Mechanism matters: the cause of fluctuations in boom–bust populations governs optimal habitat restoration strategy

Knerr

Proc. Natl. Acad. Sci. U.S.A.

et al. 2019

An important new hypothesis in landscape ecology is that extreme, decade-scale megadroughts can be potent drivers of rapid, macroscale ecosystem degradation and collapse. If true, an increase in such events under climate change could have devastating consequences for global biodiversity. However, because few megadroughts have occurred in the modern ecological era, the taxonomic breadth, trophic depth, and geographic pattern of these impacts remain unknown. Here we use ecohistorical techniques to quantify the impact of a record, pancontinental megadrought period (1891 to 1903 CE) on the Australian biota. We show that during this event mortality and severe stress was recorded in >45 bird, mammal, fish, reptile, and plant families in arid, semiarid, dry temperate, and Mediterranean ecosystems over at least 2.8 million km2 (36%) of the Australian continent. Trophic analysis reveals a bottom-up pattern of mortality concentrated in primary producer, herbivore, and omnivore guilds. Spatial and temporal reconstruction of premortality rainfall shows that mass mortality and synchronous ecosystem-wide collapse emerged in multiple geographic hotspots after 2 to 4 y of severe (>40%) and intensifying rainfall deficits. However, the presence of hyperabundant herbivores significantly increased the sensitivity of ecosystems to overgrazing-induced meltdown and permanent ecosystem change. The unprecedented taxonomic breadth and spatial scale of these impacts demonstrate that continental-scale megadroughts pose a major future threat to global biodiversity, especially in ecosystems affected by intensive agricultural use, trophic simplification, and invasive species.

Section: Resultssupporting

confidence: 79%

Historical reconstruction unveils the risk of mass mortality and ecosystem collapse during pancontinental megadrought

Knerr

Proc. Natl. Acad. Sci. U.S.A.

et al. 2019

“…With these data, we evaluated two models, a step model ( ) evaluating whether there was a demonstrable change in status (i.e., mean abundance) during the time period and a segmented model ( ) examining whether there was a change in the trend; we specifically tested for a reversal of trend from a period of decline to one of growth. We fit these models in R (R Core Team, 2018) with both the changepoint (Killick et al, 2016) and chngpt (Fong and Gilbert, 2017) packages to ensure correct model outputs (see Supplementary Datasheet S1). Assumptions of independent, normally distributed data (on a log e scale) with constant variance pre-and post-change were evaluated with Shapiro and Kolmogorov-Smirnov tests and inspection of quantile-quantile and autocorrelation plots.…”

Section: Methodsmentioning

confidence: 99%

Evidence for a Growing Population of Eastern Migratory Monarch Butterflies Is Currently Insufficient

2020

The eastern migratory population of monarch butterflies has experienced a multidecadal decline, but a recent increase in abundance (to 6.05 ha in winter 2018) has led some observers to question whether the population has reversed its long-standing decline and embarked on a trajectory of increasing abundance. We examined this possibility through changepoint analyses, assessing whether a change in trajectory existed over a 25-year times series. We found evidence of a change in trajectory in 2014, but insufficient statistical support for a significantly increasing population since that time (β = 0.285, 95% CI = −0.127, 0.697). If the population estimate for winter 2019 is ≥4.0 ha, we will then be able to credibly assert the population has been increasing since 2014. However, given estimated levels of time series variability, presumed habitat capacity and no recent change in status or trend, there was a 13.5% probability of observing a population estimate as large or larger than was reported for winter 2018. Our analyses highlight the incredible difficulty in drawing robust conclusions from annual changes in abundance over a short time series, especially for an insect that commonly exhibits considerable year-to-year variation. Thus, we urge caution when drawing conclusions regarding species status and trends for any species for which limited data are available.

“…Density-independent mortality, caused by a wide array of annually variable environmental stressors, is offset against density-dependent reproduction (Yakuba et al 2004, Drury and Dwyer 2006, Flockhart et al 2012, Marini and Zalucki 2017), and this tension between birth and death processes plays out over multiple generations and across the vastness of eastern North America (Flockhart et al 2015, Oberhauser et al2017). In some years, these processes complement one another, leading to booms or busts in the population (Himes Boor et al 2018). In other years, increases in one are offset by the other, mitigating any sizeable year-to-year change in population size.…”

mentioning

confidence: 99%

Evidence for a growing population of eastern migratory monarch butterflies is currently insufficient

Thogmartin

Szymanski

Weiser

2020

Preprint

The eastern migratory population of monarch butterflies has experienced a multi-10 decadal decline, but a recent increase in abundance (to 6.05 ha in winter 2018) has led some 11 observers to question whether the population has reversed its long-standing decline and 12 embarked on a trajectory of increasing abundance. We examined this possibility through 13 changepoint analyses, first assessing whether a change in trajectory existed and whether that 14 change was sufficient to alter our estimated risk for the population. We found evidence of a 15 change in trajectory in 2014, but insufficient statistical support for a significantly increasing 16 population since that time (β = 0.285, 95% CI = -0.127, 0.697). If the population estimate for 17 winter 2019 is ≥4.0 ha, we will then be able to credibly assert the population has been increasing 18 since 2014. However, given estimated levels of time series variability, presumed habitat capacity 19 and no recent change in status or trend, there was a 13.5% probability of observing a population 20 estimate as large or larger than was reported for winter 2018. Despite insufficient evidence for an 21 increasing population, near-term risk of quasi-extinction by 2023 has declined (mean risk 22 23 highlight the incredible difficulty in drawing robust conclusions from annual changes in 24 abundance over a short time series, especially for an insect that commonly exhibits considerable 25 year-to-year variation. Thus, we urge caution when drawing conclusions regarding species status 26 and trends for any species for which limited data are available. 27 28 Shapiro 32 Populations vary over time in their abundance, and this variability can impart uncertainty to the 33 status and trend of a species. As population dynamics approach extinction, dynamics become 34 more variable (Holmes and Fagan 2006), which means short-term highs might become higher, 35 even while abundance is declining on average. In addition to the stochastic variation in 36 abundance imposed by the environment, uncertainty in species status and trend arises from 37 population sizes most often being estimated rather than counted; trends being inferred from 38 limited duration time series; and latent characteristics of a population, such as its relation to 39 carrying capacity or quasi-extinction thresholds, generally being inferred properties rather than 40 an observable quantity. Thus, given these various sources of uncertainty, it is difficult enough to 41 determine the trajectory for a population, let alone any change that may occur in that trajectory, 42 especially one that may occur near the terminus of a time series based on limited data.43 3 Estimates of the population size of the eastern North American migratory population of 44 monarch butterflies (Danaus plexippus, hereafter monarchs) in their overwintering locations in 45 high-elevation oyamel fir (Abies religiosa) forests of central Mexico suggest a long-term decline 46 in abundance. Using a model allowing separation of observation-induced error from n...