Although some of the EverForecast species models provide output on a daily time scale, water managers and scientists in the Everglades typically evaluate hydrologic conditions and make recommendations on a weekly to monthly time scale. To target managers’ needs, we summarized EverForecast outputs on a biweekly (14 day) time step.

Humboldt was an environmental scientist even before the words environment or ecology were coined (1827 and 1875, respectively).

The field of sensory ecology is based on studying the sensory systems of animals in order to understand what they perceive in their environments and how that is going to affect their interactions with that environment (Dangles et al. 2009).

VARIABILITY IN SENSORY ECOLOGY: EXPANDING THE BRIDGEBETWEEN PHYSIOLOGY AND EVOLUTIONARY BIOLOGY

the whole world to me is a 00:19:25 kind of um Collision or or Criss-Cross or overlap between past and future

I think this is also part of  our sense of who we are as humans, as ourselves,   and the idea of the self, the individual, and  even the humans as this individual species,   these divisions are arbitrary.

So something about our   process is completely wrong. Something about our  understanding of ecology is completely wrong.   But for me, I look back at, for example,  the Daoists. To me, the Daoists understood   very deeply the complexity. Daoism really starts  with just accepting the mystery and the complexity   00:19:33 of the world and not trying to necessarily  explain it all, and then to pattern behavior   after these natural processes

Ecology and evolution provide the scientific background needed to address the biodiversity crisis; Zen provides the deeper knowing that will motivate our action to address this problem.

We will act to save “life on this planet” only if we recognize at a deep level that our “self” includes all beings. We need to recognize and feel at a deep level that ultimately we are not biologists trying to save other species. Rather, we are one emergence of life on this planet trying to save itself.

The ecologist David Barash (1973) discussed the parallels between Zen Buddhism and ecology.

The Buddhist concept of interconnectedness or emptiness (all things are empty of a separate self) is represented by the metaphor of the Jewel Net of Indra

Abstract

information ecology research mainly focuses on information ecosystems, information ecology in e-commerce, and information ecology in a network.

information ecology is an emerging field with vigorous development in recent years, and information ecology research is a multi-disciplinary subject.

A New Contribution to the Information Ecology of Humanitarian Work

Information ecology as a mind tool for repurposing of educational social networks

goal perspective, information ecologies have been designed to increase engagement with collaborative tasks (Price & Pontual-Falcão, 2011), enhance whole classroom learning (Rick, 2009), boost creative problem solving (Hilliges et al., 2007), support product design conversations (Bardill, Griffiths, Jones, & Fields, 2010), and coordinate complex collaborative working (Huang, Mynatt, & Trimble, 2006).

Information ecology was defined by Nardi and O’Day (1999) to be “a system of people, practices, values, and technologies in a particular local environment” (p. 49).

the design and integration of new technologies in learning activities cannot be studied independently of the classroom environment, less attention has been paid in learning environments

indicate that distributed cognition considers a collaborative activity taking place across individuals, artefacts and internal or external representations, as one cognitive system.

How the information ecology allows the design group to coordinate their actions? How awareness is distributed within the group when working with multiple technologies? How each one of the technologies in the ecology supports coordination and collaboration of learning activities?

an “information ecology” is a local environment enriched with multiple heterogeneous technologies, such as personal computers, handheld devices, interactive screens, which are interlinked as a unified system.

cognition cannot be tamed within the boundaries of an individual, but researchers should expand the unit of analysis to include the surrounding environment.

Information Environment

With few exceptions, most market democracies have recovered from the 2008 financial crisis. But the public has not recovered from the shock of watching supposed experts and politicians, the people who posed as the wise pilots of our prosperity, sound and act totally clueless while the economy burned. In the past, when the elites controlled the flow of information, the financial collapse might have been portrayed as a sort of natural disaster, a tragedy we should unify around our leadership to overcome. By 2008, that was already impossible. The networked public perceived the crisis (rightly, I think) as a failure of government and of the expert elites.

