https://www.pnas.org/content/116/13/6181
Scientific management of habitat, wildlife, and fish is the only way we can hope to preserve and restore species like caribou or steelhead in BC. Science means objective facts, truth, and results. Science is not an opinion. Science doesn't care if you are an anti-hunter, a hunter, or have some other agenda. If we are to have any hope of gaining and maintaining public support to protect what we value so dearly then we need to uphold science-based management as an impartial decision maker on behalf fish and wildlife, even when it may be contrary to our short-term interests.
For caribou to recover they need high quality habitat. That means lichen bearing trees which are hundreds of years old, minimal harassment from humans on snowmobiles and ATV, and natural levels of predation. Currently, logging has diminished food supplies and left many roads in its wake. Humans and predators use these roads to gain access to caribou in their habitat. Also, as a consequence of logging, moose and white-tailed deer populations have increased which in turn leads to an increase in predator numbers. The increased predator numbers and their improved access to caribou is a serious one-two punch to population numbers. If we want to protect caribou numbers while the habitat recovers, then we need a sustained predator management effort which includes both heavy culling and a reduction of their other food sources in critical caribou areas.
Ultimately, the question we need to ask ourselves is are we willing to do what it takes for our children and grandchildren to have threatened species like caribou and steelhead remain in BC. We may need to give up some access roads, we may take a hit on our forestry or fishing sectors, we may have to cull some ungulates and predators. Is it worth it? Is that something we want? I say yes.
Paper:
Saving endangered species using adaptive management
Robert Serrouya, Dale R. Seip, Dave Hervieux, Bruce N. McLellan, R.
Scott McNay, Robin Steenweg, Doug C. Heard, Mark Hebblewhite, Michael
Gillingham, and Stan Boutin
PNAS March 26, 2019 116 (13) 6181-6186; first published March 11, 2019
https://doi.org/10.1073/pnas.1816923116
Edited by James A. Estes,
University of California, Santa Cruz, CA, and approved February 6, 2019
(received for review October 2, 2018)
Significance
A replicated management experiment was conducted across >90,000 km2
to test recovery options for woodland caribou, a species that was
functionally extirpated from the contiguous United States in March 2018.
Recovery options were reductions of predators, reductions of
overabundant prey, translocations, and creating fenced refuges from
predators. Population growth was strongest where multiple recovery
options were applied simultaneously. This adaptive management study was
one of the largest predator-prey manipulations ever conducted and
provided positive results for this endangered North American ungulate.
Abstract
Adaptive
management is a powerful means of learning about complex ecosystems,
but is rarely used for recovering endangered species. Here, we
demonstrate how it can benefit woodland caribou, which became the first
large mammal extirpated from the contiguous United States in recent
history. The continental scale of forest alteration and extended time
needed for forest recovery means that relying only on habitat protection
and restoration will likely fail. Therefore, population management is
also needed as an emergency measure to avoid further extirpation.
Reductions of predators and overabundant prey, translocations, and
creating safe havens have been applied in a design covering >90,000
km2. Combinations of treatments that increased multiple vital
rates produced the highest population growth. Moreover, the degree of
ecosystem alteration did not influence this pattern. By coordinating
recovery involving scientists, governments, and First Nations,
treatments were applied across vast scales to benefit this iconic
species.
The late Graeme Caughley emphasized that naturally rare yet broadly distributed species are the most challenging to conserve (1).
These organisms will overlap with many other valuable natural
resources, creating the potential for substantial socioeconomic
conflict. Such large-landscape species also encompass many ecological
scales, inherently leading to increased uncertainty (2). Scientists have increasingly called for management experiments to help resolve such uncertainty (3),
but the challenge has been to apply treatments at sufficiently broad
scales of space and time to include relevant ecosystem processes. This
approach is referred to as adaptive management and is predicated on
creating lasting partnerships between scientists and resource managers
to test alternative hypotheses using contrasting policies (4⇓–6).
Adaptive
management was initially intended to guide the sustainable consumption
of natural resources, such as fisheries or wood fiber (4).
But can this method be successfully applied to recovering endangered
species? Many have argued that it can, but examples are rare (7, 8).
We highlight this approach using perhaps the greatest terrestrial
conservation challenge in North America: recovering woodland caribou (Rangifer tarandus caribou). These animals live across 3 million km2
from Alaska to Newfoundland, and their critical habitat overlaps
petroleum deposits and forest stands worth billions of dollars (9).
