Christopher J. Lortie 182255         1The geographic scope of local interactions.Recent progressIn this proposal, I develo...
Christopher J. Lortie 182255         2ObjectivesLONG-TERM: Recently, it has been proposed that populations of species shou...
Christopher J. Lortie 182255               3Table 1. A summary of the research program general hypothesis (i.e. long-term ...
Christopher J. Lortie 182255         4there are two distinct forms of spatial analysis developed in this research program....
Christopher J. Lortie 182255        5the new student Wheelock. Structural equation models, multivariate statistics (includ...
Christopher J. Lortie 182255       6Literature cited1.     R. E. Ricklefs, The American Naturalist 172, 741 (2008).2.     ...
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Lortie nserc application 2012


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Here is my NSERC app from last year. It got funded for 1 year instead of 5 and at the lowest level. Any feedback appreciated on how to do better grants.

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Lortie nserc application 2012

  1. 1. Christopher J. Lortie 182255 1The geographic scope of local interactions.Recent progressIn this proposal, I develop the means to effectively test the importance of coupling local interactions toregional estimates of climate thereby providing ecology and society with tangible tools to examineimportant drivers of change at multiple scales of biological organization including plants, pollinators,and communities. The platform for this research is established, and it is very novel since it encompassesplants and insects at different scales of organization. My research interests and expertise have beenevolving in the former funding cycle to this more comprehensive endpoint. I have established a strongpollinator, arthropod, and geographical toolset in my lab in addition to plant and seed communityecology expertise. My research has also been steadily moving towards conservation and end-user needsdue to HQP interests in learning both plants and arthropods and in mitigating anthropogenic change byidentifying patterns of diversity in impacted ecosystems. I have worked very hard to build a pipeline oftop students for this next cycle to conduct the research proposed herein.Collaboration with France, Chile, and the USA and the graduate program at York has allowed me tomaximize the training of HQP with my former grant and significantly extend the scope of my researchprogram. A 3-year FGS support program to increase graduate enrollment in Biology reduced theexternal student salary contribution that allowed me to heavily leverage my $18,500 NSERC DG intraining 10 MSc students (unfortunately this program has ended). I capitalized on an agreement with theUniversity of Bordeaux in France to partially fund two MSc students (completed), one PhD student(current), and several undergraduates. With respect to quality, I have successfully placed 90% of thegraduated HQPs mentored into academic and professional positions including 3 professor positions (1college and 2 university). I used broad research streams (similar approach developed herein) to trainstudents and organize research. Importantly, my former Discovery Grant of $18,500 per year (with aone-year extension due to parental leave) made large-scale ecological research challenging. I adaptedmy approach to include more collaboration with international professors (i.e. Callaway, Cavieres,Michalet, and Delzon) to reduce fieldwork costs. I kept my publication record up with syntheticecological research. I helped my students successfully write exchange grants with other countries tofund travel. I was still able to publish at a rate of 9 papers per year (career total over 60), place papers intop-tier journals, and finish 10 graduate students each with first-authored, peer-reviewed publications.My first sole-supervised PhD student is currently nearing completion with two others in progress. Thistrack record and the scientific merit of this program warrant consideration for a Discovery AcceleratorSupplement. I have built up the research systems, proofed the methodology, published 14 empiricalpapers related to the current proposal (on spatial pattern, invasion, density, climate, and disturbance) andsecured and accepted the additional PhD students needed to lead the research streams. Specifically, thegraduate students in the last several years have identified ideal study sites in each of the majorecosystems proposed to test the general objective in this new program (sites encompass importantgradients, are georeferenced to fine-scales, and interactions examined cursorily). Preliminarydownscaled climate data are also in progress for each region. I have secured funding for one PhD salaryin full through FGS internal grants and partially funded the others through merit-based specific grants(NSERC top-ups needed). Two of the leading junior scientists in regional ecology and interactions(Butterfield and Atwater) have applied for funding associated with this project (i.e. Banting Scholarshipsand Fulbright) and are collaborating with me now on preliminary baseline analyses to inform upcomingexperiments. The $500,000 in CFI funding I secured has provided my lab with the capacity to conductcutting-edge morphology and physiology measurements of plants and invertebrates. My career is thus ata critical junction. I am poised to become an international leader linking plant-plant and plant-invertebrate interactions to larger processes. As Deputy Editor-in-Chief at Oikos, I dramaticallyimproved my editing and writing with the aim of more effectively mentoring my students. This servicealso increases my capacity to network and collaborate internationally.
