, 113:14507–14514.
2. Alberti M. (2014). Eco-evolutionary
dynamics in an urbanizing planet. Trends
Ecol. EVol. 114: 1–13.
3. Alberti M., Marzluff J. and Hunt
V.M. (2017). Urban driven phenotypic changes: empirical observations and
theoretical implications for eco-evolutionary feedback. Phil. Trans. R. Soc. B., 372: 20160029.
4. Alexander T.J., Vonlanthen P. and
Seehausen O. (2017). Does eutrophication-driven evolution change aquatic
ecosystems? Phil. Trans. R. Soc. B., 372,
20160041.
5. Bailey J.K. (2009). From genes to
ecosystems: a synthesis of the effects of plant genetic factors across levels
of organization. Phil. Trans. R. Soc. B.,
364: 1607-1616.
6. Barrett S. (2016). Coordination vs.
voluntarism and enforcement in sustaining international environmental
cooperation. Proc Natl Acad Sci USA,
113:14515-14522.
7. Baskett M. L., Nisbet R. M., Kappel
C. V., Mumby P. J. and Gaines S. D. (2010). Conservation management approaches
to protecting the capacity for corals to respond to climate change: a
theoretical comparison. Glob. Change
Biol., 16: 1229-1246.
8. Bauch C.T and Galvani A.P. (2013).
Epidemiology. Social factors in epidemiology. Science, 342 (6154):47-49.
9. Bauch C.T., Sigdel R., Pharaon J.
and Anand M. (2016). Early warning signals of regime shifts in coupled
human–environment systems. Proc Natl Acad
Sci USA 113:14560-14567.
10. Becker A.D., Birger R.B., Teillant
A., Gastanaduy P. A., Wallace G.S. and Grenfell B.T. (2016). Estimating
enhanced prevaccination measles transmission hotspots in the context of
cross-scale dynamics. Proc Natl Acad Sci
USA, 113:14595-14600.
11. Benedetti-Cecchi L., Tamburello L.,
Bulleri F., Maggi E., Gennusa V. and Miller M. (2012). Linking patterns and
processes across scales: the application of scale transition theory to algal
dynamics on rocky shores. J. Exp. Biol.,
215: 977-985.
12. Brockhurst M.A., Chapman T., King
K.C., Mank J.E., Paterson S. and Hurst G.D.D. (2014). Running with the red
queen: the role of biotic conflicts in evolution. Proc. R. Soc. B 281:
20141382.
13. Brulle R.J. and Antonio R.J.
(2015). The Pope’s fateful vision of hope for society and the planet. Nat Clim Chang, 5(10): 900–901.
14. Bull J.W. and Maron, M. (2016). How
humans drive speciation as well as extinction. Proc. R. Soc. B, 283: 20160600.
15. Cairns J., Becks L., Jalasvuori M.
and Hiltunen, T. (2017). Sublethal streptomycin concentrations and lytic
bacteriophage together promote resistance evolution. Phil. Trans. R. Soc. B
372, 20160040.
16. Carlson S.M., Cunningham C.J. and Westley
P.A.H. (2014). Evolutionary rescue in a changing world. Trends Ecol. EVol, 29: 1 – 10.
17. Carmona D., Fitzpatrick C.R. and
Johnson M.T.J. (2015). Fifty years of co-evolution and beyond: integrating co-
evolution from molecules to species. Mol.
Ecol, 24: 5315 – 5329.
18. Carroll S.P.(2014). Applying
evolutionary biology to address global challenges. Science, 346: 1245993.
19. Castro M.C., et al. (2016).
Examples of coupled human and environmental systems from the extractive
industry and hydropower sector interfaces. Proc
Natl Acad Sci USA, 113:14528–14535.
20. Chapin F.S. (2003). Effects of
plant traits on ecosystem and regional processes: a conceptual framework for
predicting the consequences of global change. Ann. Bot. 91: 455-463.
21. Cheptou P.O., Hargreaves A.L.,
Bonte D. and Jacquemyn H. (2017). Adaptation to fragmentation: evolutionary
dynamics driven by human influences. Phil.
Trans. R. Soc. B, 372: 20160037.
22. Colautti R.I., Agren J. and
Anderson J.T. (2017). Phenological shifts of native and invasive species under
climate change: insights from the Boechera– Lythrum model. Phil. Trans. R. Soc. B, 372: 20160032.
