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Materials composed of spin-1 antiferromagnetic (AFM) chains are known to adopt complex ground states that are sensitive to the single-ion-anisotropy (SIA) energy ( $D$), and intrachain ( $J_0$) and interchain ( $J_{1,2}^{\prime }$) exchange energy scales. While theoretical and experimental studies have extended this model to include various other energy scales, the effect of the lack of a common SIA axis is not well explored. Here we investigate the magnetic properties of $\mathrm{Ni}\left(\mathrm{pyrimidine}\right){\left({\mathrm{H}}_2\mathrm{O}\right)}_2{\left({\mathrm{NO}}_3\right)}_2$, a chain compound where the tilting of Ni octahedra leads to a twofold alternation of the easy-axis directions along the chain. Muon-spin relaxation measurements indicate a transition to long-range order at $T_{\text{N}}=2.3\mathrm{K}$and the magnetic structure is initially determined to be antiferromagnetic and collinear using elastic neutron diffraction experiments. Inelastic neutron scattering measurements were used to find $J_0=5.107\left(7\right)\mathrm{K}, D=2.79\left(1\right)\mathrm{K},J_1^{\prime }=0.00\left(5\right)\mathrm{K}, J_2^{\prime }=0.18\left(3\right)\mathrm{K}$, and a rhombic anisotropy energy $E=0.19\left(9\right)\mathrm{K}$. Mean-field modeling reveals that the ground state structure hosts spin canting of $\varphi \approx 6.5^{\circ }$, which is not detectable above the noise floor of the elastic neutron diffraction data. Monte Carlo simulation of the powder-averaged magnetization, $M\left(H\right)$, is then used to confirm these Hamiltonian parameters, while single-crystal $M\left(H\right)$simulations provide insight into features observed in the data. Published by the American Physical Society2025 

Ecological research increasingly considers integrative relationships among phenomena at broad spatial and temporal domains. However, such large-scale inferences are commonly confounded by changing properties in the processes that govern phenomena (termed nonstationarity), which can violate assumptions underlying standard analytical methods. Changing conditions are funda-mental and pervasive features in ecology, but their influence on ecological inference and prediction increases with larger spatial and temporal domains for a host of factors. Fortunately, tools for identifying and accommodating potentially confounding spatial or temporal trends are available, and new methods are being rapidly developed. Here, we provide guidance for gaining a better understanding of nonstationarity, its causes, and how it can be addressed. Acknowledging and addressing non-constant trends in ecological patterns and processes is key to conducting large-scale research and effectively translating findings to local policies and practices.