Global Change Biology (2013) 19, 2645–2654, doi: 10.1111/gcb.12255
Predicting shifts in parasite distribution with climate change: a multitrophic level approach ROB S. A. PICKLES*†, DANIEL THORNTON*†, RICHARD FELDMAN*, ADAM MARQUES* and D E N N I S L . M U R R A Y * *Department of Biology, Trent University, Peterborough, ON, Canada, K9J 7B8, †Panthera, 8 West 40th Street, 18 Floor, New York, NY 10018, USA
Abstract Climate change likely will lead to increasingly favourable environmental conditions for many parasites. However, predictions regarding parasitism’s impacts often fail to account for the likely variability in host distribution and how this may alter parasite occurrence. Here, we investigate potential distributional shifts in the meningeal worm, Parelaphostrongylosis tenuis, a protostrongylid nematode commonly found in white-tailed deer in North America, whose life cycle also involves a free-living stage and a gastropod intermediate host. We modelled the distribution of the hosts and free-living larva as a complete assemblage to assess whether a complex trophic system will lead to an overall increase in parasite distribution with climate change, or whether divergent environmental niches may promote an ecological mismatch. Using an ensemble approach to climate modelling under two different carbon emission scenarios, we show that whereas the overall trend is for an increase in niche breadth for each species, mismatches arise in habitat suitability of the free-living larva vs. the definitive and intermediate hosts. By incorporating these projected mismatches into a combined model, we project a shift in parasite distribution accounting for all steps in the transmission cycle, and identify that overall habitat suitability of the parasite will decline in the Great Plains and southeastern USA, but will increase in the Boreal Forest ecoregion, particularly in Alberta. These results have important implications for wildlife conservation and management due to the known pathogenicity of parelaphostrongylosis to alternate hosts including moose, caribou and elk. Our results suggest that disease risk forecasts which fail to consider biotic interactions may be overly simplistic, and that accounting for each of the parasite’s life stages is key to refining predicted responses to climate change. Keywords: ecological mismatch, multitrophic, Odocoileus virginianus, parasitism, Parelaphostrongylus tenuis, species distribution modelling Received 9 July 2012 and accepted 8 April 2013
Introduction Human-induced climate change is rapidly driving shifts in species distributions, the timing of reproduction and migration events and wholesale declines in species’ population size and viability (Pounds et al., 2006). By 2050, between 15 and 35% of species could be on an irreversible trajectory towards extinction (Thomas et al., 2004), partly due to fundamental changes in the interspecific relationships between organisms both within and across trophic levels (Tylianakis et al., 2008). Increasing evidence suggests that where warming is occurring fastest, such as the polar and boreal regions, there is a decoupling of ecological interactions. For example, a mismatch in phenology of producer and consumers leads to a ‘thermal lag’ between the beginning of the spring plant-growing season and arrival of African bird migrants in Scandinavia (Saino et al., 2011) and the arrival of migratory caribou herds in Greenland Correspondence: Rob S. A. Pickles, tel. +(705) 7481011 ext. 7424, fax +(705) 748 1139, e-mail:
[email protected]
© 2013 John Wiley & Sons Ltd
(Post & Forchhammer, 2008). The result is lower reproductive success and population declines. Differing environmental tolerance among trophically interacting species may also lead to divergent geographical distributions, such as is projected to occur for some species of butterflies and their host plants (Schweiger et al., 2008, 2012). These ecological mismatches may be most pronounced in species with narrow environmental tolerance, complex life cycles and tight trophic dependency (Parmesan, 2006; Tylianakis et al., 2008). Climate change is considered to be directly influencing the emergence, spread and frequency of outbreaks of infectious disease, which is becoming an increasing concern for both human and wildlife populations (Patz et al., 1996; Kutz et al., 2005; Greer et al., 2008). It seems that often climate change may shift pathogen distribution northward (Harvell et al., 2009; Lafferty, 2009), and sometimes this may result in an overall range expansion (Epstein, 2010). Yet, it is notable that parasite distributions ultimately are governed by that of their hosts, and relaxation of a thermal constraint of a host species followed by a distribution shift will be an 2645
2646 R . S . A . P I C K L E S et al. important factor determining the ultimate geographical area available to the parasite (Van der Putten et al., 2010; Schweiger et al., 2012). Forecasting distributional changes in disease vectors typically has relied on modelling responses to abiotic variables, while ignoring biotic interactions (e.g. Rodder et al., 2010; Rose & Wall, 2011). A growing body of evidence emphasizes the importance of incorporating biotic interactions in developing accurate and precise distribution forecasts (Ara ujo & Luoto, 2007; Preston et al., 2008). This is particularly relevant to predictions of future parasite distribution because many parasitic species are multitrophic, with distinct life stages requiring multiple host stages to complete the life cycle. Consequently, predictive models based on a single stage of the parasite life cycle may suffer from error due to uncertainty regarding how the parasite will respond to climate change at each stage of its life cycle. Understanding the degree of agreement or divergence between the climate-induced responses of the parasite and its hosts is necessary to determine the most likely future pattern of parasitism and disease risk (Tylianakis et al., 2008). The responses of both hosts and free-living parasites to climate change may be similar, creating a positive feedback loop in which the distributions of all species increase, leading to an increase in the distribution and prevalence of parasitism (Brooks & Hoberg, 2007; Harris & Dun, 2010; Macnab & Barber, 2011). However, the greater complexity of the life cycles of parasites with multiple hosts and complex transmission cycles may predispose them to disruption by climate change (Rogers & Randolph, 2006), as differing environmental tolerance may lead to an ecological mismatch. Regions in which host and parasite are increasingly mismatched has interesting implications for the aforementioned predicted change in parasite distribution (i.e. Harvell et al., 2009; Lafferty, 2009) and range extent (Epstein, 2010) consequent to climate change, as well as potential for parasite adaptation and host switching (Hoberg & Brooks, 2008). Exactly how current patterns of disease and parasite distribution and prevalence are likely to change over the coming decades has been described as one of the most pressing theoretical questions in epidemiology (Moffett et al., 2007), yet with few exceptions (e.g. Acevedo et al., 2010; Stensgaard et al., 2011), surprisingly little work has modelled how host–parasite dynamics may be affected across climate change scenarios. Here, we assess whether climate change will lead to a straightforward increase in the distribution of parasites and hosts by investigating a complex parasite system involving a two-host life stage and a free-living stage. We develop separate species distribution models
for definitive and intermediate hosts and the free-living stage of the parasite and examine the degree of convergence in response to climate change projections. By combining niche models, we advance a more ecologically holistic predictive model, which we use to assess changing patterns of parasitism risk under different climate scenarios.
