No abstract

Animal populations must be able to acquire an adequate amount of nutrients to persist regardless of what environment they are in. In highly variable environments, such as drylands where food sources are limited, this potential mismatch between physiological demands and what is available in the environment is accentuated. For herbivores, the balance of macronutrients (protein and carbohydrate) is particularly important and both nutrients are highly variable in plants both spatially and temporally. Whereas it is known that many herbivores will forage multiple plants to achieve an optimal nutritional ratio (termed the intake target), it is less k nown how herbivores with different life history strategies address this in variable environments. In this study, we measured the intake targets of three grasshopper species with differing life history strategies, two migratory and one non-migratory, at three locations in New South Wales, Australia. We measured nutrient variation in plants spatially and temporally by sampling three different locations and repeated the measurement twice for one of these locations. At all three locations and both times, host plant protein differed substantially but carbohydrate content remained constant. The non-migratory grasshopper species shifted their intake target, presumably to redress nutrient imbalances. On the other hand, the two migratory grasshopper species largely maintained the same intake target, even when in a nutritionally suboptimal environment. These results suggest that non-migratory species are likely more limited in their capacity to forage for optimal diets and may rely more on digestion to survive in nutritionally suboptimal locations. In contrast, migratory grasshoppers may migrate to obtain the nutrients they need instead of redressing imbalances locally. Therefore, a strong metapopulation structure may aid in the persistence of migratory species at larger spatial scales. Since herbivores, especially insects, are important from nutrient cycling to food chains, understanding how populations persist in nutritionally variable environments is important to the overall ecosystem functioning. Further research should consider how nutritional demands drive population dynamics and how it changes with life history strategies.

No abstract

Overwintering success is an important determinant of arthropod populations that must be considered as climate change continues to influence the spatiotemporal population dynamics of agricultural pests. Using a long-term monitoring database and biologically relevant overwintering zones, we modeled the annual and seasonal population dynamics of a common pest, Helicoverpa zea (Boddie), based on three overwintering suitability zones throughout North America using four decades of soil temperatures: the southern range (able to persist through winter), transitional zone (uncertain overwintering survivorship), and northern limits (unable to survive winter). Our model indicates H. zea population dynamics are hierarchically structured with continental-level effects that are partitioned into three geographic zones. Seasonal populations were initially detected in the southern range, where they experienced multiple large population peaks. All three zones experienced a final peak between late July (southern range) and mid-August to mid-September (transitional zone and northern limits). The southern range expanded by 3% since 1981 and is projected to increase by twofold by 2099 but the areas of other zones are expected to decrease in the future. These changes suggest larger populations may persist at higher latitudes in the future due to reduced low-temperature lethal events during winter. Because H. zea is a highly migratory pest, predicting when populations accumulate in one region can inform synchronous or lagged population development in other regions. We show the value of combining long-term datasets, remotely sensed data, and laboratory findings to inform forecasting of insect pests.

Ecological phenomena operate at different spatial scales and are not uniform across landscapes or through time. One ecological theory that attempts to account for scaling and spatiotemporal variances is hierarchical patch dynamics. It introduces a hierarchical patch network with smaller spatiotemporal scales being nested within larger scales. However, few studies have modeled its presence within animal population dynamics. Locusts are an excellent model for investigating the spatiotemporal hierarchy of animal population dynamics, due to their high migratory capacity, large geographic ranges that extend across widely differing environments, and available long‐term data on distributions. Here, we investigated the influence of preceding vegetation growth on desert locust Schistocerca gregaria and Australian plague locust Chortoicetes terminifera outbreaks on three spatial levels (species range > geographic region > land unit) and between seasons. Both species are dryland herbivores with population dynamics linked to habitat productivity pulses after rain. We used NDVI data (MODIS imagery) as a measure of vegetation growth in hierarchical generalized additive models at different scales. Locust outbreaks were either preceded by vegetation growth between 78 and 32 days (Australian plague locusts) or 32 and 20 days before (desert locust) the observation. Although prior vegetation growth characterized outbreaks of both species, the temporal pattern of NDVI differed between spatiotemporal levels. All model selection criteria selected for a similar spatial hierarchy for both species: geographic region > land unit which supports the hierarchical patch dynamics paradigm. Further, it illuminates important timing differences between geographic regions and land units for preceding vegetation growth and locust outbreaks which can help locust managers identify when and where outbreaks occur. By acknowledging the spatiotemporal patterning of locust abundance, we account for heterogeneity of population dynamics throughout species ranges. Our findings demonstrate the importance of incorporating spatiotemporal variation in population models of insects and other animals.