A Picture’s Worth 1000 Data Points: Tips for incorporating photos into our science

Scientists take a lot of pictures, or at least, we should. We travel to unusual places, do unusual activities, and see unusual things, all of which can often be displayed excellently in a photograph. Before cameras, many scientists were also supreme artists. They would document the real world (and the subjects of their studies) in drawings or paintings, and the line between scientific data and artistic expression was blurry. This skill was necessary for the dispersal of information, and more information on a place or a organism can often be expressed per page in a picture than in text. A collection of dozens of paintings or drawings could represent a lifetime’s work and only exist in a handful of prints. Now, with digital photography, we can take hundreds of high-quality photos in minutes with infinite, diverse data and immediately share them with the world. But all these photos can be difficult to manage, particularly as your collection grows. So let’s talk about how we can more effectively manage our photographic

Most science-pics can fit into at least one of four categories: 1) Aesthetic, 2) Exhibitory, 3) Methodological, and 4) Systematic. Aesthetic photos should be consciously composed and executed, and serve as an artistic representation of a subject. Handy for presentations and exhibits, these can be infinitely elevated and the principles that make a quality photograph can be incorporated into any picture, scientific or not. Exhibitory photos are particularly useful to scientists seeking to communicate natural phenomenon. If we carry a camera with us while we’re in the field, we can catch examples of our study topic in action. For example, a photo of a bird eating a bug on a cactus can help you explain the idea of indirect facilitation. Again, these types of photos are useful for presentations, teaching, and publication, but do not necessarily have to be artfully composed (though an attractive photo can make your presentation stronger and more professional). Methodological photos are unique, but are present across many disciplines, as a tool or resource for explaining a protocol. For us ecologists, these photos are usually of a researcher conducting their experiment or of equipment in the field. A photo like this can help your audience better understand what exactly you were doing, especially if they are outside of your field. And finally, systematic photos are those which are taken as a part of the protocol. Just like any other measurement in a study, these photos should be taken at regular intervals, be they spatial, temporal, or event-based. These photos are often not aestetic at all, but rather are primarily functional. One should never permanently delete these photos just are you wouldn’t delete other observations–they are a part of the dataset. Indeed, this category is usually the type of photo that requries the most management, and is most likely to make your hair fall out. Large file sizes make it hard to save on your personal machine or online platform not specifically designed for uploading many photos at once (looking at you, google drive). That’s why I’m going to tell you a bit about a platform that we can use to more effectively backup, share, and manage our science-pics: Flickr.

Flickr, an online photo sharing platform, is similar to many other databases were one can have their own personal account, be on a team account, create groups, tag photos, and make your photos public or private. Free for your first 1000 photos and $50/year for unlimited uploads, the real beauty comes in it’s file management. And lucky for us, it has both a desktop and mobile version. Just like any platform, however, it has it’s disadvantages. Without careful monitoring and guidelines, it can become just as unorganized and hence unusable as any sharing platform. To benefit from Flickr’s system, you have to establish a system among your team, and make sure to follow it. Here, I’m going to walk you through uploading a photo, creating an album, and managing those photos in a clean and effective way.

Once you’ve signed in you, you can explore your profile page, which has different tabs for the different elements of your page. This is pretty self explanatory; the only part that may seem confusing is the Photostream element, which is simply a list of all public photos you’ve uploaded in order of recentness. Private photos won’t show up here.

The Ecoblender Lab Profile page. Notice the Photostream is empty because we are new account.

Our profile page won’t be very interesting, however, until we start to populate our account by uploading photos. To do this, notice the cloud and arrow in the top right corner and choose “Upload”.

To add any new photos, you’ll need to upload. You can pick the different settings and details (like what album you’re adding to) during the uploading process.

Now, to upload new photos, simply follow the prompts and fill out the left hand tabs with descriptions or tags, and chose/create the appropriate the album. You don’t need to choose an album, if you don’t, it’ll simply go to your camera roll and (if public) your photostream. However, we should always try to maintain order by depositing photos into albums, especially on a shared account.

More tags means more potential viewers! That is, if it’s public.

