Background

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DISPERSAL LIMITATION

Understanding how dispersal limitation structures plant species geographic ranges has received increasing attention in recent years, especially in light of climate change where distributional boundaries shift northward in latitude and upwards in elevation to track suitable habitat (Cunze et al., 2013; Dullinger et al., 2004). The general question of ‘Which species will be able to move and track these changing conditions?’ has brought together a wide variety of research groups, but generalizations still remain challenging (Gaston, 2009), in part due to the many unique abiotic and biotic factors that can come into effect at a species range margin (Sexton et al., 2009).

To understand how accessibility will limit geographic range shifts under future climate conditions, it  is important to understand how dispersal influences contemporary boundaries and to consider historic evolutionary and biogeographic processes. There is now increasing recognition that many plant species  are at disequilibria (of varying degrees) with suitable climate conditions and even species that appear to be locally abundant, may be dispersal limited at larger spatial scales (Cunze et al., 2013; Dullinger et al., 2004). Human induced movement of  plants across the globe and the creation of “invasive species” has acted as the major examples for  dispersal and range expansion (Sexton et al., 2009; Vitousek et al. 1997), but evidence for dispersal limitation in the absence of obvious barriers for many native species has received mixed results, suggesting it should be studied as a species level  problem in conjunction with other range limiting mechanisms (Dullinger et al., 2004; Gaston, 2009).

The lab group that I am a part of has worked primarily with Mimulus spp. (especially M. cardinalis) as  a focal study system to investigate a variety of processes across a species geographic range, including range wide demographic surveys, central and marginal studies of maladaptive gene flow, reciprocal  transplants and growth chamber studies (see lab website). This research has challenged some of the most basic assumptions of marginal populations that are often taken for granted such as reduced fitness and survivorship in edge populations (Angert, 2009).

Translocations Over the Northern Range Margin

Geographic range distribution of M. cardinalis re-created from Hiesey (1971) 

This proposed project would be a component of a several studies to address how the contemporary  northern geographic distribution of M. cardinalis is structured by habitat availability and accessibility to  unoccupied areas north of its current range. We are attempting to infer dispersal limitation by  transplanting individuals into nearby sites identified as ‘within the range’ and to sites just northeast of  Eugene Oregon that have been identified as ‘outside of the range’. Although a closely related study that transplanted M.cardinalis above of their natural elevation limit resulted in greatly reduced survivorship, it is believed that the more gradual environmental gradients and suitable conditions encountered beyond a  latitudinal range edge may allow for greater persistence north of its current distribution (Angert & Schemske, 2005). There is also special interest in the survivorship and growth of juvenile life stages in peripheral populations because of their higher sensitivities to the overall longevity of a given population (Angert, 2009; Villellas et al., 2013).

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SPECIES DISTRIBUTION MODELS 

The use of species distribution models (SDM) or bioclimatic envelope modeling has become wide spread in throughout ecology and conservation biology partly due to the continual advancement of remote sensing technology and readily available climate & environmental spatial layers (Guisan and Thuiller, 2005). With the rapid pace of climate change SDMs have been used to forecast massive geographic range shifts for species northward in latitude and upwards in elevation to track suitable habitat, but determining which species will be able to disperse to new areas and track their climate envelope has remained challenging (Corlett and Westcott, 2013; Loarie et al., 2008).

A major challenge to the SDM approach is that the models are constructed by correlating occurrence records with environmental variables. This approach assumes that contemporary range limits are at an equilibrium with climate and are not strongly influenced by dispersal limitation (Araújo et al., 2005; Guisan and Thuiller, 2005). This could be problematic if SDMs are providing overly optimistic predictions for species ability to migrate with climate change, when in reality they may be adversely affected in their native range if they fail to disperse (Corlett and Westcott, 2013).

Despite extensive use of correlative SDMs and the characterization of environmental gradient driven range margins, the empirical validation of these models through translocations has remained extremely rare (e.g. McLane and Aitken, 2012). Also, while the discussion of assisted migration through translocations has been a debated mitigation strategy, questions of the practicality and feasibility to manage translocations for a moving target have remained unanswered (Corlett & Westcott, 2013; McLane & Aitken, 2012).

Through the second half of this project I plan to build a SDM for the climate niche of Mimulus cardinalis and evaluate the population performance through a series of translocations across a gradient of predicted suitability. The SDM will be developed from surveyed presence and absence records as well as herbarium records with downscaled climate variables from ClimateWNA (Wang et al., 2012). The model algorithm will consist of an ensemble average and MaxEnt predictions to reflect the most widely used SDM methods. M. cardinalis individuals from two northern populations will be transplanted to 10 sites across the modeled predicted suitability gradient, while microsite features will remain standardized across sites. Each of 20 plots per site will have multiple life stages represented so that an estimate of lambda (finite population growth rate) can be regressed against the predicted suitability for each site.

