Global-Scale Species Mapping From Crowdsourced Data
Authors: Elijah Cole
Grant Van Horn
Alexander Shepard
Patrick Leary
Scott Loarie
Pietro Perona
Oisin Mac Aodha
Venue: Submitted to ICLR 2023 (2023)
Keywords: species distribution modeling
coordinate networks
deep learning

Abstract: Estimating the geographical range of a species from in situ observational data is a challenging and important geospatial prediction problem. Given a set of locations indicating where a species has been observed, the goal is to learn a model that can predict how likely it is for the species to be present at any other location. While this is a well-studied problem, traditional approaches are unable to take advantage of more recently available large-scale datasets that cover many locations and species. We propose a new approach that jointly estimates the geographical ranges of tens of thousands of different species simultaneously. We develop a series of benchmark evaluation tasks that measure different aspects of the species range and spatial representation learning problems. We show that our approach scales both in terms of amount of training data and species, where adding more data enables the models to learn better spatial representations that generalize to other species. Despite being only trained on weakly supervised crowdsourced data, our models can approach the predictions of current expert-developed gold standard models.

Under review as a conference paper at ICLR 2023
JOINT IMPLICIT NEURAL REPRESENTATIONS FOR GLOBAL-SCALE SPECIES MAPPING
Anonymous authors Paper under double-blind review
ABSTRACT
Estimating the geographical range of a species from sparse observations is a chal- lenging and important geospatial prediction problem. Given set of locations where species has been observed, the goal is learn model that can predict whether the species is present or absent at any location. This problem has long history in ecology, but traditional methods struggle to take advantage of emerging large-scale crowdsourced datasets which can include tens of millions of records for hundreds of thousands of species. We propose a new approach based on im- plicit neural representations that jointly estimates the geographical ranges of 47k species simultancously. We also introduce a series of benchmarks that measure different aspects of species range estimation and spatial representation learning. We find that our approach scales gracefully, making better predictions as we scale up the number of species used for training and the amount of training data per species. Despite being trained on noisy and biased crowdsourced data, our mod- els can approximate expert-develoge gold standard range maps for many species.
INTRODUCTION
We are witnessing a dramatic decline in global biodiversity, which has severe ramifications for natural resource management, food security, and ecosystem services that are crucial to human health (Watson al. 2019). If we want take effective conservation action we must understand where different species live. In ecology, this problem is known as species distribution modeling (SDM) (Elith & Leathwick 2009). Ideally we would have up-to-date global SDMs for all species. Unfortunately, we only have SDMs for a relatively small number of species and locations.
A key obstacle that most SDM methods are incompatible with the most common form of species data. An SDM must predict whether species is present or absent at any location given spatially sparse observation records. With a sufficient amount of presence-abses abbsence data records of where species has been observed to occur and where it has been confirmed to be absent this problem can be approached using standard methods from machine learning and statistics (Beery al. 2021 Most SDM methods require presence-absence data. Unfortunately, presence-absence data is scarce due to the difficulty of verifying that a species is absent from an area. Presence-only data which consists only of locations where species has been observed, with confirmed absences is much more abundant. For instance, at the end of 2022 the iNaturalist community science platform (iNa) had collected over 70M observations across 300k species, all of which is presence-only data. Though presence-only data is not without drawbacks (Hastie & Fithian 2013), it is important to develop SDM methods that can take advantage of this vast supply of data.
Deep learning is currently one of our best tools for making use of large-scale datasets. Deep neural networks also have a key advantage over many prior SDM methods because they can jointly learn the distribution of many species in the same model (Chen et al. 2017 Tang et al. 2018 Mac Aodha al. 2019). By learning representations that share information across species, the models can make improved predictions (Chen et al. 2017). However, the majority of these deep learning approaches need presence-an presence-absence data for training, which prevents them from scaling beyond the small number of species and regions for which sufficient presence-absence data is available.
'The term pressence-absence" should not be taken to convey absolute certainty about whether species present or absent. False absences and, to a lesser extent, false presences are serious concern in SDM MacKenzie al. 2002 Guillera-Arrota 2017

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Input Data Species Presence
Species Range Model
Evaluation Tasks
Species Range
Geo Prior
Geo Features
Imsage
Loc
Irpuz
Loc Enz Osz Rx0 h(ft)))
P(*LL)) P(J[))
kn
Figure 1: We show that sparse species observation data (left) be used learn meaningful geospatial representations (middle). We evaluate the ability of these models to (right): estimate species ranges, assist computer vision classifiers, and transfer other geospatial prediction tasks.
Our work makes the following contributions: (i) A novel implicit neural representation approach to joint SDM across tens of thousands of species, trained with crowdsourced presence-only data. (ii A detailed investigation of loss functions for learning from presence-one -ony data, their scaling properties, and the resulting geospatial representations. (iii) A new suite of geospatial benchmark tasks - ranging from species mapping to fine-grained visual categorization which will facilitate future research on large-scale SDM and geospatial representation learning.
2 RELATED WORK
Species distribution modeling (SDM) refers to set of methods that aim to predict where (and sometimes when and in what quantities) species of interest are likely to be found (Elith & Leathwick] 2009). The literature on SDM is vast. Readers interested in overview of SDM should see the classic review by Elith & Leathwick (2009) or the recent review of SDM for computer scientists by Beery et al. (2021). Note that we focus narrowly on the problem of estimating species range, i.c. we do not consider more complex problems like abundance estimation (Potts & Elith 2006).
Traditional approaches to SDM train conventional supervised learning models (e.g. logistic regres- sion (Pearce & Ferrier 2000), random forests (Cutler et al. 2007), etc.) to learn a mapping between hand-designed sets of environmental features (e.g. altitude, average rainfall, etc.) and species pres- ence or absence (Phillips et al. 20044] Elith et al. 2006). Readers interested in traditional SDM ap- proaches should consult Norberg et al. (2019); Valavi et al. 2021 2022) and the references therein. More recently, deep learning methods have been introduced that instead jointly represent multiple different species within the same model (Chen et al.] 2017; Botella et al., 2018b: Tang et al. 2018]] Mac Aodha et al., 2019). These models are typically trained on crowdsourced data, which can in- troduce additional challenges and biases that need to be accounted for during training (Fink et al., 2010 Chen & Gomes| 2019: Johnston et al.) 2020 Botella et al. 2021). We build on the work of Mac Aodha et al. (2019). who proposed a neural network approach that forgoes the need for en- vironmental features (as required by e.g. [Bttlll et al. (2018b); Tang et al. (2018) by learning to
predict species presence from geographical location alone.
The problem of joint SDM with presence-only data can be viewed as an instance of multi-label classification with limited supervision. In particular, it is an example of single positive labti-label learning (SPML) (Cole et al. 2021, Verelst al. 2022, Zhou et al.) 2022). The goal is to train a model that is capable of making labti-label predictions at test time, despite having only ever observed one positive label per training instance (i.e. no confirmed negative training labels). Our work connects the SPML literature and species range mapping literature, and sets up large-scale joint species distribution modeling as challenging real-world SPML task. This setting presents significant new challenges for SPML, which has previously been limited to relatively small label spaces (< 100 categories). We investigate the role of the number of categories our experiments. Some SPML methods such as ROLE (Cole et al. 2021) are not computationally viable when the label space large. One of our baselines will be the SPMI. method of Zhou et al. (2022). which scalable and attains state-of-thee the-rr- performance on standard SPML benchmarks.