Materials and methods
Study system We used the meningeal worm, Parelaphostrongylus tenuis, as the focal species of the study. This parasite is known to infect several ungulate species in North America, but is unable to complete its life cycle outside its sole definitive host, the white-tailed deer, Odocoileus virginianus (Lankester, 2001). Although the parasite causes severe neurological dysfunction in aberrant hosts such as moose, elk and caribou, domestic sheep and goats, the meningeal worm is nonpathogenic in its definitive host (Lankester, 2001). Adult meningeal worms between the meninges and cranial venous blood vessels pass eggs which migrate through the host. En-route the larvae hatch, and upon being shed with faeces, they penetrate or are eaten by a terrestrial gastropod. Inside the gastropod, the larvae develop into the L2 and L3 infective stage larvae and await their host to be eaten by a grazing cervid (Fig. 1), before migrating to the meninges and developing into adult worms (Anderson, 1963). Over 20 species of gastropod have so far been identified as being capable of maintaining P. tenuis infection, and prevalence of infection among species varies geographically. The meadow slug, Deroceras leave, is widely considered to be the most important intermediate host (Lankester, 2001) due to its broad habitat requirements and distribution and capacity to maintain a high prevalence of infection (Lankester & Anderson, 1968). The snails Discus cronkhitei and Zonitoides arboreus have also commonly been found to maintain high prevalence of P. tenuis larvae (Rowley et al., 1987; Platt, 1989; Nankervis et al., 2000; Jacques & Jenks, 2003). These three gastropods are both habitat generalists and geographically widespread.
Model development Geographical presence data for P. tenuis were obtained from literature search using the Global Mammal Parasite Database (www.mammalparasites.org) (Nunn & Alitzer, 2005) and a Google Scholar search. Where precise geographical information was lacking, the locality of occurrence was georeferenced using Google Earth 5.0 and Biogeomancer (Guralnick et al., 2006) and maps from pdfs were georeferenced directly in ArcMap (ver. 10.1) Geographical data for white-tailed deer were obtained from the databases Manisnet (www.manisnet.org) and the Global Biodiversity Information Facility (GBIF) (www.gbif.org) and data for the gastropod hosts were taken from GBIF and a Google Scholar search. In each case, only records from after 1960 were included, which agrees with the © 2013 John Wiley & Sons Ltd, Global Change Biology, 19, 2645–2654
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Shedding of L1 larvae with droppings occurs year round, peaking in spring
Consumption of gastropods while browsing. Infection peak late summer
White-tailed deer L3 larva moults to adult and begins reproduction
Gastropod Free-living L1 larva
Infection by L1larvae by slugs grazing on or near deer droppings. Infection peak early summer
L1 larva moults to L2 and L3 larva inside slug
Fig. 1 The transmission cycle of Parelaphostrongylus tenuis.
