Debashish Panda, Ph.D. 🐾

Western India Tiger Landscape|WWF India

[Debashish Panda, Ph.D. 🐾 @debashish_wild]

The study underscores the influence of human infrastructure, viz., roads and villages, on mesocarnivore presence and highlights the need for targeted conservation in the ravines.

[Debashish Panda, Ph.D. 🐾 @debashish_wild]

🚨🐺🦊New Publication Alert!🐆🦨
Our latest research, “Factors determining the occurrence and spatial-association of mesocarnivores in the semi-arid landscape of Madhya Pradesh, India,” is now published in Mammal Research (2026). Led by @nature_wayfarer
https://t.co/JEX3cFqslo

[Debashish Panda, Ph.D. 🐾 @debashish_wild]

Despite differences in body size and diet, mesocarnivores showed positive associations. Interactions with leopards and hyenas were mostly neutral or positive, except for jackals, which showed negative association with leopards.

[Bilal Habib @wildwithwolves]

First-ever IUCN Red List assessment for the Indian Wolf (Canis lupus pallipes)!
Now listed as Vulnerable (A2ae; C1) — with only 2,877–3,310 mature individuals left in the wild.
One of India’s most enigmatic predators stands on the brink — the time to act is now.
#IndianWolf #Conservation #IUCN
@moefcc @ntca_india @IBCA_official @rameshpandeyifs @IUCN @WildCanids @wildwithwolves @bhlab_india @shaheerkhan @bioacousticist @laurenhennelly1

[Trends in Ecology & Evolution @Trends_Ecol_Evo]

Online now: Water provisioning shapes carnivore community structure https://t.co/9cZnGKkObY

[MiquelTTconservation @Miquel_TT]

🌍🦴 New Publication Alert! 🍪

#Hyenas: from fossil giants to misunderstood carnivores. Explore their #evolution & ties to humanity—ecology, culture, & more.

Tribute to Björn Kurtén's legacy! 🐾@annzoolfenn

🔗 https://t.co/2aSfUlA92F

@GCCGtweet @HELICS_Lab

[Fabiola Iannarilli @Fabi_Iannarilli]

📸New paper alert📸

Interested in investigating how animal diel activity patterns vary along anthropogenic and environmental gradients? Check out our how-to guide on estimating activity using hierarchical models @FiebergJohn @science_action and John Erb

https://t.co/xD6mJdCa4x

[Methods in Ecology and Evolution @MethodsEcolEvol]

📖Published📖

Find out more about EarthRanger: an open-source platform built to help monitor, research and manage ecosystems 👇

https://t.co/qGh7aKsLtE

[British Ecological Society @BritishEcolSoc]

We are very excited about the new Pielou Review recently published in @MethodsEcolEvol

Check out @kokkonutter, Hanna Kokko’s ‘Who is afraid of modelling time as a continuous variable?’ here 👉 https://t.co/coHxktSQwa

[Susanta Nanda IFS (Retd) @susantananda3]

“I can imagine no heroism greater than motherhood.”

[Trends in Ecology & Evolution @Trends_Ecol_Evo]

Online now: The overlooked biodiversity loss https://t.co/VuKCBTKX4R

[Sanjeev Varshney @skvdst]

Swiss Government Excellence Scholarships at Doctoral and Post Doctoral Levels for research or study at Swiss universities and research/ techology institutes. - Details at https://t.co/pSZyIQD7Px.

Apply in India up to 11th November 2024

[Ecosphere @ESAEcosphere]

The cover of @ESAEcosphere's August issue has landed! With a dramatic shot by Pilar Oliva-Vidal of a bearded vulture, from a study of how these bone-eating birds find their next meal

Find the issue's #OpenAccess research here: https://t.co/xEMNIPNWk4

[Alain Zuur @Alain_Zuur]

Now available at https://t.co/RCVt648teE: 'The World of Zero-Inflated Models - Volume 2: Using GLMM.

Although this book is published under the umbrella of 'The World of Zero-Inflated Models', it also provides a good introduction to ordinary linear mixed-effects models and GLMMs.

Chapter 11 contains an extensive explanation of linear mixed-effects models. Originally, we used a dataset of bears and ants, but after discovering that the covariates only explained 2% of the variation, we decided to completely rewrite this chapter with a different dataset on painted turtles. At that point, we had forgotten that the chapter on zero-inflated binomial GLMMs also uses painted turtle data. So, we hope you like turtles. In Chapter 12, we first introduce Poisson GLMM using a squirrel dataset and discuss marginal and conditional predicted values. The chapter also covers zero-inflated Poisson and generalised Poisson GLMMs. A zero-inflated Poisson GLMM is applied to a humphead fisheries dataset in Chapter 13.

In Chapter 14, we discuss how to handle nested and crossed random effects, as well as auto-correlation, using a dataset on zero-inflated tree hyrax count data. A detailed explanation of zero-inflated binomial GLMMs is provided in Chapter 15. We use a dataset on painted turtles and also touch upon beta-binomial models. In contrast, Chapter 16 utilises beta GLMMs, zero-inflated beta GLMMs, zero-altered beta GLMMs, and ordered beta GLMMs for the analysis of zero-inflated caribou data. Finally, Chapter Chap17 presents an application of the Tweedie GLMM to zero-inflated biomass data.

[Trends in Ecology & Evolution @Trends_Ecol_Evo]

Online now: Keystone individuals – linking predator traits to community ecology https://t.co/TxkXSIg7oW

[Royal Society Publishing @RSocPublishing]

#ProcB Special Feature - The other 1%: Showcasing #science and #scientists from the #GlobalSouth and #indigenous communities - find out more on how to send us your proposal https://t.co/ATNFr6ppg5

[Susanta Nanda IFS (Retd) @susantananda3]

Melanistic tiger cub is learning to hunt in Simlipal.
Can you guess the prey??