Into (and Out of) the Weeds: Lessons Learned from my Newest Publication

Woohoo!… finally my newest publication is available via Early View in Evolutionary Applications

screenshot 2019-01-12 20.58.25

This study was a product of my Delta Science Postdoctoral Fellowship to investigate the mechanisms for effective biological control of the invasive water hyacinth in the Sacramento-San Joaquin River Delta (hereafter “Delta”).

In a nutshell:  Two weevils (insects) are currently used all over the world for the biological control of the invasive water hyacinth, including the Sacramento-San Joaquin River Delta, California. They have had variable success, with notable reduction of biomass and cover of this invasive aquatic weed in warmer climates compared to more temperate climates such as the Delta. Although temperature plays a large role in their success, I also investigated the role of genetic variation in the success of these weevils and whether there is lower genetic diversity and heterozygosity in the Delta compared to the native origin of these weevils (South America). To do this, I used polymorphic microsatellite markers  (repeating regions of DNA in the genetic blueprints of a species) to detect differences between individuals and between populations. Additionally, as myself and others noticed weevils from the field that appeared to be hybrids of these two species, I examined whether these hybrid-like weevils are genetic hybrids (meaning that they have genetic patterns representative of the genetic blueprints from both species)

In my opinion, the most important findings from this study were:

  1. We found hybrids! This is huge! These two weevils are introduced all over the world for the control of invasive water hyacinth. So now that we know hybridization occurs, it is critical since to understand how hybridization affects their success. For instance, sometimes hybrids can outperform non-hybrids (hybrid-vigor) whereas other times hybridization can decrease performance, as well as population growth (hybrid-breakdown). I am very excited however that Dr. Julie Coetzee’s laboratory in South Africa is now starting to look into the effects of hybridization between these two weevil species.. so stay tuned (I know I will!) .
    Demonstration of hybridization between the two weevils: Neochetina bruchi and N. eichhorniae
    Typical elytra markings characteristic of (a) Neochetina bruchi and (b) N. eichhorniae; compared to atypical elytra markings for (c) N. bruchi and (d) N. eichhorniae. Microsatellite markers confirmed that specimens (c & d) are hybrids. A weevil (c) from the study site in California resulted in 100% amplification of markers for N. bruchi and 80% amplification of the markers for N. eichhorniae, whereas a weevil from Texas (d) resulted in amplification of 25% of the markers for N. bruchi and 100% of the markers for N. eichhorniae.
  2. We found that low genetic variation from demographic bottlenecks (small populations of the weevils being introduced over and over again through the biological control programs), can sometimes be buffered by genetic admixture from multiple introductions. This was one of several findings from this study that was made possible through the unique combination of documented historical records from biological control programs and population genetic analyses, such as those we made with the program, FLOCK.

    Importation history and the Introduction Processes of Two Biological Control Agents of the Invasive Water Hyacinth
    Partial importation history (a, b) compared to the introduction processes predicted by FLOCK genetic analyses (c, d) of Neochetina bruchi and Neochetina eichhorniae, two weevils native to South America. Arrows depict the direction of the biological control releases and the date initially released, but do not point to the exact release site in that locality. Black lines and yellow‐filled regions represent the routes of importation history that were tested with microsatellite markers.Abbreviations are detailed in Table 1 (Hopper et al. 2019, Evolutionary Applications). Numbers next to abbreviations indicate the number of genetic sub‐clusters found from FLOCK analyses (c, d)
  3. Through combining this genetic study with a temperature performance study, we found that low genetic variation does not always hinder population adaptation or performance. This finding has been observed in other study systems, such as with the invasive Argentine ant, which has lower genetic variation in the introduced region, but is more successful than in the native range due to reduced intraspecific aggression among separate ant nests in the introduced populations. 

I also think that the lessons I learned from the process of writing this manuscript were very important, and I detail these below. 

Lesson 1: Know when to ask for help

This study culminated out of work that I did at UC Davis, advised by Dr. Ted Grosholz, and in collaboration with researchers, Dr. Paul Pratt and Dr. Kent McCue (USDA/ARS), Dr. Ruth Hufbauer (Colorado State University) and Dr. Pierre Duchesne (Université Laval, Quebec, Canada). The latter two coauthors of whom I actually contacted out of the blue during the analysis and writing portion of the study, since I felt like I needed more guidance from experts in the population genetics and data analysis field. I think knowing when to ask for help is really critical in science (no matter what your academic standing is), and it almost always improves the study to get additional opinions and critique. Think of it as a preliminary peer review before the ultimate peer review!

I also asked several folks that are experts in population genetics for advice on the collection, processing and analysis of the data before and during the start of this project, including:  Dr. Jeremy Andersen (UC Berkeley), and Dr. Rick Grosberg and Brenda Cameron (UC Davis) and Dr. Neil Tsutsui (UC Berkeley).

Lesson 2: Be Flexible, and Adapt to let the Data tell the Story

The title of this manuscript felt very suitable to me as ecological data are not always clear-cut, and sometimes it can take some time to wade through the weeds of data and figure out how to tell the accompanying story.  This is especially true for when resulting data don’t match up with your original expectations and initial story you thought you would tell. The key to this issue, is don’t try to force your old story on the data… get a second opinion if needed, and be open-minded by letting the data ‘speak’ for itself.

Lesson 3: Work Hard, Be Patient and Persistent

I think with anything that you do, sometimes a final product comes easy… and other times it seems like a long drawn out process. This project fell in the latter category, as it was my first time learning about and implementing a population genetics study, and I was working on the analysis and write-up of this study all while starting a new postdoc in an entirely new study system. I think an important aspect to finishing this project was really persistence. I spent week nights and weekends working diligently on the data analysis and writing and re-writing the paper. I also had to be patient with myself as I had to give myself time to learn the new types of analyses (which means new R packages and code!) and time to read all of the important papers in the study field.

If by chance you are also just starting a population genetic study, and feel a bit lost, please see my three-part tutorial blog posts which hopefully will provide some assistance:

  1. How-to use microsatellites for population genetics, Part I: Study Design, DNA extraction, Microsatellite Marker Design/Outsourcing
  2. Population Genetics Part II: Tips and Tricks, Multiplex PCR and Workflow of Microsatellites- the cheap way
  3. Population Genetics, Part III: Data Wrangling and Analyses

Lesson 4: Implement Self-Deadlines and Advertise them to your CoAuthors

writing_phdcomicSometimes its hard to finish something if you don’t have a deadline. So make yourself a deadline, and tell everyone about this deadline, so that you are held accountable for this timeline. I actually had some coauthors that needed me to submit this article to the journal by October 1st in order to meet some of their workplace requirements for publications. Needless to say, I pulled an all-nighter and got it in to the journal by 5am that day.. true story….

