Predicting mule deer recruitment from climate oscillations for harvest management on the northern Great Plains

• Published in: The Journal of wildlife management 2015-11, Vol.79 (8), p.1226-1238
• Main Authors: Ciuti, Simone, Jensen, William F., Nielsen, Scott E., Boyce, Mark S.
• Format: Article
• Language: English
• Subjects: aerial survey; Animal populations; Animal reproduction; Autumn; Climate change; Climate models; Deer; Fawns; harvest management; Hunting; Modeling; Mule deer; multivariate el niño southern oscillation; North Pacific index; Pacific decadal oscillation; Pacific-based climate; Population Ecology; recruitment; Southern Oscillation; Spring; Summer; survival; Temperature; Weather; Wildlife; Wildlife management; Winter
• ISSN: 0022-541X; 1937-2817
• DOI: 10.1002/jwmg.956

We analyzed a unique 51-year time series for a population of mule deer in the North Dakota badlands, USA to examine the effects of seasonal weather on autumn recruitment. Winter weather recorded prior to birth of fawns and weather conditions recorded during spring the previous year (lagged effect), but not during spring or summer after birth, were related to observed patterns in autumn recruitment. When deer density was low (approx. 1 deer/km2) during the 1960s, fawn/female ratios were high ranging from 1.1 to 1.4 when minimum temperatures during the prior winter averaged −16° C and −8° C, respectively. Likewise, during the 2000s, when deer density was high (approx. 3 deer/km2), fawn/female ratios ranged from 0.6 to 0.9 when minimum daily temperatures during the previous winter were −16° C and −8° C, respectively. Large-scale Pacific-based climatic indices (>2,000 km to the Pacific coast) were correlated with local weather and helped explain variability in autumn recruitment. Higher values of the multivariate el niño southern oscillation and Pacific decadal oscillation were correlated with warmer and drier winters in the North Dakota badlands, whereas the North Pacific Index was correlated with colder and snowy winters. The ability to predict recruitment from local weather or from broad-scale climate indices (oscillations) provides greater opportunities for conservation and management, such as adjusting harvest quotas prior to autumn harvest.