WorldClim long-term dataset used to model the species’ habitat suitability. Altogether we derived 339 presence points for P. tenuis, 2133 presence points for white-tailed deer, 274 presence points for D. leave, 1486 for Z. arboreus and 598 for D. cronkhitei. The importance of correct background extent on model performance has been repeatedly emphasized (Phillips et al., 2009; VanDerWal et al., 2009), with general agreement that the background should include the full environmental range of the species including areas reachable by the species (Phillips et al., 2009). For white-tailed deer, we included as background the known range from NatureServe (www.natureserve.org; Patterson et al., 2007), buffered to 200 km and including the range as far south as southern Mexico. This buffer was chosen to represent the availability of environmental space to whitetailed deer, and was several times the known dispersal distance of deer (Diefenbach et al., 2008). The background for the parasite P. tenuis was restricted to the white-tailed deer’s range, using the NatureServe range map. As the three gastropod species modelled are known to occur throughout North America, we included the entire continent as the background for this species. Sampling bias can strongly influence model performance when sampling effort has been highly clustered (Phillips et al., 2009), which is the case for the species in this study given their continental distribution. We countered potential sampling bias using the protocol of Elith et al. (2010) to downweight highly clustered records. First the distance between each sample locality and each background point were summed using a 10 9 10 km scale background raster, and then Gaussian kernel distance was set with a standard deviation of 200 km to represent several times the maximum © 2013 John Wiley & Sons Ltd, Global Change Biology, 19, 2645–2654
dispersal distance of deer (Diefenbach et al., 2008). The resulting bias raster produced was then reclassified to have 20 classes to avoid the effect of extreme outliers having a large effect on model performance. The complete set of 19 bioclimatic variables along with altitude were downloaded from the WorldClim database (Hijmans et al., 2005). In addition to the altitude variable and bioclimatic variables, we used two soil variables obtained from the Food and Agriculture Organisation (FAO) Harmonized World Soil Database (HWSD) (www.fao.org/nr/land/ soils/harmonized-world-soil-database/en), namely soil pH and soil drainage, which have been considered important variables influencing P. tenuis larval transmission (Lankester, 2001). Previously, a strong relationship between cervid biomass and net primary productivity (NPP) was found (Cr^ete, 1999), and we therefore included NPP as an environmental variable in modelling the white-tailed deer and meadow slug using a grid developed by Imhoff et al. (2004). As white-tailed deer also are associated with deciduous forest (Wasel et al., 2003), we included the Advanced Very High Resolution Radiometer (AVHRR) continuous tree cover grid (DeFries et al., 2000) as a predictive variable. To remove potential problems due to colinearity among environmental variables, we first conducted pairwise Pearson correlation tests on 2000 randomly selected points in each of the variables for North America (Elith et al., 2010), removing highly correlated variables (>0.85). To avoid overparameterizing the model, we then applied a pruning step, in which runs were conducted in Maxent for each species with the reduced set of 17 variables (Murray et al., 2011). From these initial runs, we selected variables which both contributed over 90% of the
2648 R . S . A . P I C K L E S et al. information to the model and that were likely to be biologically relevant to the species in question. We used the default settings in Maxent (e.g. 10 000 background points) with the exception that only hinge features were used, due to the greater ease of interpreting response curves. This is a piecewise-linear function that is largely equivalent to a generalized additive model. To validate our model performance, we partitioned our data into test and training sets. Thirty per cent of presence points were randomly set aside to test model performance. The accuracy of each model was assessed using the Area Under the Curve (AUC) statistic, with a random ranking having an AUC of 0.5, and a perfect ranking having a score of 1 (Phillips & Dudik, 2008). The relative importance of each variable was assessed using jackknifing, with 10 cross-validation replicates. Although each species’ model was run using the specified background discussed above, distributions were projected over the entire continent. We modelled habitat suitability of the focal species under future climate scenarios using the three commonly used general circulation models (GCM): the Canadian Centre for Climate Modelling and Analysis model CGCM2, the Commonwealth Scientific and Industrial Research Organization model CSIRO mk2 and the Hadley Centre for Climate Prediction and Research’s model HadCM3. Under each GCM, we also considered two different carbon emission scenarios, an upper (A2a) and a lower (B2a) (Nakicenovic & Swart, 2000). Downscaled climate grids in bioclim format for the decades 2050s and 2080s for each GCM were downloaded from the Climate Change, Agriculture and Food Security (CCAFS) website (www.ccafs-climate.org). In each future projection altitude, NPP, forest cover and soil variables were included in the model as static variables to improve overall model fit (Stanton et al., 2011). To account for variation among GCMs, we created a final projection habitat suitability map for each species by taking the mean habitat suitability value from each separate GCM.
Niche breadth and niche overlap analysis The choice of threshold used to convert a map of probability of occurrence to a binary ‘present/absent’ grid can be important in determining the resulting projection pattern (Nenzen & Ara ujo, 2011). We used a thresholdless approach for resolving the extent to which niches of the intermediate and definitive hosts overlapped with the larval parasite. We used ENM Tools (Warren et al., 2008) to investigate changes in niche overlap and breadth in each focal decade. We used the maximum specificity plus sensitivity threshold to define the range of the parasite at each focal decade. The degree of niche overlap was determined by a pairwise analysis taking the difference between species habitat suitability scores at each cell following a standardization procedure in which suitability is first summed to 1 over the geographic space, obviating the need to calculate a threshold for occurrence. The similarity statistic I (Warren et al., 2008) was calculated, which ranges from 0 (no overlap among niche models) to 1 (niche models identical). The degree of
niche breadth of each species was calculated using the inverse concentration metrics devised by Levins (1968), in which niche breadth ranges from 0 (high degree of habitat selectivity) to 1 (all habitats equally suitable). We then compared spatial patterns of niche overlap and mismatch within the projected ranges of P. tenuis by calculating the difference in habitat suitability between the parasite and each host at each time interval. We used two alternative multispecies models to explore the potential of P. tenuis to expand its distribution while accounting for biotic interactions. For this study, we assumed that each gastropod host was similarly important to P. tenuis. In the first approach, we considered the maximum habitat suitability of a host gastropod species in a given cell, given that this should represent the overall suitability of the intermediate host. In the second approach, we considered that a decline in suitability in one gastropod host would lead to an overall decline in transmission potential, so we used the mean habitat suitability of the combined gastropod models. We then used a minimum-weighting approach to combine the intermediate host, definitive host and larval parasite habitat suitability maps, considering that each element would represent a limiting factor in a transmission cycle. We projected multispecies parasite suitability maps for both future time periods (2050 and 2080) and compared these with the parasite-only suitability maps to determine the degree of consensus and divergence in projected spatial patterns of parelaphostrongylosis risk.
