How squirrels protect their caches: Location, conspicuousness during caching, and proximity to kin influence cache lifespan

Scatter-hoarding animals cannot physically protect individual caches, and instead utilize several behavioral strategies that are hypothesized to offer protection for caches. We validated the use of physically altered, cacheable food items, and determined that intraspecific pilfering among free-ranging fox squirrels (N = 23) could be assessed in the field. In this study we were able to identify specific individual squirrels who pilfered or moved caches that had been stored by a conspecific. We identified a high level of pilfering (25%) among this population. In a subsequent study, we assessed the fate of squirrel-made caches. Nineteen fox squirrels cached 294 hazelnuts with passive integrated transponder tags implanted in them. Variables collected included assessment and cache investment and protection behaviors; cache location, substrate, and conspicuousness of each cache; how long each cache remained in its original location, and the location where the cache was finally consumed. We also examined whether assessment or cache protection behaviors were related to the outcomes of buried nuts. Finally, we measured the population dynamics and heterogeneity of squirrels in this study, testing the hypothesis that cache proximity and pilferage tolerance could serve as a form of kin selection. Polymer chain reaction (PCR) was used to analyze hair samples and determine relatedness among 15 squirrels, and the potential impact of relatedness on caching behavior. Results suggested that cache protection behaviors and the lifespan of a cache were dependent on the conspicuousness of a cache. Squirrels may mitigate some of the costs of pilfering by caching closer to the caches of related squirrels than to those of non-related squirrels.

Corvids Outperform Pigeons and Primates in Learning a Basic Concept

Corvids (birds of the family Corvidae) display intelligent behavior previously ascribed only to primates, but such feats are not directly comparable across species. To make direct species comparisons, we used a same/different task in the laboratory to assess abstract-concept learning in black-billed magpies ( Pica hudsonia). Concept learning was tested with novel pictures after training. Concept learning improved with training-set size, and test accuracy eventually matched training accuracy—full concept learning—with a 128-picture set; this magpie performance was equivalent to that of Clark’s nutcrackers (a species of corvid) and monkeys (rhesus, capuchin) and better than that of pigeons. Even with an initial 8-item picture set, both corvid species showed partial concept learning, outperforming both monkeys and pigeons. Similar corvid performance refutes the hypothesis that nutcrackers’ prolific cache-location memory accounts for their superior concept learning, because magpies rely less on caching. That corvids with “primitive” neural architectures evolved to equal primates in full concept learning and even to outperform them on the initial 8-item picture test is a testament to the shared (convergent) survival importance of abstract-concept learning.

Weighted Cache Location Problem with Identical Servers

This paper extends the well-knownp-CLP with one server top-CLP withm≥2identical servers, denoted by(p,m)-CLP. We propose the closest server orienting protocol (CSOP), under which every client connects to the closest server to itself via a shortest route on given network. We abbreviate(p,m)-CLP under CSOP to(p,m)-CSOP CLP and investigate that(p,m)-CSOP CLP on a general network is equivalent to that on a forest and further to multiple CLPs on trees. The case ofm=2is the focus of this paper. We first devise an improvedO(ph2+n)-time parallel exact algorithm forp-CLP on a tree and then present a parallel exact algorithm with at mostO((4/9)p2n2)time in the worst case for(p,2)-CSOP CLP on a general network. Furthermore, we extend the idea of parallel algorithm to the cases ofm>2to obtain a worst-caseO((4/9)(n-m)2((m+p)p/p-1!))-time exact algorithm. At the end of the paper, we first give an example to illustrate our algorithms and then make a series of numerical experiments to compare the running times of our algorithms.