BioControl (2018) 63:461–473 https://doi.org/10.1007/s10526-018-9886-4 A proposal to use plant demographic data to assess potential weed biological control agents impacts on non-target plant populations . . . Bernd Blossey Andrea Davalos Wade Simmons Jianqing Ding Received: 14 November 2017 / Accepted: 25 April 2018 / Published online: 28 April 2018 The Author(s) 2018 Abstract Weed biocontrol programs aim to reduce assessments, are essential to guide weed biocontrol the spread and population growth rate of the target programs. We propose to add use of plant demography plant while stabilizing or increasing populations of (an assessment of how environmental factors and those native species considered under threat by ecological interactions, for example competition, invasive plants. This goal is not unique to weed disease or herbivory, may affect plant populations by biocontrol but applies to all other invasive plant altering survival, growth, development and reproduc- management techniques, though such information is tive rates of plant individuals) during host speciﬁcity rarely collected. Without this information, success of risk assessments of potential biological control agents. management interventions can be ambiguous, and Demographic models can reﬁne assessments of poten- regulatory agencies, the public, policy makers, funders tial impacts for those plant species that experience and land managers cannot be held accountable for some feeding or larval development during host chosen treatments. A fundamental reform, including speciﬁcity testing. Our proposed approach to focus use of demographic studies and long-term on impact on plant demography instead of attack on plant individuals is useful in appropriately gauging threats potential weed biocontrol agents may pose to Handling Editors: Mark Schwarzla¨nder, Cliff Moran and S. non-target species after ﬁeld release. Raghu. Keywords Demography Host speciﬁcity Non Electronic supplementary material The online version of target effects Risk assessment Trapa natans L. this article (https://doi.org/10.1007/s10526-018-9886-4) con- tains supplementary material, which is available to authorized Weed biocontrol users. B. Blossey (&) W. Simmons Department of Natural Resources, Cornell University, Ithaca, NY 14853, USA Introduction e-mail: firstname.lastname@example.org Biological weed control programs aim to ﬁnd organ- A. Da´valos Biological Sciences, SUNY Cortland, 1215 Bowers Hall, isms able to reduce spread and population growth rate Cortland, NY 13045, USA of target plants, while avoiding non-target impacts. The track record of weed biocontrol over the past J. Ding century is decidedly mixed, since only a third of all College of Life Sciences, Henan University, weed biocontrol programs achieve at least partial Kaifeng 475004, Henan, China 123 462 B. Blossey et al. suppression of targets (Crawley 1989; Fowler et al. 2000; Moran et al. 2005). Many biocontrol agents fail to establish, or fail to control host plants (Crawley 1989; McFadyen 1998). On the other hand, while occasionally contested, the safety record of weed biocontrol is superior to other management methods, while economic and ecological beneﬁts can be enor- mous and continue to accrue (Moran et al. 2005; Suckling and Sforza 2014). Following publications of high proﬁle cases of non- target attack by Rhinocyllus conicus Fro¨lich (Cur- culionidae) and Cactoblastis cactorum Berg (Pyrali- Fig. 1 Schematic design of typical proposed host speciﬁcity dae), respectively, changes in decision making testing protocol for potential weed biocontrol agents. Depending processes in regulatory agencies, particularly in the on life history and feeding mode of the herbivore under USA, shifted to a greater reliance on fundamental consideration, test conditions may vary. Pool 1 includes all plant species proposed for host speciﬁcity testing that are tested using host-range data, a change that further threatens release highly constrained no-choice conditions (Screen 1). Those even of highly speciﬁc agents (Hinz et al. 2014). The species that could not be eliminated in the ﬁrst screening step irony of this change in risk perception is that speciﬁc constitute pool 2 which are tested using more sophisticated and successful agents of the past would have difﬁcul- designs, such as multiple-choice tests using potted plants or ties passing through current approval processes larger cages (screen 2). Species in pool 3 include plant species that were still attacked under the more sophisticated test (Groenteman et al. 2011; Hinz et al. 2014). At a time conditions, or where larvae completed development. Tests when it is becoming increasingly evident that many utilized during screen 3 depend on herbivore feeding niches and invasive species control methods, particularly chem- logistical and regulatory considerations but include use of ical management, are unable to achieve lasting control common gardens, multiple choice tests without containment, etc. Only those species that were still attacked under the most and may in fact threaten non-target species (Ketten- realistic conditions possible in a particular program would then ring and Adams 2011; Pearson et al. 2016), we argue be considered candidate species for demographic analyses that it is time for fundamental reform of risk assess- (screen 4). Note that the pool of species shrinks with each test, ment and decision making processes in invasive plant while the realism of testing conditions and their ecological relevance