Results Using the set of environmental variables, we obtained train/test AUC values of 0.728/0.732 for whitetailed deer, 0.859/0.847 for Z. arboreus, 0.835/0.794 for D. leave, 0.861/0.830 for D. cronkhitei and 0.848/0.799 for P. tenuis. The P. tenuis distribution model corresponded well with known range limits (Lankester, 2001), and was restricted within the range of the whitetailed deer, with higher habitat suitability found in New England and the eastern seaboard, the Great Lakes region and parts of the Southwest (Fig. 2). The white-tailed deer distribution model corresponded closely with its known northern range limit. Suitability across the range was relatively homogenous and high suitability was suggested for parts of the Pacific coast including California, where the white-tailed deer is not currently found. The distribution model for the gastropods suggested that suitable habitat occurred throughout North America and the tundra region, though suitability was greatest in New England and British Colombia (Fig. 2). Based on jackknifing results, the most important variable determining the distribution of the free-living parasite was precipitation of the warmest quarter, which had the highest AUC in isolation and relatively high reduction in AUC when either variable was removed from the global model. The most important © 2013 John Wiley & Sons Ltd, Global Change Biology, 19, 2645–2654
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2080a Fig. 2 Comparison of three different models of current and future habitat suitability for Parelaphostrongylus tenuis. Increasing red indicates greater habitat suitability. Only results from emission scenario A2a are shown.
variable for the white-tailed deer was NPP, whereas the most important variable for the gastropods Z. arboreus and D. cronkhitei was the minimum temperature of the coldest month and for D. leave it was temperature seasonality (full details of the jackknifing results are provided in Table S1 of the supporting information). Under the two climate scenarios, all species exhibited shifts in habitat suitability in North America over the next 70 years (Fig. 2). The meadow slug exhibited the least change, with parts of the area currently represented by tundra becoming suitable, whereas suitability decreased in the south-eastern USA. The consensus from the three GCMs and two carbon emission scenarios is that habitat suitability for P. tenuis is predicted to shift northwards over the decades, primarily in Alberta, Saskatchewan, Ontario, Quebec and Labrador (Fig. 2). This is the case whether the parasite is modelled alone or in conjunction with the two gastropod scenarios (maximum vs. mean gastropod suitability). The increase in suitability in the north is balanced by loss of habitat suitability in the south and west of the parasite’s current range (Fig. 2). Overall niche breadth increased in all species (Fig. 3). White-tailed deer exhibited the strongest increase in niche breadth with climate change, increasing from 0.597 in 2012 to 0.741–0.734 by 2080 under scenarios © 2013 John Wiley & Sons Ltd, Global Change Biology, 19, 2645–2654
A2a and B2a respectively. Under both scenarios, the deer’s habitat suitability continued to expand in the boreal forest, with the Hudson Plain having a mean habitat suitability of 0.3 or greater by 2050. Niche breadth of the three gastropod species likewise increased in each case by a factor of 1.07–1.09 and niche breadth of P. tenuis alone increased from 1.38 to 1.44– 1.45 by 2080. Overlap of niches increased between the parasite and both D. cronkihetei and D. leave under both carbon emission scenarios. Between P. tenuis and Z. arboreus, an initial increase in overlap was followed by a slight decrease between 2050 and 2080. Under both emissions scenarios, niche overlap between P. tenuis and the white-tailed deer was relatively stable between 2012 and 2050, but then decreased markedly from 2050 until 2080 (Fig. 4). Spatial patterns of niche overlap between parasite and host within the parasite’s projected range varied among taxa, particularly within the Gastropoda (Fig. 5). In 2080, a mismatch between P. tenuis and deer arises in the northeast whereas a mismatch with Z. arboreus develops along the western fringe of P. tenuis’ range. A degree of ecological mismatch is already apparent between P. tenuis and D. leave (west) and between P. tenuis and D. cronkhitei (south). This spatial
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pattern does not change with D. leave by 2080, but intensifies with D. cronkhetei. The northward expansion of P. tenuis is associated with a high degree of niche overlap between it and D. leave, but a developing mismatch with D. cronkhetei.