10-year project by the UK Centre for Ecology and Hydrology revealed that nature-friendly farming methods boost biodiversity without reducing average yield

I installed a roof rack on my car recently, it's always full of bugs. The car itself is almost bug free.It's possible that modern aerodynamic cars are better at avoiding bugs than old cars, although I don't know if this explains the whole difference. reply parent bregma () 2 minutes ago on I think it does. I don't remember the bugs being nearly so bad back in the 1960s and 1970s as they are now where I live, but I never have to clean off the windshield of my car. When I was a youth we would fight for the privilege of squeegeeing the windshield at every gas fill-up. Now, it's hard to go out without drawing a cloud and even the dog wants to stay inside in the summer.I suspect automobile aerodynamics play a bigger role in the windscreen index reduction than ecological destruction does.

I think you're coming from an anthroprocentric perspective and behaviorialist paradigm, where natural processes are understood in the lens of humans and human actions ("The focus is on the efficient production of useful goods in ways that require minimal maintenance by letting other creatures do all the work for you"), and the reason someone does something is because it benefits themselves or other humans ("without that core benefit nobody could do it even if they wanted to and have a viable farm").The ethical principle of "Fair Share" isn't just about the yields the land owner has, but also the yields other inhabitants of an ecology have. For people who are motivated by stewardship, for example, humans obtaining benefits is not elevated into its own thing. As an example, some of the Native tribes would say something along the lines that when you plant, one is for the plants, one is for the animals, one is for the birds, one is for us. It is certainly not about maximizing production efficiencies for the benefit of humans alone.That motivation and attitude shapes the way someone views and experiences their life, and their place, and in turn shapes how we go about caring for land, caring for people, and fair share.I know I'm cheating here a bit. I'm using the work of Carol Sanford to identify world view and paradigm, and that way of thinking through these things are not spelled out in the original works of Mollison and Holmgren. Sanford's work on regenerative paradigms and living systems world view goes a long way towards sorting out the different ways people approach things in the permaculture community, and is generalizable more broadly than food systems.Regeneration is a characteristic exclusive to living systems. It's not something that can be approached from a world view that everything is a machine, or the paradigm that one can control behavior through incentives and disincentives. Only living systems can regenerate. It's the broader paradigm from which "your food (and other resources) produce themselves" comes from. Living systems are capable of growing and adapting on their own; they are nested -- so that is you and I, within larger living systems of family, community, organization, ecology. It is because of regeneration that "food and other resources produce themselves".My point in all of this is that there is a diversity of motivations and views, and the view that "without that core benefit nobody could do it even if they wanted to and have a viable farm" is not as universal as it sounds like. "The core reason to do permaculture is that your food (and other resources) produce themselves" might be your core reason, but it is not true it is the reason that everyone in the permaculture community applies permaculture.

Erving Goffman, you may recall, was a mid-twentieth century sociologist, who, in The Presentation of the Self in Everyday Life, developed a dramaturgical model of human identity and social interactions. The basic idea is that we can understand social interactions by analogy to stage performance. When we’re “on stage,” we’re involved in the work of “impression management.” Which is to say that we carefully manage how we are perceived by controlling the impressions we’re giving off. (Incidentally, media theorist Joshua Meyrowitz usefully put Goffman’s work in conversation with McLuhan’s in No Sense of Place: The Impact of Electronic Media on Social Behavior, an underrated work of media theory published in 1986.)3

It is an ecology because it is not just an empty space, like an empty classroom.

In “Dark Ecology,” Morton writes that we must cultivate a “spirituality of care” toward the objects of the world—not just the likable parts but the frightening ones. Morton suggests that, instead of burying nuclear waste, we might store it aboveground, in a visible place, where we can learn to take more responsibility for it—perhaps even building an aesthetically interesting enclosure. The kind of care Morton envisions is as interested in piles of sulfur as in trees; it is concerned with both polar bears and circuit boards. Morton wants us to care for plutonium. At a minimum, Morton thinks that this kind of caring could cure us of the idea that we are in control; it might show us that we are part of a vast network of interpenetrating entities that come to know one another without dispelling their mystery. At a maximum, Morton seems to feel that this omnidirectional, uncanny form of care could help save the world.