  2. 2. Christopher J. Lortie 182255 2ObjectivesLONG-TERM: Recently, it has been proposed that populations of species should be the primary focus atthe regional level for testing ecological and evolutionary processes (1). This regional-horizontal view ofcommunities has merit but of course is also subject to criticism (2) - namely that scale impactsecological and evolutionary processes in various ways and that communities can, in some instances, beintegral entities. As a co-author of this critique and advocate for community studies (3), I endorse theseviews. Nonetheless, I recognize that the best science often and necessarily must transcend the local.Large-scale processes such as climate, dispersal, stochastic events, and available species pool function atregional scales and thus are the appropriate scale to study communities (4-6). Importantly,anthropogenic drivers of change are certainly regional but more likely global and include factors such asincreased disturbance, novel species introductions, and a rapidly changing and highly variable climate.Studying communities in small patches at arbitrary or convenient locations is thus a questionable pursuit(7). I propose that we do not need more ecologists counting plants or arthropods using inconsistentmethodologies at small, haphazard scales. In community assembly, regional and biogeographicalprocesses are always important (8). We do need to continue to measure processes at small scales inmany instances. Plants and insects interact with other individuals immediately adjacent to them, seedsare often dispersed short distances, and the heterogeneity of highly localized microsites directly filterslarger environmental impacts. Hence, I propose a direct link of experiments testing local spatial patternand interactions with regional climate data measures. This methodology applies a replicated-regional –neither strictly regional horizontal nor strictly community – approach to the study of invasion andclimate change. The general hypothesis which encompasses all research that will be conducted inmy lab in the next funding cycle is that local interactions have the capacity to mediate large-scalepatterns and that regional effects including climate are filtered by these interactions. Hence, themajor long-term objective of this research program is (1) to link local interactions between and withinplants and arthropods to large-scale drivers and patterns of change such as climate, productivity, anddisturbance. An ancillary long-term objective is consequently (2) to test the efficacy of quantitativegeographic downscaling tools in providing useful datasets for management and process analyses inecological systems.SHORT-TERM: Climate change and invasion are ideal issues to test these objectives and specificpredictions have been developed for each that is amenable to both mensurative and manipulativeexperiments. Climate change is dramatic and alpine environments are predicted to experiencedisproportionately high rates of change (9-12) that will shift both local environmental conditions and theinteractions between plants and with pollinators and arthropods. Invasion introduces novel species to aregion and most of the dominant hypotheses are predicated upon either diversity, saturation, or escapefrom invertebrate controls (13) all of which can be assessed at regional scales and can convey resistance.I will use these two critical issues to conduct replicated experiments at appropriate regional scalesensuring all local data are georeferenced. The associated predictions are in essence nested in the largergeographic scope hypothesis developed (Table 1). Importantly, more than one trophic level will alwaysbe explored now in my lab since true communities are composed of more than plants and since theprimary objective is to examine local interactions in a geographic framework. The short-term objectivesapplied in each system thus include the following: (1) identification of keystone species and importantlocal interactions within a region/ecosystem, (2) generate downscale climate, productivity, andenvironmental estimates, (3) develop and implement appropriate manipulative experiments includingremoval/selective loss experiments to mimic change, and (4) after a 3-5 year window depending onsystem, apply spatially-explicit analyses to partition local versus regional variation and assess themagnitude of importance of various drivers using structural equation models.