Discussion Despite recent advances in modelling methods and climate forecasts, few studies have considered biotic interactions when predicting future distributions, particularly in the case of host–parasite systems (Preston et al., 2008; Stensgaard et al., 2011; Barrett et al., 2013;
Wisz et al., 2013). In this study, all models predicted an increase in habitat suitability for P. tenuis transmission across North America from now through to 2050 and 2080. However, incorporating changes in habitat suitability of the parasite’s host species suggests that despite each individual species increasing their niche breadth by 2080 in response to climate change, a concurrent increase in niche mismatch between them will lead to less than expected increases in suitability compared with modelling the parasite only by its freeliving stage (if we assume definitive host specificity is maintained). Significantly, ecological mismatch between the parasite and its hosts increased in two instances as climate warmed (Fig. 5). This led to pronounced differences in projected increases of habitat suitability among models and the potential for overestimating suitability in the northeast and northwest of the parasite’s range. The two multispecies models are therefore more conservative, implying greater losses and smaller increases of habitat suitability than the parasite-only model (Fig. 2), which partially supports Lafferty’s (2009) argument of range shift rather than range expansion as a result of climate change. Our findings suggest that changing climatic patterns have the potential to exacerbate ecological mismatches between trophically interacting species, which has implications for our ability to predict changing distribution patterns in host–parasite or predator–prey systems. Our models suggest that a degree of ecological mismatch already exists between the larval parasite niche and both the intermediate and definitive host niches. Examining the variable which contributed most to the model determining P. tenuis larval distribution, precipitation of the warmest quarter, it seems that the parasite larva can only survive in a subset of the range of its hosts, i.e. where summer rainfall is greater than 200 mm yr 1 (Fig. 6, left). Previous research has shown that survival of P. tenuis larvae is reduced by desiccation and rehydration (Shostak & Samuel, 1984), and that summer rainfall is a good predictor of likelihood of infection (Peterson & Lankester, 1991; Wasel et al., 2003). The mismatch between P. tenuis and deer, especially in northern regions, likely arises because the latter, but not the former species is constrained by winter temperature (and associated snow conditions). Minimum temperature of the coldest month is an important variable determining white-tailed deer distribution yet was not important for modelling the parasite’s distribution. Examining the response of the parasite and deer to this climatic variable suggests that larval P. tenuis can survive low temperatures (Fig. 6, right), a finding that is supported in experimental tests of its thermal tolerance (Shostak & Samuel, 1984; Forrester & Lankest© 2013 John Wiley & Sons Ltd, Global Change Biology, 19, 2645–2654
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Fig. 6 Mismatches in the environmental niche space of the different life stages of Parelaphostrongylus tenuis. Bio18 (Precipitation of warmest quarter), is an important variable determining the distribution of larval Parelaphostrongylus tenuis, whereas Bio6 (Minimum temperature of the coldest month), is an important variable determining the distribution of the definitive host, the white-tailed deer. Plots were obtained by calculating the kernel density estimate of the values of variables Bio18 and Bio6 at all the points within each parasite life stage’s threshold distribution. © 2013 John Wiley & Sons Ltd, Global Change Biology, 19, 2645–2654
2652 R . S . A . P I C K L E S et al. er, 1998). Moreover, P. tenuis larvae are known to be sheltered inside aestivating gastropods, which can protect them from extreme winter conditions (Lankester & Peterson, 1996). On the other hand, winter temperature is a limiting factor for the intermediate gastropod host species in addition to the white-tailed deer host. Therefore, increasing temperatures over time will continue to result in increasingly suitable habitat for these species in northern regions. Although P. tenuis is currently not found in Alberta or Labrador, all models suggest that environmental conditions in 2080 will nevertheless become highly suitable for P. tenuis transmission. Although Samuel & Holmes (1974) found no evidence of P. tenuis occurrence in Alberta in 1974, and a single White-tailed deer Management Unit in northeast Saskatchewan was defined as the westernmost limit of the parasite’s range in 2003 (Wasel et al., 2003), it may be the case that white-tailed deer have not yet expanded north far enough to connect the enzootic region of southern Manitoba with the region of high habitat suitability in Alberta. Anderson (1972) initially predicted that P. tenuis would expand west into the Aspen Parkland with the spread of white-tailed deer, however, this has yet to occur, leading us to agree with Wasel et al. (2003) that the drier prairies of southern Saskatchewan and southeastern Alberta represent a barrier of unsuitable habitat to larval P. tenuis. The multispecies models both suggest that the Northern Forest ecoregion will witness increasingly suitable habitat both for the parasite and its hosts, with niche overlap increasing in each instance. However, the increasing aridity of the Great Plains will lead to prominent decreases in suitability, both for important intermediate hosts such as D. leave as well as the larval parasite itself, possibly leading to extirpation in Nebraska, Oklahoma and retractions from Kansas and South Dakota. One possible consequence of increasing climatically driven ecological mismatch between a parasite and its hosts is that this mismatch may act as a catalyst driving episodic host switching, particularly where there are alternative host species in sympatry (Hoberg & Brooks, 2008). The increasing ecological mismatch between white-tailed deer and P. tenuis in Quebec for instance (Fig. 5), coupled with the presence of caribou as a dead-end host highly susceptible to infection, may present a region of increased selection pressure for host switching (Hoberg & Brooks, 2008), with the parasite evolving to complete its life cycle inside the caribou. Our work has implications for the conservation and management of the broader boreal forest cervid assemblage. White-tailed deer have expanded their range north over the last 50 years, including throughout Northern Alberta and southern parts of the