Fully embracing the principles of ecology could revolutionize every aspect of design, in substance and in style.

Open Source Ecology

If we accept the idea that the entire surface of the earth is migratory, then why not landscapes in particular? A landscape — as a scene, landschap, ecosystem, and socio-political territory — is a material assembly of moving entities, a dynamic medium which changes in quality and structure through the aggregate movements or actions of the things that constitute it.

technology can no longer be understood as a set of tools used by humans, and instead has become an ecology in which humans participate

Thereisstillanothersetofinstitutions,ifthatistherightword,Iwanttocalltoyourattentionandmakemuchof.Theseareinvisibleinstitutions:theprinciplesofethicsandmorality.Certainlyonewayoflookingatethicsandmorality,awaythatiscompatiblewiththisattemptatrationalanaly-sis,isthattheseprinciplesareagreements,consciousor,inmanycases,unconscious,tosupplymutualbenefits.Theagreementtotrusteachothercannotbebought,asIhavesaid;itisnotevennecessarilyveryeasyforittobeachievedbyasignedcontractsayingthatwewillworkwitheachother.

Outcomes
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Institutions
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Culture
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Pyscho-Ontology

At the global level none of the 20 targets have been fully achieved, though six targets have been partially achieved (Targets 9, 11, 16, 17, 19 and 20). Examining the 60 specific elements of the Aichi Biodiversity Targets, seven have been achieved and 38 show progress. Thirteen elements show no progress or indicate a move away from the target, and for two elements the level of progress is unknown. The table on the following pages provides an overview of the progress made towards each of the 20 Aichi Biodiversity Targets.

A six-word California fire ecology primer: The state is in the hole. A seventy-word primer: We dug ourselves into a deep, dangerous fuel imbalance due to one simple fact. We live in a Mediterranean climate that’s designed to burn, and we’ve prevented it from burning anywhere close to enough for well over a hundred years. Now climate change has made it hotter and drier than ever before, and the fire we’ve been forestalling is going to happen, fast, whether we plan for it or not.

We’re presently living through what feels like a remarkably turbulent time. In fact, we might be tempted to think that ours is a uniquely chaotic moment. Of course, most of us know that human beings have lived through more chaotic, violent, and calamitous times than ours. What is novel in our experience isn’t the depth of the health crisis or the scale of the protests, the economic volatility, or the political instability. What is novel is the information ecosystem in which all of this and more is unfolding. Most of us now have far greater access to information about the world, and we are—arguably, I grant—exposed to a far wider array of competing narratives attempting, without notable success, to make sense of it all. In short, it would appear that our basic sense-making technology, the narrative, is a bit glitchy, both failing to operate as we might expect and causing some issues of its own. You won’t be surprised to learn that I think Marshall McLuhan can be helpful here. While many have found McLuhan’s aphorism “the medium is the message” confounding, McLuhan actually offered a rather straightforward explanation. “The ‘message’ of any medium or technology,” McLuhan wrote, “is the change of scale or pace or pattern that it introduces into human affairs.” “The effects of technology do not occur at the level of opinions or concepts,” McLuhan added, “but alter sense ratios or patterns of perception steadily and without any resistance.”Digital media introduced a new scale, pace, and pattern to human communication, and, in this way, altered how the world is perceived. With regards to scale, we encounter an unprecedented amount of information about the world at large through digital media. With regards to pace, we encounter this information with previously unknown and unrelenting immediacy. And, with regards to pattern, we encounter it both in novel social contexts and in a form that bears greater resemblance to a database than a story.