23. Colautti R.I., Alexander J.M.,
Dlugosch K.M., Keller S.R. and Sultan S.E. (2017). Invasions and extinctions
through the looking glass of evolutionary ecology. Phil. Trans. R. Soc. B, 372: 20160031.
24. Crouse D. T., Crowder L. B. and
Caswell H. (1987). A stage-based population model for loggerhead sea-turtles
and implications for conservation. Ecology,
68: 1412-1423.
25. Darimont C.T., Carlson S.M.,
Kinnison M.T., Paquet P.C,. Reimchen T.E. and Wilmers C.C. (2009). Human
predators outpace other agents of trait change in the wild. Proc. Natl Acad. Sci. USA, 106: 952-954.
26. Dawson T. P., Jackson S. T., House
J. I., Prentice I. C. and Mace G. M. (2011). Beyond predictions: Biodiversity
conservation in a changing climate. Science,
332: 53-85.
27. Day T.(2005). Modelling the
ecological context of evolutionary change: deja vu or something new? In “Ecological Paradigms Lost: Routes to
Theory Change” (Ed. Cuddington K. and Beisner B. E.), 273-309.
28. Denny M. and Helmuth B. (2009).
Confronting the physiological bottleneck: a challenge from ecomechanics. Integr. Comp. Biol., 49: 197-201.
29. Denny M. W. and Dowd W. W. (2012).
Ecomechanics, environmental stochasticity, and the evolution of thermal safety
margins in intertidal limpets. J. Exp.
Biol., 215: 934-947.
30. Dibattista J.D., Feldheim K.A.,
Garant D., Gruber S.H. and Hendry A.P. (2011). Anthropogenic disturbance and
evolutionary parameters: a lemon shark population experiencing habitat loss. EVol. Appl., 4: 1 –17.
31. Dibattista J.D. (2008). Patterns of
genetic variation in anthropogenically impacted populations. ConserV. Genet, 9: 141–156.
32. Fisman D.N., Tuite, A.R. and Brown
K.A. (2016). Impact of El Ni~no Southern Oscillation on infectious disease
hospitalization risk in the United States. Proc
Natl Acad Sci USA 113:14589–14594.
33. Fitzpatrick M. C., Shah H. A.,
Pandey A., Bilinski A. M., Kakkar M., Clark A. D., Townsend J. P., Abbas S.S.
and Galvani A.P. (2016). One Health
approach to cost-effective rabies control in India. Proc Nat l Acad Sci USA, 113:14574-14581.
34. Forbes A.A., Powell T.H.Q,
Stelinski L.L., Smith J.J. and Feder, J.L. (2009). Sequential sympatric
speciation across trophic levels. Science,
323: 776-779.
35. Gonzalez A., Ronce O., Ferriere R.
and Hochberg M.E. (2013). Evolutionary rescue: an emerging focus at the
intersection between ecology and evolution. Phil.
Trans, R. Soc. B 368: 20120404.
36. Hamilton P.B., Rolshausen G.,
Webster T.M.U. and Tyler C.R. (2017). Adaptive capabilities and fitness
consequences associated with pollution exposure in fish. Phil. Trans. R. Soc. B 372: 20160042.
37. Hastings A. (2016). Time scales and
the management of ecological systems. Proc
Natl Acad Sci USA 113:14568–14573.
38. Heller N. E. and Zavaleta E. S.
(2009). Biodiversity management in the face of climate change: A review of 22
years of recommendations. Biol. Conserv,
142: 14-32.
39. Helmuth B., Kingsolver J. and
Carrington, E. (2005). Biophysics, physiological ecology, and climate change:
does mechanism matter? Annu. Rev.
Physiol, 67: 177- 201.
40. Henderson K.A, Bauch C.T. and Anand
M. (2016). Alternative stable states and the sustainability of forests,
grasslands, and agriculture. Proc Natl
Acad Sci USA 113:14552–14559.
41. Hendry A.P., Nosil P. and Rieseberg
L.H. (2007). The speed of ecological speciation. Funct. Ecol. 21: 455-464.
42. Hendry A.P. (2010). Evolutionary
biology in biodiversity science, conservation, and policy: a call to action. Evolution, 64: 1517-1528.
