105 Matching Annotations
  1. Nov 2019
    1. 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)

      Very interesting way to validate correlative phenotypes in communities

  2. Oct 2019
    1. 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
    1. 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.
    1. difficult to observe in situ at the microscale, hence mechanisms and time scales relevant for bacterial spatial organization remain largely qualitative.
    1. 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.
  3. Sep 2019
    1. This biosensor will help identify organic substrates that potentially support microbial growth and activity before and during nodulation
    2. 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
    1. quantifying biogeochemical fluxes resulting from these reactions remains a challenge
    2. 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
    1. identify mechanistic drivers of microbial activity, infer meaningful interaction networks, and rationally engineer microbial communities.

      Mechanistic insights in microbial ecology

  4. Aug 2019
    1. 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
    1. 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
  5. Jul 2019
    1. 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
    1. Fear of humans as apex predators has landscape-scale impactsfrom mount ain lions to mice (2019)

      Apex predators such as large carnivores can have cascading, landscape-scale impacts across wild-life communities, which could result largely from the fear they inspire, although this has yet to be experimentally demonstrated.

      Humans have supplanted large carnivores as apex predators in many systems, and similarly pervasive impacts may now result from fear of the human ‘superpredator’.

      We conducted a landscape-scale playback experiment demonstrating that the sound of humans speaking generates a landscape of fear with pervasive effects across wildlife communities.

      • Large carnivores avoided human voices and moved more cautiously when hearing humans,
      • medium-sized carnivores became more elusive and reduced foraging.
      • Small mammals evidently benefited, increasing habitat use and foraging.

      Thus, just the sound of a predator can have landscape-scale effects at multiple trophic levels.

      Our results indicate that many of the globally observed impacts on wildlife attributed to anthropogenic activity may be explained by fear of humans.

  6. May 2019
    1. 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

      The end of this formulation translates from the german, "Stoffwechsel [metabolism] mit der Natur".

      Contemporary ecological Marxists, such as John Bellamy Foster, cite this passage in support of claims that Marx's economic writings understood human relations to the environment in terms of what he called the ‘metabolism’ (Stoffwechsel) between nature and society.

      Thus, scholars such as Foster argue that Marx’s ideas offer an historical explanation for the ecological impact of capitalism on a planetary scale.

      Locating this metabolic relationship in capitalist practices of resource extraction, food production and waste, the consequences of capitalist production's intervention in the "material re-actions between himself and Nature" produce what has been variously characterized as metabolic rift or metabolic shift

  7. Jan 2019
  8. Dec 2018
    1. 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.

      Quality of ecology -- how artifacts impact the design process, technical systems that work together, and users identity

    2. 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.

      Definition of material ecology

  9. Nov 2018
    1. 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 distinction between direct causality and information content is a really useful perspective even beyond EDM.

  10. Oct 2018
    1. 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 study addressed the relative importances of shrub "resources" on a rodent community in a sage-brush dominated shrub-steppe ecosystem in southwestern Wyoming

    1. 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.
    1. 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.

      Huge surprise

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

      "Subsequent research making use of ternary phase diagrams eventually showed that in small mammal communities trophic structure is strongly related to resource use." trophic structure -the way in which organisms use food resources. to get their energy for growth and reproduction, and is often refered to in.

    1. 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).

      This loosely couples with the degrowth discourses around steady state economies and possible political ecologies

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

      Whether or not the gig economy is here to stay, the result will be widespread un- or under-employment caused by technological displacement. Whether workers are gathered into a gig economy or are outright unemployed is what remains to be seen.

  11. Aug 2018
    1. social ecology formally emerged with the work of Murray Bookchin

      We should clarify that the term "social ecology" is not Bookchin's, but, at least according to Janet Biehl's Ecology or Catastrophe: The Life of Murray Bookchin, originated with E.A. Gutkind. In 1953, Gutkind authored Community and Environment: A Discourse on Social Ecology. Use of the term may go back even further.

    2. the critique of a thing is inherent in the alternative presented

      Posing alternatives to capitalism and the nation-state simultaneously: 1) asserts the inadequacy of those institutions (a "negative" critique), and 2) asserts the superiority of the alternative being posed (a "positive" critique).

    3. We refer to the plural

      From our perspective, we are seeking to develop a social ecological theory within a broader ecosocialist movement in which there is no privileged praxis, but a plurality of mutually reinforcing practical strategies.