increases management and weed biocontrol that is guided by appropriate scientiﬁc information and open dialogue, highly speciﬁc herbivores. We propose to utilize plant not fear (Blossey 2016b). demography (an assessment of how environmental We propose that adoption of modern scientiﬁc tools factors and ecological interactions, for example com- focusing on demographic impacts of herbivores could petition, disease or herbivory, may affect plant pop- constitute a breakthrough development in maintaining ulations by altering survival, growth, development and safety while increasing the ability to select effective reproductive rates of plant individuals) (Salguero- herbivores. We consider it paramount to shift non- Gomez et al. 2015) to assess potential threats of target risk assessments away from damage to individ- candidate biocontrol agents to non-target species. This uals to population level effects expected after ﬁeld approach aims to provide a means by which to releases. We envision that traditional reductionist evaluate potential impacts to non-target plant popula- approaches (no-choice, small herbivore conﬁnements, tions. Our proposal constitutes a signiﬁcant shift in the followed by multiple-choice or potted plant experi- way weed biocontrol researchers, review panels and ments) will continue to be the mainstay of host others may look at the approval and risk assessment speciﬁcity testing. These tests are valuable because the process—but it is a scientiﬁcally valid and biologi- vast majority of test plant species will not be attacked cally meaningful one. We are not concerned by host even under constrained conditions (Fig. 1). However, use but by negative impacts to populations of non- in many programs often a few test plant species remain target species. This is not a reduction in protections that may be fed upon, are accepted for oviposition, or afforded to native species as it continues to safeguard even allow larval development (albeit at a greatly all native species or valuable introduced species that reduced rate compared to original host plants) by 123 A proposal to use plant demographic data to assess potential weed biological control… 463 have attained cultural, ornamental or agricultural ranges are always narrower than experimentally signiﬁcance. We argue that to ‘‘safeguard’’ means determined fundamental host ranges. that populations of non-target organisms are main- tained and do not suffer demographic declines due to biocontrol agent introductions. Cosmetic damage or Evidence for threats of weed biocontrol agents even substantial damage to, or death of, individuals to (rare) native plant species does not necessarily indicate demographic or ecolog- ical consequences. Reports of weed biocontrol agents attacking non- The shift we propose will ﬁnd resistance based on target species do exist, including spillover events with risk perceptions regarding safety of non-target plants substantial temporary defoliation of non-target species due to concerns that herbivores introduced to control (Blossey et al. 2001; Louda et al. 1997, 2003; Paynter introduced plants will (1) attack (rare) native species et al. 2008; Pemberton 2000; Suckling and Sforza leading to declines in populations of these species; and 2014). Comprehensive reviews assessing weed bio- (2) that diet restriction (i.e. speciﬁcity) of weed control outcomes (Blossey et al. 2001; Suckling and biocontrol agents are ‘‘ﬂuid’’ and change over time, Sforza 2014), conclude that [ 90% of all biocontrol leading to attack and unintended negative conse- agents never attack non-target species. The majority of quences for native species. We will brieﬂy review non-target feeding is attributed to spillover events and Suckling and Sforza (2014) report such attacks on 128 evidence for these concerns before further developing our proposal to use demography in host speciﬁcity risk non-target plants. Host speciﬁcity testing appears assessments. However, we ﬁrst provide a primer on unable to predict identity of these species, but physical terminology used to describe plant–herbivore interac- proximity may explain some of it (Blossey et al. 2001). tions because we believe that some differences in However, occasional or prolonged host use appears perceived risk perception are semantic. highly predictable using fundamental host range data (Paynter et al. 2015). Fewer than ten biocontrol agents have established populations on non-target species, a Terminology used in describing plant–herbivore risk that was known, and accepted by regulatory interactions and weed biocontrol programs agencies, at time of their introduction (Blossey et al. 2001). Of these, only three, R. conicus, C. cactorum Ecologists typically refer to diets of herbivores using and Trichosirocalus horridus (Panzer) (Curculion- terms like specialists or generalists (Smilanich et al. idae) may have effects that reduce populations and 2016) or more speciﬁcally monophagous (feeding on a growth rates of non-target species (Louda et al. 1997; single or few species within a genus), oligophagous Suckling and Sforza 2014; Takahashi et al. 2009). (utilizing several plant species, typically in different None of these herbivores would be approved under genera), and polyphagous (using different plant current decision making frameworks (McFadyen species in different genera and families) (Bernays 1998; Suckling and Sforza 2014). and Chapman 1994). In contrast, weed biocontrol Detailed documentation of non-target plant species researchers