Hemicellulase activity was elevated in pairwise mixtures of communities that placed interacting phylotypes together (interactions, n = 9 mixtures) but not in communities that did not place interacting phylotypes together (no interactions, n = 56 mixtures)

We also show how our toolbox can be used to deploy the FBP in planta to build auto-luminescent reporters for the study of gene-expression and hormone fluxes

Because growth conditions (i.e., fluid dynamics and nutrient composition) can also have a profound effect on when QS is important,(71-75) there is a need to study biofilm formation and QS of BNR bacteria under various potential operating conditions.

difficult to observe in situ at the microscale, hence mechanisms and time scales relevant for bacterial spatial organization remain largely qualitative.

it is unclear to what extent these results are relevant in natural habitats, as the standard assays neglect the different surface chemistries, interactions with other species, and physical constraints of natural environments.

This biosensor will help identify organic substrates that potentially support microbial growth and activity before and during nodulation

Such biosensors can reveal intriguing aspects of the environment and the physiology of the free-living soil S. meliloti before and during the establishment of nodulation, and they provide a nondestructive, spatially explicit method for examining rhizosphere soil chemical composition

quantifying biogeochemical fluxes resulting from these reactions remains a challenge

These tools provide insights into processes such as N uptake at the scale of individual root tips, allowing observation of plant–microbe interactions on scales at which they actually occur, instead of being masked by a whole‐core average

identify mechanistic drivers of microbial activity, infer meaningful interaction networks, and rationally engineer microbial communities.

we emphasize how processes and interactions at one spatial or temporal scale contribute to emergent processes and properties at larger or longer scales. Most often the emergent processes we investigate are aquatic ecosystem services, including carbon burial, greenhouse gas emissions, nutrient removal, and recreational fisheries

This article discusses, and challenges, some of the often implicit assumptions made in community studies. It suggests greater focus on ecological questions, more critical analysis of accepted concepts and consideration of the fundamental mechanisms controlling microbial processes and interactions in situ

microbial bioreporters (Table 2) report their perception of, or response to, their environments

across ecosystems? To take just one example, when fish stocks fall in Ghanaian seas, hunting of bushmeat goes up and 41 land-based species go into decline. As hyperkeystones, we unite the entire world in a chain of falling dominoes

Labour is, in the first place, a process in which both man and Nature participate, and in which man of his own accord starts, regulates, and controls the material re-actions between himself and Nature

Payeng’s afforestation project has helped preserve the island’s landscape.

Extending this notion of material ecology, the quality of ecology in feminist interaction design integrates an aware-ness of design artifacts’ effects in their broadest contexts and awareness of the widest range of stakeholders through-out design reasoning, decision-making, and evaluation. It invites interaction designers to attend to the ways that de-sign artifacts in-the-world reflexively design us [79], as well as how design artifacts affect all stakeholders.

Material ecology theory emphasizes the extent to which an artifact participates in a system of artifacts [73, 52]. This structural approach considers ways that relationships among artifacts determine their meaning in the system or ecology.

This reflects a fundamental property of EDM in that forecast performance depends solely on the information content of the data rather than on how well assumed equations match reality.To clarify the concept of nonuniqueness, consider the canonical Lorenz attractor (SI Appendix, Fig. S1A). The behavior of this system is governed by three differential equations (SI Appendix, Eq. S1). However, the axes can be rotated to produce three new coordinates, x′, y′, and z′, and the equations rewritten in terms of these new coordinates, allowing the system to be described using either representation (x, y, and z or x′, y′, and z′) as well as mixed combinations (e.g., x, y, and z′). Thus, with an infinite number of ways to rotate the system, there are an unlimited number of “true variables” and “true models.” In the case of sockeye salmon, the similar performance of different models (SI Appendix, Table S4) does not mean that one or the other model is incorrect; instead, it reflects the fact that the environmental variables are indicators of the same general mechanism, and so different variable combinations can be equally informative for forecasting recruitment.Again, we emphasize that including a variable does not imply a direct causal link—variables in an EDM model improve forecasts because they are informative; it does not mean that the included variables are proximate causes. Importantly, the converse does not hold either: a variable could be causal and yet not appear in the multivariate EDM; this might occur when multiple stochastic drivers affect recruitment in an interdependent way, necessitating that a model include measurements of all of the drivers to account for their combined effect. For example, although none of the tested variables seem to improve forecasts for the Birkenhead stock (SI Appendix, Table S4), this does not mean that these sockeye salmon are insensitive to SST, river discharge, and the PDO. Rather, it suggests that the effect of these variables may be modulated by other factors not considered here.