      Already, we can see that "Libertarian Municipalist," dual power, revolutionary syndicalist, and prefigurative approaches can be taken. Often, the praxes that emerge from the broadly ecosocialist sphere start from a high degree of theoretical agreement, but diverge strategically and not antagonistically.

    4. About

      Greetings! Potemkin here (one of the primary authors), just getting the hang of this annotation system. It's open-source. I like the idea of using annotation to facilitate deeper discussion, and perhaps as a more civilized and precise method of commenting or interacting with a website. I think this can facilitate virtual study groups and other remote collaborations. Exciting stuff!

      Please annotate, comment on blog posts that are open for comments, and let's try to build a positive, supportive, open ecosocialist community dedicated to creating Better Worlds and Brighter Futures!

    1. tri-fold pamphlet created around 2007

      I attempted to keep some of the formatting of the original, but this was not very successful. There are no doubt better overviews of Esperanto out there, but I wanted to highlight the little information I could find at the time on Esperanto's radical history, particularly among anarchists.

      I still believe in the potential of Esperanto. It's very simple and accessible for working-class and impoverished people--taking little time and with an abundance of free resources--to learn. After that, a world of potential is opened, being able to speak with any other Esperantist the world over and sharing information in a universal way.

      To me, Esperanto has the potential to facilitate a truly international revolutionary movement and its use helps dissolve borders and embodies the humanistic, anarchistic, cosmopolitan idea of "unity-in-diversity."

    1. ‘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.
  12. Jan 2018
  13. Dec 2017
    1. Feedback mechanisms provide stability such that ecosystems appear stable during some time frames but can abruptly shift to express new structures in others (9)

      We need to understand how frequently these kinds of change happen in order to understand the potential for forecasting and the best kinds of models for approaching it.

  14. Nov 2017
    1. We invite all scientists to endorse this global environmental article and engage with a new alliance concerned about global climate and environmental trends

    1. 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.

      Great description of the value of forecasting models for improving conversations between stakeholders and managers in the development of goals and expectations of outcomes.

    2. 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.

      Cautionary note on making long-term forecasts with ecological models due to decreased accuracy with forecast horizon. This issue is made clear through the proper inclusion of uncertainty in the models.

    3. 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.

      This is an exemplar of how to use complex process oriented models to inform the value of management decisions.

    4. 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.
      1. Another nice example of needing to make choices about what complexity to include in the model.
      2. An example of an explicit choice to avoid including environmental factors since they themselves would have to be forecast.
    5. 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).

      One of the things I really like about this paper is that it highlights that no matter how many statistical complexities are included in a model there are always more that could be. It isn't tractable to include them all so you choose the ones you think are most important based on available evidence and your professional judgement.

  15. Oct 2017
  16. Aug 2017
    1. 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).

      Very nice summary of some of the challenges to using models of contemporary species distributions for forecasting changes in distribution.

    2. 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).

      Recognizing where species are relative to the observed climate range will be important for understanding their potential response to changes in climate. This information should be included when using distribution models to predict changes in species distributions. Ideally this information could be used in making point estimates, but at a minimum understanding its impact on uncertainty would be a step forward.

  17. May 2017
    1. The dance (or, as I prefer to call it, the complex ecolo

      This is an interesting move in terms of form. Hayles herself is the one who introduced the term "dance" and then immediately amends it parenthetically to comment that she prefers the term "complex ecology." I'm not sure why she chose to leave both of those thoughts in, but I like it.

  18. Apr 2017
    1. A promising option for integrating theory with practice in K-12 open learning is the Tech-nological Pedagogical Content Knowledge framewor

      Knowledge Building and networked knowledge ecologies would be more updated and current examples of open learning?

      Scardamalia & BEreiter (2014) http://ikit.org/fulltext/2014-KBandKC-Published.pdf

      Knowledge ecology: http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=8D310E62BF5DC284DA14B5A6CE9F762E?doi=10.1.1.612.6430&rep=rep1&type=pdf

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

      When I made my last post about the Gaia Hypothesis, I'd already read this part, but I only just now realized that the Daisyworld simulation is actually really relevant to this. It's a model of a planet that has only white and black daisies (high and low albedo, so one reflects light and cools the planet, and the other does the reverse), and how the two species can unintentionally create and preserve homeostasis on the planet simply by following the light.

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

      James Lovelock's Gaia Hypothesis originated slightly before 1967, but he was working for NASA's Jet Propulsion Laboratory, so in many ways, he already was working off a mental image of the Earth seen from the outside.