Northwest Territories (Latham et al., 2011). This expansion seems to be driving decline in moose and woodland caribou, primarily through higher wolf numbers that are afforded through their reliance on deer (Latham et al., 2011). Our predicted increase in habitat suitability for all three stages of P. tenuis’ life cycle in the boreal forest over the next 40–70 years (Fig. 2), together with the known increase in whitetailed deer populations, means that a northward spread of the parasite is highly likely. This may well lead to an exacerbation of existing problems associated with the rising white-tailed deer population, which is known to be capable of maintaining high P. tenuis prevalence levels. Parelaphostongylosis has been implicated as a cause of decline in some populations of moose and elk (Murray et al., 2006; McIntosh et al., 2007; Lankester, 2010). However, populations of both these species also are known to be able to coexist with a white-tailed deer reservoir population. One caveat is that disease outbreaks may be more strongly driven by extreme weather patterns rather than by gradual climatic warming (Handeland & Slettbakk, 1994; Hudson et al., 2006; Rogers & Randolph, 2006; Polley et al., 2010), in which case the type of long-term climatic projections employed here may not fully represent locations of potential transmission foci in the future. In conclusion, incorporating all components of the host–parasite trophic cycle allows a refinement of forecasting parasite responses to climate change and provides a more conservative guide to shifting disease risk in the future. However, we also acknowledge that the predictions of this model assume that no rapid evolutionary adaptation will take place that will compensate the negative effects of climate change on habitat suitability. The assumption may be overly simplistic given that parasites have different geographical variants expressing local adaptations (Crofton et al., 1965; Thompson, 1982). For example, increased resistance of P. tenuis larvae to desiccation, survival in aestivating gastropods or altered larval shedding peaks may favour resilience in the south and west, where we predict suitability to decline. However, this is a problem common to virtually all projections made via species distribution models (Schwartz, 2012). Therefore, understanding the potential for members in a trophic assemblage to adapt to climate variation and incorporating this into forecasting represents the requisite next step in this modelling approach. Indeed, the possibility that ecological mismatches between parasites and their hosts may arise consequent to climate variability or other ongoing changes offers a rich topic for future research on the ecological mosaics of selection and adaptation. © 2013 John Wiley & Sons Ltd, Global Change Biology, 19, 2645–2654
P R E D I C T I N G S H I F T S I N P A R A S I T E D I S T R I B U T I O N S 2653 Acknowledgements
Jacques CN, Jenks JA (2003) Meningeal worm (Parelaphostrongylus tenuis) in South Dakota: the parasite in terrestrial gastropods. Proceedings of the South Dakota
This research was funded by a postdoctoral research fellowship awarded to R.S.A. Pickles by the Canadian Bureau of International Education. S. Wingrove helped in georeferencing occurrence data.
Academy of Science, 82, 143–151. Kutz SJ, Hoberg EP, Polley L, Jenkins EJ (2005) Global warming is changing the dynamics of Arctic host-parasite systems. Proceedings of the Royal Society B, 272, 2571–2576. Lafferty KD (2009) The ecology of climate change and infectious diseases. Ecology, 90, 888–900. Lankester MW (2001) Extrapulmonary lungworms of Cervids. In: Parasitic Diseases of
References Anderson RC (1963) The incidence, development, and experimental transmission of Pneumostrongylus tenuis Dougherty (Metastrongyloidea: Protostrongylidae) of the meninges of the white-tailed deer (Odocoileus virginianus borealis) in Ontario. Canadian Journal of Zoology, 4, 775–792. Anderson RC (1972) The ecological relationships of the meningeal worm and native cervids in North America. Journal of Wildlife Diseases, 8, 304–310. Ara ujo MB, Luoto M (2007) The importance of biotic interactions for modelling species distributions under climate change. Global Ecology and Biogeography, 16, 743–753. Acevedo P, Ruiz-Fons F, Estrada R, Marquez AL, Miranda MA, Gortazar C, Lucientes J (2010) A broad assessment of factors determining Culicoides imicola abundance: modelling the present and forecasting its future in climate change scenarios. PLoS ONE, 5, e14236. Barrett MA, Brown JL, Junge RE, Yoder AD (2013) Climate change, predictive modelling and lemur health: assessing impacts of changing climate on health and conservation in Madagascar. Biological Conservation, 157, 409–422. Brooks DR, Hoberg EP (2007) How will global climate change affect parasite–host assemblages? Trends in Parasitology, 23, doi: 10.1016/j.pt.2007.08.016. Cr^ete M (1999) The distribution of deer biomass in North America supports the hypothesis of exploitation ecosystems. Ecology Letters, 2, 223–227. Crofton HD, Whitlock JH, Glazer RA (1965) Ecology and biological plasticity of sheep nematodes. II. Genetic environmental plasticity in Haemonchus contortus. Cornell Veterinarian, 55, 251–258. DeFries R, Hansen M, Townshend JRG, Janetos AC, Loveland TR (2000) A new global 1 km data set of percent tree cover derived from remote sensing. Global Change Biology, 6, 247–254. Diefenbach DR, Long