PreprintReviewVersion 1Preserved in Portico This version is not peer-reviewed
A Review of Published and Unpublished Findings from 20 Long-term Monitoring Studies of Eastern Monarch Butterflies: the Population was Never in Danger, Despite Recent Winter Colony Declines
Version 1
: Received: 27 October 2020 / Approved: 28 October 2020 / Online: 28 October 2020 (15:32:14 CET)
How to cite:
Davis, A. A Review of Published and Unpublished Findings from 20 Long-term Monitoring Studies of Eastern Monarch Butterflies: the Population was Never in Danger, Despite Recent Winter Colony Declines. Preprints2020, 2020100597. https://doi.org/10.20944/preprints202010.0597.v1.
Davis, A. A Review of Published and Unpublished Findings from 20 Long-term Monitoring Studies of Eastern Monarch Butterflies: the Population was Never in Danger, Despite Recent Winter Colony Declines. Preprints 2020, 2020100597. https://doi.org/10.20944/preprints202010.0597.v1.
Cite as:
Davis, A. A Review of Published and Unpublished Findings from 20 Long-term Monitoring Studies of Eastern Monarch Butterflies: the Population was Never in Danger, Despite Recent Winter Colony Declines. Preprints2020, 2020100597. https://doi.org/10.20944/preprints202010.0597.v1.
Davis, A. A Review of Published and Unpublished Findings from 20 Long-term Monitoring Studies of Eastern Monarch Butterflies: the Population was Never in Danger, Despite Recent Winter Colony Declines. Preprints 2020, 2020100597. https://doi.org/10.20944/preprints202010.0597.v1.
Abstract
There are a large number of wildlife and insect species that are in trouble on this planet, and most believe that monarch butterflies in eastern North America are too, because of the well-publicized declines of their winter colonies in central Mexico in the last 25 years. A small number of studies over the last decade have cast doubt on this claim by showing declines are not evident at other stages of the annual cycle. To determine how extensive this pattern is, I conducted an exhaustive review of peer-reviewed and grey literature on (eastern) monarch population censuses and studies, conducted across all seasons, and extracted data from these sources to evaluate how monarch abundance has or has not changed over time. I identified 20 collections of data that included butterfly club reports, compilations of citizen-science observations, migration roost censuses, long-term studies of isotopic signatures, and even museum records. These datasets range in duration from 15 years to over 100 years, and I endeavored to also update each with information from the most current years. I also re-examined the winter colony data after incorporating historical records of colony measurements dating back to 1976. This represents the most complete and up-to-date synthesis of information regarding this population. When I examined the long-term trajectory within each dataset a distinct pattern emerged. Modest declines are evident within the winter colonies (over the full 45 year dataset), and, within three censuses conducted during the spring recolonization. Meanwhile, 16 completely separate monitoring studies conducted during the summer and fall (and from varying locations) revealed either no trend at all or in fact an increase in abundance. While each of these long-term studies has inherent limitations, the fact that all 16 sources of data show the same pattern is undeniable. Moreover, this evidence is consistent with recently-conducted genetic work that shows a lack of decline. Collectively, these results indicate that despite diminishing winter colonies and spring migrations, monarchs in eastern North America are capable of rebounding fully each year, implying that milkweed is not limiting within their collective range. Moreover, there is no indication from these data that the summer population was ever truly diminished by changing agricultural practices in the Midwest that reduced milkweed in crop fields within that region. It is possible that the larger population is not as dependent on Midwestern agricultural milkweed as once thought, and/or that monarchs are adapting to increasingly human-altered landscapes. These results are timely and should bear on the upcoming USFWS decision on whether the monarch requires federal protection in the United States. Importantly, they argue that despite losses of many insects globally, the eastern North American monarch population is not in the same situation.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Received:
23 December 2020
Commenter:
Leslie Ries
The commenter has declared there is no conflict of interests.