Caribou are also a key umbrella species for boreal biodiversity, and
their range covers one of the largest carbon stores on the planet—the
boreal forest (10). Most populations are in decline and extirpation is ongoing (11, 12), setting the stage for an unparalleled conflict between conservation and natural-resource economies (9).
With three barren females remaining in the only population south of the
49th parallel, caribou are the first large-mammal extirpation in recent
history from the contiguous United States (13).
The
complexity of this problem is the result of broad alterations to
ecosystem dynamics across three trophic levels: vegetation, herbivores,
and carnivores (14, 15) (Fig. 1). Even under pristine conditions, caribou are less fecund than deer (Odocoileus virginianus) or moose (Alces alces) (16) and can be more vulnerable once encountered by predators (17).
Yet, in human-altered systems, the creation of productive, early seral
forests buoy primary prey numbers such as moose and deer (18, 19). Thence, predator numbers are maintained by the more numerous moose and deer (20, 21),
creating a decoupling between predator numbers and caribou.
Consequently, caribou can decline to extinction while predators are
maintained by generalist herbivores (14, 22). This process is referred to as apparent competition (23) and affects many threatened taxa (24),
especially as climate and land-use change facilitate the spread of
generalist prey. In the well-known case of California’s Channel Island
fox (Urocyon littoralis), invasive feral pigs (Sus scrofa) subsidized predatory golden eagles (Aquila chrysaetos), causing declines in this endangered fox (25, 26).
Recovery was achieved by the simultaneous reduction of pigs and eagles.
In that case, the subsidy of overabundant prey could be reversed
relatively quickly. For woodland caribou, however, subsidies of prey
will last for decades because of long-term changes to forest age
distributions (Fig. 1). Therefore, the classic solution of protecting remaining critical habitat (27)
will not save most caribou populations because of the time needed to
recover old forests and the continental scale of disturbance (28).
In such cases, population management is needed until protection and
recovery of habitat overcome the legacy of industrial development.
Population-based recovery measures include direct predator reductions (29), prey reductions that lead to fewer predators (30, 31),
animal translocations, and the creation of short-term safe havens from
predators (predator-proof fences, i.e., maternal pens). Reducing
predators can produce immediate benefits (29, 32⇓–34) but can be unpopular because it is a proximate, short-term solution (35).
Reducing subsidized prey is one trophic level closer to the ultimate
cause, and safe havens are small (<10 ha) fenced areas that exclude
predators and protect caribou during the calving season.
Fig. 1.
Process of apparent competition [AC; (23)]
spanning three trophic levels: vegetation, herbivores, and carnivores.
AC occurs between abundant primary prey (moose and deer) and endangered
woodland caribou. In this instance, the early seral forests will last
for decades, facilitating the subsidy of primary prey. Therefore,
immediate management of large mammals (herbivores and carnivores) is
required to recover caribou until the early seral forests transition to
closed canopies. Image courtesy of Kate Broadley (Fuse Consulting,
Alberta, Canada).
Here
we contrast management experiments designed to reduce uncertainty about
how to conserve endangered caribou. The primary hypothesis was that
population declines could be reversed by removing the proximate limiting
factor, excessive predation, because broad-scale ecosystem restoration
would take decades to achieve. We included early seral forest (36) as a covariate to test the alternate hypothesis that the degree of ecosystem alteration would influence population response (27, 37).
This design essentially contrasts the proximate limiting factor of
predation with the ultimate factor of ecosystem alteration. We also
qualitatively evaluated how the intensity of treatments and population
size affected recovery. The population treatments covered large areas
(3,000–8,500 km2) and included predator removal (wolves; n = 6), subsidized-prey reduction (n = 4), predator removal plus safe havens (n = 1), and translocations of caribou (n
= 1). These were compared with six untreated, control populations. Our
synthesis revealed three conclusions that credibly inform recovery for
caribou and other endangered species. First, an adaptive management
framework, with control populations, was critical to determining if
population growth increased following a specific treatment. Second, a
treatment had to be applied intensively to produce a measurable effect.
Third, applying two treatments simultaneously produced an additive
effect on caribou population growth.
Results
We
compared the population growth rate (λ) of 12 caribou populations
before and after a treatment as well as 6 adjacent populations used as
experimental controls. Before treatments, 16 of 18 populations were in
decline (λ < 1; Fig. 2).
