bioRxiv preprint doi: https://doi.org/10.1101/2020.04.29.069559; this version posted May 1, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.
1 2 3 4
5 Leveraging biological complexity to predict
6 patch occupancy in a recent host range expansion
7
8 M. L. Forister1,2*, C. S. Philbin2,3, Z. H. Marion4, C. A. Buerkle5,
9 C. D. Dodson2,3, J. A. Fordyce6, G. W. Forister7, S. L. Lebeis8,
10 L. K. Lucas9, C. C. Nice10, Z. Gompert9
11
12
13 Affiliations:
14 1 Dept. of Biology, University of Nevada, Reno, NV 89557, USA
15 2 Hitchcock Center for Chemical Ecology, University of Nevada, Reno, NV 89557, USA
16 3 Dept. of Chemistry, University of Nevada, Reno, NV 89557, USA
17 4 School of Biology, University of Canterbury, Christchurch, New Zealand
18 5 Dept. of Botany and Program in Ecology, University of Wyoming, Laramie, WY 82071, USA
19 6 Dept. of Ecology and Evolutionary Biology, Univ. of Tennessee, Knoxville, TN 37996, USA
20 7 Bohart Museum of Entomology, University of California, Davis, CA, USA
21 8 Dept. of Microbiology, Univ. of Tennessee, Knoxville, TN 37996, USA
22 9 Dept. of Biology, Utah State University, Logan, UT 84322, USA
23 10 Dept. of Biol., Pop. and Conserv. Biol., Texas State Univ., San Marcos, TX 78666, USA
24
25 * Corresponding author. Email: [email protected]
26 27 28
29 bioRxiv preprint doi: https://doi.org/10.1101/2020.04.29.069559; this version posted May 1, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. p.2
30 Abstract:
31 Specialized plant-insect interactions are a defining feature of life on earth, yet we are only
32 beginning to understand the factors that set limits on host ranges in herbivorous insects. To
33 understand the colonization of alfalfa by the Melissa blue butterfly, we quantified arthropod
34 assemblages and plant metabolites across a wide geographic region, while controlling for climate
35 and dispersal inferred from population genomic variation. The presence of the butterfly is
36 successfully predicted by direct and indirect effects of plant traits and interactions with other
37 species. Results are consistent with the predictions of a theoretical model of parasite host range
38 in which specialization is an epiphenomenon of the many barriers to be overcome rather than a
39 consequence of trade-offs in developmental physiology.
40
41 One sentence summary:
42 The formation of a novel plant-insect interaction can be predicted with a combination of biotic
43 and abiotic factors, with comparable importance revealed for metabolomic variation in plants
44 and interactions with mutualists, competitors and enemies.
45 bioRxiv preprint doi: https://doi.org/10.1101/2020.04.29.069559; this version posted May 1, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. p.3
46
47 Main text:
48 Emerging infectious diseases and crop pests are examples of host range expansion in which an
49 organism with a parasitic life style colonizes and successfully utilizes a novel host (1). Many
50 aspects of host range are poorly understood, including why most herbivorous insects and other
51 parasites are specialized and the conditions under which new host-parasite interactions develop
52 and persist (1, 2). Reductionist approaches in focal systems have revealed key aspects of host
53 recognition (3) and other relevant mechanisms (4), but by design do not encompass context
54 dependence including interacting species and abiotic variation. Ecological studies of host range,
55 in contrast, might quantify context dependence but have not always included modern genomic
56 and metabolomic approaches (5). Here we use the colonization of alfalfa, Medicago sativa, by
57 the Melissa blue butterfly, Lycaeides melissa (Fig. 1) to present what is to our knowledge the
58 most thorough picture of a recent (within the last 200 years) host range expansion in terms of
59 number of populations studied and breadth of interacting species and host traits characterized.
60 Theoretical work in this area can be divided into two partially-overlapping groups, those
61 that emphasize developmental performance (including trade-offs in the ability to use different
62 hosts), and those that stress opportunity and constraint imposed by exogenous factors, primarily
63 natural enemies (6) and geography (7). Although developmental trade-offs in host use are rare
64 (8), it is clear that plant defenses are a barrier to insect colonization, as performance is often
65 reduced for herbivores in experiments with novel vs ancestral hosts (9). What we do not know is
66 whether the magnitude of performance effects studied in the lab will be informative under field
67 conditions. Predation pressure could, for example, remove all opportunity for successful
68 development on a novel host that would otherwise be suitable. Equally unknown is whether bioRxiv preprint doi: https://doi.org/10.1101/2020.04.29.069559; this version posted May 1, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. p.4
69 variation within and among host populations might have compensatory effects, such that a direct
70 negative effect of a particular toxin on an herbivore is balanced by similar effects on a
71 competitor.
72 Lycaeides melissa is widespread in western North America, where it can be found in
73 association with native legume (Fabaceae) host plants, and typically persists in isolated
74 subpopulations connected by limited gene flow (10). The association with alfalfa is
75 heterogenous, and most often occurs in areas where the plant has escaped cultivation. Alfalfa
76 was introduced to western North America in the mid 1800s (10), and is a poor food plant for L.