Our task is related to the growing number of works that use coordinate neural networks for implic- itly representing images (Tancik et al. 2020) and 3D scenes (Sitzmann et al. 2019), including those that perform novel-view image synthesis (Mildenhall et al. 2020). There are many design choices in these methods that are being actively studied. including the impact of the activation functions in the network (Sitzmann et al. 2019] Ramasinghe & Lucey] 2022) and the effect of different input encodings (Tancik et al. 2020 Zheng al. 2022). In most research on implicit neural representa-
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tions, there is natural choice of training objective e.g. mean squared error between the predictions and the data. In the context of presence-only species range estimation, this choice is less clear. We systematically investigate this question in our experiments.
Quantifying the performance of SDMs at scale is notoriously difficult due to the fact that we lack presence-absence data for most species and locations (Beery et al. 202T). One approach is to eval- uate performance on a small set of species (typically fewer than 300) from limited geographical regions where it is feasible to collect presence-absence data, as done in e.g. Potts & Elith (2006); Norberg el al. (2019); Valavi et al. (2022). Two of our evaluation tasks are larger-scale versions of this idea, in which we compare the performance of our models against gold standard expert range maps for around 4,000 species. An alternative evaluation approach is to measure the performance on related "proxy" task. For example, there have been a number of works that use models trained for species range estimation to assist deep image classifiers (Berg et al. 2014 Tang et al.
2015
Mac Aodha et al. 2019 Chu et al. 2019 Mai et al.) 2020 Terry et al. 2020 Skreta et al. 2020
Yang et al. 2022) By using images from platforms like iNaturalist, we can evaluate different range estimation methods on the task of aiding fine-grained image classification across tens of thousands of species. Finally, we also evaluate the spatial representations learned by our models via transfer learning, using them as inputs for suite of geospatial regression tasks. This suite of complementary benchmark tasks captures different aspects of SDM performance, and provides a starting point for large-scale SDM evaluation. See Appendix B.1 for a more detailed comparison of approaches to SDM benchmarking.
METHODS
3.1 PRELIMINARIES
Problem statement. Let x = [lon, lat] denote geographical location (i.e. longitude and latitude). Lety {0, 11$ denote the true presence (1) or absence (0) of S different species at x. We introduce z E [0, 2 to represent our observed data at x, where 2j = 1 if species is present, 2j 0 if species j is absent, and 2j Ø if we do not know whether species j is present or absent. Our goal is to develop a model that produces an estimate of y at any location x over some spatial domain X, given observed data {(x,, ) We parameterize this model as ğ = (f)() where fa :
X' - R is a location encoder and ha R - [0, 1]$ is multi-label classifier. The prediction D E [o, 1]s is our estimate of the presence or absence of each species x. Intuitively, the location encoder So learns representation of space that is used by the multi-label classifier hg to predict species presence at given location.
Input encoding. Each species observation is associated with a point x = [lom, lat]. We rescale these values so that lon, lat E [-1, 1]. Following Mac Aodha et al. (2019) we guard against boundary effects by using sinusoidal encoding for the input. The final result is an input vector
x jsin(lon), (lsol) sin(lat), cos
(1)
Alternative input encodings for related coordinate networks have been explored in existing litera- ture (Mai et al. 2020: Tancik et al.] 2220) Mai et al., 2022 Zheng et al. 2022). Their choice is orthogonal the losses we explore, thus we leave their evaluation for future work.
Implicit neural representations. Traditionally, representation learning aims to transform complex objects (e.g. images, text) into simpler objects (e.g. low-dimensional vectors) that facilitate some downstream task (e.g. classification) (Goodfellow et al. 2016). Implicit neural representations offer a different perspective, in which a signal is represented by neural network that maps the signal domain (e.g. RR for audio, IR2 for images) to the signal values (Sitzmann et al.) 2019 Tancik et al., 2020 In this work we learn an implicit neural representation for a large collection of crowdsourced species observation data, which can be viewed as a set of 2D point clouds (one for each species). This process yields an implicit neural representation for the geospatial distribution of each species, as well representation for any location on earth.
Presence-absence vs. presence-only data. Species observation datasets come in two varieties: (i) mresence-absence data consists of locations where species has been observed to be present and locations where it has been confirmed to be absent. That is, we say we have presence-absenee data f)| > 0. Unfortunately, presence-absence data
for species j if [2, Zij = 0)| 0 and [2, : 2ij
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targets at data location
targets at random location
data location
AN-SSDL
?
?
AN-SLDS
AN-fuil
unobserved ground truth
random location
Figure 2: Illustration of three basic losses from Section 3.2 For each loss (CAN- LAN- and CAN-tuu) we visualize the targets that the network is encouraged to predict. Each loss can be broken into parts: one part that updates the network's predictions at the location of training example (the data location) and one part that updates the network's predictions at another location chosen randomly (random location). Each loss has access boxes). The rest of the labels are unobserved (non-bold boxes), and the losses make different as- sumptions about those unobserved labels. LAN-SSDL (top) assumes that the species that has been confirmed present at the data location is absent from the random location. LAN-SLDS (middle) assumes that random species (other than the one that confirmed to be present) absent from the data location. LAN-ful (bottom) assumes that all unobserved species are absent from the data location, and that all species are absent from the random location. All of these losses are making imperfect approximations of the unobserved ground truth labels shown at the bottom of the figure.
confirmed positive label (bold
is costly to obtain at scale because confirming absence requires skilled observers to exhaustively search an area. (ii) Presence-only data is easier acquire and thus more abundant because absences are not collected, i.e. 2ij E {1,2} for E [N] and [S].
3.2 LEARNING FROM LARGE-SCALE PRESENCE- ONLY DATA
In the context of SPML image classification, a simple but effective approach is to assume that un- observed labels are negative (Cole et al. 2021). This approach is based on probabilistic argument: since natural images tend to have small number of categories compared to the size of the label set, the vast majority of the labels will be negative. This is also true for species distribution modeling. Given an arbitrary location and a large set of candidate species, nearly all of them will be absent. In this section we describe several simple and scalable loss functions based this idea. We illustrate three of our basic losses in Figure
"Assume negative" loss (same species, different location). This loss pairs each observation of species with a pscudo-negative for that species at another location chosen uniformly at random:
CAN CAN-SSD(($,2)
Σ | (log(ú,)) + log(1 - 1/5]1
Tppas
where hhf with r Uniform(X') and Npus E5= (zy= This approach generates pseudo-negatives across the globe, but many of them likely be "easy" because they in the ocean or far from the true species range.
"Assume negative" loss (same location, different species). This loss pairs each observation of species with a pscudo-negative at the same location for a different species:
S
CAN-SLDS($,2)
Σ
(log(( log(1 0r)
Tipos j=1
where j' Uniform({} + 1)). Intuitively, this approach generates pseudo-negatives that are aligned with the spatial distribution of the observed data.
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Full "assume negative" loss. The previous two losses are inefficient in the sense that they do not use all of the entries in y. We can combine the pseudo-nege negao-ne sampling strategies of SSD- and LAN-So LAN-SIDS SLDS and use all available predictions follows:
9
(AN-Fau($,2) z)
Σ 5=1
(es==(Aoo((0) [ayell log(1 0j) + log(1 Ø/)
(4)
where y' (fo(r) with (X)) The hyperparameter X > 0 can be used to prevent the negative labels from dominating the loss. This is equivalent to the loss from Mac Aodha et al. (2019) without their user modeling terms.
Maximum entropy loss. The current state-of-the-art in SPML image classification a technique proposed by Zhou et al. (2022). The idea is to encourage predictions for unobserved labels to maximize entropy instead forcing them to zero. Zhou et al. (2022) also includes an additional pseudo-labeling component, but omit this because the resulting empirical improvement from it is small. We can apply this idea to LAN- SSDL, LAN-SLDS. and LAN full by replacing all terms of the form "- log(1 [ pp) with terms of the form (H())" where (p) - (p log(p) + p) log(1 pp))- We name these "maximum entropy" (ME) variants LME-SSDL. LME-SLDE and LME-full-
4 EXPERIMENTS
In this section we evaluate the performance of the different losses outlined previously across a range of species and environmental prediction tasks.