43. Hendry A.P., Taylor E.B. and
McPhail J.D. (2002). Adaptive divergence and the balance between selection and
gene flow: lake and stream stickleback in the Misty system. Evolution, 56:
1199-1216.
44. Herrel A., Huyghe K., Vanhooydonck
B., Backeljau T., Breugelmans K., Grbac I., Van Damme R. and Irschick D.J. (2008). Rapid large-scale
evolutionary divergence in morphology and performance associated with
exploitation of a different dietary resource. Proc. Natl Acad. Sci. USA, 105: 4792-4795.
45. Hiltunen T., Virta M. and Laine A.L.
(2017). Antibiotic resistance in the wild: an eco-evolutionary perspective. Phil. Trans. R. Soc. B 372: 20160039.
46. Hoffmann A. A. and Sgro C. M.
(2011). Climate change and evolutionary adaptation. Nature, 470: 479-485.
47. Hofmann G. E. and Todgham A. E.
(2010). Living in the now: physiological mechanisms to tolerate a rapidly
changing environment. Annu. Rev.
Physiol., 72: 127-145.
48. Kuparinen A. and
Festa-Bianchet M. (2017). Harvest- induced evolution: insights from
aquatic and terrestrial systems. Phil.
Trans. R. Soc. B 372: 20160036.
49. Lancaster L.T., Morrison G. and Fitt R.N. (2017). Life
history trade-offs, the intensity of competition, and coexistence in novel and
evolving communities under climate change. Phil.
Trans. R. Soc. B 372: 20160046.
50. Lande R. and Arnold S. J. (1983). The measurement of
selection on correlated characters.
Evolution, 37: 1210-1226.
51. Langwig K.E., Hoyt J.R., Parise K.L., Frick W.F., Foster
J.T. and Kilpatrick A.M. (2017). Resistance in persisting bat populations after
white-nose syndrome invasion. Phil. Trans.
R. Soc. B, 372: 20160044.
52. Laurance W.F. (2014). A global strategy for road
building. Nature, 513: 229–232.
53. Levin S. A. and Lubchenco J. (2008). Resilience,
robustness, and marine ecosystem-based
management. Bioscience,
58: 27-32.
54. Levins R. (1966). The strategy of model building in
population biology. Am. Scientist,
54: 421-431.
55. Lewnard J.A. and Townsend J.P. (2016). Climatic and
evolutionary drivers of phase shifts in the plague epidemics of colonial India.
Proc Natl Acad Sci USA 113:14601-14608.
56. Lotze H.K. and Milewski I. (2004). Two centuries of
multiple human impacts and successive changes in a North Atlantic food web. Ecol. Appl, 14: 1428-1447.
57. Lubchenco J., Cerny-Chipman E.B., Reimer J.N., Levin
S.A. (2016). The right incentives enable ocean sustainability successes and
provide hope for the future. Proc Natl
Acad Sci USA 113:14507-14514.
58. Madin, J. and Connolly, S. (2012). Quantifying
population-level performance over environmental gradients on coral reefs. J. Exp. Biol, 215: 968-976.
59. Mangel M. (2016). Whales, science, and scientific
whaling in the International Court of Justice. Proc Natl Acad Sci USA 113:14523–14527.
60. May R. M. (2004). Uses and abuses of mathematics in
biology. Science, 303: 790-793.
61. Mendenhall, C.D., Shields-Estrada, A., Krishnaswami,
A.J., Daily, G.C. (2016). Quantifying and sustaining biodiversity in tropical
agricultural landscapes. Proc Natl Acad
Sci USA 113:14544–14551.
62. Moller A.P. and Mousseau T.A. (2015). Strong effects of
ionizing radiation from Chernobyl on mutation rates. Sci. Rep, 5: 8363.
63. Nisbet R. M., Jusup M., Klanjscek T. and Pecquerie L.
(2012). Integrating dynamic energy budget (DEB) theory with traditional
bioenergetic models. J. Exp. Biol,
215: 892-902.
64. Nuismer S.L., Ridenhour B.J. and
Oswald, B.P. (2007). Antagonistic coevolution mediated by phenotypic
differences between quantitative traits. Evolution,
61: 1823 – 1834.