Nothing like a little pressure to light up that writing-fire…

Lesson 5: Don’t cut corners

This goes with Lesson 3, on being patient. Towards the end of writing up a big study, you might find yourself just wanting it to be over. You would do anything to not have to think about that project or the data anymore. However, crossing that finish line is actually one of the most crucial components and can make or break your ability to get into a decent journal. Having co-authors often really helps solve this problem, as they will call you out on any cut corners (if they are doing their job), and will suggest critical improvements to the paper that maybe you were thinking about.. but were just initially too lazy to do. Also on this note.. Read the proof-version (final version before being published) of the paper word for word! You don’t want any typos in your finished product.. especially true in your Title, Abstract and Figure Legends!

Lesson 6: Celebrate at Each Stage of Completion

Be sure to acknowledge your accomplishments after you submit the manuscript the first time, after the revisions and acceptance, and after the manuscript goes In Press. After all- you worked hard to get to each of those stages, and celebration will help motivate you for the next time you have to do it all over again!

writing god

 

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Population Genetics, Part III: Data Wrangling and Analyses

So some good news!- My population genetics study on the two herbivorous biological control agents of water hyacinth: Neochetina bruchi and N. eichhorniae, was finally accepted for publication w/ minor revisions in Evolutionary Applications. I will certainly post it once it is In Press! This was one of the projects I did for my Delta Science Postdoctoral Fellowship research 

So with that, I will fulfill my promise on posting Part III of my ‘how-to’ series for population genetics using microsatellites.  To recap, Part I of this series explained what microsatellites are, and how to develop microsatellite markers, and Part II was on how to amplify and genotype these markers (the cheap way with universal fluorescent labeled tails, and multiplex pcr).

Part III (right here!) is my attempt to guide you through the jungle of population genetic analyses. I will discuss the main programs and analyses I used and how to properly format your data to make these packages and programs work!

NB_admixture_K2_K6
STRUCTURE analysis of N. bruchi across eight populations and eight loci

I am not going to go into nitty-gritty detail because the tutorial for the ‘poppr’ package in R, does a FANTASTIC job on guiding newbies (including my former self) through the process of how to import data into R, exploring the data, and then how to conduct some basic and advanced analyses. The link is here  http://grunwaldlab.github.io/Population_Genetics_in_R/index.html

Honestly- this is how I started learning how to conduct population genetic analyses in R.. I kid you not. I literally followed the above tutorial step by step and did almost all of the analyses just to get a feel for the data and how to run population genetic stats.

So- Where to start you ask?


Well, one of my collaborator/coauthors (Dr. Ruth Hufbauer-CSU) emphasized that before you analyze the data, a good first step is to know what your question is, and why you are asking those questions. Then you should base your analyses on those questions.

Here are some example questions:

  • Where did these samples/individuals originate from?
  • How many populations are there?
  • What is the genetic diversity in these populations, and are some populations more diverse than others? Genetic diversity is often based on one or more of the following: heterozygosity, allelic richness and diversity indices such as the Shannon, Simpson, or Nei)
  • Are there population genetic bottlenecks?
  • Is there inbreeding?
  • Are there hybrids (crosses between two species)?

Then of course you have to report some general marker- and population-based stats (Deviation from HWE- Hardy Weinberg Equilibrium, Linkage Disequilibrium (LD), overall expected and observed heterozygosity, (He and Ho), null alleles..etc).


Load the Data: Before you do anything, you have to load the data in a format that the programs recognize!

  •  GenAlex- Excel Based Program-useful to check data formatting, and reformat data for import into R or other programs. However the main thing I found useful was understanding just what your dataframe should look like, which the Poppr tutorial emphasizes nicely: here
  • Adegenet package in R- (Jombart et al., 2010) Converts any type of data frame or matrix or txt file to a format that you need for a specific type of analysis
    • For most of my data analyses, I used the following two formats, converting my csv to data that the packages could recognize, or that I could convert further:
      1. newdataname <- read.genalex(“datafile.csv”,genclone = FALSE)
        • you can convert this to a genepop format with the following code-
      2. newdataname2=read.genalex(“datafile.csv”)
        • #need genclone for gytpes conversion, hence don’t use genclone=FALSE
          • gtypesdata=genind2gtypes(newdataname2)
Screenshot 2018-12-08 12.48.53.png
Example dataframe for import and analysis with the Poppr R package. Areas selected in blue represent the Loci, Samples and Populations, see poppr tutorial for further examples

Basic and Advanced Stats- I suggest to use:

  • Poppr– (Kamvar et al., 2015; Kamvar et al., 2014) this package depends on loading a lot of other packages and guides you through analyses in the tutorial. One example- is as a wrapper for the ‘vegan’ package- poppr calculates genotype accumulation curve (see if you sampled enough loci and individuals),
  • Pegas-(Paradis, 2010) -calculate Linkage Disequilibrium (LD) and HWE across populations for each locus
  • PopGenReport-(Adamack et al., 2014)- calculate null-allele frequencies pairwise FST and Jost’s D analyses, compare total and average allelic richness (accounting for sample size) and the number of private alleles among populations
  • diveRsity– (Keenan et al., 2013)-Estimate the average observed (Ho) and expected (He) heterozygosity, deviations from HWE (exact test) and the average ‘inbreeding coefficient’ (FIS) for each population across all loci.  In my paper I distinguish FIS as a measure of increases in homozygosity due to genetic drift caused by a larger population being separated into sub populations, rather than due to consanguineous mating (Crow, 2010)
  • InbreedR- (Stoffel et al., 2016)-calculate g2 as a measure of inbreeding.

Hypothesis testing: 

  • Linear Mixed Models, or Generalized (GLMMs) depending on which is more suitable for your data- with the lmer function in the lme4 package (Bates et al., 2015): I used this to test for the effects of population (collection site) on genetic diversity. Implementing an LMM accounts for the variability of the microsatellite loci by modeling locus as a random effect, and collection site as a fixed effect with allelic richness or expected heterozygosity as the response variables in separate models. Stepwise model simplification (Crawley, 2013) can be performed using likelihood ratio tests. Differences across collection sites can be compared, based on 95% CI, using Tukey’s post-hoc test in the ‘multcomp’ package (Hothorn et al., 2008). Read more about mixed models here. 

Analyses of Population Structure

I suggest using several programs to see how they compare. I used:

  • STRUCTURE -as it is one of the most popular programs-(Pritchard et al., 2000). I used Clumpak (Kopelman, Mayzel, Jakobsson, Rosenberg, & Mayrose, 2015) to analyze the Best K, and to visualize and produce plots based on all of the runs from STRUCTURE outputs. Please see data-wrangling section below for more details on how to get your data into STRUCTURE, and also into Clumpak.
  • FLOCK- great program in excel (Duchesne & Turgeon, 2012), to see which populations are genetic sources for other populations, as well as determining ‘K’ the number of genetic clusters within a given population or site (useful to compare to output ‘K’s from STRUCTURE
  • ‘adegenet’– to conduct Discriminant Analysis of Principal Components (DAPC) (Jombart, Devillard, & Balloux, 2010). There is a great tutorial here:
DAPC analysis on microsatellite data (eight loci) from eight populations of N. bruchi
Used the Adegenet package in R, and the Adegenet DAPC tutorial

Of course life is never easy.. especially when you have a MacOSX and for some reason the world revolves around PCs.