This study addressed the relative importances of shrub "resources" on a rodent community in a sage-brush dominated shrub-steppe ecosystem in southwestern Wyoming

.

This, together with potential differences in resource renewal rates and predation risk may underlie the shared-preference for the semistabilized-sand habitat and thus affect the community organization.

Fire, rainfall, and particularly extreme climatic events such as El Niño can, at times, outweigh the importance of biotic factors such as competition or predation, emphasizing the importance of resource pulses associated with disturbances.

Subsequent research making use of ternary phase diagrams eventually showed that in small mammal communities trophic structure is strongly related to resource use.

Narratives that describe time as uniform and evolving throughout history towards more accelerated states have also been critiqued for theirpotential to reinforce social inequalities (Sharma 2014) and for justifyingthe appropriation of natural resources in unsustainable ways (Bastian 2012).

On the other hand, though much less likely, is the possibility of the gig economy becoming a long-term fixture of capitalism.

social ecology formally emerged with the work of Murray Bookchin

the critique of a thing is inherent in the alternative presented

We refer to the plural

About

tri-fold pamphlet created around 2007

‘Thedilemma, then, is that a right to information couldmake people worse off in terms of information.’’Elgesem then provides a contextual analysis of therole search engines play in the broader ‘‘informationecology’’ constituted by contemporary ICTs. Elgesemis able to connect the search engine dilemma withKant’s second formulation of the CategoricalImperative, ‘‘Act in such a way that you treathumanity, whether in your own person or in theperson of another, always at the same time as an endand never simply as a means.’’8Here, Elgeseminterprets Kant to mean that by ‘‘humanity,’’ Kantrefers to our ability to reason as the central propertythat makes us human. The simple point, as empha-sized in Kant’s famous example regarding lying, isthat failure to provide truthful information is a primeexample of violating the CI because false informationmakes it impossible for the recipient to exercise herrationality. By the same token, Elgesem argues that abiased search engine likewise makes it impossible forusers to exercise their rationality, and thus likewiserepresent violations of the CI.

mean- ing is relative to context

Feedback mechanisms provide stability such that ecosystems appear stable during some time frames but can abruptly shift to express new structures in others (9)

One of the primary uses of a model like this one is to improve the conversation between stakeholders and managers. The model can be valuable in helping managers and citizens arrive at realistic goals and to realize that there will be inherent risks associated with meeting those goals. For example, our analysis shows that reducing the probability of transmission by one half in five years using vaccination is not likely when we include uncertainty in the ability of managers to treat a targeted number of seronegative females. Forecasts suggested that there was virtually no chance of meeting that goal (Table 12). Similarly there was a 7% chance of reducing adult female seroprevalence below 40% using vaccination. We can nonetheless use this work to articulate what level of brucellosis suppression is feasible given current technology. For example, managers and stakeholders might agree that it is enough to be moving in the right direction with efforts to reduce risk of infection from brucellosis. In this case, a reasonable goal might be “Reduce the probability of exposure by 10% relative to the current median value.” The odds of meeting that goal using vaccination increased to 26%. With this less ambitious goal, vaccination increases the probability that the goal would be met relative to no action by a factor of only 1.4. This illustrates a fundamental trade-off in making management choices in the face of uncertainty: less ambitious goals are more likely to be met, but they offer smaller improvements in the probability of obtaining the desired outcome relative to no action.

