In rangelands, monitoring spatial regime boundaries (i.e., boundaries between ecological states) could provide early warnings of state transitions, elucidate the spatial nature of state transitions, and quantify management outcomes. Here, we test the ability of established regime shift detection methods and traditional, local-scale rangeland monitoring data to identify spatial regime boundaries in a complex rangeland system. We collected plant community composition data via point-intercept sampling at 400 evenly-spaced locations along a 4000m transect. We then applied three statistical regime shift detection methods to identify spatial regimes and compared outcomes of each statistical method. Statistical detection of spatial regimes held up to traditional field monitoring practices. Spatial regime locations matched historic plant communities in the study site going back 130 years, but we also detected a localized wildfire-driven state transition: a relict ponderosa pine (Pinus ponderosa) spatial regime transitioned to a bur oak (Quercus macrocarpa) – annual grass regime. The spatial regimes monitoring approach capitalizes on the existence of spatial boundaries between states to track ecological states as they move, expand, contract, or disappear. This is an advancement over traditional time series approaches to regime shift/state transition detection which only detect state transitions if enough sites transition. Existing local-scale rangeland monitoring, used strategically, can complement current coarse, broad-scale applications of spatial regimes monitoring by detecting subtle, fine-scale boundaries that broad-scale monitoring cannot capture.

在牧场中，监测空间格局边界（即生态状态之间的边界）可以提供状态转变的预警，阐明状态转变的空间性质，并量化管理结果。在这里，我们测试已建立的政权转移检测方法和传统的地方尺度牧场监测数据在复杂牧场系统中识别空间政权边界的能力。我们通过沿4000m断面的400个均匀间隔的位置进行点截取采样来收集植物群落组成数据。然后，我们应用了三种统计体制转变检测方法来识别空间体制，并比较每种统计方法的结果。对空间状况的统计检测不符合传统的现场监视实践。黄松（Pinus黄花鱼）的空间格局过渡到bur橡木（Quercus macrocarpa）–一年生草。空间状态监测方法利用状态之间的空间边界来跟踪生态状态的移动，扩展，收缩或消失。这是对传统时间序列方法进行状态转移/状态转换检测的一种进步，该方法仅在足够的站点转换时才检测状态转换。战略性地使用的现有地方尺度牧场监测可以通过检测大规模监测无法捕获的细微，精细边界来补充当前的空间体制监测的粗略，大规模应用。