Search for: All records

Abstract The storage effect is a plausible natural mechanism that generates balanced genetic polymorphism in temporally varying environments. Balanced polymorphism may facilitate evolutionary rescue, promoting the persistence of populations otherwise destined for extinction. However, it is unknown whether the storage effect can be established in small populations whose size is allowed to vary, and if so, whether it will lead to evolutionary rescue. In this study, we investigate whether the spatial storage effect emerges and facilitates evolutionary rescue across small populations of variable sizes that inhabit heterogeneous, temporally varying environments and exchange migrants. We use an eco-evolutionary model to examine the phenomenon under a wide set of conditions, including the magnitudes and periods of temporal variation, habitat harshness, migration rates, the degrees of spatial heterogeneity, and increasing fitness oscillations over time, all within the framework of the logistic population growth model. We find that the storage effect emerges and that it increases the persistence of populations in harsh, temporally varying habitats beyond levels expected in the absence of the mechanism. This mechanism demonstrates how rapid evolution broadens the known conditions for population persistence in the face of rapid and continuous environmental changes. 

Abstract Understanding the relationship between precipitation (PPT) and aboveground net primary productivity (ANPP) is essential for modeling the global carbon cycle. Across grassland to forest gradients, the PPT‐ANPP relationship is well defined and nonlinear. Temporal patterns within a site over time are more variable and nearly always linear. Linear relationships, however, are inconsistent with positive asymmetry, where increases in ANPP during wet years exceed declines in dry years. The double asymmetry model predicts that concave‐down nonlinearities will occur when extreme high and low PPT years are included in a time series. We tested this prediction using long‐term observational ANPP data along with rainfall manipulation experiments. By combining observational records with experimental treatments, including drought, water addition, and nitrogen addition, we found some support for the double asymmetry model. However, the response under high precipitation coupled with nitrogen addition was concave‐up, not down. By experimentally extending the range of monsoon precipitation, we found a weak but significant, nonlinear PPT‐ANPP relationship, but only when nutrient limitation was alleviated. Our results demonstrate that multiple interacting factors govern the PPT‐ANPP relationship within a site over time, challenging our ability to predict how ANPP will respond to changes in precipitation in the future. 

ABSTRACT Predicting the effects of climate change on plant and animal populations is an urgent challenge for understanding the fate of biodiversity under global change. At the surface, quantifying how climate drives the vital rates that underlie population dynamics appears simple, yet many decisions are required to connect climate to demographic data. Competing approaches have emerged in the literature with little consensus around best practices. Here we provide a practical guide for how to best link vital rates to climate for the purposes of inference and projection of population dynamics. We first describe the sources of demographic and climate data underlying population models. We then focus on best practices to model the relationships between vital rates and climate, highlighting what we can learn from mechanistic and phenomenological models. Finally, we discuss the challenges of prediction and forecasting in the face of uncertainty about climate‐demographic relationships as well as future climate. We conclude by suggesting ways forward to build this field of research into one that makes robust forecasts of population persistence, with opportunities for synthesis across species. 

Abstract Dryland productivity is highly sensitive to precipitation variability, and models predict that rainfall variability will increase in the future. Numerous studies have documented the relationship between productivity and precipitation, but most focus on aboveground production (ANPP), while the effects on belowground production (BNPP) remain poorly understood. Furthermore, previous research suggests that ANPP and BNPP are uncoupled within ecosystems, but the degree to which rainfall variability affects the interplay between aboveground and belowground production is unknown. We conducted a long‐term rainfall manipulation experiment in Chihuahuan Desert grassland to investigate how the size and frequency of growing season rain events affected BNPP and its relationship to ANPP. Experimental plots received either 12 small‐frequent rain events or 3 large‐infrequent events during the monsoon season for a total of 60 mm of added rainfall per treatment per year. All plots, including three controls, received ambient rainfall throughout the year. Total BNPP ranged from a low of 94.7 ± 38.2 g m²year⁻¹under ambient conditions to a high of 183.7 ± 44.6 g m²year⁻¹under the small‐frequent rainfall treatment. Total BNPP was highest under small‐frequent rain events, and there was no difference in BNPP between 0–15 and 15–30 cm soil depths in either rainfall treatment. ANPP and BNPP were uncorrelated within rainfall treatments, but weakly positively correlated across all plots and years. Our results contribute to a growing body of research on the importance of small rain events in drylands and provide further evidence regarding the weak coupling between aboveground and belowground processes. 

