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.
Long-term monitoring of NDVI changes by remote sensing to assess the vulnerability of threatened plants
L. Matas-Granados, M. Pizarro, L. Cayuela, D. Domingo, D. Gomez, M.B.García
https://reader.elsevier.com/reader/sd/pii/S0006320721004808?token=3C4ED16A49C1C30E9EFC75390D8515C0131B407EC5A320193C7463C6C08EF57D874331612B03280464E3FF0E3EA233AE&originRegion=eu-west-1&originCreation=20211221182422
Little is known about how much continuous landscape transformation might affect the most vulnerable elements of biodiversity. In this study, we quantified changes in the normalized difference vegetation index (NDVI) over the past 35 years across locations with threatened plants and in Natura 2000 (N2000) protected areas, in an environmentally heterogeneous region of Southern Europe. First, we estimated the intensity and duration of NDVI gains and losses based on Landsat time series using the LandTrendr algorithm in Google Earth Engine. Then, we tested: 1) whether populations of threatened plants were located in more stable sites than non-threatened plants (i.e., lower NDVI changes); 2) whether NDVI changes around populations of threatened plants differed across habitats and inside/outside N2000 areas, and 3) whether lower NDVI changes occurred in N2000 areas than unprotected areas, thereby indicating their effectiveness at preserving biodiversity. Threatened plants tended to be concentrated in sites with less change irrespective of the habitat where they occurred and their location within protected areas. Occurrence in stable sites also reduced the risk associated with small-sized populations. N2000 areas were in line with the overall greening trend but they experienced less loss events than the unprotected areas, thereby supporting their role in slowing down human-induced land cover changes. Our approach demonstrates how long-term remote sensing monitoring can help to assess the effects of both slow processes and drastic landscape transformation events on priority plants in a comprehensive and rapid manner. This method can identify hidden patterns in extensive regions and guide effective conservation management