77 melissa caterpillars, which develop into adults that can be up to 70% smaller than individuals on
78 native hosts, with direct (11) and indirect fitness consequences (12). The use of M. sativa does
79 not appear to be constrained by genetic, developmental trade-offs in L. melissa or a lack of
80 genetic variation in ability to utilize that host (13, 14). Nevertheless, unoccupied patches of M.
81 sativa have remained unoccupied by the butterfly for years or even decades, even in close
82 proximity to occupied patches (15). We studied that heterogeneity using more than 1,600
83 individual plants from 56 alfalfa locations with and without L. melissa (Fig. 1A). We find that
84 roughly three-quarters of the variation in L. melissa presence and absence at the landscape scale
85 can be predicted with a structural equation model (Fig. 2) and a suite of variables that includes
86 metabolomic variation, host patch area, the abundance of interacting arthropods, and dispersal
87 (relative rates of effective migration; Fig. 1B). The success of the model is also apparent in
88 cross-validation (Fig. 2) and null simulations of site-level properties (Supplementary Figure S7).
89 Like most butterflies in the family Lycaenidae, L. melissa caterpillars engage in a
90 facultative mutualism with ants (Fig. 1C), where caterpillars produce specialized secretions in
91 exchange for protection from natural enemies (16). Previous experimental work in this system bioRxiv preprint doi: https://doi.org/10.1101/2020.04.29.069559; this version posted May 1, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. p.5
92 found that excluding ants from individual plants reduced caterpillar survival (17). We find here
93 that ant abundance is the most influential variable or control on L. melissa presence across the 56
94 sites (Fig. 2, Fig. 3A). This is true even when considering the fact that ants facilitate
95 hemipterans (aphids, treehoppers, and other myrmecophiles), which in turn have a negative
96 competitive effect on L. melissa (Fig. 3B). The balance of ant and hempiteran effects is such that
97 the negative effect of the latter is most influential at intermediate ant densities (Fig. 3D). Similar
98 complexity arises through direct and indirect effects of metabolomic variation. Phytochemical
99 factor 4 has a direct negative association with L. melissa presence (Fig. 3C), but an indirect
100 positive effect mediated through other herbivores and their effect on ants (Fig. 2). That axis of
101 plant variation is positively associated with a number of alkaloids, among other compounds, with
102 potential herbivore toxicity (see Supplementary Table S4 and Figure S5).
103 Considering the summed totals of direct and indirect effects estimated through path
104 analysis (Fig. 2B), we find that metabolomic variation is associated with the most pronounced,
105 direct negative effects, followed closely (among negative effects) by direct and indirect
106 interactions with other arthropods and then indirect effects of plant structure. The effect of
107 specific leaf area is consistent with a previous experimental study (18), but is small compared to
108 both positive and negative indirect effects associated with plant size and the density of flowers
109 mediated through enemies and competitors (Fig. 2B). In terms of positive effects on L. melissa,
110 the importance of ants is followed by geographic factors including patch area and dispersal
111 (effective migration rates). These results demonstrate the value of studying plant variation in the
112 context of geography and interacting species. While individual components of the results
113 reported here are consistent with experimental work, other aspects are less accessible to
114 manipulation. Individual metabolites, for example, have a mix of positive and negative direct bioRxiv preprint doi: https://doi.org/10.1101/2020.04.29.069559; this version posted May 1, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. p.6
115 effects on L. melissa (Fig. 3F) as observed in a previous rearing experiment (18), while the
116 indirect effects of individual compounds are characterized more by positive effects mediated
117 through numerous other species in the wild (Fig. 3G).
118 The theory of ecological fitting suggests that novel hosts are colonized if they are "close
119 enough" to native hosts in key traits (19–22), but we have few cases in which that "close enough"
120 distance has been quantified as we have done in this system. We find diverse factors or controls
121 on colonization that are encountered in multifarious combinations (23). When all factors align,
122 butterfly populations persist on the novel host, but the diversity of challenges (plants, enemies,
123 abiotic conditions) undoubtedly makes adaptation to the novel host difficult, especially when
124 some or all of those factors likely shift in character from year to year (24). This possibility is
125 consistent with only minimal local adaptation that has been observed in alfalfa-associated
126 populations (13). Given these results, we can see the theory of host range evolution approaching
127 maturity: genetic trade-offs are possible, but rare (25); instead, it is likely that a balance of
128 factors (both positive and negative) associated with novel host use exist in any system but are
129 only infrequently encountered in combinations that allow host range expansion (26–28). Thus,
130 generalist herbivores or parasites (with many accumulated hosts) are predictably rare across
131 geographic and phylogenetic scales (29, 30). The complexity of barriers to novel host use and
132 the ecological contingency of colonization challenge our ability to forecast new crop pests or
133 emerging infectious diseases (1), but the multi-disciplinary approach illustrated here does raise
134 the promise, at least for herbivorous insects, that expansions of host range can be understood
135 given current technologies and sufficient sampling effort.
