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. 2024 May 14;121(20):e2303846121.
doi: 10.1073/pnas.2303846121. Epub 2024 May 6.

Landscape fragmentation overturns classical metapopulation thinking

Yun Tao  1   2 , Alan Hastings  3   4 , Kevin D Lafferty  5   6 , Ilkka Hanski  7 , Otso Ovaskainen  7   8   9
Affiliations

Landscape fragmentation overturns classical metapopulation thinking

Yun Tao et al. Proc Natl Acad Sci U S A. .

Abstract

Habitat loss and isolation caused by landscape fragmentation represent a growing threat to global biodiversity. Existing theory suggests that the process will lead to a decline in metapopulation viability. However, since most metapopulation models are restricted to simple networks of discrete habitat patches, the effects of real landscape fragmentation, particularly in stochastic environments, are not well understood. To close this major gap in ecological theory, we developed a spatially explicit, individual-based model applicable to realistic landscape structures, bridging metapopulation ecology and landscape ecology. This model reproduced classical metapopulation dynamics under conventional model assumptions, but on fragmented landscapes, it uncovered general dynamics that are in stark contradiction to the prevailing views in the ecological and conservation literature. Notably, fragmentation can give rise to a series of dualities: a) positive and negative responses to environmental noise, b) relative slowdown and acceleration in density decline, and c) synchronization and desynchronization of local population dynamics. Furthermore, counter to common intuition, species that interact locally ("residents") were often more resilient to fragmentation than long-ranging "migrants." This set of findings signals a need to fundamentally reconsider our approach to ecosystem management in a noisy and fragmented world.

Keywords: fragmentation; landscape ecology; metapopulation; population dynamics.

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Conflict of interest statement

Competing interests statement:The authors declare no competing interest.

Figures

Fig. 1.
Fig. 1.
Four types of landscape structure generated on a 60 × 60 lattice, with the proportional habitat covers listed in parentheses: (A) homogenous (1), (B, i–iv) lowly fragmented (0.28, 0.30, 0.28, 0.25), (C, i–iv) highly fragmented (0.11, 0.13, 0.12, 0.10), and (D) regular grid (0.11). The darkened areas represent habitat space. In D, each patch has unit length and equal nearest-neighbor distances.
Fig. 2.
Fig. 2.
Mean times to global extinction of conspecifics on four landscapes: (A) homogenous, (B) lowly fragmented, as pictured in Fig. 1 B, i, (C) highly fragmented, as pictured in Fig. 1 C, i, and (D) regular grid, plotted on a logarithmic scale. The model domains are the full-sized landscapes in Fig. 1 (top circles) and random samples of 6 × 6 lattices (bottom circles). Conspecifics vary in their spatial scale of dispersal and competition α, ranging from 1 to ∞. The rightmost column (α=∞) thus describes the effects of total habitat area on metapopulation persistence, independent of habitat arrangement. Environmental stochasticity was globally synchronized and modeled across three values of variance σr2 distinguished by line colors: 0.5 (red, green), 1 (orange, blue), and 2 (yellow, purple). In B–D, the results with and without habitat aggregation (i.e., collecting the habitat fragments into a square patch versus keeping the original habitat arrangements) are represented by blue and red circle outlines, respectively. 3,000 simulation iterations were run per system setting, each initialized with the same density of spatially randomized individuals per unit area (1,000/3,600) and continued until the metapopulation reaches global extinction or the terminal time of 5×105 generations. Mean-fecundity rate μ0=ln(1.1); competition strength b=0.2. Results for landscapes depicted in Fig. 1 B and C, ii–iv can be found in SI Appendix, Fig. S5A.
Fig. 3.
Fig. 3.
Global population densities of conspecifics on four landscapes: (A) homogenous, (B) lowly fragmented, as pictured in Fig. 1 B, i, (C) highly fragmented, as pictured in Fig. 1 C, i, and (D) regular grid. Conspecifics vary in their spatial scales of dispersal and competition α, ranging from 1 to ∞, and mean fecundity rates μ0, marked by the colors of the box outlines (red and blue). Regional stochasticity was modeled across three values of spatial scale αr, indicated by the filled colors: 1.5 (red, green), 6 (orange, blue), and ∞ (yellow, purple). Five iterations were run per parameter set, each initialized with 5,000 spatially randomized individuals and tracked for 250 generations, with only the last 50 generations shown. The horizontal lines, colored to match μ0, depict time-variant mean-field model predictions averaged across five simulation iterations and the last 50 of 250 generations. Variance of regional stochasticity σr2=0.5; competition strength b=0.2. Results for landscapes depicted in Fig. 1 B and C, ii–iv can be found in SI Appendix, Fig. S5B.
Fig. 4.
Fig. 4.
Interlaced correlograms showing spatial synchrony on four landscapes: (A) homogenous, (B) lowly fragmented, as pictured in Fig. 1 B, i, (C) highly fragmented, as pictured in Fig. 1 C, i, and (D) regular grid. Synchrony was measured by the pairwise correlations in local patch occupancy and local abundance at the final generation, illustrated using distinct color gradients. Sampling sites are identical square grids of length m=2. The left panels show the levels of synchrony between nearby populations (separated by an intersite distance lij≈2); the right panels show those between distant populations (lij≈20). Mean fecundity rates μ0 were adjusted to maintain a near-constant mean global population size N (1,000 ± 200) across all combinations of landscape structure, spatial scale of regional stochasticity αr, and spatial scale of dispersal and competition α. 5,000 simulation iterations were run per parameter set, each initialized with 5,000 spatially randomized individuals and tracked for 50 generations. Variance of regional stochasticity σr2=0.5; competition strength b=0.2. Results for landscapes depicted in Figs. 1 B and C, ii–iv can be found in SI Appendix, Fig. S5C.
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