Abstract Reordering of abundances among species is a common response in communities whether affected by anthropogenic drivers or natural disturbance. However, understanding how competitive relationships drive community dynamics under global environmental change remains limited, primarily due to uncertainties related to changes in species interactions and the scarcity of long‐term observations. By combining long‐term data and time series analysis tools, we quantified the compositional dynamics and causal interactions among functional groups of an arid grassland community under chronic nutrient enrichment for 15 years following wildfire. We hypothesized that chronic nutrient addition would promote species reordering among dominant grasses and subordinate annual forbs after wildfire, thereby increasing biomass and compositional variation over the long term. Contrary to expectations, while the abundance of the dominant grassBouteloua eriopoda(black grama) declined immediately after the wildfire, the increase in annual forbs under N addition did not occur until a decade later. Convergent cross‐mapping revealed that annuals were causally influenced by black grama abundance and maintained relatively lower abundance in control plots. However, with N addition, this causal interaction from black grama to annuals disappeared. Accordingly, temporal variability of biomass and community composition increased as the abundance of annuals rose. Combined with evidence of precipitation response, these results imply that the competitive advantage of perennial plants over annual forbs could serve as a stabilizing mechanism for community variability by limiting the response of annuals to precipitation fluctuations. However, this stabilizing process is disrupted by the cumulative effects of chronic nitrogen addition. This long‐term experiment provides new insights into the destabilizing effects of community reordering, without changes in species richness, in response to anthropogenic nutrient loading. 

Abstract The hot deserts of the southwestern United States are experiencing increased frequency, severity, and duration of drought due to anthropogenic climate change. Plant communities in these deserts differ in composition, specifically the abundance of annual and perennial species, which could differentiate responses among these ecosystems to drought. Thus, identifying how these desert plant communities respond to prolonged, severe drought is critical to assess vulnerability to climate change. We measured the response of herbaceous plant communities to 4 years of experimentally imposed severe drought in Chihuahuan, Sonoran, and Mojave Desert sites in the southwestern US.We imposed year‐round passive rain exclusion treatments with a 66% reduction in ambient rainfall for 4 years at two sites in each of the three US hot deserts. We measured plant species composition and abundance in treatment and control plots during the peak growing season.Vegetative cover increased with seasonal precipitation at all six sites. Species richness and evenness varied in response to drought across all sites over the duration of the experiment. At three of the six sites, species richness increased with seasonal precipitation and at three sites species evenness decreased with seasonal precipitation.In general, we found that community structure was linked to seasonal precipitation more so than cumulative drought in these herbaceous communities of southwestern US deserts, and that these desert communities are highly resilient following prolonged, extreme drought. 

Abstract Dominant species play a key role in plant communities, influencing the abundance and richness of subordinate species through competitive and facilitative interactions. However, generalizations about the effects of dominant plant species in grasslands can be difficult due to the many differences among communities, such as abiotic conditions and regional species pools. To overcome this issue, we conducted a dominant species removal experiment in two semiarid grassland communities at the Sevilleta National Wildlife Refuge in central New Mexico. These communities had different dominant species but similar abiotic conditions and regional species pools. We studied the effects of removing dominant species on community composition, diversity, and aboveground net primary production (ANPP) over a 23‐year period. Our results showed that dominant grasses suppressed both richness and abundance of subordinate species. In the Chihuahuan Desert grassland, the loss ofBouteloua eriopodawas only partially compensated for by subordinate species, while in the Great Plains grassland, the loss ofBouteloua graciliswas fully compensated for after 16 years. Despite increased species richness, removing dominant species reduced ANPP and resulted in a negative relationship between species richness and ANPP in both grasslands. These results have important implications for ecosystem management and conservation, highlighting the potential impact of losing dominant species on subordinate species and community dynamics. 