But the real power comes when we want to upload multiple photos at once. Unlike other photo sharing platforms and most personal machine file management systems, you can edit multiple files and assign names/descriptions/tags/people/albums to multiple files at once, easily selecting all or some of the photos you’re interested in.

Edit the details for all, one, or some of the photos you’re uploading.

Now, if you are working on a project that includes systematically taken photos, a team Flickr account can support it. It’s important to establish certain guidelines for the publication for all albums with multiple contributors, but especially for photos a part of a dataset. As an example, our lab is cataloging photos of trees on York University’s campus, and we would like to include photos of every tree in an album on Flickr. We might use the following guidelines to make sure all photos are properly documented:

  • Album title should be descriptive of the project and include the year.
  • Only systematic photos should be included in the album, methodological and fun photos should be in a separate album.
  • The photo title should be the Unique ID that the tree is recorded as in the dataset. If we have multiple photos for one tree, that’s fine! Maintain the
  • The description should include any details that distinguish it from other photos of the same tree (e.g. you might take a photo of the entire tree, a photo of a bird in the tree, and some damage to the bark, so include that detail in the description). Try to use the same one or two words if you are taking the same photos (e.g. “whole tree”, “bird”, “damage”, etc.). It should also include the photographer, the date the photo was taken, and the treatment we are interested in (for this project, it might be disturbance level).
  • We should include general tags on every photo that strangers may wish to find (e.g., science, ecology, nature, explorer, tree, bird, insect, etc.), but also a project-specific tag that includes systematic and other photos from the project.
  • The members working on the project should be added to “people”, particularly those who were collecting the data (even if they weren’t the photographer)

This is only one example, but it can be modified for all sorts of albums and projects. In fact, it doesn’t have to only be for specific projects! How often have you been working on a presentation and realized you never did take a photo of your study site? Instead of trying to track down anyone with a photo of the place (or being asked to wade through your phone’s camera roll), we can have a place to put them all immediately and know exactly who to credit (don’t forget to credit photographers!) When we systematically organize our photos, we can quickly find and share them among our peers. Much like sharing our code, our research plan, or our datasets, sharing our photos can help cultivate a culture of open science and provide resources within our own research group and beyond! Now, go follow us on Flickr, @ecoblender_lab! 🙂

Campus tree project 2020

Two super experiments.

  1. Exotic animal responses to trees and disturbance.
  2. Individual tree and patch-level dynamic responses to disturbance.
  3. Post hoc synthesis and contrast of interaction pathways.

Sample academic plans

Table, list, freeform, calendar, all good. Match your modality or even use more than one mechanism to support your journey this Fall and beyond.

Journey rabbit

Learn

Read Spatio-temporal statistics with R

Read Advanced R (skim some bits, functions)

Write

Rangeland brome papers x2

Scientific synthesis paper

Editorial on distributed learning

Dream on – notes on how to do experiments into paper

Magic paper with Mario

Synthesis papers with Mario, Nargol, and Malory

Work

Teach BIOl3250

Review apps for Diol grad committee

Support team in papers and planning

Other

Seminars on experimental science

Connect with USAID folks

Grants

Mozilla

YUFA to write a book

Journey army ants

1) Apply for NSERC and other grants

2) Readings list for PhD project

3) Select committee Members 

4) Come up with a few thesis questions

a. Possibly run a meta/Systematic review (Would need to make a super clear one though)

b. Continue with density? Maybe make areas with a bunch of replicate shrubs and see what happens. Or cut some shrubs down and see what happens

c. Maybe look at how density of shrubs has an impact on invasive grasses?

d. Maybe look at one specific animal species and compare density of that specific species to density of shrubs?

e. Or maybe take a completely new route on thing.

5) Brainstorm possible field projects for PhD (When we are going, manipulative for experiments, timeframes, etc.)

