Author: Emma Higgins

A lot of us have been there…. setting up what seems like endless 3D anole replicas, often in the tropical heat, messing around with countless iButtons (which are a nightmare to get out of the replicas), to measure operative temperature (T_e)–the temperature of the animal at equilibrium with its environment….

As frustrating as this can sometimes be, it is an integral part of measuring thermal habitat quality and availability, which as we all know, is important for such things as ectotherm energetics, abundance and predicting species responses to climate and land cover change.

However, using these 3D replicas, we only get point-based measures of T_e at randomly selected points within the survey area. These points are sampling only a very small extent of thermal habitat, and therefore may not represent the conditions mere metres away.  This method therefore does not allow us to measure T_e across the whole of the survey area at spatial resolutions relevant to the individual animal. This method is also, costly in terms of both time and money. Therefore, is there another way?

Well, we do have microclimate–biophysical modelling, which generally relies on mechanistic models that downscale broad scale (usually monthly) macro-climate (≥ 1km grid) data to estimate microclimate in specific habitats, e.g.  NicheMapR, Microclima and Microclimc (Kearney and Porter, 2017; Maclean et al., 2018; Maclean and Klinges, 2021). These estimates of microclimate must then be combined with biophysical heat exchange models to estimate animal operative temperature (T_e), e.g. the ectotherm model in NicheMapR (Kearney and Porter, 2020).

These models have revolutionized our ability to model thermal environments across broad spatial extents, especially for species distribution modelling, and new developments have the potential to model much finer variation (e.g. Microclimc), but applications at scales of individual organismal movement (e.g. cms to m) are still rare.

These limitations of existing methods are particularly pertinent given the established importance of spatial heterogeneity of thermal environment for species, particularly ectotherms, and by extension our beloved anoles (Huey, 1974; Sears and Angiletta, 2015; Sears et al., 2016).

Luckily, we as a team had already pondered, if the canopy is key for regulating ectotherm operative temperatures (T_e), then, can we predict T_e using biophysical equations relating to canopy characteristics?

Part of the field team, helping process what is certainly not an anole, whilst setting up survey plots (photo credit Adam Algar).

This was the basis of this paper, “Unoccupied Aerial Vehicles as a Tool to Map Lizard Operative Temperature in Tropical Environments.”

So, to test this, we first needed to collect canopy data – which, for anyone who has done this type of work will agree, is not so easy! This is where Unoccupied Aerial Vehicles (UAVs) come in.

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New Paper: Disentangling Controls on Animal Abundance: Prey Availability, Thermal Habitat, and Microhabitat Structure

DOI:  https://doi.org/10.1002/ece3.7930

Why are there more anoles in this plot? This is the question that we continually asked ourselves whilst setting up our 2018 survey plots on the small island of Utila, Honduras, home of the endemic Anolis bicaorum (pictured above). So in 2019 we set out from Heathrow airport, kitted up with A LOT of equipment, for our second field season, based at Kanahau Utila Research and Conservation Facility, with one of the goals being to look into just that (see previous Anole Annals posts on Utila and its anoles).

We all know that such factors as the thermal environment, prey resources and structural habitat play important roles in the lives of our beloved anoles. And we also know that you can go to one spot and be overrun with anoles, but go to a seemingly similar spot nearby and find none (usually when the field season is drawing to a close and you still don’t have a large enough sample size). But how are these aspects of anole biology linked? Specifically what determines their abundance, and its variation, at fine scales? While ecological niche theory is well developed, empirical evidence for which factors are most important, and how they interact, is still rare for many taxa, including anoles. Given rapid environmental change, understanding the drivers and mechanisms governing abundance is now more important than ever.

We measured the abundance of the endemic Anolis bicaorum across thirteen 20x20m plots along a tropical habitat gradient, using standard mark-recapture methods, based on Heckel & Roughgarden (1979). Within these plots, we also measured factors relating to the thermal habitat suitability (using 3D printed models fitted with iButtons), structural habitat (perch surveys), canopy cover (leaf area index) and prey availability (arthropod biomass and diversity). We then used N-mixture models and path analysis to disentangle direct and indirect effects of these factors on anole abundance.