typically focus on herbivores using a occasionally attacked by biocontrol agents offer single plant species. Furthermore, ‘‘use’’ in the assurances that signiﬁcant non-target effects have ecological and evolutionary literature typically refers not gone unrecognized or unreported—in this case to plants chosen for oviposition and allowing larval absence of evidence indicates evidence of absence of development in the ﬁeld, recognized as realized host such effects and not just lack of effort. A recent range in weed biocontrol. Experimental host-speci- literature survey of threats by insect herbivores to rare ﬁcity testing aims to (1) elucidate the fundamental host plants concluded that with exception of R. conicus and range (plant species acceptable for feeding, oviposi- C. cactorum, ‘‘currently this threat is either seldom tion and larval development using no-choice tests in realized (perhaps because of extensive pre-release the absence of the original host), and (2) provide screening in modern biocontrol programs) or else additional data using less constrained and increasingly seldom documented’’ (Ancheta and Heard 2011). ecologically realistic testing procedures to allow forecasting of realized host ranges. Realized ﬁeld host 123 464 B. Blossey et al. Lack of evidence for evolution of dietary Species accumulation curves on novel host plants preferences in weed biocontrol agents plateau in approximately 100 years for generalists and 500–10,000 years for specialists (Bernays and Gra- Permitting processes for biocontrol agent releases may ham 1988). However, the vast majority of phy- differ widely among countries (Sheppard and Warner tophagous insects show ‘‘phylogenetic 2016), but host speciﬁcity tests are widely standard- conservatism’’ retaining their association with plant ized (Wapshere 1974). Despite further reﬁnements taxa over millions of years with \ 10% of speciation proposed and implemented in subsequent years events including a shift to a different plant family (Briese 2005; Clement and Cristofaro 1995; Sheppard (Winkler and Mitter 2008). Biocontrol agents passing et al. 2005; USDA 2016), this sequence of testing has through host range testing, as far as we can tell from largely remained state-of-the-art, providing over- decades of observation and study, appear particularly whelmingly safe weed biocontrol agents. There is no ‘‘conservative’’. evidence that fundamental host ranges of biocontrol We now return to our argument that use of agents have evolved (Arnett and Louda 2002; Maro- demographic models should be a desired and required hasy 1996; Paynter et al. 2004; Sheppard et al. 2005; tool during risk assessment of biocontrol agents. We van Klinken and Edwards 2002), despite dire warnings are not the ﬁrst to propose such new tools (Louda et al. (Simberloff and Stiling 1996). There is, however, 2005a; Raghu et al. 2006; Sauby et al. 2017), although evidence for evolution of improved performance on we believe we are the ﬁrst to ask that this becomes part non-target plants (McEvoy et al. 2012) and we of pre-release risk assessments. We will brieﬂy acknowledge that few formal assessments have been introduce concepts of demographic modeling and made. then provide examples how demography has, and can However, occasional use, even if predicted, of be utilized in weed biocontrol. To the best of our species that are not targets of weed biocontrol, and knowledge, no biocontrol program has used demo- frequent citation of the few species with anticipated graphic information to assess risks to non-targets large negative impacts, appears to be registered by before ﬁeld releases, so we will rely on post-release non-biocontrol scientists as evidence for a poor track analyses and a hypothetical scenario involving a record and evolutionary malleable diet breadth of current target of biocontrol research, water chestnut, insect herbivores used in weed biocontrol. We argue Trapa natans L. (Lythraceae) to illustrate our that the problem arises due to the disconnect between proposal. perception of labile or rapidly evolving host ranges in weed biocontrol agents and available evidence. Weed biocontrol researchers have increased efforts to Using demography to evaluate biocontrol agent improve testing procedures to mitigate constraints of risks to non-target plants laboratory conditions that affect insect behavior to improve predictions of realized host ranges (Clement Demography and matrix population models (Caswell and Cristofaro 1995; Fowler et al. 2012; van Klinken 2001) are now common tools in biology (Caswell and and Edwards 2002). While such improvements are Salguero-Gomez 2013) and their use in invasion essential to increase reliability of predictions regard- biology and weed biocontrol is increasing (Carval- ing realized host ranges, the focus on improving heiro et al. 2008; DeWalt 2006; Eckberg et al. 2014; testing conditions appears to have prevented a discus- Kerr et al. 2016; McEvoy and Coombs 1999; Shea and sion among scientists and regulatory agencies regard- Kelly 1998; Swope et al. 2017). Technical background ing the appropriate meaning of safeguarding other is provided elsewhere (Caswell 2001; Caswell et al. species (Hinz et al. 2014). 