UTM to longitude latitude R-code

UTM-long.lat conversions

Option 1. Work with original dataframe that has locations as UTM

library(rgdal)

utms <- SpatialPoints(data[, c(“long”, “lat”)], proj4string=CRS(“+proj=utm +zone=10”)) #create UTM matrix

longlats <- spTransform(utms, CRS(“+proj=longlat”)) #transform

Option 2. Generate a new dataframe and use coordinates function instead (preserves other vectors in dataframe)

#convert UTM to long.lat

mapdata <- data

coordinates(mapdata) <- ~long+lat #similar to SpatialPoints

proj4string(mapdata) <- CRS(“+proj=utm +zone=10”) #assign projection and coordinate reference system

longlats <- spTransform(mapdata, CRS(“+proj=longlat”)) #transform

Waiting for the rain

Rainfall updates

The growing season of 2015-2016 has come and gone with disappointing results from the supposed El Niño year. The 2016-2017 season is approaching and a few had feared that it would just continue the current pattern of drought. I was especially fearful having battling drought four years in a row in my study of plant interactions. It would be nice to have a chance with at least “average” precipitation amounts. Half way through the rain season and this year looks promising. Areas of California have been seeing some pretty significant precipitation including some potential floods. While this is great news in terms of drought relief for coastal cities and the Sierra Nevada snow pack, I wonder what the consequences will be for the deserts? In particular, the Mojave always seems to be in the unluckiest of rain shadows, missing most of the precipitation that the rest of the state experiences. I took a snap shot of the rainfall and average temperatures since seeding at the end of October. Here are the results:

Interpretation

The right combination of rain, temperature, and timing are absolutely crucial in desert ecosystems in regards to how the plant composition will respond. In an older paper by Beatley (1974) is a description of how these three variables determine plant composition. From this and my own experience, the absolute minimum rain to see any annual vegetation on the ground is 2.5 cm. However, these plants usually die within a month if there is no subsequent rain. I have seen this occur in multiple years where Halloween rain is not followed by any other precipitation until mid-January. The result? Many dead plants, and a new representation for plant communities. The Mojave has seen enough rain to begin germination and this rain has all occurred within the last 3 weeks. This, plus continued cold temperatures, should encourage the persistence of annuals for at least another month. If at least one other major rain storm passes through in that time I would expect to see these plants make it to flowering. On the more westerly side of the state, my sites have been seeing fairly consistent rain. This is great news for my Panoche Hills site that likely has passed its precipitation threshold that guarantees emerged plants to flowering.

Fingers crossed as always!

Rules-of-thumb for collaboration

Rules-of-thumb for reuse of data and plots
1. If you use unpublished data from someone else, even if they are done with it, invite them to be a co-author.
2. If you use a published dataset, at the minimum contact authors, and depending on the purpose of the reuse, consider inviting them to become a co-author. Check licensing.
3. If you use plots initiated by another but in a significantly different way/for a novel purpose, invite them to be co-author (within a reasonable timeframe).
4. If you reuse the experimental plots for the exact same purpose, offer the person that set it up ‘right of first refusal’ as first author (within a fair period of time such as 1-2 years, see next rule).
5. If adding the same data to an experiment, first authorship can shift to more recent researchers that do significant work because the purpose shifts from short to long-term ecology.  Prof Turkington (my PhD mentor) used this model for his Kluane plots.  He surveyed for many years and always invited primary researchers to be co-authors but not first.  They often declined after a few years.
6. Set a reasonable authorship embargo to give researchers that have graduated/changed focus of profession a generous chance to be first authors on papers.  This can vary from 8 months to a year or more depending on how critical it is to share the research publicly.  Development pressures, climate change, and extinctions wait for no one sadly.
Rules-of-thumb for collaborative writing
1. Write first draft.
2. Share this draft with all potential first authors so that they can see what they would be joining.
3. Offer co-authorship to everyone that appropriately contributed at this juncture and populate the authorship list as firmly as possible.
4. Potential co-authors are invited to refuse authorship but err on the side of generosity with invitations.
5. Do revisions in serial not parallel.  The story and flow gets unduly challenging for everyone when track changes are layered.