We first decided on several measures for each niche factor which could determine the suitability of the habitat for the anoles. For the thermal environment, we first determined the thermal preference (T_pref) range of A. bicaorum, following Battles and Kolbe (2018). We then calculated two indices to quantify the thermal habitat quality of each plot. The first was the percent of model hours that operative temperatures (from 3D models) were within the T_pref range over the 36-hour study period for each plot. The second was the total number of degrees (°C) that the models deviated from the T_pref range across all models throughout the survey period for each plot, which included the total degrees, the degrees above and degrees below the T_pref range.

As a measure of structural microhabitat quality we determined both perch availability by counting surveys and the plot basal area (a measure of stand density), across all tree trunks, palm stems and fence posts in the plot.

For prey availability, we measured arthropod biomass (g) and diversity (Simpson and Shannon’s) from a combination of leaf litter sieving and sweep-net samples taken in each plot. Sweep-net and leaf-litter samples were combined for plot level analyses.

We also measured mean leaf area index (LAI) in each plot using an Accupar LP80 ceptometer. LAI is the one-sided area of leaves per unit ground area and is a measure of canopy density; it is expected to influence thermal environment via the interception of solar radiation (Campbell & Normal 1998; Algar et al 2018).

After we determined reasonable measures of habitat suitability for each factor, we examined univariate relationships between A. bicaorum abundance and each of our habitat variables (percent of time within T_pref, deviation from T_pref, perch number, basal area, arthropod biomass, arthropod diversity and LAI) by including each predictor as a covariate in a multinomial-Poisson mixture model of abundance. The results of the most significant and strongest relationships can be seen in the figure below.

We used these models to select a subset of these variables (one representing habitat structure, one prey availability, and one thermal quality) for subsequent path analysis; we also included LAI as the sole measure for canopy cover. We used the path analysis to evaluate the relative strength of direct and indirect effects on abundance. As we could not estimate indirect paths within a single multinomial-Poisson mixture model, we estimated abundance for the path analysis from a multinomial Poisson mixture model that included no environmental covariates, held detection rate constant, and permitted abundance to vary by plot. The results of the path analysis can be seen in the figure below.

Our results showed that thermal habitat quality and prey biomass both had positive direct effects on anole abundance. However, thermal habitat quality also influenced prey biomass, leading to a strong indirect effect on abundance. Thermal habitat quality was primarily a function of canopy density, measured as leaf area index (LAI). Despite having little direct effect on abundance, LAI had a strong overall effect mediated by thermal quality and prey biomass.

We have demonstrated the interconnectedness of abiotic and biotic components that determine habitat quality and animal abundance. Rather than identify a single strong control on abundance, we found key abiotic factors (canopy cover and thermal environment) affect abundance through multiple pathways and have effects that are mediated by biotic interactions and the niche of the focal species. In particular, our results suggest alignment of thermal niches across multiple trophic levels results in strong indirect effects of thermal environment on anole abundance. Losses of thermal habitat quality, particularly due to canopy loss, may thus have greater effects than appreciated when only direct effects are considered.

Our results demonstrate the role of multidimensional environments and niche interactions in determining animal abundance and highlight the need to consider interactions between thermal niches and trophic interactions to understand variation in abundance, rather than focusing solely on changes in the physical environment. Identifying the factors responsible for population change along habitat gradients will improve our understanding of how multidimensional environments and niches interact to determine population abundance. Which is more important than ever in this ever-changing world.

You can read the full paper here.

If you have any questions or just an interest in the work, please feel free to contact me emma.a.higgins@hotmail.com.

I would also just like to thank everyone again who was involved in this project, it was a lot of hard work, but great fun and it couldn’t have been done without the team effort.

 

References

Algar, A.C. et al. 2018. Remote sensing restores predictability of ectotherm body temperature in the world’s forests. – Glob. Ecol. Biogeogr. 27: 1412-1425. https://doi.org/10.1111/geb.12811

Campbell, G. S., and J. M. Norman. 1998. An introduction to environmental biophysics. 2nd edition. -Springer-Verlag, New York.

Battles, A.C. and Kolbe, J.J. 2018. Miami heat: Urban heat islands influence the thermal suitability of habitats for ectotherms. – Glob. Change Biol. 25: 562–576. https://doi.org/10.1111/gcb.14509

Heckel, D.G. and Roughgarden, J., 1979. A Technique For estimating the Size of Lizard Populations .Published by : Wiley on behalf of the Ecological Society of America Stable URL : http://www.jstor.org/stable/1936865 References Linked refere 60, 966–975.