4.1 MODELS
As described in Section 3.1 he which produce vector of predictions ú = h& (fo (x)) for location x. The location encoder fa implemented as the fully connected neural network shown in Figure A4 We implement the multi- label classifier ha as a single linear layer with sigmoid activations. Full implementation details can be found in Appendix
model consists of location encoder fo and multi-label classifier
There are two other model types represented Table 1 The rows tagged with "LR" are logistic regression baselines (Pearce & Ferrier 2000), in which the location encoder fo is replaced with the identity function and hg is unchanged. The "Best Discretized Grid" row does not use a location encoder, but instead makes predictions based binning the training data (Berg al. 2014). Full details for these models can be found in Appendix C These baselines allow us to quantify the importance of our deep location encoder.
4.2 TRAINING DATA
We train our models on presence-only species observation data obtained from the community science platform iNaturalist (iNa). The training set consists of 35.5 million observations covering 47,375 species observed prior to 2022. For each species, species was observed. To be included in the training set, each species needed at least 50 observa- tions. Some species more common than others, and thus the dataset is heavily imbalanced Figure A6 Later we use this data in its entirety ("All"), with different maximum observations per class ("X Class"), with different subsets of classes. See Appendix D for more details on the dataset.
have the geographical coordinate where the
4.3 EVALUATION TASKS AND METRICS
We propose four tasks for evaluating large-scale species distribution models. We give brief descrip- tions here, and provide further details in Appendix
S&T: eBird Status and Trends. This task quantifies the agreement between our presence-only maps and high-quality expert range maps from the eBird Status & Trends dataset (Fink al. 2020 covering 536 bird species. Performance measured using mean average precision (MAP), Le. computing the per-species average precision (AP) and averaging the results across species.
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IUCN: Range Maps. This task compares maps against expert range maps from the International Union for Conservation of Nature (IUCN) Red List (IUC). Unlike the bird-focused S&T task, this task covers 3,938 species from diverse taxonomic groups. Performance is measured using MAP.
Geo Prior: Geographical Priors for Computer Vision. This task measures the utility of our range maps priors for ann-igaieed image classification (Berg et al. 2014 Mac Aodha et al. 2019). For this task we collect 282.974 geo-located images from iNaturalist, covering 39,444 species from our training set. The performance metric for this task ("A Top-1") the change in image classifier top-1 accuracy when using our range maps as a geographical prior. This occurs at test time - the image classifier is never trained with geographical information. improves the performance of the classifier. Unlike S&T and IUCN, this is an indirect evaluation of range map quality since we are assessing only how useful the range maps are for downstream task.
positive value indicates that the prior
Geo Feature: Environmental Representation Learning, Instead of evaluating the range estimates of our model, this transfer learning task evaluates the quality of the underlying geospatial repre- sentation that has been learned. The goal is to predict nine different geospatial characteristics of the environment e.g. above-ground carbon, elevation, etc. First, we use the location encoder So to extract features for grid of evenly spaced locations. After splitting the locations into train and test sets, we fit a linear regressor to predict the geospatial characteristics from the extracted features. Performance is evaluated using the coefficient of determination R on the test set.
4.4 RESULTS
Deep location encoders crucial. Our method outperforms logistic regression trained with the same inputs and loss by more than 50 MAP on the S& task compare "LAN ton" and "LAN-ful (LR, Coord. Inputs)" with 1000 examples per class in Table [ The only difference is the fact that our method uses a deep location encoder. See Appendix A.2 for more details.
Environmental features are unnecessary for good performance. Our method gains only 1.1 MAP on the S&T task when we switch from coordinate inputs ("LAN-tun") to environmental feature inputs ("LAN (Env. Inputs)"). The gain much more significant for logistic regression, i.e. +15.7 MAP for "CAN (LR, Env. Inputs)" vs. "LAN-full (LR, Coord. Inputs)". It seems that our approach can successfully use sparse presence-only data to learn about the environment, so that using environmental features as input provides a marginal benefit. See Appendix A. for more.
Low-shot performance is surprisingly good. In Table we can see that LAN-full (using only 10 examples per category, i.e. 1.3% of the training data) beats the "Best Discretized Grid"' baseline (which uses all of the training data) on every task. Our models seem to be capable of capturing general spatial patterns using relatively little data. While this is encouraging, we expect that more data is necessary to capture fine detail as suggested by Figure
Pseudo-negatives that follow the data distribution usually better. CAN-SSDL and CAN- differ only in the fact that CAN-SSDL samples LAN LAN-SLDS samples pseudo-negatives from data locations (see Figure 2 We see in Table that LAN-SLDS outperforms LAN for all tasks except IUCN. This could be due to the fact that some IUCN species have ranges far from areas that are well-sampled by iNaturalist. As we can see in Figure A3 (Black Oystercatcher), LAN-SSD can behave poorly in areas with little training data. This highlights the importance of using diverse tasks study range estimation methods.
from random locations while
Joint learning across categories is beneficial, but more data is better. In Figure B we study the effect of the amount of training data on performance for the S&T task. We first note that increasing the number of training examples per species reliably and significantly improves performance. One possible mechanism for this suggested by Figure [5) which shows a more spatially detailed repre- sentation emerging with more training data. Figure 3 also shows that adding additional species to the training set (which are not in the test set) also tends to increase performance, though by smaller margin. In the right panel of Figure 3 we can see that it is better to have more training data for fewer species than less training data spread across more species. Table I shows a clear trend towards higher performance with more training data across tasks. The exception is that both LAN- and CAN full have worse performance on the Geo Prior task when using all data compared to using 1000 / class. This may be due to the extreme imbalance in the full training set, as show in Figure A6

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85
85
80
80
MAP
MAP
S&T
S&T + 4K
S&T + 8k
/ Class
S&T + 24k
100 Class
70
All
70
1000 / Class
10
102
10
10" # Train Total
10'
# Train Class
S&T range prediction performance with CAN-full full function of training size. We consider two notions size: number of training examples per class and number of classes (i.e. species). These plots visualize the same data in different ways. (Left) Each curve corresponds to model trained on a different set of species. "S&T" indicates that we only train on the species in the S&T task. For "S&T+ X" we add in species chosen uniformly random. For the "All" line we train on all 47k species. The z-axis represents the number of training examples per class. (Right) Each curve corresponds to different amount of training data per class. The points along each curve correspond to the different training sets from the left panel (i.e. "S&T", "S&T 4k" .... "All"). The x-axis represents total number of training examples. Unsurprisingly, increasing the number of training examples per category improves performance. More interestingly, adding training data for additional species (which are not evaluated at test time) improves performance as well.
Figure 3:
1.0
Enc. Coords.
0.8
CAN - SSDL
0.6
CAN SLDS
R2
CAn full
0.4
CME - SSDL
0.2
LME SLDS
0.0
CME full
AGC
ELE
LAI NTV
NOV
POD
SNC
SOM
TRC
Figure 4: Geo Feature task performance broken down by subtask. All location encoders are trained with 1000 examples per category. The regression tasks are sorted by the performance of "Enc. Co- ords." which a naive baseline method that only uses the raw encoded coordinates from Equation]] [] features. All losses outperform this naive baseline, but loss consistently outperforms the oth- ers.
Method rankings may generalize across domains. The presence-only SDM problem in this work and the SPMI. image classification problem in Cole et al. (2021) are both SPML, problems. Despite this formal equivalence, it does not seem to be case that the best current method for SPML image classification is also the best methods for presence-only SDM. Zhou et al. (2022) show that their "maximum entropy" loss performs much better than the "assume negative" loss across a number of image classification datasets. However, all of the "maximum entropy" losses in Table T (C SSDL. LME-SLDE (ME-ful) perform worse than their "assume negative" counter- parts (LaN-SSui) LAN-SLDS, LAN-fal) in almost all cases. LME-full top performer the Geo Feature task, but Figure shows that no method seems to have a commanding advantage.