  3. 3. Christopher J. Lortie 182255 3Table 1. A summary of the research program general hypothesis (i.e. long-term objective) and associatedpredictions (short-term) examining the geographic scope of interactions. Ecosystem refers to the set of sitesidentified for study. Predictions are the primary research tools for each PhD student whilst the postdoctoralfellow(s) and the professor test the overarching hypothesis. The column labeled spatial refers to which predictionexplicitly incorporates local spatial statistics such as mapping. Interaction lists the taxon tested, and ‘N’ is thetotal number of sites currently identified regionally within each ecosystem. Only sites with preliminaryinteraction and downscaled data already validated are included.Ecosystem Hypothesis & predictions Spatial Interaction N(& HQP)All Local interactions mediate large-scale patterns and regional effects 4(Butterfield) are filtered by these interactions.Alpine Cushion plants serve as ecosystem engineers for plants, arthropod, X plant-plant 9(Reid) and pollinators. plant-invert Cushion plants filter climate and downscaled data are also a significant predictor of interaction strengths. Cushion plant effects increase beta diversity. XGrassland Invertebrates can be drivers of plant invasions at large scales. X invert-plant 6(Spafford) Biogeographical contrasts explain a large proportion of the variation X in extent of invasion by exotic plants. Enemy free space is primarily shaped by local interactions but X regional processes initially set the stage.Forest Escape from invertebrate controls is not important to tree invasions in X invert-plant 12(Wheelock) Southern Ontario. Plasticity and adaptation within species explains extent of invasion by tree species. Downscaled climate data and NDVI partially explain variation in X exotic representation.Desert Both direct and indirect facilitation effects shape plant interactions. X plant-plant 12(Sotomayor) Large-scale climatic gradients explain shifts in interactions between X plants. Downscaled productivity and stress data such as soil moisture explain X local interactions – particularly indirect interactions.Literature reviewSpatial pattern analysis is useful at all scales of ecology. It has the capacity not only to map, visualize,and identify important patterns but to also provide an assessment of process. Whilst coupling patternwith process in experimental plant ecology is intuitive, this linkage has been largely underdeveloped(14). Importantly, well-designed studies that include georeferenced spatial pattern supply an anchor formore advanced analysis in the future and for linkages to other data streams such as climate orenvironmental measures. However, there has been a divide between spatial ecology and the ecology ofinteractions such as plant population or plant community ecology (15, 16). This is unfortunate sinceboth finer-scale ecology, including population genetics and ecophysiology, and larger-scale ecology,such as regional and ecosystem-level studies, have been rapidly incorporating spatial statistics andsometimes also include georeferenced data. In many respects, the intermediate scales of plant ecologyare thus the most lacking. There is every indication that the scales of organization studied at this level,such as community structure, are effectively described by both local factors and larger spatial variables(4, 7). The merging of the two scales is thus a powerful tool in understanding ecological assemblyprocesses (17) including application to pressing issues such as invasion or climate change. To be clear,
  4. 4. Christopher J. Lortie 182255 4there are two distinct forms of spatial analysis developed in this research program. The analysis of thelocal distribution of plants and invertebrates and the relationship between them within communities, andthe analysis of regional patterns of climate and larger-scale patterns of factors that can concurrentlydetermine community structure. In the former instance, formal spatial statistics such as semivariogramsor Ripley’s K function are used to assess extent, scale, and statistical significance of the distributiondetected (18), and this can be combined with traditional plant ecology manipulations such as removals(19). In the latter instance, populations or communities are compared regionally (20, 21) but preferablyvia georeferencing given the current and evolving capacity to link to empirically downscaled climateestimates (22). Downscaled climate data at specific scales have not been applied to interactions inecology and evolution. This may seem challenging, but the resolution of these approaches hasdramatically improved, and the scale of the field research I am proposing within each ecosystem is alsomuch broader. Hence, whilst this research is novel, the risk is limited since both sets of data aredelineated and structured at appropriate scales a priori.Methodology(A) FIELD: The integration of all research conducted in the lab including surveys and manipulationsand acquisition of parallel climate and regional data fields will allow for a powerful cross-ecosystem testof the overarching hypothesis. This general approach to synthesis – i.e. small, integrated experimentsbroadly distributed - has been proposed as the critical change needed for more effective hypothesistesting (23, 24). I have spent the last 3 years building a large web of georeferenced sites ready forimplementation of nested spatial surveys (5), deployment of sensor webs (25), and manipulativeexperiments including removals (26). The NSERC-funded CANPOLIN protocol (member, 4 years) willalso be applied to the alpine and grassland sites in Canada to assess pollinator diversity levels, however,I will be adding a georeferencing component to every sampling instance. The local sampling will beregionally replicated within each of the following four ecosystems: alpine, grassland, forest, and desert.In addition to specific experiments developed with the students, two protocols will be applied in eachsystem – my students and I have coined them SDD and KLRS. Scale-dependence designs (SDD) arewell articulated in the literature and clearly illustrated in a recent paper including nested plots, disjunctvariable sized plots, and subplots (5) which will be used to structure all measurements and somemanipulations. Keystone loss + replacement