6) Work on posters and other outside projects/research papers

Journey salamander

Read “Fundamentals of Statistics” Aug 2020

Research possible committee members and reach out Oct 2020

Select a committee member Dec 2020

Come up with a thesis question Dec 2020

Field Work/Data Collection Apr 2020 – Aug 2020

Data Analysis Fall 2021

Reading list for research Winter 2020

Take remote sensing course? Winter 2020

Thesis Writing Winter 2022

Hugh C. Morris Experiential Learning Fellowship application February 15, 2021

Scholarship/fellowship research Oct 1, 2020

Ontario Graduate Scholarships (OGS) application Dec 1, 2020

Journey chipmunk

Not including how ongoing work is structured.. mostly deadlines, hopeful progress and some milestones

October

Decide on topic

Find committee

Submit NSERC/OGS application

Transfer final insect survey specimens to ethanol

Some progress on synthesis

November

Come up with a review/meta

Come up with 1or 2 new chapters on top of lizard diet

Progress report

Mail first run of samples to Mark

December

Field planning

Resurrect and write-up spatial veg finally

Apply for permits if needed

Work through a review

Compile taxonomist list

Jan

Family level data input

Second round of specimens sent to Mark

Review data extracted – what is the story?

Do I need to get additional data?

Get into grad level GIS course

Feb

Learn simulations 

March

Research evaluation meeting

Submit thesis proposal to FGS

April

Submit interim report to BLM

My Q: 

How focused does a topic need to be? How related are chapters?

How do expectations differ for phd from msc

Journey hellgrammite

1) Apply for NSERC and other grants

2) Readings list for PhD project

3) Select committee Members 

4) Come up with a few thesis questions

a. Possibly run a meta/Systematic review (Would need to make a super clear one though)

b. Continue with density? Maybe make areas with a bunch of replicate shrubs and see what happens. Or cut some shrubs down and see what happens

c. Maybe look at how density of shrubs has an impact on invasive grasses?

d. Maybe look at one specific animal species and compare density of that specific species to density of shrubs?

e. e. Or maybe take a completely new route on thing.

5) Brainstorm possible field projects for PhD (When we are going, manipulative for experiments, timeframes, etc.)

6) Work on posters and other outside projects/research papers

Journey capybara

Largest focus on teaching for the fall term. Followed by restarting the greenhouse experiment and finishing seeds in October. Two papers are currently submitted and going through review, with another manuscript in preparation.

Journey kangaroo

-September’s end: finalize rough research questions and ideas to explore for PhD (human-wildlife conflict, trampling, and facilitation)-October 20th: confirm committee members-October: Collect reading list, get seeds and scale from lab to continue seed allocation side project-October 15th: Confirm all AIF Climate Activist Videos online-November: Work through reading list & submit grants (National Geogrpahic Explorer Grant, MITACS?, other small grants). Seed allocation data analyses-December 1st: first progress report-December: Continue working through reading lists, add to lists as necessary. -December’s end: Finish full draft of seed allocation paper-January: Determine 2 cognates and begin drafting cognate literature reviews-March: Finish up first full draft of proposal (due beginning of May)

Using Magic the Gathering to Teach Experimental Design

With the current circumstances in today’s world, we have all been forced to adjust to a new method of learning. With classes moving online, one of the major challenges that has arisen is keeping students interested and engaged in their studies. Online classes extremely limit student-student and student-faculty interactions, making these courses feel almost impersonal. That being said, finding a way to make these online lessons fun and engaging could both help students enhance their overall understanding of basic academic concepts (such as experimental design, report writing, and figure generation) while also allowing them to work with novel information.

This semester I am one of the TAs for Biol 3250, a course dedicated to teaching students in varying academic disciplines how to plan, conduct, and analyze scientific data collected through experimentation. Many of the labs we have designed focus on proper planning and execution of these scientific experiments, but there is one section of the course that is a bit different. For this section, students are presented with several large datasets and are tasked with designing a hypothesis, predictions, and/or predict the patterns presented in the pre-collected data. From there, students are asked to create a figure and run a statistical test to support their ideas. To keep the students engaged and interested, one of these datasets used data collected from something outside the scientific field. This data was collected from opening packs of a popular tabletop card game titled “Magic the Gathering”.

What is Magic the Gathering

Magic the Gathering is a popular tabletop card game that uses a combination of strategy and chance to win. The game, designed first in 1993, has since expanded in both size and popularity with over 35 million players worldwide. The game puts players against each other with their pre-constructed decks, where the goal is to get their opponent’s life points from 20 to 0. Packs of these cards can be purchased containing 1 Rare/Mythic, 3 Uncommon, and 9 Common cards. To see how these packs are opened and what a booster box looks like check out the Youtube channel Mario MTG. Each set released is thematically unique from the previous, taking players to worlds (known as planes) that push the bounds of one’s imagination. Players get to choose from a variety of color themes and combinations for their decks with each color representing something different.

