Predicting the distributions of predator (snow leopard) and prey (blue sheep) under climate change in the Himalaya

Future climate change is likely to affect distributions of species, disrupt biotic interactions, and cause spatial incongruity of predator–prey habitats. Understanding the impacts of future climate change on species distribution will help in the formulation of conservation policies to reduce the risks of future biodiversity losses. Using a species distribution modeling approach by MaxEnt, we modeled current and future distributions of snow leopard (Panthera uncia) and its common prey, blue sheep (Pseudois nayaur), and observed the changes in niche overlap in the Nepal Himalaya. Annual mean temperature is the major climatic factor responsible for the snow leopard and blue sheep distributions in the energy-deficient environments of high altitudes. Currently, about 15.32% and 15.93% area of the Nepal Himalaya are suitable for snow leopard and blue sheep habitats, respectively. The bioclimatic models show that the current suitable habitats of both snow leopard and blue sheep will be reduced under future climate change. The predicted suitable habitat of the snow leopard is decreased when blue sheep habitats is incorporated in the model. Our climate-only model shows that only 11.64% (17,190 km2) area of Nepal is suitable for the snow leopard under current climate and the suitable habitat reduces to 5,435 km2 (reduced by 24.02%) after incorporating the predicted distribution of blue sheep. The predicted distribution of snow leopard reduces by 14.57% in 2030 and by 21.57% in 2050 when the predicted distribution of blue sheep is included as compared to 1.98% reduction in 2030 and 3.80% reduction in 2050 based on the climate-only model. It is predicted that future climate may alter the predator–prey spatial interaction inducing a lower degree of overlap and a higher degree of mismatch between snow leopard and blue sheep niches. This suggests increased energetic costs of finding preferred prey for snow leopards – a species already facing energetic constraints due to the limited dietary resources in its alpine habitat. Our findings provide valuable information for extension of protected areas in future

Ecology of the snow leopard and the Himalayan tahr in Sagarmatha (Mt. Everest) National Park, Nepal.

Ecology of the snow leopard and the Himalayan tahr in Sagarmatha (Mt. Everest) National Park, Nepal

What Do Ecological Paradigms Offer to Conservation?

Ecological theory provides applications to biodiversity management—but often falls short of expectations. One possibility is that heuristic theories of a young science are too immature. Logistic growth predicts a carrying capacity, but fisheries managed with the Lotka-Volterra paradigm continue to collapse. A second issue is that general predictions may not be useful. The theory of island biogeography predicts species richness but does not predict community composition. A third possibility is that the theory itself may not have much to do with nature, or that empirical parameterization is too difficult to know. The metapopulation paradigm is relevant to conservation, but metapopulations might not be common in nature. For instance, empirical parameterization within the metapopulation paradigm is usually infeasible. A challenge is to determine why ecology fails to match needs of managers sometimes but helps at other. Managers may expect too much of paradigmatic blueprints, while ecologists believe them too much. Those who implement biodiversity conservation plans need simple, pragmatic guidelines based on science. Is this possible? What is possible? An eclectic review of theory and practice demonstrate the power and weaknesses of the ideas that guide conservation and attempt to identify reasons for prevailing disappointment

Evolution of cooperation: combining kin selection and reciprocal altruism into matrix games with social dilemmas.

Darwinian selection should preclude cooperation from evolving; yet cooperation is widespread among organisms. We show how kin selection and reciprocal altruism can promote cooperation in diverse 2×2 matrix games (prisoner's dilemma, snowdrift, and hawk-dove). We visualize kin selection as non-random interactions with like-strategies interacting more than by chance. Reciprocal altruism emerges from iterated games where players have some likelihood of knowing the identity of other players. This perspective allows us to combine kin selection and reciprocal altruism into a general matrix game model. Both mechanisms operating together should influence the evolution of cooperation. In the absence of kin selection, reciprocal altruism may be an evolutionarily stable strategy but is unable to invade a population of non-co-operators. Similarly, it may take a high degree of relatedness to permit cooperation to supplant non-cooperation. Together, a little bit of reciprocal altruism can, however, greatly reduce the threshold at which kin selection promotes cooperation, and vice-versa. To properly frame applications and tests of cooperation, empiricists should consider kin selection and reciprocal altruism together rather than as alternatives, and they should be applied to a broader class of social dilemmas than just the prisoner's dilemma

A modification for prisoner’s dilemma for iterative interactions.

<p>The term <i>w</i> is the probability of a player knowing the strategy of its partner. The tit-for-tat (TFT) strategy cooperates with strangers or with known TFT individuals. It plays defect with known D individuals.</p

Payoff matrix for an iterative prisoner’s dilemma with non-random interactions.

<p>Following many interactions, a portion <i>r</i> occurs with a like-individual, and only the portion 1-<i>r</i> occurs with the randomly selected individual. All-C interacts with another All-C, both <i>r</i> and 1-<i>r</i> of the time the interaction generates a payoff of <i>r</i>(<i>b</i>−<i>c</i>)+(1−<i>r</i>)(<i>b</i>−<i>c</i>), with a total payoff of “<i>r</i>(<i>b</i>−<i>c</i>)+(1−<i>r</i>)(<i>b</i>−<i>c</i>)” for All-C and TFT pair of strategies. Likewise, All-C and All-D obtains <i>r</i>(<i>b</i>−<i>c</i>)+(1−<i>r</i>)( −c), and so forth.</p

Payoff matrix for the snowdrift game.

<p>Digging out of the snowdrift gives each player a benefit of <i>b/2</i>. The cost is born by the digger (C) who splits the cost when both dig, or bears the entire cost as the sole digger. When <i>b/</i>2−<i>c</i>>0, the ESS is a mixture of C and D individuals. When <i>b/</i>2−<i>c</i>/2>0><i>b</i>/2−<i>c</i>, All-D is the sole ESS, even though there is still a social dilemma where All-C yields higher payoffs than All-D.</p

Payoff matrix for the hawk-dove game.

<p>In hawk-dove game, if both players cooperate, then they split the benefit <i>b</i>/2. When one player cooperates and the other defects, the co-operator obtains nothing but the defector the benefit, <i>b</i>. If both players defect, they split the benefit, but also incur a cost (<i>b</i>/2−<i>c</i>).</p

Payoff matrix for an iterative hawk-dove game with non-random interactions.

<p>Payoff matrix for an iterative hawk-dove game with non-random interactions.</p