How Big and How Close? Habitat Patch Size and Spacing to Conserve a Threatened Species

Abstract

Abstract. We present results of a spatially explicit, individual‐based stochastic dispersal model (HexSim) to evaluate effects of size and spacing of patches of habitat of Northern Spotted Owls (NSO; Strix occidentalis caurina) in Pacific Northwest, USA, to help advise recovery planning efforts. We modeled 31 artificial landscape scenarios representing combinations of NSO habitat cluster size (range 4–49 NSO pairs per cluster) and edge‐to‐edge cluster spacing (range 7–101 km), and an all‐habitat landscape. We ran scenarios using empirical estimates of NSO dispersal dynamics and distances and stage class vital rates (representing current population declines) and under adult survival rates adjusted to achieve an initially stationary population. Results suggested that long‐term (100‐yr) habitat occupancy rates are significantly higher with habitat clusters supporting ≥25 NSO pairs and ≤15 km spacing, and with overall landscapes of ≥35–40% habitat. Although habitat provision is key to NSO recovery, no habitat configuration provided for long‐term population persistence when coupled with currently observed vital rates. Results also suggested a key role of floaters (unpaired, nonterritorial, dispersing owls) in recolonizing vacant habitat, and that the floater population segment becomes increasingly depleted with greater population declines. We suggest additional areas of modeling research on this and other threatened species.

Analysis of Sensitivity and Uncertainty in an Individual‐based Model of a Threatened Wildlife Species

“…Several other causes of model variation and sensitivity have been analyzed elsewhere, in particular effects of including or omitting NSO dispersal barriers on the landscape, effects of Barred Owls ( Strix varia ; a key competitor) on NSO population size and trend (Wiens et al []), potential contribution of nonfederal lands as habitat (Singleton []), and size and spacing of habitat blocks (Marcot et al []). Management essentially cannot control NSO dispersal barriers of elevation and large water barriers.…”

“…We parameterized the model with 15 input parameters (Table ) representing female‐only demography with 4 stage classes (juvenile, first‐year, second‐year, and third‐year and older); paired (territory‐holding, nesting, reproductive adult) birds and unpaired (nonterritorial) “floater” birds; and 3 habitat quality (resource use) classes (low, moderate, high) based on analysis of empirical habitat selection behaviors (Singleton []). We included consideration of floater birds because previous NSO HexSim modeling suggested that they could play an important role in population persistence (Marcot et al []). Movement in the normative model consisted of stochastic dispersal of juvenile NSOs, prospecting for high‐quality habitat by which to establish a breeding territory, and exploring for habitat to establish a foraging home range (Table ; Supporting Information Appendix B).…”

“…The event sequence for each simulated year includes stochastic survivorship and reproduction by stage and resource classes, and dispersal and floater movement to prospect for suitable unoccupied habitat and to establish territories and home ranges (Figure ). Survivorship and reproduction schedules in the model draw from probability distributions calibrated to field data (Forsman et al []), and all movement events in the model are stochastic and bound by specified value ranges or algorithms parameterized to emulate known NSO movement dynamics (Marcot et al []).…”

“…For example, the NSO survival rates varied stochastically as a function of stage class, resource use class, and other factors. Also, other analyses have already established high sensitivity of population size and trend to survivorship and reproduction (Lamberson et al [], Marcot et al []). Many years of field study have provided estimates of NSO vital rates (survival and reproduction) with nearly unprecedented accuracy (Forsman et al []), and sensitivity to vital rate stochasticity can be analyzed separately (e.g., Wisdom et al [], Aberg et al []).…”

“…HexSim runs on terrestrial landscapes tiled with regular hexagons and user‐supplied details on habitat quality, species life histories, and other parameters. Recent applications of HexSim include analysis of habitat fragmentation effects on wolves ( Canis lupus ) in Manitoba (Stronen et al []) and on Ord's kangaroo rats ( Dipodomys ordii ) in Alberta, Canada (Heinrichs et al []), and evaluation of threatened populations of Northern Spotted Owls (NSO; Strix occidentalis caurina ) in a suite of hypothetical landscapes varying in absolute habitat area and size and spacing of old‐forest habitat patches in the Pacific Northwest, USA (Marcot et al []). Heinrichs et al ([]) included a brief summary of sensitivity analysis, reporting that extinction risk was largely insensitive to variations in population and habitat quality parameters, and most sensitive to decreases (but not increases) in vital rates of survival and reproduction.…”