2011; Caswell and Salguero-Gomez 2013; Williams Clearly, diets of insect herbivores change over time et al. 2001), but brieﬂy development of demographic and both generalists and specialists may acquire new models requires estimating vital rates, the transition hosts (Futuyma and Agrawal 2009). Range expansions probabilities from one life stage to another. For plants through human aided introduction of novel plants or with clear developmental stages, a stage-based model insects provide enormous ecological and evolutionary requires estimation of transitions from seed, to opportunities for herbivores to adopt new hosts. seedling, to rosette to ﬂowering plant, to seed output, 123 A proposal to use plant demographic data to assess potential weed biological control… 465 back to seed and seed bank (Davis et al. 2006; Shea Shea et al. 2005; Swope et al. 2017). These examples and Kelly 1998). Local abiotic conditions, competi- are important in recognizing that demographic tion, herbivores, stochasticity, density dependence and approaches are already an important part of the other processes may affect survival and the probability evaluation process in weed biocontrol. We will not that an individual will transition from one stage to the review these here but focus instead on efforts to help next. Vital rates can be inferred in the ﬁeld by assess demographic impacts of weed biocontrol agents monitoring cohorts of marked individuals. on non-target plants after release. Twenty years have Demographic models can aide in assessments of passed since the initial widespread criticism regarding potential impacts of proposed biocontrol agents on safety of (weed) biocontrol (Louda et al. 1997; non-target plants that could not be excluded using Pemberton 2000; Simberloff and Stiling 1996). At traditional testing sequences (Fig. 1). We propose use least ten biocontrol agents have established popula- of experiments, for example by manipulating herbi- tions on non-target species and [ 120 non-target vore access or attack rates and then measuring stage plants are reported to be attacked (Blossey et al. speciﬁc reductions in survival, recruitment, growth, 2001; Suckling and Sforza 2014), thus we expected to biomass, or seed output of non-target plants, which ﬁnd numerous publications outlining demographic can be done in common gardens, or other conﬁne- consequences, or at least attempts to evaluate conse- ments when insects are not approved for release. quences of such attacks. Our Web of Science searches uncovered few studies, which may indicate that they Constructing and populating models with data, and analyzing model performance under different scenar- either were not conducted, did not get published, or ios (often referred to as perturbation analysis) allows were deemed unimportant to conduct or fund. We comparisons of contributions made by different vital therefore focus on R. conicus and C. cactorum, species rates for overall population growth rates (Caswell that according to categorization by Suckling and 2000). The outcome of these exercises is the ability to Sforza (2014) have ‘‘massive’’ non-target impacts, forecast population growth rates (k), population plus post-release evaluations of Mogulones crucifer ﬂuctuation and potential extinction risk, and the Pallas (Curculionidae), a species approved for release sensitivity of growth rates to small changes in vital against houndstongue (Cynoglossum ofﬁcinale L.) rate values, regardless of which management action is (Boraginaceae) in Canada (Catton et al. 2016). applied (Kerr et al. 2016). We recognize that demo- Apparently, studies evaluating demographic effects graphic approaches during evaluation of potential of T. horridus beyond documentation of attack on a biocontrol agents will have to contend with many non-target plants do not exist, therefore we exclude different obstacles, the smallest among them may be this species. lack of familiarity of biocontrol scientists with demo- A high-proﬁle paper regarding non-target effects of graphic modeling (Blossey 2016b). But this is a small R. conicus (Louda et al. 1997) tabulated attack rates on price to pay for the ability to improve predictability of native thistles, but fell short of documenting demo- impacts to targets or risks to non-target organisms. graphic effects, which were strongly implied due to Furthermore, a fast growing and increasingly utilized seed limitation and large demographic impacts by open access database, COMPADRE, provides a native seed feeders on Cirsium altissimum (L.) Spreng potentially important resource to inform construction (Asteraceae) (Guretzky and Louda 1997). Additional of appropriate models for species of interest (Sal- investigations clearly documented demographic guero-Gomez et al. 2015). threats by R. conicus (Louda et al. 2005b) based both on ﬁeld and laboratory data. But effects appear context-dependent and do not occur every year and Retrospective demographic analyses for target in every location (Rand and Louda 2004; Rose et al. and non-target effects in weed biocontrol 2005). In addition, some native thistles show positive population growth rates even in the presence of and Demographic modeling has been used to understand attack by R. conicus (DePrenger-Levin et al. 2010). success or failure of weed biocontrol programs in Furthermore, results of demographic models to assess reducing target plant population growth rates (Buck- population growth rates for Platte thistle, Cirsium ley et al. 2004; DeWalt 2006; Shea and Kelly 1998; canescens Nutt. concluded that impacts may be 123 466 B. Blossey et al. substantial, but variable in space and time and not as models incorporating more than presence of herbivore catastrophic as previously feared (Rose et al. 2005). attack and other ‘‘stressors’’ are we able to gauge While