Kumar Mainali

ecologist, statistician, ML/AI specialist

[Barsee 🐶 @heyBarsee]

13 secret websites powered by AI to finish hours of work in seconds (Don't lose them):

[Kumar Mainali @kumar_mainali]

Exciting news! Our AI system for predicting wetlands with very high accuracy has been published in Science of The Total Environment and featured in 27+ media outlets. #AI #wetlands #prediction
https://t.co/3AanZrvbwD

[bioRxiv @biorxivpreprint]

CooccurrenceAffinity: An R package for computing a novel metric of affinity in co-occurrence data that corrects for pervasive errors in traditional indices https://t.co/ViHyqQ2MHS #bioRxiv

[Julien Cayla @juliencayla]

10 academic writing skills I wish I had learned as a PhD student (with updated links) A thread ⬇️ 🧵 for PhD students and junior scholars:

[Nature Computational Science @NatComputSci]

We also highlight work by @kumar_mainali and colleagues on a new metric of co-occurrence and similarity that addresses existing sensitivity issues present at more popular similarity indices (https://t.co/8urfntwKqr). https://t.co/t3UTI09OxU

[Chris Patrick @ChrisPatrickPhD]

This is really important stuff to consider if you use dissimilarity indices ever (think ordination, genetic analyses, community analyses, beta diversity, etc). Curious to see whether this will catch on!

https://t.co/1YJfZ3N2VK

[Kumar Mainali @kumar_mainali]

@vjjan91 @bio_diverse @mizoraman @PriyankaHariH @vjjan91 the paper is under open access. Please check again.

[@comecology.bsky.social @com_ecology]

@Jon_Chase03 I need to sit down, get pencil and paper, and do math before I can start singing:

"And now, the end is near
And so I face the final curtain
My friend, I'll make it clear
I'll state my case, of which I am certain"

Looking forward to spending time on this one!

[Kumar Mainali @kumar_mainali]

@Jon_Chase03 @DanMcGlinn sorry about my multi-part response... I am not used to the brevity of Twitter.

[Kumar Mainali @kumar_mainali]

@Jon_Chase03 #7) @DanMcGlinn Articles on co-occurrence indices seem to recognize that dramatically non-null association between species pairs is actually rather common, & the point and confidence-interval estimation of the degree of non-nullity ought therefore to be of scientific importance.

[Kumar Mainali @kumar_mainali]

@Jon_Chase03 #6) @DanMcGlinn In contrast, the log odds ratio is a parameter that is meaningful to interpret in the way the two species interact (ie they have greater probability of choosing the same sites because they are looking for some of the same things).

[Kumar Mainali @kumar_mainali]

@Jon_Chase03 #5) @DanMcGlinn Centering and scaling, ie standardizing, under null hypothesis is "roughly" comparable to doing p-values (also a null-hypothesis calculation), and that seems to be where Carmona and Pärtel (2020) stop.

[Kumar Mainali @kumar_mainali]

@Jon_Chase03 #4) @DanMcGlinn There is a considerable difference betn analysis done under a legitimate interpretable alternative hypothesis and that done under null hypothesis (hypergeometric distribution of co-occurrences applicable when site occupancy is truly independent across species).

[Kumar Mainali @kumar_mainali]

@DanMcGlinn @Jon_Chase03 @R_you_cereal @C_PCarmona We believe that this won't make much difference in larger-sample 2x2 tables, but definitely could in smaller tables. It's an issue of sensitivity analysis, which could be addressed in Bayesian fashion by putting a prior on the row-column totals where constraints should be soft.

[Kumar Mainali @kumar_mainali]

@Alex_Smith_Ants Totally agree on the confusion our choice of name has caused. We should do a better job in naming a metric.

[Kumar Mainali @kumar_mainali]

@LeffUfrgs The first two pages summarize the challenges with the prior methods. Fig 1 and 2 show how various indices and alpha map to CDF.

[Andy Gonzalez @bio_diverse]

Have you ever calculated Jaccard's index? I have.
Stop, and read this paper before you do so again.
It proposes a new measure of co-occurrence that changes the interpretation of patterns in your data #biodiversity
https://t.co/1IQgz0H31U

[Kumar Mainali @kumar_mainali]

@Jon_Chase03 #2) @DanMcGlinn First, for a given scenario of prevalence, the distribution is not symmetric. This means that values equidistant from the center of the null in opposite directions (e.g., 2 versus −2) indicate different strengths of positive and negative association.

[Kumar Mainali @kumar_mainali]

@Jon_Chase03 #1) @DanMcGlinn Compared to raw Jaccard, its standardization (Keil 2019, Ecosphere) is much better. We show the standardized Jaccard index correctly centers the value of zero at the center of null (section S2 and fig. S1, first column).... However, it still presents two problems.