4.5 LIMITATIONS
There are some limitations associated with our analysis. As noted, the training set is heavily imbal- anced, both in terms of the species themselves and where the data collected. This is partially because some species are more common and thus more likely to be observed than others. We do not explicitly deal with species imbalance in the training data, other than by showing that the ordering of methods does not significantly vary even when the training data is capped to the same upper limit per species, i.e. 10, 100, 1,000 examples per class in Table

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Figure 5: Visualization the 256-dimensional learned location encoder features projected to three dimensions using Independent Component Analysis. (Left) This corresponds to the CAN-full model with a maximum of 10 examples per class. The features are smooth and do not appear to encode much high frequency spatial information. (Right) In contrast, the --N-full model with maximum of 1000 examples per class, contains more high frequency information. The increase in training data appears enable this model to better encode spatially varying environmental properties.
L CAN-SSDA
LAN- SLDS
CAN-NN full
10 Positives Class
100 Positives Class
1000 Positives / Class
Present
Abbeent
Model Stare
Figure 6: Visualization of predictions for Wood Thrush when varying the amount of training data (rows) for different loss functions (columns). The model predictions are generated on a grid coor- dinates. Hexagons signify locations where hexagons are locations where the model should predict present, dark hexagons are locations where the model should predict absent, and locations without hexagons All models improve their range maps when given access to more data, as expected. LAN-SSDL over estimates the range with few examples, and refines the range with additional data, while -AN-full starts off conservative with few training examples and expands more data is available. The range predicted by SLDSSLDS is somewhere in- mn-between. The magnitude of the score values also inter- esting, with both CAN SSDL and CAN CLN-SEDS predicting more locations confidently than LAN-fill
evaluate the model outputs using S&T data. Light
not used for S&T evaluation.
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Table 1: Results for four geospatial tasks: S&T (eBird Status & Trends species mapping), TUCN (IUCN species mapping), Geo Prior (fine-grained image classification with a geographical prior). and Geo Feature (geographical feature regression). The tasks and metrics are defined Section 4.3 We assess performance as a function of the loss function and the amount of training data ("# Class"). The logistic regression ("LR") and "Best Discretized Grid" baselines do not have an entry for the Geo Feature task as they do not learn location encoder. "LAN- full (Env. Inputs)" does not have an entry for the Geo Feature task because it trained on environmental features which may be
closely related to the evaluation task.
S&T
IUCN
Geo Prior
Geo Feature
Loss
# Class
(MAP)
(MAP)
Top-1] I)
(Mean R²)
SSN-
65.65
95.94
0.671
CAN-SSUI
100
73.54
97.54
+4.7
0.731
LAN-SSDL
1000
76.52
98.02
+4.8
0.725
CAN-SSDI.
AlI
77.35
98.11
+4,9
0.731
LAN-SLDS
TO
72.09
92.74
+4.8
0.705
CAN-SLDS
100
79.66
95.06
+6.2
0.724
LAN-SLDS
1000
82,68
94,45
+6,2
0.729
LAN-SLDS
All
82.98
95.36
+5.9
0.744
CAN-fall
TO
74.78
96.70
+4.5
0.699
CAN-full
100
81.83
97.78
+6.6
0.735
CAN-fall
1000
84.80
98.27
+6.2
0,746
-aN-
85.15
98.19
+5.0
0.743
AaN-fall (Eny. Inputs)
1000
85.94
Baselines:
CAN-Fall full (LR, Coord. Inputs)
Pearce Ferrier
2000
31.46
49.25
-0.4
fal (LR, Coord, Inputs)
Pearce Ferrer
2000
100
30.05
44.95
CAN-ful full (LR, Coord. Inputs)
Pearce Ferrier
2000
1000
32.14
39.49
LAN-full full (LR, Env. Inputs)
(Peurce & Ferrier
2000
1000
47.83
L LME-SSDL (Zhou al. 2022
1000
72.48
96.56
+1.6
0.708
CME-SLDS (Zhou et al
2022
1000
81.86
93.62
0.728
CME-Ful full (Zhou
2022
1000
82.52
94.71
+1.5
0.747
Best Discretized Cind (Bery
2014
All
72.87
94.42
Lap (Mac Aodha al. 2019
1000
81.97
97.73
+5.7
0.718
Reliably evaluating the performance of models for species range estimation is long standing and challenging question. To address this issue, we present a suite of complementary benchmarks that attempt to evaluate different facets of this spatial prediction problem. However, obtaining ground truth distribution data for tens of thousands of species remains very difficult problem. While we believe our benchmarks to be significant step forward, they are likely to have blind spots.
Finally, extreme care should be taken before making conservation decisions based on the outputs of models such as the ones presented here. Our goal in this work is to demonstrate the promise of large-scale representation learning for species distribution modeling. Our models have not been calibrated validated beyond the experiments illustrated above.
CONCLUSION
We explored the problem of species range estimation through the lens of representation learning. Across a range of spatial prediction tasks, we showed that adding more and diverse data improves model performance. In doing so, also made connections to recent literature on training implicit coordinate networks and learning multi-label classifiers from limited supervision. We hope that our work will encourage more machine learning researchers work this important problem.
While the initial results are encouraging, there are still many avenues for future work. For example, currently our models do make use of any temporal input information (Mac Aodha al., 2019). we do not account for the spatial biases present in the data (Chen & Gomes] 2019), and our neural network models could be adapted to have better inductive biases for encoding spatially varying signals (Ramasinghe & Lucey 2022).

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Appendix
A ADDITIONAL RESULTS
A.1 How DOES OUR APPROACH COMPARE TO TRADITIONAL SDM?
Our approach can handle large-scale SDM in a way that most traditional SDMs cannot. However, for context compare our results to classic SDM. One of the most scalable SDM approaches in the ecology literature is to use logistic regression with environmental features as input (Pearce & train this method using GPU-accelerated batch-based optimization. Full implementation details are provided in Appendix C.3.1
Ferrier 2000). Crucially,
In Figure AT
compare approach against logistic regression. We try both approaches with environmental feature inputs and with coordinate inputs. First we note that our method has decisive advantage over logistic regression all cases. Second, we observe that using environmental features as input (instead of coordinates) improves the performance of logistic regression significantly (+15,7 MAP) and improves the performance of our method slightly (+1.1 MAP). These facts are consistent with the claim that our method is able to learn rich representation of the environment from species co-occurrence patterns alone, without the need for environmental covariates.
80
60
MAP
40
20
0
LR
Ours Coords.
Ours
Coords.
Env.
Env.
Figure Al: Results for the S&T task. All models are trained with 1000 examples per class using the CAN-full loss. We compare logistic regression ("LR") to our approach ("Ours"), using either coordinates ("Coords.") environmental covariates ("Env.") inputs.
A.2 WHAT THE EFFECT OF THE ARCHITECTURE OF THE LOCATTION ENCODER?
Location encoder depth. As discussed in Appendix the location encoder fo used throughout the paper has one input layer followed by four residual layers. We now consider the importance of the number of layers in the encoder. Figure A2 shows a large performance increase when we go from the logistic regression model (0 hidden layers) to the next largest model (4 hidden layers). Performance is fairly stable beyond that point, marginally increasing (8 hidden layers) and then dropping (12 hidden layers). We suggest that the performance drop for the deepest models may be due to the fact that, with enough capacity, there is need to share information across species.