  • White: The color of order, community, and peace.
  • Blue: The color representing knowledge, perfection, and control.
  • Black: The color of resurrection, opportunity, success, and satisfaction
  • Red: The color of freedom, strength, and destruction
  • Green: The color of nature, growth, beauty, and harmony

So How Does This Tie Into Learning?

So I bet you are wondering now, “How could a fun tabletop card game possibly have any connection to an experimental design course?” The best answer to that is that it teaches students to work with large amounts of data while also showing that experimental designing can be fun! Boxes of these Magic the Gathering sets were opened with each individual card inputted into the data as a datapoint. Each pack ranged from 14-15 datapoints and after opening several boxes, well over 6000 data points (and soon more) have been collected. Each individual data point in itself looked at various aspects of each card such as name, color, ability, etc.

With this large dataset now compiled, students are free to generate any possible questions or hypotheses they could think of. “What is the probability of getting a mythic card”, “On average how many foil (shiny) cards can you get in a box”, “which set has a larger number of higher value cards.” These are just some small questions that could be addressed by analyzing this dataset. We want to show students that you can run an experiment on anything, even a fun game, and we want them to have a fun and unique way of working with big datasets. Being able to find new and inventive ways to keep students engaged in their studies is quite a task. Since everything is now done virtually online, it is important to try and new and fun ways of both engaging and teaching students.

For those who want to see how these data points were collected the Youtube channel Mario MTG goes through a bunch of box openings (Shameless plugin haha). On this channel, I go through opening boxes of the newest sets of magic the Gathering and also provide some commentary on the cards, prices, news, formats, and the overall future of the game.

The link to the channel can also be found below!!!

https://yorku.zoom.us/j/99086573797?pwd=aVJwRlRJblpnNFcrNW5jM2xWMXlxQT09

Fall 2020 goals

Personal

  1. Stay healthy.
  2. Be safe.
  3. Connect with team.
  4. Learn something new.

Deliverables

  1. Academic plan.
  2. MSc and PhD committees built and summoned.
  3. Read a pile of papers on existing focus and begin a new direction too.
  4. Proposal.
  5. Complete and submit outstanding synthesis papers.
  6. Book and schedule progress reports.
  7. Prep deck and present at report meeting.

Strategy

  1. Set hard and soft deadlines.
  2. Populate google calendar.
  3. Identify stepping stones to larger goals and make lists breaking each deliverable down.
  4. Select someone to be accountable to.
  5. Identify collaborations and update them on stepping stones.
  6. Keep track of progress (Gantt charts, notes, meetings, GitHub repos with issues to track).
  7. Plan breaks from the screen.
get the lands out so you have the mana to spend

SC/BIOL 4000 8.0 BIOLOGY HONOURS THESIS

This Fall will be remote for the majority of academic activities. The 8.0 credit honours thesis program is approximately 8mos in duration. The student leads an independent research project. As a team, we would like to work with two individuals on one of the following projects. Each student will work with a graduate student or postdoc and Chris Lortie.

a. Each project is individually implemented by the student safely.
b. Zoom calls with Chris and the co-mentor to ideate, solve, and plan will be used to collaborate in addition to editing datasheets and docs.
c. It will be beneficial for the student to have experience in R and be able to work independently.
d. The pre-reqs are listed online for BIOL4000 are here and currently include students their final year with a BIOL GPA of at least 6.0.

Tree forest dynamics at YorkU

(1) Subway effects on trees and woodlots. A census of tree forest dynamics and individual tree changes on YorkU campus. Jenna Braun, Mike Belanger, and I dug through census records compiled by the YorkU master gardener in 2012 and 2013. Over 5000 trees were tagged on campus and their size and health were recorded. The data are here. This is a fantastic project and opportunity to revisit a superb dataset in R and also resample some or many of the trees. We now have a subway rumbling away underneath campus, and we can check trees near and far from the line and test the hypothesis that disturbance belowground influences tree growth and health.

