Crop Damage by Wild Herbivores:Insights Obtained from Optimization Models

2016

J. Trop. Ecol.

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Abstract:The nilgai (Boselaphus tragocamelus) is a widespread species in India that forages in forest as well as on agricultural lands. In Tadoba-Andhari Tiger Reserve, India, it typically takes to crop-raiding at night, while it rests and forages in forest during the daytime. We studied changes in herding and vigilance behaviour during foraging in forest versus in agricultural lands and monsoon versus post-monsoon in the years 2012–2015. We recorded number of individuals (herd size), sex-age composition and number of individuals per unit area of herd's spread (compactness) for every herd under observation using instantaneous scan sampling in forest (176 herds) and farms (321 herds), while spatial trends in herd size on agricultural lands were studied using transect sampling at night. Vigilance behaviour was studied using focal-animal sampling in forest (n = 91) and farms (n = 52) by choosing a single individual per herd under 15 min of observation. Herd sizes were significantly larger in forest (monsoon, median = 3, interquartile range (IQR) = 2–6, post-monsoon, median = 5, IQR = 3–8) than on farms adjacent to forest (monsoon = 3, IQR = 1–5, post-monsoon = 4, IQR = 2–5) and further decreased non-linearly with distance from the forest edge. Herds were more compact, i.e. with smaller inter-individual distance in forests than on farms. Crop-raiding was found to be female-biased, and adult males as well as newborn calves were observed on agricultural lands significantly less frequently. The median vigilance frequency was significantly higher on farms (1.4 min−1) as compared with forests (0.205 min−1) but the median unit scan duration was significantly less in farms (6 s) compared with forest (60 s). The observed differences are likely to be due to difference in the nature of risk faced in the two habitats. In forest, detection of ambush predators such as tigers that occur at a low density, requires careful watch and larger herds increase the chances of detection. In contrast, detection of guarding farmers on agricultural lands who are present at a higher density and make their presence conspicuous to drive away crop raiders would need a glance of smaller time duration. As crop-raiding occurs at night, moonlight is likely to affect the frequency of crop-raiding but we did not find evidence for any deterrent effect of moonlight on the frequency of crop-raiding. The data suggest that the nilgai exhibits substantial behavioural plasticity in response to different nature and levels of risks faced in the two habitats.

Section: Introductionmentioning

confidence: 99%

Differences in behaviour of the nilgai (Boselaphus tragocamelus) during foraging in forest versus in agricultural land

Bayani

2016

J. Trop. Ecol.

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“…In such cases the overhead cost of foraging will increase. However Watve et al (2016a) show that in models that optimize the benefit cost difference, overheads do not change the position of the optimum in relation to the curve. Therefore even in this set of assumptions the relationship of A with R remains unchanged.…”

mentioning

confidence: 90%

“…Foraging optimization has been an important focus of interest in behavioural ecology with substantial theoretical as well as empirical inputs. Animals have been modelled as well as demonstrated to optimize their foraging strategies to maximize nutrient gains (Clark & Mangel 1986;Kamil et al 2012;Parker et al 1990;Watve et al 2016aWatve et al , 2016b. Presence of a predator substantially alters behavioural and physiological patterns of a population (Preisser et al 2005).…”

Section: Foraging Optimizationmentioning

confidence: 99%

Foraging theory and the propensity to be obese: an alternative to thrift

Baig

Lokhande³

et al. 2018

Preprint

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The evolutionary origin of obesity is classically believed to be genetic or developmentally induced thrift, as an adaptation to ancestral feast and famine conditions. However, recently the thrift family of hypotheses have attracted serious criticism necessitating alternative thinking. Optimization of foraging behaviour is an important aspect of behavioural evolution. For a species evolved for optimizing nutritional benefits against predation or other foraging risks, reduction in foraging risk below a threshold dramatically increases the steady-state body weight. In modern life where feeding is detached from foraging, the behavioural regulation mechanisms are likely to fail resulting into escalation of adiposity. At a proximate level the signalling pathways for foraging optimization involve fear induced signal molecules in the brain including Cocaine and Amphetamine Regulated Transcript (CART) interacting with adiposity signals such as leptin. While leptin promotes the expression of the fear peptides, the fear peptides promote anorectic action of leptin.This interaction promotes foraging drive and risk tolerance when the stored energy is low and suppresses hunger and foraging drive when the perceived risk is high.The ecological model of foraging optimization and the molecular model of interaction of these peptides converge in the outcome that the steady state adiposity is an inverse square root function of foraging risk. The foraging optimization model is independent of thrift or insurance hypotheses, but not mutually exclusive. We review existing evidence and suggest testable predictions of the model.Understanding obesity simultaneously at proximate and ultimate levels is likely to suggest effective means to curb the obesity epidemic.

“…However, why it is appropriate to use the ratio in some examples and difference in others is not explained. For single unit investment optimization, although both ratio models and difference models are used multiple times in literature, the question when a ratio model is appropriate and when a difference model is appropriate was not formally addressed until recently (Watve et al, 2016., Watve & Ojas 2020). We argue here that the outcomes of ratio versus difference optimization can be substantially different; clarity in the conditions under which ratio or difference optimization is appropriate resolves many long standing questions and conundrums; and this distinction gives new clarity about some of the old debates.…”

Section: Introductionmentioning

confidence: 99%

“…Complex organisms exhibit much behavioral plasticity and have evolved the ability to judge the cost benefits in a given context and optimize their behavior contextually. Behavioral optimization models have been used to explain human behavior in nutritional (Nettle et al, 2017), ecological (Watve et al, 2016) and social context (Purshouse & McAlister 2013). There is considerable debate over the application of optimality to humans (Driscoll 2009;Rahnev & Denison 2018) particularly in experimental tests of perceptual decision making.…”

Section: Introductionmentioning

confidence: 99%

The ratio versus difference optimization and its implications for optimality theory

Shinde

Patwardhan

2020

Preprint

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Among the classical models of optimization, some models maximize the ratio of returns per investment, others maximize the difference between returns and investment. However, the question under what conditions use of the ratio model is appropriate and under what conditions a difference model should be used remained unaddressed until recently. The question is important because the strategies indicated by ratio optimum can be substantially different than the ones suggested by difference optimum. We make a general case here for the set of conditions for appropriate use of ratio versus difference optimum. When the investable amount is perceived as limiting but not the investment opportunities a ratio optimum is appropriate and when the investment opportunities are perceived to be limiting but not the investable amount, difference optimum is appropriate. Taking examples of Concorde fallacy, parental investment, r and K selection, nectar production, pollinator behavior, protein synthesis and stability, viral burst size and human economic behavior we show that the ratiodifference distinction in optimization models resolves many long standing debates and conundrums in evolution, behavior and economics.