Residual modules vs. traditional layers. The residual network used by us was chosen to allow us to directly compare to existing related work that uses the same architecture (Mac Aodha et al. 2019). Figure A2 compares this architecture against simpler multi-layer perceptron (MLP) architecture. Here the traditional MLP is comparable, and in fact performs slightly better. Though there is not much difference in performance, future work should consider using a traditional MLP for simplicity.
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80
70
MAP
60
50
40
Residual MLP
MLP
30
4 Encoder Depth ( Hidden Layers)
8
12
Figure A2: Results for the S&T task under different design choices for the location encoder ar- chitecture. All models are trained with 100 examples per class. We compare the location encoder architecture used throughout the paper ("Residual MLP") against simple MLP as a function of the number of layers the encoder. Note that one residual layer corresponds to two traditional layers, so for the purposes of this figure the encoder used throughout the paper has 8 layers (8 ordinary layers- residual layers). The O-layer model is identical for both architectures, and is equivalent to logistic regression.
A.3 ADDITIONAL. QUALITATIVE RESULTS
To build some intuition for the behavior of LAN-SSDL- CAN- SLNSSLDS and CAN-full we compare these losses on three species that are known have interesting ranges in Figure A3
B ADDITIONAL DISCUSSION
B. HOw THE BENCHMARK TASKS PROPOSED THIS PAPER COMPARE EXISTING SDM BENCHMARKS?
Presence-only SDM notoriously tricky to evaluate (Beery et al. 2021 and there are few public benchmark datasets available for the task. Here will discuss the two most relevant lines of prior work that have approached this evaluation problem (one from the ecology community and one from the machine learning community), and discuss where our benchmark is similar and different.
To the best of our knowledge, Elith et al. (2006) was the first attempt to systematically compare presence-only SDM algorithms across many species and locations. That work compared 16 SDM algorithms on a collection of taxonomically-spedii
datasets from different regions, covering a total of 226 species. Presence-only data was used for training, presence-absence data was used for evaluation. Unfortunately the data was not made publicly available until Elith et al. (2020). There two main issues with this benchmark. First, the benchmark is not suitable for studying large- scale joint SDM. It has small number of species overall, and there are at most 54 covered in any region. Second, the species in the dataset are anonymized. This makes impossible to use their dataset to study large-scale SDM, because we cannot increase the size of their training with external data nor can we evaluate our trained models on their test data.
Another line of work comes from the GeoLifeCLEF series of datasets and competitions (Botella cet al.) 2018a| 2019 Deneu et al. 2020 Cole et al. 2020 Lorieul et al. 2021 2022). These bench- marks represent an attempt to scale up presence-only SDM, with the 2022 dataset covering 17k species with 1.6M species observations the U.S. and France. As in our benchmark, all of their train- ing data is drawn from community science projects. The primary limitation of the GeoLifeCLEF benchmarks is that they use biased presence-only data at test time, evaluating the problem as an information retrieval task instead of spatial prediction task.
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Figure A3: Loss function comparison (columns) for three different species of birds (rows). All models were trained with 1000 examples per class. See Figure 6 for an explanation of the plots. These species were chosen for visualization because their ranges have interesting complementary properties. (Top Row) Barn Swallow is species that occurs across the globe. (Middle Row) Blue Rock- Thrush is species whose range goes from the data rich Western Palearctic biogeographic realm. through a data sparse area, and back to a more data rich area of eastem Asia. (Bottom Row) Black Oystereatcher ii a species whose range hugs the west coast the United States. Unlike in the rest of the paper, these visualizations for Black Oystercatchet do not use mask to filter out predictions from non- non-land regions. Here, we specifically wanted to see if the models learned to follow the coastline. We observe that CAN SLDS incorrectly expands the range into the Pacific.
LN full
creteras
Necu Sork Sore
CAN LAN-SLDS SLDS
Abontt
Preaer
LAN SSDL
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Our benchmark can be viewed as significant expansion of the GeoLifeCLEF line of work. Instead of being geographically limited to France and the U.S., we allow data from anywhere in the world. Instead of evaluating with biased presence-only data, (220)) However, unlike Elith et al. (2020), we work at a large scale that allows us to study data scaling in SDM. Our indirect evaluation tasks (Geo Prior and Geo Feature) add complementary dimensions to presence-absence evaluation, and have no counterpart in Cole et al. (2020) or Elith al. 2020)).
presence-absence data like Elith et al.
B.2 ENVIRONMENTAL COVARIATES VS. COORDINATES
One important characteristic of any SDM is whether not it is spatially explicit. Spatially explicit SDMs include geospatial coordinates as part of the model input (Domisch et al. 2019). Traditional covariate- SDMs include only environmental features (e.g. altitude, distance to roads, average temperature, etc.) in the input (Elith & Leathwick] 2009).
Covariate-based SDMs are often understood to reflect habitat suitabiliry, because they learn relationship between environmental characteristics and observed species occurrence patterns. A covariate-based SDM will make the same predictions for all locations with same covariates, even if those locations are on different continents. Furthermore, covariates sets must be selected by hand and are often limited in their spatial resolution and coverage.
By contrast, spatially explicit SDMs can model the fact that a species may be present one location and absent in another, even if those two locations have similar characteristics, However, spatially explicit models are unlikely to generalize to locations that are spatially distant from the training data such locations are simply out of distribution.
Our proposed approach is spatially explicit, so our goal is not to learn from data in one location and extrapolate to distant locations. Instead, our goal is observation data to approximate high-quality expert range maps. Our locations of interest are the same during training and testing. The difference is the training data source and quality. See Meerow let al. (2014) for more nuanced discussion of extrapolation vs. interpolation and the role of model complexity in SDM.
abundant (but noisy and biased) species
B.3 THE ROLE OF TIME
Some species are immobile (e.g. trees), while others (e.g. birds) may occupy different areas at dif-
ferent times of the year. For this reason, there has long been interest in the temporal dynamics of species distributions (Collins & Glenn 1991 Guisan & Rahbek 2011). However, traditional SDMs
use environmental features as input, which seldom include temporal structure (Elith et al. 2020 Norberg et al.) 2019). For instance, the famous WorldClim bioclimatic variables used in many SDM
therefore not unusual for papers on SDM to make no explicit considerations for temporal information. Similarly, in this work we do not use temporal information during training or evaluation. However, we consider this to be an interesting area for future work.
papers are non-temporal (Hijmans et al. 2005).
C IMPLEMENTATION DETAILS
C.I NETWORK ARCHITECTURE
Unless otherwise specified, we use the network in Figure A4 for our location encoder fa. This is identical to the architecture in Mac Aodha et al. (2019), and similar architectures have been used for other tasks (Martinez et al., 2017). The right side of the figure shows the network structure, consisting of one standard linear layer and four residual layers. The left side of the figure shows the structure of a single residual layer. Note that all layers have the same number of nodes. Every layer of the network has 256 nodes and use p 0.5 for the dropout probability.
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C.2 TRAINING DETAILS
Environment. All models were trained Amazon AWS p3 25.21arrge instance with a Tesla V100 GPU and 60 GB RAM. The model training code was written in PyTorch (v1.7.0).
Hyperparameters, All models were trained for 10 epochs using batch size of 2048. We used the Adam optimizer with an exponential learning rate decay schedule of
learning rate initial learning ratex epoch 0.98
where epoch {0, ... 9). For each model we tuned the learning over (1e-2, le 1e-3, 1e-4) and selected the model with the best validation performance on the "S&T" task. For LAN- and CGP we set À 2048.
Linear
ReLUU
Dropout
Linear
ReLU
ReLU
Residual Layer
Residual Layer
Layer
Layer
Figure A4: Network diagram for the fully connected network (with residual connections) which we for our location encoder fe.
