Search for: All records

The widespread extirpation of megafauna may have destabilized ecosystems and altered biodiversity globally. Most megafauna extinctions occurred before the modern record, leaving it unclear how their loss impacts current biodiversity. We report the long-term effects of reintroducing plains bison ( Bison bison ) in a tallgrass prairie versus two land uses that commonly occur in many North American grasslands: 1) no grazing and 2) intensive growing-season grazing by domesticated cattle ( Bos taurus ). Compared to ungrazed areas, reintroducing bison increased native plant species richness by 103% at local scales (10 m 2 ) and 86% at the catchment scale. Gains in richness continued for 29 y and were resilient to the most extreme drought in four decades. These gains are now among the largest recorded increases in species richness due to grazing in grasslands globally. Grazing by domestic cattle also increased native plant species richness, but by less than half as much as bison. This study indicates that some ecosystems maintain a latent potential for increased native plant species richness following the reintroduction of native herbivores, which was unmatched by domesticated grazers. Native-grazer gains in richness were resilient to an extreme drought, a pressure likely to become more common under future global environmental change. 

Diverse communities of large mammalian herbivores (LMH), once widespread, are now rare. LMH exert strong direct and indirect effects on community structure and ecosystem functions, and measuring these effects is important for testing ecological theory and for understanding past, current, and future environmental change. This in turn requires long-term experimental manipulations, owing to the slow and often nonlinear responses of populations and assemblages to LMH removal. Moreover, the effects of particular species or body-size classes within diverse LMH guilds are difficult to pinpoint, and the magnitude and even direction of these effects often depends on environmental context. Since 2008, we have maintained the Ungulate Herbivory Under Rainfall Uncertainty (UHURU) experiment, a series of size-selective LMH exclosures replicated across a rainfall/productivity gradient in a semi-arid Kenyan savanna. The goals of the UHURU experiment are to measure the effects of removing successively smaller size classes of LMH (mimicking the process of size-biased extirpation) and to establish how these effects are shaped by spatial and temporal variation in rainfall. The UHURU experiment comprises three LMH-exclusion treatments and an unfenced control, applied to 9 randomized blocks of contiguous 1-ha plots (n = 36). The fenced treatments are: “MEGA” (exclusion of megaherbivores, elephant and giraffe); “MESO” (exclusion of herbivores ≥40 kg); and “TOTAL” (exclusion of herbivores ≥5 kg). Each block is replicated three times at three sites across the 20-km rainfall gradient, which has fluctuated over the course of the experiment. The first five years of data were published previously (Ecological Archives E095-064) and have been used in numerous studies. Since that publication, we have (a) continued to collect data following the original protocols, (b) improved the taxonomic resolution and accuracy of plant and small-mammal identifications, and (c) begun collecting several new data sets. Here, we present updated and extended raw data from the first 12 years of the UHURU experiment (2008–2019). Data include daily rainfall data throughout the experiment; annual surveys of understory plant communities; annual censuses of woody-plant communities; annual measurements of individually tagged woody plants; monthly monitoring of flowering and fruiting phenology; every-other-month small-mammal mark-recapture data; and quarterly large-mammal dung surveys. There are no copyright restrictions; notification of when and how data are used is appreciated and users of UHURU data should cite this data paper when using the data. 

Multiple, simultaneous environmental changes, in climatic/abiotic factors, interacting species, and direct human influences, are impacting natural populations and thus biodiversity, ecosystem services, and evolutionary trajectories. Determining whether the magnitudes of the population impacts of abiotic, biotic, and anthropogenic drivers differ, accounting for their direct effects and effects mediated through other drivers, would allow us to better predict population fates and design mitigation strategies. We compiled 644 paired values of the population growth rate ( λ ) from high and low levels of an identified driver from demographic studies of terrestrial plants. Among abiotic drivers, natural disturbance (not climate), and among biotic drivers, interactions with neighboring plants had the strongest effects on λ . However, when drivers were combined into the 3 main types, their average effects on λ did not differ. For the subset of studies that measured both the average and variability of the driver, λ was marginally more sensitive to 1 SD of change in abiotic drivers relative to biotic drivers, but sensitivity to biotic drivers was still substantial. Similar impact magnitudes for abiotic/biotic/anthropogenic drivers hold for plants of different growth forms, for different latitudinal zones, and for biomes characterized by harsher or milder abiotic conditions, suggesting that all 3 drivers have equivalent impacts across a variety of contexts. Thus, the best available information about the integrated effects of drivers on all demographic rates provides no justification for ignoring drivers of any of these 3 types when projecting ecological and evolutionary responses of populations and of biodiversity to environmental changes. 

Most studies of the ecological effects of climate change consider only a limited number of weather drivers that could affect populations, though we know that multiple weather drivers can simultaneously affect population growth rate. Multiple drivers could simultaneously increase/decrease one vital rate, or one may increase a vital rate while another decreases the same vital rate. Considering the impact of multiple weather drivers on vital rates is particularly important in a changing climate, in which correlations among drivers may not be preserved in the future. We used a long‐term dataset on the endangered red‐cockaded woodpecker (Dryobates borealis) to understand how multiple weather drivers jointly affect survival and reproductive vital rates and then assessed the contributions of individual weather drivers to historical trends in vital rates over time. We found that vital rates were often influenced by more than one weather driver and that weather drivers most commonly exerted opposing effects. For instance, some weather drivers increased vital rates over time, while others acted in the opposite direction, decreasing vital rates over time. Importantly, the historical correlations among weather drivers are almost always projected to change in the future climate, such that future trends in vital rates may not match historical trends. For example, we do not find historical trends in adult survival, but changing correlations among weather drivers could generate future trends in this vital rate. Our work provides an example of how multiple weather drivers can control a variety of vital rates and also illustrates how changes in the correlation structure of weather drivers through time might substantially affect future trends in individual and population performance.

 

Structured demographic models are among the most common and useful tools in population biology. However, the introduction of integral projection models (IPMs) has caused a profound shift in the way many demographic models are conceptualized. Some researchers have argued that IPMs, by explicitly representing demographic processes as continuous functions of state variables such as size, are more statistically efficient, biologically realistic, and accurate than classic matrix projection models, calling into question the usefulness of the many studies based on matrix models. Here, we evaluate how IPMs and matrix models differ, as well as the extent to which these differences matter for estimation of key model outputs, including population growth rates, sensitivity patterns, and life spans. First, we detail the steps in constructing and using each type of model. Second, we present a review of published demographic models, concentrating on size‐based studies, which shows significant overlap in the way IPMs and matrix models are constructed and analyzed. Third, to assess the impact of various modeling decisions on demographic predictions, we ran a series of simulations based on size‐based demographic data sets for five biologically diverse species. We found little evidence that discrete vital rate estimation is less accurate than continuous functions across a wide range of sample sizes or size classes (equivalently bin numbers or mesh points). Most model outputs quickly converged with modest class numbers (≥10), regardless of most other modeling decisions. Another surprising result was that the most commonly used method to discretize growth rates for IPM analyses can introduce substantial error into model outputs. Finally, we show that empirical sample sizes generally matter more than modeling approach for the accuracy of demographic outputs. Based on these results, we provide specific recommendations to those constructing and evaluating structured population models. Both our literature review and simulations question the treatment of IPMs as a clearly distinct modeling approach or one that is inherently more accurate than classic matrix models. Importantly, this suggests that matrix models, representing the vast majority of past demographic analyses available for comparative and conservation work, continue to be useful and important sources of demographic information.