series (KLRS) is a combination of two commonapproaches to studying interactions and also marries two of my most significant contributions (positiveinteractions by keystone species and density dependence experiments) by (i) removing/leaving intactkeystone species (KL) such as shrubs/cushions/dominant grasses/trees depending on the system (26, 27)with (ii) artificially added density replacement series (RS) of the associated species (28) in concert withthe first treatment. In BC, Reid has mapped mountain sites for cushion plant associations withpollinators and arthropods. These local interactions will be nested within regional climate data toexplore the relevance of regional climatic drivers on alpine ecosystem engineer species and pollinators.Orthogonal removal experiments of both plants and pollinators will then be used to infer causation.Plant invasion will be studied in grasslands in Ontario from an invertebrate perspective by Spafford toassess whether arthropods are passengers or drivers of change and to assess the capacity for native plantsto provide enemy free spaces (29). Pan traps, sweep netting, pitfall traps, and video observation of focalinvasive plants will be used to survey the region (30) and species addition experiments used to assesspotential resistance (31). Plant-plant and plant-arthropod interactions will be examined in SouthAmerica by Sotomayor to contrast the relative importance of direct versus indirect interactions inmodifying local diversity on regional gradients. This will be accomplished by spatial pattern analyses ofshrub versus open microsites, species removals, and seedbank manipulations (18). Finally, Lamarque(current PhD) is completing his degree and has built two 1500 tree common gardens to study invasionand change and mapped 85 forests locally and 62 forests in France to estimate extent of invasion by non-native tree species (32). This system is thus well established for the two standard protocols proposed by
  5. 5. Christopher J. Lortie 182255 5the new student Wheelock. Structural equation models, multivariate statistics (including CCA), andformal gradient analyses will be used to analyze the datasets for regional patterns. Meta-analyses willbe used to compare strength and sign of effect sizes across all ecosystems.(B) GEOGRAPHIC TOOLS: Climate and hydrological information for all studies will be drawn frompublicly available results of global and regional land data assimilation system including but not limitedto spatially distributed estimates of evapotranspiration, NDVI, precipitation, relative humidity, airtemperature, surface soil moisture, root zone soil moisture, and soil temperature. Land data assimilationsystems (LDAS) merge advanced numerical models with satellite and in situ meteorological andhydrological observations to produce optimal estimates of land surface states and fluxes. As a defaulttool, the Global Land Data Assimilation System (33) will serve as the primary source of data providing3 hourly meteorological and hydrological fields at 25 km resolution from 1979 onwards. For siteslocated in Southern Ontario and the United States, the North American Land Data Assimilation System(NLDAS, 12.5 km resolution) will be used to supplement GLDAS (34), and for South American sitesthe South American Land Data Assimilation System (SALDAS) will be employed (35). Regionalphysiography (particularly in alpine zones) and local ecological processes (particularly in grasslands andforests) can have a significant impact on local climate conditions. Hence, re-analysis data on climateand meteorology will be further downscaled using empirical-statistical methodologies (36),topographical deterministic methods (37), and satellite-based methods (38) to increase accuracy for eachregion. All downscaling routines will be done with R statistical software. Additional image processingand data extraction will be done with NCAR Command Language (NCL) that is also an open-sourcescripting language. The analyses will be compared with standard products such as Worldclim in orderto evaluate the utility of the energy balance fields proposed.ImpactThe use of geographic tools in other disciplines is exploding. It provides access to datasets that explainbroad patterns and identify novel avenues of research. This program has direct implications for ecology,geography, and management of many Canadian ecosystems. Firstly, climate and invasion are dominantanthropogenic drivers of change in all ecosystems. By coupling local interactions to less limitedgeographic scales, we have a larger scope of inference in understanding the relative importance ofinteractions such as plant-pollinator associations or plant-plant facilitation in mediating climate changeor invasion effects. In both instances, maintaining current levels of native diversity and avoiding speciesloss is facilitated by this research in that species may respond to perturbation differently and identifyingthe scale and generality of relationships that maintain biotic assemblages is critical for conservation.Secondly, linking climatic and hydrological databases such as the Global Land Data AssimilationSystem to local interactions is crucial in increasing their utility for ecologists and environmentalscientists. This research program provides a general protocol to resolve the scale mismatches that thusfar have prevented effective coupling and will lead to novel extended geographic interaction studies inother biotic systems. Importantly, I would love to see Canada positioned as a leader in the rapidlyevolving field of geographic tool application to the biological measures needed for conservation. Whilstthis new field of bioclimate is certainly now established and expanding, this particular proposal is verynovel. I will link climate to highly localized measures of interaction strengths. There are dynamic notstatic estimates of ecological processes on the ground. Climate downscaling and remote sensingapproaches have been applied to fine-scale estimates of microclimate or vegetation cover, but there havebeen no linkages of regional variables to important interactions such as foundational plant-plant or plant-pollinator interactions. Hence, anchoring climate to interactions between plants and with insectsprovides insight to several critical ecosystem services that are unexplored at this scale.