      Sidenote, but I first encountered the Gaia Hypothesis because the game SimEarth (which is built around modeling and playing with the concept) had a whole essay about it bundled in the game. I was way too young to really grasp the game without blatantly cheating (which feels like a worrying allegory), but I really remember the essay, along with SimCity's hidden essays on urban design and the character of cities. I'm trying to think if any video game since the Sim series has had a similar connection to an academic discipline.

  19. Mar 2017
    1. Is it appropriate when studying rhizomes to concentrate on 'flowers'?

      We are attracted to what blooms.

      We forget to take into account the undergrowth.

    1. Sachs Harbour

      Sachs Harbour is located in the Inuvik region of the Northwest Territories, Canada and is situated on the southwestern coast of Banks Island in the Inuvialuit Settlement Region. According to the 2011 census, the population was 112 people. The principle languages spoken in the town are Inuvialuktan and English. The economy is primarily based upon hunting and trapping, but tourism also plays a small role. Residents also engage in ice fishing- harvesting fish from the Amundsen Gulf and Beaufort Sea. Banks Island is ecologically significant for being home to the largest goose colony in North America and is home to three quarters of the world’s population of muskoxen. Barren-ground caribou and polar bears are also seen on the island. On April 26, 2006, the world’s first documented wild-born grizzly-polar bear hybrid was shot near the town. The town has a Visitor Reception Centre that presents the Aulavik National Park and Inuvialuit culture to visitors to the Banks island and serves as a center for community activities. The town is of historical significance for a number of ships sent out to the Arctic Bay by the British Admiralty to find the lost expedition of James Franklin that became trapped in the ice for three years and was abandoned by its crew. One ships primary investigator and captain was Robert McClure who was able to identify the fabled North West Passage- a waterway across the top of North America that would allow passage to Asia from Banks Island. Only few have made this passage since due to icy and dangerous waters, but as the earth warms there may be a day when this passage becomes common. Sachs Harbour is in the Arctic tundra climate zone, which is characterized by long and extremely cold winters. Since many of the activities of the residents in the community revolve aroundfishing hunting,and travel, many residents have considerable knowledge of weather conditions, permafrost, and erosion patterns. Because of climate changes in recent years, many local residents fear that their knowledge of weather patterns may not be as useful as the weather becomes harder to predict. Since the climate has been changing, the sea ice is breaking up earlier than usual taking seals farther south in the summer. Seals are a main food source for the town. Climate change is bringing many other changes to the island’s ecology as well; salmon appeared for the first time in nearby waters between 1999 and 2001, new species of birds are migrating- including robins and barn swallows, and more flies and mosquitos have been appearing. Additionally, there is estimated to be 4 to 12 billion barrels of commercially recoverable oil in the Beaufort Sea and between 13 and 63 trillion cubic feet of natural gas. As the climate continues to warm it will be easier to access these resources, which could potentially damage the ecology of the island if not managed properly.

      Citations Babaluk , John A., James D. Reist, James D. Johnson, and Lionel Johnson. " First Records of Sockeye (Oncorhynchus nerka) and Pink Salmon (O. gorbuscha) from Banks Island and Other Records of Pacific Salmon in Northwest Territories, Canada." Http://pubs.aina.ucalgary.ca. June 2000. Accessed March 9, 2017. http://pubs.aina.ucalgary.ca/arctic/Arctic53-2-161.pdf.

      Callow, Lin. "Oil and Gas Exploration & Development Activity Forecast." Http://www.beaufortrea.ca. March 2013. Accessed March 2017. http://www.beaufortrea.ca/wp-content/uploads/2012/03/NCR-5358624-v4-BREA_-_FINAL_UPDATE_-_EXPLORATION_AND_ACTIVITY_FORECAST-__MAY_2013.pdf.

      Canada, Government Of Canada Statistics. "Census Profile." Census Program. May 31, 2016. Accessed March 09, 2017. http://www12.statcan.gc.ca/census-recensement/2011/dp-pd/prof/details/page.cfm?Lang=E&Geo1=CSD&Code1=6101041&Geo2=PR&Code2=61&Data=Count&SearchText=Sachs Harbour&SearchType=Begins&SearchPR=01&B1=All&GeoLevel=PR&GeoCode=6101041&TABID=1.

      "Observed Climate Change Impacts in Sachs Harbour, Canada." Observed Climate Change Impacts in Sachs Harbour, Canada. Accessed March 09, 2017. http://www.greenfacts.org/en/arctic-climate-change/toolboxes/observed-climate-change-impacts.htm.