ES, Rosenberry CS, Wallingford BD, Smith DR (2008) Modeling distribution of dispersal distances in white-tailed deer. Journal of Wildlife Management, 72, 1296–1303. Elith J, Kearney M, Phillips S (2010) The art of modelling range-shifting species. Methods in Ecology and Evolution, 1, 330–342. Epstein P (2010) The ecology of climate change and infectious diseases: comment. Ecology, 91, 925–928. Forrester SG, Lankester MW (1998) Over-winter survival of first-stage larvae of Parelaphostrongylus tenuis (Nematoda : Protostrongylidae). Canadian Journal of Zoology, 710, 704–710. Greer A, Ng V, Fisman D (2008) Climate change and infection diseases in North America: the road ahead. Canadian Medical Association Journal, 178, 715–722. Guralnick RP, Wieczorek J, Beaman R, Hijmans RJ (2006) BioGeomancer: automated georeferencing to map the world’s biodiversity data. PLoS Biology, 4, 1908–1909. Handeland K, Slettbakk T (1994) Outbreaks of clinical cerebrospinal elaphostrongylosis in reindeer (Rangifer tarandus tarandus) in Finnmark, Norway, and their relation to climatic conditions. Journal of Veterinary Medicine Series B, 41, 407–410. Harris CN, Dun RR (2010) Using host associations to predict spatial patterns in the species richness of the parasites of North America. Ecology Letters, 13, 1411– 1418. Harvell D, Alitzer S, Cattadori IM, Harrington L, Weil E (2009) Climate change and wildlife diseases: when does the host matter the most? Ecology, 90, 912–920. Hijmans RS, Cameron SE, Parra JL et al. (2005) Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 25, 1965– 1978. Hoberg EP, Brooks DR (2008) A Macroevolutionary mosaic: episodic host-switching, geographical colonization and diversification in complex host-parasite systems. Journal of Biogeography, 35, 1533–1550. Hudson PJ, Cattadori IM, Boag B, Dobson AP (2006) Climate disruption and parasite–host dynamics: patterns and processes associated with warming and the frequency of extreme climatic events. Journal of Helminthology, 80, 175–182. Imhoff ML, Bounoua L, DeFries R et al. (2004) The consequences of urban land transformation on net primary productivity in the United States. Remote Sensing of Environment, 89, 434–443.
© 2013 John Wiley & Sons Ltd, Global Change Biology, 19, 2645–2654
Wild Mammals, 2nd edn (eds Samuel WM, Pybus MJ, Kocan AA), pp. 228–278. Iowa State University Press, Ames, IA. Lankester MW (2010) Understanding the impact of meningeal worm, Parelaphostrongylus tenuis, on moose populations. Alces, 46, 53–70. Lankester MW, Anderson RC (1968) Gastropods as intermediate hosts of Pneurnostrongylus tenuis Dougherty of white-tailed deer. Canadian Journal of Zoology, 46, 373–383. Lankester MW, Peterson WJ (1996) The possible importance of deer wintering yards in the transmission of Parelaphostrongylus tenuis to white-tailed deer and moose. Journal of Wildlife Diseases, 32, 31–38. Latham ADM, Latham MC, McCutchen NA, Boutin S (2011) Invading white-tailed deer change wolf-caribou dynamics in northeastern Alberta. Journal of Wildlife Management, 75, 204–212. Levins R (1968) Evolution in Changing Environments. Monographs in Population Biology, Vol 2. Princeton University Press, Princeton, NJ. Macnab V, Barber I (2011) Some (worms) like it hot: fish parasites grow faster in warmer water, and alter host thermal preferences. Global Change Biology, 18, 1540–1548. McIntosh T, Rosatte R, Campbell D et al. (2007) Evidence of Parelaphostronylus tenuis infections in free-ranging elk (Cervus elephas) in southern Ontario. Canadian Veterinary Journal, 48, 1146–1154. Moffett A, Shackelford N, Sarkar S (2007) Malaria in Africa: vector species’ niche models and relative risk maps. PLoS ONE, 2, e824. Murray DL, Cox EW, Ballard WB et al. (2006) Pathogens, nutritional deficiency, and climate influences on a declining moose population. Wildlife Monographs, 166, 1–166. Murray KA, Retallick RWR, Puschendorf R et al. (2011) Assessing spatial patterns of disease risk to biodiversity: implications for the management of the amphibian pathogen, Batrachochytrium dendrobatidis. Journal of Applied Ecology, 48, 163– 173. Nakicenovic N, Swart R (2000) A Special Report of Working Group III of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK. Nankervis PJ, Samuel WM, Schmitt SM, Sikarskie JG (2000) Ecology of the meningeal worm, Parelaphostrongylus tenuis (Nematoda), in white-tailed deer and terrestrial gastropods of Michigan’s Upper Peninsular with implications for moose. Alces, 36, 163–181. Nenzen HK, Ara ujo MB (2011) Choice of threshold alters projections of species range shifts under climate change. Ecological Modelling, 222, 3346–3354. Nunn CL, Alitzer SM (2005) The global mammal parasite database: an online resource for infectious disease records in wild primates. Evolutionary Anthropology, 14, 1–2. Parmesan C (2006) Ecological and evolutionary responses to recent climate change. Annual Review of Ecological Systematics, 37, 637–669. Patterson BD, Ceballos G, Sechrest W et al. (2007) Digital Distribution Maps of the Mammals of the Western Hemisphere, version 3.0. NatureServe, Arlington, VA. Patz JA, Epstein PR, Burke TA, Balbus JM (1996) Global climate change and emerging infectious diseases. Journal of the American Medical Association, 275, 217–223. Peterson WJ, Lankester MW (1991) Aspects of the epizootology of Parelaphostrongylus tenuis in a white-tailed deer population. Alces, 27, 183–192. Phillips SJ, Dudik M (2008) Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography, 31, 161–175. Phillips SJ, Dudık M, Elith J et al. (2009) Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecological Applications, 19, 181–197. Platt TR (1989) Gastropod intermediate hosts of Parelaphostrongylus tenuis (Nematoda: Metastrongyloidea) from Northwestern Indiana. Journal of Parasitology, 75, 519– 523. Polley L, Hoberg E, Kutz S (2010) Climate change, parasites and shifting boundaries. Acta Veterinaria Scandinavica, 52, 51–55. Post E, Forchhammer C (2008) Climate change reduces reproductive success of herbivore through trophic mismatch. Philosophical Transactions of the Royal Society B, 363, 2367–2373.