Comment:
I have found several substantive flaws in this summary of monarch data sets and I do my best to address each here although this does not rise to the level of an exhaustive review. I am going to avoid making comments about the health of the monarch population, that is not my goal here. I am simply going to comment on each of the data sets and reported trends in Table 1. As for the patterns presented in Fig. 2, I would suggest that it is difficult to evaluate them because scant information about methods were provided and the x-axis isn’t even labeled making it difficult to evaluate. Instead, I focus on a dataset-by-dataset summary of the population trends reported in Table 1 from what I consider to be the most informative datasets (structured surveys) to the least (opportunistic).
MOST INFORMATIVE: STRUCTURED SURVEY OF ADULTS (data collected along fixed routes or defined areas that are repeated over time and some metric of effort is collect, e.g., the amount of time spent surveying). This includes so-called “Pollard walks” and “Counts” as well as MLMP surveys, WWF surveys and roost censuses at fall funnel points.
WWF winter colonies. It is not disputed that the numbers have been declining since the mid-1990s (before that is harder to say) and that is what is reported here.
NABA summer counts: No change as reported in Inamine et al. 2016. However, Ries et al. 2015b showed a near-significant breakpoint indicated a potential decline in summer populations but this was not significant. Saunders et al. 2019 also showed no decline in the summer population from 2004-2015 using NABA summer counts. Ries et al. 2015a,b both point out that a lack of statistical trends could be caused by not accounting for biases in survey placement and that this should be a focus of further research.
NABA spring counts were reported to be declining in Inamine et al. 2016. Data density is extremely low here and an analysis that I published (Ries et al. 2015a) of the same data showed no significant trend.
IL Butterfly Monitoring Network: Davis reports no trend based on personnel communication with the program leader, and this aligns with what is published in Ries et al. 2015b and would only be strengthened by more up-to-date numbers as are reported here. However, this ignores the fact that these same IL data were used in Saunders et al. 2018 and there they showed that declines were evident from 1994-2003 in Ohio and Illinois in counties with more crop cover as glyphosate use increased. According to results in that study, numbers stabilized at lower levels in 2003. These results suggest a decline during the period of glyphosate adoption although are not conclusive on this point due to limited spatial scope of study (IL and OH).
Ohio Butterfly Monitoring Network: Reported as “ambiguous, no clear trend” even though monarchs were reported as declining in Wepprich’s 2019 paper. Wepprich’s paper did not focus on monarchs and so I do not suggest that it is unreasonable to question the strength of the trend given that the first two years were unusually high (as noted) but then I would want to see a reanalysis of the data without those years (not shown in Fig. 2 or supplement). The data are shown in the supplement, but not analyzed for a trend without the two early-year outliers.
I also would point out the following with respect to analyzing trends from these structured survey programs.
1) Pleasants et al. 2017 demonstrate how an increase of abundance in good habitat on breeding grounds could also be due to loss of habitat in the wider countryside and this would not be obvious if poor habitat is not surveyed, something we know is often the case. Thus, we know that increases in abundance in good-quality habitat could present a biased picture and we also know that habitat quality has rarely been accounted for in published studies of monarchs (including Ries et al. 2015a,b, Inamine et al. 2016, Saunders et al. 2019, Wepprich et al. 2019, but see Saunders et al. 2018).
2) To repeat this important point: when estimating population sizes from sampling, samples should ideally be randomly placed if one wants to extrapolate values over a larger inference space and we know that surveys of butterflies performed by monitoring networks are not randomly placed. This issue is a big one and it should be stated up front, because all we can do is try and account for this statistically, but this is difficult so at the very least, we need to be careful about inferring abundances of monarchs throughout the summer breeding range. Again, Ries et al. 2015a,b both highlight the issue of sampling bias and say that a major goal of research should be to account for these biases. Only Saunders et al. 2018 used a statistical correction for bias and then found that declines were evident in counties as glyphosate adoption increased and this was stronger for counties with higher amounts of crop cover.