    1. Dr. Ian McTaggart-Cowan

      Dr. Ian McTaggart-Cowan was a professor of zoology and Dean of graduate studies at the University of British Columbia (UBC), where he founded and lead the first university-based wildlife conservation department in Canada. Referred to as the "Father of Canadian Ecology,” he was one of the founders of the study of environmental ecology in Canada, and was appointed to the board of The Nature Trust of British Columbia by the Prime Minister of Canada, where he served as director for 33 years. Dr. McTaggart-Cowan graduated from UBC and completed his PhD at the University of California at Berkeley. Since then, he has received many awards and honors for his research and dedication to the research and conservation of wildlife in Canada. His accomplishments also include founding the National Research Council of Canada, serving as Chair of the Environmental Council of Canada, the inaugural and 19-year Chair of the Public Advisory Board of the BC Habitat Conservation Trust Fund Foundation, keystone member (and later Chair) of the Birds of British Columbia author team, chancellor of the University of Victoria, and advocate for whaling commissions in support of its prohibition.

      At UBC, Dr. McTaggart-Cowan oversaw the research of more than 100 students and continued to inspire generations of academics. During the 1950s and 1960s, he produced television nature programs on CBC (Canada Broadcasting Corporation) such as Fur and Feathers, The Living Seas, and The Web of Life that were aired internationally in hopes of inspiring the youth to advocate for conservation and its research. Dr. McTaggart-Cowan also had a strong political voice and convinced the Canadian government to hire professional wildlife biologists for the country’s wildlife programs.

      West, All Points. "Canadian Conservation Leader and TV Nature Program Pioneer Profiled in New Biography." CBCnews. October 15, 2015. Accessed March 06, 2017. http://www.cbc.ca/news/canada/british-columbia/ian-mctaggart-cowan-bio-shines-light-on-pioneering-tv-nature-program-host-1.3271571.

      "Ian McTaggart Cowan." The Nature Trust of British Columbia. Accessed March 06, 2017. http://www.naturetrust.bc.ca/ian-mctaggart-cowan/.

  20. Feb 2017
    1. 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.

      This difficulty in comparing algorithms (due to differences in forest type, location, and assessment strategy used for different algorithms) indicates a clear need for set of open data and centralized assessment to allow different methods to be competed against one another to determine the best routes forward.

      This kind of approach has been very successful in other image analysis problems (e.g., ImageNET).

      The National Ecological Observatory Network data seems ideal for doing something like this. Data is/will be available for a variety of different systems and with LiDAR, Hyperspectral, RGB, and field data for large numbers of plots.

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

      This difficulty of applying across forest types is central to the challenges of developing approaches that can be applied to continental scale data collection like that being conducted by NEON. Overcoming this challenge will likely require incorporating ecological information into models, not just the remote sensing, and determining how to choose and adjust different approaches to get the best delineations possible based on information about the forest type/location.

  21. Jan 2017
    1. 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.

      Interesting point about the potential importance of spatiotemporal interactions.

    2. 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.

      This is an interesting approach to forecasting future climate values with variation.

      1. Use GCMs to predict long-term change in climate condition
      2. Add this change to the observed time-series
      3. Simulate off of this adjusted time-series

      Given short-term variability may be important, that it is not the focus of the long-term GCM models, and that the goal here is modeling equilibrum (not transitional) dynamics, this seems like a nice compromise approach to capture both long-term and short-term variation in climate.

    3. 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.

      This seems like a perfectly reasonable approach in this context. As models like this are scaled up to larger spatial extents the proportion of locations with zero abundance will increase and so generalizing the use of this approach will require a different approach to handling zeros.

    4. 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.

      This seems to imply that only a single environmental time-series was used across all of the spatial locations. This is reasonable given the spatial extent of the data, but it will be necessary to allow location specific environmental time-series to allow this to be generalized to large spatial extents.

    5. 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).

      The fact that most species distribution models treat locations within a species range as being of equivalent quality for the species regardless of whether there are 2 or 2000 individuals of that species is a core weakness of the occupancy based approach to modeling these problems. Approaches, like those in this paper, that attempt to address this weakness are really valuable.

  22. Dec 2016
    1. 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.

      Our abilities to manage natural systems are also typically limited to a small range of space and time scales.