Abstract Integral projection models (IPMs) are widely used for studying continuously size‐structured populations. IPMs require a growth sub‐model that describes the probability of future size conditional on current size and any covariates. Most IPM studies assume that this distribution is Gaussian, despite calls for non‐Gaussian models that accommodate skewness and excess kurtosis. We provide a general workflow for accommodating non‐Gaussian growth patterns while retaining important covariates and random effects. Our approach emphasizes visual diagnostics from pilot Gaussian models and quantile‐based metrics of skewness and kurtosis that guide selection of a non‐Gaussian alternative, if necessary. Across six case studies, skewness and excess kurtosis were common features of growth data, and non‐Gaussian models consistently generated simulated data that were more consistent with real data than pilot Gaussian models. However, effects of “improved” growth modeling on IPM results were moderate to weak and differed in direction or magnitude between different outputs from the same model. Using tools not available when IPMs were first developed, it is now possible to fit non‐Gaussian models to growth data without sacrificing ecological complexity. Doing so, as guided by careful interrogation of the data, will result in models that better represent the populations for which they are intended. 

Abstract Agroecosystems, which include row crops, pasture, and grass and shrub grazing lands, are sensitive to changes in management, weather, and genetics. To better understand how these systems are responding to changes, we need to improve monitoring and modeling carbon and water dynamics. Vegetation Indices (VIs) are commonly used to estimate gross primary productivity (GPP) and evapotranspiration (ET), but these empirical relationships are often location and crop specific. There is a need to evaluate if VIs can be effective and, more general, predictors of ecosystem processes through time and across different agroecosystems. Near‐surface photographic (red‐green‐blue) images from PhenoCam can be used to calculate the VI green chromatic coordinate (G_CC) and offer a pathway to improve understanding of field‐scale relationships between VIs and GPP and ET. We synthesized observations spanning 76 site‐years across 15 agroecosystem sites with PhenoCam G_CCand GPP or ET estimates from eddy covariance (EC) to quantify interannual variability (IAV) in the relationship between GPP and ET and G_CCacross. We uncovered a high degree of variability in the strength and slopes of the G_CC∼ GPP and ET relationships (R² = 0.1 ‐ 0.9) within and across production systems. Overall, G_CCis a better predictor of GPP than ET (R² = 0.64 and 0.54, respectively), performing best in croplands (R² = 0.91). Shrub‐dominated systems exhibit the lowest predictive power of G_CCfor GPP and ET but have less IAV in slope. We propose that PhenoCam estimates of G_CCcould provide an alternative approach for predictions of ecosystem processes. 

ABSTRACT Cover crops, a promising strategy to increase soil organic carbon (SOC) storage in croplands and mitigate climate change, have typically been shown to benefit soil carbon (C) storage from increased plant C inputs. However, input‐driven C benefits may be augmented by the reduction of C outputs induced by cover crops, a process that has been tested by individual studies but has not yet been synthesized. Here we quantified the impact of cover crops on organic C loss via soil erosion (SOC erosion) and revealed the geographical variability at the global scale. We analyzed the field data from 152 paired control and cover crop treatments from 57 published studies worldwide using meta‐analysis and machine learning. The meta‐analysis results showed that cover crops widely reduced SOC erosion by an average of 68% on an annual basis, while they increased SOC stock by 14% (0–15 cm). The absolute SOC erosion reduction ranged from 0 to 18.0 Mg C⁻¹ ha⁻¹ year⁻¹and showed no correlation with the SOC stock change that varied from −8.07 to 22.6 Mg C⁻¹ ha⁻¹ year⁻¹at 0–15 cm depth, indicating the latter more likely related to plant C inputs. The magnitude of SOC erosion reduction was dominantly determined by topographic slope. The global map generated by machine learning showed the relative effectiveness of SOC erosion reduction mainly occurred in temperate regions, including central Europe, central‐east China, and Southern South America. Our results highlight that cover crop‐induced erosion reduction can augment SOC stock to provide additive C benefits, especially in sloping and temperate croplands, for mitigating climate change.