3) Different data sets had different temporal scopes. For instance, Kincaid et al. 2019 report no change in population from 2006-2019 (reflecting when they started collecting data) whereas the main period of glyphosate adoption was during the 1990s and no change in population seen during this time period would be consistent with Saunders et al. 2018 that showed no change with respect to additional glyphosate after 2004 and Saunders et al. 2019 that showed no change in monarch population sizes from 2004-2015.
4) It’s unclear what to conclude from Monarch Larvae data since those show numbers per milkweed stem and one might expect higher densities if milkweed were to become limiting.
FALL ROOST DATA at Peninsula Point, Long Point, Cape May, and Point Pelee: I have not worked much with roost data, but I will say that these are censuses and thus require less consideration of bias, but inferences are also somewhat limited to these particular sites. However, since these are funnel points, I don’t dispute that they may be indicative of the general population size to their north.
LEAST INFORMATIVE: OPPORTUNISTIC DATA (Data collected without known protocols related to effort and no information about effort is collected or reported). This include museum specimens, individual observations reported to portals like iNaturalist or eButterfly and also “field trips” where a checklist is reported (often with abundances). Note that eButterfly includes both individual sightings and complete checklists.
Extracting trends from presence-only opportunistic data is extremely difficult if not impossible. Presence-only data come from museum or single observations (no checklist). The potential to do this remains highly controversial among statistical ecologists. Further, unless effort is somehow accounted for via some proxy, which is difficult from opportunistic accounts and even field trips that produce checklists because effort is not quantified or reported, then tracking trends is problematic, to say the least! My personal opinion is that the science is still out on the ability to account for abundances sufficiently to perform a trend analysis from presence-only data so I would discount any current publication that claim to do this. This means that I personally would not put any weight on the presence-only opportunistic data sets reported in Table 1. That includes data from Journey North and Museum records. I want to further note that the study on museum records was published by me (Ries et al. 2019), and I never suggested that monarch populations were increasing (the trend reported by Table 1) – my point was that the data can't be trusted - and that the "increasing trend" observed was based on occurrences of an average of only 2.3 monarchs per year. Indeed, the title of my paper is: “Tracking trends in monarch abundance over the 20th century is currently impossible using museum records”. I’m not sure how much more clear I could possibly be about my conclusions; I used the word “impossible” to describe our ability to identify population trends – yet somehow, this paper was used as evidence that monarchs were increasing!
Other opportunistic data include the Ontario Butterfly Atlas, eButterfly and the Massachusetts butterfly club. These were based on published analyses and I address each one of these separately:
Crewe et al. 2019 used the Ontario Butterfly Atlas data and showed no trend in monarch abundances from the year 2003-2017 using a list length correction. List length is an analysis that uses the length of a reported checklist as a proxy of effort. It is one of the better ways of dealing with opportunistic data, but it has its flaws and there are ways to improve it, especially by using benchmarks (see Isaac et al. 2014). I’ll note that the error bars in the monarch estimates are HUGE! And the normal ups and downs of the monarch population are largely absent – and so the “evidence” that there is no trend is extremely weak (not even accounting for the general fact that lack of a significant result is simply a failure to reject the null hypothesis of no difference!).
Flockhart et al. 2019 used eButterfly and Journey North data in a species distribution model and showed no change in range size from 2000-2015. Using presence-only (opportunistic) data for ranges has a long history but problems with accounting for effort make tracking changes in range size problematic and that would include trends in range size. It is always difficult to separate effort from results. In any case, the range size in Canada may not be indicative of overall population size and the authors do not seem to make any claims to that effect.