136
137 bioRxiv preprint doi: https://doi.org/10.1101/2020.04.29.069559; this version posted May 1, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. p.7
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211 ACKNOWLEDGEMENTS:
212 Thanks to the Hitchcock Center for Chemical Ecology. Thanks to Sarah Flanagan and Kate Bell
213 for help with collections, and Josh Jahner, Su'ad Yoon and Josh Harrison whose travels in the
214 Great Basin discovered many important field sites.
215 Funding: National Science Foundation grant DEB-1638793 to MLF and CDD, DEB-1638768 to
216 ZG, DEB-1638773 to CCN, DEB-1638922 to JAF, and DEB-1638602 to CAB; MLF was
217 additionally supported by a Trevor James McMinn professorship.
218 Author contributions: Overall concept and approach by M.L.F., C.S.P, C.A.B, C.D.D., J.A.F.,
219 S.L.L., L.K.K., C.C.N. and Z.G. Data collection by Z.H.M., C.S.P, G.W.F and M.L.F. Data
220 analysis by M.L.F., C.S.P., Z.G., J.A.F., C.C.N., and Z.H.M. All authors read and edited the
221 manuscript.
222 Competing interests: The authors declare no competing interests. Data availability: The data
223 analyzed in this study will be available on Dryad.
224
225 SUPPLEMENTARY MATERIALS:
226 Materials and Methods
227 Supplementary Results
228 Figures S1-S7
229 Tables S1-S13 bioRxiv preprint doi: https://doi.org/10.1101/2020.04.29.069559; this version posted May 1, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. p.11
230
231
232
233
234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 Fig. 1. Map of study locations, dispersal surface, and images of butterflies, ants and 260 caterpillar. (A) Solid symbols (circles and triangles) are focal alfalfa locations from which 261 arthropods and plants were collected: blue sites are locations where the Melissa blue butterfly 262 (Lycaeides melissa) has colonized the novel host; green sites are alfalfa locations not colonized 263 by the butterfly. Open symbols (circles and squares) are locations used in the quantification of 264 gene flow; in some cases (where an open circle appears within a blue circle), sites were 265 represented in both datasets. (B) Effective migration surface used to generate covariates 266 representing rates of effective dispersal (blue is faster than average, red is slower). (C) L. 267 melissa caterpillar being tended by mutualist ants on alfalfa (photo by CCN). (D) Female and 268 (E) male L. melissa butterflies (photos by MLF). 269 270 271 bioRxiv preprint doi: https://doi.org/10.1101/2020.04.29.069559; this version posted May 1, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. p.12
272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 Fig. 2. Structural equation model and summary of direct and indirect effects. (A) Path 303 diagram illustrates coefficients estimated in structural equation model predicting L. melissa 304 presence and absence across the landscape, as well as abundance of ants, tended herbivores, 305 other herbivores and predators (model fit: Fisher's C = 67.66, P = 0.995). Negative effects are 306 indicated by red lines, positive effects by gray lines; width of lines are scaled to the magnitude of 307 the coefficients. For the endogenous variables, two numbers are shown within ovals: R2 values 308 (on top) and observed-vs-predicted correlations (below) from leave-one-out cross validation. 309 Color coding of exogenous variables indicates plant metabolomics data (green), plant structural 310 traits (violet), geographic variables (brown), and climate (yellow); color coding also corresponds 311 to bar chart (B), which summarizes relative magnitude of direct and indirect effects (solid and 312 hashed bars, respectively), both positive and negative. For example, climate has a modest 313 positive direct effect, a smaller positive indirect effect (mediated through tended herbivores), and 314 a larger negative indirect effect (through predators). 315 316 317 bioRxiv preprint doi: https://doi.org/10.1101/2020.04.29.069559; this version posted May 1, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. p.13
318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 Fig. 3. Illustration of effects for a subset of variables predicting L. melissa presence and 353 absence across the landscape. (A, B and C) Partial effects of ants, tended herbivores, and 354 phytochemical factor 4 on L. melissa occupancy; in other words, these are the effects of those 355 individual factors while controlling for other factors predicting occupancy (see paths in Fig. 2). 356 (D and E) Predicted probability of patch occupancy across a range of values for ant and tended 357 herbivore abundance (D) and for ant abundance and phytochemical factor 4 (E) where it can be 358 seen, for example, that at high values of phytochemical factor 4, a higher abundance of ants is 359 needed before the probability of occupancy rises. (F and G) direct and indirect effects of 849 360 metabolomic features, both positive (blue) and negative (orange); see text for more details on 361 calculation of individual effects. Formica ant and Campylenchia treehopper (one of the more 362 abundant ant-tended herbivores at our study sites) illustrations by MLF; the alkaloid shown 363 above (C) is medicanine (see Supplementary Figure S5 for additional examples).