Migratory birds modulate niche tradeoffs in rhythm with seasons and life history

Abstract

Movement is a key means by which animals cope with variable environments. As they move, animals construct individual niches composed of the environmental conditions they experience. Niche axes may vary over time and covary with one another as animals make tradeoffs between competing needs. Seasonal migration is expected to produce substantial niche variation as animals move to keep pace with major life history phases and fluctuations in environmental conditions. Here, we apply a time-ordered principal component analysis to examine dynamic niche variance and covariance across the annual cycle for four species of migratory crane: common crane (Grus grus, n = 20), demoiselle crane (Anthropoides virgo, n = 66), black-necked crane (Grus nigricollis, n = 9), and white-naped crane (Grus vipio, n = 9). We consider four key niche components known to be important to aspects of crane natural history: enhanced vegetation index (resources availability), temperature (thermoregulation), crop proportion (preferred foraging habitat), and proximity to water (predator avoidance). All species showed a primary seasonal niche "rhythm" that dominated variance in niche components across the annual cycle. Secondary rhythms were linked to major species-specific life history phases (migration, breeding, and nonbreeding) as well as seasonal environmental patterns. Furthermore, we found that cranes' experiences of the environment emerge from time-dynamic tradeoffs among niche components. We suggest that our approach to estimating the environmental niche as a multidimensional and time-dynamical system of tradeoffs improves mechanistic understanding of organism-environment interactions.

Keywords: animal movement; ecological niche; life history; migration.

Fig. 1.

(A) GPS tracks from 104 individuals of 4 species of crane spanning Africa, Asia, and Europe: common crane (G. grus; green, n = 20), demoiselle crane (A. virgo; red; n = 66), black-necked crane (G. nigricollis; orange, n = 9), and white-naped crane (G. vipio; pink, n = 9). (B) Traditional PCA among species reduces dimensionality of environmental variables (here water proximity, proportion crops within 300 m, EVI, and land surface temperature; see Materials and Methods) but ignores within-species tradeoffs among variables (niche components) and temporal dynamics. Points represent weeks. (C) GPS tracks and time series of two niche components (water proximity and EVI) for a single individual black-necked crane suggest tradeoffs between proximity to risk (low values of water proximity suggest high predation exposure) and reward (high EVI indicates relatively high primary productivity). Values are scaled to individual-specific empirical quantiles.

Fig. 2.

We considered four “niche components” (Top Left) with a priori ecological interpretations relevant to crane natural history. By organizing data as a weekly time series (Middle Left) before dimension reduction via PCA we can ask three questions about time-dynamic relationships among variables. (Q1, Top Right) Are there tradeoffs among niche components over the course of a full annual cycle? Opposing PC loadings indicate negative covariance (a tradeoff) among niche components, whereas concurring and orthogonal vectors imply positive covariance and independent covariance, respectively. Vector length indicates relative variance compared to other niche components; thus, very short vectors suggest niche components that do not vary strongly. (Q2, Middle Right) Are environmental niches conserved across seasons? By rotating the original data points back into environmental space (i.e., PC space) we can view the degree of overlap in niche axes among seasons. Seasons with little or no overlap imply use of different niches between seasons. (Q3, Bottom) How do particular axes of variation (PCs) contribute to variance in niche components over time? By plotting PC scores over time, we observe the relative degree to which each week of data contributes to overall variation in that axis. Scores close to zero imply very little variance in that week, whereas scores deviating from zero suggest large (positive or negative) variance contributed by that particular week and reveal how particular axes of niche variation associate with major phases of life history (e.g., migrations).

Fig. 3.

(Top row) PC loadings for each species reveal patterns of covariance among niche components over the course of an annual cycle. Orthogonal loadings imply uncorrelated variance, suggesting modulation of niche components across the annual cycle rather than direct tradeoffs. Opposing loadings suggest a tradeoff among niche components (as in black-necked crane between water proximity and EVI). Shorter vectors suggest low variance in a niche component over the course of a year. (Bottom row) Species’ seasonal niche variation plotted using the first 2 PCs. Niches are differentiated when there is little overlap in environmental space across seasons (e.g., summer and winter in demoiselle crane). Niches are consistent between seasons (tracked) when overlapping in PC space (e.g., spring and fall in white-naped crane).

Fig. 4.

Annual niche dynamics and their components. Line plots show PC scores per week (i.e., per sample) for the first two PCs in relation to spring and fall migration (green and orange shaded areas, respectively). Values that deviate from zero indicate weeks that contributed relatively more to that PC. Bar plots show niche component loadings on that PC. Similar to the scores, niche component loadings that deviate from zero indicate greater relative contribution of a certain covariate to that PC. Temporal trends in PC scores reveal seasonal patterns of variation in the niche components indicated by the bar plots. For example, for demoiselle crane, PC1 is composed almost entirely of water proximity and crop proportion and exhibits a temporal trend broadly matching patterns of seasonality. On the other hand, PC2 for that species shows strong variation during spring and fall migration but less so during winter or summer stationary periods. This migration-associated PC is driven primarily by variance in temperature.