A big data-model integration approach for predicting epizootics and population recovery in a keystone species.

Infectious diseases pose a significant threat to global health and biodiversity. Yet, predicting the spatiotemporal dynamics of wildlife epizootics remains challenging. Disease outbreaks result from complex nonlinear interactions among a large collection of variables that rarely adhere to the assumptions of parametric regression modeling. We adopted a nonparametric machine learning approach to model wildlife epizootics and population recovery, using the disease system of colonial black-tailed prairie dogs (BTPD, Cynomys ludovicianus) and sylvatic plague as an example. We synthesized colony data between 2001 and 2020 from eight USDA Forest Service National Grasslands across the range of BTPDs in central North America. We then modeled extinctions due to plague and colony recovery of BTPDs in relation to complex interactions among climate, topoedaphic variables, colony characteristics, and disease history. Extinctions due to plague occurred more frequently when BTPD colonies were spatially clustered, in closer proximity to colonies decimated by plague during the previous year, following cooler than average temperatures the previous summer, and when wetter winter/springs were preceded by drier summers/falls. Rigorous cross-validations and spatial predictions indicated that our final models predicted plague outbreaks and colony recovery in BTPD with high accuracy (e.g., AUC generally >0.80). Thus, these spatially explicit models can reliably predict the spatial and temporal dynamics of wildlife epizootics and subsequent population recovery in a highly complex host-pathogen system. Our models can be used to support strategic management planning (e.g., plague mitigation) to optimize benefits of this keystone species to associated wildlife communities and ecosystem functioning. This optimization can reduce conflicts among different landowners and resource managers, as well as economic losses to the ranching industry. More broadly, our big data-model integration approach provides a general framework for spatially explicit forecasting of disease-induced population fluctuations for use in natural resource management decision-making.

A meta-analysis of the effects of common management actions on the nest success of North American birds.

Management strategies for the recovery of declining bird populations often must be made without sufficient data to predict the outcome of proposed actions or sufficient time and resources necessary to collect these data. We quantitatively reviewed studies of bird management in Canada and the United States to evaluate the relative efficacy of 4 common management interventions and to determine variables associated with their success. We compared how livestock exclusion, prescribed burning, removal of predators, and removal of cowbirds (Molothrus ater) affect bird nest success and used meta-regression to evaluate the influence of species and study-specific covariates on management outcomes. On average, all 4 management interventions increased nest success. When common species and threatened, endangered, or declining species (as defined by long-term trend data from the North American Breeding Bird Survey) were analyzed together, predator removal was the most effective management option. The difference in mean nest success between treatment and control plots in predator-removal experiments was more than twice that of either livestock exclusion or prescribed burning. However, when we considered management outcomes from only threatened, endangered, or declining species, livestock exclusions resulted in the greatest mean increase in nest success, more than twice that of the 3 other treatments. Our meta-regression results indicated that between-species variation accounted for approximately 86%, 40%, 35%, and 7% of the overall variation in the results of livestock-exclusion, prescribed-burn, predator-removal, and cowbird-removal studies, respectively. However, the covariates we tested explained significant variation only in outcomes among prescribed-burn studies. The difference in nest success between burned and unburned plots displayed a significant, positive trend in association with time since fire and was significantly larger in grasslands than in woodlands. Our results highlight the importance of comparative studies on management effects in developing efficient and effective conservation strategies.

Inbreeding depression varies with investment in sex in a facultative parthenogen.

The reproductive mode of facultative parthenogens allows recessive mutations that accumulate during the asexual phase to be unmasked following sexual reproduction. Longer periods of asexual reproduction should increase the accumulation of deleterious mutations within individuals, reduce population-level genetic diversity via competition and increase the probability of mating among close relatives. Having documented that the investment in sexual reproduction differs among populations and clones of Daphnia pulicaria, we ask if this variation is predictive of the level of inbreeding depression across populations. In four lake populations that vary in sex investment, we raised multiple families (mother, field-produced daughter, laboratory-produced daughter) on high food and estimated the fitness reduction in both sexually produced offspring relative to the maternal genotype. Inbred individuals had lower fitness than their field-produced siblings. The magnitude of fitness reduction in inbred offspring increased as population-level investment in sex decreased. However, there was less of a fitness reduction following sex in the field-produced daughters, suggesting that many field-collected mothers were involved in outcross mating.