After treatments began, 8 of 12 treated populations showed λ increases
of 0.04–0.28, and 6 of these 8 achieved stable or increasing λ (λ ≥ 1).
None of the control populations had positive population growth during
treatments. The most pronounced increase occurred within the Klinse-Za
(KZA) population (λ = 0.86–1.14), where the combination of wolf removal
plus maternal penning resulted in a near-doubling of population size,
from 36 to 67 animals between 2013 and 2018 (SI Appendix, Table S1). The adjacent control populations, Graham (GRA) and Wolverine (WOL), continued to decline at λ = 0.65 and 0.86 (Fig. 2).
Fig. 2.
Population
growth rates (λ; 1 = stability) before and after treatments were
initiated, with controls matched by a similar time period (SI Appendix, Table S1).
Solid arrows indicate λ > 1. Population values apply to the
beginning of treatment. Black outlines show woodland caribou range
boundaries. (Inset) current (gray) and historic (dashed line)
distribution in the contiguous United States and Canada. ALP, À la
Pêche; CON, Columbia North; COS, Columbia South; FBQ, Frisby Queest;
GRA, Graham; GRH, Groundhog; HAS, Hart South; KSI, Kennedy Siding; KZA,
Klinse-Za; LSM, Little Smoky; PAR, Parsnip; PUS, Purcells South; QUI,
Quintette; RPC, Redrock–Prairie Creek; SCE, Scott East; SSE, South
Selkirks; WGS, Wells Gray South; WOL, Wolverine.
An ANCOVA revealed that the effect of treatment (five levels; Table 1)
explained 44.2% of the variation in change to λ (Δλ), with positive
effects for wolf reduction and wolf reduction + penning. Percentage
alteration of forest cover explained only 4.2% of the variation in Δλ (SI Appendix, Fig. S1 and Table S2). The ANCOVA with both treatment and forest alteration was less parsimonious and explained less variation (ΔAICc = 4.68, R2 = 0.42; see SI Appendix, Table S3)
than the effect of treatment alone. Six of the treated populations
numbered <50 animals at the start of a treatment, and only one of
these (KZA) achieved positive population growth (λ = 1.14) when
subjected to two treatments simultaneously. Only two of the larger
treated populations (>50 animals) did not achieve an increased λ
following treatments: Parsnip (PAR) and À la Pêche (ALP). Both had low
intensity of management applied (SI Appendix, Table S1).
In PAR, moose were reduced by 40% compared with Columbia North (CON),
where moose were reduced by >80% and λ increased by 0.064–1.02. In
ALP, wolf reduction was applied only to the winter range during the
first eight years of treatment and λ did not increase. The treatment was
then expanded to the entire range for three years and λ increased from
0.92 to 1.10 (SI Appendix, Table S1). The US/Canada transboundary South Selkirks (SSE) population was small (n = 18) when wolf removal was initiated and expanded only to the Canadian portion of the range (Fig. 2);
the population declined from 18 to 3 barren females as of March 2018.
In summary, caribou λ did not respond in the three herds with low
treatment intensity (SSE, PAR, and ALP), but when ALP transitioned from
low to high intensity, λ increased from 0.92 to 1.10. Finally, the
translocation of 20 animals to Purcells South (PUS) in 2012 did not
improve λ, with only 4 remaining animals in March 2018.
Table 1.
Analysis of covariance explaining change in λ (Δλ) based on treatments for woodland caribou
Discussion
By
focusing on the ultimate recovery metric, caribou population growth, we
demonstrated clear benefits of an adaptive management framework applied
to endangered species over an enormous landscape. Reducing one limiting
factor improved λ, but the greatest increase occurred when two limiting
factors were reduced simultaneously. The implementation of wolf
reductions followed by penning within KZA illustrates the iterative
nature of adaptive management. Given that penning is designed to
increase recruitment and wolf reduction increases adult survival,
implementing both achieved the highest λ. And critically, pairing
populations experiencing treatments with controls that received no
similar recovery actions strengthened our inferences.
Intensity
of treatment, both numerically and spatially, was a key factor in
detecting a population response. In all three instances where treatment
intensity was limited, no caribou response was observed. These results
follow previous studies suggesting that predation rates should not
change linearly with prey density, partially because of
density-dependent processes (31, 38, 39).