65. Pagnutti C., Bauch C.T. and Anand,
M. (2013). Outlook on a worldwide forest transition. PLoS One, 8(10):e75890.
66. Palumbi S. R. (2001). Humans as the
worldʼs
greatest evolutionary force. Science,
293: 1786-1790.
67. Parmesan C. (2006). Ecological and
evolutionary responses to recent climate change. Annu. Rev. Ecol. Evol. Syst., 37: 637-669.
68. Parmesan C. and Yohe G. (2003). A
globally coherent fingerprint of climate change impacts across natural systems.
Nature, 421: 37-42.
69. Roemer J.E. (2010). The ethics of
intertemporal distribution in a warming planet. Environ Resour Econ, 48(3):363–390.
70. Rogalski M.A., Gowler C.D., Shaw
C.L., Hufbauer R.A. and Duffy, M.A. (2017). Human drivers of ecological and
evolutionary dynamics in emerging and disappearing infectious disease systems. Phil. Trans. R. Soc. B 372: 20160043.
71. Rowland E. L., Davison J. E.
and Graumlich L. J. (2011). Approaches
to evaluating climate change impacts on species: a guide to initiating the
adaptation planning process. Environ.
Manage, 47: 322-337.
72. Roy D., Lucek K., Walter R.P. and
Seehausen, O. (2015). Hybrid ‘superswarm’ leads to rapid divergence and
establishment of populations during a biological invasion. Mol. Ecol., 24: 5394 – 5411.
73. Saccheri, I. and Hanski, I. (2006).
Natural selection and population dynamics. Trends
Ecol., 21: 341-347.
74. Salmo P., Nilsson J.F., Nord A., Bensch
S. and Isaksson, C. (2016). Urban environment shortens telomere length
in nestling great tits, Parus major. Biol.
Lett., 12: 20160155.
75. Schluter, D. (1988). Estimating the
form of natural-selection on a quantitative trait. Evolution, 42: 849-861.
76. Schoener T. W. (1986). Mechanistic
approaches to community ecology – a new reductionism. Am. Zool. 26: 81-106. Sci
USA 113:14544–14551.
77. Seehausen O., Takimoto G., Roy, D.
and Jokela, J. (2008). Speciation reversal and biodiversity dynamics with
hybridization in changing environments. Mol.
Ecol., 17: 30-44.
78. Sharpe D.M.T. and Hendry A.P. (2009). Life history change in
commercially exploited fish stocks: an analysis of trends across studies. EVol.
Appl., 2: 260-275.
79. Strauss S.Y. (2014). Ecological and
evolutionary responses in complex communities: implications for invasions and
eco-evolutionary feedbacks. Oikos,
123: 257- 266.
88. Svensson E.I. and Raberg, L.
(2010). Resistance and tolerance in animal enemy-victim coevolution. Trends Ecol., 25: 267–274.
81. Turcotte M.M., Araki H., Karp D.S.,
Poveda K., Whitehead S.R. (2017). The eco-evolutionary impacts of domestication
and agricultural practices on wild species. Phil. Trans. R. Soc. B 372: 20160033.
82. Van de Koppel J., Bouma T. and Herman P. (2012). Upscaling
biomechanical interactions in estuarine ecosystems. J. Exp. Biol., 215, 962-967.
83. Vonlanthen
P., Bittner D., Hudson A.G., Young K.A., Muller R., Lundsgaard-Hansen B., Roy D.,
Di Piazza S., Largiader C.R., Seehausen O. (2012). Eutrophication causes
speciation reversal in whitefish adaptive radiations. Nature, 482: 357–362.
84. Walsh S. J. and Mena C.F. (2016).
Interactions of social, terrestrial, and marine sub-systems in the Galapagos
Islands, Ecuador. Proc Natl Acad Sci USA,
113:14536–14543.
85. Wood C.W. and Brodie E.D.I. (2016).
Evolutionary response when selection and genetic variation covary across
environments. Ecol. Lett, 19:
1189–1200.
86. Yang L.H. and Rudolf V.H.W. (2010).
Phenology, ontogeny and the effects of climate change on the timing of species
interactions. Ecol. Lett, 13: 1–10.
87. Zarnetske P.L., Skelly D.K. and
Urban, M.C. (2012). Biotic multipliers of climate change. Science, 336: 1516 – 151.
.jpg)
Comments
Post a Comment