    2. 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

      This strong distinction between "forecasts" and "scenarios" seems like a rather arbitrary distinction on the surface. There are forecasting approaches that attempt to account for uncertainty in a broad array of things including uncertainty in the generating model. Many of the examples in Principles of Forecasting by J. Scott Armstrong are what would be described as "scenario" based approaches here. Likewise some of the approaches employed by forecasters in Superforecasting by Tetlock & Gardner involve developing a range of scenarios.

      Scenarios in general need to have a reasonable probability of occurrence to be usefully included in decision making. So at least at some minimum threshold it a probability is being associated with scenarios. Going one step further and assigning a probability to each member of a set of scenarios would result in a probabilistic forecast.

      In short, it seems to me that scenario development is, in many cases, a kind of forecasting. It may involve large uncertainties and it may currently be associated with different kinds of decision making, like choosing management practices that are robust to may possible models, but these can both be accomplished in other ways. Using language that implies that these are completely distinct approaches seems likely to cause confusion and unnecessary terminological debate.

  23. Nov 2016
    1. 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.

      The market is quite different from ecology in the sense that it responds to the predictions/forecasts that are made about it. The idea of the "efficient market" is one of the reasons why modeling the market is believed to be inherently difficult.

      It is also worth noting that investing in index funds is based on a simple model, that the market always increases at rates greater than inflation in the long run. In other words, an inclination towards index funds suggests that some aspects of the market are forecastable at some time-scales. Paying attention to what aspects of ecological systems are less susceptible to surprises (and at what scales) would be a useful route forward.

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

      The validity of this statement depends on what is meant by "commonplace". Given the question asked I think the results substantiate "major surprises occur at least once in most long-term studies". However, if the average long-term study involves thousands of observations/results then it's unclear to me if "at least one" surprise clearly supports "commonplace".

      The paper discusses this point further down in the paragraph, but it is a really important point since one of the overall messages of the paper is that the prevalence of surprises makes prediction difficult.

    3. 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

      I understand the need to explain what kind of "surprises" are being looked for. That said "providing several well-known examples of ecological surprises" immediately before asking about whether they are encountered also feels a bit like priming. Providing an even number of abstract examples that represent cases that both would and would not have been considered "surprises" seems less likely to bias the respondents.

      Also, the main question isn't really whether or not "surprises" occur, that is already taken as a given, it is how prevalent are they. It would have been interesting to include a question about what proportion of observations from the researchers site were considered to be surprising.

    1. 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 ).

      Interesting information on the history of scenario based forecasting.

    2. 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 ).

      This distinction between "prediction" and "forecast" is not something I've generally seen in either the ecological forecasting literature or the forecasting literature more generally. This use is backed only by a citation to a guest editorial in a zine (think newsletter), so while I appreciate the need to be clear about uncertainty I don't think this treatment of the terminology is a particularly effective way to accomplish this.

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

      The success of this model with only small amounts of training data is encouraging for other areas of ecology and environmental science where the available time-series may be short.

    2. 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.

      This recognition of additional factors that could influence fire, and the fact it more complex models using the same data may be able to indirectly use some of these influences is really valuable. It is, in effect, positing that latent variables associated with some of these causes may be associated with measurable aspects of SST.

    3. This is a nice example of chaining together separate pieces of knowledge to understand what form of forecasting model might be successful. Large scale climate phenomena -> variation in precipitation -> variation in fire season severity.

    1. My thoughts on Climatic Associations of British Species Distributions Show Good Transferability in Time but Low Predictive Accuracy for Range Change by Rapacciuolo et al. (2012).

    2. 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.

      It's interesting that the ensembles perform best overall but more poorly for predicting changes in occupancy. It seems possible that ensembling multiple methods is basically resulting in a more static prediction, i.e., something closer to a naive baseline.

    3. 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].

      This will be an ongoing challenge for species distribution modeling, because most of the data appropriate for these purposes is not collected in such a way as to allow the straightforward application of standard detection probability/occupancy models. This could potentially be addressed by developing models for detection probability based on species and habitat type. These models could be built on smaller/different datasets that include the required data for estimating detectability.

    4. an average 87% of grid squares maintaining the same occupancy status; similarly, all climatic variables were also highly correlated between time periods (ρ>0.85, p<0.001 for all variables). As a result, models providing a good fit to early distribution records can be expected to return a reasonable fit to more recent records (and vice versa), regardless of whether relevant predictors of range shift have actually been captured. Previous studies have warned against taking strong model performance on calibration data to indicate high predictive accuracy to a different time period [20], [24]–[26]; our results indicate that strong model performance in a different time period, as measured by widespread metrics, may not indicate high predictive accuracy either.

