Detecting early-warning
signals of concern in plant populations
with a Citizen Science network. Are threatened and other
priority species for conservation performing worse?
María B. García | José L. Silva | Pablo Tejero | Iker Pardo. Journal of Applied Ecology
DOI: 10.1111/1365-2664.13890
1. Long-term monitoring of biodiversity is a fundamental part of environmental management, and Citizen Science (CS) approaches are increasing their contribution to such endeavour. CS plant monitoring programmes, however, almost exclusively report on the species presence, which can be used to detect changes in distribution or occupancy areas, but not to assess their local extinction risk. To anticipate the collapse of local populations, we need information on population sizes, trends, temporal fluctuations and threats. This is particularly important in the case of priority species (threatened, endangered and those that need special protection). 2. Here we describe the working protocol of the ‘Adopt a plant’ programme, a collaborative network that is currently monitoring 332 populations of 204 plant taxa (threatened, of community interest, common, rare and habitat indicators) across a heterogeneous landscape in NE Spain. Coordinated by scientists, participants estimate population sizes, record disturbances and follow scientifically rigorous sampling methods to track plant abundances year after year in fixed representative areas within populations. Two simple indices are estimated from that information: the overall trend (mean population abundance change, as percentage; PAch) and temporal fluctuations (standard deviation of annual changes; PAchsd). 3. The potential of this ongoing high-quality dataset is demonstrated through the analysis of 242 populations monitored over 3–10 years. Stability is the dominant trend (mean PAch: +0.14%), with priority species having similar PAch and lower PAchsd than non-priority ones. Regardless of the priority status, small populations performed worse than large ones. Only 8% of studied populations faced direct human threats. 4. Synthesis and applications. The ‘Adopt a plant’ collaborative monitoring programme was launched in NE of Spain to produce standardized indices of abundance change and other early-warning signals of concern or risk of population collapse. Such information is crucial to report the conservation status of threatened plants, and plants of Community interest (Habitats Directive). By analysing hundreds of populations, we found that priority plants experienced few threats and did not perform worse than non-priority ones. This unexpected finding evidences the importance of gathering massive demographic information to refine conservation priorities and to achieve a more comprehensive assessment of flora's vulnerability.
A critical comparison of integral projection and matrix projection models for demographic analysis.
Doak DF; E Waddle; RE Langendorf; AM Louthan; NI Chardon; RR Dibner; DA Keinath; E Lombardi; C Steenbock; RK Shriver; C Linares; MB Garcia; WC Funk; SW Fitzpatrick; WF Morris; and ML Peterson. 2021.
https://doi.org/10.1002/ecm.1447
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.
Phenotypic plasticity masks range-wide genetic differentiation for vegetative but not reproductive traits in a short-lived plant
Villellas J, ... MB Garcia, and YM Buckley. Ecology Letters 10.1111/ele.13858 (IF: 8.665, Ecology: 6/169; 2019). Autorea: https://doi.org/10.22541/au.160975628.85388662/v1
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.