(2) Other ecological hypotheses relevant to urban forest dynamics (disturbance, new buildings, edge or center of campus etc). There are at least two projects here. The students can work independently and still split up the work of testing more than question or hypothesis. One individual can (re)sample trees from 2012 and 2013 near/far subway lines, and a second student can examine any other ecological question with disturbance, new buildings, or how sets of trees are doing in different ecological contexts on a university campus.

Desert ecology data analyses

We have many open datasets ready to go for deep analysis work if you are competent in R (or Python but we work in R in the lab). Many spatial questions, niche questions, or use or plant and animal survey data and join them to new data on climate or downscaled remote data if you are game for that adventure. Here are a few examples.

Vegetation under shrubs and in the open in the Central California Deserts.
Data.

Desert arthropod diversity patterns in California.
Data.

Camera traps and birds of the deserts.
Data.

In each instance, the workflow will include a few Zooms to plan analyses and additional data lookups, then the student researcher digs in!

To apply

If you meet the pre-reqs and are in your final year in YorkU Biology, please email lortieatyorkudotca, and we will set up an interview with you for the team!

Proposed guidelines for presentation of ecological community responses to a key factor (or set thereof)

Observations on accessibility of evidence and coherence in reported findings

In collaboratively writing this week as a team and in reviewing the literature with a specific meta-question in mind (how best to present community ecology, today, for the specific papers we are working on), we have a formula for a REALLY solid and clear presentation of results for some papers to consider – primarily for those that examined the response of a community (animals, birds, pollinators, etc.) to a key factor such as shrubs, shelters, water, light, or density to name a few ideas. This trend in reporting included competition and facilitation papers not just keystone plant species effects on other species.


FIG 1. Gift to the reader.

Surprise, this is for you. Thank you for getting this far into the paper or even scrolling to a figure. No sarcasm here – everyone busy and there a lot of potential papers to read in ecology and evolution.

This figure is thus the MAIN POINT of paper.
Show them the hypothesis ‘worked’! We defined ‘worked’ as including enough complexity to address how well and when (i.e. community response by phylum or season). So, reading a paper on light and competition on COMMUNITIES (plants or any taxa), we skim to the first figure and EXPECT to see, well you guessed it, light on x-axis then y-some measure of how the community responded – big picture results. We also expected to see some facet or color in data or some level illustrating how well the factor worked so to speak. Is light level always important? Or, does it depend on something?  Almost ALL current papers include that second factor.

Like this or even better really.

The reader is like ‘OH I got it’. Density is important or microhabitat important, but it depends on season because birds fly around a lot and migrate too.


FIG 2. Show something about the species in community.

Imagine a reviewer for a journal such as Journal of Animal Ecology or really any eco-journal.  The editor will try for an ecologist that knows something about desert mammals, birds, or the bees if the paper is about those communities. These readers will expect a second plot to be about composition or show species. Bird people (plant people too when we read community response papers about plants) want to be able see a plot and go OHYA I know that species OR aha I suspected NOT all species responded the exact same way to this key driver.

There are least three options for a STRONG second plot about species.

a. Relative frequencies.
A stacked bar or line plot or something that lists out species and shows their relative frequencies by at least one, prefereably two, key level(s). Rank abundance plots nice but not so common now.



b. A composition plot from an ordination analysis
One that shows something really deep about community OR actually shows species in the ordination plots with labels.



c. A cool species network plot
A plot that shows not only the species BUT how their connections changes based on the key factor(s).

FIG 3. Mechanism or other key ecological context that illuminates WHY the community responded to key factor(s).


Optional (and depends on study of course) but can illustrate how another key moderator IF needed such as RDM, temperature, etc mediates the community outcomes. OR, show the mechanism that explains fig 1 and 2. OR, zoom in on a key finding such as species by functional group, migratory status, etc.



Summary

Fig 1 – Main finding with enough detail to encompass predictions or how well and when hypothesis works (or not).
Fig 2 – Show composition or species because this is a community response paper.
Fig 3 – Show mechanism, zoom in on how community responded (functional groups), or show a really important finding that is strongly related to Fig 1 but you did not want clutter up or make it even more complex.