Data from the MA Butterfly Club has been most rigorously assessed for population trends using list-length methods. Although list length methods have pros and cons, the MA butterfly club has very high data density and a limited population of people going on these trips and so there is likely less variability in effort compared to other collections of field trip data (pure conjecture on my part). Data on trends from the MA butterfly club was first reported by Breed et al. (2013). This is undoubtedly the most rigorous analysis of the MA butterfly data in terms of abundance trends and in this paper, monarchs are reported to be declining – yet this was not reflected in Table 1. Instead a novel analysis is based on “sum of monarchs/number of reports” which does not reflect best statistical practices for analyzing checklist data was reported instead.
In summary, I think that the picture painted in this preprint includes many faulty conclusions based on the studies or datasets cited. Again, I am not making any statements about the collective evidence one way or the other about the trajectory of the monarch population. Instead, I just present my own personal individual assessment that the trends presented in Table 1 for many (not all) of the data sets presented in this preprint include are flawed. I suggest that this preprint should be ignored until it has gone through the peer-review process which I believe would require substantial revisions in order to be accepted in any ecological journal.
Breed, G. A., Stichter, S., & Crone, E. E. (2013). Climate-driven changes in northeastern US butterfly communities. Nature Climate Change, 3(2), 142-145.
Crewe, T. L., Mitchell, G. W., & Larrivée, M. (2019). Size of the Canadian breeding population of monarch butterflies is driven by factors acting during spring migration and recolonization. Frontiers in Ecology and Evolution, 7, 308.
Tyler Flockhart, D. T., Larrivée, M., Prudic, K. L., & Ryan Norris, D. (2019). Estimating the annual distribution of monarch butterflies in Canada over 16 years using citizen science data. Facets, 4(1), 238-253.
Inamine, H., Ellner, S. P., Springer, J. P., & Agrawal, A. A. (2016). Linking the continental migratory cycle of the monarch butterfly to understand its population decline. Oikos, 125(8), 1081-1091.
Isaac, N. J., van Strien, A. J., August, T. A., de Zeeuw, M. P., & Roy, D. B. (2014). Statistics for citizen science: extracting signals of change from noisy ecological data. Methods in Ecology and Evolution, 5(10), 1052-1060.
Kinkead, K. E., T. M. Harms, S. J. Dinsmore, P. W. Frese, and K. T. Murphy. 2019. Design implications for surveys to monitor monarch butterfly population trends. Frontiers in Ecology and Evolution 7: 11.
Pleasants, J. M., Zalucki, M. P., Oberhauser, K. S., Brower, L. P., Taylor, O. R., & Thogmartin, W. E. (2017). Interpreting surveys to estimate the size of the monarch butterfly population: Pitfalls and prospects. PLoS One, 12(7), e0181245.
Ries, L., Taron, D. J., & Rendón-Salinas, E. (2015a). The disconnect between summer and winter monarch trends for the eastern migratory population: Possible links to differing drivers. Annals of the Entomological Society of America, 108(5), 691-699.
Ries, L., Oberhauser, K., Taron, D., Battin, J., Rendon-Salinas, E., Altizer, S., & Nail, K. (2015b). Connecting eastern monarch population dynamics across their migratory cycle. Monarchs in a changing world: Biology and conservation of an iconic insect. Cornell University Press, Ithaca, NY, 268-281.
Ries, L., Zipkin, E. F., & Guralnick, R. P. (2019). Tracking trends in monarch abundance over the 20th century is currently impossible using museum records. Proceedings of the National Academy of Sciences, 116(28), 13745-13748.
Saunders, S. P., Ries, L., Oberhauser, K. S., Thogmartin, W. E., & Zipkin, E. F. (2018). Local and cross‐seasonal associations of climate and land use with abundance of monarch butterflies Danaus plexippus. Ecography, 41(2), 278-290.
Saunders, S. P., Ries, L., Neupane, N., Ramírez, M. I., García-Serrano, E., Rendón-Salinas, E., & Zipkin, E. F. (2019). Multiscale seasonal factors drive the size of winter monarch colonies. Proceedings of the National Academy of Sciences, 116(17), 8609-8614.