R. conicus should have never been approved for impacts appropriately. As in the case of R. conicus, release, current evidence is of widespread attack on anticipated ‘‘massive’’ impacts of C. cactorum are, native Cirsium species, but evidence for predicted according to published studies, not currently materi- massive negative demographic non-target effects alizing in the ﬁeld. (sensu Suckling and Sforza 2014) has not been Risk assessment after release of M. crucifer, a root presented at this time. feeding weevil that attacked some Boraginaceae, The accidental introduction of C. cactorum to including some US native and rare plants, during host North America (Pemberton 1995) raised concerns speciﬁcity testing (De Clerck-Floate and Sch- over safety of native North and Central American warzla¨nder 2002), provides a good example of an Opuntia spp. (Cactaceae) (Vigueras and Portillo application of matrix population models. Canadian 2001), particularly for rare endemics, such as Opuntia authorities granted release permits and M. crucifer corallicola Small where only 12 known individuals established and began to spread in British Columbia, existed in the Florida Keys (Johnson and Stiling 1996). prompting fears about non-target attacks upon arrival Follow-up work, including using plant demography, in the USA (Andreas et al. 2008). Additional host over the past two decades has delivered a more reﬁned speciﬁcity testing, including ﬁeld tests in British view of realized threats. While initial introductions to Columbia, also documented non-target attack by M. Nevis and St. Kitts in the Lesser Antilles to control crucifer but found minor adult feeding and infrequent weedy native Opuntia spp. was ill advised, a survey larval development, despite ability of the weevil to 50 years after C. cactorum releases showed that the complete development under no-choice conditions targeted native species Opuntia triacantha (Willd.) (De Clerck-Floate and Schwarzla¨nder 2002). Sweet and Opuntia stricta (Haw.) Haw. remain under Subsequent monitoring showed that M. crucifer did biological control while the native tree pear Consolea not establish at sites where C. ofﬁcinale was absent rubescens (Salm-Dyck ex DC) Lem. (Cactaceae) was (Catton et al. 2015) and attack of non-target species not attacked and the cultivated and naturalized Opun- tapered off within a few meters (Catton et al. 2014), tia cochenillifera (L.) Mill showed limited feeding by including during spillover events. Furthermore, C. cactorum (Pemberton and Liu 2007). In the detailed demographic work on Hackelia micrantha Southeastern USA, C. cactorum has spread rapidly, (Eastw.) J. L. Gentry (Boraginaceae), a native plant resulting in variable impacts depending on cactus species regularly attacked in the ﬁeld, demonstrated species, often resulting in size decreases and reduction that while population growth rates for C. ofﬁcinale in relative growth rates (Sauby et al. 2017). Jezorek were reduced below replacement rates (k \ 1), H. et al. (2012) summarized these ﬁndings as follows: micrantha beneﬁtted from C. ofﬁcinale reductions ‘‘although C. cactorum should still be considered a (Catton et al. 2016). These results indicate that while threat, particularly for rare opuntioids, overall survival individual H. micrantha are being attacked and allow along the west central Florida coast is currently high larval development of M. crucifer, the species is safe and plants that are able to survive C. cactorum attack and suffers no harm at the population level (Catton are not being reduced in size, possibly because they et al. 2016). Similar demographic experiments with possess traits that render them more tolerant of C. rare plants that are part of the fundamental host range cactorum damage. Our ﬁndings suggest that an of M. crucifer could help evaluate real (vs. feared) assumption of severe negative effects of an invasive threats to other US native Boraginaceae. species, based on its effects in other regions or over These examples showcase the value of detailed short periods of time, may not always be justiﬁed’’. In demographic studies to assess how attack by biocon- the case of the rare endemic O. corallicola, detailed trol agents may, or may not, contribute to harm, or studies and restoration efforts revealed that salinity, endangerment of non-target species. Only through moisture conditions, hurricanes, trampling by deer, such detailed work are we able to separate anecdotal and stem rot over the past two decades were more observation of attack from contributions of many important demographic threats than C. cactorum factors (habitat loss and fragmentation, inbreeding (Stiling et al. 2000). Only by developing detailed depression, succession, disturbance, climate, abiotic 123 A proposal to use plant demographic data to assess potential weed biological control… 467 conditions, competition, other natural enemies, etc.) growth and reproduction of T. natans at different that affect plant demography simultaneously. larval densities (0–50 L per rosette) (Ding and To the best of our knowledge, no biocontrol Blossey 2005; Ding et al. 2006b). We combine program has used a demographic analysis to assess herbivore impact data with demographic plant data herbivore impact on non-target plants pre-release or as obtained in outdoor aquatic mesocosms to build part of a release petition. Raghu et al. (2006) proposed demographic models that project future plant popula- to use modeling a priori, but this recommendation tion dynamics