Indeed, caribou in both the PAR moose reduction and the associated Hart
South (HAS) control continued to decline, likely because moose were
reduced by only 40%. Similarly, when wolves were reduced over just a
portion of ALP and SSE, caribou λ did not improve. But when the
treatment was adaptively expanded to the entire range of ALP, λ
increased substantially. Conclusions from these actions are becoming
clear—half measures erode public confidence when the outcome is unlikely
to achieve recovery. Resources should be directed strategically and
toward recovery treatments of sufficient intensity to achieve results.
Finally, as with many translocations (40), moving 20 caribou to PUS was unsuccessful because most of these animals were shortly killed by predators (41), driving home Caughley’s primary message of first removing agents of decline before attempting such actions (1).
The
appeal of adaptive management lies with the simple logic of using
management actions to test a hypothesis and, if possible, to test
alternate hypotheses with contrasting policies (4, 6). These actions should follow detailed modeling of the system to help minimize risks of unintended consequences (3, 31, 42)
but also to refute or validate conceptual models of ecosystem dynamics.
For example, previous theory suggested caution when removing subsidized
prey because of demographic time lags of predators and depensatory
predation that can exacerbate declines of rare prey (31, 38). An empirical example occurred within our system when deer populations crashed in 1997 and cougars (Puma concolor) switched to eating caribou (see ref. 31).
This information must be adaptively incorporated into recovery plans,
but can create imbalances in study designs and implementation. In our
case, the lack of replication for some treatments—for example,
translocations—may weaken inferences. However, when considered in light
of independent studies indicating that animal translocations often fail (40), even with caribou (43),
inferences are consistent. Similarly, the combination of treatments
(penning and wolf reduction in KZA) can make it challenging to
definitively conclude which treatment was strongest. Indeed, balanced
and replicated factorial experiments are a laudable goal, but we agree
with Krebs’ (44) synthesis of Caughley’s perspective on uncertainty in conservation (1):
“Several suspected agents of decline may have to be removed at once… It
is better to save the species than to achieve scientific purity.” We
hope this approach will encourage others to pursue a priori planned
designs or retrospective approaches to adaptive management. Nonetheless,
social and logistical barriers to implementation are immense, primarily
due to real or perceived impacts on human values (4). Consequently, according to Westgate et al. (7),
only 1% of studies that have attempted adaptive management report any
response metrics. The plight of woodland caribou has likely reduced
these barriers, enabling partnerships across political jurisdictions,
among academics, First Nations, managers, industry, and conservationists
(45).
The global spread of generalist species through habitat modification and climate change (46)
will continue to exacerbate the endangerment and extirpation of species
via complex ecological mechanisms such as apparent competition. In many
cases, recovery will involve the reduction of expanding prey or
abundant native predators. Although six caribou populations grew within
highly disturbed landscapes, intensive management was required to
achieve this outcome. Support for direct predator reduction is likely to
wane (35)
unless the ultimate cause of decline, habitat alteration, is addressed.
In the case of caribou, like many other endangered species,
anthropogenic alterations of forested ecosystems are the ultimate cause
of declines. Habitat protection for caribou varies considerably across
jurisdictions, but is greatest within the Southern Mountain ecotype,
where 22,000 km2 of remaining old forest have been protected from forest cutting in legal land reserves (47).
This protection has resulted in 5 of 18 caribou ranges in this study
having similar or higher levels of forest gain than forest loss (36) (SI Appendix, Table S1).
In such areas, the degree of intensive population management needed to
recover caribou is expected to diminish over time. However, in areas
where habitat loss exceeds habitat recovery, intensive population
treatments will have to be ongoing until there is a change in how
natural resources are valued.
Methods
Our
study included 18 caribou populations in Alberta, British Columbia, and
Idaho, of which 12 were subjected to government-led management actions
(hereafter referred to as treatments in an adaptive management context)
and 6 were controls. We chose only 6 control populations to be
conservative in matching ecological conditions as closely as possible to
the treatment populations. However, almost all caribou populations in
western Canada were rapidly declining; for example, during the same
period, populations in Alberta were declining at a mean rate of −8% per
year (48). The 12 treated populations in our study were subjected to four recovery actions; (i) predator reductions, (ii) prey reductions, (iii) translocation, and/or (iv) maternal penning (Fig. 2).
Although
controversial in many conservation settings, there is a long history of
predator (and prey) reduction to recover endangered species (34, 49), from removing feral goats (Capra spp.), to recover endangered island fauna (50), to removal of golden eagles on the Channel Islands, to recover the endangered Channel Island fox (25).