Wepprich, T., Adrion, J. R., Ries, L., Wiedmann, J., & Haddad, N. M. (2019). Butterfly abundance declines over 20 years of systematic monitoring in Ohio, USA. PLoS One, 14(7), e0216270.
Commenter: Leslie Ries
The commenter has declared there is no conflict of interests.
MOST INFORMATIVE: STRUCTURED SURVEY OF ADULTS (data collected along fixed routes or defined areas that are repeated over time and some metric of effort is collect, e.g., the amount of time spent surveying). This includes so-called “Pollard walks” and “Counts” as well as MLMP surveys, WWF surveys and roost censuses at fall funnel points.
WWF winter colonies. It is not disputed that the numbers have been declining since the mid-1990s (before that is harder to say) and that is what is reported here.
NABA summer counts: No change as reported in Inamine et al. 2016. However, Ries et al. 2015b showed a near-significant breakpoint indicated a potential decline in summer populations but this was not significant. Saunders et al. 2019 also showed no decline in the summer population from 2004-2015 using NABA summer counts. Ries et al. 2015a,b both point out that a lack of statistical trends could be caused by not accounting for biases in survey placement and that this should be a focus of further research.
NABA spring counts were reported to be declining in Inamine et al. 2016. Data density is extremely low here and an analysis that I published (Ries et al. 2015a) of the same data showed no significant trend.
IL Butterfly Monitoring Network: Davis reports no trend based on personnel communication with the program leader, and this aligns with what is published in Ries et al. 2015b and would only be strengthened by more up-to-date numbers as are reported here. However, this ignores the fact that these same IL data were used in Saunders et al. 2018 and there they showed that declines were evident from 1994-2003 in Ohio and Illinois in counties with more crop cover as glyphosate use increased. According to results in that study, numbers stabilized at lower levels in 2003. These results suggest a decline during the period of glyphosate adoption although are not conclusive on this point due to limited spatial scope of study (IL and OH).
Ohio Butterfly Monitoring Network: Reported as “ambiguous, no clear trend” even though monarchs were reported as declining in Wepprich’s 2019 paper. Wepprich’s paper did not focus on monarchs and so I do not suggest that it is unreasonable to question the strength of the trend given that the first two years were unusually high (as noted) but then I would want to see a reanalysis of the data without those years (not shown in Fig. 2 or supplement). The data are shown in the supplement, but not analyzed for a trend without the two early-year outliers.
I also would point out the following with respect to analyzing trends from these structured survey programs.
1) Pleasants et al. 2017 demonstrate how an increase of abundance in good habitat on breeding grounds could also be due to loss of habitat in the wider countryside and this would not be obvious if poor habitat is not surveyed, something we know is often the case. Thus, we know that increases in abundance in good-quality habitat could present a biased picture and we also know that habitat quality has rarely been accounted for in published studies of monarchs (including Ries et al. 2015a,b, Inamine et al. 2016, Saunders et al. 2019, Wepprich et al. 2019, but see Saunders et al. 2018).
2) To repeat this important point: when estimating population sizes from sampling, samples should ideally be randomly placed if one wants to extrapolate values over a larger inference space and we know that surveys of butterflies performed by monitoring networks are not randomly placed. This issue is a big one and it should be stated up front, because all we can do is try and account for this statistically, but this is difficult so at the very least, we need to be careful about inferring abundances of monarchs throughout the summer breeding range. Again, Ries et al. 2015a,b both highlight the issue of sampling bias and say that a major goal of research should be to account for these biases. Only Saunders et al. 2018 used a statistical correction for bias and then found that declines were evident in counties as glyphosate adoption increased and this was stronger for counties with higher amounts of crop cover.
3) Different data sets had different temporal scopes. For instance, Kincaid et al. 2019 report no change in population from 2006-2019 (reflecting when they started collecting data) whereas the main period of glyphosate adoption was during the 1990s and no change in population seen during this time period would be consistent with Saunders et al. 2018 that showed no change with respect to additional glyphosate after 2004 and Saunders et al. 2019 that showed no change in monarch population sizes from 2004-2015.