under different beetle herbivory sce- followed rejection of a herbivore by Australian narios. These data were initially collected and the authorities due to minor feeding on a non-target plant. demographic model built to assess utility of each We believe weed biocontrol researcher should strive herbivore as a biocontrol agent. By considering, for to make this standard practice when promising species sake of argument, T. natans as a non-target species, we fail traditional testing sequences (Fig. 1). Embracing can show how biocontrol programs can use demogra- this approach can lead to important collaborations phy in risk evaluation for non-target species. Full with those concerned about native species and descriptions of the herbivore impact studies are academics with specialized knowledge (Clewley available elsewhere (Ding and Blossey 2005; Ding et al. 2012). et al. 2006b) and results and details of our experimen- To further develop our proposal to use plant tal design to collect demographic data in aquatic demography models in pre-introduction risk assess- mesocosms using four different plant populations ments, we now turn to T. natans. We present data collected in Massachusetts (MA), Rhode Island (RI), collection procedures used to develop a demographic New York (NY), and Virginia (VA), USA are detailed model for T. natans. For the sake of argument, we in Supplementary Materials, Section 2. assume T. natans to be a non-target species, and we further assume that two different chrysomelid beetles Model development are potential biocontrol agents that failed to be cleared in traditional host speciﬁcity screening. We incorpo- We evaluated differences in population growth rate of rate the feeding impacts of each herbivore into the T. natans with periodic matrix population models. demographic model to evaluate the risk each agent Periodic matrix models are suited to explore within may pose to plant demography. year transitions of annual organisms, such as T. natans (Caswell 2001). These models partition life history transitions into m phases deﬁned by a matrix (B ) that Evaluation of two herbivores attacking T. natans projects the population into the next phase (h; where h =1…m). Population over entire cycles is given by The Eurasian T. natans is a ﬂoating aquatic annual the product of period matrices: A ¼ B B ...B ; m m1 1 plant invasive in North America where it is attacked by where A is the annual transition matrix. We partitioned the native water lily leaf beetle, Galerucella nym- T. natans life cycle into three phases (Fig. 2): fall- phaeae L. while the extremely similar Galerucella spring (seedbank or new seeds germinate), spring– birmanica Jacoby (Chrysomelidae) attacks T. natans summer (rosettes grow small or large), and summer- in Asia (Ding and Blossey 2005; Ding et al. 2006a, b). fall (reproduction). We classiﬁed individuals in each Both herbivores are multivoltine and while G. life stage as seedbank, seeds, small or large plants and nymphaeae is oligophagous with multiple host races determined plant size as a function of surface area. We (Cronin et al. 1999), G. birmanica is host-speciﬁc to T. used census data from the common garden to estimate natans, although it occasionally lays a few eggs on transition and germination rates and published data to water shield, Brasenia schreberi J. Gmelin (Cabom- calculate seedbank longevity (Kunii 1988) (Supple- baceae), the only other plant reported to be attacked in mentary Materials, Table S3). We calculated annual the ﬁeld (Ding et al. 2006a) (natural histories of T. population growth rate (k) given by the dominant natans, B. schreberi, G. nymphaeae and G. birmanica eigenvalue of A and used bootstrap methods to are provided in Supplementary Materials, Section 1). estimate 95% conﬁdence intervals for each population Both herbivores were evaluated as potential biocontrol (deﬁned as the 2.5 and 97.5% quantiles from a agents in experiments that assessed their impact on distribution based on resampling values with 123 468 B. Blossey et al. Fig. 2 Seasonal life cycle diagram and periodic matrices for T. seedbank or previous fall until spring, Bs plant survival and natans. Each row represents a season and each circle a life stage growth from spring to fall in the same year and Bu reproduction (SB: seedbank, S: seed, Sm: small plant, Lg: large plant). of small and large plants. Parameters are deﬁned in Supple- Periodic matrix Bf represents germination of seeds from mental Materials, Table S3 replacement holding sample size constant for 1000 parameters (FSM and FLG, fertility for small and large iterations) (Caswell 2001). We also conducted elas- plants, respectively), but in this case fertility values ticity analyses on each periodic matrix to evaluate were drawn from a Poisson distribution. which transition most inﬂuenced growth rate (Caswell To account for density dependent effects, which 2001; Smith et al. 2005). result in decreased T. natans fertility and plant size (Groth et al. 1996), we modeled fertility values as a Non-target threat simulation density-dependent parameter, such that number of seeds produced by small (FSM) or large (FLG) plants We examined how presence and typical attack of G. was dependent on the number of T. natans plants (P) in nymphaeae or of G. birmanica affects demography of the previous season (s) T. natans (Ding and Blossey 2005; Ding et al. 2006b). ðÞ 1ðzP Þ s1 We modeled effects of each scenario on T. natans F ¼ ðÞ 1ðzP Þ s1 1 þ e populations over ten time