Here are some Data-wrangling tips for getting data into STRUCTURE and ClumpaK 

  • To get my data into the STRUCTURE format, I used the function ‘genind2structure’ that I found online here. Then in R, I used: genind2structure(inputdata, file=”outputdata.txt”, pops=TRUE).
  • Following this , you will need to:
    • DELETE THE GENALEX HEADERS
    • GET RID OF ANY ‘_’ IN THE TEXT FILE
    • GET RID OF LETTERS IN POP FILE, REPLACE WITH #S
    • DELETE IND AND POP HEADER
    • SAVE AS TXT FILE (TABS DELIMINATED)
    • RUN PERL SCRIPT Below..
    • since I have a MacOSX, I had to convert from DOS to UNIX with terminal program before loading in STRUCTURE by using similar code to this: while($_ = <>){s/\r\n|\n|\r/\n/g;print “$_\n”;}
      and you can find more info here .
    • DON’T TOUCH FILE AFTER THIS.. TA DA!
    • To get my files into the Clumpak web processor, I had to use a different zip-program (Zipfiles4PC) than what the MacOSx does, as for some reason Clumpak couldn’t process- Mac-zipped files.

Ok.. I think that is enough for now.. but really.. If I can emphasize one thing it is to go through the whole Poppr tutorial to get a handle of how to analyze data in R, and a feel for YOUR data!

 

 

Hot off the Press: Cool Temperature Performance of a Biological Control Agent of the Invasive Water Hyacinth

Wow.. I can’t believe May was my last post.. ugh! Ive been swamped with starting my new postdoc, moving into a new place, and writing an NSF-OCE grant! Anyhow, I am back and will try to be more regular again!

I am excited to announce a new article that is hot off the press! I coauthored this article with Dr. Angelica Reddy (first author) and Dr. Paul Pratt at the USDA, along with researchers from Argentina and Uruguay. You can read it here with free access for 50 days: Article in Biological Control.

This study was in conjunction with some of the work I did as a Delta Science Postdoctoral Fellow to investigate the mechanisms limiting the current biological control of invasive water hyacinth (Eichhornia crassipes) in the Sacramento-San Joaquin River Delta in northern California, USA (hereafter ‘Delta’). Classical biological control uses natural enemies (predators/herbivores, parasitoids and parasites) to control invasive populations of weeds, pests and disease vectors in the introduced range. 
Successful biological control agents can reduce pest populations below threshold levels that cause problems for humans and native species. Once established, biological control can provide a sustainable, long-lasting management option.

In a previous study, my coauthors and I conducted a one-year field survey 34 years after the initial releases of several biological control agents of water hyacinth in the Delta (Hopper et al. 2017). We found that two biological control agents, the herbivorous weevils, N. bruchi and N. eichhorniae (Coleoptera), were still present in the Delta and the associated tributaries. Although N. bruchi was broadly distributed throughout the Delta, N. eichhorniae was only found in the southernmost tributaries. Densities of N. bruchi during the warmer months in the Delta are comparable to densities in other regions with successful control of water hyacinth, but were not high enough year-round to reduce water hyacinth biomass and cover. Thus one idea to improve control is to re-introduce the more rare weevil, N. eichhorniae, in order to increase its abundance and distribution in the Delta and compliment the impacts of the existing weevil populations.

ChillyWeevilOne theory for the difference in the current abundance and distribution between these two weevil species is that the present biotype of N. eichhorniae in the Delta is less cold-tolerant than N. bruchi. Thus, the researchers from the USDA and myself were interested in determining whether a cold-temperature biotype of N. eichhorniae is present. If a cold-tolerant biotype exists, then the goal will be to screen it in the quarantine (host range tests and pathogen screening), access the necessary permits, and then release it into the Delta to improve the performance of the current population of N. eichhorniae and ultimately enhance the control of the invasive water hyacinth in the Delta. 

To achieve these goals, we (Reddy et al. 2019) examined the cool temperature performance and cold tolerance of four populations of the biological control agent, N. eichhorniae. These populations consisted of N. eichhorniae from: the Delta (California: USA), a population within the native range (Uruguay), and two temperate populations (Kubusi River, Stutterheim, South Africa and Jilliby, Australia). The geo-locations of these populations are noted as red markers in the green-highlighted regions on the map.

NE_NB_Map_for BLOGIn this study, we measured life history parameters of these weevil populations under temperatures occurring in the Delta during the cooler seasons (Fall and Winter). These life-history parameters included: Egg survivorship and development, juvenile (larval and pupal) survivorship and development, adult fecundity and adult longevity. I then used these parameters to construct stage-structured matrix models and calculate the intrinsic growth rates, doubling times, generation times and reproductive potential of each of these populations (as I have detailed in a previous blog and linked here).

In summary, Reddy et al. (2018) found that the population from Jilliby, Australia had the highest intrinsic rate of increase under conditions simulating Fall temperatures in the Delta due to the fact this population had the highest fecundity compared to all of the other populations (including the existing population residing in the Delta). Permission is thus being sought to release the Australian population into the Delta to improve the biological control of the invasive water hyacinth.

Please read the published paper for more details! 

And I will be back to update you with much more soon, especially on the results from my study on the population genetics of both of these weevils (N. bruchi and N. eichhorniae), using these same populations pictured above and many others! I finally submitted this manuscript for review.. so stay tuned!

 

New Post-doc Position at USC!

I am taking a small break from my blog tutorials on using microsatellite markers in population genetic studies to make an exciting announcement: I recently started a new 1-year Post-doc position at the University of Southern California in Dr. Dave Caron’s laboratory (more time pending funding from fellowships)!

USC-Dornsife-Cardinal-Black-on-White-RGBAlthough it is sad that my Delta Science fellowship is over, as it was a wonderful opportunity, I will still be working/writing hard to finish up my publications from this work and I will of course share these with all of you as they are published.phd011817s

In the mean time- I am moving back into marine study systems to examine the diversity and function of protists in the marine phytoplankton community!  Click here to check out the fascinating research in Dave Caron’s lab.  In addition to dabbling in several different ongoing projects in Dave’s lab- I am also very excited about starting up some of my own projects (pending funding) on the abundance, diversity and consequences of parasite-host interactions in the phytoplankton community.  As some of you might already know- I am an extreme parasite enthusiast, and only recently have researchers started to examine the potential abundance and importance of parasites in the marine phytoplankton community!