Journals for synthesis

Colleagues and I were checking through current journal listings that either explicitly focus on synthesis such as systematic reviews or include a section that is frequently well represented with synthesis contributions. Most journals in ecology, evolution, and environmental science that publish primary standard, research articles nonetheless also offer the opportunity for these papers too, but it can be less frequent or sometimes less likely to accept different forms of synthesis (i.e. systematic reviews in particular versus meta-analyses).

List

Diverse synthesis contributions very frequent
Conservation Letters (Letters)
Perspectives in Science
Perspectives in Plant Ecology, Evolution and Systematics
Diversity & Distributions
Ecology Letters
TREE
Oikos
Biological Reviews
Annual review of ecology, evolution, systematics
Letters to Nature
Frontiers in Ecology and the Environment
PLOS ONE (many systematic reviews)
Environmental Evidence
Biology Letters
Quarterly Review of Biology

Frequent synthesis contributions with some diversity in formats
Global Ecology and Biogeography
Annals of Botany
New Phytologist
Ecography
Ecological Applications
Functional Ecology
Proceedings of the the Royal Society B
Ecology and Evolution

Progress Report – Fall 2016

Several weeks ago I completed my first progress report for my MSc program. This involved a giving a short presentation (slides above) followed by a question/discussion period. My thesis focuses on pollination facilitation – non-competitive pollinator sharing between plant species that improves the reproductive success of at least one of the participants. I will be investigating these interactions in the Mojave Desert, a biodiversity hotspot supporting 659 species of bees and 1680 annual plants.

Why spatial? The study of ecology is normally separated into hierarchies, however, we know that these different levels are integrated and interact despite studying them in isolation. All interactions take place in space, and so explicitly including spatial dimensions to a study can be a way of connecting these levels, leading to a deeper understanding of the observed interactions.

It can be a little intimidating to stand in front of your committee and tell them your ideas, but they are there to support you. I received some great feedback which I am using retool my experimental design in preparation of the upcoming field season. Advice: Be careful about your clipart choices! I used a picture of queen honeybee (they don’t pollinate!!) in an interaction diagram explaining pollination facilitation. This isn’t as bad as the infamous biology textbook “Bees of the World” showing a pollinating fly on the cover, but it was noticed right away.

How to use colour in manuscripts

untitled

I thought this was a very helpful guide on using colour in figures. There are a few rules, but one comment from the whole document stands out. “If colour serves a purpose, but something other than colour would do the job better, avoid using it”.

http://www.perceptualedge.com/articles/visual_business_intelligence/rules_for_using_color.pdf

Here are the simple rules:

1. If you want different objects of the same color in a table or graph to look
the same, make sure that the background—the color that surrounds
them—is consistent.

2. If you want objects in a table or graph to be easily seen, use a background
color that contrasts sufficiently with the object.

3. Use color only when needed to serve a particular communication goal.

4. Use different colors only when they correspond to differences of meaning
in the data.

5.  Use soft, natural colors to display most information and bright and/or dark
colors to highlight information that requires greater attention.

6.  When using color to encode a sequential range of quantitative values,
stick with a single hue (or a small set of closely related hues) and vary
intensity from pale colors for low values to increasingly darker and brighter
colors for high values.

7.  Non-data components of tables and graphs should be displayed just
visibly enough to perform their role, but no more so, for excessive salience
could cause them to distract attention from the data.

8.  To guarantee that most people who are colorblind can distinguish groups
of data that are color coded, avoid using a combination of red and green in
the same display.

9. Avoid using visual effects in graphs.

 temp

ESA 2016 – my highlights

Functional traits

I have known for a while that functional traits are at the core of ecology. Unfortunately, for most of my experiments I focus solely on biomass and abundance. However, at ESA this year I noticed many researchers who measured community abundance and used a secondary database such as TRY for extracting functional traits per plots. This is a great re-purposing of already collected data to be reused to answer different questions. I am definitely going to consider analyzing data sets I currently have the species composition for to answer questions about functional diversity rather than typical species richness.

functional traits

Statistics

There was a considerable amount of NMDS usage this year, furthering that ecological analyses are becoming more complicated and requiring ordinations. Although ecologists may love NMDS, there is a preference in the statistical world to avoid it. I won’t get into the pros and cons here, but I believe ordinations such as PCoA, CA and RDA can accomplish the same as NMDS with less limitations. I will need to explore this further but I found this document a good starting point. Permuted ANOVAs were also another particularly popular statistical test and one I need to find a good R package for.