      This highlights the importance of comparing forecasts to baseline predictions to determine the skill of the forecast vs. the basic stability of the pattern.

    5. Most variation in the prediction accuracy of SDMs – as measured by AUC, sensitivity, CCRstable, CCRchanged – was among species within a higher taxon, whilst the choice of modelling framework was as important a factor in explaining variation in specificity (Table 4 and Table S4). The effect of major taxonomic group on the accuracy of forecasts was relatively small.

      This suggests that it will be difficult to know if a forecast for a particular species will be good or not, unless a model is developed that can predict which species will have what forecast qualities.

    6. The correct classification rate of grid squares that remained occupied or remained unoccupied (CCRstable) was fairly high (mean±s.d.  = 0.75±0.15), and did not covary with species’ observed proportional change in range size (Figure 3B). In contrast, the CCR of grid squares whose occupancy status changed between time periods (CCRchanged) was very low overall (0.51±0.14; guessing randomly would be expected to produce a mean of 0.5), with range expansions being slightly better predicted than range contractions (0.55±0.15 and 0.48±0.12, respectively; Figure 3C).

      This is a really important result and my favorite figure in this ms. For cells that changed occupancy status (e.g., a cell that has occupied at t_1 and was unoccupied at t_2) most models had about a 50% chance of getting the change right (i.e., a coin flip).

    7. The consensus method Mn(PA) produced the highest validation AUC values (Figure 1), generating good to excellent forecasts (AUC ≥0.80) for 60% of the 1823 species modelled.

      Simple unweighted ensembles performed best in this comparison of forecasts from SDMs for 1823 species.

    8. Quantifying the temporal transferability of SDMs by comparing the agreement between model predictions and observations for the predicted period using common metrics is not a sufficient test of whether models have actually captured relevant predictors of change. A single range-wide measure of prediction accuracy conflates accurately predicting species expansions and contractions to new areas with accurately predicting large parts of the distribution that have remained unchanged in time. Thus, to assess how well SDMs capture drivers of change in species distributions, we measured the agreement between observations and model predictions of each species’ (a) geographic range size in period t2, (b) overall change in geographic range size between time periods, and (c) grid square-level changes in occupancy status between time periods.

      This is arguably the single most important point in this paper. It is equivalent to comparing forecasts to simple baseline forecasts as is typically done in weather forecasting. In weather forecasting it is typical to talk about the "skill" of the forecast, which is how much better it does than a simple baseline. In this case the the baseline is a species range that doesn't move at all. This would be equivalent to a "naive" forecast in traditional time-series analysis since we only have a single previous point in time and the baseline is simply the prediction based on this value not changing.

    9. Although it is common knowledge that some of the modelling techniques we used (e.g., CTA, SRE) generally perform less well than others [32], [33], we believe that their transferability in time is not as well-established; therefore, we decided to include them in our analysis to test the hypothesis that simpler statistical models may have higher transferability in time than more complex ones.

      The point that providing better/worse fits on held out spatial training data is not the same was providing better forecasts is important especially given the argument about simpler models having better transferability.

    10. We also considered including additional environmental predictors of ecological relevance to our models. First, although changes in land use have been identified as fundamental drivers of change for many British species [48]–[52], we were unable to account for them in our models – like most other published accounts of temporal transferability of SDMs [20], [21], [24], [25] – due to the lack of data documenting habitat use in the earlier t1 period; detailed digitised maps of land use for the whole of Britain are not available until the UK Land Cover Map in 1990 [53].

      The lack of dynamic land cover data is a challenge for most SDM and certainly for SDM validation using historical data. If would be interesting to know, in general, how much better modern SDMs become based on held out data when land cover is included.

    11. Great Britain is an island with its own separate history of environmental change; environmental drivers of distribution size and change in British populations are thus likely to differ somewhat from those of continental populations of the same species. For this reason, we only used records at the British extent to predict distribution change across Great Britain.

      This restriction to Great Britain for the model building is a meaningful limitation since Great Britain will typically represent a small fraction of the total species range for many of the species involved. However this is a common issue for SDMs and so I think it's a perfectly reasonable choice to make here given the data availability. It would be nice to see this analysis repeated using alternative data sources that cover spatial extents closer to that of the species range. This would help determine how well these results generalize to models built at larger scales.

    12. (1) Are climate-based SDMs transferable in time? (2) Can they capture drivers of expansion and contraction of species geographic ranges? (3) What is the relative effect of methodological and taxonomic variation on prediction accuracy?