We show that these uncertainties combine to assure that long-term predictions, e.g., 20 years in Peterson et al. (1991); 35 years in Ebinger et al. (2011); 30 years in Treanor et al. (2010) will be unreliable because credible intervals on forecasts expand rapidly with increases in the forecast horizon (Table 9). Long-range forecasts will include an enormous range of probable outcomes. This finding urges caution in making long-term forecasts with ecological models.

Evaluation of alternatives proceeded in three steps. We first obtained the posterior process distribution of the state at some point in the future, given no action, and calculated the probability that the goal will be met (Fig. 3A). The no-action alternative can be considered a null model to which alternative actions can be compared. Next, we approximated the posterior process distribution at the same point in the future assuming that we have implemented an alternative for management and calculated the probability that the goal will be met (Fig. 3B). Finally, we calculated the ratio of the probability of meeting our goal by taking action over the probability if we take no action. This ratio quantifies the net effect of management (Fig. 3C) and permits statements such as “Taking the proposed action is five times more likely to reduce seroprevalence below 40% relative to taking no action.”This process for evaluating alternative actions explicitly incorporates uncertainties in the future state of the population in the presence and absence of management. A useful feature of this approach is that the weight of evidence for taking action diminishes as the uncertainty in forecasts increases. That is, increasing uncertainty in forecasts compresses the hatched area in Fig. 3C. This result encourages caution in taking action. Also useful is the inverse relationship between the absolute probability that a goal will be met by management and the probability that it will be met relative to taking no action. As the ambition of objectives increases (e.g., the dashed line in Fig. 3 moves to the left), the absolute probability that the management action will be achieved declines (the hatched area in Fig. 3B shrinks), but the probability of success relative to taking no action increases (the hatched area in Fig. 3C expands). This feature represents a fundamental trade-off in choosing goals and actions that are present in all management decisions: objectives that are not ambitious are easy to meet by applying management, but they might be met almost as easily by taking no action.

The model omits covariates describing weather conditions, e.g., drought severity, which have been included in other models of bison population dynamics in Yellowstone (Fuller et al. 2007a). We justify this omission because our central objective was to develop a forecasting model. We we use the term forecast to mean predictions of future states accompanied by coherent estimates of uncertainty arising from the failure of the model to represent all of the influences that shape the population's future trajectory.

The model is not spatially explicit. Although there is evidence that the population is made up of two different herds that spend their summers in the northern and central portions of Yellowstone National Park (Olexa and Gogan 2007), we justify our decision to treat the population without spatial structure as a first approximation of its behavior and because recent evidence suggests that substantial movement between herds occurs annually (Gates et al. 2005, Fuller et al. 2007, White and Wallen 2012).

Thus, predicting species responses to novel climates is problematic, because we often lack sufficient observational data to fully determine in which climates a species can or cannot grow (Figure 3). Fortunately, the no-analog problem only affects niche modeling when (1) the envelope of observed climates truncates a fundamental niche and (2) the direction of environmental change causes currently unobserved portions of a species' fundamental niche to open up (Figure 5). Species-level uncertainties accumulate at the community level owing to ecological interactions, so the composition and structure of communities in novel climate regimes will be difficult to predict. Increases in atmospheric CO2 should increase the temperature optimum for photosynthesis and reduce sensitivity to moisture stress (Sage and Coleman 2001), weakening the foundation for applying present empirical plant–climate relationships to predict species' responses to future climates. At worst, we may only be able to predict that many novel communities will emerge and surprises will occur. Mechanistic ecological models, such as dynamic global vegetation models (Cramer et al. 2001), are in principle better suited for predicting responses to novel climates. However, in practice, most such models include only a limited number of plant functional types (and so are not designed for modeling species-level responses), or they are partially parameterized using modern ecological observations (and thus may have limited predictive power in no-analog settings).