2654 R . S . A . P I C K L E S et al. Pounds JA, Bustamante MR, Coloma LA et al. (2006) Widespread amphibian extinctions from epidemic disease driven by global warming. Nature, 439, 161–167.
Thompson RCA (1982) Intraspecific variation and parasite epidemiology. In: Parasites, their World and Ours (eds Mettrick DF, Desser SS), pp. 369–378. Elsevier Biomedical
Preston KL, Rotenberry JT, Redak RA, Allen MF (2008) Habitat shifts of endangered species under altered climate conditions: importance of biotic interactions. Global Change Biology, 14, 2501–2515. Rodder D, Kielgast J, Lotters S (2010) Future potential distribution of the emerging amphibian chitrid fungus under anthropogenic climate change. Diseases of Aquatic Organisms, 92, 201–207, doi:10.3354/dao02197. Rogers SD, Randolph SE (2006) Climate change and vector-borne disease. Advances in
Press, Amsterdam. Tylianakis JM, Didham RK, Bascompte J, Wardle DA (2008) Global change and species interactions in terrestrial ecosystems. Ecology Letters, 11, 1351–1363. Van der Putten WH, Macel M, Visser ME (2010) Predicting species distribution and abundance responses to climate change: why it is essential to include biotic interactions across trophic levels. Philosophical Transactions of the Royal Society B, 365, 2025–2034.
Parisitology, 62, 345–378. Rose H, Wall R (2011) Modelling the impact of climate change on spatial patterns of disease risk: sheep blowfly strike by Lucilia sericata in Great Britain. International Journal of Parasitology, 41, 739–746. Rowley MA, Loker ES, Pagels JF, Montali RJ (1987) Terrestrial gastropods hosts of Parelaphostrongylus tenuis at the National Zoological Park’s Conservation and
VanDerWal J, Shoo LP, Graham C, Williams SE (2009) Selecting pseudo-absence data for presence-only distribution modeling: how far should you stray from what you know? Ecological Modelling, 220, 589–594. Warren DL, Glor RE, Turelli M (2008) Environmental niche equivalency versus conservatism: quantitative approaches to niche evolution. Evolution, 62, 2868– 2883.
Research Center, Virginia. The Journal of Parasitology, 73, 1084–1089. Saino N, Ambrosini R, Rubolini D et al. (2011) Climate warming, ecological mismatch at arrival and population decline in migratory birds. Proceedings of the Royal Society B, 278, 835–842. Samuel W, Holmes JC (1974) Search for elaphostrongyline parasites in cervids from Alberta. Canadian Journal of Zoology, 52, 401–403. Schwartz MW (2012) Using niche models with climate projections to inform
Wasel SM, Samuel WM, Crichton V (2003) Distribution and ecology of meningeal worm, Parelaphostrongylus tenuis (Nematoda), in northcentral North America. Journal of Wildlife Diseases, 39, 338–346. Wisz MS, Pottier J, Kissling WD et al. (2013) The role of biotic interactions in shaping distributions and realised assemblages of species: implications for species distribution modelling. Biological Reviews, 88, 15–50.
conservation management decisions. Biological Conservation, 155, 149–156. Schweiger O, Settele J, Kudrna O, Klotz S, K€ uhn I (2008) Climate change can cause spatial mismatch of trophically interacting species. Ecology, 89, 3472–3479. Schweiger O, Heikkinen RK, Harpke A et al. (2012) Increasing range mismatching of interacting species under global change is related to their ecological characteristics. Global Ecology and Biogeography, 21, 88–99. Shostak AW, Samuel WM (1984) Moisture and temperature effects on survival and infectivity of first-stage larvae of Parastrongylus odocoileus and P. tenuis (Nematoda: Metastrongyloidea). The Journal of Parasitology, 70, 261–269. Stanton JC, Pearson RG, Horning N et al. (2011) Combining static and dynamic variables in species distribution models under climate change. Methods in Ecology and Evolution, 3, 349–357. Stensgaard A-S, Utzinger J, Vounatsou P et al. (2011) Large-scale determinants of intestinal schistosomiasis and intermediate host snail distribution across Africa: does climate matter? Acta Tropica, doi:10.1016/j.actatropica.2011.11.010 [Epub ahead of print]. Thomas CD, Cameron A, Green RE et al. (2004) Extinction risk from climate change. Nature, 427, 145–148.
Supporting Information Additional Supporting Information may be found in the online version of this article: Table S1. Jack-knifing results of the relative importance of environmental variables used in modelling the larval parasite, intermediate and definitive hosts. AUC values for the model when each variable is used alone are shown together with the model AUC when each variable is removed. The train/test AUC of the complete model is provided below the species name. Values are means of 10 cross-validate replicates.
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