      These are three of the crucial questions that need to be answered about the performance of SDMs for forecasting. To this list I would add:

      (4) Are the uncertainties associated with SDM forecasts accurate?

    13. Unfortunately, assessing whether they do is notoriously difficult since their main aim is to predict events that are yet to occur [20]; most studies thus measure the transferability of their models using a subset or re-sampled set of the distribution records used to build the models, a limited approach that can greatly inflate estimates of predictive accuracy [20]. For this reason, an emerging approach for estimating the true transferability of SDMs has been to validate model predictions against independent field records documenting shifts in species distributions to novel time periods [20]–[26] and regions [27]–[31]. However, published accounts of such independent model validation have generally lacked methodological or taxonomic breadth.

      The relatively small number of efforts to determine how predictive SDMs are for the future state of species distributions remains an ongoing issue in 2016. This kind of work is crucial to understanding the biases and uncertainty associated with current approaches to distribution modeling.

    1. Despite the absence of mechanistic information about the underlying ecological processes, the relatively simple SSR method consistently outperformed the control models over near-term prediction horizons. This result was robust across all simulations and all life stages of the experimental data. Moreover, the SSR model achieved this feat using only a single time series, whereas the control model used all times series simultaneously (it is an ecologically unrealistic scenario to assume we know the model and have time series for all of the relevant variables). Other analyses have shown that multivariate SSR methods (35) improve with additional information (14, 36), suggesting that the performance of the SSR model tested here represents a lower bound on forecast accuracy attainable with this general approach.

      Readers of this paper should also take a look at the exchange following this paper when interpreting the overall results:

      http://www.pnas.org/content/110/42/E3975.full http://www.pnas.org/content/110/42/E3976.full

      In short, Hartig & Dormann (2013) show that using methods designed to improve fitting under chaotic conditions that the control model outperforms other models in the case of the logistic model. Perretti et al. respond by showing an example where these methods fail and argue that the true model is never known anyway.

    2. In contrast, the linear forecasting methods were often no better than a prediction of the mean of the test set (represented by SRMSE = 1; Figs. 1 and 2). The performance of the control model varied depending on the system, but it too was often no better than a mean predictor.

      This comparison to the mean of the test set is a nice example of using a baseline to contextualize the strength of the forecast. This is commonly done in weather forecasting, in which case accuracy relative to the mean or other baseline is described as "skill".

    3. To emulate ecologically realistic conditions, we added log-normal observation error with a CV of 0.2 to all of the training sets. The forecast accuracy was evaluated using the test-set time series without observation error.

      It would be interesting to know the results in the absence of this additional error. This low-error scenario seems likely to benefit the deterministic models.

    4. All control models included log-normal process and observation error, and parameter values that resulted in chaotic or near-chaotic deterministic dynamics.

      This is an important choice for this study because:

      1. It makes the job of fitting the models more challenging
      2. It makes forecasting from the deterministic process in the presence of error inherently challenging
      3. The SSR method is designed for chaotic data
    5. In real systems we often lack time series of important driving variables or species; thus, for this additional reason, this comparison represents a very optimistic scenario for the mechanistic modeling approach.

      This is a really important point regarding the potential strength of time-series only forecasting model (i.e., those not using external co-variates). The error resulting from uncertainty in the forecast of the external driver, and its propagation into the resulting forecasts of the focal outcome, may (in some cases) overwhelm any benefits derived from having the external driver. This won't be true if the external driver is important, and relatively forecastable, but it is worth keeping in mind when comparing forecasting methods.

  24. Sep 2016
    1. one can obtain good indications for theexistence of alternative attractors from field data, but theycan never be conclusive. There is always the possibilitythat discontinuities in time series or spatial patternsare due to discontinuities in some environmental factor.Alternatively, the system might simply have a thresholdresponse (Figure 2b).

      While these different potential processes behind particular patterns are not all associated with alternative stable states, they are all associated with regime shifts (barring some very narrow definition of "regime shift").

    2. Catastrophic regime shifts inecosystems: linking theory toobservation

      The title of this paper is about "regime shifts", but most of the paper focuses "alternative stable states". My impression is that "alternative stable states" and their associated attractors represent one possible cause of regime shifts, so it's interesting that that is where all of the emphasis is placed.

    3. A graphical model of a vegetation-water feedback

      This model directly assumes a critical transition from no vegetation to full vegetation at a transition point along a precipitation gradient, so the non-linear transition of the system is built in to the model. If vegetation increased smoothly with precipitation we wouldn't expect alternative stable states or regime shifts. This isn't to say that the model isn't useful for illustrative purposes, just that it basically assumes in the notable result.