steps (t) and 10,000 iterations (i) and parametrized the model with values we set z, the regression parameter, to 0.0005, indicat- estimated from the common garden and the literature ing weak density-dependent effects. To model effects (Supplementary Materials, Table S3). We modeled of management scenarios, we estimated average stochastic variation following a two-step procedure in number of seeds produced by small or large plants order to incorporate temporal variation and parametric (FSM or FLG, respectively) and weighted the value by uncertainty in model predictions (McGowan et al. rate of fertility decrease according to scenario. At each 2011). We modeled temporal variation in survival time step we estimated annual growth rate (k) as ratio rates and transition terms for each realization i and between population size at current year (N ) and t?1 time step t as a beta distributed random variable with previous year (N ). Values of k [ 1 indicate increas- parameters a and b derived from the mean survival ing populations while values of k \ 1 indicate i i (or transition) rate, l , such that l = a /(a ? b ) and declining populations. We conducted all analysis i i i i i r = l (1 - l )/(a ? b ? 1). We incorporated using package popbio (Stubben and Milligan 2007) i i i i i parametric uncertainty in the replication loop by in R Core Team (2016). sampling l from a beta distribution, and r from an i i inverse Gaussian distribution with mean m (m = 0.001) and shape parameter k (k = 0.0001). We followed the same two-step approach for fertility 123 A proposal to use plant demographic data to assess potential weed biological control… 469 Results of demographic analyses hypothetical scenario of T. natans as a potential non- target, G. birmanica would be a grave threat to Plant area, seed output and germination varied signif- continued existence of the species. If the goal were to icantly among populations and number of seeds per safeguard T. natans, our results show that of these two plant was positively correlated with plant area (Sup- extremely similar herbivores, G. nymphaeae would be plementary Materials, Fig. S1; Tables S1, S2). a safe biocontrol agent while releases of G. birmanica Asymptotic population growth rates varied signiﬁ- should not be considered. cantly across populations (Supplementary Materials, Section 3). Elasticity analyses indicated that matrix Implications for demographic assessments of non- elements representing germination of new seed and target effects growth into large plants had the greatest inﬂuence on k (Supplementary Materials, Fig. S3). We considered a hypothetical example of T. natans as a non-target plant and evaluated potential threats by Results from modelled simulations two herbivores that can successfully feed, oviposit and develop on the species. Under current decision making Our model predicted that T. natans populations will processes, biocontrol scientists or regulators would continue to grow until carrying capacity or habitat not consider G. nymphaeae a safe biocontrol agent requirements do not allow further expansion, despite because fundamental and ﬁeld host range include T. continued attack by G. nymphaeae (Fig. 3). This natans. Field evidence from [ 100 years of associa- indicates that if T. natans were a non-target species, G. tion with T. natans in North America, and results from nymphaeae would not constitute a demographic threat our demographic modeling efforts, however, clearly to T. natans populations despite regular feeding, indicate that G. nymphaeae is no demographic threat oviposition and larval development. In contrast, our to T. natans. Furthermore, there is no evolutionary model predicts that T. natans populations will be tendency, despite enormous opportunity, to improve greatly reduced when attacked by G. birmanica. After larval performance on the novel host, and female ten years simulated populations increased from 1000 choice retains its preference for oviposition on the plants to [ 40,000 plants when attacked by G. original host, even when larvae developed on T. nymphaeae, but declined to near zero individuals natans (Ding and Blossey 2009). The traditional risk under G. birmanica attack (Fig. 3). Thus, under our assessment process, as currently being implemented, would eliminate a potentially important biocontrol agent due to safety concerns regarding attack on T. natans, but this would be fundamentally unjustiﬁed given the realized demographic impact of G. nym- phaeae. Our demographic assessments correctly pre- dict what is evident in the ﬁeld: G. nymphaeae does not affect T. natans populations, while G. birmanica attack can lead to rapid and severe population growth rate declines. Discussion Reports of non-target attack by weed biocontrol agents after their release has a chilling effect on agent approvals in ongoing programs, funding and recruit- Fig. 3 Simulated population size (continuous line) and popu- ment (Moran and Hoffmann 2015). Land managers, lation growth rate (dashed line) of a T. natans population under ecologists, conservationists and weed biocontrol sci- attack by either the leaf beetle G. nymphaeae or G. birmanica. entists spent enormous amounts of time discussing Model data are integrated means derived by collecting demographic data for four different T. natans populations implications, focusing on the two high proﬁle cases of 123 470 B. Blossey et al. R. conicus and C. cactorum, while critics implied that native plants. Pivoting to new risk assessment proce- non-target