Recently, researchers in the Tara-Oceans Expedition found that parasitic interactions were the most abundant pattern in the global marine phytoplankton interactome (Lima-Mendez et al. 2015). Results from the V9-18S tag-sequence processing revealed parasite-host associations that included the copepod parasites: Blastodinium (Dinophyceae: Blastodiniaceae), Ellobiopsis (Marine Alveolate Group I: Ellobiopsidae), and Vampyrophrya (Ciliophora: Oligohymenophorea: Foettingeriida) and alveolate parasitoids of dinoflagellates and ciliates (Lima-Mendez et al. 2015). The alveolate parasitoids in particular were recognized for their top-down effects on zooplankton and microphytoplankton (Lima-Mendez et al. 2015).

 

Screenshot 2018-05-12 12.38.19
Figure from: Lima-Mendez G, Faust K, Henry N, et al. (2015) Ocean plankton. Determinants of community structure in the global plankton interactome. Science 348, 1262073.

Parasitoids are parasites that kill their host in order to complete their development (Lafferty and Kuris 2002) and increased abundance of alveolate parasitoids have been linked to declines of dinoflagellate blooms (Coats et al. 1996, Coats 1999, Chambouvet et al. 2008, Mazzillo 2011, Jephcott et al. 2016) and have been shown to regulate their dinoflagellate host populations in laboratory experiments (Noren 2000, Coats and Park 2002). The most researched alveolate parasitoids include several strains of Amoebophrya ceratii (Marine Alveolate Group II: Syndiniales) . These parasitoids have small flagellated infective stages that penetrate and multiply inside the dinoflagellate host cell, and produce numerous infective flagellates after killing and exiting the host (Cachon & Cachon 1987; Jephcott et al. 2016). For example A. ceratii can produce 60-400 new infective dinospores from its host in less than 48 hours (Chambouvet et al. 2008; Mazzillo 2011), and the generalist parasitoid, Parvilucifera sinerae, can produce 170 to > 6000 zoospores per sporangium, depending on the species and size of its host (Garces et al. 2013), with zoospore release within 72 hours of infecting a host (Alacid et al. 2015).

Below for your viewing pleasure is an example of these parasitoids- the life cycle diagram and life-cycle stages from Alacid et al. 2015, and Alacid et al. 2016 (respectively) of the generalist parasitoid Parvilucifera sinerae, in its host dinoflagellates.

So now of course the question you might have is: “why do we need to research these parasites/parasitoids further?” Well, we simply do not  know enough about these amazing parasite-host interactions, and most of our knowledge is currently limited to the photic zone of the ocean, and concentrated on just a few of these parasite species (there are many parasites out there just waiting to be discovered!). For those of you that don’t think ‘not knowing enough’ merits more work- my reply to this is that: mortality rates in the phytoplankton community have an incredible significance regarding the total primary production and biogeochemical processes in the ocean. However, how can we account for the mortality rates in the phytoplankton community and consequences for primary production if we are not accounting for a large % of contribution to mortality due to parasites that have not yet been characterized? And this folks.. is the reason why this research should be funded (aside from the obvious fact that parasites are absolutely fascinating, and the evolution and ecology of parasites can tell us a lot about related free-living species as well (that is another blog topic I will save for the future).

Of course my new Post-doc research in this field is still a bit tentative as it depends on gaining further funding- but in the mean time I am posting some lovely photos of parasites (Euduboscquella spp.) in tintinnid ciliate hosts (Eutintinnus spp.) that I have been finding from some local net tows (marine sampling nets that concentrates organisms of different size classes). So exciting- it is like a treasure hunt every time!

 

References (highly recommended reads also!)

Alacid E, Rene A, Garces E (2015) New insights into the parasitoid Parvilucifera sinerae life cycle: the development and kinetics of infection of a bloom-forming dinoflagellate host. Protist 166, 677-699.

Alacid, E., Park, M. G., Turon, M., Petrou, K. & Garces, E. (2016) A game of Russian roulette for a generalist dinoflagellate parasitoid: host susceptibility is the key to success. Front Microbiol 7, 769.

Cachon J, Cachon M (1987) Parasitic dinoflagellates. In: Biology of dinoflagellates, pp. 571-610. Blackwell, New York.

Coats DW (1999) Parasitic life styles of marine dinoflagellates. Journal of Eukaryotic Microbiology 46, 402-409.

Coats DW, Adam EJ, Gallegos CL, Hedrick S (1996) Parasitism of photosynthetic dinoflagellates in a shallow subestuary of Chesapeake Bay, USA. Aquatic Microbial Ecology 11, 1-9.

Coats DW, Park MG (2002) Parasitism of photosynthetic dinoflagellates by three strains of Amoebophrya (Dinophyta): Parasite survival, infectivity, generation time, and host specificity. Journal of Phycology 38, 520-528.

Chambouvet A, Morin P, Marie D, Guillou L (2008) Control of toxic marine dinoflagellate blooms by serial parasitic killers. Science 322, 1254-1257.

Garces E, Alacid E, Bravo I, Fraga S, Figueroa RI (2013) Parvilucifera sinerae (Alveolata, Myzozoa) is a generalist parasitoid of dinoflagellates. Protist 164, 245-260.

Jephcott TG, Alves-De-Souza C, Gleason FH, et al. (2016) Ecological impacts of parasitic chytrids, syndiniales and perkinsids on populations of marine photosynthetic dinoflagellates. Fungal Ecology 19, 47-58.

Lafferty KD, Kuris AM (2002) Trophic strategies, animal diversity and body size. Trends in Ecology & Evolution 17, 507-513.

Mazzillo FFM (2011) Novel insights on the dynamics and consequence of harmful algal blooms in the California Current System: from parasites as bloom control agents to human toxin exposure PhD dissertation, University of California, Santa Cruz.

Lima-Mendez G, Faust K, Henry N, et al. (2015) Ocean plankton. Determinants of community structure in the global plankton interactome. Science 348, 1262073.

Population Genetics Part II: Tips and Tricks, Multiplex PCR and Workflow of Microsatellites- the cheap way

No this blog is not a belated April fools joke… there really is a method to save thousands of dollars on microsatellite marker multiplex and genotyping! If you are just reading my blog for the first time, this is Part II following up on my last blog: How-to use microsatellites for population genetics, Part I: Study Design, DNA extraction, Microsatellite Marker Design/Outsourcing.

When I first set out to work with microsatellites, I was on a budget and I had never had experience with multiplex PCR, genotyping or fluorescent markers before- so there was definitely an uphill learning curve.

To start (assuming you already have your microsatellite markers- see previous blog for this)- the next step is to order your fluorescent markers to see how things work in multiplex PCR. FYI-Don’t order your Liz Size Standard until you are done troubleshooting and checking things on gels because it expires kinda quick! Plus it ships really fast (at least if you are in the USA). 

What are fluorescent markers you ask? If you look at the microsatellite genotyping peaks above, the different color peaks correspond to the different fluorescent tails that are ‘attached’ to the microsatellite primers. FAM= blue, PET = red, VIC =green, and NED = yellow. This way, you can see the different colors and know what markers they correspond to. The orange peaks are from the 600 Liz size standard which enables you to actually calibrate the size of the peaks. This is very easy if you use a program like Geneious as they have a microsatellite plugin.  I will detail more of the data handling in Part III of this blog series.