  6. 6. Christopher J. Lortie 182255 6Literature cited1. R. E. Ricklefs, The American Naturalist 172, 741 (2008).2. R. W. Brooker et al., American Naturalist 174, 919 (2009).3. C. J. Lortie et al., Oikos 107, 63 (2004).4. K. Cottenie, Ecology Letters 8, 1175 (2005).5. B. Sandel, A. B. Smith, Oikos 188, 1284 (2009).6. T. Rajaniemi, D. E. Goldberg, R. Turkington, A. R. Dyer, Ecology Letters 9, 121 (2006).7. M. A. Leibold et al., Ecology Letters 7, 601 (2004).8. J. N. Thompson, The Geographic Mosaic of Coevolution. . (The University of Chicago Press, Chicago, IL, USA, 2005).9. C. Körner, Alpine plant life., (Springer, Berlin, ed. 2nd, 2003).10. R. W. Brooker, in Positive Plant Interactions and Community Dynamics., F. I. Pugnaire, Ed. (CRC Press, Boca Raton, FL, 2010), pp. 59-78.11. G. Walther et al., Nature 416, 389 (2002).12. R. E. Moritz, C. M. Bitz, E. J. Steig, Science 297, 1497 (2002).13. R. M. Callaway, J. L. Maron, Trends in Ecology and Evolution 21, 369 (2006).14. E. J. B. McIntire, A. Fajardo, Ecology 90, 46 (2009).15. D. Tilman, P. Kareiva, Spatial ecology: the role of space in population dynamics and interspecific interactions., (Princeton University Press, Princeton, New Jersey, 1997).16. D. J. Murrell, D. W. Purves, R. Law, Trends in Ecology and Evolution 16, 529 (2001).17. J. Lu, L. Jiang, L. Yu, Q. Sun, PlosOne 6, e19762 (2011).18. C. J. Lortie, M. Munshaw, J. DiTomaso, J. Hierro, Oikos 119, 428 (2010).19. R. M. Callaway et al., Nature 417, 844 (2002).20. Z. Kikvidze et al., Alpine Ecology doi: 10.1007/s00035-010-0085-x., (2011).21. D. Kikidze et al., Ecology 86, 1395 (2005).22. A. M. Hancock et al., PLoS Genet 7, e1001375 (2011).23. P. A. Ioannidis, Plos Medicine 2, e124 (2005).24. E. Stokstad, Science 334, 308 (2011).25. K. Delin, in Wireless Sensor Networks: A Systems Perspective. . (Artech House., Norwood, MA, 2005).26. S. Diaz, A. J. Symstad, F. S. Chapin, D. A. Wardle, L. F. Huenneke, Trends in Ecology and Evolution 18, 140 (2003).27. S. K. M. Ernest, J. H. Brown, Science 292, 101 (2001).28. P. A. Jolliffe, Journal of Ecology 88, 371 (2000).29. H. Poykko, Oikos 120, 564 (2011).30. R. H. Gibson, B. Knott, T. Eberlein, J. Memmott, Oikos 120, 822 (2011).31. J. P. Grime, Journal of Vegetation Science 13, 457 (2002).32. L. J. Lamarque, S. Delzon, M. Sloan, C. J. Lortie, Ecography in press, (2012).33. M. Rodell et al., Bulletin of the American Meteorological Society 85, 381 (Mar, 2004).34. K. E. Mitchell et al., Journal of Geophysical Research-Atmospheres 109, (Apr 9, 2004).35. L. G. G. de Goncalves et al., Journal of Hydrometeorology 10, 999 (2009/08/01, 2009).36. R. E. Benestad, I. Hanssen-Bauer, D. Chen, Empirical-statistical downscaling. (World Scientific Pub Co Inc., 2008).37. G. E. Liston, K. Elder, Journal of Hydrometeorology 7, 217 (2006).38. B. F. Zaitchik, A. K. Macalady, L. R. Bonneau, R. B. Smith, International Journal of Climatology 26, 743 (2006).