4) It’s unclear what to conclude from Monarch Larvae data since those show numbers per milkweed stem and one might expect higher densities if milkweed were to become limiting.
FALL ROOST DATA at Peninsula Point, Long Point, Cape May, and Point Pelee: I have not worked much with roost data, but I will say that these are censuses and thus require less consideration of bias, but inferences are also somewhat limited to these particular sites. However, since these are funnel points, I don’t dispute that they may be indicative of the general population size to their north.
LEAST INFORMATIVE: OPPORTUNISTIC DATA (Data collected without known protocols related to effort and no information about effort is collected or reported). This include museum specimens, individual observations reported to portals like iNaturalist or eButterfly and also “field trips” where a checklist is reported (often with abundances). Note that eButterfly includes both individual sightings and complete checklists.
Extracting trends from presence-only opportunistic data is extremely difficult if not impossible. Presence-only data come from museum or single observations (no checklist). The potential to do this remains highly controversial among statistical ecologists. Further, unless effort is somehow accounted for via some proxy, which is difficult from opportunistic accounts and even field trips that produce checklists because effort is not quantified or reported, then tracking trends is problematic, to say the least! My personal opinion is that the science is still out on the ability to account for abundances sufficiently to perform a trend analysis from presence-only data so I would discount any current publication that claim to do this. This means that I personally would not put any weight on the presence-only opportunistic data sets reported in Table 1. That includes data from Journey North and Museum records. I want to further note that the study on museum records was published by me (Ries et al. 2019), and I never suggested that monarch populations were increasing (the trend reported by Table 1) – my point was that the data can't be trusted - and that the "increasing trend" observed was based on occurrences of an average of only 2.3 monarchs per year. Indeed, the title of my paper is: “Tracking trends in monarch abundance over the 20th century is currently impossible using museum records”. I’m not sure how much more clear I could possibly be about my conclusions; I used the word “impossible” to describe our ability to identify population trends – yet somehow, this paper was used as evidence that monarchs were increasing!
Other opportunistic data include the Ontario Butterfly Atlas, eButterfly and the Massachusetts butterfly club. These were based on published analyses and I address each one of these separately:
Crewe et al. 2019 used the Ontario Butterfly Atlas data and showed no trend in monarch abundances from the year 2003-2017 using a list length correction. List length is an analysis that uses the length of a reported checklist as a proxy of effort. It is one of the better ways of dealing with opportunistic data, but it has its flaws and there are ways to improve it, especially by using benchmarks (see Isaac et al. 2014). I’ll note that the error bars in the monarch estimates are HUGE! And the normal ups and downs of the monarch population are largely absent – and so the “evidence” that there is no trend is extremely weak (not even accounting for the general fact that lack of a significant result is simply a failure to reject the null hypothesis of no difference!).
Flockhart et al. 2019 used eButterfly and Journey North data in a species distribution model and showed no change in range size from 2000-2015. Using presence-only (opportunistic) data for ranges has a long history but problems with accounting for effort make tracking changes in range size problematic and that would include trends in range size. It is always difficult to separate effort from results. In any case, the range size in Canada may not be indicative of overall population size and the authors do not seem to make any claims to that effect.
Data from the MA Butterfly Club has been most rigorously assessed for population trends using list-length methods. Although list length methods have pros and cons, the MA butterfly club has very high data density and a limited population of people going on these trips and so there is likely less variability in effort compared to other collections of field trip data (pure conjecture on my part). Data on trends from the MA butterfly club was first reported by Breed et al. (2013). This is undoubtedly the most rigorous analysis of the MA butterfly data in terms of abundance trends and in this paper, monarchs are reported to be declining – yet this was not reflected in Table 1. Instead a novel analysis is based on “sum of monarchs/number of reports” which does not reflect best statistical practices for analyzing checklist data was reported instead.