Why multiplex PCR? Simple.. it is faster (& cheaper if you troubleshoot things quick).PCR Reaction for Multiplex PCR of Microsatellite Markers

Many folks use multiplex genotyping, where you do singleplex PCR with all the separate microsatellite markers, and then you add for example 4 non-overlapping microsatellite marker amplified products (from your singleplex PCR) into a well together for downstream genotyping (more on that process later). This process saves money, since instead of genotyping a single marker for 1$.. you can genotype 4 markers for 1$! However- singleplex PCR takes forever if you have a lot of samples and markers!!!! If you have 400 individual DNA extracts, and each sample requires 10 markers of genotyping.. this means 4000 PCR REACTIONS- YIKES- that would result in carpal tunnel in a heartbeat!

Multiplex PCR in contrast allows you to add 2-4, (or more) microsatellite markers with fluorescent tails into the PCR mix, making sure that markers with the same fluorescent tails don’t overlap in size (ie- a FAM marker amplified product of 100-250 bp compared to another FAM marker amplified product of ~300-400 bp size range- should be fine to put together in a mixture). Whereas you don’t need to worry as much if they are similar size but have different fluorescent markers (such as FAM (blue) versus NED (yellow)). There are pull-up issues, and inhibition issues.. but that is why you will need to test everything out first anyhow before running your final assays.

As for myself, two papers were key in learning how to streamline microsatellite multiplex and genotyping: Blacket et al. 2012 and Culley et al. 2013. Both studies utilized four universal fluorescent tails (different ones in the different studies)- so that all you need to do is to add the non-fluorescent tail (just the ATCGs) of the corresponding fluorescent tail (the ATCGs + the FAM, VIC, NED or PET fluorescent marker) to your forward primer, and then use pig-tails on your reverse primers (such as GT, GTT, GTTT- depending on your reverse primer). Cullen et al. 2013 has an appendix which actually walks you through every step, including the reaction concentration of each Forward primer +tail, Reverse primer +pig-tail, and Fluorescent marker +tail in the final multiplex mix. I ended up using the four universal tails in Blacket et al. 2012, and then used Culley’s reaction mixes.

Screenshot 2018-04-01 12.55.20
Universal Tails used with PCR fluorophores, from Blacket, M.J., Robin, C., Good, R.T., Lee, S.F., Miller, A.D. 2012. Universal primers for fluorescent labelling of PCR fragments–an efficient and cost-effective approach to genotyping by fluorescence. Molecular ecology resources 12, 456-463.

Below is a great pictorial image of how this works (from Blacket et al. 2012)

Screenshot 2018-04-01 12.54.34
Multiplex PCR with universal tails: Process from Blacket, M.J., Robin, C., Good, R.T., Lee, S.F., Miller, A.D. 2012. Universal primers for fluorescent labelling of PCR fragments–an efficient and cost-effective approach to genotyping by fluorescence. Molecular ecology resources 12, 456-463.

In addition to reading these papers (and their supplementary material) thoroughly, I recommend the following: 

  1. Talk to as many people as you can before you start/ while you are getting started -you always learn fabulous tips and tricks as well as what not to do!
  2. Use the multiplex manager program -this will help you simulate what markers are compatible with each other based on the estimated product sizes, the melting temperatures (Tm) and the specific tails and flourophores you want to use. This will help you think about the different multiplex reactions that you can use.
  3.  Order the Qiagen Multiplex Plus Kit– this will streamline everything! I accidentally ordered the regular multiplex kit.. which is an older version and slower- so I had to stick with it once I got started. However- the Plus version enables you to use a faster PCR protocol! This kit is the same thing as their old ‘Type-it kit”, just better.
  4. Order a set of just the universal tails without the fluorophores attached first, in addition to the forward primers with the universal tails, and the reverse primers with the pig tails. This will let you try running all the multiplex reactions out and test them on a gel to make sure you have everything working before you waste your precious flourophores which are expensive. This is also a cheap thing- 4x $6 max.. 24$ to try out a bunch of stuff before spending the big money is well worth it! By the way  you will order all of your flourophores (reporter dyes) attached to the universal tail or the forward primer (the latter is a more expensive technique) from Thermo Fisher Scientific as they have patents on all of them except for FAM, which you can buy cheaper from Sigma or other companies (IDTdna, Elim BioPharm…etc)
  5. When you have everything working (bands are where you expect them to be, and no large gaps between bands which indicate that the msat markers are targeting multiple regions of the DNA sequence rather than one region)- Then order black, sterile micro centrifuge tubes (this link is just an example- but any sterile brand will work)- this will be what you make all your fluorescent primer ‘party’ mixes in, which will protect the fluorescence from degradation- preventing you from having to order more and save you money and time!
  6. Color code everything -from the tops of your individual fluorescent forward-reverse-fluorescent primer mixes (FRT: Forward-tail +Reverse-pigtail +Fluorophore-Tail) to your tubes with primer party mixes (all four FRT), to your  multiplex pcr reaction mixes and to your excel files for the pcr-plates and genotyping plates.
  7. Be organized when you pipette things onto your pcr-plate, and into your genotyping plate, see below image on how I organize pipetting into a pcr-plate, with the samples in the large tubes being moved in the order that I add things to the plate. I close the lid of each tube and move it to the upper tray after I add it to the plate so I don’t lose my place. I also use my tips from the box in order so that I can look at my tip-box as well to see where I should be. IMG_7752
  8. Talk to the genotyping facility about if they permit a ‘troubleshooting’ run (free-of-charge) so that you can test the amount of final pcr product to add into each genotyping well. I used 0.5 ul pcr product (that had amplified products of four markers) with 11 ul genotyping mix (Liz size standard in Hi-di formamide)

Random Tips/Interesting findings:

  • The Liz size-standard has strict “keep in dark and don’t-freeze’ instructions when it is shipped to you. Be sure to put it in the fridge at 4 degrees and NOT in the freezer! Unfortunately it comes in a styrofoam box w/ ice-packs with no-outside labels instructing to not freeze…and so the mailing department or your lab technician might accidentally put it in the freezer (speaking from personal experience…)- The company (Thermo Fisher Scientific) was very nice in shipping me a replacement because of this issue.  However… due to this occurrence I had the opportunity to test whether or not the Liz -size standard would still work when frozen for 6 hours, and when frozen for 24 hours….Results: The size standard still works great when frozen for 6 hours and for 24 hours ! With that said.. obviously don’t purposely test this, but if it is accidentally frozen- chances are you are still ok!
  • I also found that the Liz size-standard works great and is consistent for at least 2 months beyond its written expiry date…
    • For both of these tests I had unexpired and unfrozen Liz-size standard to compare these tests to. No p-value available.. just my experience 😉
  • As for the genotyping- I used less Liz Size Standard than recommended (and so did everyone that I talked to). My specific reaction mixes were the following: 0.5 ul PCR product + 0.5 ul Liz Size standard, and 10.5 ul Hi-Di Formamide per genotyping reaction well. I know a lot of folks that use 0.5 ul PCR product + 0.2 ul Liz-size standard+ 9.3 ul Hi-Di Formamide.. and they have great success as well. I tried the latter mix  and it worked, but because my pcr products had such high fluorescence (and I was over troubleshooting my primer fluorophore mix concentrations- I decided to  instead increase my size-standard so that I could better separate the noise from the signals). My genotyping mix and final primer ‘party’ mixes result in the initial genotype peaks image of this blog- so you can see what I mean by high sample peaks compared to the size-standard.
  • As for the Hi-Di Formamide- I noticed that this does not have good results when you leave it in the fridge overnight and try to use it the next day for a second genotyping plate. However- I had good results with using Hi-Di Formamide that underwent 1-3 freeze-thaw cycles. Thus my advice is that you never leave it at room temperature or in the fridge if you have extra, but also to avoid too many freeze-thaws.
  • Additionally- Im sure you will find this out- but NEVER freeze your pcr-products after the multiplex pcr with the flourescent markers, or after you add the liz-size standard and hi-di formamide. If you can’t get your samples to the genotyping facility right away, then be sure to do a quick denature (95 degrees for 5 min) post combining the pcr product w/ the Liz size standard and hi-di formamide and then just keep in the fridge (and in the dark!) until the next day or two.

References

Blacket, M.J., Robin, C., Good, R.T., Lee, S.F., Miller, A.D. 2012. Universal primers for fluorescent labelling of PCR fragments–an efficient and cost-effective approach to genotyping by fluorescence. Molecular ecology resources 12, 456-463.

Culley, T.M., Stamper, T.I., Stokes, R.L., Brzyski, J.R., Hardiman, N.A., Klooster, M.R., Merritt, B.J. 2013. An efficient technique for primer development and application that integrates fluorescent labeling and multiplex PCR. Appl Plant Sci 1.

 

 

 

 

 

 

 

 

How-to use microsatellites for population genetics, Part I: Study Design, DNA extraction, Microsatellite Marker Design/Outsourcing

So… you want to use microsatellite markers to assess the genetic variation and population structure of your focal study organism? Well if you are anything like me two years ago.. then you have no idea where to start. Otherwise- congratulations if you are already an expert- in which case you probably don’t need to read on 🙂

SeeHearSpeak
“See No Weevil, Hear No Weevil, Speak No Weevil”                                                                          Illustration by Jacki Whisenant, contracted by Julie Hopper. Copyright 2017.

Two years ago, I was just like you (and these weevils above), and felt a bit overwhelmed and lost in undertaking the large task of designing microsatellite markers and genotyping these markers for the two weevils species (Neochetina bruchi and N. eichhorniae) that I have discussed in previous posts. 

Very briefly to recap on my work:  these two weevil species are used all over the world for the biological control of the invasive water hyacinth, including the Sacramento-San Joaquin River Delta, California. They have had variable success, with notable reduction of biomass and cover of water hyacinth in warmer climates compared to more temperate climates such as the Delta. Although temperature plays a large role in their success, I am also investigating the role of genetic variation and particularly whether there is lower genetic diversity and heterozygosity in the Delta compared to the native origin of these weevils (Uruguay and Argentina).

In Part I- (this blog), I will detail the how-to’s of sampling design and strategy, and the development of (or outsourcing) microsatellite markers.

In Part II- (next blog) I will discuss how to make your final microsatellite marker selections, and the workflow of multiplex PCR and genotyping.

In Part III- (come back in a month!) I will detail how to analyze the data with various R-packages and other computer programs, and how to format the data files correctly for these programs.

On this note, please research your study system thoroughly, as every organism is different and may require different sampling strategies and methods than I detail here for two diploid beetle species (Insecta). Additionally.. my overview below on Part I- is very brief and I definitely skip small steps to be succinct. Also my suggestions are not the only way to do things and below this blog, I post links to several other great resources. Lastly- This work is currently in prep for publication and I will post an update again after publication.


Part I: 

 

Figure from: Grunwald et al. 2017, Phytopathology
Figure from: Grunwald, N.J., Everhart, S.E., Knaus, B.J., Kamvar, Z.N. 2017. Best Practices for Population Genetic Analyses. Phytopathology 107, 1000-1010.

Sampling Design and Strategy:

First before you start sampling or ordering primers- make sure that you have a solid study question with a testable hypothesis, and a good study framework.

Next: all of the power in your genetic analyses (aka, accuracy and ability to detect differentiation among populations, etc.) depend on: 1) your sample quality (aka DNA quality), the number of samples (replicates) per treatment or location, 2) the number of high quality microsatellite markers (e.g.quality relating to two important characteristics: markers are polymorphic -having 2 or more alleles per locus-with more being better, and the markers lack true null alleles), 3) the robustness of your PCR  – whether the PCR conditions are truly suitable for your markers, and whether they can result in reproducible data, 4) the assumptions of the data and 5) the choice of statistical tests and whether the tests are truly suitable for the data.

I will cover the latter (regarding statistical tests) in a future blog, but for today I would like to focus on the ideal # of samples and the # of polymorphic markers. There has been debate about how many samples and how many markers are necessary for robust studies, and if you study an endangered species -sometimes you just have to work with what you got!

In a perfect world– you will want to make up for what you lack in samples with microsatellite markers (loci) and vice versa. So if you have a lower end of replicates, then you will want a higher number of microsatellite markers (# of loci, and more important is to have polymorphic loci with 2 or more alleles/locus) to test for each individual (replicate), and again vice-versa. There are a couple great papers that discuss sampling strategies and study design that you should definitely check out, particularly the one noted in the figure above (Grunwald et al. 2017), as well as Hale et al. 2012 which states that 25-30 individuals per population should be sufficient to accurately estimate allele frequencies given population (with some caveats). Caveats being that obviously, 25-30 individuals per population would likely NOT be enough if you only have four microsatellite markers, particularly if these markers are not polymorphic or very variable (variability referencing to the # of alleles per locus- the more the better!).. so keep this in mind. In general, with that many samples- 10-15 polymorphic markers should be fine (although the more the better), but again this depends on your study question and study system. Also, more samples might be necessary if you are interested in population differentiation (population genetic structure). In fact, in a landscape genetics study, Landguth et al. 2012 demonstrated that increasing the number of loci (and particularly having more variable loci) is more likely to increase the power of population genetic inferences compared to increasing the number of individuals.

You can also test your samples with genotype accumulation curves to see if you have captured the majority of genetic variation (I used the poppr package in R for this and will discuss more on poppr and its primer in Part III of this blog series).