Population reduction of wolves, however, is especially controversial
given their heightened conservation status in the United States, and
important trophic role (51).
Nonetheless, wolves are nowhere near endangered or threatened in Canada
and are widely distributed there, and conservative population estimates
are >14,000 wolves in just Alberta and British Columbia (52). Field studies confirm that wolves are a leading cause of mortality and are the proximate cause of caribou declines (14, 22, 32, 53⇓⇓–56).
Moreover, federal and provincial policies and legislation explicitly
list predator and prey reduction as a required recovery action, along
with habitat recovery, to recover endangered woodland caribou under
Canada’s Species at Risk Act (37, 57, 58).
Finally, predator removal was coordinated by provincial agencies
usually via helicopter shooting [similar to the removal of feral goats
on Galapagos, for example (50)] under the authority of the respective provincial wildlife Acts (59).
Prey reductions were conducted through licensed hunting of moose by
sport hunters, also through the authority of provincial wildlife acts
and policies. Thus, despite the ethical issues surrounding removal of
vertebrates (wolves, moose) to recover caribou (60),
methods were permitted and enabled by federal and provincial
legislation and policies. No university personnel were involved in
planning or conducting predator reductions, thus obviating the need for
university animal care review or approvals (see ref. 60).
Similarly, caribou translocations in British Columbia were conducted
exclusively by government staff supervised by the provincial wildlife
veterinarian.
Caribou populations were monitored for
responses to treatments between 2004 and 2018, whereas pretreatment
monitoring dated back to 1994 (SI Appendix, Table S1).
The 18 populations spanned four recognized caribou ecotypes: boreal,
northern mountain, central mountain, and southern mountain (61). Boreal are classified by COSEWIC [Committee on the Status of Endangered Wildlife in Canada (62)] as threatened (n = 1 population); northern (n = 2), as of special concern; central (n = 6) and southern (n = 9), as endangered (61). Despite variation in their listed status, the bulk of our populations were endangered; thus, we use the term endangered to refer to the status of caribou throughout. Our response metric was the finite rate of population change (λ) (63)
or, more specifically, the change in λ (Δλ) before and after
treatments. There are two approaches to estimating λ of caribou
populations depending on behavioral and habitat differences among
ecotypes. The first approach is to estimate population growth rate using
aerial surveys in areas where aerial sightability is high (64). In these cases, λ was calculated as λaerial = (Nt/N0)(1/t) (63).
The second uses survival of radio-collared animals and population-level
recruitment rates to estimate λ using a simple unstructured population
model, the recruitment-mortality equation (65): λRM = S/(1 − R), where S is annual survival of adult females and R is recruitment.
For populations in British Columbia (n
= 15), there are three ecotypes of woodland caribou (central, southern,
and northern), and aerial survey methods differ slightly due to
ecological differences. For the southern mountain ecotype (n = 9), survey estimates have been validated with 153 radio-collared animals. When snow depth exceeds 300 cm (3)
in the upper subalpine, where the caribou dwell during late winter
surveys, sightability is greater than 90%. Surveys were conducted only
under such conditions, making population estimation straightforward. For
the other six populations in British Columbia (central and northern
ecotypes), mark-resight (54)
with radio-marked caribou was used to correct population sizes, or all
individuals were marked or identified through camera traps (66). Populations in Alberta (n
= 3) are difficult to aerially survey because caribou live in dense
coniferous forest, so population trend and associated uncertainty were
estimated based on λRM (48), using the adjustment of ref. 67 to account for the delayed age at first reproduction of caribou. DeCesare et al. (67) showed that the λRM equation is algebraically identical to a Leftokvich stage matrix with three stages and thus provides identical results, but λRM
is the convention used for monitoring woodland caribou. Although
population estimates were not available in Alberta, minimum caribou
observed indicated that all three populations had >50 animals at the
start of treatments (57). Calibration and validation of the two approaches to estimating λ have been extensive (64, 67, 68). Serrouya et al. (64) compared λ for populations where both data sources (λaerial and λRM)
were available, and found the correlation to be 0.78. This suggests
that both metrics were comparable and that any biases within a
population would be minimal over time because the same method (λaerial or λRM)
was always used for each population. Additional details on the
reliability of λ estimates presented in previously published studies can
be found in the SI Appendix.