While significant progress has been made towards model selection and development (Elith et al., 2010; Guisan and Thuiller, 2005); this case study will be a critical empirical experimental evaluation of the overall correlative approach to SDMs which has remained absent. If a species is at a climatic equilibrium then the predicted suitability from a SDM should mirror real world population performance, while if a range boundary is driven largely by dispersal limitation then this correlative modeling approach will not be valid, especially in relation to projected range shifts with climate change. By using M. cardinalis as a case study it is possible to integrate correlative SDMs with a more holistic understanding of range limits within this study system. 

M.cardinalis distribution (points) with bioclimatic suitability envelope. 

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Angert, A. (2009) The niche, limits to species’ distributions, and spatiotemporal variation in demography across the elevation ranges of two monkeyflowers. PNAS, 106(2): 19693-19698. 

Angert, A.L., & Schemske, D.W. (2005) The evolution of species distributions: reciprocal transplants across the elevation  ranges of Mimulus cardinalis and M. lewisii. Evolution, 59(8): 1671-1684. 

Araújo, M.B., Pearson, R.G., & Rahbek, C. (2005). Equilibrium of species’ distributions with climate. Ecography, 28(5), 693-95.

Austin, M.P. (2002) Spatial prediction of species distribution: an interface between ecological theory and statistical modeling. Ecological Modeling, 157(3): 101-111. 

Corlett, R. T., & Westcott, D. A. (2013). Will plant movements keep up with climate change? Trends in Ecology & Evolution, 28(8), 482–8. doi:10.1016/j.tree.2013.04.003 

Cunze , S., Heydel, F., & Tackenberg, O. (2013) Are plant species able to keep pace with the rapidly changing climate? PLoSONE, 8(7): e67909. doi:10.1371/journal.pone.0067909 

Dullinger, S., Dirnböck, T., & Grabherr, G. (2004) Modeling climate change-driven tree line shifts: relative effects of temperature increase, dispersal and invisibility. Journal Ecology, 92: 241-252. 

Elith, J., & Leathwick, J.R. (2009) Species distribution models: ecological explanation and prediction across space and time. Annual review of Ecology and Evolution, 40:677-697. 

Elith, J., Kearney, M., & Phillips, S. (2010) The art of modeling range-shifting species. Methods in Ecology and Evolution. 1, 330-342. doi: 10.1111/j.2041-210X.2010.00036.x 

Gaston, K.J. (2009) Geographic range limits: achieving synthesis. Proceedings of the Royal Society of Biology, 276(1661): 1395 1406. 

Guisan, A., & Thuiller, W. (2005). Predicting species distribution: offering more than simple habitat models. Ecology Letters, 8(9), 993–1009. doi:10.1111/j.1461-0248.2005.00792.x

Loarie, S. R., Carter, B. E., Hayhoe, K., McMahon, S., Moe, R., Knight, C. A, & Ackerly, D. D. (2008). Climate change and the future of California’s endemic flora. PloS One, 3(6), e2502. doi:10.1371/journal.pone.0002502.

McLane, S. C., & Aitken, S. N. (2012). Whitebark pine (Pinus albicaulis) assisted migration potential: testing establishment north of the species range. Ecological applications, 22(1), 142–53. Retrieved from: http://www.ncbi.nlm.nih.gov/pubmed/22471080 

Sexton, J.P., McIntyre, P.J., Angert, A.L, & Rice, K.J. (2009) Evolution and ecology of species range limits. Annual Review Ecology Evolutionary Systematics, 40:415-436. 

Villellas, J., Ehrlén, J., Olesen, J.M., Braza, R., & García, M.B. (2013) Plant performance in central and northern peripheral populations of the widespread Plantago coronopus. Ecography, 36: 136 145. 

Vitousek, P.M., D’Antonio, C.M., Loope, L.L., Rejmanek, M., & Westbrooks, R. (1997) Introduced species: a significant component of human-caused global change. New Zealand Journal Ecology, 21(1):1-16. 

Wang, T., Hamann, A., Spittlehouse, D.L., Murdock, T.Q. (2012) ClimateWNA – High-resolution spatial climate data for Western North America. Journal of Applied Meteorology and Climatology, 51, 16-29.