Disclaimer
Preferences from a week of work on reading and writing with an attempted laser-beam focus.

The Beetles and the Bees: Interactions Between Herbivores and Pollinators

Check out our newly published article in the open access journal PeerJ: A systematic review of the direct and indirect effects of herbivory on plant reproduction mediated by pollination.

Figure 2: Mechanisms of damage by herbivores that can impact pollination and therefore seed set.
Solid lines represent direct interactions and dotted lines indirect interactions. The two main pathways are direct (direct damage to floral tissue influences pollinators; shown lighter in orange) and indirect (damage to vegetative tissue indirectly effects floral traits; shown darker in blue). Lines and boxes in black represent interactions and steps shared by both pathways. The dotted lines represent the net indirect interaction of plant damage on pollinators (and pollination) that was the focus of this review.

Any gardener knows the havoc that herbivores can have on their plants; whether it’s the rabbits eating their cabbage or beetles damaging their prized roses. Herbivores can devastate a floral display or chew away at the leaves until a plant is too sickly to produce flowers or fruit. However, a discerning gardener will know that not all insects are bad for their vegetable garden; rather they should hope for some bees if they expect to see a good yield of tomatoes or strawberries. Both herbivores and pollinators can influence the yield of fruits and seeds for many plants worldwide and therefore impact both crop yields and plant reproduction. The effects of both types of interactions have been repeatedly tested by the scientific community; however, by their nature, these types of studies must simplify things in order to isolate the specific effect of one species on another and therefore neglect the multitude of other species present that might change this interaction. A caterpillar may chew the petal of a flower that a bee then passes over because the flower is no longer perfect or a rabbit may eat the leaves of a plant that, in consequence, only produces one small flower that is easily overlooked. Bees and other pollinators make choices when they choose flowers to visit, and damage to the plant (whether the flower or not) can result in flowers that are less attractive to pollinators. Similarly, the part of the plant attacked (i.e. leaves, roots, stem, or flowers) should certainly impact this choice differently. Damaging flowers directly reduces the appeal of the flower, and removing it entirely certainly eliminates the possibility of pollination; but how does damage to the leaves, roots and stem change the flowers? Damage to these parts can reduce the overall quality of a flower, whether the flower is smaller or produces less nectar, or if there are simply fewer flowers overall.

This dance between herbivores and pollinators is the subject of the review we recently published in the journal PeerJ: “A systematic review of the direct and indirect effects of herbivory on plant reproduction mediated by pollination.” We collected peer-reviewed studies that examined the effects of herbivores and pollinators on plant reproduction. One of our conclusions is evident simply by the number of studies we found; out of a total of 4,304 studies that turned up in a search on the search engine Web of Science, only 59 studies fit our criteria. That is, not many studies look at both herbivores and pollination. Half of these studies looked at damage to flowers and only about a third looked at damage to any of leaves, roots, and stems (the remainder looked at general damage to any tissue). When tightening our criteria to studies that compare damage to flowers and other parts of the plant, we only found three studies. In our paper we discuss in depth the ways herbivores and pollinators interact can call for more studies to compare damage to both flowers (direct damage) and other parts of plants (indirect damage).

Reflections

While Chris (my co-author) has completed and published many systematic reviews, this was my first. Gathering and sifting through this much data was certainly an experience and quite the grind. However, I have learned many lessons from this process, both about systematic reviews in general and about handling data. One lesson is to make sure to well-document everything in your process, because you will end up going back to look, months or even years later. So, document it, organize it, and if you can, automate it! However, the most important lesson I learned was the importance of a clear question and idea in advance. By the time I had collected all my data and knew the studies well, I felt as though I had lost the entire point of the review. I didn’t have a clue what to do next. It took going back to my original notes and having a discussion with Chris to remember why we had started off on this journey to begin with and to identify what questions we were trying to answer.

Overall, this systematic review gave me an excellent insight into the review process, both what to do and what not to do. However, the content itself provided a firm basis for my own practical field research. I have been in the process of implementing some of my own experiments contrasting the effects of damage to different plant tissues both in the field and in the greenhouse.