In eastern North America, the high pollen abundances of temperate tree taxa (Fraxinus, Ostrya/Carpinus, Ulmus) in these highly seasonal climates may be explained by their position at the edge of the current North American climate envelope (Williams et al. 2006; Figure 3). This pattern suggests that the fundamental niches for these taxa extend beyond the set of climates observed at present (Figure 3), so that these taxa may be able to sustain more seasonal regimes than exist anywhere today (eg Figure 1), as long as winter temperatures do not fall below the −40°C mean daily freezing limit for temperate trees (Sakai and Weiser 1973).

The dance (or, as I prefer to call it, the complex ecolo

A promising option for integrating theory with practice in K-12 open learning is the Tech-nological Pedagogical Content Knowledge framewor

the movement of plants 13,.:, .. ,..i... 1• toward the sun.

Shortly after this im-age was released, the modem environmentalist movement in the United States began

Is it appropriate when studying rhizomes to concentrate on 'flowers'?

Sachs Harbour

Dr. Ian McTaggart-Cowan

Comparison of ITCD algorithms is challenging when there are differences in study focus, study area, data applied, and accuracy assessment method used. Before 2005, the few studies that compared methods generally tested approaches on a common dataset.

Additionally, it is often challenging to apply an algorithm developed in one forest type to another area.

We also did not allow different portions of our study area to respond to climate in different ways. Doing so would require spatially varying climate effects and a substantial increase in computational time. However, in future applications, it will be important to allow climate effects to vary over space to better capture reality. Conn et al. (2015) provide examples of how such spatiotemporal interactions can be included in abundance models. We might expect climate effects to interact with spatial covariates such as soil type, slope, and aspect.

To simulate equilibrium sagebrush cover under projected future climate, we applied average projected changes in precipitation and temperature to the observed climate time series. For each GCM and RCP scenario combination, we calculated average precipitation and temperature over the 1950–2000 time period and the 2050–2098 time period. We then calculated the absolute change in temperature between the two time periods (ΔT) and the proportional change in precipitation between the two time periods (ΔP) for each GCM and RCP scenario combination. Lastly, we applied ΔT and ΔP to the observed 28-year climate time series to generate a future climate time series for each GCM and RCP scenario combination. These generated climate time series were used to simulate equilibrium sagebrush cover.

Our process model (in Eq. (2)) includes a log transformation of the observations (log(yt − 1)). Thus, our model does not accommodate zeros. Fortunately, we had very few instances where pixels had 0% cover at time t − 1 (n = 47, which is 0.01% of the data set). Thus, we excluded those pixels from the model fitting process. However, when simulating the process, we needed to include possible transitions from zero to nonzero percent cover. We fit an intercept-only logistic model to estimate the probability of a pixel going from zero to nonzero cover: yi∼Bernoulli(μi)(8)logit(μi)=b0(9)where y is a vector of 0s and 1s corresponding to whether a pixel was colonized (>0% cover) or not (remains at 0% cover) and μi is the expected probability of colonization as a function of the mean probability of colonization (b0). We fit this simple model using the “glm” command in R (R Core Team 2014). For data sets in which zeros are more common and the colonization process more important, the same spatial statistical approach we used for our cover change model could be applied and covariates such as cover of neighboring cells could be included.

Our approach models interannual changes in plant cover as a function of seasonal climate variables. We used daily historic weather data for the center of our study site from the NASA Daymet data set (available online: http://daymet.ornl.gov/). The Daymet weather data are interpolated between coarse observation units and capture some spatial variation. We relied on weather data for the centroid of our study area.

Because SDMs typically rely on occurrence data, their projections of habitat suitability or probability of occurrence provide little information on the future states of populations in the core of their range—areas where a species exists now and is expected to persist in the future (Ehrlén and Morris 2015).

Our abilities to make observations are limited to a small range of space and time scales (8), limiting our capacity for understanding ecosystems and forecasting how they will respond to local and global change.