impacts are potentially widespread, but not dures and lines of evidence will take some time but we recognized (Louda et al. 2003). Almost 20 years have argue it is essential and well justiﬁed. passed since the initial publications and while we have We acknowledge that biological control, like every been provided with some evidence for negative other management technique, is not risk free and that demographic consequences by R. conicus, the same ecological surprises may occur. For example, it cannot be said for C. cactorum. Importantly, there is appears that identity of plant species attacked during not a single publication documenting negative popu- outbreak or spillover events is unpredictable (Blossey lation level impacts on any other non-target plant et al. 2001). But these are temporary events with no species by weed biocontrol agents. Observation of demographic consequences for attacked plant species herbivorous biocontrol agents feeding on other plants and do not constitute host shifts. Assessing potential are widespread, indicating that evidence of their non- non-target effects is the ethical thing to do, but without target feeding is being recognized, collected and use of demographic information on targets and non- reported, but their attack appears inconsequential at targets biocontrol scientists and practitioners are the population level. vulnerable to accusations of inappropriate conduct Unless there are unrecognized, or unpublished data and may get blamed for population declines or on more widespread negative demographic conse- extinctions, whether these accusations are true or not. We argue that we should acknowledge our quences by approved weed biocontrol agents, the feared threats to native plants currently do not appear responsibilities for safeguarding the continued exis- to exist. This is entirely overlooked by regulators, tence of populations of native or non-target species, conservationists and ecologists who may equate and that demographic approaches provide a powerful feeding on non-target plants with demographic threats. tool for evaluating ecological risk. To the best of our The reliance on data about fundamental host range knowledge, with the exception of R. conicus or C. tests has been called risk-averse, but it ignores the cactorum, there is no weed biocontrol agent that has increasing realized impacts of invasive plants on negatively affected populations of non-target plants. native biota (Downey and Paterson 2016), and Given that hundreds of control agents have been increasing herbicide use by land managers that result released over 100 years across the world, this track in widespread and documented detrimental long-term record is exemplary (Winston et al. 2014). But we also effects to species of conservation concern (Kettenring may miss out on very promising herbivores, because and Adams 2011). We emphasize that society and the current regulatory climate makes scientiﬁcally citizens are entitled to have questions regarding questionable and poorly justiﬁed decisions (Cristofaro performance of invasive species management methods et al. 2013; Fowler et al. 2012; Groenteman et al. 2011; answered to make informed decisions about priorities, Hinz et al. 2014). risk acceptance and funding (Blossey 2016a, b), but Our proposal to utilize demographic approaches in this requires appropriate assessments and interpreta- forecasting agent efﬁcacy, and potential impacts on tion, not fear mongering and it should apply to all non-target species, would go a long way in improving management methods (Kerr et al. 2016; Pearson et al. the standing of the discipline—one grounded in theory 2016). and applying modern tools—while retaining our Use of herbivore impact studies and demographic exemplary track record in safeguarding native species. models in the way we have described here would be an This does not make weed biocontrol inherently more important tool to evaluate efﬁcacy and safety of risky. On the contrary, we would be able to focus on potential biocontrol agents, yet it is completely effective agents with a proven track record of impact unutilized despite its promise (Blossey 2016b). Use on demography of invasive plants, and lack of of plant (and herbivore) demography would be an demographic impact on non-targets. Delivering this important advancement in improving scientiﬁc rigor information to society and decision makers and and predictability of weed biocontrol programs, albeit regulators will constitute part of the accountability we acknowledge it will not be an easy transition. For we should require from all of those engaged in far too long, the focus has been narrowly on funda- invasive species management and stewardship (Blos- mental host ranges and not allowing any attack on sey 2016a, b; Hare and Blossey 2014). 123 A proposal to use plant demographic data to assess potential weed biological control… 471 Acknowledgements We appreciate support and assistance we Carvalheiro LG, Buckley YM, Ventim R, Fowler SV, Memmott have received from those supplying rosettes from local J (2008) Apparent competition can compromise the safety populations. We appreciate help with ﬁeld work and common of highly speciﬁc biocontrol agents. Ecol Lett 11:690–700 garden by Audrey Bowe and Jennifer Price for help with model Caswell H (2000) Prospective and retrospective perturbation development. Comments by several anonymous reviewers analyses: their roles in conservation biology. 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