Like many ecosystem management cases (32),
the intensity of treatments varied across areas. For example, neither
prey nor predator reductions were ever 100%. In the SSE population, wolf
removal occurred only on the Canadian portion of the range (Fig. 2).
For the ALP population, treatment occurred on the winter range from
2007 to 2014 and then expanded to the winter and summer range from 2015
to 2017 (SI Appendix, Table S1). To index the intensity of treatment, we reported the number of wolves per 1,000 km2
removed per year; for moose, we reported the percentage reduction from
the peak population size. The CON population also had a maternal penning
trial that began in 2014, although this was a pilot study that was
designed not to affect λ but to test the concept on a low number of
animals (<20% of females). To isolate the effect of the moose
reduction treatment, and to avoid a confound caused by maternal penning
for caribou, comparisons in the Revelstoke (REV) study area (SI Appendix, Table S1)
were ended in 2013 for the treated populations—CON, Columbia South
(COS), Frisby-Queest (FBQ)—and the adjacent control populations (WGS and
GRH). Isolating the effect of the moose reduction was important because
this recovery tool had not been used before (30)
in the context of apparent competition (unlike wolf reductions, which
have been applied more frequently in this and other studies). Similarly,
localized winter feeding of caribou occurred in the Kennedy Siding
(KSI) population from 2014 to 2018, but was not formally considered a
treatment. Results indicated no effect on λ, but some improvement to
body condition was noted (66).
It was not just treatments that varied between populations, as the ultimate cause of population declines is habitat alteration (37, 58). We used an index of habitat alteration from remotely sensed forest loss data derived from Landsat (36)
to control for the ultimate driver of caribou population trends:
habitat alteration. The covariate was the proportion disturbed (early
seral forest caused primarily by logging or petroleum development; ref. 36)
within a population range, which was converted using the logit link.
The proportion of early seral forest was included to test the hypothesis
that less altered areas were more likely to have increased λ as a
result of a treatment. Previous analyses showed that more early seral
forests predicted lower caribou recruitment, as revealed in a national
meta-analysis spanning 35 populations in the federal recovery strategy (37)
and supported by theory and empirical studies across Canada. To
contextualize the length of time that population treatments would be
required, habitat alteration was also stratified by forest loss and
forest gain based on the definition of ref. 36.
We
conducted an ANCOVA to test our hypotheses by explaining Δλ as a result
of recovery treatments and the proportion disturbed in each caribou
range, with nontreatment (control) populations set as the intercept. For
statistical analyses, λ was converted to the instantaneous rate of
increase (r), λ = er (63), because r is centered on 0 and normally distributed. The dependent variable was the log response ratio, Δr, defined as ln (λafter) − ln (λbefore)—that
is, the difference in population growth rates before vs. after
treatments. Population size and treatment intensity were estimated
quantitatively as described earlier, but were treated as qualitative
factors for three reasons: (i) limited degrees of freedom are inherent in large-scale studies, (ii) population size was not available for the three herds in Alberta, and (iii)
we did not have a common currency among treatment types to quantify
intensity. All statistics were performed in R using the base lm package (69).
Acknowledgments
C.
Gray, M. Dickie, and K. Benesh helped with data extraction and GIS
analyses; and L. DeGroot conducted the SSE surveys. The West Moberly and
Saulteau First Nations were instrumental in implementing treatments for
KZA. Funding was provided by the Alberta and British Columbia
provincial governments, Idaho Fish and Game, and Parks Canada for the
caribou surveys we conducted. M.H. acknowledges funding from NASA
through the Arctic Boreal Vulnerability Experiment (ABoVE) (Grant
NNX15AW71A).
Footnotes
- ↵1To whom correspondence should be addressed. Email: serrouya@ualberta.ca.
- Author contributions: R. Serrouya, D.R.S., B.N.M., D.C.H., M.G., and S.B. designed research; R. Serrouya, D.R.S., D.H., B.N.M., R.S.M., and D.C.H. performed research; R. Serrouya and M.H. analyzed data; and R. Serrouya, D.R.S., D.H., B.N.M., R.S.M., R. Steenweg, D.C.H., M.H., M.G., and S.B. wrote the paper.
- The authors declare no conflict of interest.
- This article is a PNAS Direct Submission.
- This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1816923116/-/DCSupplemental.
- Copyright © 2019 the Author(s). Published by PNAS.
This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).