A range of information sources, which can include models, is used to develop alternative plausible trajectories of ecosystems; uncertainties about the future are represented by the range of conditions captured by the ensemble of scenarios. In contrast, forecasts narrowly limit uncertainties to those associated with a single potential outcome that is assumed to be predictable

Practices in the field of financial investing provide a good analogy to the stance we suggest for ecological predictions. A great deal of money and effort has been used to model the best ways to maximize investment returns (certainly more money and effort than has been used to refine ecological predictions). Although this work has resulted in greatly increased understanding of economic systems, the risks and limitations of using sophisticated economic models to make investments has led more and more investors to instead use simple, safe index funds. Essentially this is the recognition that the models and expert opinions are of exceptionally little value in making accurate, long-range predictions in this field and that precautionary strategies are a far better alternative.

First, major surprises are commonplace in the experience of field ecologists.

After explaining our project and providing several well-known examples of ecological surprises, we asked the recipients whether or not they had encountered any such events in the course of their field studies

Scenarios were initially developed by Herbert Kahn in response to the difficulty of creating accurate forecasts ( Kahn & Wiener 1967; May 1996 ). Kahn worked at the RAND Corporation, an independent research institute with close ties to the U.S. military. He produced forecasts based on several constructed scenarios of the future that differed in a few key assumptions ( Kahn & Wiener 1967 ). This approach to scenario planning was later elaborated upon at SRI International ( May 1996 ), a U.S. research institute, and at Shell Oil (  Wack 1985a, 1985b, Schwartz 1991; Van der Heijden 1996 ).

Prediction means different things to different technical disciplines and to different people ( Sarewitz et al. 2000 ). A reasonable definition of an ecological prediction is the probability distribution of specified ecological variables at a specified time in the future, conditional on current conditions, specified assumptions about drivers, measured probability distributions of model parameters, and the measured probability that the model itself is correct ( Clark et al. 2001 ). A prediction is understood to be the best possible estimate of future conditions. The less sensitive the prediction is to drivers the better ( MacCracken 2001 ). Whereas scientists understand that predictions are conditional probabilistic statements, nonscientists often understand them as things that will happen no matter what they do ( Sarewitz et al. 2000; MacCracken 2001 ).In contrast to a prediction, a forecast is the best estimate from a particular method, model, or individual. The public and decision-makers generally understand that a forecast may or may not turn out to be true ( MacCracken 2001 ). Environmental scientists further distinguish projections, which may be heavily dependent on assumptions about drivers and may have unknown, imprecise, or unspecified probabilities. Projections lead to “if this, then that” statements ( MacCracken 2001 ).

we used 2001–2009 fire counts detected by the Moderate Resolution Imaging Spectroradiometer (MODIS)

Fire season severity, here defined as the sum of satellite-based active fire counts in a 9-month period centered at the peak fire month, depends on multiple parameters that influence fuel moisture levels and fire activity in addition to precipitation, including vapor pressure deficits, wind speeds, ignition sources, land use decisions, and the duration of the dry season. As a result, the relationship between FSS and SSTs may be more complex than the relationships between precipitation and SSTs described above.

Whilst the consensus method we used provided the best predictions under AUC assessment – seemingly confirming its potential for reducing model-based uncertainty in SDM predictions [58], [59] – its accuracy to predict changes in occupancy was lower than most single models. As a result, we advocate great care when selecting the ensemble of models from which to derive consensus predictions; as previously discussed by Araújo et al. [21], models should be chosen based on aspects of their individual performance pertinent to the research question being addressed, and not on the assumption that more models are better.

Finally, by assuming the non-detection of a species to indicate absence from a given grid cell, we introduced an extra level of error into our models. This error depends on the probability of false absence given imperfect detection (i.e., the probability that a species was present but remained undetected in a given grid cell [73]): the higher this probability, the higher the risk of incorrectly quantifying species-climate relationships [73].