Another great talk from the conference discussed the advantages of the Negative Binomial distributions particularly for species abundance distributions. The talk discussed the tendency to fit discrete count data into log-normal distributions or Poisson. Often these distributions do not fit the data, while negative binomial does. The author A. Rominger promised me that he would be publishing an article detailing this commonality later in the year. I will hold him to that! In the mean time, here is an R package that he developed for species abundance distributions and the negative binomial distribution called pika.negative binomial distribution

Species distribution modeling

I had the opportunity to sit in on a talk by C. Merow that discussed used “expert maps” to further refined predictions of species distributions. It was a great talk and introduced a new concept to refine species predictions. Using the Map of Life (MOL), ecologists create species boundary maps that are would go into an SDM. The maps are compatible with MaxEnt and were shown to be effective at better predicting species occurrence. While a much better technique, I think the major limitation is that specific species will not have these maps delineated yet. However, until MOL catches up, big picture questions can be addressed with a greater degree of accuracy. The talk also reminded me I need to learn more Point Process Poisson modeling

The other instance SDMs that I thought was extremely informative was by Leung discussing co-occurrence models for invasion. This models have strong similarities with our work except instead of testing how an invasive species co-occurs with native species, we are trying to determine how a facilitating benefactor species interacts with neighbouring species. The way this is done is either using a proxy measurement or by using multi-species models. The example Leung used for a proxy measurement was boat movement in Ontario as a function of invasibility for a particular non-native species. I though this was a great idea and something I will consider for my next SDM experiment. The multi-species models is something that was a bit more complicated and that I will need to explore further.

Species distributions

General

Overall ESA went well! The attendance appeared a bit lower than last year, but I felt I was still super busy. It was a great experience and I learned a lot more that the highlights I am listing here. We also received some great feedback on micronet on how to improve it and I go some ideas to develop an R package that hopefully satisfies everyone’s micro-environmental challenges. A full list of my participation at ESA below:

A test of the stress-gradient hypothesis including both abiotic stress and consumer pressure during an extreme drought year

How to Set Up Automated Sensors Arrays for Measuring Micro-Environmental Characteristics and Synthesize to Larger Scales

Microenvironmental change as a mechanism to study global change

The use of shrubs as a tool for re-establishing native annuals to an invaded arid shrub land

 

esa2016

Precipitation mediates the mechanism of facilitation in a Californian Desert

ESA 101 at Fort Lauderdale is coming up! I will be presenting on recent findings from an experiment we conducted over two years. I am extremely excited for both the presentation and the results! Here is the slide deck, statistical analyses and program outline.

https://afilazzola.github.io/water.consumer/

Background/Question/Methods
The stress gradient hypothesis original purposed the frequency of plant interactions along countervailing gradients of abiotic stress and consumer pressure. However, research to date has studied these two stressors in isolation rather than together, thereby potentially neglecting the interaction of these factors on plant composition. In the arid central valley of California, we artificially manipulated a soil moisture gradient and erected animal exclosures to examine the interactions between dominant shrubs and the subordinate annual community. We conducted this experiment in an extreme drought year (2014) and a year of above-average rainfall (2016).

Results/Conclusions

Shrubs positive affected the abundance and biomass of the annual community at all levels of soil moisture and consumer pressure. In the drought year, shrub facilitation and water addition produced similar positive effect sizes on plant communities; however, the shrub facilitation effect was significantly greater in watered plots. During the year with higher rainfall, there was no observed water or exclosure effect, but shrubs still significantly increased biomass of the subordinate plants. Shrubs and positive interactions maintain productivity of annual plant communities at environmental extremes despite reductions in droughts stress or consumer pressure and these positive effects are even more pronounced with water addition. The relationship between consumer pressure and abiotic stress on plant interactions is non-linear particularly since shrubs can facilitate understorey plants through a series of different mechanisms.