Kelvyn Bladen

Beef cattle on rangelands typically graze grass monocultures that become nutritionally and phytochemically constrained during summer, reducing animal performance and health. We tested whether patches of forages (i.e., “islands”) embedded within smooth bromegrass (SBG) paddocks enable cows to self‐select complementary forages that may improve animal performance. Fifteen Angus cow–calf pairs grazed three SBG paddocks (five pairs/paddock), each with three 30 × 40 m forage islands (three islands/paddock) comprising adjacent strips of alfalfa‐ALF, sainfoin‐SF, forage kochia‐KH, small burnet‐SB, and a patch of mixed weeds‐MW. Across three 15‐day periods, cows accessed one island/paddock for 1 h each evening. Cows were video‐recorded simultaneously, and the forage species grazed by each cow was recorded at 2‐min intervals using instantaneous scan sampling. Caged biomass quantified proportional forage availability for preference–biomass comparisons and availability‐based multispecies indices, while scan‐based preference was analysed using binomial GLMMs, FSI using a linear mixed‐effects model, and the remaining selectivity, forage‐mixing, and turnover indices descriptively. Scans on forage use shifted across periods ( p < 0.001): scans on MW declined from 30%–35% to 15%–20% of total scans, while those on SF and ALF increased to 25%–30% and 15%–20%, respectively. SF had the highest overall selection ratio ( w = 1.53), and ALF shifted from avoidance to positive selection. Within bouts, FSI peaked early, then declined as the diversity of forage items selected increased; MS trended toward 0, and turnover decreased. Forage islands promoted cattle preference and selectivity for nutrient‐ and phytochemical‐rich legumes and broadened dietary choice within SBG monoculture paddocks, potentially supporting their health and performance while reducing environmental impacts.

Provides methods for interpretable machine learning with an emphasis on local feature importance estimation. The package implements the CLIQUE framework for computing observation-level importance values. Additional tools are included for visualizing multi-class partial dependence relationships across predictors and response classes.

Climate change is rapidly altering high-latitude coastal wetlands through increasing temperatures, more frequent flooding, and changing herbivore abundance and distribution. Examining plant functional trait responses to these drivers provides insight into how plant communities are shaped by climate change. We used a one-year full-factorial mesocosm experiment in coastal Alaska to assess immediate responses of two size-related traits (vegetative height and leaf area) and two leaf economics traits (specific leaf area [SLA] and leaf dry matter content [LDMC]) to warming, flooding, and goose herbivory in three dominant species across two wetland communities (Lowland and Upland) differing in landscape position. We sampled different sedges ( Carex rariflora and Carex lyngbyei ) and the same deciduous dwarf-shrub ( Salix fuscescens ) in the two wetlands. Warming increased plant size for the sedge (leaf area: + 15%) and the shrub (height: + 15%; leaf area: + 19%) in the Lowland wetland only. Flooding promoted acquisitive trait values for the Lowland sedge (SLA: + 8%; LDMC: − 6%) and conservative values for the Upland shrub (SLA: − 9%; LDMC: + 11%). The shrub thus showed clear wetland-specific responses to warming and flooding. Herbivory triggered coordinated responses across all three species, consistently decreasing size-related trait values (height: − 17–35%; leaf area: − 33–50%) and promoting acquisitive trait values (SLA: + 14–40%; LDMC: − 12–23%). Despite the absence of interactions, each driver contributed significantly to trait variation, underscoring the value of multifactorial approaches. The combination of consistent (herbivory) and context-dependent (flooding and warming) responses highlights the complexity of trait responses and improves predictions of rapid phenotypic adjustments in coastal high-latitude wetlands.

Turnover in species composition often lags behind the pace of climate change, resulting in mismatches between climate and communities. However, the impact of these community‐climate disequilibria on ecosystem functions is rarely considered, and current methods for measuring disequilibria assume that species ranges were, until recently, in equilibrium with climate. Here, we develop a simple theoretical model to address both of these problems by linking community‐climate disequilibrium with ecosystem functioning. We show how disequilibrium can impair functioning in the near‐term even when climate change is expected to enhance functioning in the long‐term. Responses are most likely to change over time in communities where turnover is slow, the impact of disequilibrium counteracts the direct effects of climate on ecosystem function, and pre‐existing disequilibrium is large. These findings emphasise the importance of precise and unbiased estimates of community‐climate disequilibria for improving ecological forecasts. By fitting our model to time series of both climate and ecosystem function from a metacommunity simulation, we show the potential for community‐climate disequilibrium to be inferred without direct knowledge about species' distributions or climatic tolerances. We end by outlining a research agenda to apply dynamic disequilibrium concepts and test novel hypotheses across diverse ecosystems.