C.3 BASELINES
C.3.1 LOGISTIC REGRESSION WITH ENVIRONMENTAL COVARIATES
This section discusses implementation of logistic regression with environmental covariates, whose performance discussed in Appendix A.I
The architecture for this approach is equivalent to using our method but replacing the location en- coder fa with the identity function. Then we can principle use any of our loss functions for training. All other training details follow Appendix
This approach uses environmental covariates as input, not raw coordinates. This means that environ- mental covariates are required for both training and evaluation. We use the elevation and bioclimatic rasters from WorldClim 2.1 (Fick & Hijmans 2017) at the arc-minute spatial resolution. We normalize each covariate independently by subtracting the mean and dividing by standard devia- tion (ignoring NaN values). We the replace NaN values with zeros. Note that we use these same environmental covariates the inputs model in Appendix A.1
C.3.2 DISCRETIZED GRID
Our baseline system for the S&T, IUCN, and Geo Prior tasks a simple spatial binning method. Once choose resolution level, the H3 geospatial indexing library (H3W) defines collection of W hexagons (H....,, Hw that partition the globe. For instance, W -2,016,830 at resolution level 5. We show discretized grid results for few different resolution choices in Table Al Below we describe the discretized grid baseline in more detail.
For the S&T and IUCN tasks, compute score for any hexagon and species follows:
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Table A1: Discretized Grid baseline results test data when using various hexagon resolution for 'triningg' the model. As this baseline does not learn location encoder, it is not possible to evaluate the Geo Feature task.
Species Range
IUCN
Geo Prior
Geo Feature
Hex Res
# Cls.
MAP
MAP
Å Top-1
R²
All
61.50
94.42
3,5
All
70.05
90.43
4.1
All
72.87
81.07
3.1
All
62.85
69.22
-0.9
1. We compute the number of occurrences of species j in hex as
N
Durj
[x.EH.] ( [sy=]]
(5)
for S W and S.SS.
2. Let H; be hexagon we wish to evaluate at test time. For any species S < S, we compute prediction for H,
Vj max Rwj
(6)
That is, 0j measures how often species j was observed in H, (relative to how often species j occurred in the location where it was observed most often). These predictions always fall between and 1, which ensures that they are compatible with the average precision metrics we use for S&T and IUCN evaluation.
For the Geo Prior task, the first step is the same but the second step is different:
1. We the number of occurrences of species j in hex w as
1 [x
for & w S W and SJSS.
2. Let H, be a hexagon we wish to evaluate at test time. For any species $ S S, compute prediction for H,
9 1 [m]
That is, any species which were not observed in H, are "ruled out" for the downstream image classification problem.
D TRAINING DATASET
D.I DATASET CONSTRUCTION
Our training data was collected by the users of the community science platform iNaturalist (iNa). iNaturalist users take photographs plants and animals, which they then upload to platform. Other users review these images and attempt to identify the species. The final species labels are decided by the consensus the community. Each species observation consists of an image and associated metadata indicating when, where, and by whom the observation was made. iNaturalist data only contains presence observations, i.e. we do not have access to any confirmed absences in training data.
Specifically, our training data was sourced from the iNaturalist AWS Open Datase in May 2022. We began by filtering the species observations according the following rules:
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oeservabons
Figure A5: Histogram of the locations of the observations from our iNaturalist training set. Here we bin the data for all 35 million observations across 47,375 species. Darker colors indicate fewer observations, brighter colors indicate more. The training data is biased towards North America, Europe, and New Zealand.
Number of Observations
Number of Observations
10a
105
10a
10a
10a
10a
10a
10000
20000 30000 40000
100000 200000 300000 400000
Sorted Taxon ID
Sorted Observer ID
Figure A6: Summary statistics for our training data. (Left) Distribution of observations over species. (Right) Distribution of observations over users.
1. Observations must have valid valid longitude and latitude data.
2. Observations must identified to species level by the iNaturalist community. Obser- vations which can only be identified to coarser levels of specificity are discarded.
3. Observations must have research grade status, which indicates that there consensus from the iNaturalist community regarding their taxonomic identity.
After this filtering process, species with fewer than 50 observations were removed from the dataset. We also remove any species which are marked as inactive") Finally, we only included observations made prior to 2022. This will enable temporal split from 2022 onward to he used test set in the future. After filtering, we were left with 35,500,262 valid observations from 47,375 distinct species. A visualization of the geographical distribution of the resulting data can be seen in Figure (AS] As Figure A6 shows, our training data is heavily imbalanced, reflecting the natural frequency with which they reported to iNaturalist (Van Hom et al. 2018
D.2 CHANGING THE NUMBER OF TRAINING EXAMPLES PER CATEGORY
In the main paper we consider the impact of the number of observations per species in the training by training on different sub-sampled datasets. We construct these datasets by choosing k obser-
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vations per species, uniformly at random. We set seed to ensure that are always using the same observations per category. We also make certain that sampled datasets are nested, so the dataset with y examples per category is superset of the dataset with lg < k1 examples per category. If category has fewer than & observations, we use them all.
D.3 CHANGING THE NUMBER OF TRAINING CATEGORIES
In the main paper we also consider the impact of the number of species in the training set. In particular, we consider the following nested subsets:
The set of 536 bird species in the eBird Status & Trends dataset (Fink et al. 2020).
The eBird Status & Trends species plus an additional A randomly selected species, where A (4000, 8000, 24000).
E EVALUATION TASKS
Here we provide additional details on the benchmark tasks used in the main paper. For each task, we outline the dataset properties, how it was collected, and the evaluation metrics used.
E.1 S&T: EBIRD STATS AND TRENDS
Task: The goal of this task is to evaluate how effective models trained on noisy crowdsourced data from iNaturalist from Fink et al. (2020) to evaluate our range predictions. This dataset consists of estimated range maps for 536 species of birds predominately found in North America, but also other regions. The range maps are the output of an expert crafted model (Fink et al. 2020) that has been trained on tens of millions of presence and absence observations, makes use additional expert knowledge to perform data filtering, and uses rich environmental covariates as input. While not without its own limitations, we treat this data as 'ground truth' for evaluation purposes as is developed using much higher quality data compared to what we use to train our models.
predicting species range maps. We the eBird Status & Trends data
Dataset: We perform several pre-processee pre-processing steps on the raw data. We first spatially bin the data using hexagons at resolution five as defined by the H3 project 2, 016, 842 hexagons cover the world at this resolution. each with an average area of 252.9km Our goal is to predict the presence or absence of given species each hexagon using the eBird Status Trends output as 'psuedo' ground truth. The evaluation regions are restricted to those where the eBird Status & Trends models have determined that there is sufficient data to make a prediction for given species. Thus, the set of evaluation regions can vary from species to species. For example, Melozone abert i has 23,282 locations with known presence or absence, of which 271 are deemed present. On the other hand, Columba livia has 67,251 locations with known presence absence, of which 39,754 are deemed present.
The eBird Status & Trends data provides species presence and absence information for each location over the course the year. For the purposes of our evaluation, we collapse all time points to one, and count hexagon region being presence for given bird if the output of their model is greater than zero for any week in the year for that species.
We create validation split by choosing 50% of the hexes with known presence/absence status uniformly at random for each species. This is in line with the notion of generalization we care about. That is, we want know how well we can reproduce expert range maps from raw, potentially noisy, data, not how well we can make predictions in previously unseen locations.
Evaluation: We use mean average precision (MAP) as our evaluation metric, only evaluating on valid regions for a given species.
http://h3geo.org
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Black Crake
Chiloe Wigeon
Figure A7: Here we show two example species from the IUNC range map evaluation task. The blue points represent positive observations, i.e. where the expert generated range maps indicate that the species is likely to be found. In contrast, the red points indicate absences.