With that said.. If I would have known 1 year ago what I know now…. I would have asked for folks around the world to collect more weevils for me, and I would have extracted more DNA!  Just remember.. not all of your DNA extractions are going to end up working out..due to various human error and/or preservation issues. Thus its always good to add at least 10-20 more samples than you think you need!

map_with_labels_pop_gen
Sampling locations of Neochetina bruchi and N. eichhorniae individuals that I used for the focal population genetics study (Hopper et al. In Prep). Thanks to all those who sent me weevils!

Designing or Outsourcing Microsatellite Marker Design: 

  • Marker Outsource Options: I want to first be upfront in that I actually ended up outsourcing this component of my study as I was going through a tough time and taking care of my dad who had metastatic cancer via at-home hospice care in Columbus, Ohio for two months. Needless to say- I was working remotely then, which made the decision to outsource this part of the lab work an easy decision. I researched a lot of outsource options and in the end I went with the cheaper and most recommended option by several colleagues- the Savannah River Ecology Lab at the University of Georgia. In the end I have mixed opinions on their work and please email me if you would like more info and I will detail the ups and downs.
  • Brief Workflow for designing microsatellite markers: 
    1. First! Check the literature to make sure microsatellite markers have not already been developed for your species or a sister species (the latter of which will sometimes work). Using previously developed markers is obviously the easiest and cheapest route!
    2. If the markers have not already been developed: Obtain high quality and high molecular weight DNA Extractions. I love doing 5% Chelex DNA extractions, but the resulting DNA can be full of PCR inhibitors- so I always use the second half of the DNAeasy kit to purify and clean up my DNA samples. You can also buy replacement spin columns for these kits way cheaper from Epoch Life Science. Then quantify them on a nano-drop or a similar DNA quantification instrument and additionally run them on a gel to make sure that you have ≥100 uL of ≥50 ng/uL of >10kb DNA per sample.
    3. Send to a sequencing facility (Illumina with paired ends >150bp preferred)
    4. Clean up sequences/fix Errors and Run a program called “Pal_finder”, or use a similar program. Pal_finder can analyze 454 or paired-end Illumina sequences ( ~150bp from each end).  This program sends possible primers to Primer3 for primer design and searches for how often each primer and primer pair occur.

    5. Filter the resulting data set by only including: a) sequences for which primers can be designed (e.g. enough flanking sequence) and b) primer pairs that occurred 1-3 times. Then, sort by motif length (di, tri, tetra, etc.) to quickly find tri or tetra nucleotide repeats and look to see if the motif was found in both directions of the sequence (which can be bad as they typically end up being smaller PCR products, but this depends on your goals). Finally, order a bunch of the primers that look promising-say 48 primer pairs to start, and test them out on a subset of 24 individuals, with an equal distribution of these individuals across all your study locations, or select individuals that you think will have a lot of variation. See Initial PCR testing in the next Blog. 

To be continued…

References

Grunwald, N.J., Everhart, S.E., Knaus, B.J., Kamvar, Z.N. 2017. Best Practices for Population Genetic Analyses. Phytopathology 107, 1000-1010.

Hale, M.L., Burg, T.M., Steeves, T.E. 2012. Sampling for microsatellite-based population genetic studies: 25 to 30 individuals per population is enough to accurately estimate allele frequencies. PloS one 7, e45170.

Landguth, E.L., Fedy, B.C., Oyler-McCance, S.J., Garey, A.L., Emel, S.L., Mumma, M., Wagner, H.H., Fortin, M.-J., Cushman, S.A. 2012. Effects of sample size, number of markers, and allelic richness on the detection of spatial genetic pattern. Molecular ecology resources 12, 276-284.

Helpful Resources on Getting Started for Part I

Lecture on Intro to Microsatellites

Part II Stage Structured Matrix Models: Measuring the Intrinsic Rate of Increase in PopBio, R

Woohoo- I am officially done with my lab work for the population genetics study on the two biological control agents (Neochetina bruchi and N. eichhorniae) of the invasive water hyacinth. I will post a blog updating you on the how-to’s and my results soon! Finally – I am done driving back and forth to the Bay Area- and currently just working on data analysis and writing up everything here in LA. Stay tuned!

phd053104s

In the mean time…

As I promised, here is the Part II to my recent blog:  How-To: Stage-Structured Matrix Models. 

In this last blog, I discussed the importance of stage-structured matrix models in calculating the intrinsic rate of increase of organisms with developmental stages (such as the weevils!) and detailed how to construct a stage-structured matrix models in excel. Again here is that file: Julies_tutorial_example_matrix_for_popbio

So now that you have your matrix.. what do you do next?

1st: Convert your matrix into a csv file such as the one I created below based on the excel file above. Just remember- don’t include the headers or row names. I am unable to upload, so I am pasting a picture of the matrix in excel below. Screenshot 2018-01-29 11.24.16

2nd: Save this file as a csv to your working directory that you use in the R statistical program. If you haven’t used R before, then go to the R website to download the program, and refer to the below links on how to set up R, your working directory, and how to import files:

1) http://sphweb.bumc.bu.edu/otlt/MPH-Modules/BS/R/R1_GettingStarted/R1_GettingStarted8.html

2) http://www.r-tutor.com/r-introduction/data-frame/data-import

Also I highly recommend downloading R studio before you start as well -makes everything a lot easier- download R studio here

3rd: Install the ‘popbio’ package. You can read about this package here.

4th: Run the below code!

#below tutorial on getting a matrix model into R and analyzing with pop bio
library(popbio)
library(popdemo)
help(popbio) #this provides more information on this package
tutorial.mm=read.table(file=”tutorial_matrix_pop_bio.csv”, sep=”,”, header=FALSE)
tutorial.mm
tutorial.L=lambda(tutorial.mm)
tutorial.L
intrinsic=log(tutorial.L)
intrinsic

#You should obtain an intrinsic rate of increase of  0.0217994

5th: If you want to analyze the stable stages, reproductive values, net reproductive rate , generation time,and conduct an eigen analysis…. Then you will also need to do the following: 

#below to get more information:
###############NEED TO FIRST LIST STAGES
stages<-c(“egg”,”1st_2nd_larvae”,”3rd_larvae”, “pupae”,”pre_rep”, “adult”)
colnames(tutorial.mm)<-stages
rownames(tutorial.mm)<-stages
tutorial.matrix=as.matrix(tutorial.mm)
tutorial.matrix
stable.stage(tutorial.matrix)
reproductive.value(tutorial.matrix)
eigen.analysis(tutorial.matrix)
fundamental.matrix(tutorial.matrix)
net.reproductive.rate(tutorial.matrix)
generation.time(tutorial.matrix)

#Congratulations! You did it!

 

 

Work featured in Estuary News, December Issue

Currently I am still plowing away in the Bay Area at the last samples for the population genetics study on the weevil biocontrol agents that I will detail in a later post. I have a new-found carpal tunnel from all of the pipetting.. lol. I try to even it out with constant rolling on my favorite foam roller!