Beef production on chemically uniform grass monocultures can limit nutrient synchrony and contribute to uneven pasture use. We evaluated whether supplementing tannins with bioactive plant secondary compounds improves foraging dynamics and landscape use by beef cattle grazing a meadow bromegrass monoculture in ways aligned with rangeland sustainability. Twenty-four Angus cow–calf pairs were allocated to six 3.6-ha paddocks (four pairs/paddock), randomly assigned to Control (Ctrl; n = 3) or Tannin treatment (TT; n = 3). Animals received 1 kg/cow/day of DDGs, with TT receiving an added 0.4% tannins (2:1 condensed:hydrolyzable). Grazing occurred during four 15-day periods (July–September) across two years. Data were analyzed with mixed-effects models. Tannins did not alter biomass removal or cow weight loss (p > 0.05). However, TT cows exhibited longer evening grazing (2.9 vs. 2.1 h), fewer standing-to-lying transitions (5.7% vs. 7.3%), and more even spatial grazing distribution (CV = 1.861 vs. 2.13; p < 0.05), and greater water consumption (147 vs. 121 L/day; p < 0.01). Average daily gain of calves was numerically greater in TT compared to Ctrl (1.03 vs. 0.93 kg/day; p = 0.27). Collectively, these shifts promoted by tannins point to enhanced evening intake opportunities and reduced patch overuse, outcomes consistent with improved welfare and more uniform pasture utilization two pillars of sustainable grazing. Increased water demand under tannins highlights a management consideration for arid systems. Overall, moderate tannin inclusion was compatible with sustainable grazing by promoting even pasture use and potentially improving nutrient use efficiency without compromising intake.

We investigated how tannin supplementation influences the behavior and performance of beef cattle grazing a meadow bromegrass monoculture. Twenty-four Angus cow–calf pairs were allocated to six 3.6-ha paddocks (four pairs/paddock), randomly assigned to Control (Ctrl; n = 3) or Tannin treatment (TT; n = 3). Animals received 1 kg/cow/day of DDGs, with TT receiving an added 0.4% tannins (2:1 condensed:hydrolyzable). Grazing occurred during four 15-day periods (July–September) across two years. Data were analyzed with mixed-effects models. Tannins did not alter biomass removal or cow weight loss (p > 0.05). However, TT cows exhibited longer evening grazing (2.9 vs. 2.1 h), fewer standing-to-lying transitions (5.7% vs. 7.3%), and more even grazing distribution (CV = 1.86 vs. 2.13; p < 0.05), and greater water consumption (147 vs. 121 L/day; p < 0.01). Average daily gain of calves was numerically greater in TT compared to Ctrl (1.03 vs. 0.93 kg/day), although this difference was not significant (p = 0.2699). Findings indicate that tannin supplementation extended evening grazing, reduced rumination, and promoted even distribution, indicative of increased forage selectivity. Fewer standing-to-lying transitions indicated reduced restlessness, potentially improving welfare. Increased water intake highlights a physiological adjustment that may require management in water-limited rangelands.

This study classified cows’ foraging behaviors using machine learning (ML) models evaluated through random test split (RTS) and cross-validation (CV) data partition methods. Models included Perceptron, Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Random Forest (RF), and XGBoost (XGB). These models classified activity states (active vs. static), foraging behaviors (grazing (GR), resting (RE), walking (W), ruminating (RU)), posture states (standing up (SU) vs. lying down (LD)), and posture combinations with rumination and resting behaviors (RU_SU, RU_LD, RE_SU, and RE_LD). XGB achieved the highest accuracy for state classification (74.5% RTS, 74.2% CV) and foraging behavior (69.4% CV). RF outperformed XGB in other classifications, including GR, RE, and RU (62.9% CV vs. 56.4% RTS), posture (83.9% CV vs. 79.4% RTS), and behaviors-by-posture (58.8% CV vs. 56.4% RTS). Key predictors varied: speed and Actindex were crucial for GR and W when increasing and for RE and RU when decreasing. X low values were linked to RE_SU and RU_SU, while X and Z influenced RE_LD more. RTS showed higher accuracy in activity states classification while CV in foraging behaviors and by posture classification. These results emphasize CV in RF’s reliability in managing complex behavioral patterns and the importance of continuous recording devices and movement data to monitor cattle behavior accurately.