E.2 ICUN: RANGE MAPS
The goal of this task is similar to the previous one, i.c. to predict the geographical range of a set of species. However, instead of ground truth' target maps that are estimated by another model, here we use expert curated range maps from the International Union for Conservation of Nature (IUCN) [] This set of data contains a more taxonomically diverse set of species compared to the Stats and Trends task, as it contains mammal and fish species, among others, in addition to birds.
Task:
Dataset: We select subset of 3,938 species that overlap with our training species. For each species, we take the expert generated range polygon(s), which indicates the geographical range of a given species, and sample In addition, we sample another set of points, uniformly at random outside those polygons(s) and denote them as absence observations. presence and half absence. A visualization of the resulting sampling process is shown in Figure A7
of points inside the polygons(s) and denote them as presence observations.
total, we sample 2,000 observations per species, half
Note that the absolute performance on this dataset in Table I often well above 90%. We believe this is due to the fact that the absence samples are "easy" because many of them are far away from the presence samples. Alternative sampling processes could have been used, e.g. with more emphasis on the boundary regions between presence and absence areas as denoted by the expert maps. However, as the expert maps cannot necessarily be assumed to be the objective ground truth (i.c. species ranges can shift over time), we instead opted for the strategy outlined above.
Evaluation: Again we use mean average precision (MAP) as the evaluation metric, which results in single score for model averaged across all species.
E.3
GEO PRIOR: GEOGRAPHICAL PRIORS FOR COMPUTER VISION
Task: The goal of this task is combine the outputs of the models trained for species range esti- mation on the iNaturalist dataset with computer vision image classifier predictions. This evaluation protocol has also been explored in other work, e.g. [Berg et al. (2014); Mac Aodha et al. (2019). We simply weight the probabilistic image classifier predictions for given image with the species pancy predictions from the location where that image was taken. The intuition that the occupancy prediction down-weights the probability of given species being predicted by the vision model if the range estimation model predicts that the species is not likely to be present at that location.
Dataset: For the vision classifier, we use image classification model developed by the iNaturalist community science platform 6 This model is an Xception network (Chollet 2017) that has been trained on 55,000 different taxonomic entities (i.e. classes) from over 27 million images. We take the predictions from the final classification layer of the classifier, and do not apply any of their sophisticated taxonomic post- post-processing. There are a total of 49,333 species in the set of 55,000 classes, the rest are higher order groups, e.g, genera. The images used to train the image classifier come from observations that were added to iNaturalist prior December 2021.
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We then constructed test set consisting of all research grade observations (i.e. those observations for which there is a consensus from the iNaturalist community as to which species is present in the image). The images in the test set only contain the set of species that were observed at training time, i.e. we do not consider the open-set prediction problem. The observations were selected from between January and May 2022 ensure that they did not overlap with the training set. We take most ten observations per species, which results in 282,974 total observations from 39, 19,444 species. In practice, many species do not have 10 observations. In total there are 2,721 species, with 9,808 total images, that are not present in our range estimation training set. For each of the 282,974 observations, we extract the predictions from the deep image classifier across all 39,444 remaining species.
Performance evaluated in terms of top-1 accuracy, where the ground truth species label is provided by the iNaturalist community. Without using any information about where an image was taken, the computer vision model alone obtains an accuracy of 75.4%, which increases to 90.4% for top-5 accuracy. During evaluation, if species is not present in a range model, we simply set the output for the range model for that species to 1.0.
Evaluation:
E.4 GEO FEATURE: ENVIRONMENTAN REPRESENTATION LEARNING
This task aims to evaluate how well features extracted from deep models trained to perform species range estimation can generalize to other dense spatial prediction tasks. Unlike the other benchmark tasks that use the species occupancy outputs directly, this is transfer learning task. We remove the classification head he and evaluate the trained location encoder fo in terms of down- stream environmental prediction tasks. The intuition is that a model that is effective at range esti- mation may have learned a good representation of the local environment. If so, that representation should be transferable to other environmental tasks with minimal adaptation.
Task:
This task is inspired by the linear evaluation protocol that commonly used in self-supervised learning, e.g. Chen et al.] (2020). In that setting, the features of the backbone model are frozen and a linear classifier is trained on them to evaluate how effective they are on various downstream classification tasks. In our case, instead of classification, environmental properties from the learned feature representations of our range estimation models. A related evaluation protocol was recently used in Rolf et al. (2021) for the case of evaluating models trained on remote sensing data.
aim to regress various continuous
The task contains nine different environmental data layers which have been collected using Google Earth Engine| The nine data layers are described in Table A2 For each of the layers, we have rasterized the data that the entire globe is represented pixel represents the measured value for given layer for the geographical region encompassed by the pixel. Example images can be seen in Figure A8
Dataset:
2004 4008 pixel image. Each
Evaluation: For evaluation, we crop the region of interest to the contiguous United States and grid into training and test cells. The spatial resolution of the training and testing cells are illustrated in Figure A8 (right). Note, we simply ignore locations that are not in the training or test sets, e.g. the ocean. This results in 51,422 training points and 50.140 test points. Features are then extracted from the location encoder for the spatial coordinates specified in the training split, and then linear ridge regressor is trained on the train pixels and evaluated on the held out test pixels. The input features are normalized to the range [0, I]. We cross validate the regularization weighting term a of the regressor on the training set, exploring the set a E {0.0001, 0.001, 0.01, 0.01,0.1, 1.0, 10.0). Performance is reported as the coefficient of determination R² on the test pixels, averaged across all nine layers.
F REPRODUCIBILITY STATEMENT
The information needed to implement and train the models outlined in this paper provided in Section C In addition, the different training losses evaluated are described in Section 3.2 Upon acceptance, we will provide code enable researchers to build on our results.
hutps///artteegineggggllcc.cm
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AGC:Above Ground Carbon
ELE; Elevation
Train / Test Split
Figure A8: Here we illustrate of the nine evaluation layers used in the Geo Feature prediction task (left and middle). On the right we indicate which regions contain pixels that are in the train or test split, or simply ignored during evaluation.
Table A2: Description and sources of the nine environmental spatial layers that part of Geo
Feature prediction task.
Name
Task
Units
Range to 129
AGC
Above ground living biomass carbon stock density of combined woody and herbaceous cover in 2010.
Mg C/ha
NASA/ORNI./bi omass carbon density/v1 agb
ELE
GMTED2010: Global multi-resolution terrain elevation data 2010. Masked land only. USGS / /GMTED201C be75
meters
-457 to 8746
LAI
The sum of the one-sided green leaf area per unit ground area. CCXK///CCMC/LL LAADALAAAATAAA LAI_AV _AVE
(sce previous)
0 to 65531
-2020
NTV
Percent of a pixel which is covered by non-tree vegeta- tion. JAXA/ JAXA/GCOMS CCRR///COMM/L////// LAND/ LAII /V2 LAI AVE
%
to 100
20020
NOV
Percent of a pixel which is not vegetated.
%
0 to 100
D0DIS/006//0D4000 Percent No:Vegetated
POD
UN adjusted estimated population density.
persons km
778120
CIESIN//PRv411/GPP UNWPP-AGC AWWPe-Adjusted
Fopulation Density
unwpp-adjusted population density
SNC
Normalized difference snow index snow cover. KCCIS/006/M00100A 2019
(see previous)
to 100
NDSI_Snow.Cover
SOM
Soil moisture, derived using onc-dimensional soil wa- mm ter balance model. IDANO EP SCORTERRACIIMAN scil 2020 The trees. tres.canopy.cover: 2000-2020
to 8882
TRC
percentage of pixel area covered by SASSA/MEASURS//CFEE
to 100
G
ETHICS
This work makes use of species observation data provided by the iNaturalist community. We ex- plicitly only use data from the public data exports from iNaturalist to ensure that data, that is already available publicly, related to sensitive species (e.g. those who are at risk of extinction) is used by our models. As noted in the limitations section in the main paper, extreme care needs be taken when attempting to interpret any species range predictions from the models presented in this paper. The models have not been evaluated on those types of downstream tasks.