In the mean time-I am thrilled to share the current December issue of SF Estuary News with you, as they recently featured my work on exploring the mechanisms limiting the current biological control of the invasive water hyacinth in the Sacramento-San Joaquin River Delta in California. Check out the profile here: SF Estuary December Issue Profile. And here is the link to the pdf file

Thank you again to the Delta Science Council and California Sea Grant for  funding this work through the Delta Science Fellowship!

Screenshot 2017-12-15 10.56.27

 

 

How-To: Stage-Structured Matrix Models

Happy Thanksgiving everyone!

So this week I constructed several stage-structured matrix models- aka Lefkovitch models- to estimate the finite and intrinsic rate of increase of the weevils Neochetina eichhorniae and N. bruchi under laboratory simulated Fall and Winter conditions in the Sacramento-San Joaquin River Delta. This work is in conjunction with a postdoctoral researcher- Angelica Reddy and Paul Pratt’s laboratory at the USDA to test the temperature performance of these biological control agents.

As you know, if insects are adapted to warm weather- they don’t perform very well in colder temperatures and this can be very applicable to biological control agents (such as the two Neochetina weevil species) that are brought from their tropical origins to colder regions to control invasive species.  Below is a cute cartoon I had a scientific illustrator Jacki Whisenant draw for me, and for a new children’s book we are writing…stay tuned!

ChillyWeevil
Copyright 2017, illustration by Jacki Whisenant and made for Julie Hopper

Because we are working in the laboratory on these two species we are able to gather a lot of life history parameters of the weevils undergoing Fall and Winter conditions. These parameters include: development time and survivorship of the different insect stages (egg, III instars of larva, pupa, pre-reproductive adult and reproductive adult), as well as the emerging sex-ratios, and longevity and daily and lifetime fecundity of the reproductive female adults.

From these parameters we can conduct several different analyses to approximate the finite rate of increase, intrinsic rate of increase, generation time, doubling time and net reproductive rate of a species to understand more about their potential population growth rates (which of course is important for biological control).

My favorite way to approximate these population growth parameters for insects is to use a stage-structured matrix model (Lefkovitch model). There are other methods you can use as well- but I won’t go into that here. If you would like to read more see the citations at the end of this blog.

Instead, I will provide a how-to tutorial since while I was working on these matrix models as a graduate student- I realized there is a lack of tutorials on the web on how to construct these models in an intuitive manner. I got lucky  as both my PhD adviser and one of my lab mates (whom had already done the research on stage-structured matrix models) helped me understand how to construct and interpret the models. In the name of paying it forward- I am attaching here an excel worksheet that has all of the calculations and formulas that demonstrate how to construct these stage-structured matrix models (see link). 

Julies_tutorial_example_matrix_for_popbio

In my next blog- I will detail how to use this resulting matrix and input it into the package popbio (Stubben and Milligan 2007) for calculation of finite rate of increase (lambda), intrinsic rate of increase (r), doubling time, generation time, net reproductive rate and much more!

Disclaimer- this is for insect stage-structured matrix models only as calculations differ for plants and vertebrates typically.

Here are some of the calculations that are built into the excel formulas: 

Screenshot 2017-11-22 12.53.45

Below is another screenshot of the file: 

Screenshot 2017-11-22 12.57.42

Below is a diagram from the famous study on Loggerhead sea turtles that explains the flow of this matrix better. However be aware that the matrix above and in the attached excel sheet-calculates gamma as 1/duration which is very different than the famous example on turtles (below), and from any matrix with plants- mainly due to life history differences among plant, invertebrates and vertebrates.

Screenshot 2017-11-22 13.10.04.png
A Stage-Based Population Model for Loggerhead Sea Turtles and Implications for Conservation Author(s): Deborah T. Crouse, Larry B. Crowder, Hal Caswell Source: Ecology, Vol. 68, No. 5, (Oct., 1987), pp. 1412-1423

References 

Caswell H (2001) Matrix Population Models: Construction, Analysis, and Interpretation. Sinauer Associates, Sunderland


Resources:

Awesome powerpoint by Chris Free at Rutgers 

Another awesome tutorial on primarily the Leslie matrix from UCSC

 

 

Invasive Weed Alert! Alligatorweed in the Delta and Suisun Marsh, California

I just received word from Louise Conrad (Department of Water Resources) that there have been recent sightings of Alligatorweed (Alternanthera philoxeroides) in both Suisun Marsh and the Tower Bridge marina in the east Delta. This is a new, noxious, weed to the Sacramento-San Joaquin River Delta system and we should all be on the look out (see photo above and below).

This invasive weed, native to South America, forms floating mats but it is rooted in sediment and has submerged, floating, and emergent forms. This invasive weed can survive a wide range of environmental conditions making it particularly threatening to both aquatic and terrestrial ecosystems.    For more information on this weed- please see: http://www.sms.si.edu/irlspec/alternanthera_philoxeroides.htm

In California, currently this weed has been documented near Grizzly Island in August, 2017 and from two other sites further up the Montezuma Slough channel in September.

The introduction point is unknown, but it is clear that this weed is in the Sacramento River and has now moved into Suisun Marsh.  Other naturalized locations can be expected.  A rapid response to the alligator weed in Suisun is warranted before this invasion compromises planned tidal wetland restoration projects.

On a related note, the invasive yellow flag iris (Iris pseudacorus), and  Ludwigia hexapetala  (Uruguayan primrose-wllow) are also spreading in these same areas and should be reported as well if found (see details for contact info below).

 

Alternanthera_philoxeroides_discoverlife.org
High densities of Alligatorweed (Alternanthera philoxeroides), forming dense mats. Also called “Pig Weed”. Photo credit: http://www.discoverlife.org

KEY ACTION POINTS: If you find new populations of alligatorweed (or the other weeds mentioned above) please take photos, GPS points (can be via your smart phone), and a voucher specimen (if possible) to send/email to the State Taxonomist, Genevieve Walden, at CDFA Genevieve.Walden@cdfa.ca.gov

 

It is crucial to notify Genevieve Walden as we need to document the extent of the problem. If we are not able to control the spread of this weed immediately it will result in similar issues and problems resulting from Brazilian Waterweed, Water Hyacinth and Water Primrose.

What happens if this weed spreads you ask?


Biological control is a possibility, and in fact, one of the biological control agents, the alligatorweed flea beetleAgasicles hygrophila, has the distinction of being the first biocontrol insect released in the U.S. in order to combat an invasive aquatic weed!

ahygrophila1
Agasicles hygrophila, Photo Credit: http://www.sms.si.edu/

Overall, management impacts on alligator weed by the alligatorweed flea beetle have resulted in a dramatic decrease in the amount of infested aquatic habitat since the insect was first released.

However, the most effective and easiest solution to combating alligatorweed in the California Delta and Suisun Marsh regions is to prevent it from spreading in the first place!