    4. The obvious intuitive explanation for a sudden dra-matic change in nature is the occurrence of a sudden largeexternal impact. However, theoreticians have long stressedthat this need not be the case. Even a tiny incrementalchange in conditions can trigger a large shift in somesystems if a critical threshold known as ‘catastrophicbifurcation’ is passed[11].

      Distinguishing between these two possibilities will be difficult since in most cases we lack a sufficiently thorough understanding of the processes driving the system to know for sure whether an important process has undergone a sudden change.

  25. Apr 2016
    1. Beavers are great for water conservation: they create ponds by damming creeks, and they also dig to make them deeper.

      We could be collecting the rain water that flows off our roofs (and also elsewhere), but very few people do -- at least not in the US.

  26. Dec 2015
  27. Jun 2015
    1. Demographics data with respect to age distributions and fecundity can be used to study human populations.

      textbook reading 36.9- The human population continues to increase, but the growth rate is slowing.

      textbook reading 36.10- Age structures reveal social and economic trends.

    2. A population can produce a density of individuals that exceeds the system’s resource availability.

      36.5- multiple factors may limit population growth

    3. Introduction of species

      Textbook section 37.13- Invasive species can devastate communities.

      Not Mathematical

    4. The structure of a community is measured and described in terms of species composition and species diversity.

      textbook 37.10- Species diversity includes relative abundance and species richness.

    1. The comparison between the model and the experts is based on the species distribution models (SMDs), not on actual species occurrences, so the observed difference could be due to weakness in the SDM predictions rather than the model outperforming the experts. The explanation for this choice in Footnote 4 is reasonable, but I wonder if it could be addressed by rarifying the sampling appropriately.

  28. Sep 2014
    1. Admittedly Chevron does attempt, in the US, to carve out a brand image for itself, but the brand is largely based on a promise of quality rather than an arbitrary emotional or lifestyle association. The argument I'm making is that, if inception actually works, then we would expect to see a lot more of it in the (rather large) market for gas stations.

      And to strengthen the author's point, I think Chevron has ramped up it's branding not as an attempt to distinguish itself from other brands but to repair the damage its brand has incurred as the result of environmental activists raising awareness of its ecological and social injustices.

  29. Jan 2014
    1. but scientists' understanding of the emergent spatial dynamics at the population level has not kept pace, in large part due to an absence of appropriate tools for data handling and statistical analysis.

      Tools gap needs to be filled to improve understanding of emergent spatial dynamics.

    1. NSF Advances in Biological Informatics: "Informatics tools for population-level animal movements." with T. Mueller, P. Leimgruber, A. Royle, and J. Calabrese. Thomas Mueller, an Assistant Research Scientist in my lab, leads this project. Also on this grant, postdoc Chris Fleming is investigating theoretical aspects of animal foraging and statistical issues associated with empirical data on animal movements. This project is developing innovative data management and analysis tools that will allow scientists and conservation managers to use animal relocation and tracking data to study movement processes at the population-level, focusing on the interrelationship of multiple moving individuals. We are developing and testing these new tools using datasets on Mongolian gazelles, whooping cranes, and blacktip sharks. More information is available on the Movement Dynamics Homepage.
    1. My project seeks to develop computer models that simulate and link behavioral movement mechanisms which can be either based on memory, perceptual cues or triggered by environmental factors. It explores their efficiency under different scenarios of resource distributions across time and space. Finally it tries to integrate empirical data on resource distributions as well as movements of moving animals, such as satellite data on primary productivity and satellite tracking data of Mongolian gazelles.
    2. News Thomas Mueller and Bill Fagan receive a new NSF Bioinformatics grant Collaborators Peter Leimgruber Smithsonian Institution Volker Grimm Centre for Environmental Research - UFZ, Leipzig Kirk A. Olson University of Massachusetts Todd K. Fuller University of Massachusetts George B. Schaller Wildlife Conservation Society Nuria Selva Institute of Nature Conservation, Krakow

      Collaborators

      • Peter Leimgruber, Smithsonian Institution
      • Volker Grimm, Centre for Environmental Research - UFZ, Leipzig
      • Kirk A. Olson, University of Massachusetts
      • Todd K. Fuller, University of Massachusetts
      • George B. Schaller, Wildlife Conservation Society
      • Nuria Selva, Institute of Nature Conservation, Krakow