24


Reviews:
[{"review_id": "Q1fSgUPEdu", "reviewer": "Reviewer_apN4", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "In this paper, the authors proposed a new approach(with 3 different loss functions) to jointly estimate the geographical ranges of tens of thousands of different species simultaneously. At the same time, the authors develop a series of experiments under different settings to justify the effectiveness and robustness of the proposed methods.", "summary_of_the_review": "Overall, this article proposes a new approach to achieve the goal of estimating the geographical ranges of species simultaneously, but the method lacks novelty. The experiment results from four different can justify the effectiveness of the proposed method, but the authors didn’t compare their methods with other state-of-the-art methods (ML methods and statists methods). ", "strength_and_weaknesses": "Strength:\n1.\tClearly specify the background and problem definition.\n2.\tGive a clear description of the proposed methods (three different loss functions).\n3.\tThe author created four different evaluation tasks to justify the effectiveness of the proposed model. The experiment analysis is very clear and easy to understand. In particular, the author shows that the three proposed loss functions have different performances in different tasks. \nWeaknesses:\n1.\tAlthough the authors mentioned the proposed loss functions in the methodology section, it did not show the overall architecture of the proposed model. Which makes readers not fully understand the proposed methods.\n2.\tAlthough the author shows great experiment results and analysis in the evaluation section, the author didn’t compare their proposed method with state-of-the-art methods(ML methods and statistic methods). Thus, we don’t know whether the proposed methods outperform other methods or not.\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "KtBuxBPAEXH", "reviewer": "Reviewer_Jszv", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "The paper proposes an approach for species distribution modelling based on using location information only and presence-only information. To deal with presence-only the paper explores different loss functions how one may add pseudo absence labels. The paper also proposes a set of benchmarking tasks.", "summary_of_the_review": "I honestly do not know whether this kind of work is in scope of ICLR. I completely accept that it may be just my bias but I haven’t seen such application papers before. There is no novelty on the actual machine learning side. The novelty comes from using the new input (location only) and presenting the new benchmarking tasks. \n\nAside from this, the only major issue with the paper is that there is no much comparison with the existing methods. \n\nGiving benefit of the doubt regarding the fitness of the topic to ICLR, I am leaning towards acceptance. \n\n*Question for the rebuttal*: Can the authors explain why other existing methods were not used in comparison? Am I missing something and they are incomparable beyond the fact that they would use more information than the proposing approach?\n", "strength_and_weaknesses": "**Strengths**:\n* The approach to use location information only appears to be novel which is surprising considering the previous approaches utilising much richer data for the same task\n* Benchmarking tasks illustrating how the location data can be used\n* Exploration of different strategies for pseudo absence labels\n\n**Weaknesses**:\n* Lack of comparison with the existing methods. I appreciate it would be unfair comparison as the other methods use more information, e.g. environmental data, but it would put this approach in the context of the existing literature\n* [Potential]. May be not in the scope for ICLR (see details below)\n* Crowdsourced data in the title is a bit misleading. There is nothing on normal crowdsourcing tasks in the paper. ”Crowdsourced data” here only refers as not too reliable and not too costly. \n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "I59z5wYdMUQ", "reviewer": "Reviewer_f3dW", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "The paper proposes a method to handle the species distribution modeling (SDM) problem at a global scale, which is challenging and important. Traditional models need presence-absence data for training and are unable to take advantage of large-scale data. In contrast, with several scalable loss functions, the method in this paper can use large-scale presence-only data for the SDM problem. The paper also proposes 4 benchmark evaluation tasks to measure different aspects of the SDM problem and presents the experimental result conducted on these tasks to test the effectiveness of the proposed method.\n", "summary_of_the_review": "This paper addresses an important problem, i.e., species range estimation at a global scale from large-scale, presence-only, crowdsourced data, and provides 4 new benchmark tasks. However, the presentation of this paper needs improvement and the technique novelty of this paper is not sufficient. Also, this article lacks sound experiments and reasonable analysis. Due to these reasons, we recommend rejecting the paper.\n", "strength_and_weaknesses": "Strength:\nThe paper works on a significant application in ecological domain: species range estimation at a global scale from large-scale, presence-only, crowdsourced data.\nThe proposed 4 benchmark evaluation tasks and the corresponding datasets may encourage and help domain researchers who want to work on species distribution problem.\n\nWeakness:\nMost citations in the article do not meet the format requirements.\nThe presentation of the paper needs improvement. Firstly, there are some typos and grammar errors in this paper, e.g., line 4 of Section 1: to understanding -> to understand, line 2 of the 2nd paragraph of Section 2: decision tress -> decision trees, line 4 of Section 4.6: explicitly attempted -> explicitly attempt. Secondly, the description of the method is hard to understand. The paper needs to add illustrations for the proposed loss function, highlight the innovative contribution, explain and justify these loss functions in detail.\nThe technical novelty of this paper is not sufficient. The method in this paper only adopts the framework in \"Elijah Cole, Oisin Mac Aodha, Titouan Lorieul, Pietro Perona, Dan Morris, and Nebojsa Jojic. Multi-label learning from single positive labels. In CVPR, 2021.\" to define the loss functions, and use some classic neural network architecture to train the vector representation of space.\nThe experiment result is not convincing since the paper only evaluates the performance of the methods proposed in this paper and their variants on some self-collected datasets. The author needs to make comparisons to state of the art alternative methods and test the effectiveness of the proposed method on some widely recognized benchmarks in the species distribution modeling research domain.\nThe analysis of the experiment result is insufficient. The author only demonstrates the result, e.g., the “best” loss depends on the task, and method rankings may not generalize across domains. The paper does not explain these results in detail.\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "HKAautLfcR", "reviewer": "Reviewer_eJKM", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This paper proposes a novel method that estimates the geographical ranges of different species based on presence-only data. This paper combines the idea of learning multi-label classifiers from limited supervision with implicit coordinate networks to improve the performance of species range estimation. The authors conduct a series of experiments to show the effectiveness of the proposed method.", "summary_of_the_review": "Overall, the paper has its merits to show a new method for species range estimate, and improve the performance of the model. However, due to some key issues that have not been well addressed, this paper can hardly meet the high-quality requirements of ICLR.", "strength_and_weaknesses": "This study is intuitively and technically sound. In addition, the paper is presented clearly and is easy to follow and reproduce. The main idea of the proposed method is simple but effective.\n\nHowever, there are several weaknesses:\n- Overall, the novelty of this paper is limited. The method proposed in this paper seems a combination of existing works, which mainly builds on the work of Mac Aodha et al. (2019) and Cole et al. (2021). Furthermore, the discussion of the main idea and the structure of the proposed method is too brief. I suggest the author discuss the main contributions of this paper and highlight the innovation compared with existing works, and describe the proposed framework more structurally.\n- The analysis of the experimental results is somewhat weak. It is mentioned in Section 2 that the authors will attempt to answer the question about what training loss is most effective, but in the section of experiments, this topic has not been discussed in depth, since no loss is best across all tasks according to the experimental results, it’s better to discuss about why this happened, and analyze which types of tasks the different loss are applicable to? Furthermore, the authors conduct several variants to highlight the importance of the deeper location encoder in terms of learning an effective representation of space, however, the proposed method is only compared with the “single layer” case, and it is not convincing enough to support the claim, and why the authors choose the network with four residual layers for location encoder?\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}]

Author rebuttal:

Decision: Reject