In summary, I think that the picture painted in this preprint includes many faulty conclusions based on the studies or datasets cited. Again, I am not making any statements about the collective evidence one way or the other about the trajectory of the monarch population. Instead, I just present my own personal individual assessment that the trends presented in Table 1 for many (not all) of the data sets presented in this preprint include are flawed. I suggest that this preprint should be ignored until it has gone through the peer-review process which I believe would require substantial revisions in order to be accepted in any ecological journal.
Leslie Ries
Associate Professor, Biology
Georgetown University
www.butterflyinformatics.org
Literature cited
Breed, G. A., Stichter, S., & Crone, E. E. (2013). Climate-driven changes in northeastern US butterfly communities. Nature Climate Change, 3(2), 142-145.
Crewe, T. L., Mitchell, G. W., & Larrivée, M. (2019). Size of the Canadian breeding population of monarch butterflies is driven by factors acting during spring migration and recolonization. Frontiers in Ecology and Evolution, 7, 308.
Tyler Flockhart, D. T., Larrivée, M., Prudic, K. L., & Ryan Norris, D. (2019). Estimating the annual distribution of monarch butterflies in Canada over 16 years using citizen science data. Facets, 4(1), 238-253.
Inamine, H., Ellner, S. P., Springer, J. P., & Agrawal, A. A. (2016). Linking the continental migratory cycle of the monarch butterfly to understand its population decline. Oikos, 125(8), 1081-1091.
Isaac, N. J., van Strien, A. J., August, T. A., de Zeeuw, M. P., & Roy, D. B. (2014). Statistics for citizen science: extracting signals of change from noisy ecological data. Methods in Ecology and Evolution, 5(10), 1052-1060.
Kinkead, K. E., T. M. Harms, S. J. Dinsmore, P. W. Frese, and K. T. Murphy. 2019. Design implications for surveys to monitor monarch butterfly population trends. Frontiers in Ecology and Evolution 7: 11.
Pleasants, J. M., Zalucki, M. P., Oberhauser, K. S., Brower, L. P., Taylor, O. R., & Thogmartin, W. E. (2017). Interpreting surveys to estimate the size of the monarch butterfly population: Pitfalls and prospects. PLoS One, 12(7), e0181245.
Ries, L., Taron, D. J., & Rendón-Salinas, E. (2015a). The disconnect between summer and winter monarch trends for the eastern migratory population: Possible links to differing drivers. Annals of the Entomological Society of America, 108(5), 691-699.
Ries, L., Oberhauser, K., Taron, D., Battin, J., Rendon-Salinas, E., Altizer, S., & Nail, K. (2015b). Connecting eastern monarch population dynamics across their migratory cycle. Monarchs in a changing world: Biology and conservation of an iconic insect. Cornell University Press, Ithaca, NY, 268-281.
Ries, L., Zipkin, E. F., & Guralnick, R. P. (2019). Tracking trends in monarch abundance over the 20th century is currently impossible using museum records. Proceedings of the National Academy of Sciences, 116(28), 13745-13748.
Saunders, S. P., Ries, L., Oberhauser, K. S., Thogmartin, W. E., & Zipkin, E. F. (2018). Local and cross‐seasonal associations of climate and land use with abundance of monarch butterflies Danaus plexippus. Ecography, 41(2), 278-290.
Saunders, S. P., Ries, L., Neupane, N., Ramírez, M. I., García-Serrano, E., Rendón-Salinas, E., & Zipkin, E. F. (2019). Multiscale seasonal factors drive the size of winter monarch colonies. Proceedings of the National Academy of Sciences, 116(17), 8609-8614.
Wepprich, T., Adrion, J. R., Ries, L., Wiedmann, J., & Haddad, N. M. (2019). Butterfly abundance declines over 20 years of systematic monitoring in Ohio, USA. PLoS One, 14(7), e0216270.